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Searching and Reporting: Cryptographic protocols that support searching and reporting on encrypted data – any information about the plaintext not deducible from the search criteria are guaranteed to be hidden. Fully Homomorphic Encryption: Cryptographic protocols that support operations on the underlying plaintext of an encryption – any information about the plaintext is guaranteed to be hidden. Secure Data Aggregation: Aggregating data without compromising privacy. Key Management Provenance End-point Input Validation: Mechanism to validate whether input data is coming from an authenticated source, such as digital signatures. Syntactic: Validation at a syntactic level. Semantic: Semantic validation is an important concern. Generally, semantic validation would validate typical business rules such as a due date. This capability can extend to the relationship of the particular file format and that the constructs which may be based on a standard can have a variance that may poise a denial of service in the form of a lockup of an application. This may also be observed even when using data translators which may not recognize the particular variant. Protocols and data formats may be altered by a vendor using reserved data field for example that will allow their products to have capabilities that differentiate their products from other products. This may also appears in differences in versions of systems for consumer devices, such as mobile devices such as cell phones. The semantic of a message and the data that is to be transported should be validated to assure at minimum conformant with standards. The use of digital signatures will be important to provide assurance that the data has been verified using a validator or data checker. This may be important to provide assurance that data from a sensor or from the data provider. This capability is important particularly if the data is to be Transformed or in the Curation of the data. If that data fails to meet the requirements it may be discarded and if that data continues to present a problem the source may be restricted in its ability to submit the data. These types of errors would be logged and prevented from being able to be disseminated to consumers. The Big Data system is a special case where use of digital signatures will be very important. Communication Integrity: Integrity of data in transit, enforced for example by using TLS.
|
Searching and Reporting: Cryptographic protocols that support searching and reporting on encrypted data – any information about the plaintext not deducible from the search criteria are guaranteed to be hidden. Fully Homomorphic Encryption: Cryptographic protocols that support operations on the underlying plaintext of an encryption – any information about the plaintext is guaranteed to be hidden. Secure Data Aggregation: Aggregating data without compromising privacy. Key Management Provenance End-point Input Validation: Mechanism to validate whether input data is coming from an authenticated source, such as digital signatures. Syntactic: Validation at a syntactic level. Semantic: Semantic validation is an important concern. Generally, semantic validation would validate typical business rules such as a due date. This capability can extend to the relationship of the particular file format and that the constructs which may be based on a standard can have a variance that may poise a denial of service in the form of a lockup of an application. This may also be observed even when using data translators which may not recognize the particular variant. Protocols and data formats may be altered by a vendor using reserved data field for example that will allow their products to have capabilities that differentiate their products from other products. This may also appears in differences in versions of systems for consumer devices, such as mobile devices such as cell phones. The semantic of a message and the data that is to be transported should be validated to assure at minimum conformant with standards. The use of digital signatures will be important to provide assurance that the data has been verified using a validator or data checker. This may be important to provide assurance that data from a sensor or from the data provider. This capability is important particularly if the data is to be Transformed or in the Curation of the data. If that data fails to meet the requirements it may be discarded and if that data continues to present a problem the source may be restricted in its ability to submit the data. These types of errors would be logged and prevented from being able to be disseminated to consumers. The Big Data system is a special case where use of digital signatures will be very important. Communication Integrity: Integrity of data in transit, enforced for example by using TLS.
| 1
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M0110_v9_2689493468.docx
|
task-1430-681
| 22
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Transformation Data Centric Security such as Usually feature of O.S. ↔ Data identity/policy-based encryption Infrastructure Policy management for access control E.g.: Windows group policy for event log Computing on the encrypted data: Vendor, platform-specific searching/filtering/deduplicate/fully homomorphic encryption Audits Complex – audits possible throughout Data Securing Data Storage and Transaction logs Vendor, platform-specific Infrastructure Key Management CSO, SIEM product keys Security Best Practices for non-relational TBD data stores Security against DoS attacks N/A Data Provenance E.g., how know an intrusion record was actually associated w/ specific workstation General Analytics for security intelligence Feature Event detection Feature Forensics Feature Consumer Digital Media Usage General Description of the Industry / Use Case: Content owners license data for usage by consumers through presentation portals, e.g., Netflix, iTunes, etc. Usage is Big Data, including demographics at user level, patterns of use such as play sequence, recommendations, content navigation. Mapping to the Security Reference Architecture: RA Component Security & Privacy Topic Use Case Mapping Sources → End-Point Input Validation Varies, vendor-dependent. Spoofing Transformation is possible. E.g., Protections afforded by securing Microsoft Rights Management Services [10]. S/MIME Real Time Security Monitoring Content creation security Data Discovery and Classification Discovery / classification possible across media, populations, channels Secure Data Aggregation Vendor-supplied aggregation
|
Transformation ↔ Data Infrastructure Data Centric Security such as identity/policy-based encryption Usually feature of O.S. Policy management for access control E.g.: Windows group policy for event log Computing on the encrypted data: searching/filtering/deduplicate/fully homomorphic encryption Vendor, platform-specific Audits Complex – audits possible throughout Data Infrastructure Securing Data Storage and Transaction logs Vendor, platform-specific Key Management CSO, SIEM product keys Security Best Practices for non-relational data stores TBD Security against DoS attacks N/A Data Provenance E.g., how know an intrusion record was actually associated w/ specific workstation General Analytics for security intelligence Feature Event detection Feature Forensics Feature Consumer Digital Media Usage General Description of the Industry / Use Case: Content owners license data for usage by consumers through presentation portals, e.g., Netflix, iTunes, etc. Usage is Big Data, including demographics at user level, patterns of use such as play sequence, recommendations, content navigation. Mapping to the Security Reference Architecture: RA Component Security & Privacy Topic Use Case Mapping Sources → Transformation End-Point Input Validation Varies, vendor-dependent. Spoofing is possible. E.g., Protections afforded by securing Microsoft Rights Management Services [10]. S/MIME Real Time Security Monitoring Content creation security Data Discovery and Classification Discovery / classification possible across media, populations, channels Secure Data Aggregation Vendor-supplied aggregation
| 0.88662
|
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M0110_v9_2689493468.docx
|
task-1430-681
| 30
|
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] |
possible ordering of a set of videos for a user (household) within a given context in real-time; maximize movie consumption. Use Case Description Digital movies stored in cloud with metadata; user profiles and rankings for small fraction of movies for each user. Use multiple criteria – content based recommender system; user-based recommender system; diversity. Refine algorithms continuously with A/B testing. Current Compute(System) Amazon Web Services AWS Solutions Storage Uses Cassandra NoSQL technology with Hive, Teradata Networking Need Content Delivery System to support effective streaming video Software Hadoop and Pig; Cassandra; Teradata Big Data Data Source Add movies institutionally. Collect user rankings and Characteristics (distributed/centralized) profiles in a distributed fashion Volume (size) Summer 2012. 25 million subscribers; 4 million ratings per day; 3 million searches per day; 1 billion hours streamed in June 2012. Cloud storage 2 petabytes (June 2013) Velocity Media (video and properties) and Rankings (e.g. real time) continually updated Variety Data varies from digital media to user rankings, (multiple datasets, user profiles and media properties for mashup) recommendations Variability (rate of Very competitive business. Need to aware of other change) companies and trends in both content (which Movies are hot) and technology. Need to investigate new business initiatives such as Netflix sponsored content Big Data Science Veracity (Robustness Success of business requires excellent quality of (collection, Issues) service curation, Visualization Streaming media and quality user-experience to analysis, allow choice of content action) Data Quality Rankings are intrinsically “rough” data and need robust learning algorithms Data Types Media content, user of user rankings profiles, “bag” Data Analytics Recommender systems and streaming video delivery. Recommender systems are always personalized and use logistic/linear regression, elastic nets, matrix factorization, clustering, latent Dirichlet allocation, association rules, gradient boosted decision trees and others. Winner of Netflix competition (to improve ratings by 10%) combined over 100 different algorithms. Big Data Specific Analytics needs continued monitoring and improvement. Challenges (Gaps) Big Data Specific Mobile access important Challenges in Mobility Security & Privacy Need to preserve privacy for users and digital rights for media.
|
possible ordering of a set of videos for a user (household) within a given context in real-time; maximize movie consumption. Use Case Description Digital movies stored in cloud with metadata; user profiles and rankings for small fraction of movies for each user. Use multiple criteria – content based recommender system; user-based recommender system; diversity. Refine algorithms continuously with A/B testing. Current Solutions Compute(System) Amazon Web Services AWS Storage Uses Cassandra NoSQL technology with Hive, Teradata Networking Need Content Delivery System to support effective streaming video Software Hadoop and Pig; Cassandra; Teradata Big Data Characteristics Data Source (distributed/centralized) Add movies institutionally. Collect user rankings and profiles in a distributed fashion Volume (size) Summer 2012. 25 million subscribers; 4 million ratings per day; 3 million searches per day; 1 billion hours streamed in June 2012. Cloud storage 2 petabytes (June 2013) Velocity (e.g. real time) Media (video and properties) and Rankings continually updated Variety (multiple datasets, mashup) Data varies from digital media to user rankings, user profiles and media properties for recommendations Variability (rate of change) Very competitive business. Need to aware of other companies and trends in both content (which Movies are hot) and technology. Need to investigate new business initiatives such as Netflix sponsored content Big Data Science (collection, curation, analysis, action) Veracity (Robustness Issues) Success of business requires excellent quality of service Visualization Streaming media and quality user-experience to allow choice of content Data Quality Rankings are intrinsically “rough” data and need robust learning algorithms Data Types Media content, user profiles, “bag” of user rankings Data Analytics Recommender systems and streaming video delivery. Recommender systems are always personalized and use logistic/linear regression, elastic nets, matrix factorization, clustering, latent Dirichlet allocation, association rules, gradient boosted decision trees and others. Winner of Netflix competition (to improve ratings by 10%) combined over 100 different algorithms. Big Data Specific Challenges (Gaps) Analytics needs continued monitoring and improvement. Big Data Specific Challenges in Mobility Mobile access important Security & Privacy Need to preserve privacy for users and digital rights for media.
| 0.847789
|
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M0180_v2_1513248729.docx
|
task-1430-702
| 17
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data and irregular simulation computation Study 3. variety of nature of graph system output datasets processing (platform) depends on the 3. summary of 3. Charm++ on complexity of the various runs of multi-nodes model simulation (software) 4. network file system (storage) 5. infiniband network (networking) 1. traditional and 1. large scale 1. computing 1. multi-levels 1. protection of 1. data fusion 1. efficient new architecture modeling for infrastructure detail network PII on from variety of method of Social for dynamic various events which can representations individuals used dta sources moving data Contagion distributed (disease, capture 2. visualization in modeling 2. data Modeling processing on emotions, with interactions 2. data protection consistency and for commondity behaviors, etc.) interactions on and secure no corruption Planning clusters 2. scalable fusion various social platform for 3. preprocessing 2. fine-resolution between events via the computation of raw data models and combined Internet datasets in to datasets (infrastructure) support Twitter 3. multi-levels 2. file servers network traffic analysis while and databases 3. huge data generate (platform) storage per year sufficient results 3. Ethernet and 24 quickly Infiniband networking (networking) 4. specialized simulators, open source software, and proprietary modeling (application) 5. huge user accounts across country boundaries (networking) 1. special 1. Web Services 1. expandable 1. access by 1. Federated 1. data storage -- dedicated or based, Grid on-demand based mobile users identity and archiving, overlay sensor based services, storage resource 2. advanced / management for data exchange and network relational for global users rich / high mobile and integration LifeWatch 2. storage databases and 2. cloud definition researchers and 2. data lifecycle Distributed, NoSQL community visualization mobile sensors management: historical and 2. personalized resource required 3. 4D 2. access control data provenance, trends data virtual labs visualization and accounting referral integrity 25 archiving 3. grid and cloud computational and identification 3. data sources based resources models traceability back distributed, and 4. data analysed to initial include incrementally observational observation and and/or real-time data monitoring at varying rates 3. [In addition to facilities, sensor due to variations original source network, and in source data,] processed satellites. processes. (secondary) data
|
Study data 3. variety of output datasets depends on the complexity of the model and irregular nature of graph processing 3. summary of various runs of simulation simulation system (platform) 3. Charm++ on multi-nodes (software) 4. network file system (storage) 5. infiniband network (networking) computation 24 Social Contagion Modeling for Planning 1. traditional and new architecture for dynamic distributed processing on commondity clusters 2. fine-resolution models and datasets in to support Twitter network traffic 3. huge data storage per year 1. large scale modeling for various events (disease, emotions, behaviors, etc.) 2. scalable fusion between combined datasets 3. multi-levels analysis while generate sufficient results quickly 1. computing infrastructure which can capture interactions on various social events via the Internet (infrastructure) 2. file servers and databases (platform) 3. Ethernet and Infiniband networking (networking) 4. specialized simulators, open source software, and proprietary modeling (application) 5. huge user accounts across country boundaries (networking) 1. multi-levels detail network representations 2. visualization with interactions 1. protection of PII on individuals used in modeling 2. data protection and secure platform for computation 1. data fusion from variety of dta sources 2. data consistency and no corruption 3. preprocessing of raw data 1. efficient method of moving data 25 and LifeWatch 1. special dedicated or overlay sensor network 2. storage Distributed, historical and trends data archiving 3. data sources distributed, and include observation and monitoring facilities, sensor network, and satellites. 1. Web Services based, Grid based services, relational databases and NoSQL 2. personalized virtual labs 3. grid and cloud based resources 4. data analysed incrementally and/or real-time at varying rates due to variations in source processes. 1. expandable on-demand based storage resource for global users 2. cloud community resource required 1. access by mobile users 2. advanced / rich / high definition visualization 3. 4D visualization computational models 1. Federated identity management for mobile researchers and mobile sensors 2. access control and accounting 1. data storage and archiving, data exchange and integration 2. data lifecycle management: data provenance, referral integrity and identification traceability back to initial observational data 3. [In addition to original source data,] processed (secondary) data --
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M0245_v2_8244561618.docx
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task-1430-727
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] |
country boundaries (networking) 1. special 1. Web Services 1. expandable 1. access by 1. Federated 1. data storage and -- dedicated or based, Grid on-demand based mobile users identity archiving, data Biodiversity overlay sensor based services, storage resource 2. advanced / management for exchange and and network relational for global users rich / high mobile integration LifeWatch 2. storage databases and 2. cloud definition researchers and 2. data lifecycle Distributed, NoSQL community visualization mobile sensors management: data historical and 2. personalized resource required 3. 4D 2. access control provenance, referral trends data virtual labs visualization and accounting integrity and archiving 3. grid and cloud computational identification 3. data sources based resources models traceability back to distributed, and 4. data analysed initial observational include incrementally data observation and/or real-time 3. [In addition to and monitoring at varying rates original source facilities, due to variations data,] processed sensor in source (secondary) data network, and processes. may be stored for satellites. 5. a variety of future uses 25 4. wide variety data, analytical 4. provenance (and of data, and modeling persistent including tools to support identification (PID)) satellite analytics for control of data, diverse scientific algorithms, and climate communities. workflows and weather 6. parallel data 5. curated data, photos, streams and (authorized) video, sound streaming reference data (i.e. analytics species names lists), recordings… 5. multi-type 7. access and algorithms, software data integration of code, workflows combination multiple and linkage distributed potentially databases unlimited data variety 6. data streaming -- -- 1. GPU -- -- -- -- 2. high Large-scale performance MPI Deep and HPC Learning Infiniband cluster 3. libraries for single-machine or single-GPU 26 computation are available (e.g., BLAS, CuBLAS, MAGMA, etc.), distributed computation of dense BLAS-like or LAPACK-like
|
country boundaries (networking) 25 Biodiversity and LifeWatch 1. special dedicated or overlay sensor network 2. storage Distributed, historical and trends data archiving 3. data sources distributed, and include observation and monitoring facilities, sensor network, and satellites. 4. wide variety of data, including satellite climate and weather data, photos, video, sound recordings… 5. multi-type data combination and linkage potentially unlimited data variety 6. data streaming 1. Web Services based, Grid based services, relational databases and NoSQL 2. personalized virtual labs 3. grid and cloud based resources 4. data analysed incrementally and/or real-time at varying rates due to variations in source processes. 5. a variety of data, analytical and modeling tools to support analytics for diverse scientific communities. 6. parallel data streams and streaming analytics 7. access and integration of multiple distributed databases 1. expandable on-demand based storage resource for global users 2. cloud community resource required 1. access by mobile users 2. advanced / rich / high definition visualization 3. 4D visualization computational models 1. Federated identity management for mobile researchers and mobile sensors 2. access control and accounting 1. data storage and archiving, data exchange and integration 2. data lifecycle management: data provenance, referral integrity and identification traceability back to initial observational data 3. [In addition to original source data,] processed (secondary) data may be stored for future uses 4. provenance (and persistent identification (PID)) control of data, algorithms, and workflows 5. curated (authorized) reference data (i.e. species names lists), algorithms, software code, workflows -- 26 Large-scale Deep Learning -- -- 1. GPU 2. high performance MPI and HPC Infiniband cluster 3. libraries for single-machine or single-GPU computation are available (e.g., BLAS, CuBLAS, MAGMA, etc.), distributed computation of dense BLAS-like or LAPACK-like -- -- -- --
| 0.319587
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M0245_v4_9775320899.docx
|
task-1430-729
| 54
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develops methods and tools for the diagnosis and intercomparison of general circulation models (GCMs) that simulate the global climate. The need for innovative analysis of GCM climate simulations is apparent, as increasingly more complex models are developed, while the disagreements among these simulations and relative to climate observations remain significant and poorly understood. The nature and causes of these disagreements must be accounted for in a systematic fashion in order to confidently use GCMs for simulation of putative global climate change. Variability (rate of Data is produced by codes running at change) supercomputer centers. During runtime, intense periods of data i/O occur regularly, but typically consume only a few percent of the total run time. Runs are carried out routinely, but spike as deadlines for reports approach. Big Data Science Veracity (Robustness Data produced by climate simulations is plays a (collection, Issues) and Quality large role in informing discussion of climate change curation, simulations. Therefore it must be robust, both from analysis, the standpoint of providing a scientifically valid action) representation of processes that influence climate, but also as that data is stored long and transferred world-wide to collaborators and other scientists. Visualization Visualization is crucial to understanding a system as complex as the Earth ecosystem. Data Types Earth system scientists are being inundated by an explosion of data generated by ever-increasing resolution in both global models and remote sensors. Data Analytics There is a need to provide data reduction and analysis web services through the Earth System Grid (ESG). A pressing need is emerging for data analysis capabilities closely linked to data archives. Big Data Specific The rapidly growing size of datasets makes scientific analysis a challenge. Challenges (Gaps) The need to write data from simulations is outpacing supercomputers’ ability to accommodate this need. Big Data Specific Challenges in Mobility Data from simulations and observations must be shared among a large widely distributed community. Security & Privacy Requirements Highlight issues for ESGF is in the early stages of being adapted for use in two additional generalizing this use domains: biology (to accelerate drug design and development) and case (e.g. for ref. energy (infrastructure for California Energy Systems for the 21st Century architecture) (CES21)). More Information (URLs)
|
develops methods and tools for the diagnosis and intercomparison of general circulation models (GCMs) that simulate the global climate. The need for innovative analysis of GCM climate simulations is apparent, as increasingly more complex models are developed, while the disagreements among these simulations and relative to climate observations remain significant and poorly understood. The nature and causes of these disagreements must be accounted for in a systematic fashion in order to confidently use GCMs for simulation of putative global climate change. Variability (rate of change) Data is produced by codes running at supercomputer centers. During runtime, intense periods of data i/O occur regularly, but typically consume only a few percent of the total run time. Runs are carried out routinely, but spike as deadlines for reports approach. Big Data Science (collection, curation, analysis, action) Veracity (Robustness Issues) and Quality Data produced by climate simulations is plays a large role in informing discussion of climate change simulations. Therefore it must be robust, both from the standpoint of providing a scientifically valid representation of processes that influence climate, but also as that data is stored long and transferred world-wide to collaborators and other scientists. Visualization Visualization is crucial to understanding a system as complex as the Earth ecosystem. Data Types Earth system scientists are being inundated by an explosion of data generated by ever-increasing resolution in both global models and remote sensors. Data Analytics There is a need to provide data reduction and analysis web services through the Earth System Grid (ESG). A pressing need is emerging for data analysis capabilities closely linked to data archives. Big Data Specific Challenges (Gaps) The rapidly growing size of datasets makes scientific analysis a challenge. The need to write data from simulations is outpacing supercomputers’ ability to accommodate this need. Big Data Specific Challenges in Mobility Data from simulations and observations must be shared among a large widely distributed community. Security & Privacy Requirements Highlight issues for generalizing this use case (e.g. for ref. architecture) ESGF is in the early stages of being adapted for use in two additional domains: biology (to accelerate drug design and development) and energy (infrastructure for California Energy Systems for the 21st Century (CES21)). More Information (URLs)
| 0.914474
|
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M0245_v4_9775320899.docx
|
task-1430-729
| 198
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] |
Use Case Requirements Summary Data Data Security & Lifecycle Use Case Sources Capabilities Consumer Privacy Management Others Transformation 1. large -- 1. large -- 1. Title 13 data 1. long-term -- document centralized preservation of data Census 2010 format from a storage (storage) as-is for 75 years and 2000 centralized 2. long-term storage preservation at the bit-level 3. curation process 1 including format transformation 4. access and analytics processing after 75 years 5. needs to make sure no data loss 1. distributed 1. crawl and 1. large data 1. high relevancy 1. security policy 1. pre-process for 1. mobile data sources index from storage and high recall virus scan search with NARA: 2. large data distributed data 2. various from search 2. file format similar Search, storage sources storages such as 2. high accuracy identification Retrieve, 3. bursty data 2. various NetApps, from 3. indexing from Preservation range from GB analytics Hitachi, categorization of 4. categorize records desktop to hundreds of processing Magnetic tapes records TB including 3. various 4. wide variety ranking, data storages such as of data formats categorization, NetApps, including detect PII data Hitachi, 2 unstructured 3. pre-processing Magnetic tapes and structured of data data 4. long-term 5. distributed preservation data sources in management fo different clouds large varied datasets. 5. hugh amount of data with high relevancy and recall. 1. data size 1. analytics are 1. software 1. data 1. improving 1. high veracity on 1. mobile approximately required for includes Hadoop, visualization for recommendation data and systems access Statistical one petabyte recommendation Spark, Hive, R, data review, systems that must be very robust. Survey systems, SAS, Mahout, operational reduce costs and The semantic Response continued Allegrograph, activity and improve quality integrity of monitoring and MySQL, Oracle, general analysis. while providing conceptual metadata general survey Storm, It continues to confidentiality concerning what improvement. BigMemory, evolve. safeguards that exactly is measured 3 Cassandra, Pig are reliable and and the resulting publically limits of inference auditable. remain a challenge 2. both confidential and secure all data. All processes must be auditable for security and
|
Use Case Requirements Summary Use Case Data Sources Transformation Capabilities Data Consumer Security & Privacy Lifecycle Management Others 1 Census 2010 and 2000 1. large document format from a centralized storage -- 1. large centralized storage (storage) -- 1. Title 13 data 1. long-term preservation of data as-is for 75 years 2. long-term preservation at the bit-level 3. curation process including format transformation 4. access and analytics processing after 75 years 5. needs to make sure no data loss -- 2 NARA: Search, Retrieve, Preservation 1. distributed data sources 2. large data storage 3. bursty data range from GB to hundreds of TB 4. wide variety of data formats including unstructured and structured data 5. distributed data sources in different clouds 1. crawl and index from distributed data sources 2. various analytics processing including ranking, data categorization, detect PII data 3. pre-processing of data 4. long-term preservation management fo large varied datasets. 5. hugh amount of data with high relevancy and recall. 1. large data storage 2. various storages such as NetApps, Hitachi, Magnetic tapes 1. high relevancy and high recall from search 2. high accuracy from categorization of records 3. various storages such as NetApps, Hitachi, Magnetic tapes 1. security policy 1. pre-process for virus scan 2. file format identification 3. indexing 4. categorize records 1. mobile search with similar from desktop 3 Statistical Survey Response 1. data size approximately one petabyte 1. analytics are required for recommendation systems, continued monitoring and general survey improvement. 1. software includes Hadoop, Spark, Hive, R, SAS, Mahout, Allegrograph, MySQL, Oracle, Storm, BigMemory, Cassandra, Pig 1. data visualization for data review, operational activity and general analysis. It continues to evolve. 1. improving recommendation systems that reduce costs and improve quality while providing confidentiality safeguards that are reliable and publically auditable. 2. both confidential and secure all data. All processes must be auditable for security and 1. high veracity on data and systems must be very robust. The semantic integrity of conceptual metadata concerning what exactly is measured and the resulting limits of inference remain a challenge 1. mobile access
| 0.399532
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M0245_v5_6066621242.docx
|
task-1430-730
| 44
|
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identification of structures (lexical units, linguistic rules, etc.) 1. a set of 1. environments 1. large file 1. client side -- -- -- network to run various system (storage) visualization CINET for topologies file network and 2. various Network to study graph graph analysis network Science theoretic tools connectivity properties and 2. dynamic grow (networking) behaviors of of the networks 3. existing various 3. asynchronous computing algorithms and real time cluster 2. synchronous 4. EC2 30 asynchronous distributed computing and real time computing cluster synchronous 4. different 5. various graph distributed parallel libraries, computing algorithms for management different toos, databases, partitioning semantic web schemes for tools efficient operation 1. large 1. analytic 1. PERL, Python, 1. analytic flows 1. security -- -- amounts of algorithms C/C++, Matlab, involving users requirements for NIST semi-annotated working with R development protecting Information web pages, written language, tools. Create sensitive data Access tweets, images, speech, human ground-up test while enabling Division video imagery, etc. and measurement meaningful 2. scaling must generally applications developmental be tested against performance truthing to real or realistic evaluation. larger data, data. Shared It’s intrinsic and extremely evaluation annotation challenging to testbeds must uncertainty engineer artificial protect the 31 measurement, data that intellectual performance sufficiently property of measurement captures the analytic for variability of real algorithm incompletely data involving developers annotated data, humans measuring analytic performance for heterogeneous data and analytic flows involving users 1. process key 1. needs to 1. iRODS data 1. general 1. Federate -- -- format types provide general management visulaization across existing 32 DataNet NetCDF, analytics software workflows authentication (iRODS) HDF5, Dicom workflows 2. environments
|
identification of structures (lexical units, linguistic rules, etc.) 30 CINET for Network Science 1. a set of network topologies file to study graph theoretic properties and behaviors of various algorithms 2. asynchronous and real time synchronous distributed computing 1. environments to run various network and graph analysis tools 2. dynamic grow of the networks 3. asynchronous and real time synchronous distributed computing 4. different parallel algorithms for different partitioning schemes for efficient operation 1. large file system (storage) 2. various network connectivity (networking) 3. existing computing cluster 4. EC2 computing cluster 5. various graph libraries, management toos, databases, semantic web tools 1. client side visualization -- -- -- 31 NIST Information Access Division 1. large amounts of semi-annotated web pages, tweets, images, video 2. scaling to larger data, intrinsic and annotation uncertainty measurement, performance measurement for incompletely annotated data, measuring analytic performance for heterogeneous data and analytic flows involving users 1. analytic algorithms working with written language, speech, human imagery, etc. must generally be tested against real or realistic data. It’s extremely challenging to engineer artificial data that sufficiently captures the variability of real data involving humans 1. PERL, Python, C/C++, Matlab, R development tools. Create ground-up test and measurement applications 1. analytic flows involving users 1. security requirements for protecting sensitive data while enabling meaningful developmental performance evaluation. Shared evaluation testbeds must protect the intellectual property of analytic algorithm developers -- -- 32 DataNet (iRODS) 1. process key format types NetCDF, HDF5, Dicom 1. needs to provide general analytics workflows 1. iRODS data management software 2. 1. general visulaization workflows 1. Federate across existing authentication environments -- -- truthing
| 0.253556
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M0245_v5_6066621242.docx
|
task-1430-730
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omic, and simulation datasets across different semantic, spatial, and temporal scales 3. link diverse data to novel HPC simulation 1. 1. Custom 1. custom 1. phone based -- -- 1. phone heterogeneous Software: software like input and access based input DOE-BER Diverse data EddyPro, EddyPro and and access AmeriFlux with different Custom analysis analysis software and domains and software, R, like R, python, FLUXNET scales, python, neural neural netowrks, Networks Translation networks, Matlab Matlab across diverse 2. analytics datasets that includes data cross domains mining, data and scales quality 2. link to many assessment, other cross-correlation environment across datasets, 50 and biology data assimilation, datasets data 3. link to HPC interpolation, Climate and statistics, quality other assessment, data simulations fusion, etc. 4. link to European data sources and projects 5. access data from 500 distributed sources 1. diverse data 1. new machine 1. SQL -- 1. privacy and -- 1. mobile from Smart learning analytics databases, CVS anonymization access for Grid sensors, to predict files, HDFS by aggregation clients City planning, consumption (platform) 51 forecasting weather, 2. R/Matlab, in Smart utilities 2, data Weka, Hadoop Grids updated every (platform) 15 minutes Use Case Detail Requirements To assist in interpreting this section of the report: This section is first organized by category [Data Sources, Transformation, etc.] Within each category is a list of numbered requirement statements.
|
omic, and simulation datasets across different semantic, spatial, and temporal scales 3. link diverse data to novel HPC simulation 50 DOE-BER AmeriFlux and FLUXNET Networks 1. heterogeneous Diverse data with different domains and scales, Translation across diverse datasets that cross domains and scales 2. link to many other environment and biology datasets 3. link to HPC Climate and other simulations 4. link to European data sources and projects 5. access data from 500 distributed sources 1. Custom Software: EddyPro, Custom analysis software, R, python, neural networks, Matlab 1. custom software like EddyPro and analysis software like R, python, neural netowrks, Matlab 2. analytics includes data mining, data quality assessment, cross-correlation across datasets, data assimilation, data interpolation, statistics, quality assessment, data fusion, etc. 1. phone based input and access -- -- 1. phone based input and access 51 forecasting in Smart Grids 1. diverse data from Smart Grid sensors, City planning, weather, utilities 2, data updated every 15 minutes 1. new machine learning analytics to predict consumption 1. SQL databases, CVS files, HDFS (platform) 2. R/Matlab, Weka, Hadoop (platform) -- 1. privacy and anonymization by aggregation -- 1. mobile access for clients Use Case Detail Requirements To assist in interpreting this section of the report: This section is first organized by category [Data Sources, Transformation, etc.] Within each category is a list of numbered requirement statements.
| 0.498336
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M0245_v5_6066621242.docx
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task-1430-730
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Data Analytics Object identification (type, size, color) and tracking. Pattern analysis of object (did the truck observed every Weds. afternoon take a different route today or is there a standard route this person takes every day). Crowd behavior/dynamics (is there a small group attempting to incite a riot. Is this person out of place in the crowd or behaving differently? Economic activity is the line at the bread store, the butcher, or the ice cream store, are more trucks traveling north with goods than trucks going south Has activity at or the size of stores in this market place increased or decreased over the past year. Fusion of data with other data to improve quality and confidence. Big Data Specific Processing the volume of data in NRT to support alerting and situational awareness. Challenges (Gaps) Big Data Specific Getting data from mobile sensor to processing Challenges in Mobility Security and Privacy Significant – sources and methods cannot be compromised the enemy should not be Requirements able to know what we see. Highlight issues for Typically this type of processing fits well into massively parallel computing such as generalizing this use provided by GPUs. Typical problem is integration of this processing into a larger cluster case (e.g. for ref. capable of processing data from several sensors in parallel and in NRT. architecture) Transmission of data from sensor to system is also a large challenge. More Information Motion Imagery Standards - (URLs) Some of many papers on object identity/tracking: General Articles on the need: Defense> Use Case 15: Intelligence Data Processing and Analysis Use Case Title Intelligence Data Processing and Analysis Vertical (area) Defense (Intelligence) Author/ Company/Email David Boyd/Data Actors/Stakeholders Senior Civilian/Military Leadership and their roles and Field Commanders responsibilities Intelligence Analysts Warfighters
|
Data Analytics Object identification (type, size, color) and tracking. Pattern analysis of object (did the truck observed every Weds. afternoon take a different route today or is there a standard route this person takes every day). Crowd behavior/dynamics (is there a small group attempting to incite a riot. Is this person out of place in the crowd or behaving differently? Economic activity is the line at the bread store, the butcher, or the ice cream store, are more trucks traveling north with goods than trucks going south Has activity at or the size of stores in this market place increased or decreased over the past year. Fusion of data with other data to improve quality and confidence. Big Data Specific Challenges (Gaps) Processing the volume of data in NRT to support alerting and situational awareness. Big Data Specific Challenges in Mobility Getting data from mobile sensor to processing Security and Privacy Requirements Significant – sources and methods cannot be compromised the enemy should not be able to know what we see. Highlight issues for generalizing this use case (e.g. for ref. architecture) Typically this type of processing fits well into massively parallel computing such as provided by GPUs. Typical problem is integration of this processing into a larger cluster capable of processing data from several sensors in parallel and in NRT. Transmission of data from sensor to system is also a large challenge. More Information (URLs) Motion Imagery Standards - Some of many papers on object identity/tracking: General Articles on the need: Defense> Use Case 15: Intelligence Data Processing and Analysis Use Case Title Intelligence Data Processing and Analysis Vertical (area) Defense (Intelligence) Author/ Company/Email David Boyd/Data Actors/Stakeholders and their roles and responsibilities Senior Civilian/Military Leadership Field Commanders Intelligence Analysts Warfighters
| 0.883826
|
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M0363_v1_4695311424.docx
|
task-1430-762
| 77
|
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water column observatories, is geographically distributed in key sites of European waters, presently consisting of thirteen sites. aims at integrating the existing European EPOS facilities in solid Earth science into one coherent multidisciplinary RI, and to increase the accessibility and usability of multidisciplinary data from seismic and geodetic monitoring networks, volcano observatories, laboratory experiments and computational simulations enhancing worldwide interoperability in Earth Science. dedicates to the monitoring of greenhouse gases ICOS (GHG) through its atmospheric, ecosystem and ocean networks. The ICOS network includes more than 30 atmospheric and more than 30 ecosystem primary long term sites located across Europe, and additional secondary sites. It also includes three Thematic Centres to process the data from all the stations from each network, and provide access to these data. is a infrastructure for biodiversity LifeWatch “virtual” and ecosystem research with services mainly provided through the Internet. Its Common Facilities is coordinated and managed at a central European level; and the serve as specialized LifeWatch Centres facilities from member countries (regional partner facilities) or research communities. provides, deploys and operates an array of Euro-Argo around 800 floats contributing to the global array (3,000 floats) and thus provide enhanced coverage in the European regional seas. makes continuous measurements of the EISCAT- 3D, geospace environment and its coupling to the Earth's atmosphere from its location in the auroral zone at the southern edge of the northern polar vortex, and is a distributed infrastructure. Variable data size. e.g., Volume (size) The amount of data within the is depending on EMSO the instrumentation and configuration of the observatory between several MBs to several GB per data set. Within the EIDA network is currently EPOS, providing access to continuous raw data coming from approximately more than 1000 stations recording about 40GB per day, so over 15 TB per year. EMSC stores a Database of 1.85 GB of earthquake parameters, which is constantly growing and updated with refined information. 222705 events – 632327 origins – 642555 – magnitudes Within EISCAT 3D raw voltage data will reach 40PB/year in 2023.
|
water column observatories, is geographically distributed in key sites of European waters, presently consisting of thirteen sites. EPOS aims at integrating the existing European facilities in solid Earth science into one coherent multidisciplinary RI, and to increase the accessibility and usability of multidisciplinary data from seismic and geodetic monitoring networks, volcano observatories, laboratory experiments and computational simulations enhancing worldwide interoperability in Earth Science. ICOS dedicates to the monitoring of greenhouse gases (GHG) through its atmospheric, ecosystem and ocean networks. The ICOS network includes more than 30 atmospheric and more than 30 ecosystem primary long term sites located across Europe, and additional secondary sites. It also includes three Thematic Centres to process the data from all the stations from each network, and provide access to these data. LifeWatch is a “virtual” infrastructure for biodiversity and ecosystem research with services mainly provided through the Internet. Its Common Facilities is coordinated and managed at a central European level; and the LifeWatch Centres serve as specialized facilities from member countries (regional partner facilities) or research communities. Euro-Argo provides, deploys and operates an array of around 800 floats contributing to the global array (3,000 floats) and thus provide enhanced coverage in the European regional seas. EISCAT- 3D, makes continuous measurements of the geospace environment and its coupling to the Earth's atmosphere from its location in the auroral zone at the southern edge of the northern polar vortex, and is a distributed infrastructure. Volume (size) Variable data size. e.g., The amount of data within the EMSO is depending on the instrumentation and configuration of the observatory between several MBs to several GB per data set. Within EPOS, the EIDA network is currently providing access to continuous raw data coming from approximately more than 1000 stations recording about 40GB per day, so over 15 TB per year. EMSC stores a Database of 1.85 GB of earthquake parameters, which is constantly growing and updated with refined information. 222705 – events 632327 – origins 642555 – magnitudes Within EISCAT 3D raw voltage data will reach 40PB/year in 2023.
| 0.869691
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M0363_v1_4695311424.docx
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task-1430-762
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] |
Appendix C: Use Case Requirements Summary Requirements were extracted from each use case within seven characteristic categories introduced in Section 3.1. The number of requirements within each category varied for each use case. Table C-1 contains the use case specific requirements. Table C-1: Use Case Specific Requirements Use Case Data Data Capabilities Data Security and Lifecycle Other Sources Transformation Consumer Privacy Management 1 1. Large -- 1. Large -- 1. Title 13 data 1. Long-term -- Census 2010 document format centralized preservation of data and 2000 from centralized storage (storage) as-is for 75 years storage 2. Long-term preservation at the bit level 3. Curation process including format transformation 4. Access and analytics processing after 75 years 5. No data loss 2 1. Distributed 1. Crawl and index 1. Large data 1. High 1. Security 1. Pre-process for 1. Mobile NARA: Search, data sources from distributed storage relevancy and policy virus scan search with Retrieve, 2. Large data data sources 2. Various storage high recall 2. File format similar Preservation storage 2. Various analytics systems such as from search identification interfaces/ 3. Bursty data processing NetApps, Hitachi, 2. High 3. Indexing results from ranging from including ranking, magnetic tapes accuracy from 4. Records desktop gigabytes to data categorization categorization hundreds of categorization, of records terabytes detection of PII 3. Various 4. Wide variety of data storage data formats 3. Data systems such including as NetApps, unstructured and 4. Long-term Hitachi, structured data preservation magnetic 5. Distributed management of tapes
|
Appendix C: Use Case Requirements Summary Requirements were extracted from each use case within seven characteristic categories introduced in Section 3.1. The number of requirements within each category varied for each use case. Table C-1 contains the use case specific requirements. Table C-1: Use Case Specific Requirements Use Case Data Sources Data Transformation Capabilities Data Consumer Security and Privacy Lifecycle Management Other 1 Census 2010 and 2000 1. Large document format from centralized storage -- 1. Large centralized storage (storage) -- 1. Title 13 data 1. Long-term preservation of data as-is for 75 years 2. Long-term preservation at the bit level 3. Curation process including format transformation 4. Access and analytics processing after 75 years 5. No data loss -- 2 NARA: Search, Retrieve, Preservation 1. Distributed data sources 2. Large data storage 3. Bursty data ranging from gigabytes to hundreds of terabytes 4. Wide variety of data formats including unstructured and structured data 5. Distributed 1. Crawl and index from distributed data sources 2. Various analytics processing including ranking, data categorization, detection of PII data 3. Data 4. Long-term preservation management of 1. Large data storage 2. Various storage systems such as NetApps, Hitachi, magnetic tapes 1. High relevancy and high recall from search 2. High accuracy from categorization of records 3. Various storage systems such as NetApps, Hitachi, magnetic tapes 1. Security policy 1. Pre-process for virus scan 2. File format identification 3. Indexing 4. Records categorization 1. Mobile search with similar interfaces/ results from desktop
| 0.53896
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M0363_v1_4695311424.docx
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task-1430-762
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Materials Data data repositories independent Visualization proprietary quality (currently for for more than variables need to for materials sensitive data poor or no process) Manufacturing 500,000 be collected to discovery from 2. Tools to commercial create robust many mask materials datasets independent proprietary 2. Many varieties variables information of datasets 2. 3. Text, graphics, Visualization and images tools for materials 1. Data streams 1. High-throughput 1. Massive 1. 1. Sandbox as 1. Validation and 1. Mobile from computing (150,000 cores) search independent uncertainty applications peta/exascale data analysis legacy for growing working areas quantification (UQ) (apps) to Materials centralized for web-like infrastructure materials data between of simulation with access Genomics simulation responsiveness (infrastructure) different data experimental data materials systems 2. Mashup of 2. GPFS (storage) stakeholders 2. UQ in results from genomics 2. Distributed simulation outputs 3. MonogDB 2. Policy-driven multiple datasets information web dataflows across codes systems federation of from central 3. Search and (platform) datasets gateway to users crowd-driven with 4. 10 GB computation networking backend, flexibility 5. Various analytic for new targets tools such as 4. MapReduce and PyMatGen, search to join FireWorks, VASP, simulation and ABINIT, NWChem, experimental data BerkeleyGW, varied community codes 6. Large storage (storage) 7. Scalable and object store (platform)
|
Materials Data for Manufacturing data repositories for more than 500,000 commercial materials 2. Many varieties of datasets 3. Text, graphics, and images independent variables need to be collected to create robust datasets Visualization for materials discovery from many independent variables 2. Visualization tools for materials proprietary sensitive data 2. Tools to mask proprietary information quality (currently poor or no process) Materials Genomics 1. Data streams from peta/exascale centralized simulation systems 2. Distributed web dataflows from central gateway to users 1. High-throughput computing data analysis for web-like responsiveness 2. Mashup of simulation outputs across codes 3. Search and crowd-driven with computation backend, flexibility for new targets 4. MapReduce and search to join simulation and experimental data 1. Massive (150,000 cores) legacy infrastructure (infrastructure) 2. GPFS (storage) 3. MonogDB systems (platform) 4. 10 GB networking 5. Various analytic tools such as PyMatGen, FireWorks, VASP, ABINIT, NWChem, BerkeleyGW, varied community codes 6. Large storage (storage) 7. Scalable and object store (platform) 1. search for growing materials data 1. Sandbox as independent working areas between different data stakeholders 2. Policy-driven federation of datasets 1. Validation and uncertainty quantification (UQ) of simulation with experimental data 2. UQ in results from multiple datasets 1. Mobile applications (apps) to access materials genomics information
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(networking) 1. Traditional and 1. Large-scale 1. Computing 1. Multi-level 1. Protection of 1. Data fusion from 1. Efficient Social new architecture modeling for infrastructure detailed PII of variety of data method of Contagion for dynamic various events that can capture network individuals used sources (i.e., Stata moving data Modeling for distributed (disease, emotions, human-to-human in modeling data files) Planning processing on behaviors, etc.) interactions on 2. Data 2. Data consistency commodity 2. Scalable fusion various social 2. protection and and no corruption clusters between combined events via the Visualization secure platform 3. Preprocessing of 2. Fine-resolution datasets Internet with for raw data models and 3. Multi-level (infrastructure) interactions computation datasets to analysis while 2. File servers and support Twitter generating databases network traffic sufficient results (platform) 3. Huge data quickly 3. Ethernet and storage Infiniband supporting annual networking data growth (networking) 4. Specialized simulators, open source software, and proprietary modeling (application) 5. Huge user accounts across country boundaries (networking) 1. Special 1. Web-based 1. Expandable 1. Access by 1. Federated 1. Data storage and -- Biodiversity dedicated or services, mobile users identity archiving, data and LifeWatch overlay sensor services, storage resource 2. Advanced/ management exchange and network relational for global users for mobile integration 2. Storage: databases, NoSQL 2. Cloud researchers and 2. Data lifecycle distributed, 2. Personalized community visualization mobile sensors management: data historical, and virtual labs resource required 3. 4D 2. Access provenance, referral
|
(networking) Social Contagion Modeling for Planning 1. Traditional and new architecture for dynamic distributed processing on commodity clusters 2. Fine-resolution models and datasets to support Twitter network traffic 3. Huge data storage supporting annual data growth 1. Large-scale modeling for various events (disease, emotions, behaviors, etc.) 2. Scalable fusion between combined datasets 3. Multi-level analysis while generating sufficient results quickly 1. Computing infrastructure that can capture human-to-human interactions on various social events via the Internet (infrastructure) 2. File servers and databases (platform) 3. Ethernet and Infiniband networking (networking) 4. Specialized simulators, open source software, and proprietary modeling (application) 5. Huge user accounts across country boundaries (networking) 1. Multi-level detailed network 2. Visualization with interactions 1. Protection of PII of individuals used in modeling 2. Data protection and secure platform for computation 1. Data fusion from variety of data sources (i.e., Stata data files) 2. Data consistency and no corruption 3. Preprocessing of raw data 1. Efficient method of moving data Biodiversity and LifeWatch 1. Special dedicated or overlay sensor network 2. Storage: distributed, historical, and 1. Web-based services, services, relational databases, NoSQL 2. Personalized virtual labs 1. Expandable storage resource for global users 2. Cloud community resource required 1. Access by mobile users 2. Advanced/ visualization 3. 4D 1. Federated identity management for mobile researchers and mobile sensors 2. Access 1. Data storage and archiving, data exchange and integration 2. Data lifecycle management: data provenance, referral --
| 0.295087
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M0363_v1_4695311424.docx
|
task-1430-762
| 181
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various and real-time computing cluster algorithms synchronous 4. EC2 computing 2. Asynchronous distributed cluster and real-time computing 5. Various graph synchronous 4. Different parallel libraries, distributed algorithms for management computing different tools, databases, partitioning semantic web schemes for tools efficient operation 1. Large amounts 1. Test analytic 1. PERL, Python, 1. Analytic 1. Security -- -- NIST of algorithms working C/C++, Matlab, R flows involving requirements Information web with written development users for protecting Access pages, tweets, language, speech, tools; creation of sensitive data Division images, video human imagery, ground-up test while enabling 2. Scaling etc. against real or and measurement meaningful to larger realistic data; applications developmental data, intrinsic and challenge: performance annotation engineering evaluation; uncertainty artificial data that shared measurement, sufficiently evaluation performance captures the testbeds that measurement for variability of real protect the incompletely data involving intellectual annotated data, humans property of measuring analytic analytic algorithm performance for developers heterogeneous data and analytic flows involving users 1. Process key 1. Provision of 1. iRODS data 1. General 1. Federate -- -- DataNet format types general analytics management visualization across existing (iRODS) NetCDF, HDF5, workflows needed software workflows authentication
|
various algorithms 2. Asynchronous and real-time synchronous distributed computing and real-time synchronous distributed computing 4. Different parallel algorithms for different partitioning schemes for efficient operation computing cluster 4. EC2 computing cluster 5. Various graph libraries, management tools, databases, semantic web tools NIST Information Access Division 1. Large amounts of web pages, tweets, images, video 2. Scaling to larger data, intrinsic and annotation uncertainty measurement, performance measurement for incompletely annotated data, measuring analytic performance for heterogeneous data and analytic flows involving users 1. Test analytic algorithms working with written language, speech, human imagery, etc. against real or realistic data; challenge: engineering artificial data that sufficiently captures the variability of real data involving humans 1. PERL, Python, C/C++, Matlab, R development tools; creation of ground-up test and measurement applications 1. Analytic flows involving users 1. Security requirements for protecting sensitive data while enabling meaningful developmental performance evaluation; shared evaluation testbeds that protect the intellectual property of analytic algorithm developers -- -- DataNet (iRODS) 1. Process key format types NetCDF, HDF5, 1. Provision of general analytics workflows needed 1. iRODS data management software 1. General visualization workflows 1. Federate across existing authentication -- --
| 0.203562
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M0363_v1_4695311424.docx
|
task-1430-762
| 185
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Dicom 2. interoperability environments 2. Real-time and across storage through batch data and network Generic protocol types Security Service API and pluggable authentication modules (GSI, Kerberos, InCommon, Shibboleth) 2. Access controls on files independent of the storage location 1. Integration of -- 1. Software: -- 1. Significant 1. Integration of -- The Discinnet metadata Symfony-PHP, but optional metadata Process approaches Linux, MySQL security and approaches across across disciplines privacy disciplines including secure servers and anonymization 1. All data types, 1. Data graph 1. Cloud 1. Efficient -- -- -- Semantic image to text, processing community Graph-Search structures to 2. RDBMS resource required protein sequence visualization needed 1. Multiple 1. Standard 1. High-volume -- 1. Multiple -- -- Light source streams of bioinformatics data transfer to security and beamlines data to be tools (BLAST, remote batch privacy stored and HMMER, multiple processing requirements analyzed later alignment and resource to be satisfied 2. Sample data to phylogenetic tools,
|
Dicom 2. Real-time and batch data 2. interoperability across storage and network protocol types environments through Generic Security Service API and pluggable authentication modules (GSI, Kerberos, InCommon, Shibboleth) 2. Access controls on files independent of the storage location The Discinnet Process 1. Integration of metadata approaches across disciplines -- 1. Software: Symfony-PHP, Linux, MySQL -- 1. Significant but optional security and privacy including secure servers and anonymization 1. Integration of metadata approaches across disciplines -- Semantic Graph-Search 1. All data types, image to text, structures to protein sequence 1. Data graph processing 2. RDBMS 1. Cloud community resource required 1. Efficient visualization needed -- -- -- Light source beamlines 1. Multiple streams of data to be stored and analyzed later 2. Sample data to 1. Standard bioinformatics tools (BLAST, HMMER, multiple alignment and phylogenetic tools, 1. High-volume data transfer to remote batch processing resource -- 1. Multiple security and privacy requirements to be satisfied -- --
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M0363_v1_4695311424.docx
|
task-1430-762
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47 200 TB (current), 500 Data analyzed Re-analysis datasets are MapReduce or the Data mining customized Atmospheric TB within 5 years incrementally inconsistent in format, like, SciDB or other for specific event types Turbulence resolution, semantics, and scientific database metadata; interpretation/analysis of each of these input streams into a common product 48 Up to 30 PB/year 42 GB/second Variety across simulation National Center for Need analytics next to Climate Studies from 15 end-to-end from simulations groups and between Atmospheric Research data storage simulations at observation and simulation (NCAR) PIO library and NERSC, more at utilities NCL and NCO, other HPC centers parallel NetCDF 49 — — From omics of the microbes PFLOWTran, postgres, Data mining, data quality DOE-BER in the soil to watershed HDF5, Akuna, NEWT, assessment, Subsurface hydro-biogeochemistry, etc. across Biogeochemistry from observation to datasets, reduced model simulation development, statistics, quality assessment, data fusion 50 — Streaming data Flux data merged with EddyPro, custom Data mining, data quality DOE-BER from ~150 towers biological, disturbance, and analysis software, R, assessment, AmeriFlux and in AmeriFlux and other ancillary data Python, neural correlation across FLUXNET over 500 towers networks, Matlab datasets, data Networks distributed assimilation, data globally collecting interpolation, statistics, flux quality assessment, data measurements fusion 51 4 TB/year for a city Streaming data Tuple-based: timeseries, R/Matlab, Weka, Forecasting models, Consumption with 1.4 million from millions of database rows; graph-based: Hadoop; GIS-based machine learning models, forecasting in sensors, such as Los sensors network topology, customer visualization time series analysis, Smart Grids Angeles connectivity; some semantic clustering, motif data for normalization detection, complex event processing, visual network analysis
|
47 Atmospheric Turbulence 200 TB (current), 500 TB within 5 years Data analyzed incrementally Re-analysis datasets are inconsistent in format, resolution, semantics, and metadata; interpretation/analysis of each of these input streams into a common product MapReduce or the like, SciDB or other scientific database Data mining customized for specific event types 48 Climate Studies Up to 30 PB/year from 15 end-to-end simulations at NERSC, more at other HPC centers 42 GB/second from simulations Variety across simulation groups and between observation and simulation National Center for Atmospheric Research (NCAR) PIO library and utilities NCL and NCO, parallel NetCDF Need analytics next to data storage 49 DOE-BER Subsurface Biogeochemistry — — From omics of the microbes in the soil to watershed hydro-biogeochemistry, from observation to simulation PFLOWTran, postgres, HDF5, Akuna, NEWT, etc. Data mining, data quality assessment, across datasets, reduced model development, statistics, quality assessment, data fusion 50 DOE-BER AmeriFlux and FLUXNET Networks — Streaming data from ~150 towers in AmeriFlux and over 500 towers distributed globally collecting flux measurements Flux data merged with biological, disturbance, and other ancillary data EddyPro, custom analysis software, R, Python, neural networks, Matlab Data mining, data quality assessment, across datasets, data assimilation, data interpolation, statistics, quality assessment, data fusion 51 Consumption forecasting in Smart Grids 4 TB/year for a city with 1.4 million sensors, such as Los Angeles Streaming data from millions of sensors Tuple-based: timeseries, database rows; graph-based: network topology, customer connectivity; some semantic data for normalization R/Matlab, Weka, Hadoop; GIS-based visualization Forecasting models, machine learning models, time series analysis, clustering, motif detection, complex event processing, visual network analysis correlation
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vary by several orders of magnitude, such as several hundred thousand genes to a billion genes 21 1. Distributed EHR 1. Data integration 1. data 1. Efficient 1. Protection of 1. Data annotated 1. Mobile data using ontological warehouse, open data health data in based on domain access Diabetes 2. Over 5 million annotation and source indexed accordance ontologies or patients with taxonomies Hbase visualization with privacy taxonomies thousands of 2. Parallel retrieval 2. needed policies and 2. Traceability of properties each algorithms for both supercomputers, legal data from origin and many more indexed and cloud and parallel requirements, (initial point of derived from custom searches; computing e.g., HIPAA. collection) through primary values identification of 3. I/O intensive 2. Security use 3. Each record: a data of interest; processing policies for 3. Data conversion range of patient cohorts, 4. HDFS storage different user from existing data data patients’ meeting 5. custom code to roles warehouse into RDF property values, certain criteria, develop new triples average of 100 patients sharing properties from controlled similar stored data. vocabulary characteristics values, and 3. Distributed average of 1,000 graph mining continuous values algorithms, pattern 4. No real-time, analysis and graph but data updated indexing, pattern periodically; data searching on RDF timestamped with triple graphs the time of 4. Robust statistical observation (time analysis tools to the value is manage false recorded) discovery rates, 5. Two main determine true
|
vary by several orders of magnitude, such as several hundred thousand genes to a billion genes 21 Diabetes 1. Distributed EHR data 2. Over 5 million patients with thousands of properties each and many more derived from primary values 3. Each record: a range of data property values, average of 100 controlled vocabulary values, and average of 1,000 continuous values 4. No real-time, but data updated periodically; data timestamped with the time of observation (time the value is recorded) 5. Two main 1. Data integration using ontological annotation and taxonomies 2. Parallel retrieval algorithms for both indexed and custom searches; identification of data of interest; patient cohorts, patients’ meeting certain criteria, patients sharing similar characteristics 3. Distributed graph mining algorithms, pattern analysis and graph indexing, pattern searching on RDF triple graphs 4. Robust statistical analysis tools to manage false discovery rates, determine true 1. data warehouse, open source indexed Hbase 2. supercomputers, cloud and parallel computing 3. I/O intensive processing 4. HDFS storage 5. custom code to develop new properties from stored data. 1. Efficient data visualization needed 1. Protection of health data in accordance with privacy policies and legal requirements, e.g., HIPAA. 2. Security policies for different user roles 1. Data annotated based on domain ontologies or taxonomies 2. Traceability of data from origin (initial point of collection) through use 3. Data conversion from existing data warehouse into RDF triples 1. Mobile access
| 0.310916
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M0394_v1_4746659136.docx
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task-1430-774
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longitudinal data data are available hard drive for test 5. Rich relational for a small number data data comprising of subjects) multiple tables, 3. Learning different data algorithms to types such as identify skews in imaging, EHR, data, so as to not demographic, to (incorrectly) genetic, and model noise natural language 4. Generalized and data requiring refined learned rich models for representation application to 6. Unpredictable diverse sets of data arrival rates, 5. Challenge: often real time acceptance of data in different modalities (and from disparate sources) 23 1. File-based 1. 1. Movement of 1. 1. Protection of 1. Data quality, -- World synthetic and very large volume Visualization PII on ability to capture the Population population, either of data for individuals used traceability of Scale centralized or computation, like visualization in modeling quality from distributed sites supercomputer (networking) 2. Data computation Study 2. Large volume performance 2. Distributed protection and of real-time 2. Unstructured MPI-based secure platform output data and irregular simulation system for 3. Variety of nature of graph (platform) computation output datasets processing 3. Charm++ on depending on the 3. Summary of multi-nodes model’s various runs of (software) complexity simulation 4. Network file system (storage) 5. Infiniband
|
longitudinal data 5. Rich relational data comprising multiple tables, different data types such as imaging, EHR, demographic, genetic, and natural language data requiring rich representation 6. Unpredictable arrival rates, often real time data are available for a small number of subjects) 3. Learning algorithms to identify skews in data, so as to not to (incorrectly) model noise 4. Generalized and refined learned models for application to diverse sets of data 5. Challenge: acceptance of data in different modalities (and from disparate sources) hard drive for test data 23 World Population Scale Study 1. File-based synthetic population, either centralized or distributed sites 2. Large volume of real-time output data 3. Variety of output datasets depending on the model’s complexity 1. and computation, like supercomputer performance 2. Unstructured and irregular nature of graph processing 3. Summary of various runs of simulation 1. Movement of very large volume of data for visualization (networking) 2. Distributed MPI-based simulation system (platform) 3. Charm++ on multi-nodes (software) 4. Network file system (storage) 5. Infiniband 1. Visualization 1. Protection of PII on individuals used in modeling 2. Data protection and secure platform for computation 1. Data quality, ability to capture the traceability of quality from computation --
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2. Link to many mining, data other quality environment and assessment, biology datasets cross-correlation 3. Link to HPC across datasets, climate and other data assimilation, simulations data 4. Link to interpolation, European data statistics, quality sources and assessment, data projects fusion, etc. 5. Access to data from 500 distributed sources 51 1. Diverse data 1. New machine 1. SQL databases, -- 1. Privacy and -- 1. Mobile from smart grid learning analytics CVS files, HDFS anonymization access for sensors, city to predict (platform) by aggregation clients Forecasting planning, consumption 2. R/Matlab, in Smart weather, utilities Weka, Hadoop Grids 2. Data updated (platform) every 15 minutes
|
2. Link to many other environment and biology datasets 3. Link to HPC climate and other simulations 4. Link to European data sources and projects 5. Access to data from 500 distributed sources mining, data quality assessment, cross-correlation across datasets, data assimilation, data interpolation, statistics, quality assessment, data fusion, etc. 51 Forecasting in Smart Grids 1. Diverse data from smart grid sensors, city planning, weather, utilities 2. Data updated every 15 minutes 1. New machine learning analytics to predict consumption 1. SQL databases, CVS files, HDFS (platform) 2. R/Matlab, Weka, Hadoop (platform) -- 1. Privacy and anonymization by aggregation -- 1. Mobile access for clients
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FEES TO BE REMITTED THROUGH INDIAN BANK MULTI-UTILITY CHALAN Amount per Type of Service Period of Issue Certificate (in Rupees) Transcript / Attestation of Within 10 Years 300 Diploma Certificate Provisional Diploma Certificate Before 10 Years 450 Consolidated Mark Statement / Mark Statements *Amount mentioned above is exclusive of Bank Charges. ***
|
FEES TO BE REMITTED THROUGH INDIAN BANK MULTI-UTILITY CHALAN Type of Service Period of Issue Amount per Certificate (in Rupees) Transcript / Attestation of Diploma Certificate Provisional Diploma Certificate Consolidated Mark Statement / Mark Statements Within 10 Years 300 Before 10 Years 450 *Amount mentioned above is exclusive of Bank Charges. ***
| 0.74267
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M03ApplicationForTranscriptOrAttestation.docx
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task-1430-780
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processing (DB) techniques for 5. Huge amounts consumer of storage (2 PB) personalization 6. I/O intensive 4. Robust learning processing algorithms 5. Continued analytic processing based on monitoring and performance results 8 1. Distributed 1. Dynamic 1. Petabytes of 1. Search time 1. Access 1. Data purge after 1. Mobile Web Search data sources fetching content text and rich of ≈0.1 control certain time interval search and 2. Streaming data over the network media (storage) seconds 2. Protection of (a few months) rendering 3. Multimedia 2. Linking of user 2. Top 10 sensitive 2. Data cleaning content profiles and social ranked results content network data 3. Page layout (visual) 9 -- 1. Robust backup 1. Hadoop -- 1. Strong -- -- Business algorithm 2. Commercial security for Continuity 2. Replication of cloud services many and Disaster recent changes applications Recovery Within a Cloud System 10 1. Centralized and 1. Tracking items 1. Internet -- 1. Security -- -- Cargo real-time based on the connectivity policy Shipping distributed unique sites/sensors identification with its sensor information, GPS coordinates 2. Real-time
|
System processing techniques for consumer personalization 4. Robust learning algorithms 5. Continued analytic processing based on monitoring and performance results (DB) 5. Huge amounts of storage (2 PB) 6. I/O intensive processing 8 Web Search 1. Distributed data sources 2. Streaming data 3. Multimedia content 1. Dynamic fetching content over the network 2. Linking of user profiles and social network data 1. Petabytes of text and rich media (storage) 1. Search time of ≈0.1 seconds 2. Top 10 ranked results 3. Page layout (visual) 1. Access control 2. Protection of sensitive content 1. Data purge after certain time interval (a few months) 2. Data cleaning 1. Mobile search and rendering 9 Business Continuity and Disaster Recovery Within a Cloud -- 1. Robust backup algorithm 2. Replication of recent changes 1. Hadoop 2. Commercial cloud services -- 1. Strong security for many applications -- -- 10 Cargo Shipping 1. Centralized and real-time distributed sites/sensors 1. Tracking items based on the unique identification with its sensor information, GPS coordinates 2. Real-time 1. Internet connectivity -- 1. Security policy -- --
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M0627_v1_8528813905.docx
|
task-1430-863
| 175
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Highlight issues for A wide array of programs in the earth sciences are working on challenges that cross the generalizing this use same domains as this project. case (e.g. for ref. architecture) More Information Under development (URLs) Earth, Environmental and Polar Science> Use Case 50: AmeriFlux and FLUXNET Use Case Title DOE-BER AmeriFlux and FLUXNET Networks Vertical (area) Research: Earth Science Author/Company/Email Deb Agarwal, Lawrence Berkeley Lab. Actors/Stakeholders AmeriFlux scientists, Data Management Team, ICOS, DOE TES, USDA, NSF, and Climate and their roles and modelers. responsibilities AmeriFlux Network and FLUXNET measurements provide the crucial linkage between Goals organisms, ecosystems, and process-scale studies at climate-relevant scales of landscapes, regions, and continents, which can be incorporated into biogeochemical and climate models. Results from individual flux sites provide the foundation for a growing body of synthesis and modeling analyses. AmeriFlux network observations enable scaling of trace gas fluxes (CO2, water vapor) Use Case Description across a broad spectrum of times (hours, days, seasons, years, and decades) and space. Moreover, AmeriFlux and FLUXNET datasets provide the crucial linkages among organisms, ecosystems, and process-scale studies—at climate-relevant scales of landscapes, regions, and continents—for incorporation into biogeochemical and climate models Current Compute(System) NERSC Solutions Storage NERSC Networking ESNet Software EddyPro, Custom analysis software, R, python, neural networks, Matlab. towers in AmeriFlux and over 500 towers distributed ≈150 Big Data Data Source globally collecting flux measurements. Characteristics (distributed/centralized) Volume (size) Velocity (e.g. real time) Variety The flux data is relatively uniform, however, the (multiple datasets, biological, disturbance, and other ancillary data needed to mashup) process and to interpret the data is extensive and varies widely. Merging this data with the flux data is challenging in today’s systems. Variability (rate of change)
|
Highlight issues for generalizing this use case (e.g. for ref. architecture) A wide array of programs in the earth sciences are working on challenges that cross the same domains as this project. More Information (URLs) Under development Earth, Environmental and Polar Science> Use Case 50: AmeriFlux and FLUXNET Use Case Title DOE-BER AmeriFlux and FLUXNET Networks Vertical (area) Research: Earth Science Author/Company/Email Deb Agarwal, Lawrence Berkeley Lab. Actors/Stakeholders and their roles and responsibilities AmeriFlux scientists, Data Management Team, ICOS, DOE TES, USDA, NSF, and Climate modelers. Goals AmeriFlux Network and FLUXNET measurements provide the crucial linkage between organisms, ecosystems, and process-scale studies at climate-relevant scales of landscapes, regions, and continents, which can be incorporated into biogeochemical and climate models. Results from individual flux sites provide the foundation for a growing body of synthesis and modeling analyses. Use Case Description AmeriFlux network observations enable scaling of trace gas fluxes (CO2, water vapor) across a broad spectrum of times (hours, days, seasons, years, and decades) and space. Moreover, AmeriFlux and FLUXNET datasets provide the crucial linkages among organisms, ecosystems, and process-scale studies—at climate-relevant scales of landscapes, regions, and continents—for incorporation into biogeochemical and climate models Current Solutions Compute(System) NERSC Storage NERSC Networking ESNet Software EddyPro, Custom analysis software, R, python, neural networks, Matlab. Big Data Characteristics Data Source (distributed/centralized) ≈150 towers in AmeriFlux and over 500 towers distributed globally collecting flux measurements. Volume (size) Velocity (e.g. real time) Variety (multiple datasets, mashup) The flux data is relatively uniform, however, the biological, disturbance, and other ancillary data needed to process and to interpret the data is extensive and varies widely. Merging this data with the flux data is challenging in today’s systems. Variability (rate of change)
| 0.842263
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M0627_v1_8528813905.docx
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20 1. Multiple 2. Scalable RDBMS 1. Huge data 1. Real-time 1. Login 1. Methods to -- Comparative centralized data for heterogeneous storage interactive security: improve data quality Analysis for sources biological data parallel bulk username and 2. Data clustering, 2. Proteins and 2. Real-time rapid loading password classification, and their structural and parallel bulk capability 2. Creation of reduction Genomes features, core loading 2. Interactive user account to 3. Integration of genomic data, 3. Oracle RDBMS, Web UI, submit and new data/content new types of SQLite files, flat backend access dataset into the system’s omics data such text files, Lucy (a to system via data store and data as version of Lucene) batch job web interface annotation transcriptomics, for keyword computation 3. Single methylomics, and searches, BLAST submission capability proteomics databases, from the UI. (SSO) describing gene USEARCH 3. Download expression databases of assembled 3. Front real-time 4. Linux cluster, and annotated web UI Oracle RDBMS datasets for interactive; server, large offline analysis backend data memory machines, 4. Ability to loading standard Linux query and processing that interactive hosts browse data keeps up with 5. Sequencing and via interactive exponential comparative web UI growth of analysis techniques 5. Visualize sequence data for highly complex data structure due to the rapid data at different drop in cost of 6. Descriptive levels of sequencing statistics resolution; technology ability to view 4. abstract Heterogeneous, complex, of highly structural, and similar data hierarchical biological data
|
20 Comparative Analysis for and Genomes 1. Multiple centralized data sources 2. Proteins and their structural features, core genomic data, new types of omics data such as transcriptomics, methylomics, and proteomics describing gene expression 3. Front real-time web UI interactive; backend data loading processing that keeps up with exponential growth of sequence data due to the rapid drop in cost of sequencing technology 4. Heterogeneous, complex, structural, and hierarchical biological data 2. Scalable RDBMS for heterogeneous biological data 2. Real-time rapid and parallel bulk loading 3. Oracle RDBMS, SQLite files, flat text files, Lucy (a version of Lucene) for keyword searches, BLAST databases, USEARCH databases 4. Linux cluster, Oracle RDBMS server, large memory machines, standard Linux interactive hosts 5. Sequencing and comparative analysis techniques for highly complex data 6. Descriptive statistics 1. Huge data storage 1. Real-time interactive parallel bulk loading capability 2. Interactive Web UI, backend batch job computation submission from the UI. 3. Download of assembled and annotated datasets for offline analysis 4. Ability to query and browse data via interactive web UI 5. Visualize data structure at different levels of resolution; ability to view abstract of highly similar data 1. Login security: username and password 2. Creation of user account to submit and access dataset to system via web interface 3. Single capability (SSO) 1. Methods to improve data quality 2. Data clustering, classification, reduction 3. Integration of new data/content into the system’s data store and data annotation --
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M0627_v1_8528813905.docx
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task-1430-863
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37 1. ≈1 PB/year 1. Advanced 1. MPI, OpenMP, 1. -- -- -- DOE becoming 7 analysis and C, C++, F90, Interpretation Extreme PB/year of visualization FFTW, viz of results Data from observational techniques and packages, Python, using data capabilities to FFTW, numpy, advanced Sky Survey support Boost, OpenMP, visualization interpretation of ScaLAPCK, PSQL techniques results from and MySQL and detailed databases, Eigen, capabilities simulations cfitsio, astrometry.net, and Minuit2 2. Methods/ tools to address supercomputer I/O subsystem limitations 38 1. 20 TB of 1. Analysis on both 1. Standard -- -- 1. Links between -- Large Survey data/day the simulation and astrophysics remote telescopes Data for observational data reduction and central analysis Cosmology simultaneously software as well sites 2. Techniques for as Perl/Python handling Cholesky wrapper scripts decomposition for 2. Oracle RDBMS, thousands of Postgres psql, simulations with GPFS and Lustre matrices of order 1 file systems and million on a side tape archives 3. Parallel image storage 39 1. Real-time data 1. Experimental 1. Legacy 1. Histograms 1. Data 1. Data quality on -- Particle from accelerator data from ALICE, computing and model fits protection complex apparatus Physics at and analysis ATLAS, CMS, LHB infrastructure (visual) LHC instruments 2. Histograms, (computing 2. scatter-plots with nodes)
|
37 DOE Extreme Data from Sky Survey 1. ≈1 PB/year becoming 7 PB/year of observational data 1. Advanced analysis and visualization techniques and capabilities to support interpretation of results from detailed simulations 1. MPI, OpenMP, C, C++, F90, FFTW, viz packages, Python, FFTW, numpy, Boost, OpenMP, ScaLAPCK, PSQL and MySQL databases, Eigen, cfitsio, astrometry.net, and Minuit2 2. Methods/ tools to address supercomputer I/O subsystem limitations 1. Interpretation of results using advanced visualization techniques and capabilities -- -- -- 38 Large Survey Data for Cosmology 1. 20 TB of data/day 1. Analysis on both the simulation and observational data simultaneously 2. Techniques for handling Cholesky decomposition for thousands of simulations with matrices of order 1 million on a side 1. Standard astrophysics reduction software as well as Perl/Python wrapper scripts 2. Oracle RDBMS, Postgres psql, GPFS and Lustre file systems and tape archives 3. Parallel image storage -- -- 1. Links between remote telescopes and central analysis sites -- 39 Particle Physics at LHC 1. Real-time data from accelerator and analysis instruments 2. 1. Experimental data from ALICE, ATLAS, CMS, LHB 2. Histograms, scatter-plots with 1. Legacy computing infrastructure (computing nodes) 1. Histograms and model fits (visual) 1. Data protection 1. Data quality on complex apparatus --
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InCadence Strategic Services NASA AlloyCloud, Inc. Pw Carey Philippe Journeau William Miller Compliance Partners, LLC ResearXis MaCT USA Wo Chang Pavithra Kenjige Bob Natale NIST PK Technologies Mitre Corporation Yuri Demchenko Orit Levin Rod Peterson University of Amsterdam Microsoft U.S. Department of Veterans Affairs Frank Farance Eugene Luster Ann Racuya-Robbins Consultant U.S. Defense Information Systems World Knowledge Bank Agency/R2AD LLC Geoffrey Fox Russell Reinsch Ashok Malhotra Indiana University Center for Government Interoperability Oracle Ian Gorton John Rogers Bill Mandrick CMU HP L3 Data Tactics Nancy Grady Robert Marcus Arnab Roy SAIC ET-Strategies Fujitsu Karen Guertler Lisa Martinez Mark Underwood Consultant Consultant Krypton Brothers; Synchrony Financial William Vorhies Keith Hare JCC Consulting, Inc. Predictive Modeling LLC Alicia Zuniga-Alvarado Consultant
|
InCadence Strategic Services Pw Carey Compliance Partners, LLC Wo Chang NIST Yuri Demchenko University of Amsterdam Frank Farance Consultant Geoffrey Fox Indiana University Ian Gorton CMU Nancy Grady SAIC Karen Guertler Consultant Keith Hare JCC Consulting, Inc. NASA Philippe Journeau ResearXis Pavithra Kenjige PK Technologies Orit Levin Microsoft Eugene Luster U.S. Defense Information Systems Agency/R2AD LLC Ashok Malhotra Oracle Bill Mandrick L3 Data Tactics Robert Marcus ET-Strategies Lisa Martinez Consultant AlloyCloud, Inc. William Miller MaCT USA Bob Natale Mitre Corporation Rod Peterson U.S. Department of Veterans Affairs Ann Racuya-Robbins World Knowledge Bank Russell Reinsch Center for Government Interoperability John Rogers HP Arnab Roy Fujitsu Mark Underwood Krypton Brothers; Synchrony Financial William Vorhies Predictive Modeling LLC Alicia Zuniga-Alvarado Consultant
| 0.326165
|
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M0635_v1_5650594125.docx
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task-1430-866
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images, hundreds of real-time analytic recognition and tracking, translation; video thousands of partially challenges from document analysis, person/object detection ground-truthed video multiple data dialogue, and tracking; event clips, terabytes of streams multimedia search/extraction detection; smaller fully imagery/document test matching; novelty collections detection; structural semantic temporal analytics 32 Petabytes, hundreds Real time and batch Rich iRODS Supports general DataNet (iRODS) of millions of files analysis workflows 33 Small as metadata to Real time Can tackle arbitrary Big Data Symfony-PHP, Linux, -- The Discinnet Big Data MySQL Process 34 A few terabytes Evolving in time Rich Database Data graph processing Semantic 35 50 to 400 GB per day, Continuous stream Images Octopus for Volume reconstruction, Light Source total ≈400 TB of data, but analysis Tomographic feature identification, Beamlines need not be real Reconstruction, Avizo () etc. time and FIJI (a distribution of ImageJ) 36 ≈100 TB total Nightly update runs Images, spectra, time series, Custom data processing Detection of rare events Catalina increasing by 0.1 TB a processes in real catalogs pipeline and data and relation to existing Time Transient night accessing PBs of time analysis software diverse data Survey base astronomy data, 30 TB a night from successor LSST in 2020s 37 Several petabytes Analysis done in Image and simulation data MPI, FFTW, viz New analytics needed to DOE Extreme from Dark Energy batch mode with packages, numpy, analyze simulation Data from Survey and Zwicky data from Boost, OpenMP, results Cosmological Sky Transient Factory, observations and ScaLAPCK, PSQL and Survey simulations > 10 PB simulations MySQL databases, updated daily Eigen, cfitsio, astrometry.net, and
|
images, hundreds of thousands of partially ground-truthed video clips, terabytes of smaller fully test collections real-time analytic challenges from multiple data streams recognition and tracking, document analysis, dialogue, multimedia search/extraction translation; video person/object detection and tracking; event detection; imagery/document matching; novelty detection; structural semantic temporal analytics 32 DataNet (iRODS) Petabytes, hundreds of millions of files Real time and batch Rich iRODS Supports general analysis workflows 33 The Discinnet Process Small as metadata to Big Data Real time Can tackle arbitrary Big Data Symfony-PHP, Linux, MySQL -- 34 Semantic A few terabytes Evolving in time Rich Database Data graph processing 35 Light Source Beamlines 50 to 400 GB per day, total ≈400 TB Continuous stream of data, but analysis need not be real time Images Octopus for Tomographic Reconstruction, Avizo () and FIJI (a distribution of ImageJ) Volume reconstruction, feature identification, etc. 36 Catalina Transient Survey ≈100 TB total increasing by 0.1 TB a night accessing PBs of base astronomy data, 30 TB a night from successor LSST in 2020s Nightly update runs processes in real time Images, spectra, time series, catalogs Custom data processing pipeline and data analysis software Detection of rare events and relation to existing diverse data 37 DOE Extreme Data from Cosmological Sky Survey Several petabytes from Dark Energy Survey and Zwicky Transient Factory, simulations > 10 PB Analysis done in batch mode with data from observations and simulations updated daily Image and simulation data MPI, FFTW, viz packages, numpy, Boost, OpenMP, ScaLAPCK, PSQL and MySQL databases, Eigen, cfitsio, astrometry.net, and New analytics needed to analyze simulation results Time
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M0637_v1_6301062872.docx
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task-1430-868
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Statistical traditional survey MySQL, Oracle, operational auditable for conceptual Survey sources, Storm, activity, and security and metadata Response government BigMemory, general confidentiality concerning what administrative data Cassandra, Pig analysis; as required by exactly is measured sources, and continual various legal and the resulting sources evolution statutes limits of inference) from the digital economy 5 1. Real-time 1. Real-time -- -- 1. Strong -- 1. Mobile Cloud ingestion of data analytics security and access for privacy Finance constraints 6 1. File-based 1. Standard 1. Amazon Elastic 1. 1. Access 1. Metadata 1. Windows Mendeley documents with machine learning Compute Cloud reporting controls for management from Android and constant new and analytics (EC2) with HDFS tools who reads what PDF extraction iOS mobile uploads libraries (infrastructure) 2. content 2. Identification of devices for 2. Variety of file 2. Efficient scalable 2. S3 (storage) Visualization document content types such as and parallelized 3. Hadoop tools such as duplication deliverables PDFs, social way to match (platform) networking 3. Persistent from network log files, between 4. Scribe, Hive, graph, identifier Windows client activities documents Mahout, Python scatterplots, 4. Metadata desktops images, 3. Third-party (language) etc. correlation between spreadsheet, annotation tools or 5. Moderate data repositories presentation files publisher storage (15 TB such as CrossRef, watermarks and with 1 TB/ month) PubMed, and Arxiv cover pages 6. Batch and processing 7 1. User profiles 1. Streaming video 1. Hadoop 1. Streaming 1. Preservation 1. Continued ranking 1. Smart Netflix and ranking contents to (platform) and rendering of users, and updating based interface Movie information multiple clients 2. Pig (language) media privacy and on user profile and accessing Service 2. Analytic 3. Cassandra and digital rights for analytic results movie processing for Hive media content on matching client 4. Huge numbers mobile interest in movie of subscribers, platforms selection ratings, and
|
Statistical Survey Response traditional survey sources, government administrative data sources, and sources from the digital economy MySQL, Oracle, Storm, BigMemory, Cassandra, Pig operational activity, and general analysis; continual evolution auditable for security and confidentiality as required by various legal statutes conceptual metadata concerning what exactly is measured and the resulting limits of inference) 5 Cloud for Finance 1. Real-time ingestion of data 1. Real-time analytics -- -- 1. Strong security and privacy constraints -- 1. Mobile access 6 Mendeley 1. File-based documents with constant new uploads 2. Variety of file types such as PDFs, social network log files, client activities images, spreadsheet, presentation files 1. Standard machine learning and analytics libraries 2. Efficient scalable and parallelized way to match between documents 3. Third-party annotation tools or publisher watermarks and cover pages 1. Amazon Elastic Compute Cloud (EC2) with HDFS (infrastructure) 2. S3 (storage) 3. Hadoop (platform) 4. Scribe, Hive, Mahout, Python (language) 5. Moderate storage (15 TB with 1 TB/ month) 6. Batch and processing 1. reporting tools 2. Visualization tools such as networking graph, scatterplots, etc. 1. Access controls for who reads what content 1. Metadata management from PDF extraction 2. Identification of document duplication 3. Persistent identifier 4. Metadata correlation between data repositories such as CrossRef, PubMed, and Arxiv 1. Windows Android and iOS mobile devices for content deliverables from Windows desktops 7 Netflix Movie Service 1. User profiles and ranking information 1. Streaming video contents to multiple clients 2. Analytic processing for matching client interest in movie selection 1. Hadoop (platform) 2. Pig (language) 3. Cassandra and Hive 4. Huge numbers of subscribers, ratings, and 1. Streaming and rendering media 1. Preservation of users, privacy and digital rights for media 1. Continued ranking and updating based on user profile and analytic results 1. Smart interface accessing movie content on mobile platforms
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M0637_v1_6301062872.docx
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task-1430-868
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] |
2. Output the form of Open Geospatial Consortium web features or standard geospatial files (shape files, KML) 15 1. Much real-time 1. Analytics: Near 1. Tolerance of 1. Geospatial 1. Protection of 1. Data provenance -- Intelligence data with Real (NRT) unreliable overlays (GIS) data against (e.g. tracking of all Data processing at alerts based on networks to and network unauthorized transfers and Processing near-real time (at patterns and warfighter and diagrams access or transformations) and Analysis worst) baseline changes remote sensors (primary disclosure and over the life of the 2. Data in 2. Up to hundreds visualizations) tampering data disparate silos, of petabytes of must be data supported by accessible modest to large through a clusters and semantically clouds integrated data 3. Hadoop, space Accumulo (Big 3. Diverse data: Table), Solr, NLP text files, raw (several variants), media, imagery, Puppet (for video, audio, deployment and electronic data, security), Storm, human-generated custom data applications, visualization tools 16 1. 1. A 1. Hadoop, Hive, 1. Results of 1. Data 1. Standardize, 1. Security EMR Data Heterogeneous, comprehensive R. Unix-based analytics consumer aggregate, and across mobile high-volume, and consistent 2. Cray provided for direct access to normalize data from devices
|
2. Output the form of Open Geospatial Consortium web features or standard geospatial files (shape files, KML) 15 Intelligence Data Processing and Analysis 1. Much real-time data with processing at near-real time (at worst) 2. Data in disparate silos, must be accessible through a semantically integrated data space 3. Diverse data: text files, raw media, imagery, video, audio, electronic data, human-generated data 1. Analytics: Near Real (NRT) alerts based on patterns and baseline changes 1. Tolerance of unreliable networks to warfighter and remote sensors 2. Up to hundreds of petabytes of data supported by modest to large clusters and clouds 3. Hadoop, Accumulo (Big Table), Solr, NLP (several variants), Puppet (for deployment and security), Storm, custom applications, visualization tools 1. Geospatial overlays (GIS) and network diagrams (primary visualizations) 1. Protection of data against unauthorized access or disclosure and tampering 1. Data provenance (e.g. tracking of all transfers and transformations) over the life of the data -- 16 EMR Data 1. Heterogeneous, high-volume, 1. A comprehensive and consistent 1. Hadoop, Hive, R. Unix-based 2. Cray 1. Results of analytics provided for 1. Data consumer direct access to 1. Standardize, aggregate, and normalize data from 1. Security across mobile devices
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M0637_v1_6301062872.docx
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task-1430-868
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diverse data view of data across supercomputer use by data data as well as disparate sources sources sources and over 3. Teradata, consumers/ to the results of 2. Reduce errors and 2. Volume: > 12 time PostgreSQL, stakeholders, analytics bias million entities 2. Analytic MongoDB i.e., those who performed by 3. Common (patients), > 4 techniques: 4. Various, with did not informatics nomenclature and billion records or information significant I/O actually research classification of data points retrieval, NLP, intensive perform the scientists and content across (discrete clinical machine learning processing analysis; health service disparate observations), decision models, specific researchers aggregate of > 20 maximum visualization 2. Protection of challenging in the TB raw data likelihood techniques all health data health IT space, as 3. Velocity: estimators, in compliance the taxonomies 500,000 to 1.5 Bayesian networks with continue to evolve— million new governmental SNOMED, transactions per regulations International day 3. Protection of Classification of 4. Variety: data in Diseases (ICD) 9 and formats include accordance future ICD 10, etc. numeric, with data structured providers, numeric, policies. structured 4. Security and text, discrete privacy policies nominal, discrete unique to a ordinal, discrete data subset structured, binary 5. Robust large blobs security to (images and prevent data video) breaches 5. Data evolve over time in a highly variable fashion 17 1. High-resolution 1. 1. Legacy system 1. 1. Security and 1. Human 1. 3D Pathology spatial digitized and cloud Visualization privacy annotations for visualization
|
diverse data sources 2. Volume: > 12 million entities (patients), > 4 billion records or data points (discrete clinical observations), aggregate of > 20 TB raw data 3. Velocity: 500,000 to 1.5 million new transactions per day 4. Variety: formats include numeric, structured numeric, structured text, discrete nominal, discrete ordinal, discrete structured, binary large blobs (images and video) 5. Data evolve over time in a highly variable fashion view of data across sources and over time 2. Analytic techniques: information retrieval, NLP, machine learning decision models, maximum likelihood estimators, Bayesian networks supercomputer 3. Teradata, PostgreSQL, MongoDB 4. Various, with significant I/O intensive processing use by data consumers/ stakeholders, i.e., those who did not actually perform the analysis; specific visualization techniques data as well as to the results of analytics performed by informatics research scientists and health service researchers 2. Protection of all health data in compliance with governmental regulations 3. Protection of data in accordance with data providers, policies. 4. Security and privacy policies unique to a data subset 5. Robust security to prevent data breaches disparate sources 2. Reduce errors and bias 3. Common nomenclature and classification of content across disparate challenging in the health IT space, as the taxonomies continue to evolve— SNOMED, International Classification of Diseases (ICD) 9 and future ICD 10, etc. 17 Pathology 1. High-resolution spatial digitized 1. 1. Legacy system and cloud 1. Visualization 1. Security and privacy 1. Human annotations for 1. 3D visualization
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SAR Synthetic Aperture Radar SDLC/HDLC Software Development Life Cycle/Hardware Development Life Cycle SDN software-defined networking SEC U.S. Securities and Exchange Commission SFA 2.0 Scientific Focus Area 2.0 Science Plan SIEM Security Incident/Event Management SIOS Svalbard Integrated Arctic Earth Observing System SOAP Simple Object Access Protocol SOX Sarbanes–Oxley Act of 2002 SPADE Support for Provenance Auditing in Distributed Environments SPR Security and Privacy Requirements SSH Secure Shell SSO Single sign-on capability tf-idf term frequency–inverse document frequency TPR Transformation Provider Requirements UA University of Arizona UAVSAR Unmanned Air Vehicle Synthetic Aperture Radar UI user interface UPS United Parcel Service UQ uncertainty quantification vCDS virtual Climate Data Server VO Virtual Observatory VOIP Voice over IP WALF Wide Area Large Format Imagery WLCG Worldwide LHC Computing Grid XBRL extensible Business Related Markup Language XML Extensible Markup Language ZTF Zwicky Transient Factory
|
SAR Synthetic Aperture Radar SDLC/HDLC Software Development Life Cycle/Hardware Development Life Cycle SDN software-defined networking SEC U.S. Securities and Exchange Commission SFA 2.0 Scientific Focus Area 2.0 Science Plan SIEM Security Incident/Event Management SIOS Svalbard Integrated Arctic Earth Observing System SOAP Simple Object Access Protocol SOX Sarbanes–Oxley Act of 2002 SPADE Support for Provenance Auditing in Distributed Environments SPR Security and Privacy Requirements SSH Secure Shell SSO Single sign-on capability tf-idf term frequency–inverse document frequency TPR Transformation Provider Requirements UA University of Arizona UAVSAR Unmanned Air Vehicle Synthetic Aperture Radar UI user interface UPS United Parcel Service UQ uncertainty quantification vCDS virtual Climate Data Server VO Virtual Observatory VOIP Voice over IP WALF Wide Area Large Format Imagery WLCG Worldwide LHC Computing Grid XBRL extensible Business Related Markup Language XML Extensible Markup Language ZTF Zwicky Transient Factory
| 1
|
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M0637_v1_6301062872.docx
|
task-1430-868
| 289
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"parameters": { "hadoop": { "zookeeper.quorum": [ "IP", "IP", "IP"] } } } } } The deployment has as inputs parameters defining the applied function and the input data. Both Map/Reduce function and data objects define a parameter, which specifies the location it is retrieved from. For instance, source the ``file://'' Uniform Resource Identifier (URI) indicates sending a directory structure from the local file system and the ``ftp://'' indicates that the data should be fetched from a File Transfer Protocol (FTP) resource. It is the framework's responsibility to materialize an instantiation of the desired environment along with the function and data. Object 4.37: Map/Reduce { "mapreduce": { "function": { "source": "file://.", "args": {} }, "data": { "source": "ftp:///...", "dest": "/data" }, "fault_tolerant": true, "backend": {"type": "hadoop"} } } Additional parameters include the and parameters. The former flag indicates if the fault\_tolerant backend deployment should operate in a fault tolerant mode. For instance, in the case of Hadoop, this may Map/Reduce mean configuring automatic failover of name nodes using Zookeeper. The parameter accepts an object backend describing the system providing the workflow. This may be a native deployment of Hadoop, or a Map/Reduce special instantiation using other frameworks such as Mesos. A function prototype is defined earlier. Key properties are that functions describe their input parameters and generated results. For input parameters, the and respectively describe the objects buildInputs systemBuildInputs that should be evaluated and system packages that should be present before this function can be installed. The attribute describes how to apply this function to its input data. Parameters affecting the evaluation of the eval function may be passed in as the attribute. The results of the function application can be accessed via the args object, which is a mapping from arbitrary keys (e.g., data, processed, model) to an object representing the outputs result.
|
"parameters": { "hadoop": { "zookeeper.quorum": [ "IP", "IP", "IP"] } } } } } The Map/Reduce deployment has as inputs parameters defining the applied function and the input data. Both function and data objects define a source parameter, which specifies the location it is retrieved from. For instance, the ``file://'' Uniform Resource Identifier (URI) indicates sending a directory structure from the local file system and the ``ftp://'' indicates that the data should be fetched from a File Transfer Protocol (FTP) resource. It is the framework's responsibility to materialize an instantiation of the desired environment along with the function and data. Object 4.37: Map/Reduce { "mapreduce": { "function": { "source": "file://.", "args": {} }, "data": { "source": "ftp:///...", "dest": "/data" }, "fault_tolerant": true, "backend": {"type": "hadoop"} } } Additional parameters include the fault\_tolerant and backend parameters. The former flag indicates if the Map/Reduce deployment should operate in a fault tolerant mode. For instance, in the case of Hadoop, this may mean configuring automatic failover of name nodes using Zookeeper. The backend parameter accepts an object describing the system providing the Map/Reduce workflow. This may be a native deployment of Hadoop, or a special instantiation using other frameworks such as Mesos. A function prototype is defined earlier. Key properties are that functions describe their input parameters and generated results. For input parameters, the buildInputs and systemBuildInputs respectively describe the objects that should be evaluated and system packages that should be present before this function can be installed. The eval attribute describes how to apply this function to its input data. Parameters affecting the evaluation of the function may be passed in as the args attribute. The results of the function application can be accessed via the outputs object, which is a mapping from arbitrary keys (e.g., data, processed, model) to an object representing the result.
| 0.876539
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M0641_v1_7213461555.docx
|
task-1430-872
| 43
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Agriculture, Commerce, Defense, Energy, Health and Human Services, Homeland Security, Transportation, Treasury, and Veterans Affairs. NIST would like to acknowledge the specific contributions to this volume, during Version 1, and/or Version 2 activities, by the following NBD-PWG members: Zane Harvey Mark Underwood David Boyd QuantumS3 Krypton Brothers InCadence Strategic Solutions Synchrony Financial Haiping Luo Frank Farance Gregor von Lasewski Department of the Treasury Consultant Indiana University Gary Mazzaferro Geoffrey Fox Timothy Zimmerlin Russell Reinsch Indiana University Consultant Center for Government Nancy Grady Interoperability SAIC Arnab Roy Fujitsu
|
Agriculture, Commerce, Defense, Energy, Health and Human Services, Homeland Security, Transportation, Treasury, and Veterans Affairs. NIST would like to acknowledge the specific contributions to this volume, during Version 1, and/or Version 2 activities, by the following NBD-PWG members: David Boyd InCadence Strategic Solutions Frank Farance Consultant Geoffrey Fox Indiana University Nancy Grady SAIC Zane Harvey QuantumS3 Haiping Luo Department of the Treasury Gary Mazzaferro Russell Reinsch Center for Government Interoperability Arnab Roy Fujitsu Mark Underwood Krypton Brothers Synchrony Financial Gregor von Lasewski Indiana University Timothy Zimmerlin Consultant
| 0.625082
|
[
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M0671_v1_0731201855.docx
|
task-1430-880
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Naïve Bayes Fast Low Scales over vary large datasets. Speed at the expense of accuracy Neural Network Slow Low Centering and Scaling, Remove Highly Correlated Predictors Fast Low Scales over medium size K Nearest Neighbors datasets Ridge Fast High Centering and Scaling Regression Nearest Fast Medium Shrunken Centroids MARS Fast High Slow Low Accurate Bagged / Boosted Trees Unsupervised learning problems do not have labeled data and can be classified into two subgroups: clustering algorithms and dimensionality reduction techniques. Clustering algorithms attempt to find underlying structure in the data by determining groups of similar data. Dimensionality reduction algorithms are typically used for preprocessing of datasets prior to the application of other algorithms. lists common clustering Table 10 algorithms, and lists common dimensionality reduction techniques. Table 11 Table 10. Unsupervised Clustering Algorithms Name Pre-Processing Interpretability Notes Missing value sensitivity, Medium Scales over large datasets for K -means Centering and Scaling clustering tasks, must specify number of clusters (k) Must specify number of Fuzzy c-means clusters (k) Specify k for probability tasks Must specify number of Gaussian clusters (k) Must specify number of Hierarchical clusters (k) DBSCAN Do not have to specify number of clusters (k) While technically dimension reduction may be a preprocessing technique, which transforms predictors, usually driven for computational reasons, some consider dimensionality reduction (or data reduction) techniques a class of unsupervised algorithms because they are also a solution for unlabeled data.
|
Naïve Bayes Fast Low Scales over vary large datasets. Speed at the expense of accuracy Neural Network Slow Low Centering and Scaling, Remove Highly Correlated Predictors K Nearest Neighbors Fast Low Scales over medium size datasets Ridge Regression Fast High Centering and Scaling Nearest Shrunken Centroids Fast Medium MARS Fast High Bagged / Boosted Trees Slow Low Accurate Unsupervised learning problems do not have labeled data and can be classified into two subgroups: clustering algorithms and dimensionality reduction techniques. Clustering algorithms attempt to find underlying structure in the data by determining groups of similar data. Dimensionality reduction algorithms are typically used for preprocessing of datasets prior to the application of other algorithms. Table 10 lists common clustering algorithms, and Table 11 lists common dimensionality reduction techniques. Table 10. Unsupervised Clustering Algorithms Name Pre-Processing Interpretability Notes K -means Missing value sensitivity, Centering and Scaling Medium Scales over large datasets for clustering tasks, must specify number of clusters (k) Fuzzy c-means Must specify number of clusters (k) Gaussian Specify k for probability tasks Must specify number of clusters (k) Hierarchical Must specify number of clusters (k) DBSCAN Do not have to specify number of clusters (k) While technically dimension reduction may be a preprocessing technique, which transforms predictors, usually driven for computational reasons, some consider dimensionality reduction (or data reduction) techniques a class of unsupervised algorithms because they are also a solution for unlabeled data.
| 0.80937
|
[
0,
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18,
19,
20,
21,
22,
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25,
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40,
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44,
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M0671_v1_0731201855.docx
|
task-1430-880
| 32
|
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Foundations Treasure Hunt On this page you will find the age ranges for the categories used for developmental indicators (infants, younger toddlers, older toddlers, younger preschoolers, and older preschoolers) (Hint – look in pages 1-7!) : _____ On this page you will find a text box that summarizes the intended uses for Foundations (Hint – look in pages 1-7!): _____ On this page you will find a summary of the document’s organization and guidance on how to think about developmental domains and their overlap (Hint – look in pages 1-7!): _____ Where does the document list how goals and developmental indicators should and should not be used? (Hint – look in pages 1-7!): _____ On what pages of Foundations are the subdomains and goals for each of the five domains listed? (Hint – look beyond pages 1-7!): _____
|
Foundations Treasure Hunt On this page you will find the age ranges for the categories used for developmental indicators (infants, younger toddlers, older toddlers, younger preschoolers, and older preschoolers) (Hint – look in pages 1-7!) : _____ On this page you will find a text box that summarizes the intended uses for Foundations (Hint – look in pages 1-7!): _____ On this page you will find a summary of the document’s organization and guidance on how to think about developmental domains and their overlap (Hint – look in pages 1-7!): _____ Where does the document list how goals and developmental indicators should and should not be used? (Hint – look in pages 1-7!): _____ On what pages of Foundations are the subdomains and goals for each of the five domains listed? (Hint – look beyond pages 1-7!): _____
| 1
|
[
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M1%20Treasure%20Hunt_0.docx
|
task-1430-942
| 0
|
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497,
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] |
Pirimicarb NEDI: 91% of the ADI NESTI as % of the ARfD 2-6 years 2+ years Peppers 1 9 15 Spices *0.05 <1 <1 Strawberry 3 28 7 Pirimiphos-methyl Dietary exposure estimate Kiwifruit Omit 2 not required Procymidone NEDI: 38% of the ADI NESTI as % of the ARfD 2-6 years 2+ years Strawberry *0.02 1 <1 Propazine Dietary exposure estimate not required Lupin Omit *0.1 Propiconazole NEDI: 8% of the ADI NESTI as % of the ARfD 2-6 years 2+ years Blackberries 1 <1 <1 Boysenberry 1 <1 <1 Raspberries, red, black 1 <1 <1 Spices *0.1 <1 <1 Pyraclostrobin NEDI: 2% of the ADI NESTI as % of the ARfD 2-6 years 2+ years Blackberries 4 6 1 Blueberries 4 6 1 Boysenberry 4 6 1 Raspberries, red, black 4 6 1 Strawberry 1 19 5 Pyrazophos Dietary exposure estimate Cucumber Omit T2 not required Pyriproxyfen NEDI: 6% of the ADI Grapes 2.5 Quinclorac NEDI: <1% of the ADI NESTI as % of the ARfD 2-6 years 2+ years Cranberry 1.5 <1 <1 Sethoxydim Dietary exposure estimate Bergamot Omit *0.1 not required Burnet, salad Omit *0.1 Chervil Omit *0.1 Dill, seed Omit *0.1 Fennel, bulb Omit 0.2 Fennel, seed Omit *0.1 Herbs [except thyme] Omit *0.1 Kaffir lime leaves Omit *0.1 Lemon grass Omit *0.1 Lemon verbena (fresh weight) Omit *0.1 Mizuna Omit *0.1 Rose and dianthus (edible flowers) Omit *0.1 Strawberry Omit 0.1 Thyme Omit 0.5 Spectinomycin Dietary exposure estimate Goat milk Omit *2 not required
|
Pirimicarb NEDI: 91% of the ADI NESTI as % of the ARfD Peppers Spices Strawberry 1 *0.05 3 2-6 years 9 <1 28 2+ years 15 <1 7 Pirimiphos-methyl Dietary exposure estimate not required Kiwifruit Omit 2 Procymidone NEDI: 38% of the ADI NESTI as % of the ARfD Strawberry *0.02 2-6 years 1 2+ years <1 Propazine Dietary exposure estimate not required Lupin Omit *0.1 Propiconazole NEDI: 8% of the ADI NESTI as % of the ARfD Blackberries Boysenberry Raspberries, red, black Spices 1 1 1 *0.1 2-6 years <1 <1 <1 <1 2+ years <1 <1 <1 <1 Pyraclostrobin NEDI: 2% of the ADI NESTI as % of the ARfD Blackberries Blueberries Boysenberry Raspberries, red, black Strawberry 4 4 4 4 1 2-6 years 6 6 6 6 19 2+ years 1 1 1 1 5 Pyrazophos Dietary exposure estimate not required Cucumber Omit T2 Pyriproxyfen NEDI: 6% of the ADI Grapes 2.5 Quinclorac NEDI: <1% of the ADI NESTI as % of the ARfD Cranberry 1.5 2-6 years <1 2+ years <1 Sethoxydim Dietary exposure estimate not required Bergamot Burnet, salad Chervil Dill, seed Fennel, bulb Fennel, seed Herbs [except thyme] Kaffir lime leaves Lemon grass Lemon verbena (fresh weight) Mizuna Rose and dianthus (edible flowers) Strawberry Thyme Omit *0.1 Omit *0.1 Omit *0.1 Omit *0.1 Omit 0.2 Omit *0.1 Omit *0.1 Omit *0.1 Omit *0.1 Omit *0.1 Omit *0.1 Omit *0.1 Omit 0.1 Omit 0.5 Spectinomycin Dietary exposure estimate not required Goat milk Omit *2
| 0.526809
|
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M1009-MRLs-SD2.docx
|
task-1431-11
| 5
|
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Hub Planning devices Status Date adopted by Council Highview Highview Park Year 1-3 in Corporate Park Masterplan Business Plan (2013-2017) Allen Swanbourne Year 3-5 in Corporate Park Masterplan Business Plan (2013-2017) Princess Nil - - Road Croydon Nil - - Street Floreat Nil - - Asquith Housing Diversity Study Complete, project 28 November 2006 Study ongoing Street Hampden Hampden Road Ongoing 23 August 2011 Road Study Broadway Broadway Study Ongoing 23 August 2011 Waratah Waratah Avenue Ongoing 27 March 2012 Avenue Placemaking Strategy Captain Stirling Highway Approved Draft 13 October 2009 Stirling Special Control Area The priority of hubs is able to scheduled through Council resolutions and budget processes, with no work to commence prior to budget allocation. Priority will be given to hubs that are involved in ongoing projects, followed by those without existing planning projects. In some cases, projects have already been initiated and have commenced. These projects will draw from the Local Hubs Framework principles but not processes. Relevant legislation This document sits below a range of legislation from both state and local government, creating a suite of legislation for local hubs. State Planning Policy 4.2 Activity Centres Monitor and review Reviewing this Framework is important to ensure that it remains relevant. Objectives To ensure that the overall Framework is remains relevant. To ensure the content of the overall Framework is accurate.
|
Hub Planning devices Status Date adopted by Council Highview Park Highview Park Masterplan Year 1-3 in Corporate Business Plan (2013-2017) Allen Park Swanbourne Masterplan Year 3-5 in Corporate Business Plan (2013-2017) Princess Road Nil - - Croydon Street Nil - - Floreat Nil - - Asquith Street Housing Diversity Study Study Complete, project ongoing 28 November 2006 Hampden Road Hampden Road Study Ongoing 23 August 2011 Broadway Broadway Study Ongoing 23 August 2011 Waratah Avenue Waratah Avenue Placemaking Strategy Ongoing 27 March 2012 Captain Stirling Stirling Highway Special Control Area Approved Draft 13 October 2009 The priority of hubs is able to scheduled through Council resolutions and budget processes, with no work to commence prior to budget allocation. Priority will be given to hubs that are involved in ongoing projects, followed by those without existing planning projects. In some cases, projects have already been initiated and have commenced. These projects will draw from the Local Hubs Framework principles but not processes. Relevant legislation This document sits below a range of legislation from both state and local government, creating a suite of legislation for local hubs. State Planning Policy 4.2 Activity Centres Monitor and review Reviewing this Framework is important to ensure that it remains relevant. Objectives To ensure that the overall Framework is remains relevant. To ensure the content of the overall Framework is accurate.
| 0.806537
|
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Provide an image enhancer Manager for public use at the Community Nedlands and Mt Service Centres Claremont Libraries. Provide a modified bus for Manager eligible community Community members to access the Service Centres services provided by Nedlands Community Care. Provide Well Aged Manager programs for the Community community. Service Centres Provide an audio loop for Manager hearing impaired clients in Community Centre Based Day Care Service Centres facility. Provide technology classes Manager and/or workshops at Community Nedlands Community Care. Service Centres Provide social activities Manager aimed at inclusion and Community
|
Provide an image enhancer for public use at the Nedlands and Mt Claremont Libraries. Manager Community Service Centres Provide a modified bus for eligible community members to access the services provided by Nedlands Community Care. Manager Community Service Centres Provide Well Aged programs for the community. Manager Community Service Centres Provide an audio loop for hearing impaired clients in Centre Based Day Care facility. Manager Community Service Centres Provide technology classes and/or workshops at Nedlands Community Care. Manager Community Service Centres Provide social activities aimed at inclusion and Manager Community
| 0.471444
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M13%207116%20%20%20Disability%20Access%20and%20Inclusion%20Plan%20DAIP%202013%20-%202018_0.docx
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task-1431-143
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Poultry meat (in the fat) 0.5 APVMA Etoxazole 5 Hops, dry 7 15 United States <1 <1 Tea, green, black 15 Tea, Green, Black (black, Codex <1 <1 fermented and dried) 15 Fenbuconazole 12 Cranberry 0.5 1 United States <1 <1 Fenbutatin-oxide 95 Not required Not required Grapes [except wine grapes] 5 Grapes 5 United States Fenitrothion Not required Not required Oilseeds Delete T0.1 APVMA Add 0.1 Pulses Delete T0.1 APVMA Add 0.1 Fenpropathrin 33 Stone fruits [except cherry 1.4 Commodity not listed United States Worst case: Worst case: and peach] 95 - Nectarine 59 - Nectarine Fenpyroximate 29 Cherries 2 Commodity not listed United States 85 29 Grapes 1 0.1 United States 86 75 Hops, dry 10 10 United States 29 11 Tea, green, black 0.1 Commodity not listed European Union <1 <1 Fenvalerate Not required Not required Pome fruits Delete 1 APVMA Stone fruits Delete 1 APVMA Flonicamid Chemical not listed 8 Hops, dry 7 United States 16 6 Flubendiamide 20 Spices 0.02 Commodity not listed European Union <1 <1 Tea, green, black 0.02 Tea, Green, Black (black, European Union <1 <1 fermented and dried) 50 Fluopyram 17 Cherries 0.6 0.7 United States 2 <1
|
Poultry meat (in the fat) 0.5 APVMA Etoxazole 5 Hops, dry 7 15 United States <1 <1 Tea, green, black 15 Tea, Green, Black (black, fermented and dried) 15 Codex <1 <1 Fenbuconazole 12 Cranberry 0.5 1 United States <1 <1 Fenbutatin-oxide 95 Not required Not required Grapes [except wine grapes] 5 Grapes 5 United States Fenitrothion Not required Not required Oilseeds Delete T0.1 Add 0.1 APVMA Pulses Delete T0.1 Add 0.1 APVMA Fenpropathrin 33 Stone fruits [except cherry and peach] 1.4 Commodity not listed United States Worst case: 95 - Nectarine Worst case: 59 - Nectarine Fenpyroximate 29 Cherries 2 Commodity not listed United States 85 29 Grapes 1 0.1 United States 86 75 Hops, dry 10 10 United States 29 11 Tea, green, black 0.1 Commodity not listed European Union <1 <1 Fenvalerate Not required Not required Pome fruits Delete 1 APVMA Stone fruits Delete 1 APVMA Flonicamid Chemical not listed 8 Hops, dry 7 United States 16 6 Flubendiamide 20 Spices 0.02 Commodity not listed European Union <1 <1 Tea, green, black 0.02 Tea, Green, Black (black, fermented and dried) 50 European Union <1 <1 Fluopyram 17 Cherries 0.6 0.7 United States 2 <1
| 0.877973
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M1010-MRLs-CFS-SD1.docx
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task-1431-19
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Spring onion 15 Boscalid Commodities of plant origin: Boscalid Commodities of animal origin: Sum of boscalid, ” Fenbutatin oxide nicotinamide and the glucuronide conjugate of Bis[tris(2-methyl-2-phenylpropyl)tin]-oxide “ nicotinamide, expressed as boscalid equivalents Grapes [except wine grapes] 5 “ Grapes 5 ” Fenitrothion ” Fenitrothion Buprofezin “ Buprofezin Oilseeds 0.1 “ Pulses [except soya bean (dry)] 0.1 Grapes 2.5 ” ” Fluxapyroxad Carfentrazone-ethyl Commodities of plant origin: Fluxapyroxad Carfentrazone-ethyl Commodities of animal origin for enforcement: Fluxapyroxad “ Hops, dry 0.1 “ Barley 3 ” Chlorantraniliprole ” Plant commodities and animal commodities other Forchlorfenuron than milk: Chlorantraniliprole Forchlorfenuron Milk: Sum of chlorantraniliprole, “ Grapes 0.03 and ” Glyphosate Sum of glyphosate and Aminomethylphosphonic acid (AMPA) metabolite, expressed as glyphosate expressed as chlorantraniliprole “ “ Soya bean (dry) 20 Fruiting vegetables, cucurbits 0.5 Legume vegetables 2 ” Imazamox ” Imazamox Chlorpyrifos “ Chlorpyrifos Soya bean (dry) 0.1 “ Citrus fruits 1 ” Imazapic ” Sum of imazapic and its hydroxymethyl derivative Cypermethrin “ Cypermethrin, sum of isomers Sugar cane 0.1 “ Grapes 2 ” Imazapyr ” Imazapyr Cyprodinil “ Cyprodinil Maize 0.1 “ Grapes 3 ” Imidacloprid ” Sum of imidacloprid and metabolites containing the Dimethomorph 6-chloropyridinylmethylene moiety, expressed as Sum of E and Z isomers of dimethomorph imidacloprid “ “ Grapes 3 Grapes 1 Onion, bulb 0.6 Potato 0.05 ” Shallot 0.6
|
Boscalid Commodities of plant origin: Boscalid Commodities of animal origin: Sum of boscalid, nicotinamide and the glucuronide conjugate of nicotinamide, expressed as boscalid equivalents “ Grapes 5 ” Buprofezin Buprofezin “ Grapes 2.5 ” Carfentrazone-ethyl Carfentrazone-ethyl “ Hops, dry 0.1 ” Chlorantraniliprole Plant commodities and animal commodities other than milk: Chlorantraniliprole Milk: Sum of chlorantraniliprole, and expressed as chlorantraniliprole “ Fruiting vegetables, cucurbits 0.5 Legume vegetables 2 ” Chlorpyrifos Chlorpyrifos “ Citrus fruits 1 ” Cypermethrin Cypermethrin, sum of isomers “ Grapes 2 ” Cyprodinil Cyprodinil “ Grapes 3 ” Dimethomorph Sum of E and Z isomers of dimethomorph “ Grapes 3 Onion, bulb 0.6 Potato 0.05 Shallot 0.6 Spring onion 15 ” Fenbutatin oxide Bis[tris(2-methyl-2-phenylpropyl)tin]-oxide “ Grapes [except wine grapes] 5 ” Fenitrothion Fenitrothion “ Oilseeds 0.1 Pulses [except soya bean (dry)] 0.1 ” Fluxapyroxad Commodities of plant origin: Fluxapyroxad Commodities of animal origin for enforcement: Fluxapyroxad “ Barley 3 ” Forchlorfenuron Forchlorfenuron “ Grapes 0.03 ” Glyphosate Sum of glyphosate and Aminomethylphosphonic acid (AMPA) metabolite, expressed as glyphosate “ Soya bean (dry) 20 ” Imazamox Imazamox “ Soya bean (dry) 0.1 ” Imazapic Sum of imazapic and its hydroxymethyl derivative “ Sugar cane 0.1 ” Imazapyr Imazapyr “ Maize 0.1 ” Imidacloprid Sum of imidacloprid and metabolites containing the 6-chloropyridinylmethylene moiety, expressed as imidacloprid “ Grapes 1 ”
| 0.588986
|
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M1010-MRLs-CFS.docx
|
task-1431-20
| 18
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Institutions of Higher Education may of to in the upper right block below. revise “State Ohio” “Owner” If the Project is locally administered, delete the signature in the lower left block and the “Owner’s Concurrence” associated rows of the table below. SIGNATURES IN WITNESS WHEREOF, the parties hereto have executed this Agreement as of the date set forth below: STATE OF OHIO «INSERT CM’S NAME» Signature Signature Printed Name Printed Name Title Title Date OWNER’S CONCURRENCE by «insert name» Owner’s Signature Printed Name Title Purchase Order No. If the CM is a corporation, partnership, sole proprietorship, or limited liability company, use the table above. If the CM is a joint venture or special purpose entity, use the table below. Then delete the unused table and this note. STATE OF OHIO «INSERT CM’S NAME» by «insert name» by «insert Contracting name» Joint Venturer/Member’s Authority’s Signature Signature Printed Name Printed Name Title Title Date
|
Institutions of Higher Education may revise “State of Ohio” to “Owner” in the upper right block below. If the Project is locally administered, delete the “Owner’s Concurrence” signature in the lower left block and the associated rows of the table below. SIGNATURES IN WITNESS WHEREOF, the parties hereto have executed this Agreement as of the date set forth below: «INSERT CM’S NAME» STATE OF OHIO Signature Signature Printed Name Printed Name Title Title Date OWNER’S CONCURRENCE by «insert Owner’s name» Signature Printed Name Title Purchase Order No. If the CM is a corporation, partnership, sole proprietorship, or limited liability company, use the table above. If the CM is a joint venture or special purpose entity, use the table below. Then delete the unused table and this note. «INSERT CM’S NAME» STATE OF OHIO by «insert Joint Venturer/Member’s name» by «insert Contracting Authority’s name» Signature Signature Printed Name Printed Name Title Title Date
| 0.799397
|
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M170-00_52_23-Agreement_Form_CMR18.docx
|
task-1431-265
| 5
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Workshop on Electric Useful for handling projects (UG & Approved Binoj Kumar A.C Asst. Profs, June 28-30, Machine Design using PG) based on machine modeling and 78000 &Meera Khalid EEE 2016 ANSYS analysis. Familiarization of Approved Advanced laboratory Useful for technical staff for handling Dr. Vincent G Asso. Prof & June 21-23, Equipment in advanced equipment. 85000 &Samkumar T T/I 2016 Electrical and Electronics Engg. Sooraj G& T/I &Asst. STTP on Computer June 20-25, Based on demand from technical staff 120000 Approved Umesh A C Prof., ECE Hardware 2016 of various departments, this course has Maintenance and been organized. Networking Dr.Vineetha S Asst. Profs, STTP on June 13-18, Since it is useful for handling B.Tech 130000 Approved &Anu Bonia CSE 2016 & M.Tech Projects, it is Francis database recommended. and Tools Nisha K K& Raji Asst. Profs, STTP on Graph July 11-16, Faculty would get acquainted with 180000 Approved R Pillai CSE Algorithms and 2016 various algorithms which is useful for Computational research Geometry STTP on Cyber June 20-26, The course being an interdisciplinary 100000 Approved System P A Padmakumar Security and Tools for 2016 one, and useful for all engineering Analyst, CSE Ethical Hacking faculty, it is recommended Workshop on July 5-8, The training programme is intended 105000 Approved Enhancing Individual 2016 for administrative and technical staff. Dr.C.Sathish Excellence for Career It is expected to improve self Professor Kumar Effectiveness awareness and self organization in the participants, thereby improve their individual contribution to the institute. Dr.Jayaprasad P Asst. Prof., STTP on Data June 20-26, Intended for faculty getting trained 120000 Approved N Mathematics Analysis using 2016 with advanced software packages in Computational Mathematics Softwares
|
Binoj Kumar A.C &Meera Khalid Asst. Profs, EEE Workshop on Electric Machine Design using ANSYS June 28-30, 2016 Useful for handling projects (UG & PG) based on machine modeling and analysis. 78000 Approved Dr. Vincent G &Samkumar T Asso. Prof & T/I Familiarization of Advanced laboratory Equipment in Electrical and Electronics Engg. June 21-23, 2016 Useful for technical staff for handling advanced equipment. 85000 Approved Sooraj G& Umesh A C T/I &Asst. Prof., ECE STTP on Computer Hardware Maintenance and Networking June 20-25, 2016 Based on demand from technical staff of various departments, this course has been organized. 120000 Approved Dr.Vineetha S &Anu Bonia Francis Asst. Profs, CSE STTP on database and Tools June 13-18, 2016 Since it is useful for handling B.Tech & M.Tech Projects, it is recommended. 130000 Approved Nisha K K& Raji R Pillai Asst. Profs, CSE STTP on Graph Algorithms and Computational Geometry July 11-16, 2016 Faculty would get acquainted with various algorithms which is useful for research 180000 Approved P A Padmakumar System Analyst, CSE STTP on Cyber Security and Tools for Ethical Hacking June 20-26, 2016 The course being an interdisciplinary one, and useful for all engineering faculty, it is recommended 100000 Approved Dr.C.Sathish Kumar Professor Workshop on Enhancing Individual Excellence for Career Effectiveness July 5-8, 2016 The training programme is intended for administrative and technical staff. It is expected to improve self awareness and self organization in the participants, thereby improve their individual contribution to the institute. 105000 Approved Dr.Jayaprasad P N Asst. Prof., Mathematics STTP on Data Analysis using Computational Softwares June 20-26, 2016 Intended for faculty getting trained with advanced software packages in Mathematics 120000 Approved
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M17bog.docx
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task-1431-290
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Department of Electrical Engineering Remarks by BoG Amt Name of Details of Industry Prob Date No of Expected Topic Target Sl.No Coordinators Experts of Pgm. Days (Rs.) Outcome Dr. Dinesh Students will get Approved Er. Anurag practical Design of Sasikumar, . Prof knowledge in the high Power Graffito Systems One April 2016 Tech 15000 design and Rectifiers Trivandrum Day students implementation of high power rectifiers Students will Approved Dr. Prince A, get knowledge Assoc. Prof in the field of Dr. Manohar Singh, power system Engineering Officer protection. grade-3, Student will get Power System Central Power knowledge on One Protection Research Institute April 2016 students 25000 Day research topics (CPRI), Ministry of of the in the field of Power,Government power system of India , Bangalore and research through industrial experience
|
Department of Electrical Engineering Sl.No Topic Name of Coordinators Details of Industry Experts Prob Date of Pgm. No of Days Target Amt (Rs.) Expected Outcome Remarks by BoG Design of high Power Rectifiers Dr. Dinesh Prof Er. Anurag Sasikumar, Graffito Systems Trivandrum April 2016 One Day Tech students 15000 Students will get practical knowledge in the design and implementation of high power rectifiers Approved Power System Protection Dr. Prince A, Assoc. Prof Dr. Manohar Singh, Engineering Officer grade-3, Central Power Research Institute (CPRI), Ministry of Power,Government of India , Bangalore April 2016 One Day students of the 25000 Students will get knowledge in the field of power system protection. Student will get knowledge on research topics in the field of power system and research through industrial experience Approved .
| 0.270166
|
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task-1431-290
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B6.Students Paper Presentation Name of Sl No Amt Student Desig.& Dept. Date Topic Institution (Rs.) Paper Titled: Exploring the effect of proximity Institute of marketing as a new marketing strategy tool in 1st&2nd April Jith John S3,MTech Technology and Indian retail markets: A review @ International 2016 7500/- Francis IEM Science, conference on Developments in Management Ghaziabad, UP ,Technology &Science (ICDMTB 2016) Paper Titled: Productivity enhancement in Institute of manufacturing industries through values stream 1st&2nd April S3,MTech Technology and Vishnulal N R mapping: A review @ International conference 2016 7500/- IEM Science, on Developments in Management ,Technology Ghaziabad, UP &Science (ICDMTB 2016) Paper Titled: Study on perception of new generation customers towards a footwear Institute of 1st&2nd April Muhammed S3,MTech manufacturing company :A review @ Technology and 2016 7500/- Thabsheer A IEM International conference on Developments in Science, Management ,Technology &Science (ICDMTB Ghaziabad, UP 2016) Paper Titled: A review on cost of quality Institute of 1st&2nd April S3,MTech analysing models @ International conference Technology and Aneesh M 2016 7500/- IEM on Developments in Management ,Technology Science, &Science (ICDMTB 2016) Ghaziabad, UP Paper Titled: Intervention strategies for manual Institute of material handling tasks :A review @ 1st&2nd April S4,MTech Technology and Ebin Mathew International conference on Developments in 2016 7500/- IEM Science, Management ,Technology &Science (ICDMTB Ghaziabad, UP 2016) BoG accorded sanction for attending the workshop. BoG recommended that faculty who is the co author may also attend the conference and the name of the faculty who is the co author should be included in the proposal.
|
B6.Students Paper Presentation Sl No Name of Student Desig.& Dept. Topic Institution Date Amt (Rs.) Jith John Francis S3,MTech IEM Paper Titled: Exploring the effect of proximity marketing as a new marketing strategy tool in Indian retail markets: A review @ International conference on Developments in Management ,Technology &Science (ICDMTB 2016) Institute of Technology and Science, Ghaziabad, UP 1st&2nd April 2016 7500/- Vishnulal N R S3,MTech IEM Paper Titled: Productivity enhancement in manufacturing industries through values stream mapping: A review @ International conference on Developments in Management ,Technology &Science (ICDMTB 2016) Institute of Technology and Science, Ghaziabad, UP 1st&2nd April 2016 7500/- Muhammed Thabsheer A S3,MTech IEM Paper Titled: Study on perception of new generation customers towards a footwear manufacturing company :A review @ International conference on Developments in Management ,Technology &Science (ICDMTB 2016) Institute of Technology and Science, Ghaziabad, UP 1st&2nd April 2016 7500/- Aneesh M S3,MTech IEM Paper Titled: A review on cost of quality analysing models @ International conference on Developments in Management ,Technology &Science (ICDMTB 2016) Institute of Technology and Science, Ghaziabad, UP 1st&2nd April 2016 7500/- Ebin Mathew S4,MTech IEM Paper Titled: Intervention strategies for manual material handling tasks :A review @ International conference on Developments in Management ,Technology &Science (ICDMTB 2016) Institute of Technology and Science, Ghaziabad, UP 1st&2nd April 2016 7500/- BoG accorded sanction for attending the workshop. BoG recommended that faculty who is the co author may also attend the conference and the name of the faculty who is the co author should be included in the proposal.
| 0.584349
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M17bog.docx
|
task-1431-290
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APPENDIX F A Few Critical Things to Keep in Mind When Responding to an RFP for Consolidated Technology Services This document is explanatory only and has no consequence on the processes stated in any particular procurement. Please do not submit this checklist, it is merely a resource. _______ Note critical items such as: mandatory 1. Read the document. entire requirements; supplies/services required; submittal dates; number of copies required for submittal; funding amount and source; contract requirements (i.e., contract performance security, insurance requirements, performance and/or reporting requirements, etc.). 2. _______ Note the procurement officer's name, address, phone numbers and e-mail address. This is the only person you are allowed to communicate with regarding the RFP and is an excellent source of information for any questions you may have. 3. _______ Take advantage of the and period. Submit your “question answer” questions to the RFP Coordinator by the due date listed in the Schedule of Events and view the answers given in the formal issued for the RFP. “addenda” All addenda issued for an RFP are posted on the website and will State’s include all questions asked and answered concerning the RFP. 4. _______ Follow the format required in the RFP when preparing your response. Provide point-by-point responses to all sections in a clear and concise manner. Make sure to address each subpart. 5. _______ Provide complete answers/descriptions. Read and answer all questions and requirements. assume the State or evaluator/evaluation committee will Don’t know what your company capabilities are or what items/services you can provide, even if you have previously contracted with the State. The proposals are evaluated based solely on the information and materials provided in your response. 6. _______ Check the website for RFP addenda. Before submitting your State’s response, check the website at State’s http://cts.wa.gov/procurement/procurement.aspx to see whether any addenda were issued for the RFP. _______ to make sure that you have 7. Review and read the RFP document again addressed all requirements and have followed all of the instructions. Once you have done that, read the RFP document again.
|
APPENDIX F A Few Critical Things to Keep in Mind When Responding to an RFP for Consolidated Technology Services This document is explanatory only and has no consequence on the processes stated in any particular procurement. Please do not submit this checklist, it is merely a resource. 1. _______ Read the entire document. Note critical items such as: mandatory requirements; supplies/services required; submittal dates; number of copies required for submittal; funding amount and source; contract requirements (i.e., contract performance security, insurance requirements, performance and/or reporting requirements, etc.). 2. _______ Note the procurement officer's name, address, phone numbers and e-mail address. This is the only person you are allowed to communicate with regarding the RFP and is an excellent source of information for any questions you may have. 3. _______ Take advantage of the “question and answer” period. Submit your questions to the RFP Coordinator by the due date listed in the Schedule of Events and view the answers given in the formal “addenda” issued for the RFP. All addenda issued for an RFP are posted on the State’s website and will include all questions asked and answered concerning the RFP. 4. _______ Follow the format required in the RFP when preparing your response. Provide point-by-point responses to all sections in a clear and concise manner. Make sure to address each subpart. 5. _______ Provide complete answers/descriptions. Read and answer all questions and requirements. Don’t assume the State or evaluator/evaluation committee will know what your company capabilities are or what items/services you can provide, even if you have previously contracted with the State. The proposals are evaluated based solely on the information and materials provided in your response. 6. _______ Check the State’s website for RFP addenda. Before submitting your response, check the State’s website at http://cts.wa.gov/procurement/procurement.aspx to see whether any addenda were issued for the RFP. 7. _______ Review and read the RFP document again to make sure that you have addressed all requirements and have followed all of the instructions. Once you have done that, read the RFP document again.
| 0.887883
|
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M18-RFP-038.docx
|
task-1431-298
| 25
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SCHEDULE [1] Standard 1.4.2 is varied by inserting in alphabetical order in Schedule 1 “ Coumatetralyl Pig, edible offal of [except liver] T0.003 Pig fat T*0.001 Pig liver T0.004 Pig meat T*0.001 ” “ Warfarin Pig, edible offal [except liver] T0.007 Pig fat T0.007 Pig liver T0.04 Pig meat T0.007 ” Attachment B Explanatory Statement – 1. Authority Section 13 of the Food Standards Australia New Zealand Act 1991 (the FSANZ Act) provides that the functions of Food Standards Australia New Zealand (the Authority) include the development of standards and variations of standards for inclusion in the Australia New Zealand Food Standards Code (the Code). Division 2 of Part 3 of the FSANZ Act specifies that the Authority may prepare a proposal for the development or variation of food regulatory measures, including standards. This Division also stipulates the procedure for considering a proposal for the development or variation of food regulatory measures. The Authority prepared Proposal M1012 to consider introducing certain temporary maximum residue limits (MRLs) for residues of agricultural and veterinary (agvet) chemicals that may occur in food, in order to align standards with the Australian Pesticides and Veterinary Medicines Authority (APVMA) temporary MRLs for coumatetralyl and warfarin in pork commodities. The Authority considered the Proposal in accordance with Division 2 of Part 3 and has approved a draft variation. Following consideration by the Australia and New Zealand Ministerial Forum on Food Regulation, section 92 of the FSANZ Act stipulates that the Authority must publish a notice about the standard or draft variation of a standard. Section 94 of the FSANZ Act specifies that a standard, or a variation of a standard, in relation to which a notice is published under section 92 is a legislative instrument, but is not subject to parliamentary disallowance or sunsetting under the Legislative Instruments Act 2003. 2. Purpose
|
SCHEDULE [1] Standard 1.4.2 is varied by inserting in alphabetical order in Schedule 1 “ Coumatetralyl Pig, edible offal of [except liver] T0.003 Pig fat T*0.001 Pig liver T0.004 Pig meat T*0.001 ” “ Warfarin Pig, edible offal [except liver] T0.007 Pig fat T0.007 Pig liver T0.04 Pig meat T0.007 ” Attachment B – Explanatory Statement 1. Authority Section 13 of the Food Standards Australia New Zealand Act 1991 (the FSANZ Act) provides that the functions of Food Standards Australia New Zealand (the Authority) include the development of standards and variations of standards for inclusion in the Australia New Zealand Food Standards Code (the Code). Division 2 of Part 3 of the FSANZ Act specifies that the Authority may prepare a proposal for the development or variation of food regulatory measures, including standards. This Division also stipulates the procedure for considering a proposal for the development or variation of food regulatory measures. The Authority prepared Proposal M1012 to consider introducing certain temporary maximum residue limits (MRLs) for residues of agricultural and veterinary (agvet) chemicals that may occur in food, in order to align standards with the Australian Pesticides and Veterinary Medicines Authority (APVMA) temporary MRLs for coumatetralyl and warfarin in pork commodities. The Authority considered the Proposal in accordance with Division 2 of Part 3 and has approved a draft variation. Following consideration by the Australia and New Zealand Ministerial Forum on Food Regulation, section 92 of the FSANZ Act stipulates that the Authority must publish a notice about the standard or draft variation of a standard. Section 94 of the FSANZ Act specifies that a standard, or a variation of a standard, in relation to which a notice is published under section 92 is a legislative instrument, but is not subject to parliamentary disallowance or sunsetting under the Legislative Instruments Act 2003. 2. Purpose
| 0.988412
|
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M1012%20Amendments%20to%20Std%201.4.2%20AppR.docx
|
task-1431-30
| 11
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Fruit, stone, group 12 Cherries None 0.01 New US Chemical not listed <1% <1% 0.01 Pyridaben 94% Hops, dry None 10 New US Hop, dried cones 10 Chemical not listed N/A N/A Pyrimethanil 32% Berries and other small fruits [except T5 None Deleted FSANZ grapes; strawberry] Berries and other Berries and other small fruits small fruits [except Blackberries 15 Blackberries 15; Not Not [except 15 New Codex blueberries; Raspberries 15 Raspberries 15 (2016) required required grapes; grapes; strawberry] strawberry] T5 Berries Not Not and other required required small fruits Blueberries Blueberries [except 8 New Codex Blueberries 8 8 (2016) grapes; strawberry] T5 Onion, bulb, subgroup Not Not Onion, bulb 0.1 0.2 Increased US Onion, bulb 0.2 (2008) 3-07A 0.2 required required Pome fruits 7 15 Increased Codex Pome fruits 15 Pome fruits 15 (2014) Not Not required required Not Not Potato *0.01 0.05 Increased Codex Potato 0.05 Potato 0.05 (2008) required required Vegetable, tuberous Not Not Sweet potato None 0.05 New US and corm, subgroup 1C Commodity not listed required required 0.05 Quintozene Banana 1 None Deleted APVMA Banana
|
Cherries None 0.01 New US Fruit, stone, group 12 0.01 Chemical not listed <1% <1% Pyridaben 94% Hops, dry None 10 New US Hop, dried cones 10 Chemical not listed N/A N/A Pyrimethanil 32% Berries and other small fruits [except grapes; strawberry] T5 None Deleted FSANZ Berries and other small fruits [except blueberries; grapes; strawberry] Berries and other small fruits [except grapes; strawberry] T5 15 New Codex Blackberries 15 Raspberries 15 Blackberries 15; Raspberries 15 (2016) Not required Not required Blueberries Berries and other small fruits [except grapes; strawberry] T5 8 New Codex Blueberries 8 Blueberries 8 (2016) Not required Not required Onion, bulb 0.1 0.2 Increased US Onion, bulb, subgroup 3-07A 0.2 Onion, bulb 0.2 (2008) Not required Not required Pome fruits 7 15 Increased Codex Pome fruits 15 Pome fruits 15 (2014) Not required Not required Potato *0.01 0.05 Increased Codex Potato 0.05 Potato 0.05 (2008) Not required Not required Sweet potato None 0.05 New US Vegetable, tuberous and corm, subgroup 1C 0.05 Commodity not listed Not required Not required Quintozene Banana 1 None Deleted APVMA Banana
| 0.59958
|
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M1014%20MRLs%20(2016)%20SD1%20DEAs.docx
|
task-1431-34
| 26
|
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(the following is to be filled out at the end of the workshop) PLEASE CIRCLE TO WHAT EXTENT YOU AGREE OR DISAGREE WITH THE FOLLOWING STATEMENTS: Neither Strongly Strongly Agree Agree nor Disagree Agree Disagree Disagree Subject matter was adequately 5 4 3 2 1 covered by the Contingency Planning Guide and the reading materials in this workshop guide. Workshop was suitable for my 5 4 3 2 1 background and experience Workshop was well-paced 5 4 3 2 1 The draft CP is of good quality. 5 4 3 2 1 The Draft CP produced represents 5 4 3 2 1 the consensus of this working group Workshop participants were 5 4 3 2 1 encouraged to take an active part. 7. I am confident that this plan will lead 5 4 3 2 1 to a better humanitarian response if the planned-for scenario does occur. 8. Was the workshop length: Correct? Too short? Too long ? 9. Were there: Just enough participants? Too few? Too many ? 10. Which areas, if any, received too much or too little time? 11. Do you have any suggestions that you feel could improve this workshop or the CP draft that was produced? 12. Any other comments?
|
(the following is to be filled out at the end of the workshop) PLEASE CIRCLE TO WHAT EXTENT YOU AGREE OR DISAGREE WITH THE FOLLOWING STATEMENTS: Strongly Agree Agree Neither Agree nor Disagree Disagree Strongly Disagree Subject matter was adequately covered by the Contingency Planning Guide and the reading materials in this workshop guide. 5 4 3 2 1 Workshop was suitable for my background and experience 5 4 3 2 1 Workshop was well-paced 5 4 3 2 1 The draft CP is of good quality. 5 4 3 2 1 The Draft CP produced represents the consensus of this working group 5 4 3 2 1 Workshop participants were encouraged to take an active part. 5 4 3 2 1 7. I am confident that this plan will lead to a better humanitarian response if the planned-for scenario does occur. 5 4 3 2 1 8. Was the workshop length: Correct? Too short? Too long ? 9. Were there: Just enough participants? Too few? Too many ? 10. Which areas, if any, received too much or too little time? 11. Do you have any suggestions that you feel could improve this workshop or the CP draft that was produced? 12. Any other comments?
| 0.850429
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M1_2_5_1%20IFRC%20developing%20a%20contingency%20plan.docx
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task-1431-378
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[
158,
837,
196,
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[
201,
837,
288,
853
]
] |
required required Phosmet 92% Currants, black, Currants (black, red None 2 New EU Commodity not listed 2% 1% red, white and white) Not Phosphorous acid required Berries and other small fruits [except Not Not T50 None Deleted APVMA riberries; required required strawberry] Not Not Grapes None 200 New APVMA required required Piperonyl butoxide 73% All other foods Not Not except animal food None 0.5 New FSANZ required required commodities Not Not Herbs None 8 New APVMA required required Pirimicarb Coriander (leaves, Not Not T20 None Deleted APVMA roots, stems) required required Not Not Herbs T20 None Deleted APVMA required required Not Not Hops, dry 0.5 None Deleted APVMA required required Not Not Lemon balm T20 None Deleted APVMA required required Profenofos 1% All other foods Not Not except animal food None 0.02 New FSANZ required required commodities Peppers, chili None 3 New Codex Peppers Chili Peppers Chili 3 (2012) <1% <1% Peppers Chili, dried 20 Peppers, chili (dry) None 20 New Codex Peppers Chili, dried <1% <1% (2012) Not Propachlor required Garlic 2.5 None Deleted APVMA Not Not
|
required required Phosmet 92% Currants, black, red, white None 2 New EU Currants (black, red and white) Commodity not listed 2% 1% Phosphorous acid Not required Berries and other small fruits [except riberries; strawberry] T50 None Deleted APVMA Not required Not required Grapes None 200 New APVMA Not required Not required Piperonyl butoxide 73% All other foods except animal food commodities None 0.5 New FSANZ Not required Not required Herbs None 8 New APVMA Not required Not required Pirimicarb Coriander (leaves, roots, stems) T20 None Deleted APVMA Not required Not required Herbs T20 None Deleted APVMA Not required Not required Hops, dry 0.5 None Deleted APVMA Not required Not required Lemon balm T20 None Deleted APVMA Not required Not required Profenofos 1% All other foods except animal food commodities None 0.02 New FSANZ Not required Not required Peppers, chili None 3 New Codex Peppers Chili Peppers Chili 3 (2012) <1% <1% Peppers, chili (dry) None 20 New Codex Peppers Chili, dried Peppers Chili, dried 20 (2012) <1% <1% Propachlor Not required Garlic 2.5 None Deleted APVMA Not Not
| 0.55738
|
[
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168,
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171,
172,
173,
174,
175,
176,
177
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M1015%20-%20Supporting%20Document%201.docx
|
task-1431-38
| 24
|
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Check that your answers are sensible, significant figures, units and that you have documented your answer with comments and statements of physical principles. The problem type is free fall uniformly accelerated motion in – the vertical Y direction: = -g = -9.81 m.s-2 and the displacement corresponds to the a s changes in the vertical position of the ball. We can solve the problem using the equations (1) (2) (3) (4) (5) Case (a) Initial state: = 0 = 0 = 0 = -9.81 m.s-2 t s u a = -g Final state when = 1.23 s: = ? m = ? m.s-1 t s v Eq(1) Eq(2) Final state when = -40.0 m (as ball strikes ground): = ? s v = ? s t
|
Check that your answers are sensible, significant figures, units and that you have documented your answer with comments and statements of physical principles. The problem type is free fall – uniformly accelerated motion in the vertical Y direction: a = -g = -9.81 m.s-2 and the displacement s corresponds to the changes in the vertical position of the ball. We can solve the problem using the equations (1) (2) (3) (4) (5) Case (a) Initial state: t = 0 s = 0 u = 0 a = -g = -9.81 m.s-2 Final state when t = 1.23 s: s = ? m v = ? m.s-1 Eq(1) Eq(2) Final state when s = -40.0 m (as ball strikes ground): v = ? t = ? s
| 0.697371
|
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m1_aconstant.docx
|
task-1431-387
| 10
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Unit vectors Specify the units Physical Symbol Scalar S.I. Other quantity vector unit units time minute scalar: second hour s day year time interval minute scalar: second hour s day year distance scalar: metre mm travelled m km displacement mm (position vector m km vector) average speed scalar: m.s-1 km.h-1 speed scalar: m.s-1 km.h-1 (instantaneous) average vector m.s-1 km.h-1 velocity velocity vector m.s-1 km.h-1 (instantaneous) average vector m.s-2 acceleration acceleration vector m.s-2
|
Unit vectors Specify the units Physical quantity Symbol Scalar vector S.I. unit Other units time scalar: second s minute hour day year time interval scalar: second s minute hour day year distance travelled scalar: metre m mm km displacement (position vector) vector m mm km average speed scalar: m.s-1 km.h-1 speed (instantaneous) scalar: m.s-1 km.h-1 average velocity vector m.s-1 km.h-1 velocity (instantaneous) vector m.s-1 km.h-1 average acceleration vector m.s-2 acceleration vector m.s-2
| 0.355998
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m1_B.docx
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task-1431-394
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average speed S.I. units average velocity N.B. very different values for the average speed and average velocity. Often the same symbol is used for speed and velocity. You always need to state whether the symbol represents the speed or velocity. Our tram speeds up continually until it reaches the Origin, it then slows down and stops at the right end of the track. In the return journey, it continually speeds up until it reaches the Origin it then slows down and stops at the left end of the track. The speed and velocity are always changing. Hence, it is more useful to talk around instantaneous values rather than average values. The instantaneous velocity is a vector quantity whose magnitude is equal to the instantaneous speed and who instantaneous direction is in the same sense to that in which the particle is moving at that instant. The velocity vector is always tangential to the trajectory of the particle at that instant. Remember, speed and velocity are
|
average speed S.I. units average velocity N.B. very different values for the average speed and average velocity. Often the same symbol is used for speed and velocity. You always need to state whether the symbol represents the speed or velocity. Our tram speeds up continually until it reaches the Origin, it then slows down and stops at the right end of the track. In the return journey, it continually speeds up until it reaches the Origin it then slows down and stops at the left end of the track. The speed and velocity are always changing. Hence, it is more useful to talk around instantaneous values rather than average values. The instantaneous velocity is a vector quantity whose magnitude is equal to the instantaneous speed and who instantaneous direction is in the same sense to that in which the particle is moving at that instant. The velocity vector is always tangential to the trajectory of the particle at that instant. Remember, speed and velocity are
| 1
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m1_dva.docx
|
task-1431-401
| 4
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] |
required required Not Not Olive oil, crude T2 None Deleted APVMA required required Not Not Olives T1 None Deleted APVMA required required Peppers None T0.1 New APVMA 14% 4% Persimmon, None 0.5 New APVMA 6% 2% American Potato None *0.01 New APVMA 3% 1% Not Not Pulses 0.1 None Deleted APVMA required required Root and tuber Not Not *0.01 None Deleted APVMA vegetables required required Not Not Strawberry *0.01 None Deleted APVMA required required Vegetables [except garlic; lettuce, head; Not Not 0.1 None Deleted APVMA lettuce, leaf; onion, required required bulb; root and tuber vegetables] Methomyl 44% Not Not Blackberries 2 None Deleted APVMA required required Not Not Coffee beans T1 None Deleted APVMA required required Not Not Fig T0.7 None Deleted APVMA required required Fruiting vegetables, other than cucurbits Not Not None 1 New APVMA [except peppers; required required sweet corn Fruiting vegetables, other Not Not 1 None Deleted APVMA than cucurbits required required [except peppers]
|
required required Olive oil, crude T2 None Deleted APVMA Not required Not required Olives T1 None Deleted APVMA Not required Not required Peppers None T0.1 New APVMA 14% 4% Persimmon, American None 0.5 New APVMA 6% 2% Potato None *0.01 New APVMA 3% 1% Pulses 0.1 None Deleted APVMA Not required Not required Root and tuber vegetables *0.01 None Deleted APVMA Not required Not required Strawberry *0.01 None Deleted APVMA Not required Not required Vegetables [except garlic; lettuce, head; lettuce, leaf; onion, bulb; root and tuber vegetables] 0.1 None Deleted APVMA Not required Not required Methomyl 44% Blackberries 2 None Deleted APVMA Not required Not required Coffee beans T1 None Deleted APVMA Not required Not required Fig T0.7 None Deleted APVMA Not required Not required Fruiting vegetables, other than cucurbits [except peppers; sweet corn None 1 New APVMA Not required Not required Fruiting vegetables, other than cucurbits [except peppers] 1 None Deleted APVMA Not required Not required
| 0.511376
|
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M1015_Supporting%20Document%201.docx
|
task-1431-41
| 19
|
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Not Not Strawberry None *0.1 New EU Strawberries Chemical not listed required. required <1%5 Not Trichlorfon required Tree nuts Not Not Macadamia nuts 0.1 Maintained APVMA 0.1 required required Not Not Tree nuts 0.1 None Deleted APVMA required required Not Tridemorph required Not Not Banana T*0.05 None Deleted APVMA required required Not Not Barley 0.1 None Deleted APVMA required required Fruiting Not Not vegetables, 0.1 None Deleted APVMA required required cucurbits Trifloxystrobin 23% Raspberries, red, Raspberries (red and Not Not None 3 New EU Commodity not listed black yellow) required required Trifluralin 9% All other foods Not Not except animal food None 0.01 New FSANZ required required commodities Not Not Almonds None 0.05 New US Nut, tree, group 14 Chemical not listed required required Not Tylosin required Not Not Fish muscle T*0.002 None Deleted APVMA required required
|
Strawberry None *0.1 New EU Strawberries Chemical not listed Not required Not required. <1%5 Trichlorfon Not required Macadamia nuts Tree nuts 0.1 0.1 Maintained APVMA Not required Not required Tree nuts 0.1 None Deleted APVMA Not required Not required Tridemorph Not required Banana T*0.05 None Deleted APVMA Not required Not required Barley 0.1 None Deleted APVMA Not required Not required Fruiting vegetables, cucurbits 0.1 None Deleted APVMA Not required Not required Trifloxystrobin 23% Raspberries, red, black None 3 New EU Raspberries (red and yellow) Commodity not listed Not required Not required Trifluralin 9% All other foods except animal food commodities None 0.01 New FSANZ Not required Not required Almonds None 0.05 New US Nut, tree, group 14 Chemical not listed Not required Not required Tylosin Not required Fish muscle T*0.002 None Deleted APVMA Not required Not required
| 0.462045
|
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M1015_Supporting%20Document%201.docx
|
task-1431-41
| 28
|
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Change Date June 25, 2015 a. Resolution Use the table below to resolve shipping exceptions for source material sent to for Common the scanning vendors. Exceptions Exception Type VA Action (CACI) VA Action (SMS) RMN or UPS In the CACI (Omega) portal In the Data Dimensions (dd) Tracker Tracking Keying Error/Numbers search for the record on the search for the record using the Not Found in Reconciliation tab, and Search feature, and Vendor Portal if the record does not exist if the record does not exist under under the RMN or tracking the RMN or tracking number, then number, then create a new use the Create function to enter it entry, or as a new shipment be sure to save the new entry if the record is keyed in once all the information is incorrectly, then entered, and click Shipping then Search and enter either the RMN or once entered and saved, use the tracking number as it is Search function to confirm that the currently in the system manifest entry was successfully click Search then SELECT created, or if a record exists but is next to the entry, and entered incorrectly, such as the edit the tracking number and wrong UPS tracking number being RMN fields on the right side associated with an RMN, ROs as necessary and click Update should Record. use the Search function to look up existing entries with the relevant RMN and UPS tracking number double-click on the search result update the entry to reflect the information provided in the exception description, and do use Create to try to add a not new manifest entry, as it will result in a duplicate keying error. Extra Folder Not ROs should not request the return of Determine if the Veteran's on Manifest or extra claims folder exceptions except documents should have been sent Folder does not as a last resort. The preferred method for conversion. If the Veteran have DCS. of resolution is falls within the parameters of the VBMS exclusions list, request the folder be returned to the RO. use VBMS to create a new DCS sheet(s) for the extra record(s) If there is no VBMS exclusion
|
Change Date June 25, 2015 a. Resolution for Common Exceptions Use the table below to resolve shipping exceptions for source material sent to the scanning vendors. Exception Type VA Action (CACI) VA Action (SMS) RMN or UPS Tracking Keying Error/Numbers Not Found in Vendor Portal In the CACI (Omega) portal search for the record on the Reconciliation tab, and if the record does not exist under the RMN or tracking number, then create a new entry, or if the record is keyed in incorrectly, then click Shipping then Search and enter either the RMN or tracking number as it is currently in the system click Search then SELECT next to the entry, and edit the tracking number and RMN fields on the right side as necessary and click Update Record. In the Data Dimensions (dd) Tracker search for the record using the Search feature, and if the record does not exist under the RMN or tracking number, then use the Create function to enter it as a new shipment be sure to save the new entry once all the information is entered, and once entered and saved, use the Search function to confirm that the manifest entry was successfully created, or if a record exists but is entered incorrectly, such as the wrong UPS tracking number being associated with an RMN, ROs should use the Search function to look up existing entries with the relevant RMN and UPS tracking number double-click on the search result update the entry to reflect the information provided in the exception description, and do not use Create to try to add a new manifest entry, as it will result in a duplicate keying error. Extra Folder Not on Manifest or Folder does not have DCS. Determine if the Veteran's documents should have been sent for conversion. If the Veteran falls within the parameters of the VBMS exclusions list, request the folder be returned to the RO. If there is no VBMS exclusion ROs should not request the return of extra claims folder exceptions except as a last resort. The preferred method of resolution is use VBMS to create a new DCS sheet(s) for the extra record(s)
| 0.74163
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M21-1III_ii_1_F.docx
|
task-1431-591
| 10
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26 27 28 29 30 L3-1 to 3-7 Quiz L4-2 Parallel & L4-3 Graphing lines L4-4 Investigating graphs Non-Instruction day Perpendicular Lines of linear functions L4-1 Slope of a Line Hand-in Text pages 354 to 356 November 2 3 4 5 6 L4-1 to 4-4 Quiz No Classes L4-5 Slope-point Form Work period L4-6 Slope-Intercept Form Parent-Teacher Interviews 11:00 to 7:00 9 10 11 12 13 L4-7 General Form Work period Remembrance day L4-8 Love those linear L4-5 to 4-7 Quiz equations Doomsday #3 Review Hand-in 16 17 18 19 20 Doomsday #3 L5-1 Solving Linear Work period L5-2 Solving Linear Non-Instruction day Equations Graphically Equations Substitution – – Lecture? Lecture? 23 24 25 26 27 Work period L5-3 Solving Linear Work period L5-4 Linear Equations Work period – Equations Elimination Possible solutions – Lecture? Lecture? 30 December 1 2 3 4 L5-5 Love solving those L5-1 to 5-4 Quiz L6-1 Imperial Measures of L6-2 Math Lab: Measuring Non-instruction day linear equations Length Length and Distance Hand-in Hand-in Text pages 13 to 15 7 8 9 10 11 L6-1 to 6-3 Quiz L6-3 Relating SI and Work period L6-5 Volumes of Right L6-6 Surface Area & Imperial Units Pyramids & Right Cones Volume of a Sphere L6-4 Surface Areas of Right Pyramids & Right Cones 14 15 16 17 18 L6-7 Solving Problems L6-8 Love those L6-4 to 6-7 Quiz Work period L7-2 Using the trig ratios Involving Objects measurements areas and volumes L7-1 Trigonometry – tangent sine cosine Hand-in Tuesday Wednesday Thursday Friday
|
26 27 28 29 30 L3-1 to 3-7 Quiz L4-1 Slope of a Line L4-2 Parallel & Perpendicular Lines L4-3 Graphing lines L4-4 Investigating graphs of linear functions Hand-in Text pages 354 to 356 Non-Instruction day November 2 3 4 5 6 L4-1 to 4-4 Quiz L4-5 Slope-point Form Work period No Classes Parent-Teacher Interviews 11:00 to 7:00 L4-6 Slope-Intercept Form 9 10 11 12 13 L4-7 General Form Work period Remembrance day L4-8 Love those linear equations Hand-in L4-5 to 4-7 Quiz Doomsday #3 Review 16 17 18 19 20 Doomsday #3 L5-1 Solving Linear Equations – Graphically Lecture? Work period L5-2 Solving Linear Equations – Substitution Lecture? Non-Instruction day 23 24 25 26 27 Work period L5-3 Solving Linear Equations – Elimination Lecture? Work period L5-4 Linear Equations – Possible solutions Lecture? Work period 30 December 1 2 3 4 L5-5 Love solving those linear equations Hand-in L5-1 to 5-4 Quiz L6-1 Imperial Measures of Length L6-2 Math Lab: Measuring Length and Distance Hand-in Text pages 13 to 15 Non-instruction day 7 8 9 10 11 L6-3 Relating SI and Imperial Units L6-1 to 6-3 Quiz L6-4 Surface Areas of Right Pyramids & Right Cones Work period L6-5 Volumes of Right Pyramids & Right Cones L6-6 Surface Area & Volume of a Sphere 14 15 16 17 18 L6-7 Solving Problems Involving Objects L6-8 Love those measurements areas and volumes Hand-in L6-4 to 6-7 Quiz L7-1 Trigonometry – tangent sine cosine Work period L7-2 Using the trig ratios Tuesday Wednesday Thursday Friday
| 0.647793
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M10c%202015-16%20sem%201%20gen.docx
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task-1431-60
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Monday January 4 5 6 7 8 L7-1 to 7-3 Quiz L7-3 Math Lab: Measuring L7-4 Applying trig ratios Work period L7-5 Applying trig ratios – – an Inaccessible Height part I part II Hand-in Math text pages 84 to 86 11 12 13 14 15 Work period L7-6 Love those L7-4 to 7-5 Quiz Doomsday #4 Final Exam review trigonometry problems Doomsday #4 Review Last day of classes Hand-in 18 19 20 21 22 Final exam 25 26 27 28 29 Deadline All work must be in and complete by 2:00 pm
|
Monday January 4 5 6 7 8 L7-3 Math Lab: Measuring an Inaccessible Height Hand-in Math text pages 84 to 86 L7-1 to 7-3 Quiz L7-4 Applying trig ratios – part I Work period L7-5 Applying trig ratios – part II 11 12 13 14 15 Work period L7-6 Love those trigonometry problems Hand-in L7-4 to 7-5 Quiz Doomsday #4 Review Doomsday #4 Final Exam review Last day of classes 18 19 20 21 22 Final exam 25 26 27 28 29 Deadline All work must be in and complete by 2:00 pm
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M10c%202015-16%20sem%201%20gen.docx
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task-1431-60
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For more information on DEA, Reference: see , and . Benefits for children of Vietnam Veterans who are born with spina bifida ( g. Eligibility for Chapter 18 benefits) are dependent on the character of discharge. That is, regardless not Benefits of the characterization of the service of the Veteran parent, individuals may receive Chapter 18 benefits if the necessary Vietnam service and relationship requirements are met. For more information on eligibility and development for Chapter Reference: 18 benefits, see M21-1, Part VI, 2.B.
|
Reference: For more information on DEA, see , and . g. Eligibility for Chapter 18 Benefits Benefits for children of Vietnam Veterans who are born with spina bifida ( benefits) are not dependent on the character of discharge. That is, regardless of the characterization of the service of the Veteran parent, individuals may receive Chapter 18 benefits if the necessary Vietnam service and relationship requirements are met. Reference: For more information on eligibility and development for Chapter 18 benefits, see M21-1, Part VI, 2.B.
| 0.810339
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M21-1III_ii_6.docx
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task-1431-604
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,whichever results in the higher numeral. When the puretone threshold is 30 decibels or less at 1000 Hz Important: and 70 decibels or more at 2000 Hz, the Roman numeral obtained by using the appropriate table will be elevated to the next higher Roman numeral. For more information on evaluating hearing loss based on Reference: exceptional patterns of hearing impairment, see . When an examiner certifies that speech discrimination scores are not o. Evaluating Hearing Loss appropriate or cannot be obtained, typically indicated with a test “cannot When Speech (CNT)” designation on examination, in accordance with use Table VIA in . Discrimination Scores Are Not An examiner indicates that speech discrimination scores are not Example: Appropriate or appropriate due to inconsistent results. Cannot Be Obtained p. Considering disease is characterized by episodic attacks with subsequent Meniere’s Hearing subsiding of symptoms following the attack. A Veteran may be totally deaf Impairment during the attack with return to normal hearing when the attack ends. Due to Therefore, in evaluating hearing impairment under , the puretone thresholds Meniere’s or speech discrimination percentages are not required to meet the provisions Disease of as hearing impairment associated with disease is often transient. Meniere’s In some cases, hearing loss may not recede following an attack of Important: disease and instead results in a permanent loss of hearing that Meniere’s meets the definition of hearing impairment under . In such circumstances, award benefits under the DC that results in the highest percentage for the Veteran. q. Determining Use the table below to determine whether reexamination is necessary. the Need for Reexamination A single examination is often sufficient to meet the qualifying Note: conditions of permanence under . If … Then … the extent of hearing loss in an do not routinely schedule individual claim has been reexamination. satisfactorily established by an examination
|
,whichever results in the higher numeral. Important: When the puretone threshold is 30 decibels or less at 1000 Hz and 70 decibels or more at 2000 Hz, the Roman numeral obtained by using the appropriate table will be elevated to the next higher Roman numeral. Reference: For more information on evaluating hearing loss based on exceptional patterns of hearing impairment, see . o. Evaluating Hearing Loss When Speech Discrimination Scores Are Not Appropriate or Cannot Be Obtained When an examiner certifies that speech discrimination scores are not appropriate or cannot be obtained, typically indicated with a “cannot test (CNT)” designation on examination, in accordance with use Table VIA in . Example: An examiner indicates that speech discrimination scores are not appropriate due to inconsistent results. p. Considering Hearing Impairment Due to Meniere’s Disease Meniere’s disease is characterized by episodic attacks with subsequent subsiding of symptoms following the attack. A Veteran may be totally deaf during the attack with return to normal hearing when the attack ends. Therefore, in evaluating hearing impairment under , the puretone thresholds or speech discrimination percentages are not required to meet the provisions of as hearing impairment associated with Meniere’s disease is often transient. Important: In some cases, hearing loss may not recede following an attack of Meniere’s disease and instead results in a permanent loss of hearing that meets the definition of hearing impairment under . In such circumstances, award benefits under the DC that results in the highest percentage for the Veteran. q. Determining the Need for Reexamination Use the table below to determine whether reexamination is necessary. Note: A single examination is often sufficient to meet the qualifying conditions of permanence under . If … Then … the extent of hearing loss in an individual claim has been satisfactorily established by an examination do not routinely schedule reexamination.
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M21-1III_iv_4_SecB.docx
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task-1431-612
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The resultant disabling effects of a TBI event beyond those that follow d. TBI Residuals immediately from the acute injury to the brain are known as or TBI residuals TBI sequelae. The signs and symptoms of TBI residuals can be organized into the three main categories of physical, cognitive, and behavioral/emotional residuals for evaluation purposes. Examples of TBI residuals in each of the three categories may include, but are not limited to, those listed below. Physical Cognitive Behavioral/Emotional Apraxia (inability to execute Dementias (pre-senile Depression purposeful, previously learned Alzheimer’s type, dementia motor tasks, despite physical pugilistica, post traumatic ability and willingness) dementia) Aphasia (difficulty Attention and concentration Agitation and communicating orally and/or in deficits irritability writing) Paresis (muscle weakness or Memory, processing, and Impulsivity incomplete paralysis) learning impairment Plegia (paralysis or stroke) Language deficiencies Aggression Dysphagia (difficulty Planning difficulties Anxiety swallowing) Disorders of balance and Judgment and control difficulties Posttraumatic stress coordination disorder Diseases of hormone deficiency Reasoning and abstract thinking limitations Parkinsonism Self-awareness limitations Nausea/vomiting Headaches Dizziness Blurred vision Seizure disorder Sensory loss Weakness Sleep disturbance TBI residuals can resolve in a short period of time or can persist Note: chronically or even permanently. Chronic TBI residuals may include some or all of the clinical signs that developed immediately during the TBI event. Others (such as seizures or spasticity) may have a delayed onset. e. Determining A claim for SC for TBI may also be worded as a claim for or “head injury,” the Issues in A claim document mentioning any of the above must be “concussion.” TBI Cases sympathetically read and understood as a claim for identifiable TBI all residuals that can be attributed to one or more TBI events.
|
d. TBI Residuals The resultant disabling effects of a TBI event beyond those that follow immediately from the acute injury to the brain are known as TBI residuals or TBI sequelae. The signs and symptoms of TBI residuals can be organized into the three main categories of physical, cognitive, and behavioral/emotional residuals for evaluation purposes. Examples of TBI residuals in each of the three categories may include, but are not limited to, those listed below. Physical Cognitive Behavioral/Emotional Apraxia (inability to execute purposeful, previously learned motor tasks, despite physical ability and willingness) Dementias (pre-senile Alzheimer’s type, dementia pugilistica, post traumatic dementia) Depression Aphasia (difficulty communicating orally and/or in writing) Attention and concentration deficits Agitation and irritability Paresis (muscle weakness or incomplete paralysis) Memory, processing, and learning impairment Impulsivity Plegia (paralysis or stroke) Language deficiencies Aggression Dysphagia (difficulty swallowing) Planning difficulties Anxiety Disorders of balance and coordination Judgment and control difficulties Posttraumatic stress disorder Diseases of hormone deficiency Reasoning and abstract thinking limitations Parkinsonism Self-awareness limitations Nausea/vomiting Headaches Dizziness Blurred vision Seizure disorder Sensory loss Weakness Sleep disturbance Note: TBI residuals can resolve in a short period of time or can persist chronically or even permanently. Chronic TBI residuals may include some or all of the clinical signs that developed immediately during the TBI event. Others (such as seizures or spasticity) may have a delayed onset. e. Determining the Issues in TBI Cases A claim for SC for TBI may also be worded as a claim for “head injury,” or “concussion.” A claim document mentioning any of the above must be sympathetically read and understood as a claim for all identifiable TBI residuals that can be attributed to one or more TBI events.
| 0.785304
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M21-1III_iv_4_SecG.docx
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task-1431-616
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For more information on References: the rating activity, see M21-1, Part III, Subpart iv, 1.1, and issues that require a rating decision, see M21-1, Part III, Subpart ii, 7.3.b. c. Components The rating decision is composed of a explanation of the Narrative of the Rating determination on benefit entitlement and a containing information Codesheet Decision about the claimant, the current decision, past decisions and the current state of entitlement to compensation and pension benefits. To the maximum extent possible, rating decisions should not include Note: mailing address information or RO-specific address information. Should a field be required in a system, placeholders should be used such as dashes or spaces. For more information on References: rating decision see M21-1 Part III, Subpart iv, 6.C.5 Narrative, the see M21-1 Part III, Subpart iv, 6.D, and Codesheet, the coded conclusion on the see M21-1 Part III, Subpart iv, 6.E. Codesheet, d. Rating The table below provides information about the sections of a rating decision. Decision Sections For more information on the rating decision format, see the Reference: Rating Decision Sections Description Contains the Narrative Introduction for each issue considered Decision, Evidence for each issue Reasons for Decision, considered, and References. Contains the Codesheet data table Jurisdiction coded conclusion SPECIAL NOTATION and TEMPLATE fields, and signature(s). Rating decisions are prepared using VBMS-R, which incorporates the latest e. Rating Decision rating decision format, includes tools to help ensure rating decision Automation
|
References: For more information on the rating activity, see M21-1, Part III, Subpart iv, 1.1, and issues that require a rating decision, see M21-1, Part III, Subpart ii, 7.3.b. c. Components of the Rating Decision The rating decision is composed of a Narrative explanation of the determination on benefit entitlement and a Codesheet containing information about the claimant, the current decision, past decisions and the current state of entitlement to compensation and pension benefits. Note: To the maximum extent possible, rating decisions should not include mailing address information or RO-specific address information. Should a field be required in a system, placeholders should be used such as dashes or spaces. References: For more information on rating decision Narrative, see M21-1 Part III, Subpart iv, 6.C.5 the Codesheet, see M21-1 Part III, Subpart iv, 6.D, and the coded conclusion on the Codesheet, see M21-1 Part III, Subpart iv, 6.E. d. Rating Decision Sections The table below provides information about the sections of a rating decision. Reference: For more information on the rating decision format, see the Rating Decision Sections Description Narrative Contains the Introduction Decision, for each issue considered Evidence Reasons for Decision, for each issue considered, and References. Codesheet Contains the data table Jurisdiction coded conclusion SPECIAL NOTATION and TEMPLATE fields, and signature(s). e. Rating Decision Automation Rating decisions are prepared using VBMS-R, which incorporates the latest rating decision format, includes tools to help ensure rating decision
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M21-1III_iv_6_SecC.docx
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task-1431-623
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sufficiency, and transfers disability and entitlement data into a corporate Using VBMS-R database of claimants who have applied for VA benefits. For more information on VBMS-R, see . Reference: 2. Introduction This topic contains information on the part, including Introduction Introduction and Introduction, Introduction. January 20, 2015 Change Date The purpose of the is to a. Purpose of Introduction the Introduction identify the claimant, and acknowledge the qualifying service, including any special Veteran’s considerations relevant to the claim, such as former prisoner of war (FPOW) status. The level of detail in the introduction depends on the complexity of Note: each issue. The may be generated using the narrative assistance function in b. Generating Introduction the VBMS-R or may be composed manually. Use the guidelines listed below Introduction when manually composing the Introduction. Write directly to the claimant. is acceptable usage. “You” Include all periods of service. Use the format month/day/year to month/day/year. Service dates must be checked for accuracy. Note: For more information on using the narrative assistance function, Reference: see the .
|
Using VBMS-R sufficiency, and transfers disability and entitlement data into a corporate database of claimants who have applied for VA benefits. Reference: For more information on VBMS-R, see . 2. Introduction Introduction This topic contains information on the Introduction part, including Introduction, and Introduction. Change Date January 20, 2015 a. Purpose of the Introduction The purpose of the Introduction is to identify the claimant, and acknowledge the Veteran’s qualifying service, including any special considerations relevant to the claim, such as former prisoner of war (FPOW) status. Note: The level of detail in the introduction depends on the complexity of each issue. b. Generating the Introduction The Introduction may be generated using the narrative assistance function in VBMS-R or may be composed manually. Use the guidelines listed below when manually composing the Introduction. Write directly to the claimant. “You” is acceptable usage. Include all periods of service. Use the format month/day/year to month/day/year. Note: Service dates must be checked for accuracy. Reference: For more information on using the narrative assistance function, see the .
| 0.695045
|
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M21-1III_iv_6_SecC.docx
|
task-1431-623
| 2
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Change Date January 18, 2016 a. Disposition Once an 800 series work item has been reviewed, determine if follow-up of an 800 Series action is required. Work Item After Review See the table below for actions to take after reviewing the 800 series work item. Then ... If… no action is required PCAN the 800 series work item using the Msg Rvwd, no action reason “VETSNET necessary” additional action is required PCLR the 800 series work item using the Msg Rvwd, work under reason “VETSNET and proper EP,” work under the proper end product (EP). Do not PCLR an 890WI Exception: (SAH/SHA) until final action is taken under the proper EP. When action is required, use the date of the 800 series work item as the date b. Establishing Date of Claim of claim for the controlling EP. When 800 Series Work Do use the date of review as the date of claim for the Important: not Item Requires controlling EP. Action For more information on 890WIs (SAH/SHA), see Reference: M21-1, Part IX, Subpart i, 3.3.c, and M21-1, Part IX, Subpart i, 3.3.g. c. Prioritizing All 800 series work items affecting payments require prompt attention. 800 Series Work Item ROs and PMCs must consistently prioritize 800 series work items that potentially result in an underpayment, overpayment, or potential financial hardship. d. EP for In addition to establishing the appropriate controlling EP when an 800 series Potential work item requires action, establish an EP 693 to control for potential Underpayment underpayment overpayment of benefits. Use the date of the 800 series or or work item as the date of claim for the EP 693. Overpayment If an EP 600 is subsequently established to provide notice of a proposed adverse action
|
Change Date January 18, 2016 a. Disposition of an 800 Series Work Item After Review Once an 800 series work item has been reviewed, determine if follow-up action is required. See the table below for actions to take after reviewing the 800 series work item. If… Then ... no action is required PCAN the 800 series work item using the reason “VETSNET Msg Rvwd, no action necessary” additional action is required PCLR the 800 series work item using the reason “VETSNET Msg Rvwd, work under proper EP,” and work under the proper end product (EP). Exception: Do not PCLR an 890WI (SAH/SHA) until final action is taken under the proper EP. b. Establishing Date of Claim When 800 Series Work Item Requires Action When action is required, use the date of the 800 series work item as the date of claim for the controlling EP. Important: Do not use the date of review as the date of claim for the controlling EP. Reference: For more information on 890WIs (SAH/SHA), see M21-1, Part IX, Subpart i, 3.3.c, and M21-1, Part IX, Subpart i, 3.3.g. c. Prioritizing 800 Series Work Item All 800 series work items affecting payments require prompt attention. ROs and PMCs must consistently prioritize 800 series work items that potentially result in an underpayment, overpayment, or potential financial hardship. d. EP for Potential Underpayment or Overpayment In addition to establishing the appropriate controlling EP when an 800 series work item requires action, establish an EP 693 to control for potential underpayment or overpayment of benefits. Use the date of the 800 series work item as the date of claim for the EP 693. If an EP 600 is subsequently established to provide notice of a proposed adverse action
| 0.783971
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M21-1III_v_10_SecA.docx
|
task-1431-642
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Beneficiary regarding the competency based on the evidence of record. beneficiary’s Takes No Action For more information about the actions to take after the response Reference: period ends, see M21-1MR, Part III, Subpart v, 9.B.2.b, starting with Step 5. The VSC collocated with the fiduciary hub of jurisdiction will handle all l. Processing a NOD Received appeals of incompetency determination. After a Final Rating of If a notice of disagreement (NOD) is received after a final rating of Incompetency incompetency, prepare a statement of the case (SOC) send the SOC to the beneficiary, or fiduciary, if applicable, and the or representative, and beneficiary’s fiduciary’s enclose VA Form 9, Appeal to Board of Veterans’ Appeals. When preparing the SOC, carefully consider whether the Important: inclusion of certain information could be detrimental to the state beneficiary’s of mind. A decision to exclude information on this basis must be supported by a opinion and advice concerning the appropriate way to physician’s communicate the information directly to the beneficiary. Fiduciary hubs will handle all appeals regarding the selection of a Note: fiduciary. For more information on References: sending a SOC, see M21-1, Part I, 5.D.3, and releasing information that may be detrimental to the state of beneficiary’s mind, see M21-1, Part I, 5.D.2.d and e. 4. Information About the Brady Act Introduction This topic contains information about the Brady Act, including effect of the Brady Act on incompetent beneficiaries relief from the Brady Act requirements initial steps to take upon receipt of a request for relief required paragraphs for National Instant Criminal Background Check System (NICS) relief development letter consent form that must accompany the NICS relief development letter general information about decisions to grant or deny a request for relief evidence Veterans Service Representatives (VSRs) must consider when deciding a request for relief
|
Beneficiary Takes No Action regarding the beneficiary’s competency based on the evidence of record. Reference: For more information about the actions to take after the response period ends, see M21-1MR, Part III, Subpart v, 9.B.2.b, starting with Step 5. l. Processing a NOD Received After a Final Rating of Incompetency The VSC collocated with the fiduciary hub of jurisdiction will handle all appeals of incompetency determination. If a notice of disagreement (NOD) is received after a final rating of incompetency, prepare a statement of the case (SOC) send the SOC to the beneficiary, or fiduciary, if applicable, and the beneficiary’s or fiduciary’s representative, and enclose VA Form 9, Appeal to Board of Veterans’ Appeals. Important: When preparing the SOC, carefully consider whether the inclusion of certain information could be detrimental to the beneficiary’s state of mind. A decision to exclude information on this basis must be supported by a physician’s opinion and advice concerning the appropriate way to communicate the information directly to the beneficiary. Note: Fiduciary hubs will handle all appeals regarding the selection of a fiduciary. References: For more information on sending a SOC, see M21-1, Part I, 5.D.3, and releasing information that may be detrimental to the beneficiary’s state of mind, see M21-1, Part I, 5.D.2.d and e. 4. Information About the Brady Act Introduction This topic contains information about the Brady Act, including effect of the Brady Act on incompetent beneficiaries relief from the Brady Act requirements initial steps to take upon receipt of a request for relief required paragraphs for National Instant Criminal Background Check System (NICS) relief development letter consent form that must accompany the NICS relief development letter general information about decisions to grant or deny a request for relief evidence Veterans Service Representatives (VSRs) must consider when deciding a request for relief
| 0.849256
|
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M21-1III_v_9_SecB.docx
|
task-1431-654
| 17
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Department of Veterans Affairs M21-1, Part IV, Subpart ii Veterans Benefits Administration June 3, 2015 Washington, DC 20420 Transmittal Sheet The table below describes the changes included in this revision of Veterans Changes Included in Benefits Manual M21-1, Part IV, “Compensation DIC, and Death This Revision Subpart Compensation Benefits,” ii, “Compensation.” Notes: The term “regional office” (RO) also includes pension management center (PMC), where appropriate. Unless otherwise noted, the term the official, “claims folder” refers to numbered, Department of Veterans Affairs (VA) repository whether paper – or electronic for all documentation relating to claims that a Veteran and/or – his/her dependent(s) file with VA. Minor editorial changes have also been made to update incorrect or obsolete references update obsolete terminology, where appropriate remove references to specific claims-processing systems, where doing so does not affect the clarity of the instructions or information provided renumber each topic based on the standard that the first topic in each section is Topic 1 reassign alphabetical designations to individual blocks and repaginate, where necessary, to account for new and/or deleted blocks within a topic update section and topic titles to more accurately reflect their content clarify block labels and/or block text, and bring the document into conformance with M21-1 standards. Reason(s) for the Change Citation To incorporate guidance that discuss metastasis cancer. M21-1, Part IV, Subpart ii, Chapter 1, Section C, Topic 1, Block a (IV.ii.1.C.1.a) To add additional guidance that informs the target audience to refrain IV.ii.1.C.1.e from divulging confidential information concerning the nuclear weapons location. To incorporate guidance that informs the target audience how to IV.ii.1.C.1.f redact classified information in a Veterans Benefits Management System (VBMS) claims folder. IV.ii.1.C.2.a To relocate guidance from old IV.ii.1.C.1.a. that discusses the centralized processing of all radiation claims. To add guidance that informs the target audience a radiogenic disease must be confirmed prior to transferring a claim to the Jackson Regional Office (RO), and
|
Department of Veterans Affairs M21-1, Part IV, Subpart ii Veterans Benefits Administration June 3, 2015 Washington, DC 20420 Transmittal Sheet Changes Included in This Revision The table below describes the changes included in this revision of Veterans Benefits Manual M21-1, Part IV, “Compensation DIC, and Death Compensation Benefits,” Subpart ii, “Compensation.” Notes: The term “regional office” (RO) also includes pension management center (PMC), where appropriate. Unless otherwise noted, the term “claims folder” refers to the official, numbered, Department of Veterans Affairs (VA) repository – whether paper or electronic – for all documentation relating to claims that a Veteran and/or his/her dependent(s) file with VA. Minor editorial changes have also been made to update incorrect or obsolete references update obsolete terminology, where appropriate remove references to specific claims-processing systems, where doing so does not affect the clarity of the instructions or information provided renumber each topic based on the standard that the first topic in each section is Topic 1 reassign alphabetical designations to individual blocks and repaginate, where necessary, to account for new and/or deleted blocks within a topic update section and topic titles to more accurately reflect their content clarify block labels and/or block text, and bring the document into conformance with M21-1 standards. Reason(s) for the Change Citation To incorporate guidance that discuss metastasis cancer. M21-1, Part IV, Subpart ii, Chapter 1, Section C, Topic 1, Block a (IV.ii.1.C.1.a) To add additional guidance that informs the target audience to refrain from divulging confidential information concerning the nuclear weapons location. IV.ii.1.C.1.e To incorporate guidance that informs the target audience how to redact classified information in a Veterans Benefits Management System (VBMS) claims folder. IV.ii.1.C.1.f To relocate guidance from old IV.ii.1.C.1.a. that discusses the centralized processing of all radiation claims. To add guidance that informs the target audience a radiogenic disease must be confirmed prior to transferring a claim to the Jackson Regional Office (RO), and IV.ii.1.C.2.a
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k. MCASC is primarily responsible for Responsibilities of the MCASC identifying post-Vietnam Era record(s) required to document the stressors, or providing confirmation that the claimed stressor(s) cannot be corroborated using records in its custody. A regional office (RO) cannot deny a claim for PTSD based Important: solely on the absence of a verified stressor until MCASC or NARA has confirmed the RO’s negative findings. The following information must be included in each e-mail request to l. Information to Include in MCASC for records needed to document a claimed stressor: Record Requests to the last name Veteran’s MCASC designation of the unit of assignment at the time of the stressful Veteran’s Related to a event down to the lowest possible level Claimed the most specific date(s), at minimum the month and year, during which the Stressor stressful event occurred geographical location where stressful event took place description of the stressful event, and the name and e-mail address of the VA Point of Contact. Notes: Do include in the request any medical information or statement(s) from not the Veteran. MCASC only researches unit command chronology. Therefore, personal identification information, such as SSN, service number, date of birth, and dates of service should not be included in the request. Submit a request to MCASC after exhausting all efforts to document only the claimed stressor(s) through other means, including Virtual VA and/or military web sites. official Include a copy of the e-mail request in the claims folder. Veteran’s References: For e-mail address, see M21-1, Part IV, Subpart ii, 1.D.3.b. MCASC’s For a sample of an e-mail request to MCASC for stressor verification, see M21-1, Part IV, Subpart ii, 1.D.3.m. Below is a sample of the text to include in the e-mail to MCASC for a m. Sample to determination as to the availability of records required to corroborate a MCASC for stressor that occurred during in the Marine Corps after the Vietnam Determination Era. as to Availability of To: Marine Corps Archives and Special Collections Records to Corroborate From: XYZ Regional Office
|
k. Responsibilities of the MCASC MCASC is primarily responsible for identifying post-Vietnam Era record(s) required to document the stressors, or providing confirmation that the claimed stressor(s) cannot be corroborated using records in its custody. Important: A regional office (RO) cannot deny a claim for PTSD based solely on the absence of a verified stressor until MCASC or NARA has confirmed the RO’s negative findings. l. Information to Include in Record Requests to MCASC Related to a Claimed Stressor The following information must be included in each e-mail request to MCASC for records needed to document a claimed stressor: the Veteran’s last name designation of the Veteran’s unit of assignment at the time of the stressful event down to the lowest possible level the most specific date(s), at minimum the month and year, during which the stressful event occurred geographical location where stressful event took place description of the stressful event, and the name and e-mail address of the VA Point of Contact. Notes: Do not include in the request any medical information or statement(s) from the Veteran. MCASC only researches unit command chronology. Therefore, personal identification information, such as SSN, service number, date of birth, and dates of service should not be included in the request. Submit a request to MCASC only after exhausting all efforts to document the claimed stressor(s) through other means, including Virtual VA and/or official military web sites. Include a copy of the e-mail request in the Veteran’s claims folder. References: For MCASC’s e-mail address, see M21-1, Part IV, Subpart ii, 1.D.3.b. For a sample of an e-mail request to MCASC for stressor verification, see M21-1, Part IV, Subpart ii, 1.D.3.m. m. Sample to MCASC for Determination as to Availability of Records to Corroborate Below is a sample of the text to include in the e-mail to MCASC for a determination as to the availability of records required to corroborate a stressor that occurred during in the Marine Corps after the Vietnam Era. To: Marine Corps Archives and Special Collections From: XYZ Regional Office
| 0.808096
|
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M21-1IV_ii_1_SecD.docx
|
task-1431-658
| 22
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Section E. Claims Based on Service in Southwest Asia Overview This section contains the following topics: In This Section Topic Topic Name 1 Developing Claims Based on Southwest Asia Service 2 Examinations in Claims Based on Southwest Asia Service 1. Developing Claims Based on Southwest Asia Service This topic contains information on developing Southwest Asia claims, Introduction including action to take when the Veteran alleges exposure to environmental hazards but claims no disability initial development action on a new Southwest Asia service claim an exhibit of the Section 5103 notice letter attachment for Southwest Asia claims verifying Southwest Asia service using DD Form 214, Certificate of Release or Discharge from Active Duty requesting service department records in a Southwest Asia claim requesting medical evidence in claims under 38 CFR 3.317, and considering whether to clarify or corroborate lay statements November 5, 2015 Change Date A claim is not substantially complete if a Veteran alleges exposure to a. Action to Take When the environmental hazards during service, but does not identify the medical Veteran Alleges condition or symptom for which service connection (SC) is claimed. In cases Exposure to such as these Environmental Hazards but inform the Veteran that he/she identify a specific disability, since must Claims No exposure in and of itself is not a disability, and Disability ask the Veteran to identify the disability(ies) that resulted from exposure to environmental hazards during service. Important: Do not process the claim as a denial. Whenever possible, telephone the Veteran to obtain the information needed
|
Section E. Claims Based on Service in Southwest Asia Overview In This Section This section contains the following topics: Topic Topic Name 1 Developing Claims Based on Southwest Asia Service 2 Examinations in Claims Based on Southwest Asia Service 1. Developing Claims Based on Southwest Asia Service Introduction This topic contains information on developing Southwest Asia claims, including action to take when the Veteran alleges exposure to environmental hazards but claims no disability initial development action on a new Southwest Asia service claim an exhibit of the Section 5103 notice letter attachment for Southwest Asia claims verifying Southwest Asia service using DD Form 214, Certificate of Release or Discharge from Active Duty requesting service department records in a Southwest Asia claim requesting medical evidence in claims under 38 CFR 3.317, and considering whether to clarify or corroborate lay statements Change Date November 5, 2015 a. Action to Take When the Veteran Alleges Exposure to Environmental Hazards but Claims No Disability A claim is not substantially complete if a Veteran alleges exposure to environmental hazards during service, but does not identify the medical condition or symptom for which service connection (SC) is claimed. In cases such as these inform the Veteran that he/she must identify a specific disability, since exposure in and of itself is not a disability, and ask the Veteran to identify the disability(ies) that resulted from exposure to environmental hazards during service. Important: Do not process the claim as a denial. Whenever possible, telephone the Veteran to obtain the information needed
| 0.870922
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M21-1IV_ii_1_SecE.docx
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task-1431-660
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unique circumstance challenges those who must determine a disability rating. While individual exposure levels cannot be accurately determined, DoD considers constrictive bronchiolitis (initially diagnosed as to be “bronchiolitis obliterans”) plausibly associated with exposure to the 2003 Mishraq State sulfur fire event. This health effect has been scientifically associated with high exposures to SO2 . Both sulfur dioxide and hydrogen sulfide are gases that can produce irritation and reddening of the nose and throat, eye irritation/pain, and coughing. At high levels, sulfur dioxide can burn the skin and can cause severe airway obstruction, hypoxemia, pulmonary edema, and even death. The firefighters involved with suppressing this fire experienced irritation, minor burns, and other effects such as blood-tinged nasal mucous. Some have been found to have long-term respiratory conditions such as “constrictive bronchiolitis.” Note: If the claim is for a respiratory condition possibly related to the sulfur fire exposure consider requesting tests for be conducted in addition to “bronchiolitis” other respiratory testing, while noting that many standard test results may be normal. This information is not meant to influence examiners rendering opinions concerning the etiology of any particular disability; but rather to ensure that such opinions are fully informed based on all known objective facts. Therefore, when rendering opinions requested by rating authorities for a disability potentially related to such exposure, please utilize this information objectively and together with the remaining evidence, including lay evidence, in the record. Veteran’s _____________________ Adjudication Authority 12. Exhibit 4: Fact Sheet Qarmat Ali Water Treatment Plant in Basrah, Iraq August 7, 2015 Change Date a. Fact Sheet: Fact Sheet for Claims of Exposure Based on the Qarmat Ali Water Treatment Qarmat Ali is shown below. Plant in Basrah, Iraq, Water Treatment Plant
|
unique circumstance challenges those who must determine a disability rating. While individual exposure levels cannot be accurately determined, DoD considers constrictive bronchiolitis (initially diagnosed as “bronchiolitis obliterans”) to be plausibly associated with exposure to the 2003 Mishraq State sulfur fire event. This health effect has been scientifically associated with high exposures to SO2 . Both sulfur dioxide and hydrogen sulfide are gases that can produce irritation and reddening of the nose and throat, eye irritation/pain, and coughing. At high levels, sulfur dioxide can burn the skin and can cause severe airway obstruction, hypoxemia, pulmonary edema, and even death. The firefighters involved with suppressing this fire experienced irritation, minor burns, and other effects such as blood-tinged nasal mucous. Some have been found to have long-term respiratory conditions such as “constrictive bronchiolitis.” Note: If the claim is for a respiratory condition possibly related to the sulfur fire exposure consider requesting tests for “bronchiolitis” be conducted in addition to other respiratory testing, while noting that many standard test results may be normal. This information is not meant to influence examiners rendering opinions concerning the etiology of any particular disability; but rather to ensure that such opinions are fully informed based on all known objective facts. Therefore, when rendering opinions requested by rating authorities for a disability potentially related to such exposure, please utilize this information objectively and together with the remaining evidence, including lay evidence, in the Veteran’s record. _____________________ Adjudication Authority 12. Exhibit 4: Fact Sheet Qarmat Ali Water Treatment Plant in Basrah, Iraq Change Date August 7, 2015 a. Fact Sheet: Qarmat Ali Water Treatment Plant Fact Sheet for Claims of Exposure Based on the Qarmat Ali Water Treatment Plant in Basrah, Iraq, is shown below.
| 0.919695
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M21-1IV_ii_1_SecI.docx
|
task-1431-664
| 32
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the presence of symptoms sufficient to diagnose the specific disorder at least three months prior to diagnosis. FGIDs do not include structural gastrointestinal diseases, such as Important: inflammatory bowel disease (such as ulcerative colitis or Crohn's disease) and gastroesophageal reflux disease, as these conditions are considered to be organic or structural diseases characterized by abnormalities seen on x-ray, endoscopy, or through laboratory tests. The effective date of the amendment to to include FGIDs was July 15, Note: 2011. k. Examination In general, when a Veteran with GW service claims symptoms fitting the Requirement description of an MUCMI, as described in , before proceeding with a for MUCMIs ensure that an examination with a medical opinion has been decision, obtained. The opinion must classify the symptom as an MUCMI without conclusive pathology or etiology before SC under can be awarded. If the diagnosis shown in medical records involves one of the Exception: MUCMIs described in (chronic fatigue syndrome, fibromyalgia, or functional gastrointestinal disorders), then SC is appropriate and a VA examination may only be necessary to determine current level of severity. Chronic multi-symptom illnesses of partially explained etiology and l. Partially Understood pathophysiology, such as diabetes and multiple sclerosis, are not considered Chronic medically unexplained and cannot be considered a qualifying chronic disability for purposes of . Illnesses When adjudicating conditions with partially explained etiology, SC can only be awarded on another basis such as direct SC under or presumptive SC under and . m. The presumptive period for manifestation of qualifying chronic disability Presumptive under Period for Manifestation begins on the date following last performance of active military, naval, or of Disability air service in the Southwest Asia theater of operations during the GW, and Under 38 CFR extends through December 31, 2016. 3.317 specifies the following 13 categories of signs or symptoms that may be n. Signs and Symptoms of manifestations of an undiagnosed illness or an MUCMI Undiagnosed
|
the presence of symptoms sufficient to diagnose the specific disorder at least three months prior to diagnosis. Important: FGIDs do not include structural gastrointestinal diseases, such as inflammatory bowel disease (such as ulcerative colitis or Crohn's disease) and gastroesophageal reflux disease, as these conditions are considered to be organic or structural diseases characterized by abnormalities seen on x-ray, endoscopy, or through laboratory tests. Note: The effective date of the amendment to to include FGIDs was July 15, 2011. k. Examination Requirement for MUCMIs In general, when a Veteran with GW service claims symptoms fitting the description of an MUCMI, as described in , before proceeding with a decision, ensure that an examination with a medical opinion has been obtained. The opinion must classify the symptom as an MUCMI without conclusive pathology or etiology before SC under can be awarded. Exception: If the diagnosis shown in medical records involves one of the MUCMIs described in (chronic fatigue syndrome, fibromyalgia, or functional gastrointestinal disorders), then SC is appropriate and a VA examination may only be necessary to determine current level of severity. l. Partially Understood Chronic Illnesses Chronic multi-symptom illnesses of partially explained etiology and pathophysiology, such as diabetes and multiple sclerosis, are not considered medically unexplained and cannot be considered a qualifying chronic disability for purposes of . When adjudicating conditions with partially explained etiology, SC can only be awarded on another basis such as direct SC under or presumptive SC under and . m. Presumptive Period for Manifestation of Disability Under 38 CFR 3.317 The presumptive period for manifestation of qualifying chronic disability under begins on the date following last performance of active military, naval, or air service in the Southwest Asia theater of operations during the GW, and extends through December 31, 2016. n. Signs and Symptoms of Undiagnosed specifies the following 13 categories of signs or symptoms that may be manifestations of an undiagnosed illness or an MUCMI
| 0.846857
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M21-1IV_ii_2_secD.docx
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task-1431-672
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As held in, 19 Vet.App. 1 (2004) the Veteran is not required to Important: provide evidence linking a qualifying chronic disability listed in to events in service, as long as the Veteran meets all other requirements in . Because the course of an undiagnosed illness cannot be predicted, monitor it i. Considering the Need for a by establishing the necessary controls for a future examination within 24 Future months of the last examination of record. Examination of an Undiagnosed At the expiration of the control period, review the evidence of record to Illness determine whether reexamination is necessary. 3. General Information About Rating Decisions for Qualifying Disabilities Introduction This topic contains general information about rating decisions for qualifying disabilities, including stating the issue in rating decisions for undiagnosed qualifying chronic disabilities language for the section of the rating decision Decision referencing relevant dates in the rating decision termination or reduction of benefits previously awarded under , and Southwest Asia participation in VA-sponsored research projects. Veterans’ Change Date June 3, 2015 State the issue in the rating decision in claims for undiagnosed qualifying a. Stating the Issue in Rating chronic disabilities as [specify signs or symptoms] Service connection for as Decisions for due to a qualifying chronic disability. Undiagnosed Qualifying Chronic Disabilities
|
Important: As held in, 19 Vet.App. 1 (2004) the Veteran is not required to provide evidence linking a qualifying chronic disability listed in to events in service, as long as the Veteran meets all other requirements in . i. Considering the Need for a Future Examination of an Undiagnosed Illness Because the course of an undiagnosed illness cannot be predicted, monitor it by establishing the necessary controls for a future examination within 24 months of the last examination of record. At the expiration of the control period, review the evidence of record to determine whether reexamination is necessary. 3. General Information About Rating Decisions for Qualifying Disabilities Introduction This topic contains general information about rating decisions for qualifying disabilities, including stating the issue in rating decisions for undiagnosed qualifying chronic disabilities language for the Decision section of the rating decision referencing relevant dates in the rating decision termination or reduction of benefits previously awarded under , and Southwest Asia Veterans’ participation in VA-sponsored research projects. Change Date June 3, 2015 a. Stating the Issue in Rating Decisions for Undiagnosed Qualifying Chronic Disabilities State the issue in the rating decision in claims for undiagnosed qualifying chronic disabilities as Service connection for [specify signs or symptoms] as due to a qualifying chronic disability.
| 0.8279
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M21-1IV_ii_2_secD.docx
|
task-1431-672
| 13
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endocrine system 79 8879 central nervous system 80 8880 miscellaneous neurological 81 8881 cranial nerve paralysis 82 8882 cranial nerve neuritis 83 8883 cranial nerve neuralgia 84 8884 peripheral nerve paralysis 85 8885 peripheral nerve neuritis 86 8886 peripheral nerve neuralgia 87 8887 epilepsies 89 8889 psychotic disorders 92 8892 organic mental 93 8893 psychoneurotic 94 8894 psychophysiologic 95 8895 dental and oral 99 8899 The table below contains examples of analogous codes that may be used g. Examples of Analogous when evaluating undiagnosed illnesses manifest by the 13 signs or symptoms Codes for found in . For the second code, use a DC with rating criteria that most Undiagnosed accurately evaluates manifestations of the disability. Disabilities This list does not contain all possible analogous codes. Note: For more information on the 13 signs or symptoms of an Reference: undiagnosed illness, see . If the symptom is Then the hyphenated DC is … … abnormal weight loss 8873-7328, (resection of intestine). cardiovascular signs or symptoms 8870-7013, (tachycardia). cardiovascular signs or symptoms 8870-7005, (arteriosclerotic heart disease (ASHD)). fatigue 8863-6354, (chronic fatigue syndrome). fatigue 8877-7700, (anemia). gastrointestinal signs or symptoms 8873-7305, (ulcer), or 8873-7319, (irritable bowel syndrome). headache 8881-8100, (migraine headaches). joint pain 8850-5002, (rheumatoid arthritis). menstrual disorders 8876-7622, (uterus displacement). muscle pain 8850-5021, (myositis). neurologic signs or symptoms 8885-85__, (peripheral neuropathy). neuropsychological signs or 8893-9300, (organic mental symptoms disorder).
|
endocrine system 79 8879 central nervous system 80 8880 miscellaneous neurological 81 8881 cranial nerve paralysis 82 8882 cranial nerve neuritis 83 8883 cranial nerve neuralgia 84 8884 peripheral nerve paralysis 85 8885 peripheral nerve neuritis 86 8886 peripheral nerve neuralgia 87 8887 epilepsies 89 8889 psychotic disorders 92 8892 organic mental 93 8893 psychoneurotic 94 8894 psychophysiologic 95 8895 dental and oral 99 8899 g. Examples of Analogous Codes for Undiagnosed Disabilities The table below contains examples of analogous codes that may be used when evaluating undiagnosed illnesses manifest by the 13 signs or symptoms found in . For the second code, use a DC with rating criteria that most accurately evaluates manifestations of the disability. Note: This list does not contain all possible analogous codes. Reference: For more information on the 13 signs or symptoms of an undiagnosed illness, see . If the symptom is … Then the hyphenated DC is … abnormal weight loss 8873-7328, (resection of intestine). cardiovascular signs or symptoms 8870-7013, (tachycardia). cardiovascular signs or symptoms 8870-7005, (arteriosclerotic heart disease (ASHD)). fatigue 8863-6354, (chronic fatigue syndrome). fatigue 8877-7700, (anemia). gastrointestinal signs or symptoms 8873-7305, (ulcer), or 8873-7319, (irritable bowel syndrome). headache 8881-8100, (migraine headaches). joint pain 8850-5002, (rheumatoid arthritis). menstrual disorders 8876-7622, (uterus displacement). muscle pain 8850-5021, (myositis). neurologic signs or symptoms 8885-85__, (peripheral neuropathy). neuropsychological signs or symptoms 8893-9300, (organic mental disorder).
| 0.89893
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M21-1IV_ii_2_secD.docx
|
task-1431-672
| 18
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[
737,
849,
791,
869
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[
573,
866,
648,
886
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] |
Section F. Compensation Based on Individual Unemployability (IU) Overview In This Section This section contains the following topics: Topic Topic Name 1 General Information on IU Claims 2 Evaluating Evidence to Establish Entitlement to IU 3 Evaluating Evidence of Self-Employment or Employment With a Tightly Held Corporation 1. 4 Preparing a Rating Decision Addressing the Issue of IU General 5 Special Considerations in IU Claims 6 Exhibit 1: Rating Decision Addressing to Veteran’s Failure on Complete a Field Examination 7 Exhibit 2: Rating Decision Addressing to Respond Veteran’s Failure IU to a Notice of Proposed Adverse Action After Failure to Complete a Claims Field Examination 8 Exhibit 3: Rating Decision Addressing the Subsequent Receipt of Evidence That Restores IU After Failure to Complete a Field Examination 9 Poverty Threshold Information Introduction This topic contains general information on IU claims, including establishing entitlement to a total disability rating for compensation based on IU (TDIU) unemployable vs. unemployed definition of substantially gainful employment, and definition of marginal employment. Change Date November 9, 2015
|
Section F. Compensation Based on Individual Unemployability (IU) Overview In This Section This section contains the following topics: Topic Topic Name 1 General Information on IU Claims 2 Evaluating Evidence to Establish Entitlement to IU 3 Evaluating Evidence of Self-Employment or Employment With a Tightly Held Corporation 4 Preparing a Rating Decision Addressing the Issue of IU 5 Special Considerations in IU Claims 6 Exhibit 1: Rating Decision Addressing Veteran’s Failure to Complete a Field Examination 7 Exhibit 2: Rating Decision Addressing Veteran’s Failure to Respond to a Notice of Proposed Adverse Action After Failure to Complete a Field Examination 8 Exhibit 3: Rating Decision Addressing the Subsequent Receipt of Evidence That Restores IU After Failure to Complete a Field Examination 9 Poverty Threshold Information 1. General on IU Claims Introduction This topic contains general information on IU claims, including establishing entitlement to a total disability rating for compensation based on IU (TDIU) unemployable vs. unemployed definition of substantially gainful employment, and definition of marginal employment. Change Date November 9, 2015
| 0.895331
|
[
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22,
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M21-1IV_ii_2_SecF.docx
|
task-1431-674
| 0
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Department of Veterans Affairs M21-1, Part IV, Subpart ii Veterans Benefits Administration July 30, 2015 Washington, DC 20420 Transmittal Sheet The table below describes the changes included in this revision of Veterans Changes Included in Benefits Manual M21-1, Part IV, “Compensation DIC, and Death This Revision Subpart Compensation Benefits,” ii, “Compensation.” Notes: The also includes pension management center term “regional office” (RO) (PMC), where appropriate. The term also includes “Veterans Service Center Manager” (VSCM) Pension Management Center Manager (PMCM), where appropriate. Unless otherwise noted, the term the official, “claims folder” refers to numbered, Department of Veterans Affairs (VA) repository whether paper – or electronic for all documentation relating to claims that a Veteran and/or – his/her dependent(s) file with VA. Reason(s) for the Change Citation M21-1, Part IV, To provide guidance on how to send the Subpart ii, Chapter 2, Counseling/Evaluation/Rehabilitation (CER) folder to the scanning Section F, Topic 2, vendor when the contents of the CER folder have evidentiary value. Block i To add references to Appeals Management Center (AMC) (IV.ii.2.F.2.i) procedures for obtaining VR&E records, and shipping requirements. To correct the reference that refers to the AMC procedure for IV.ii.2.F.2.j requesting VR&E records. Rescissions None Authority By Direction of the Under Secretary for Benefits Signature Thomas J. Murphy, Director Compensation Service LOCAL REPRODUCTION AUTHORIZED Distribution
|
Department of Veterans Affairs M21-1, Part IV, Subpart ii Veterans Benefits Administration July 30, 2015 Washington, DC 20420 Transmittal Sheet Changes Included in This Revision The table below describes the changes included in this revision of Veterans Benefits Manual M21-1, Part IV, “Compensation DIC, and Death Compensation Benefits,” Subpart ii, “Compensation.” Notes: The term “regional office” (RO) also includes pension management center (PMC), where appropriate. The term “Veterans Service Center Manager” (VSCM) also includes Pension Management Center Manager (PMCM), where appropriate. Unless otherwise noted, the term “claims folder” refers to the official, numbered, Department of Veterans Affairs (VA) repository – whether paper or electronic – for all documentation relating to claims that a Veteran and/or his/her dependent(s) file with VA. Reason(s) for the Change Citation To provide guidance on how to send the Counseling/Evaluation/Rehabilitation (CER) folder to the scanning vendor when the contents of the CER folder have evidentiary value. To add references to Appeals Management Center (AMC) procedures for obtaining VR&E records, and shipping requirements. M21-1, Part IV, Subpart ii, Chapter 2, Section F, Topic 2, Block i (IV.ii.2.F.2.i) To correct the reference that refers to the AMC procedure for requesting VR&E records. IV.ii.2.F.2.j Rescissions None Authority By Direction of the Under Secretary for Benefits Signature Thomas J. Murphy, Director Compensation Service Distribution LOCAL REPRODUCTION AUTHORIZED
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Section C. Initial Screening Policies Overview In this Section This section contains the following topics: Topic Topic Name See Page Recording the Date of Receipt 1-C-2 Claimant Signatures 1-C-5 Claims From Foreign Residents 1-C-7 Claims for Ancillary Benefits 1-C-8 Handling Claims From Employees and Their 1-C-9 Relatives Claims From Veterans Who Are Receiving 1-C-12 Retirement Pay or Received Separation Benefits Claims From Survivor Benefit Plan (SBP) 1-C-16 Beneficiaries 10. Recording the Date of Receipt This topic contains policies for recording the date of receipt on documents the Introduction Department of Veterans Affairs (VA) receives, including , and . Change Date September 16, 2014 Each document the Department of Veterans Affairs (VA) receives in any of a. Requirement to Stamp the its facilities or locations where it has a presence must be stamped with the Date of Receipt date of receipt. Most of the time, this activity occurs in a regional office’s on All (RO’s) mailroom. Incoming Documents Upon identification of a document in the Veterans Service Center (VSC) that bears no date stamp, attempt to determine when VA received the document.
|
Section C. Initial Screening Policies Overview In this Section This section contains the following topics: Topic Topic Name See Page Recording the Date of Receipt 1-C-2 Claimant Signatures 1-C-5 Claims From Foreign Residents 1-C-7 Claims for Ancillary Benefits 1-C-8 Handling Claims From Employees and Their Relatives 1-C-9 Claims From Veterans Who Are Receiving Retirement Pay or Received Separation Benefits 1-C-12 Claims From Survivor Benefit Plan (SBP) Beneficiaries 1-C-16 10. Recording the Date of Receipt Introduction This topic contains policies for recording the date of receipt on documents the Department of Veterans Affairs (VA) receives, including , and . Change Date September 16, 2014 a. Requirement to Stamp the Date of Receipt on All Incoming Documents Each document the Department of Veterans Affairs (VA) receives in any of its facilities or locations where it has a presence must be stamped with the date of receipt. Most of the time, this activity occurs in a regional office’s (RO’s) mailroom. Upon identification of a document in the Veterans Service Center (VSC) that bears no date stamp, attempt to determine when VA received the document.
| 0.85173
|
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M21-1MRIII_ii_1_SecC.docx
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task-1431-719
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If go to the next step. yes, If go to Step 7. no, 4 To update the BIRLS record in Share select the BIRLS UPDATE command enter the first and last name, and claimant’s select SUBMIT. 5 Under the POWER OF ATTORNEY SEARCH command, use the LOCATE field to look up the POA Was the POA found using the LOCATE field? If select UPDATE and go to Step 7. yes, If go to the next step. no, 6 Perform an accreditation of VSOs search using the . Was the POA found in the ? If yes, follow the procedure in M21-1, Part III, Subpart ii, 3.C.6 to have the POA added to the corporate database add a note to VBMS or MAP-D select the POWER OF ATTORNEY SEARCH command use the code 099 as the POA in the LOCATE field select UPDATE, and go to the next step. If no, add a note to VBMS or MAP-D explaining why the POA could not be updated, and forward the information to the Agent and Attorney Fee Coordinator (AAFC) for follow up with the POA. For more information on in References: AAFC’s responsibilities handling non-accredited POA submissions, see M21-1, Part I, Subpart i, 3.C.1.d M21-1, Part I, Subpart i, 3.C.6, and M21-1, Part I, Subpart i, 3.C.7. 7 Is there an EP pending? If go to the next step. yes, If go to Step 9. no, 8 To update the pending issue in Share select the PIF INQUIRY command make note of the specific EP and claim label, and select READY select the PIF CHANGE command and enter the
|
If yes, go to the next step. If no, go to Step 7. 4 To update the BIRLS record in Share select the BIRLS UPDATE command enter the claimant’s first and last name, and select SUBMIT. 5 Under the POWER OF ATTORNEY SEARCH command, use the LOCATE field to look up the POA Was the POA found using the LOCATE field? If yes, select UPDATE and go to Step 7. If no, go to the next step. 6 Perform an accreditation of VSOs search using the . Was the POA found in the ? If yes, follow the procedure in M21-1, Part III, Subpart ii, 3.C.6 to have the POA added to the corporate database add a note to VBMS or MAP-D select the POWER OF ATTORNEY SEARCH command use the code 099 as the POA in the LOCATE field select UPDATE, and go to the next step. If no, add a note to VBMS or MAP-D explaining why the POA could not be updated, and forward the information to the Agent and Attorney Fee Coordinator (AAFC) for follow up with the POA. References: For more information on AAFC’s responsibilities in handling non-accredited POA submissions, see M21-1, Part I, Subpart i, 3.C.1.d M21-1, Part I, Subpart i, 3.C.6, and M21-1, Part I, Subpart i, 3.C.7. 7 Is there an EP pending? If yes, go to the next step. If no, go to Step 9. 8 To update the pending issue in Share select the PIF INQUIRY command make note of the specific EP and claim label, and select READY select the PIF CHANGE command and enter the
| 0.88765
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M21-1MRIII_ii_3_SecC.docx
|
task-1431-724
| 13
|
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M21-1, Part III, Subpart iv, 4.I.2.d. 4. Hemic and Lymphatic Conditions This topic contains information about hemic and lymphatic conditions, Introduction including the definition of sickle cell disease the definition of sickle cell anemia the definition of and bone marrow transplant stem cell transplant inheritance of sickle cell trait inheritance of sickle cell anemia characteristics of sickle cell anemia mechanism of inheritance of sickle hemoglobin assigning a permanent and total evaluation for multiple myeloma assigning a permanent and total evaluation for chronic lymphocytic leukemia (CLL) considering claims for SC of mycosis fungoides, and evaluating mycosis fungoides July 5, 2015 Change Date a. Definition: is a generic term for all disorders characterized by the Sickle cell disease Sickle Cell presence of sickle hemoglobin (Hb S), in the red blood cells and includes Disease sickle cell anemia sickle cell trait, and other hemoglobinopathies such as sickle cell thalassemia, and sickle-hemoglobin C disease. The phenomenon of sickling of red blood cells is a hereditary Note: abnormality that of itself usually produces few ill effects. b. Definition: is a hereditary and familial disorder characterized Sickle cell anemia Sickle Cell clinically by symptoms of Anemia anemia
|
M21-1, Part III, Subpart iv, 4.I.2.d. 4. Hemic and Lymphatic Conditions Introduction This topic contains information about hemic and lymphatic conditions, including the definition of sickle cell disease the definition of sickle cell anemia the definition of bone marrow transplant and stem cell transplant inheritance of sickle cell trait inheritance of sickle cell anemia characteristics of sickle cell anemia mechanism of inheritance of sickle hemoglobin assigning a permanent and total evaluation for multiple myeloma assigning a permanent and total evaluation for chronic lymphocytic leukemia (CLL) considering claims for SC of mycosis fungoides, and evaluating mycosis fungoides Change Date July 5, 2015 a. Definition: Sickle Cell Disease Sickle cell disease is a generic term for all disorders characterized by the presence of sickle hemoglobin (Hb S), in the red blood cells and includes sickle cell anemia sickle cell trait, and other hemoglobinopathies such as sickle cell thalassemia, and sickle-hemoglobin C disease. Note: The phenomenon of sickling of red blood cells is a hereditary abnormality that of itself usually produces few ill effects. b. Definition: Sickle Cell Anemia Sickle cell anemia is a hereditary and familial disorder characterized clinically by symptoms of anemia
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M21-1MRIII_iv_4_SecI.docx
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task-1431-749
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Section B. Division of Responsibilities for Processing Benefits Delivery at Discharge (BDD) and Quick Start Claims Overview In This Section This section contains the following topics: Topic Topic Name See Page 3 Regional Office (RO) and Intake Site 2-B-2 Responsibilities 4 Intake Site Responsibilities Unique to BDD 2-B-16 Claims 5 Intake Site Responsibilities Unique to Quick 2-B-21 Start Claims BDD Rating Activity Site (RAS) 2-B-24 6 Responsibilities Quick Start Consolidated Processing Site (CPS) 2-B-35 7 Responsibilities 3. Regional Office (RO) and Intake Site Responsibilities Introduction This topic contains information about the responsibilities of intake sites and regional offices in handling pre-discharge claims, including definition of an intake site pre-discharge claim coordinators pre-discharge outreach initial actions upon receipt of a pre-discharge claim circumstances requiring additional/alternative intake site action(s) claims filed more than 180 days prior to discharge claims filed with no known date of discharge intake site responsibilities to update Modern Award Processing Development (MAP-D) pre-discharge claims without service treatment records (STRs) handling pre-discharge claims folders pre-discharge claims with existing paperless claims folders, and folder flash for Benefits Delivery at Discharge (BDD) and Quick Start claims.
|
Section B. Division of Responsibilities for Processing Benefits Delivery at Discharge (BDD) and Quick Start Claims Overview In This Section This section contains the following topics: Topic Topic Name See Page 3 Regional Office (RO) and Intake Site Responsibilities 2-B-2 4 Intake Site Responsibilities Unique to BDD Claims 2-B-16 5 Intake Site Responsibilities Unique to Quick Start Claims 2-B-21 6 BDD Rating Activity Site (RAS) Responsibilities 2-B-24 7 Quick Start Consolidated Processing Site (CPS) Responsibilities 2-B-35 3. Regional Office (RO) and Intake Site Responsibilities Introduction This topic contains information about the responsibilities of intake sites and regional offices in handling pre-discharge claims, including definition of an intake site pre-discharge claim coordinators pre-discharge outreach initial actions upon receipt of a pre-discharge claim circumstances requiring additional/alternative intake site action(s) claims filed more than 180 days prior to discharge claims filed with no known date of discharge intake site responsibilities to update Modern Award Processing Development (MAP-D) pre-discharge claims without service treatment records (STRs) handling pre-discharge claims folders pre-discharge claims with existing paperless claims folders, and folder flash for Benefits Delivery at Discharge (BDD) and Quick Start claims.
| 0.90522
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M21-1MRIII_i_2_SecB.docx
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task-1431-760
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Name: ____________________________ Title: ___________________ k. MAP-D or When writing a letter to obtain information from the Veteran regarding a VBMS Letters PTSD claim based on personal trauma, use MAP-D or VBMS and select the to Use to appropriate personal trauma special issue on the CONTENTIONS screen Obtain Information MST (claims for any conditions, mental or physical (including PTSD), From the resulting from MST) Veteran in PTSD Personal Trauma (claims for PTSD resulting from a non-sexual – Claims Based personal trauma), or on Personal Non-PTSD Personal Trauma (claims for any condition, mental or physical Trauma (other than PTSD), resulting from a non-sexual personal trauma). Enclose to solicit details of the claim. VA Form 21-0781a Letters used by ROs to solicit details concerning a combat Important: stressful incident are inappropriate for PTSD claims based on personal trauma. l. Process for Review the claim and all attached documents. Request STRs and the entire Obtaining personnel folder from the appropriate records custodian, if necessary. Service Records in Claims VSRs must work closely with Rating Veterans Service Representatives Note: Based on (RVSRs) when developing personal trauma cases. Personal Trauma Because a personal trauma is an extremely personal and sensitive issue m. Problems Associated With many incidents of personal trauma are not officially reported, and Development in the victims of this type of in-service trauma may find it difficult to produce Claims Based evidence to support the occurrence of the stressor. on Personal Trauma It is often necessary to seek alternative evidence that may demonstrate the presence of markers. The term refers to evidentiary signs, events, or marker circumstances indicating a that the claimed stressor occurred such possibility as reports, lay statements, or behavioral changes that may be associated with the approximate timeframe of the claimed stressor. For information on References: alternative sources for information, see M21-1, Part IV, Subpart ii, 1.D.5.n, and behavioral changes in claims based on personal trauma, see M21-1, Part III, Subpart iv, 4.H.4.d.
|
Name: ____________________________ Title: ___________________ k. MAP-D or VBMS Letters to Use to Obtain Information From the Veteran in Claims Based on Personal Trauma When writing a letter to obtain information from the Veteran regarding a PTSD claim based on personal trauma, use MAP-D or VBMS and select the appropriate personal trauma special issue on the CONTENTIONS screen MST (claims for any conditions, mental or physical (including PTSD), resulting from MST) PTSD – Personal Trauma (claims for PTSD resulting from a non-sexual personal trauma), or Non-PTSD Personal Trauma (claims for any condition, mental or physical (other than PTSD), resulting from a non-sexual personal trauma). Enclose VA Form 21-0781a to solicit details of the claim. Important: Letters used by ROs to solicit details concerning a combat stressful incident are inappropriate for PTSD claims based on personal trauma. l. Process for Obtaining Service Records in Claims Based on Personal Trauma Review the claim and all attached documents. Request STRs and the entire personnel folder from the appropriate records custodian, if necessary. Note: VSRs must work closely with Rating Veterans Service Representatives (RVSRs) when developing personal trauma cases. m. Problems Associated With Development in Claims Based on Personal Trauma Because a personal trauma is an extremely personal and sensitive issue many incidents of personal trauma are not officially reported, and the victims of this type of in-service trauma may find it difficult to produce evidence to support the occurrence of the stressor. It is often necessary to seek alternative evidence that may demonstrate the presence of markers. The term marker refers to evidentiary signs, events, or circumstances indicating a possibility that the claimed stressor occurred such as reports, lay statements, or behavioral changes that may be associated with the approximate timeframe of the claimed stressor. References: For information on alternative sources for information, see M21-1, Part IV, Subpart ii, 1.D.5.n, and behavioral changes in claims based on personal trauma, see M21-1, Part III, Subpart iv, 4.H.4.d.
| 0.780555
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M21-1MRIV_ii_1_SecD.docx
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task-1431-774
| 33
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Once the report of the examination, completed 6 to 12 months following j. Expediting Reevaluation of separation, is received by the rating activity, reevaluation of the SC the SC disability(ies) should be expedited. Disability(ies) in A Veteran who, without good cause, fails to report for the VA Note: Prestabilization examination scheduled in connection with a prestabilization rating under is Ratings subject to adjustment of benefits under For more information on References: a to report for an examination, see M21-1, Part IV, Veteran’s failure Subpart ii, 3.B, and denials when a Veteran fails to report for an examination, see M21-1Part III, Subpart v, 2.A.6. In some cases, VA assigns a 50 or 100 percent prestabilization rating for one k. Handling Prestabilization or more, but not all, of the same disabilities for which the Veteran received Ratings When severance pay. Severance Pay Is Awarded In such cases, assign separate evaluations for the disabilities for which severance pay was awarded which will assist authorization activity in processing an award. The provisions of operate similarly to prestabilization ratings. They both l. Handling Certain Mental state that qualifying significant disabilities that exist at service discharge will Disorders That be evaluated at the 50 percent level or higher. Cause Discharge However, under From Service the only covered condition is a mental disorder that develops in service as a result of a highly stressful event, and is significant enough to cause release from active service, and the evaluations are assigned under the regular rating schedule. Where a qualifying mental disorder is at issue consider whether the schedular rating available under makes a pre-stabilization rating under inappropriate. For more information on evaluations in cases of service discharge Reference: from mental disorders caused by traumatic stress, see , and M21-1, Part III, Subpart iv, 4.H.1.h.
|
j. Expediting Reevaluation of the SC Disability(ies) in Prestabilization Ratings Once the report of the examination, completed 6 to 12 months following separation, is received by the rating activity, reevaluation of the SC disability(ies) should be expedited. Note: A Veteran who, without good cause, fails to report for the VA examination scheduled in connection with a prestabilization rating under is subject to adjustment of benefits under References: For more information on a Veteran’s failure to report for an examination, see M21-1, Part IV, Subpart ii, 3.B, and denials when a Veteran fails to report for an examination, see M21-1Part III, Subpart v, 2.A.6. k. Handling Prestabilization Ratings When Severance Pay Is Awarded In some cases, VA assigns a 50 or 100 percent prestabilization rating for one or more, but not all, of the same disabilities for which the Veteran received severance pay. In such cases, assign separate evaluations for the disabilities for which severance pay was awarded which will assist authorization activity in processing an award. l. Handling Certain Mental Disorders That Cause Discharge From Service The provisions of operate similarly to prestabilization ratings. They both state that qualifying significant disabilities that exist at service discharge will be evaluated at the 50 percent level or higher. However, under the only covered condition is a mental disorder that develops in service as a result of a highly stressful event, and is significant enough to cause release from active service, and the evaluations are assigned under the regular rating schedule. Where a qualifying mental disorder is at issue consider whether the schedular rating available under makes a pre-stabilization rating under inappropriate. Reference: For more information on evaluations in cases of service discharge from mental disorders caused by traumatic stress, see , and M21-1, Part III, Subpart iv, 4.H.1.h.
| 0.839152
|
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M21-1MRIV_ii_2_secJ.docx
|
task-1431-779
| 3
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For more information on effective date of convalescence ratings, Reference: see . f. Extension of Extensions of temporary total awards are authorized under when justified by Convalescent the facts. Ratings A temporary total evaluation granted for any of the criteria in can be extended to a total duration of six months (in incremental amounts of one, two or three months). Further extensions of one to six months beyond the initial six month period may be made with the following limitations: entitlement must have been established under (surgery with severe postoperative residuals or immobilization of one or more major joint), and approval of the VSCM is required. g. Requesting Generally, unless required by the diagnostic code, reexamination is not Future required following a period in which a temporary total rating was assigned Examinations under . The RVSR should review the evidence of record and assign a new post-convalescence evaluation based on the residual disability following the convalescence period. Only request a VA examination before the end of the total disability period when the evidence is inadequate to determine the residual disability and assign a schedular evaluation after the convalescence period. Assignment of an evaluation lower than 100 percent after the temporary h. convalescence period does not, itself, implicate . The grant of a temporary Evaluations total evaluation with an appropriate schedular evaluation equal to or greater and 38 CFR than the pre-convalescence running award rate taking effect thereafter does 3.105(e) not create a reduction or discontinuance in compensation payments currently being termination of these total ratings will not be made. states: “The subject to §3.105(e) of this chapter.” If the residual disability is less disabling than the rating in effect Exception: prior to the assignment of temporary total disability under so that assigning the appropriate post-convalescence evaluation would result in a reduction or discontinuance in compensation payments made under the running award: assign the temporary 100 percent convalescent evaluation as provided in M21-1, Part IV, Subpart ii, 2.J.4.d assign the evaluation in effect prior to hospitalization effective immediately thereafter, and propose reduction in accordance with to the lower rate supported by the evidence.
|
Reference: For more information on effective date of convalescence ratings, see . f. Extension of Convalescent Ratings Extensions of temporary total awards are authorized under when justified by the facts. A temporary total evaluation granted for any of the criteria in can be extended to a total duration of six months (in incremental amounts of one, two or three months). Further extensions of one to six months beyond the initial six month period may be made with the following limitations: entitlement must have been established under (surgery with severe postoperative residuals or immobilization of one or more major joint), and approval of the VSCM is required. g. Requesting Future Examinations Generally, unless required by the diagnostic code, reexamination is not required following a period in which a temporary total rating was assigned under . The RVSR should review the evidence of record and assign a new post-convalescence evaluation based on the residual disability following the convalescence period. Only request a VA examination before the end of the total disability period when the evidence is inadequate to determine the residual disability and assign a schedular evaluation after the convalescence period. h. Evaluations and 38 CFR 3.105(e) Assignment of an evaluation lower than 100 percent after the temporary convalescence period does not, itself, implicate . The grant of a temporary total evaluation with an appropriate schedular evaluation equal to or greater than the pre-convalescence running award rate taking effect thereafter does not create a reduction or discontinuance in compensation payments currently being made. states: “The termination of these total ratings will not be subject to §3.105(e) of this chapter.” Exception: If the residual disability is less disabling than the rating in effect prior to the assignment of temporary total disability under so that assigning the appropriate post-convalescence evaluation would result in a reduction or discontinuance in compensation payments made under the running award: assign the temporary 100 percent convalescent evaluation as provided in M21-1, Part IV, Subpart ii, 2.J.4.d assign the evaluation in effect prior to hospitalization effective immediately thereafter, and propose reduction in accordance with to the lower rate supported by the evidence.
| 0.901844
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M21-1MRIV_ii_2_secJ.docx
|
task-1431-779
| 12
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Per VHA may request determinations from VBA including, but not limited d. Determinations to, whether VBA Makes for a dental condition or disability is a result of combat wounds Dental a dental condition or disability is a result of service trauma Treatment the Veteran has a noncompensable SC dental condition or disability Purposes the Veteran has a compensable SC disability. the Veteran is totally disabled due to an SC disability, or the Veteran has former Prisoner of War (FPOW) status. Notes: This does not mean a formal rating determination is always required. In many cases eligibility for outpatient dental treatment can be decided by VHA without VBA rating action based information of record. Examples include: one-time dental treatment for Veterans who apply within 180 days after military discharge (Class II) Prisoner of War status (Class II(c)) , and total disability (Class IV) In lieu of a dental treatment rating decision annotate VA Form that the Veteran is totally disabled. For more information on Reference: conditions that can be SC only for treatment purposes, see , and verification of FPOW status for dental eligibility, see . As specified in VBA will develop and decide a claim for service connection e. When to Prepare a for treatment purposes VHA only after Rating for determines that a Veteran meets the basic eligibility requirements of and Dental that VBA make a determination (provide required information or requests Treatment issuing a rating decision). Purposes Provide the determination requested. Important: only Veterans who have a noncompensable dental condition (including tooth f. Service Trauma for disorders for which service connection for compensation purposes cannot be Dental granted) resulting from combat wounds or service trauma may receive any Treatment reasonably necessary treatment for the SC dental condition under Class II(a). Purposes If VHA provides a requesting a dental trauma rating, VA Form 10-7131 prepare a rating identifying the specific tooth number(s) that were injured and identify the service trauma. , 607 F.3d 802 (Fed. Cir, 2010) determined that means Note: service trauma an injury or wound produced by an external physical force, for example striking the mouth on a vehicle, striking the mouth when falling, and biting
|
d. Determinations VBA Makes for Dental Treatment Purposes Per VHA may request determinations from VBA including, but not limited to, whether a dental condition or disability is a result of combat wounds a dental condition or disability is a result of service trauma the Veteran has a noncompensable SC dental condition or disability the Veteran has a compensable SC disability. the Veteran is totally disabled due to an SC disability, or the Veteran has former Prisoner of War (FPOW) status. Notes: This does not mean a formal rating determination is always required. In many cases eligibility for outpatient dental treatment can be decided by VHA without VBA rating action based information of record. Examples include: one-time dental treatment for Veterans who apply within 180 days after military discharge (Class II) Prisoner of War status (Class II(c)) , and total disability (Class IV) In lieu of a dental treatment rating decision annotate VA Form that the Veteran is totally disabled. Reference: For more information on conditions that can be SC only for treatment purposes, see , and verification of FPOW status for dental eligibility, see . e. When to Prepare a Rating for Dental Treatment Purposes As specified in VBA will develop and decide a claim for service connection for treatment purposes only after VHA determines that a Veteran meets the basic eligibility requirements of and requests that VBA make a determination (provide required information or issuing a rating decision). Important: Provide only the determination requested. f. Service Trauma for Dental Treatment Purposes Veterans who have a noncompensable dental condition (including tooth disorders for which service connection for compensation purposes cannot be granted) resulting from combat wounds or service trauma may receive any reasonably necessary treatment for the SC dental condition under Class II(a). If VHA provides a VA Form 10-7131 requesting a dental trauma rating, prepare a rating identifying the specific tooth number(s) that were injured and identify the service trauma. Note: , 607 F.3d 802 (Fed. Cir, 2010) determined that service trauma means an injury or wound produced by an external physical force, for example striking the mouth on a vehicle, striking the mouth when falling, and biting
| 0.835002
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m21-1mrix_ii_2.docx
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task-1431-782
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a. Determining VA accredited attorneys and agents may only charge fees for services What provided after the RO has issued a decision and a NOD has been filed with Constitutes a respect to a “case.” Case When making a determination on fees apply the following principles: An original claim, a reopened claim, and a claim for increase are each separate even if they address the same disability. “cases,” Each request for DIC, Pension, or Death Pension is a separate “case.” Where the benefit sought is disability compensation, the case consists of an assertion of entitlement to compensation based on a specific disability. Distinct and separate symptoms are distinct disabilities and, therefore, distinct cases. However, different diagnoses assigned to the same symptoms do not represent different disabilities and, therefore, are not necessarily separate cases. Examples in following blocks illustrate what constitutes a for the “case” purpose of determining whether fees may be charged. For more information on References: The case concept, see 10 Vet.App. 104 (1997) (docket no. 96-0017) , Distinct symptomatology, see , 6 Vet. App. 259 (1994), Different diagnoses assigned to the same symptoms, see , 23 Vet. App. 1 (2009). b. Example 1 Facts – What The Veteran seeks service connection for (1) posttraumatic stress disorder Constitutes a (PTSD) and (2) hearing loss. “Case” The RO denies service connection for PTSD and defers the issue of service connection for hearing loss. After the Veteran files an NOD with the denial of service connection for PTSD, the RO awards service connection for hearing loss. Result The request for service connection for PTSD and the request for Veteran’s service connection for hearing loss constitute separate cases because they involve distinct disabilities. Fees may not be paid based on the award of service connection for hearing loss because the award was made in the initial decision, before an NOD could have been filed. c. Example 2 Facts – What On August 4, 2008, the Veteran files a claim for depression. At that time, Constitutes a the Veteran was not receiving any VA benefits and did not have any other “Case” claims pending. The RO denied service connection for depression and the
|
a. Determining What Constitutes a Case VA accredited attorneys and agents may only charge fees for services provided after the RO has issued a decision and a NOD has been filed with respect to a “case.” When making a determination on fees apply the following principles: An original claim, a reopened claim, and a claim for increase are each separate “cases,” even if they address the same disability. Each request for DIC, Pension, or Death Pension is a separate “case.” Where the benefit sought is disability compensation, the case consists of an assertion of entitlement to compensation based on a specific disability. Distinct and separate symptoms are distinct disabilities and, therefore, distinct cases. However, different diagnoses assigned to the same symptoms do not represent different disabilities and, therefore, are not necessarily separate cases. Examples in following blocks illustrate what constitutes a “case” for the purpose of determining whether fees may be charged. References: For more information on The case concept, see , 10 Vet.App. 104 (1997) (docket no. 96-0017) Distinct symptomatology, see , 6 Vet. App. 259 (1994), Different diagnoses assigned to the same symptoms, see , 23 Vet. App. 1 (2009). b. Example 1 – What Constitutes a “Case” Facts The Veteran seeks service connection for (1) posttraumatic stress disorder (PTSD) and (2) hearing loss. The RO denies service connection for PTSD and defers the issue of service connection for hearing loss. After the Veteran files an NOD with the denial of service connection for PTSD, the RO awards service connection for hearing loss. Result The Veteran’s request for service connection for PTSD and the request for service connection for hearing loss constitute separate cases because they involve distinct disabilities. Fees may not be paid based on the award of service connection for hearing loss because the award was made in the initial decision, before an NOD could have been filed. c. Example 2 – What Constitutes a “Case” Facts On August 4, 2008, the Veteran files a claim for depression. At that time, the Veteran was not receiving any VA benefits and did not have any other claims pending. The RO denied service connection for depression and the
| 0.873151
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M21-1MRI_3_SecC.docx
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task-1431-789
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Section C. Income Verification Match (IVM) Overview This section contains the following topic: In This Section _______________________________________________________________
|
Section C. Income Verification Match (IVM) Overview In This Section This section contains the following topic: _______________________________________________________________
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This topic contains information on effective dates of adjustment for pension Introduction and DIC, including Parents’ the effective date for Improved Pension and DIC, and Parents’ payer and recipient verify Department of Veterans Affairs (VA) information. Change Date March 30, 2015 a. Effective In Improved Pension and DIC cases, the effective date of reduction Parents’ Date: or termination is the first of the month following the month the income was Improved first received. Pension and DIC Parents’ In the notice of proposed adverse action, ask the beneficiary to furnish VA with the date income was first received. If the income payer or income recipient does not furnish the specific Note: date that the income was first paid, but shows the calendar year it was paid, assume that it was received in January and propose to adjust effective February 1. If both the payer and the income recipient respond within the 30-day waiting b. Payer and Recipient period verifying the information VA received from the match, send a notice Verify VA of proposed adverse action based on the income report. recipient’s Information If there is only one income year, there is no need to mention the reply Note: from the payer. 7. Differing Responses From Payer and Recipient
|
Introduction This topic contains information on effective dates of adjustment for pension and Parents’ DIC, including the effective date for Improved Pension and Parents’ DIC, and payer and recipient verify Department of Veterans Affairs (VA) information. Change Date March 30, 2015 a. Effective Date: Improved Pension and Parents’ DIC In Improved Pension and Parents’ DIC cases, the effective date of reduction or termination is the first of the month following the month the income was first received. In the notice of proposed adverse action, ask the beneficiary to furnish VA with the date income was first received. Note: If the income payer or income recipient does not furnish the specific date that the income was first paid, but shows the calendar year it was paid, assume that it was received in January and propose to adjust effective February 1. b. Payer and Recipient Verify VA Information If both the payer and the income recipient respond within the 30-day waiting period verifying the information VA received from the match, send a notice of proposed adverse action based on the income recipient’s report. Note: If there is only one income year, there is no need to mention the reply from the payer. 7. Differing Responses From Payer and Recipient
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261,
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407,
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480,
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487,
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558,
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566,
825,
612,
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[
620,
825,
738,
853
]
] |
The MAPR for any given claimant is determined by a. General Information on MAPR the type of payee (Veteran, surviving spouse, or child) the number of established dependents, and whether the beneficiary is eligible for the Aid and Attendance (A&A) or Housebound MAPR. A change in any of these factors, such as the loss of a dependent or grant of A&A, changes the MAPR and the amount of the pension payable, per . A higher MAPR applies to a beneficiary who is found in need of A&A Note: or is Housebound. A&A and Housebound benefits are also referred to as Special Monthly Pension (SMP). b. If Social Security (SS) benefits are increased as the result of an SS Increase adjustment (COLA), Pension MAPRs are increased by a like in MAPR percentage at the same time, per . For References: current and historical MAPRs, see , and more information on adjustment of Pension based on a change in income due to an SS COLA, see . 3. Veterans Who Are Married to Each Other This topic contains information on Veterans who are married to each other, Introduction including the combined rate payable to Veterans married to each other the award procedures in Veteran-married-to-Veteran cases determining the file number to use in combined awards estranged married Veterans, and mandatory election from protected pension in Veteran-married-to-Veteran cases. June 27, 2015 Change Date
|
a. General Information on MAPR The MAPR for any given claimant is determined by the type of payee (Veteran, surviving spouse, or child) the number of established dependents, and whether the beneficiary is eligible for the Aid and Attendance (A&A) or Housebound MAPR. A change in any of these factors, such as the loss of a dependent or grant of A&A, changes the MAPR and the amount of the pension payable, per . Note: A higher MAPR applies to a beneficiary who is found in need of A&A or is Housebound. A&A and Housebound benefits are also referred to as Special Monthly Pension (SMP). b. Increase in MAPR If Social Security (SS) benefits are increased as the result of an SS adjustment (COLA), Pension MAPRs are increased by a like percentage at the same time, per . References: For current and historical MAPRs, see , and more information on adjustment of Pension based on a change in income due to an SS COLA, see . 3. Veterans Who Are Married to Each Other Introduction This topic contains information on Veterans who are married to each other, including the combined rate payable to Veterans married to each other the award procedures in Veteran-married-to-Veteran cases determining the file number to use in combined awards estranged married Veterans, and mandatory election from protected pension in Veteran-married-to-Veteran cases. Change Date June 27, 2015
| 0.897028
|
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M21-1V_iii_1_SecE.docx
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task-1431-804
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Department of Veterans Affairs M21-1, Part III, Subpart i Veterans Benefits Administration July 31, 2015 Washington, DC 20420 Transmittal Sheet The table below describes the changes included in this revision of Veterans Changes Included in Benefits Manual M21-1, Part III, Subpart i, “General Claims Process,” This Revision of the Veterans “Structure Service Center (VSC).” Notes: The term “regional office” (RO) also includes pension management center (PMC), where appropriate. Unless otherwise noted, the term the official, “claims folder” refers to numbered, Department of Veterans Affairs (VA) repository whether paper – or electronic for all documentation relating to claims that a Veteran and/or – his/her survivors file with VA. Minor editorial changes have also been made to update incorrect or obsolete references update the term to “VCAA notice” “section 5103 notice” update the term to “notification letter” “decision notice” update obsolete terminology, where appropriate remove references to specific claims-processing systems, where doing so does not affect the clarity of the instructions or information provided renumber each topic based on the standard that the first topic in each section is Topic 1 reassign alphabetical designations to individual blocks, where necessary, to account for new and/or deleted blocks within a topic update section and topic titles to more accurately reflect their content clarify block labels and/or block text, and bring the document into conformance with M21-1 standards. Reason(s) for the Change Citation To update instructions in notes sublabel for Step 6 of table concerning M21-1, Part III, actions a Physical Evaluation Board Liasion Officer (PEBLO) should Subpart i, Chapter 2, take on a Section D, Topic 2, “referral package.” Block a (III.i.2.D.2.a) To add Block b which contains a sample copy of the Service III.i.2.D.2.b Department Memorandum of Complete and Current Service Treatement Records (STRs). III.i.2.D.3.a To add the items a proper referral package includes to Step 2 of table. To provide information in Step 4 of table about initial interview completed within 10 days. To add table in Step 5 about determining with embedded “If/Then” Consolidated Veterans Records System (COVERS) if a claims folder
|
Department of Veterans Affairs M21-1, Part III, Subpart i Veterans Benefits Administration July 31, 2015 Washington, DC 20420 Transmittal Sheet Changes Included in This Revision The table below describes the changes included in this revision of Veterans Benefits Manual M21-1, Part III, “General Claims Process,” Subpart i, “Structure of the Veterans Service Center (VSC).” Notes: The term “regional office” (RO) also includes pension management center (PMC), where appropriate. Unless otherwise noted, the term “claims folder” refers to the official, numbered, Department of Veterans Affairs (VA) repository – whether paper or electronic – for all documentation relating to claims that a Veteran and/or his/her survivors file with VA. Minor editorial changes have also been made to update incorrect or obsolete references update the term “VCAA notice” to “section 5103 notice” update the term “notification letter” to “decision notice” update obsolete terminology, where appropriate remove references to specific claims-processing systems, where doing so does not affect the clarity of the instructions or information provided renumber each topic based on the standard that the first topic in each section is Topic 1 reassign alphabetical designations to individual blocks, where necessary, to account for new and/or deleted blocks within a topic update section and topic titles to more accurately reflect their content clarify block labels and/or block text, and bring the document into conformance with M21-1 standards. Reason(s) for the Change Citation To update instructions in notes sublabel for Step 6 of table concerning actions a Physical Evaluation Board Liasion Officer (PEBLO) should take on a “referral package.” M21-1, Part III, Subpart i, Chapter 2, Section D, Topic 2, Block a (III.i.2.D.2.a) To add Block b which contains a sample copy of the Service Department Memorandum of Complete and Current Service Treatement Records (STRs). III.i.2.D.2.b To add the items a proper referral package includes to Step 2 of table. To provide information in Step 4 of table about initial interview completed within 10 days. To add embedded “If/Then” table in Step 5 about determining with Consolidated Veterans Records System (COVERS) if a claims folder III.i.2.D.3.a
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if priority processing of a claim/correspondence associated with an EP 139 is necessary, or if a rating decision is ready for promulgation, and the evidence necessary to process the claim/correspondence associated with the EP 139 is of record. Although Compensation Service and the contractor are generally Important: responsible for processing claims/correspondence associated with an EP 139, SOOs remain responsible for responding to inquiries they receive through the Inquiry Routing and Information System (IRIS) or from members of Congress regarding such claims/correspondence. Compensation Service periodically releases batches of decision notices that b. Batch Generation and reflect a denial of entitlement to additional disability compensation for a Release of dependent when the claimant is a Veteran with a combined disability rating Decision that is than 30 percent less and Notices That Reflect a Denial a 130-series EP is pending a concurrently pending rating EP, without or of Entitlement an EP 020 and a 130-series EP are concurrently pending. to Additional Benefits Upon release of a decision notice, the system that generated the notice clears the 130-series EP, and uploads a copy of the decision notice into the eFolder. Veteran’s For information about rating EPs, see M21-4, Appendix B, Reference: Section II.
|
if priority processing of a claim/correspondence associated with an EP 139 is necessary, or if a rating decision is ready for promulgation, and the evidence necessary to process the claim/correspondence associated with the EP 139 is of record. Important: Although Compensation Service and the contractor are generally responsible for processing claims/correspondence associated with an EP 139, SOOs remain responsible for responding to inquiries they receive through the Inquiry Routing and Information System (IRIS) or from members of Congress regarding such claims/correspondence. b. Batch Generation and Release of Decision Notices That Reflect a Denial of Entitlement to Additional Benefits Compensation Service periodically releases batches of decision notices that reflect a denial of entitlement to additional disability compensation for a dependent when the claimant is a Veteran with a combined disability rating that is less than 30 percent and a 130-series EP is pending without a concurrently pending rating EP, or an EP 020 and a 130-series EP are concurrently pending. Upon release of a decision notice, the system that generated the notice clears the 130-series EP, and uploads a copy of the decision notice into the Veteran’s eFolder. Reference: For information about rating EPs, see M21-4, Appendix B, Section II.
| 0.82612
|
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M21_1III_i_4_SecE.docx
|
task-1431-831
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Missouri Judicial Yes 60 330 No Not Confirmation/Ratification Holder Date of Applicable Convey – Confirmation Transfer of Ratification Custody of Sale Retain or 3rd Party – Confirmed Sale Date with No Transfer of Custody Missouri Non-Judicial Sale No Results of Date of Sale 60 330 Yes 365 Sale Montana Judicial Confirmation/Ratification Yes Holder Date of 150 450 Yes 365 Convey - Confirmation Transfer of Ratification Custody of Sale Retain or 3rd Party – Confirmed Sale Date with No Transfer of Custody Montana Non-Judicial Sale No Results of Date of Sale 150 450 No Not Sale Applicable Nebraska Judicial of Sale Yes Holder Date of 180 420 No Not Convey – Confirmation Applicable Transfer of Ratification Custody of Sale Retain or 3rd Party – Confirmed
|
Missouri Judicial Confirmation/Ratification Yes Holder Convey – Transfer of Custody Retain or 3rd Party – Confirmed Sale Date with No Transfer of Custody Date of Ratification of Sale 60 330 No Not Applicable Missouri Non-Judicial Sale No Results of Sale Date of Sale 60 330 Yes 365 Montana Judicial Confirmation/Ratification Yes Holder Convey - Transfer of Custody Retain or 3rd Party – Confirmed Sale Date with No Transfer of Custody Date of Ratification of Sale 150 450 Yes 365 Montana Non-Judicial Sale No Results of Sale Date of Sale 150 450 No Not Applicable Nebraska Judicial of Sale Yes Holder Convey – Transfer of Custody Retain or 3rd Party – Confirmed Date of Ratification of Sale 180 420 No Not Applicable Confirmation Confirmation Confirmation
| 0.596223
|
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M26-3%20Appendix%20G%20-%205-2017.docx
|
task-1431-857
| 7
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North Carolina N North Dakota N Ohio Y All Counties Not Applicable Two-Thirds of Sheriff's Appraised Results of Sale Sheriff's Appraised Value Value Oklahoma Y All Counties Not Applicable Two-Thirds of Sheriff's Appraised Results of Sale Sheriff's Appraised Value Value Oregon N Pennsylvania N Puerto Rico Y All Counties Not Applicable Up to 3 sales Original Loan Results of Sale 1st - Original Loan Amount or Net Amount or NV Value (whichever 2nd - 2/3 Original is greater) Loan Amount or NV 3rd - 1/2 Original Loan Amount or NV Rhode Island N South Carolina N South Dakota Y All Counties WebLGY Not Applicable WebLGY Not Applicable Appraised Value Appraised Value Tennessee N Texas N Utah N Vermont Y All Counties Total Not Applicable Total Results of Sale Indebtedness Indebtedness Virgin Islands N Virginia N Washington N
|
North Carolina N North Dakota N Ohio Y All Counties Not Applicable Two-Thirds of Sheriff's Appraised Value Sheriff's Appraised Value Results of Sale Oklahoma Y All Counties Not Applicable Two-Thirds of Sheriff's Appraised Value Sheriff's Appraised Value Results of Sale Oregon N Pennsylvania N Puerto Rico Y All Counties Not Applicable Up to 3 sales 1st - Original Loan Amount or NV 2nd - 2/3 Original Loan Amount or NV 3rd - 1/2 Original Loan Amount or NV Original Loan Amount or Net Value (whichever is greater) Results of Sale Rhode Island N South Carolina N South Dakota Y All Counties WebLGY Appraised Value Not Applicable WebLGY Appraised Value Not Applicable Tennessee N Texas N Utah N Vermont Y All Counties Total Indebtedness Not Applicable Total Indebtedness Results of Sale Virgin Islands N Virginia N Washington N
| 0.708738
|
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M26-3%20Appendix%20G%20-%205-2017.docx
|
task-1431-857
| 18
|
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CONTENTS CHAPTER 14. CLAIMS PARAGRAPH PAGE 14-2 14.01 Claims (38 CFR 36.4324)………………………………...….. 14-2 14.02 Eligibility for Claim Payment…..…..……....……......……... 14.03 Claim 14-3 Payment Calculation…………...………..………........ 14.04 Allowable Claim Items for 14-10 VA Reimbursement...…..…....... 14.05 VA 14-17 Determines Credit to Indebtedness…………….……….. 14.06 VA Calculates Claim 14-18 Payment……..……………......……... 14.07 Other Review Considerations on Claim 14-19 Payment……….….. 14.08 Credits to 14-20 Claim…………………………………………….. 14.09 Servicer Receives Claim 14-20 Payment....…………….……......... 14-21 14.10 Supplemental Claims…………………………………….….. 14.11 Claim Process for Mobile Homes (38 CFR 14-21 36.4824)……….. 14.12 Funds Received By After Claim 14-21 Payment....………..…….....
|
CONTENTS CHAPTER 14. CLAIMS PARAGRAPH PAGE 14.01 Claims (38 CFR 36.4324)………………………………...….. 14-2 14.02 Eligibility for Claim Payment…..…..……....……......……... 14-2 14.03 Claim Payment Calculation…………...………..………........ 14-3 14.04 Allowable Claim Items for VA Reimbursement...…..…....... 14-10 14.05 VA Determines Credit to Indebtedness…………….……….. 14-17 14.06 VA Calculates Claim Payment……..……………......……... 14-18 14.07 Other Review Considerations on Claim Payment……….….. 14-19 14.08 Credits to Claim…………………………………………….. 14-20 14.09 Servicer Receives Claim Payment....…………….……......... 14-20 14.10 Supplemental Claims…………………………………….….. 14-21 14.11 Claim Process for Mobile Homes (38 CFR 36.4824)……….. 14-21 14.12 Funds Received By After Claim Payment....………..……..... 14-21
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CONTENTS CHAPTER 12. QUITCLAIM DEEDS PARAGRAPH PAGE 12-2 12.01 Quitclaim Deeds……………………………………………. 12.02 Requesting a Quitclaim 12-2 Deed………………………......…... 12.03 Executing a Quitclaim 12-3 Deed……………………..…….........
|
CONTENTS CHAPTER 12. QUITCLAIM DEEDS PARAGRAPH PAGE 12.01 Quitclaim Deeds……………………………………………. 12-2 12.02 Requesting a Quitclaim Deed………………………......…... 12-2 12.03 Executing a Quitclaim Deed……………………..……......... 12-3
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Wisconsin Judicial - Yes Holder Date of 330 510 No Not Owner Convey – Applicable Occupied Transfer of Ratification Custody of Sale Retain or 3rd Party – Confirmed Sale Date with No Transfer of Custody Wisconsin Judicial - Yes Holder Date of 240 510 No Not Tenant Convey – Applicable Occupied Transfer of Ratification Custody of Sale Retain or 3rd Party – Confirmed Sale Date with No Transfer of Custody Wisconsin Non-Judicial Yes Holder Date of 330 510 No Not Convey – Applicable Transfer of Ratification Custody of Sale Retain or 3rd Party – Confirmed Sale Date with No Transfer of Custody
|
Wisconsin Judicial - Owner Occupied Yes Holder Convey – Transfer of Custody Retain or 3rd Party – Confirmed Sale Date with No Transfer of Custody Date of Ratification of Sale 330 510 No Not Applicable Wisconsin Judicial - Tenant Occupied Yes Holder Convey – Transfer of Custody Retain or 3rd Party – Confirmed Sale Date with No Transfer of Custody Date of Ratification of Sale 240 510 No Not Applicable Wisconsin Non-Judicial Yes Holder Convey – Transfer of Custody Retain or 3rd Party – Confirmed Sale Date with No Transfer of Custody Date of Ratification of Sale 330 510 No Not Applicable
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M26-4%20Appendix%20G%20-%205-%202017.docx
|
task-1431-877
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- ELEMENTS OF General Program of Requirements indicating the square footage and number of all program spaces Project’s LEED registration with GBCI LEED point checklist indicating which points the owner and and design team has selected as “yes”, “possible”, “no” Minutes of the Eco-Charrette Printout of EPA Target Finder Data Sheet Narrative summary explaining how the additional project funding (3 percent) will contribute to the Energy Credits - ELEMENTS OF SCHEMATIC DESIGN General LEED checklist indicating which points the owner and design team have identified and as “yes”, “possible”, “no” Printout of any changes to EPA Target Finder Data Sheet Evidence of the most recent parametric modeling of energy consumption Most recent Monthly Energy Consumption by fuel by end use, from Energy Model Most recent Annual Energy Consumption by fuel by end use, from Energy Model Monthly Peak Demand by fuel by end use, from Energy Model Annual Peak Demand by fuel by end use, from Energy Model Narrative summary explaining how the additional project funding (3 percent) will contributed to the Energy Credits Architectural Singleline drawings showing complete building layout, identifying the various major areas, core areas and their relationships Exterior wall section indicating location of openings, and overall thermal transfer value for each element of the exterior wall/envelope Plan and Section views, indicating the design approach to day lighting Identify roof system, deck, membrane flashing and drainage technique and indicate overall combined heat transfer coefficient value for exterior wall/envelope Show exterior building elevations identifying proposed shell finishes (include all exterior surfaces, doors, and windows) Site plan with building located and overall grading plan with preliminary contours Preliminary site development such as access road paving, walls and outside support buildings, and paved parking lots landscaping, geothermal wellfields, bio-swales, constructed wetlands, composting facilities, natural areas, site utilities (existing and new) should be shown Gross and net area calculations separated to show conformance with Program of Requirements Each space to be annotated with net square footage Building Code type and occupancy information
|
- ELEMENTS OF General Program of Requirements indicating the square footage and number of all program spaces Project’s LEED registration with GBCI LEED point checklist indicating which points the owner and design team has selected as “yes”, “possible”, and “no” Minutes of the Eco-Charrette Printout of EPA Target Finder Data Sheet Narrative summary explaining how the additional project funding (3 percent) will contribute to the Energy Credits - ELEMENTS OF SCHEMATIC DESIGN General LEED checklist indicating which points the owner and design team have identified as “yes”, “possible”, and “no” Printout of any changes to EPA Target Finder Data Sheet Evidence of the most recent parametric modeling of energy consumption Most recent Monthly Energy Consumption by fuel by end use, from Energy Model Most recent Annual Energy Consumption by fuel by end use, from Energy Model Monthly Peak Demand by fuel by end use, from Energy Model Annual Peak Demand by fuel by end use, from Energy Model Narrative summary explaining how the additional project funding (3 percent) will contributed to the Energy Credits Architectural Singleline drawings showing complete building layout, identifying the various major areas, core areas and their relationships Exterior wall section indicating location of openings, and overall thermal transfer value for each element of the exterior wall/envelope Plan and Section views, indicating the design approach to day lighting Identify roof system, deck, membrane flashing and drainage technique and indicate overall combined heat transfer coefficient value for exterior wall/envelope Show exterior building elevations identifying proposed shell finishes (include all exterior surfaces, doors, and windows) Site plan with building located and overall grading plan with preliminary contours Preliminary site development such as access road paving, walls and outside support buildings, and paved parking lots landscaping, geothermal wellfields, bio-swales, constructed wetlands, composting facilities, natural areas, site utilities (existing and new) should be shown Gross and net area calculations separated to show conformance with Program of Requirements Each space to be annotated with net square footage Building Code type and occupancy information
| 0.979355
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M110-05.K12-Minimum_Stage_Submission.docx
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task-1431-88
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b. In addition, original title documentation must be forwarded to the VA-assigned technician by the settlement date provided in approval letter. The following is considered acceptable VA’s title documentation: 1. Original mortgage or DOT, or a copy certified by a local authority, with all assignments and any subordination agreements. 2. Original mortgage note from origination endorsed to the Secretary of Veterans Affairs. 3. Original title insurance policy, naming the Secretary of Veterans Affairs as a mortgagee’s co-insured, and an updated policy or endorsement naming the Secretary as insured as of the date of the recorded assignment. 4. Recorded assignment of mortgage to the Secretary of Veterans Affairs. c. Upon receipt of all title documentation, VA will submit the documents to the appropriate Loan Guaranty National Practice Group (NPG) for review. VA does not certify claim payment until the title has been approved by the NPG. If the title is not acceptable, VA may reassign the loan back to the servicer and deny refunding of the loan.
|
b. In addition, original title documentation must be forwarded to the VA-assigned technician by the settlement date provided in VA’s approval letter. The following is considered acceptable title documentation: 1. Original mortgage or DOT, or a copy certified by a local authority, with all assignments and any subordination agreements. 2. Original mortgage note from origination endorsed to the Secretary of Veterans Affairs. 3. Original mortgagee’s title insurance policy, naming the Secretary of Veterans Affairs as a co-insured, and an updated policy or endorsement naming the Secretary as insured as of the date of the recorded assignment. 4. Recorded assignment of mortgage to the Secretary of Veterans Affairs. c. Upon receipt of all title documentation, VA will submit the documents to the appropriate Loan Guaranty National Practice Group (NPG) for review. VA does not certify claim payment until the title has been approved by the NPG. If the title is not acceptable, VA may reassign the loan back to the servicer and deny refunding of the loan.
| 0.953668
|
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M26-4%20Chapter%209%20Refunds%20-%20November%202016.docx
|
task-1431-894
| 3
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People with disabilities are individuals, no matter how they acquired their disability, what sort of disability they have or how severe their disability is. People with disabilities have the right to be respected and treated with dignity. People with disabilities have the same human rights and responsibilities as any member of the Australian community. People with disabilities have the same rights as any member of the Australian community to realise their potential for intellectual, physical, social, emotional, sexual and spiritual development. People with disabilities have the same rights as any member of the Australian community to make their own decisions about their lifestyle. People with disabilities have a right to be protected from neglect, abuse, intimidation and exploitation. People with disabilities have the same rights as any member of the Australian community to access the assistance and support that will enable them to exercise their rights, discharge their responsibilities and attain a reasonable quality of life. The Disability Discrimination Act (1992) Guide Dogs SA/NT recognises the definition of “disability” provided in the Disability Discrimination Act 1992. People who have a disability might have: total or partial loss of the person’s bodily or mental functions total or partial loss of a part of the body the presence in the body of organisms causing disease or illness the presence in the body of organisms capable of causing disease or illness the malfunction, malformation or disfigurement of a part of the person’s body a disorder or malfunction that results in the person learning differently from a person without the disorder or malfunction a disorder, illness or disease that affects a person’s thought processes, perception of reality, emotions or judgment or that results in disturbed behaviour. It includes a disability that: presently exists previously existed but no longer exists may exist in the future is imputed to a person. Guide Dogs SA/NT will conduct its business in a way that: Promotes an increased awareness of the rights, needs, and contribution by people with disabilities as valued members of the community. Supports access and equity for all South Australians with disabilities and prevents discrimination on the basis of age, gender, sexuality, race, ethnicity, religion, language and culture.
|
People with disabilities are individuals, no matter how they acquired their disability, what sort of disability they have or how severe their disability is. People with disabilities have the right to be respected and treated with dignity. People with disabilities have the same human rights and responsibilities as any member of the Australian community. People with disabilities have the same rights as any member of the Australian community to realise their potential for intellectual, physical, social, emotional, sexual and spiritual development. People with disabilities have the same rights as any member of the Australian community to make their own decisions about their lifestyle. People with disabilities have a right to be protected from neglect, abuse, intimidation and exploitation. People with disabilities have the same rights as any member of the Australian community to access the assistance and support that will enable them to exercise their rights, discharge their responsibilities and attain a reasonable quality of life. The Disability Discrimination Act (1992) Guide Dogs SA/NT recognises the definition of “disability” provided in the Disability Discrimination Act 1992. People who have a disability might have: total or partial loss of the person’s bodily or mental functions total or partial loss of a part of the body the presence in the body of organisms causing disease or illness the presence in the body of organisms capable of causing disease or illness the malfunction, malformation or disfigurement of a part of the person’s body a disorder or malfunction that results in the person learning differently from a person without the disorder or malfunction a disorder, illness or disease that affects a person’s thought processes, perception of reality, emotions or judgment or that results in disturbed behaviour. It includes a disability that: presently exists previously existed but no longer exists may exist in the future is imputed to a person. Guide Dogs SA/NT will conduct its business in a way that: Promotes an increased awareness of the rights, needs, and contribution by people with disabilities as valued members of the community. Supports access and equity for all South Australians with disabilities and prevents discrimination on the basis of age, gender, sexuality, race, ethnicity, religion, language and culture.
| 1
|
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M366%20Disability%20Action%20%26%20%20Inclusion%20Plan%202015-2017%20v2.0.docx
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December 8.2.9 Include a standard line on every relevant Manager 2016 official Guide Dogs SA/NT publication to Marketing and advise the reader the material is Communications available in alternative formats (or can be provided upon request). In place – 8.2.10 Develop a clear procedure for all staff on EM Client Services continuous how to convert print to various EM Mobility and Dog improvement alternative formats. Services required. December 2016 In place – 8.2.11 Ensure newly recruited client services EM Client Services continuous staff and existing staff are appropriately EM Mobility and Dog improvement trained and updated on the use of Services assistive equipment, as well as the role required. June 2017 and operation of communication aids and translating and interpreting services. Improvement to 8.2.12 Ensure the Guide Dogs SA/NT website Manager the website meets international access standards – Marketing and access has been i.e. meets the World Wide Web Communications undertaken. Consortium (W3C) Web Content Further work Accessibility Guidelines (WCAG2.0). needs to be undertaken to reach full compliance with (WCAG2.0)June 2017 8.2.13 Undertake regular maintenance of the Manager Ongoing Guide Dogs SA/NT website to ensure it Marketing and meets evolving international standards. Communications In Place 8.2.14 Ensure the (DAIP) is accessible through EM S Client Services Ongoing the Guide Dogs SA/NT website and EM Mobility and Dog June 2016 available in alternative formats such as Services large print, Braille and audio upon Manager request. Provide the (DAIP) in various Marketing and community languages on a needs basis. Communications December 8.2.15 Promote the use and awareness of EM Client Services 2016 adaptive technology devices (kits) to the broader community to promote safety, Independence and inclusion for people with sensory loss in specialised environments, e.g. Hospitals, hotels resorts, nursing homes etc.
|
8.2.9 Include a standard line on every relevant official Guide Dogs SA/NT publication to advise the reader the material is available in alternative formats (or can be provided upon request). Manager Marketing and Communications December 2016 8.2.10 Develop a clear procedure for all staff on how to convert print to various alternative formats. EM Client Services EM Mobility and Dog Services In place – continuous improvement required. December 2016 8.2.11 Ensure newly recruited client services staff and existing staff are appropriately trained and updated on the use of assistive equipment, as well as the role and operation of communication aids and translating and interpreting services. EM Client Services EM Mobility and Dog Services In place – continuous improvement required. June 2017 8.2.12 Ensure the Guide Dogs SA/NT website meets international access standards – i.e. meets the World Wide Web Consortium (W3C) Web Content Accessibility Guidelines (WCAG2.0). Manager Marketing and Communications Improvement to the website access has been undertaken. Further work needs to be undertaken to reach full compliance with (WCAG2.0)June 2017 8.2.13 Undertake regular maintenance of the Guide Dogs SA/NT website to ensure it meets evolving international standards. Manager Marketing and Communications Ongoing 8.2.14 Ensure the (DAIP) is accessible through the Guide Dogs SA/NT website and available in alternative formats such as large print, Braille and audio upon request. Provide the (DAIP) in various community languages on a needs basis. EM S Client Services EM Mobility and Dog Services Manager Marketing and Communications In Place Ongoing June 2016 8.2.15 Promote the use and awareness of adaptive technology devices (kits) to the broader community to promote safety, Independence and inclusion for people with sensory loss in specialised environments, e.g. Hospitals, hotels resorts, nursing homes etc. EM Client Services December 2016
| 0.67648
|
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m366-disability-action-inclusion-plan-2015-2017-v2-0.docx
|
task-1432-231
| 10
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behavior; Technology and industry; Economic development and land use; International travel and commerce; Microbial adaptation and change; and Breakdown of public health measures. There have been other groupings of causative factors proposed related to re-emerging infections and in some instances we do not yet have a clue as to how new agents have appeared in animal and human populations. The problem of emerging infections is well exemplified by the many examples of new and emerging infectious diseases that have impacted upon localized populations and/or geographical areas over the past several decades. Human immunodeficiency virus (HIV)/AIDS, first identified in 1981, portrays the significant impact that an infectious disease can have on the world. Presently HIV/AIDS is the fourth leading cause of death in the world and it remains the leading cause of death in Africa. The economic havoc it has created worldwide is frightening and its impact upon all peoples will remain embedded on mankind for decades. More geographically localized, but still creating worldwide concern, have been the haemorrhagic fevers, Nipah virus, and monkeypox. And more recently sudden acute respiratory syndrome (SARS) exemplifies how the occurrence of a new and dangerous infectious disease can monopolize governmental activities, cause fear and hysteria, have a significant impact on the economy throughout the world and on the freedom of movement of people. We are bold in our attempts to control infectious diseases. We have eradicated one disease (smallpox) and two other diseases are in the final stages of eradication (poliomyelitis and dracunculiasis). These eradication programmes demonstrate how international collaboration and co-operation can significantly benefit the world. However, our goals must be realistic, that is, initiation of an eradication programme must be limited to the few diseases for which this is a valid goal. Control and prevention should be our main
|
behavior; Technology and industry; Economic development and land use; International travel and commerce; Microbial adaptation and change; and Breakdown of public health measures. There have been other groupings of causative factors proposed related to re-emerging infections and in some instances we do not yet have a clue as to how new agents have appeared in animal and human populations. The problem of emerging infections is well exemplified by the many examples of new and emerging infectious diseases that have impacted upon localized populations and/or geographical areas over the past several decades. Human immunodeficiency virus (HIV)/AIDS, first identified in 1981, portrays the significant impact that an infectious disease can have on the world. Presently HIV/AIDS is the fourth leading cause of death in the world and it remains the leading cause of death in Africa. The economic havoc it has created worldwide is frightening and its impact upon all peoples will remain embedded on mankind for decades. More geographically localized, but still creating worldwide concern, have been the haemorrhagic fevers, Nipah virus, and monkeypox. And more recently sudden acute respiratory syndrome (SARS) exemplifies how the occurrence of a new and dangerous infectious disease can monopolize governmental activities, cause fear and hysteria, have a significant impact on the economy throughout the world and on the freedom of movement of people. We are bold in our attempts to control infectious diseases. We have eradicated one disease (smallpox) and two other diseases are in the final stages of eradication (poliomyelitis and dracunculiasis). These eradication programmes demonstrate how international collaboration and co-operation can significantly benefit the world. However, our goals must be realistic, that is, initiation of an eradication programme must be limited to the few diseases for which this is a valid goal. Control and prevention should be our main
| 1
|
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m3727.docx
|
task-1432-235
| 21
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Facilitator hands out the Advice Letter Worksheet Writing Youth are allowed fifteen minutes to write. Facilitator moves from group to group, giving (15 min) advice. Reading/clearing up of Facilitator asks several youth to read their letters, and clears up any misconceptions in misconceptions the letters. (3 min) Give each reader a round of applause. CELEBRATE (5 min) Youth celebrate their Cookie Party! learning. Handout cookies (or other treats) and congratulate youth on a job well done. Developed by Thrive Foundation for Youth. Creative Commons Attribution-Noncommerial-Share Alike 3.0. U.S. License. 2010. This document can be shared and adapted by users for educational, non-commercial purposes.
|
Writing (15 min) Reading/clearing up of misconceptions (3 min) Facilitator hands out the Advice Letter Worksheet Youth are allowed fifteen minutes to write. Facilitator moves from group to group, giving advice. Facilitator asks several youth to read their letters, and clears up any misconceptions in the letters. Give each reader a round of applause. CELEBRATE (5 min) Youth celebrate their learning. Cookie Party! Handout cookies (or other treats) and congratulate youth on a job well done. Developed by Thrive Foundation for Youth. Creative Commons Attribution-Noncommerial-Share Alike 3.0. U.S. License. 2010. This document can be shared and adapted by users for educational, non-commercial purposes.
| 0.840146
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m3_what_the_growth_mindset_means_to_you.docx
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task-1432-341
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there. “Advanced.” Deviation 0 -1 0 Though some of the errors in work shown are below the overall volume of Identified Issues N/A errors is not significant N/A enough to place this response in the “Below Proficient” category. Calibration Training-Student Sample #2 Anchored Advanced Performance Level Panelist Panelist 1 Panelist 2 Panelist 3 Performance Level Advanced Advanced Advanced Assigned Very few errors in the SR and The overall score and student’s The work shown CR sections. The one incorrect the quality of work shown is Performance Level is easy to follow CR answer was off by less higher than what I have seen Rationale and mostly than 1 degree, indicating a from most of the 9th graders I correct. minor miscalculation. have worked with. Deviation 0 0 0 Identified Issues N/A N/A N/A Calibration Training-Student Sample #3 Anchored Below Proficient Performance Level Panelist Panelist 1 Panelist 2 Panelist 3 Performance Level Below Proficient Below Proficient Below Proficient Assigned Even if the CR answers Student did not follow The student missed Performance Level were correct, there is directions for many CR items. and/or skipped well Rationale still little to no work Most answers are labeled with over half of the items shown to justify them. incorrect units of measure. on the assessment. Deviation 0 0 0 Identified Issues N/A N/A N/A Build Phase-Meeting 2 Meeting Date: May 8, 2014 Panelists/Qualifications: P. Lopez (District Department Chair), D. Freeman (High School Instructional Coach), N. Bryant (High School Instructional Coach) Subject/Grade Level of Assessment/Task: Mathematics Grade 9 End of Course (EOC) Exam Performance Levels and Descriptors-Draft Set (from Meeting 1) Advanced Proficient Below Proficient Students at this level consistently Students at this level frequently, Students at this level inconsistently, and effectively demonstrate accurately, and satisfactorily inadequately, and partially
|
“Advanced.” there. Deviation 0 -1 0 Identified Issues N/A Though some of the errors in work shown are below the overall volume of errors is not significant enough to place this response in the “Below Proficient” category. N/A Calibration Training-Student Sample #2 Anchored Performance Level Advanced Panelist Panelist 1 Panelist 2 Panelist 3 Performance Level Assigned Advanced Advanced Advanced Performance Level Rationale The work shown is easy to follow and mostly correct. Very few errors in the SR and CR sections. The one incorrect CR answer was off by less than 1 degree, indicating a minor miscalculation. The student’s overall score and the quality of work shown is higher than what I have seen from most of the 9th graders I have worked with. Deviation 0 0 0 Identified Issues N/A N/A N/A Calibration Training-Student Sample #3 Anchored Performance Level Below Proficient Panelist Panelist 1 Panelist 2 Panelist 3 Performance Level Assigned Below Proficient Below Proficient Below Proficient Performance Level Rationale Even if the CR answers were correct, there is still little to no work shown to justify them. Student did not follow directions for many CR items. Most answers are labeled with incorrect units of measure. The student missed and/or skipped well over half of the items on the assessment. Deviation 0 0 0 Identified Issues N/A N/A N/A Build Phase-Meeting 2 Meeting Date: May 8, 2014 Panelists/Qualifications: P. Lopez (District Department Chair), D. Freeman (High School Instructional Coach), N. Bryant (High School Instructional Coach) Subject/Grade Level of Assessment/Task: Mathematics Grade 9 End of Course (EOC) Exam Performance Levels and Descriptors-Draft Set (from Meeting 1) Advanced Proficient Below Proficient Students at this level consistently and effectively demonstrate Students at this level frequently, accurately, and satisfactorily Students at this level inconsistently, inadequately, and partially
| 0.742346
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M4.4_Handouts.docx
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task-1432-383
| 18
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] |
Module 4, Handout 6: Teacher Rubric At-a-Glance The 4 Standards, 16 Indicators, and 33 elements in the Model Rubric for teachers: (The complete Massachusetts Model System for Educator Evaluation Part III: Guide to Rubrics and Model Rubrics for Superintendent, Administrator, and Teacher is available at .) Standard I: Standard III: Standard II: Standard IV: Curriculum, Planning, and Family and Community Teaching All Students Professional Culture Assessment Engagement A. Curriculum and Planning Indicator A. Instruction Indicator A. Engagement Indicator A. Reflection Indicator 1. Subject Matter Knowledge 1. Quality of Effort and Work 1. Parent/Family Engagement 1. Reflective Practice 2. Child and Adolescent Development 2. Student Engagement 2. Goal Setting 3. Rigorous Standards-Based Unit 3. Meeting Diverse Needs Design 4. Well-Structured Lessons B. Assessment Indicator B. Learning Environment Indicator B. Collaboration Indicator B. Professional Growth Indicator 1. Variety of Assessment Methods 1. Safe Learning Environment 1. Learning Expectations 1. Professional Learning and Growth 2. Adjustments to Practice 2. Collaborative Learning 2. Curriculum Support Environment 3. Student Motivation C. Analysis Indicator C. Cultural Proficiency Indicator C. Communication Indicator C. Collaboration Indicator 1. Analysis and Conclusions 1. Respects Differences 1. Two-Way Communication 1. Professional Collaboration 2. Sharing Conclusions With 2. Maintains Respectful 2. Culturally Proficient Colleagues Environment Communication 3. Sharing Conclusions With Students D. Expectations Indicator D. Decision-Making Indicator 1. Clear Expectations 1. Decision-making 2. High Expectations 3. Access to Knowledge E. Shared Responsibility Indicator 1. Shared Responsibility F. Professional Responsibilities Indicator 1. Judgment 2. Reliability and Responsibility
|
Module 4, Handout 6: Teacher Rubric At-a-Glance The 4 Standards, 16 Indicators, and 33 elements in the Model Rubric for teachers: (The complete Massachusetts Model System for Educator Evaluation Part III: Guide to Rubrics and Model Rubrics for Superintendent, Administrator, and Teacher is available at .) Standard I: Curriculum, Planning, and Assessment Standard II: Teaching All Students Standard III: Family and Community Engagement Standard IV: Professional Culture A. Curriculum and Planning Indicator 1. Subject Matter Knowledge 2. Child and Adolescent Development 3. Rigorous Standards-Based Unit Design 4. Well-Structured Lessons A. Instruction Indicator 1. Quality of Effort and Work 2. Student Engagement 3. Meeting Diverse Needs A. Engagement Indicator 1. Parent/Family Engagement A. Reflection Indicator 1. Reflective Practice 2. Goal Setting B. Assessment Indicator 1. Variety of Assessment Methods 2. Adjustments to Practice B. Learning Environment Indicator 1. Safe Learning Environment 2. Collaborative Learning Environment 3. Student Motivation B. Collaboration Indicator 1. Learning Expectations 2. Curriculum Support B. Professional Growth Indicator 1. Professional Learning and Growth C. Analysis Indicator 1. Analysis and Conclusions 2. Sharing Conclusions With Colleagues 3. Sharing Conclusions With Students C. Cultural Proficiency Indicator 1. Respects Differences 2. Maintains Respectful Environment C. Communication Indicator 1. Two-Way Communication 2. Culturally Proficient Communication C. Collaboration Indicator 1. Professional Collaboration D. Expectations Indicator 1. Clear Expectations 2. High Expectations 3. Access to Knowledge D. Decision-Making Indicator 1. Decision-making E. Shared Responsibility Indicator 1. Shared Responsibility F. Professional Responsibilities Indicator 1. Judgment 2. Reliability and Responsibility
| 0.660409
|
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M4Handouts.docx
|
task-1432-430
| 11
|
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852
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] |
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