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on Earth. == Cultures on simulated asteroid/meteorite materials == To quantify the potential amounts of life in biospheres, theoretical astroecology attempts to estimate the amount of biomass over the duration of a biosphere. The resources, and the potential time-integrated biomass were estimated for planetary systems, for habitable zones around stars, and for the galaxy and the universe. Such astroecology calculations suggest that the limiting elements nitrogen and phosphorus in the estimated 1022 kg carbonaceous asteroids could support 6·1020 kg biomass for the expected five billion future years of the Sun, yielding a future time-integrated BIOTA (BIOTA, Biomass Integrated Over Times Available, measured in kilogram-years) of 3·1030 kg-years in the Solar System, a hundred thousand times more than life on Earth to date. Considering biological requirements of 100 W kg−1 biomass, radiated energy about red giant stars and white and red dwarf stars could support a time-integrated BIOTA up to 1046 kg-years in the galaxy and 1057 kg-years in the universe. Such astroecology considerations quantify the immense potentials of future life in space, with commensurate biodiversity and possibly, intelligence. Chemical analysis of carbonaceous chondrite meteorites show that they contain extractable bioavailable water, organic carbon, and essential phosphate, nitrate and potassium nutrients. The results allow assessing the soil fertilities of the parent asteroids and planets, and the amounts of biomass that they can sustain. Laboratory experiments showed that material from the Murchison meteorite, when ground into a fine powder and combined with Earth's water and air, can provide the nutrients to support a variety of organisms including bacteria (Nocardia asteroides), algae, and plant cultures such as potato and asparagus. The microorganisms used organics in the carbonaceous meteorites as the carbon source. Algae and plant cultures grew well also on Mars meteorites because of their high bio-available phosphate contents. The Martian materials achieved
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soil fertility ratings comparable to productive agricultural soils. This offers some data relating to terraforming of Mars. Terrestrial analogues of planetary materials are also used in such experiments for comparison, and to test the effects of space conditions on microorganisms. The biomass that can be constructed from resources can be calculated by comparing the concentration of elements in the resource materials and in biomass (Equation 1). A given mass of resource materials (mresource) can support mbiomass, X of biomass containing element X (considering X as the limiting nutrient), where cresource, X is the concentration (mass per unit mass) of element X in the resource material and cbiomass, X is its concentration in the biomass. m b i o m a s s , X = m r e s o u r c e , X c r e s o u r c e , X c b i o m a s s , X {\displaystyle m_{biomass,\,X}=m_{resource,\,X}{\frac {c_{resource,\,X}}{c_{biomass,\,X}}}} (1) Assuming that 100,000 kg biomass supports one human, the asteroids may then sustain about 6e15 (six million billion) people, equal to a million Earths (a million times the present population). Similar materials in the comets could support biomass and populations about one hundred times larger. Solar energy can sustain these populations for the predicted further five billion years of the Sun. These considerations yield a maximum time-integrated BIOTA of 3e30 kg-years in the Solar System. After the Sun becomes a white dwarf star, and other white dwarf stars, can provide energy for life much longer, for trillions of eons. (Table 2) == Effects of wastage == Astroecology also concerns wastage, such as the leakage of biological matter into space. This will cause an exponential decay of space-based biomass as given by Equation (2), where M (biomass 0) is the
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mass of the original biomass, k is its rate of decay (the fraction lost in a unit time) and biomass t is the remaining biomass after time t. Equation 2: M b i o m a s s ( t ) = M b i o m a s s ( 0 ) e − k t {\displaystyle M_{biomass}(t)=M_{biomass}(0)e^{-kt}\,} Integration from time zero to infinity yields Equation (3) for the total time-integrated biomass (BIOTA) contributed by this biomass: Equation 3: B I O T A = M b i o m a s s ( 0 ) k {\displaystyle BIOTA={\frac {M_{biomass}(0)}{k}}} For example, if 0.01% of the biomass is lost per year, then the time-integrated BIOTA will be 10,000 M b i o m a s s ( 0 ) {\displaystyle M_{biomass}(0)} . For the 6·1020 kg biomass constructed from asteroid resources, this yields 6·1024 kg-years of BIOTA in the Solar System. Even with this small rate of loss, life in the Solar System would disappear in a few hundred thousand years, and the potential total time-integrated BIOTA of 3·1030 kg-years under the main-sequence Sun would decrease by a factor of 5·105, although a still substantial population of 1.2·1012 biomass-supported humans could exist through the habitable lifespan of the Sun. The integrated biomass can be maximized by minimizing its rate of dissipation. If this rate can be reduced sufficiently, all the constructed biomass can last for the duration of the habitat and it pays to construct the biomass as fast as possible. However, if the rate of dissipation is significant, the construction rate of the biomass and its steady-state amounts may be reduced allowing a steady-state biomass and population that lasts throughout the lifetime of the habitat. An issue that arises is whether we should build immense amounts of life
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that decays fast, or smaller, but still large, populations that last longer. Life-centered biotic ethics suggests that life should last as long as possible. == Galactic ecology == If life reaches galactic proportions, technology should be able to access all of the materials resources, and sustainable life will be defined by the available energy. The maximum amount of biomass about any star is then determined by the energy requirements of the biomass and by the luminosity of the star. For example, if 1 kg biomass needs 100 Watts, we can calculate the steady-state amounts of biomass that can be sustained by stars with various energy outputs. These amounts are multiplied by the life-time of the star to calculate the time-integrated BIOTA over the life-time of the star. Using similar projections, the potential amounts of future life can then be quantified. For the Solar System from its origins to the present, the current 1015 kg biomass over the past four billion years gives a time-integrated biomass (BIOTA) of 4·1024 kg-years. In comparison, carbon, nitrogen, phosphorus and water in the 1022 kg asteroids allows 6·1020 kg biomass that can be sustained with energy for the 5 billion future years of the Sun, giving a BIOTA of 3·1030 kg-years in the Solar System and 3·1039 kg-years about 1011 stars in the galaxy. Materials in comets could give biomass and time-integrated BIOTA a hundred times larger. The Sun will then become a white dwarf star, radiating 1015 Watts that sustains 1e13 kg biomass for an immense hundred million trillion (1020) years, contributing a time-integrated BIOTA of 1033 years. The 1012 white dwarfs that may exist in the galaxy during this time can then contribute a time-integrated BIOTA of 1045 kg-years. Red dwarf stars with luminosities of 1023 Watts and life-times of 1013 years can
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contribute 1034 kg-years each, and 1012 red dwarfs can contribute 1046 kg-years, while brown dwarfs can contribute 1039 kg-years of time-integrated biomass (BIOTA) in the galaxy. In total, the energy output of stars during 1020 years can sustain a time-integrated biomass of about 1045 kg-years in the galaxy. This is one billion trillion (1020) times more life than has existed on the Earth to date. In the universe, stars in 1011 galaxies could then sustain 1057 kg-years of life. == Directed panspermia == The astroecology results above suggest that humans can expand life in the galaxy through space travel or directed panspermia. The amounts of possible life that can be established in the galaxy, as projected by astroecology, are immense. These projections are based on information about 15 billion past years since the Big Bang, but the habitable future is much longer, spanning trillions of eons. Therefore, physics, astroeclogy resources, and some cosmological scenarios may allow organized life to last, albeit at an ever slowing rate, indefinitely. These prospects may be addressed by the long-term extension of astroecology as cosmoecology. == See also == Cosmology Meteorites == References == == External links == Astro-Ecology / Science of expanding life in space AstroEthics / Ethics of expanding life in space Panspermia-Society / Science and ethics of expanding life in space
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Dr A.R.D. Prasad is an Indian Library and Information Science Academic, Information professional and Information scientist. Dr Prasad teaches at Documentation Research and Training Centre (DRTC), Bangalore as Professor of Library and Information Science and he is retired Head of DRTC, which is India's only proper ischool with a very strong research program. His areas of specialisation include Artificial intelligence-Applications in LIS, Natural language processing, Digital Libraries, Hypertext and Multimedia applications, Institutional repository, Open-source software used in Libraries, Open Access to Information, Semantic Web Technology, Free and open source software etc. His other area of interests are Mythology, Buddhism, Philosophy and Indian History. He is pioneer in the promotion and development of Open-source software used in Libraries and Information Centres in India, Open access (publishing) and Open Access movement. He is visiting Faculty of University of Trento, Italy. == Early life == He has Master of Arts (M.A), M.Phil. in Philosophy, BLIS, ADIS (from DRTC, ISI) and obtained his doctorate (PhD) on "Application of Natural Language Processing Tools and Technique in Developing Subject Indexing Languages" from Karnatak University, Dharwad. == Career == Dr. Prasad is senior faculty at Documentation Research and Training Centre (DRTC). He joined DRTC as a lecturer on 4 June 1990. == Memberships and association == Member, Working Group, National Knowledge Commission, Government of India. Member, DSpace Governance Advisory Committee Member, Project Evaluation committee on E-Infrastructure, European Commission, Brussels. Member, UGC Curriculum Development Committee Member, UGC ETDs Guidelines (Electronic Theses and Dissertations) Member, Retro-conversion committee of National Library of India Consultant, United Nations-Food and Agriculture Organization (UN-FAO) A Fulbright Scholar(1999) Mentor, Google Summer of Code Invited Speaker, Indian National Science Congress, 2006, Hyderabad == Literary and scientific activities == Currently he is active in European Commissions FP7-FET project on Living Knowledge. He is Editor of the Proceedings
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of the International Conference on Semantic Web and Digital Libraries (ICSD-2007). He is also guest Editor of Online Information Review, V.32(4), 2008, special edition on Semantic Web and Web Design. == Published work == === Peer reviewed articles in international Periodicals === Devika P. Madalli and A.R.D. Prasad: Vyasa: a knowledge representation system for automatic management of analytico-synthetic system. accepted for the 7th International Conference of Society for Knowledge Organisation. 10–13 July Granada, Spain. Pijush Kanti Panigrahi and A.R.D. Prasad : An Inference Engine for Time Isolates of Colon Classification Schedule. 6th International Study Conference on Classification Research organised by FID/CR and University College London. June 16?18, 1997, London. Devika V. Aptagiri, Gopinath M.A. and A. R. D. Prasad. A knowledge Representation Paradigm for Automating POPSI. Knowledge Organization. V 22(3/4). 1995. pp. 162–167. A.R.D. Prasad. Prometheus: An Automatic Indexing System. 4th International Conference of the ISKO. 15–18 July 1996. Washington, DC. M.A.Gopinath and A.R.D.Prasad. A Knowledge Representation Model for Analytico-Synthetic Classification. In: Knowledge Organization and Quality Management, Edited by Hanne Albrechtsen and Susanne Oernager. Indeks Verlag, Frankfurt. 1994. Durga Shankar Rath and ARD Prasad. Heuristics for identification of Bibliographic Elements from Title Pages. Approved for publication in International Cataloguing and Bibliographic Control. To be published in December 2003. ARD Prasad and Durga Shankar Rath. Heuristics for identification of Bibliographic Elements from Verso of Title Pages. Sent for publication in Library Hitech. Ralf Depping: vascoda.de and the system of the German virtual subject libraries. In: Prasad, A.R.D. & Madalli, D.P. (eds.): International Conference on Semantic Web and Digital Libraries (ICSD 2007), 21–23 February 2007: 304–314. (pdf file, 730 KB Archived 18 July 2011 at the Wayback Machine) === Papers in Indian Periodicals === A.R.D. Prasad. Z39.50 for Retrieving MARC21 Records in Batch mode. In Workshop on Information Resource Management, Documentation Research
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& Training Centre, ISI, Bangalore, 13–15 March 2002. A. R. D. Prasad. Metadata:Cataloguing of Web Resources Using Dublin Core and Marc21. In Digital Libraries:Content Creation, Access and Management. USEFI-IIT Delhi workshop. IIT, New Delhi. 18–22 December 2001. A. R. D. Prasad. A Brief Introduction of Z39.50. NACLIN- 2001. University of Hyderabad. 6–9 November 2001. A R D Prasad. Creation of Digital Libraries in Indian Languages Using Unicode. Paper presented at the Joint Workshop on Digital Libraries. USEFI, DRTC, and University of Mysore. Mysore. 12–16 March 2001. A.R.D. Prasad. Working with Digital Information using WWWISIS on Linux. In. CALIBER 2001. INFLIBNET and University of Pune, Pune, 15–16 March 2001. pp. 32–45. A R D Prasad. Using Multimedia Database with WWWISIS on Win9x/NT. Paper presented at the Workshop on Multimedia and Internet Technologies. Documentation Research Training Centre. Bangalore. 26–28 February 2001. A R D Prasad : Chaos! Thy Name is Internet. In: DRTC Workshop on Information Management including ISO 9000 QMS. DRTC, ISI Bangalore. 6–8 January 1999. A R D Prasad and Smitha Srishaila. File formats for Multimedia. Article published in the book Multimedia: its applications in Library and Information Science. T.R. Publications, Madras. 1998. Unit writer for the Postgraduate Diploma in Library Automation and Networking, Distance Education Programme. Editor. University of Hyderabad, Hyderabad. 1998. A R D Prasad and A Aruna. A Prototype LAN Model for Indian Universities. Paper presented at the National Seminar on Information Technology Applications in Libraries and Information Centers. Vidyasagar University. Medinipur. 24–25 March 1998 A R D Prasad and A Aruna. Penelope's Web: New challenges to Librarians. 17th Annual Convention and Conference of the SIS on Virtual Libraries: Internet based Library and Information Services. University of Hyderabad, Hyderabad. 12–14 March 1998. A R D Prasad: Internet: Connectivity Issues. Paper presented in the DRTC Workshop on Practical
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Orientation to Internet. DRTC, Bangalore. 28–30 Jan 1998. A.R.D Prasad: Browsers: Interface to the Web. Paper presented in the DRTC Workshop on Practical Orientation to Internet.DRTC, Bangalore 28–30 Jan 1998. A.R.D Prasad: Customising Web browsing through Internet Channels. Paper presented in the DRTC Workshop on Practical Orientation to Internet. DRTC, Bangalore 28–30 Jan 1998. A.R.D Prasad: Interfacing the Web: An overview of alternatives. Paper presented in the DRTC Workshop on Practical Orientation to Internet. DRTC, Bangalore. 28–30 Jan 1998. Madhuchanda Bhattacharyya and A R D Prasad. Internet Webcasters. Paper presented in the 21st All India IASLIC Conference on Information Superhighway: its impact on Library and Information Services in India, Tamil Nadu Agricultural University, Coimbatore, 26–29 December 1997. A.R.D Prasad, Smitha Srishaila and P.H.Akkamahadevi: Natural Language Interface to Databases. Paper Accepted for the 16th Annual Convention and Conference of the Society for Information Science. 29–31 January 1997. Bhuvaneshwar. A.R.D Prasad and Prasenjit Kar: Self Sufficiency versus Resource Sharing: Implications in Library networks. Paper presented in the XVII National Seminar of IASLIC. 10–13 Dec 1996. Calcutta. A.R.D Prasad: Some reflections on the Impact of Information Technology on Library Science Profession. Paper presented in the DRTC workshop on Advances in Information Technology. Oct 28–30,1996. Bangalore. A.R.D Prasad and Devika V Aptagiri: Multimedia Technology: An overview of hardware aspects. Paper presented in the DRTC workshop on Advances in Information Technology. Oct 28–30,1996. Bangalore. Smitha Srishaila and A.R.D Prasad: An Overview of Multimedia File Formats. Paper presented in the DRTC workshop on Advances in Information Technology. Oct 28–30,1996. Bangalore. A.R.D. Prasad. IDA: A retrospective conversion software for OCLC, LC, BNB and Bookfind CD-ROM Databases. DESIDOC Bulletin of Information Technology, V 15(3). May 1995. pp 13–17. Devika V. Aptagiri, Sudhanshu Bala Satapathy and A.R.D. Prasad. Optical Character Recognition ib building bibliographic databases. In. XX IASLIC Conference.
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26–29 Dec 1995. Lucknow. Devika V.Aptagiri and A.R.D.Prasad. Computer Assisted Thesaurus Construction. In: Research and Teaching in Classification and Indexing, Edited by M.A.Gopinath. DRTC Annual Seminar, 9–11 August, 94. Bangalore. A.R.D. Prasad and Ranjan Sinha Takur. Automatic Identification of Key Terms from Book Titles In: Research and Teaching in Classification. A.R.D. Prasad and C.R. Karisiddappa. Declarative programming and Thesaurus Construction. In: Database Production and Distribution, Edited by N.Seshagiri, I.K. Ravichandra Rao and N.V.Satyanarayana. Tata Mc Graw-Hill, New Delhi. 1993. A.R.D. Prasad and Bidyut Baran Kar. Parsing Boolean Expressions Using Definite Clause Grammar. Library Science with a slant to Documentation, v.31 (1), March, 94. pp. 24–26. A.R.D. Prasad. Introduction to Artificial Intelligence In: Artificial Intelligence and its Applications to Library and Information Work, Edited by A.R.D. Prasad. DRTC Refresher Seminar, 26–28 May, 93. Bangalore. A.R.D. Prasad. Logic and Logic Programming In: Artificial Intelligence and its Applications to Library and Information Work, Edited by A.R.D. Prasad, Drtc Refresher Seminar, 26–28 May 93, Bangalore. A.R.D. Prasad. Natural Language Processing In: Artificial Intelligence and its Applications to Library and Information Work, Edited by A.R.D. Prasad, DRTC Refresher Seminar, 26–28 May 93, Bangalore. A.R.D. Prasad. Towards a Common command Language. In: Energizing Library and Information Services, Edited by M.A. Gopinath. DRTC Refresher Seminar, 20—22 May 1992. Bangalore. A.R.D. Prasad. Natural Language Processing Techniques in Information retrieval: An Overview. In: Information Retrieval, Edited by I.K.Ravichandra Rao. Annual Seminar, 5–7 Feb, '92. Bangalore. A.R.D. Prasad. Guidelines for design and development for Computer Assisted Instruction (CAI) Software. In: 9th IATLIS Conference held at Visakhapatnam, 9–11 August 1992. A.R.D. Prasad. Optical Character Recognition. In: Current Research in Library and Information Science, Edited by S. Seetharama and C.R. Karisiddappa. RBSA Publishers, Jaipur, 1993. A.R.D. Prasad. Database Management Systems. In: Dimensions of Library and Information Science. Edited by V. Venkatappaiah, Concept
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Publishers, 1990. A.R.D. Prasad. A Case for Artificial Intelligence in Library and Information Science Curriculum. In: Specialization in Library and Information Science Education. 8th IATLIS Conference held at Bangalore, 17—19 January 1990. == References == == External links == http://www.grl2020.net/uploads/position_papers/A.R.D.%20Prasad.pdf Archived 23 July 2011 at the Wayback Machine A.R.D. Prasad | Documentation Research and Training Centre http://www.ignca.nic.in/PDF_data/kn_digital001_pdf_data/T2a_Development_Digital_Repository.pdf https://web.archive.org/web/20110721150059/http://ir.inflibnet.ac.in/dxml/bitstream/handle/1944/551/323-329(cal%2007).pdf?sequence=1 http://crl.du.ac.in/ical09/invitations.pdf Wayback Machine
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A behavioral cusp is any behavior change that brings an organism's behavior into contact with new contingencies that have far-reaching consequences. A behavioral cusp is a special type of behavior change because it provides the learner with opportunities to access new reinforcers, new contingencies, new environments, new related behaviors (generativeness) and competition with archaic or problem behaviors. It affects the people around the learner, and these people agree to the behavior change and support its development after the intervention is removed. The concept has far reaching implications for every individual, and for the field of developmental psychology, because it provides a behavioral alternative to the concept of maturation and change due to the simple passage of time, such as developmental milestones. The cusp is a behavior change that presents special features when compared to other behavior changes. == History == The concept was first proposed by Sidney W. Bijou, an American developmental psychologist. The idea of the cusp was to link behavioral principles to rapid spurts in development (see Behavior analysis of child development). A behavioral cusp as conceptualized by Jesus Rosales-Ruiz and Donald Baer in 1997 is an important behavior change that affects future behavior changes. The behavioral cusp, like the reinforcer, is apprehended by its effects. Whereas a reinforcer acts on a single response or a group of related responses, the effects of a behavioral cusp regulate a large number of responses in a more distant future. The concept has been compared to a developmental milestone; however, not all cusps are milestones. For example, learning to play soccer is not a milestone, but it was life-changing for Pelé. As a result of learning to kick grapefruits (the initial important change or cusp), Pelé accessed (1) new environments, (2) new reinforcers, (3) new soccer moves, (4) dropped competing behaviors
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(smoking), and (5) gained international acclaims for his skill. Soccer is not a developmental milestone because it is not a necessary skill in most environments. == Properties == The following properties are special features of a behavioral change that lead to more change, and an increased likelihood of social adaptation, independence, and cultural fitness. === New reinforcers === New reinforcers are accessible and enrich the perspective of the learner. Additionally these reinforcers may lead to an increase in the variety of behaviors. If the reinforcers are promoting health and social behaviors, they will lead to an improved quality of life. ==== Case example ==== A child who learns to open a door may access the swing for the first time and learns to use the swing. Here, the new skill (swinging motion is the reinforcer) may lead to more complex and social activities such as (1) turn taking, (2) asking someone to share the swing, (3) taking turns pushing someone, which in turn (4) may provide more social opportunities to speak and (5) interact with the play partners, etc. ==== Case non-example ==== A child learns to open a door and walks outside. He finds some ants behind a shrubbery and watches the ants. His parents are looking for him, they get worried and are calling him. The child is unusually mesmerized by columns of ants on the ground and does not hear the calls. His parents find him shortly after, but they are frantic from their 5-minute search and accidentally scare him from going outside. In this non-example, learning to open doors that lead outside resulted in consequences that did not directly benefit the child and maybe decrease important skills related to exploration and search. In this case, no new reinforcers were contacted and learning to open the backyard
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door (that has a special latch) was effectively a waste of time because the child's parents don't usually approve being alone in the backyard. === New contingencies of reinforcement === New contingencies are responsible for the selection of novel and more adaptive behaviors while decreasing problematic or archaic behaviors. Contingencies of reinforcement (before > R > Reinforcer) produce and maintain each and every learned behavior. New contingencies establish the control of new stimuli over our behaviors, and therefore make us more sensitive and aware of our surrounding. === New environments === New environments are geographical and/or virtual areas of potential change (receiving environments). New environments regulate, maintain, and set the micro-cultural boundaries for reinforcers (and punishers), and their antecedents. They include tools and stakeholders controlling the pace and content of instruction and, as a result, they regulate boundary of what the learner learns (e.g., school curriculum). Practitioners are confident that their cusp will lead to the desired behavior and open the door to new environments. New environments must contain some of the stakeholders' preferences and reinforcers to create lasting positive reinforcement practices for the learner. An important consideration is the time (and timing) of cusp events and ensuing behaviors in the context of historical events--history. === Generativeness === Generativeness describes the ability of the receiving environment to regulate novel responses, functions, values or response products derived from the original cusp response. Some proposals have been put forward to explain how conscious organisms achieve passing into new frames of reference. Semiotic Matrix Theory (SMT), its pansemiosis, describes falsifiable existential and cognitive heuristics of recognizing Energy requirements, Safety concerns and Possibility or Opportunity as “passing” functions. For a behavior, it is the ability to recombine or merge into more complex units, or the ability to contact environments. === Competition with archaic behaviors
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=== Behavior competition is the ability of cusp behaviors to displace previously established behaviors on a continuum of intensity and rate, across repertoires, and environments. Competing archaic behaviors occur on a corresponding continuum of severity. An example of this was illustrated in Frank Sulloway's research dealing, among other findings, with the likelihood that first versus second borns would embrace new ideas and act upon them. In the context of behavioral cusps, second-borns possessed and understood information that could allow them to 'see beyond' the limitations of archaic notions of "evolution" or the ideological reprimands of organized and conservative religion. From a semiotic perspective, everyday communication provide us with important cues about how interlocutors reveal the degree to which behavioral cusps are or have taken place. === Effect on stakeholders === Effect on others comes from the learner's behavior affecting the stakeholders who control reinforcers and punishers in a specific environment. It is important to identify these stakeholders' motivations and reinforcers in selecting potential cusps. Effect refers to the changes in values and behaviors of the stakeholder resulting from a cusp in the learner. The initial and gradually more complex behaviors that constituted the entry point for an important behavior change that, once initiated, so profoundly alters, displaces, or transforms one's behavioral repertoire that it renders preexisting behavioral repertoires obsolete. A behavioral cusp is an important behavior change that alters the probability of the learner's future repertoires and interactions with stakeholders' repertoires. === Social validity === Social validity is an indicator of social acceptability of a behavior and its consequences for the stakeholders representing the communities which the learner is accessing or will access. Some seemingly insignificant changes in a stakeholder may dramatically affect the learner. All stakeholders (e.g., government officials, teachers, parents, and other interventionists) should agree to the goals,
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methods, and tools for the intervention and the norms from the local community suggest the boundaries of what should be learned. == Applications == === Life span/development guidelines === The behavioral cusp has implications for the selection and sequencing of skills during the life span. While milestones are mainly concerned with the chronology of behaviors, the concept of behavioral cusp is concerned with the fitness of the behavior within a context or a receiving environment. As Rosales-Ruiz and Baer (1997) stated, "One child's cusp may be another child's waste of time." Thus, there is a great need for empirically-based guidelines in making decisions related to the initial selection of skills. === Prediction and control of development over longer periods of time === The applications of the concepts are related to the prediction, selection, and retention of successful and adaptive behaviors to the treatment of childhood autism, Down syndrome, and other developmental disabilities – they are also humane and based on evidence from the field of behavior analysis. The first applications of the concept derive from a set of guidelines proposed by Bosch and Fuqua in The Journal of Applied Behavior Analysis. === Development of new technology === A new technology and methodology, necessary to measure the effects of a small change over time, will reveal a strong dependence on the initial conditions selected by a cusp specialist (butterfly effect). == Future research == Future research will elucidate the nature and parameters of the criteria and the tools used in the selection and sequencing of skills. As importantly, the existing parameters (proposed by Rosales-Ruiz, Baer, Bosch, & Fuqua) provides justifications for behavioral interventions. == See also == Applied behavior analysis Attachment in children Behavior analysis of child development Behaviorism Child development Child development stages Child psychology Critical period Early childhood education
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Face validity Feral child Functional analysis (psychology) Pedagogy Play (activity) Professional practice of behavior analysis Psychological behaviorism == References ==
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The molecular formula C30H44O9 (molar mass: 548.66 g/mol, exact mass: 548.2985 u) may refer to: Cymarin Peruvoside, or cannogenin thevetoside
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A script lichen, or graphid lichen, is a member of a group of lichens which have spore producing structures that look like writing on the lichen body. The structures are elongated and narrow apothecia called lirellae, which look like short scribbles on the thallus. "Graphid" is derived from Greek for "writing". An example is Graphis mucronata. == References ==
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Louis Heyns Du Preez (born 9 July 1962) is a South African professor of zoology who specialises in parasitology and herpetology at the North-West University. Du Preez is best known for his research on South African frog species, writing a widely used wildlife guide for the frogs of Southern Africa, and contributions to global parasitology with special focus studies on polystome worms. His contributions to polystome research have led to a recently discovered Malagasy frog species, Blommersia dupreezi, being named in his honour. == Biography == Du Preez grew up in Ficksburg and started his tertiary education at the University of the Free State located in the same province he was brought up. In 1986 he obtained his Master of Science degree with the thesis titled 'Polystoma australis (Monogenea): aspekte van ontwikkeling en gedrag wat betrekking het op rekrutering en vestiging'. From 1989 to 1990 he was a school teacher in Bloemfontein. From 1991 to 1993 he was Head of the Department of Herpetology at the National Museum in Bloemfontein. In 1994 he obtained a PhD degree from the University of the Free State with a thesis titled 'Study of factors influencing the nature and extent of host-specificity among polystomatids (Polystomatidae: Monogenoidea) parasitic in Anura of southern Africa' under the mentorship of Dawid Kok. Du Preez then progressed to being the Senior Lecturer of Zoology at the University of the Free State from 1996 to 2000. From 2001 to 2004 he was appointed associate professor. In 2002 he established the African Amphibian Research Conservation Group and was later promoted Full Professor of Zoology in 2005 at North-West University. In 2011 du Preez was elected Chair of the Zoology Department at North-West University. Throughout his career he has conducted research in several countries across the world including France, United States, Nigeria,
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Brazil and China. He is a member of the Zoological Society of Southern Africa, the Herpetological Association of Africa, the Suid-Afrikaanse Akademie vir Wetenskap en Kuns, the Parasitological Association of Southern Africa and the Microscopy Association of Africa. == Publications == Louis du Preez published several books and over 100 scientific articles. In addition to several parasite, frog, and reptile species that are new to science, du Preez's species descriptions include the frog species Breviceps carruthersi and Breviceps passmorei from the Rain Frog family (Brevicipitidae), and Hyperolius howelli from the Reed Frog family (Hyperoliidae). === Books === Field guide and key to the frogs & toads of the Free State, 1996 (ISBN 978-0-86886-549-2) Field Guide to the frogs and toads of the Vredefort Dome World Heritage Site, 2006 (ISBN 978-1-86822-517-0) Bios: an integrated approach to life sciences teaching and learning, 2007 (ISBN 978-1-920140-06-9) A complete guide to the frogs of Southern Africa, 2009; 2015 (ISBN 978-1-77007-446-0) Frogs and frogging in South Africa, 2011 (ISBN 978-1-77007-914-4) Turtle Polystomes of the world: Neopolystoma, Polystomoidella & Polystomoides, 2011 (ISBN 978-3-639-36517-7) A Bilingual Field Guide to the Frogs of Zululand (or Isiqondiso Sasefilidini Esindimimbili Ngamaxoxo AkwelaKwaZulu in IsiZulu), 2017 (ISBN 978-1-928224-19-8) Frogs of Southern Africa: a complete guide, 2017 (ISBN 978-1-77584-636-9) === Scientific publications === Source: Du Preez, Louis H., and Dawid J. Kok. "Syntopic occurrence of new species of Polystoma and Metapolystoma (Monogenea: Polystomatidae) in Ptychadena porosissima in South Africa." Systematic Parasitology 22.2 (1992): 141–150. doi:10.1007/BF00009606 Du Preez, Louis H., and Dawid J. Kok. "Supporting experimental evidence of host specificity among southern African polystomes (Polystomatidae: Monogenea)." Parasitology Research 83 (1997): 558–562. doi:10.1007/s004360050297 Du Preez, LH, and L. H. S. Lim. "Neopolystoma liewi sp. n.(Monogenea: Polystomatidae) from the eye of the Malayan box turtle (Cuora amboinensis)." Folia Parasitologica 47.1 (2000): 11–15. https://folia.paru.cas.cz/pdfs/fol/2000/01/03.pdf Verneau, Olivier,
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et al. "A view of early vertebrate evolution inferred from the phylogeny of polystome parasites (Monogenea: Polystomatidae)." Proceedings of the Royal Society of London. Series B: Biological Sciences 269.1490 (2002): 535–543. doi:10.1098/rspb.2001.1899 Weldon, Ché, et al. "Origin of the amphibian chytrid fungus." Emerging Infectious Diseases 10.12 (2004): 2100. doi:10.3201/eid1012.030804 Du Preez, Louis H., and Milton F. Maritz. "Demonstrating morphometric protocols using polystome marginal hooklet measurements." Systematic Parasitology 63.1 (2006): 1–15. doi:10.1007/s11230-005-5496-5 Mendelson III, Joseph R., et al. "Confronting amphibian declines and extinctions." Science 313.5783 (2006): 48–48. doi:10.1126/science.1128396 Andreone, Franco, et al. "The challenge of conserving amphibian megadiversity in Madagascar." PLOS Biology 6.5 (2008): e118. doi:10.1371/journal.pbio.0060118 Du Preez, Louis H., et al. "Reproduction, larval growth, and reproductive development in African clawed frogs (Xenopus laevis) exposed to atrazine." Chemosphere 71.3 (2008): 546–552. doi:10.1016/j.chemosphere.2007.09.051 Petzold, Alice, et al. "A revision of African helmeted terrapins (Testudines: Pelomedusidae: Pelomedusa), with descriptions of six new species." Zootaxa 3795.5 (2014): 523–548. doi:10.11646/zootaxa.3795.5.2 Meyer, Leon, et al. "Parasite host-switching from the invasive American red-eared slider, Trachemys scripta elegans, to the native Mediterranean pond turtle, Mauremys leprosa, in natural environments." Aquatic Invasions 10.1 (2015): 79–91. doi:10.3391/ai.2015.10.1.08 Du Preez, Louis H., and Michelle Van Rooyen. "A new polystomatid (Monogenea, Polystomatidae) from the mouth of the North American freshwater turtle Pseudemys nelsoni." ZooKeys 539 (2015): 1. doi:10.3897/zookeys.539.6108 Du Preez, Louis H., and Olivier Verneau. "Eye to eye: classification of conjunctival sac polystomes (Monogenea: Polystomatidae) revisited with the description of three new genera Apaloneotrema ng, Aussietrema ng and Fornixtrema ng." Parasitology Research 119.12 (2020): 4017–4031. doi:10.1007/s00436-020-06888-w Du Preez, Louis Heyns, Marcus Vinícius Domingues, and Olivier Verneau. "Classification of pleurodire polystomes (Platyhelminthes, Monogenea, Polystomatidae) revisited with the description of two new genera from the Australian and Neotropical Realms." International Journal for Parasitology: Parasites and Wildlife 19 (2022): 180–186. doi:10.1016/j.ijppaw.2022.09.004 Landman, Willem, et al.
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"Metapolystoma ohlerianum n. sp.(Monogenea: Polystomatidae) from Aglyptodactylus madagascariensis (Anura: Mantellidae)." Acta Parasitologica (2023): 1–15. doi:10.1007/s11686-023-00668-z Verneau, Olivier, Gerald R. Johnston, and Louis Du Preez. "A quantum leap in the evolution of platyhelminths: Host-switching from turtles to hippopotamuses illustrated from a phylogenetic meta-analysis of polystomes (Monogenea, Polystomatidae)." International Journal for Parasitology 53.5–6 (2023): 317–325. doi:10.1016/j.ijpara.2023.03.001 === Species descriptions === Du Preez has described or contributed to the description of at least 24 polystome species, 8 polystome genera, and more than 10 other non-polystome parasite species that have frogs or reptiles as their hosts. In herpetology, du Preez has described or contributed to the description of at least 20 new frog species and 6 reptile species. ==== Amphibians ==== Breviceps carruthersi Du Preez, Netherlands, and Minter, 2017 Breviceps passmorei Minter, Netherlands, and Du Preez, 2017 Gephyromantis cornucopia Miralles, Köhler, Glaw, Wollenberg Valero, Crottini, Rosa, Du Preez, Gehring, Vieites, Ratsoavina, and Vences, 2023 Gephyromantis feomborona Miralles, Köhler, Glaw, Wollenberg Valero, Crottini, Rosa, Du Preez, Gehring, Vieites, Ratsoavina, and Vences, 2023 Gephyromantis fiharimpe Vences, Köhler, Crottini, Hofreiter, Hutter, Du Preez, Preick, Rakotoarison, Rancilhac, Raselimanana, Rosa, Scherz, and Glaw, 2022 Gephyromantis kremenae Miralles, Köhler, Glaw, Wollenberg Valero, Crottini, Rosa, Du Preez, Gehring, Vieites, Ratsoavina, and Vences, 2023 Gephyromantis mafifeo Miralles, Köhler, Glaw, Wollenberg Valero, Crottini, Rosa, Du Preez, Gehring, Vieites, Ratsoavina, and Vences, 2023 Gephyromantis matsilo Vences, Köhler, Crottini, Hofreiter, Hutter, Du Preez, Preick, Rakotoarison, Rancilhac, Raselimanana, Rosa, Scherz, and Glaw, 2022 Gephyromantis mitsinjo Miralles, Köhler, Glaw, Wollenberg Valero, Crottini, Rosa, Du Preez, Gehring, Vieites, Ratsoavina, and Vences, 2023 Gephyromantis oelkrugi Vences, Köhler, Crottini, Hofreiter, Hutter, Du Preez, Preick, Rakotoarison, Rancilhac, Raselimanana, Rosa, Scherz, and Glaw, 2022 Gephyromantis portonae Vences, Köhler, Crottini, Hofreiter, Hutter, Du Preez, Preick, Rakotoarison, Rancilhac, Raselimanana, Rosa, Scherz, and Glaw, 2022 Gephyromantis pedronoi Vences, Köhler, Andreone, Craul, Crottini, Du Preez, Preick,
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Rancilhac, Rödel, Scherz, Streicher, Hofreiter, and Glaw, 2021 Gephyromantis sergei Miralles, Köhler, Glaw, Wollenberg Valero, Crottini, Rosa, Du Preez, Gehring, Vieites, Ratsoavina, and Vences, 2023 Hyperolius friedemanni Mercurio and Rödel in Channing, Hillers, Lötters, Rödel, Schick, Conradie, Rödder, Mercurio, Wagner, Dehling, Du Preez, Kielgast, and Burger, 2013 Hyperolius howelli Du Preez and Channing, 2013 Hyperolius inyangae Channing in Channing, Hillers, Lötters, Rödel, Schick, Conradie, Rödder, Mercurio, Wagner, Dehling, Du Preez, Kielgast, and Burger, 2013 Hyperolius jacobseni Channing in Channing, Hillers, Lötters, Rödel, Schick, Conradie, Rödder, Mercurio, Wagner, Dehling, Du Preez, Kielgast, and Burger, 2013 Hyperolius lupiroensis Channing in Channing, Hillers, Lötters, Rödel, Schick, Conradie, Rödder, Mercurio, Wagner, Dehling, Du Preez, Kielgast, and Burger, 2013 Hyperolius rwandae Dehling, Sinsch, Rodel, and Channing in Channing, Hillers, Lötters, Rödel, Schick, Conradie, Rödder, Mercurio, Wagner, Dehling, Du Preez, Kielgast, and Burger, 2013 Tomopterna adiastola Channing and Du Preez, 2020 ==== Reptiles ==== Pelomedusa barbata Petzold, Vargas-Ramírez, Kehlmaier, Vamberger, Branch, Du Preez, Hofmeyr, Meyer, Schleicher, Široký, and Fritz, 2014 Pelomedusa kobe Petzold, Vargas-Ramírez, Kehlmaier, Vamberger, Branch, Du Preez, Hofmeyr, Meyer, Schleicher, Široký & Fritz, 2014 Pelomedusa neumanni Petzold, Vargas-Ramírez, Kehlmaier, Vamberger, Branch, Du Preez, Hofmeyr, Meyer, Schleicher, Široký & Fritz, 2014 Pelomedusa schweinfurthi Petzold, Vargas-Ramírez, Kehlmaier, Vamberger, Branch, Du Preez, Hofmeyr, Meyer, Schleicher, Široký & Fritz, 2014 Pelomedusa somalica Petzold, Vargas-Ramírez, Kehlmaier, Vamberger, Branch, Du Preez, Hofmeyr, Meyer, Schleicher, Široký & Fritz, 2014 Pelomedusa variabilis Petzold, Vargas-Ramírez, Kehlmaier, Vamberger, Branch, Du Preez, Hofmeyr, Meyer, Schleicher, Široký & Fritz, 2014 ==== Nematodes ==== Amphibiophilus bialatus Svitin, Kuzmin, Harnoster & Du Preez, 2020 Amphibiophilus mooiensis Svitin & Du Preez, 2018 Camallanus sodwanaensis Svitin, Truter, Kudlai, Smit & Du Preez, 2019 Cosmocerca daly Harnoster, du Preez & Svitin, 2022 Cosmocerca monicae Harnoster, du Preez & Svitin, 2022 Cosmocerca makhadoensis Harnoster, du Preez & Svitin, 2022 Pseudocapillaria (Ichthyocapillaria) bumpi
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Svitin, Bullard, Dutton, Netherlands, Syrota, Verneau & du Preez, 2021 Serpinema cayennense Harmoster, Svitin & Du Preez, 2019 Rhabdias delangei Kuzmin, Svitin, Harnoster & du Preez, 2020 Rhabdias blommersiae Kuzmin, Junker, du Preez & Bain, 2013 ==== Flatworms ==== Aussietrema queenslandense (Pichelin, 1995) Du Preez & Verneau, 2020 Aussietrema spratti (Pichelin, 1995) Du Preez & Verneau, 2020 Emoleptalea mozambiquensis Curran, Dutton, Warren, du Preez & Bullard, 2021 Eupolystoma namibiense Du Preez, 2015 Fornixtrema elizabethae (Platt, 2000) Du Preez & Verneau, 2020 Fornixtrema guianense (Du Preez, Badets, Héritier & Verneau, 2017) Du Preez & Verneau, 1920 Fornixtrema liewi (Du Preez & Lim, 2000) Du Preez & Verneau, 2020 Fornixtrema grossi (Du Preez & Morrison, 2012) Du Preez & Verneau, 2020 Fornixtrema palpebrae (Strelkov, 1950) Du Preez & Verneau, 2020 Fornixtrema scorpioides (Du Preez, Badets, Héritier & Verneau, 2017) Du Preez & Verneau, 2020 Indopolystoma hakgalense (Crusz & Ching, 1975) Chaabane, Verneau & Du Preez, 2019 Indopolystoma indicum (Diengdoh & Tandon, 1991) Chaabane, Verneau & Du Preez, 2019 Indopolystoma parvum Chaabane, Verneau & Du Preez, 2019 Indopolystoma viridi Chaabane, Verneau & Du Preez, 2019 Indopolystoma zuoi (Shen, Wang & Fan, 2013) Chaabane, Verneau & Du Preez, 2019 Manotrema brasiliensis (Viera, Novelli, Sousa & SouzaLima, 2008) du Preez, Domingues & Verneau, 2022 Manotrema fuquesi (Mañe-Garzón & Gil, 1962) du Preez, Domingues & Verneau, 2022 Manotrema uruguayensis (Mañe-Garzón & Gil, 1961) du Preez, Domingues & Verneau, 2022 Metapolystoma ansuanum Landman, Verneau, Raharivololoniaina & Du Preez, 2021 Metapolystoma falcatum Landman, Verneau, Raharivololoniaina & Du Preez, 2021 Metapolystoma multiova Landman, Verneau, Raharivololoniaina & Du Preez, 2021 Metapolystoma ohlerianum Landman, Verneau, Vences & Du Preez, 2023 Metapolystoma porosissimae Du Preez & Kok, 1992 Metapolystoma theroni Landman, Verneau, Raharivololoniaina & Du Preez, 2021 Metapolystoma vencesi Landman, Verneau, Raharivololoniaina & Du Preez, 2021 Nanopolystoma brayi Du Preez, Wilkinson &
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Huyse, 2008 Nanopolystoma lynchi Du Preez, Wilkinson & Huyse, 2008 Nanopolystoma tinsleyi Du Preez, Badets & Verneau, 2014 Pleurodirotrema chelodinae (MacCallum, 1918) du Preez, Domingues & Verneau, 2022 Pleurodirotrema macleayi (Rohde, 1984) du Preez, Domingues & Verneau, 2022 Pleurodirotrema novaeguineae (Fairfax, 1990) du Preez, Domingues & Verneau, 2022 Polystoma goeldii Sales, Du Preez, Verneau & Domingues, 2022 Polystoma knoffi Du Preez & Domingues, 2019 Polystoma okomuensis Aisien, Du Preez & Imasuen, 2010 Polystoma testimagnum Du Preez & Kok, 1993 Polystoma travassosi Du Preez & Domingues, 2019 Polystomoides cayensis (Du Preez, Badets, Héritier & Verneau, 2017) Chaabane, Du Preez, Johnston & Verneau, 2022 Polystomoides aspidonectis (MacCallum, 1919) Chaabane, Du Preez, Johnston & Verneau, 2022 Polystomoides cayensis (Du Preez, Badets, Héritier & Verneau, 2017) Chaabane, Du Preez, Johnston & Verneau, 2022 Polystomoides cyclovitellum (Caballero, Zerecero & Grocott, 1957) Chaabane, Du Preez, Johnston & Verneau, 2022 Polystomoides domitilae (Caballero, 1938) Chaabane, Du Preez, Johnston & Verneau, 2022 Polystomoides euzeti (Combes & Ktari, 1976) Chaabane, Du Preez, Johnston & Verneau, 2022 Polystomoides orbiculare (Stunkard, 1916) Chaabane, Du Preez, Johnston & Verneau, 2022 Polystomoides rugosa (MacCallum, 1918) Chaabane, Du Preez, Johnston & Verneau, 2022 Polystomoides scriptanus Héritier, Verneau, Smith, Coetzer & Du Preez, 2017 Polystomoides soredensis Héritier, Verneau, Smith, Coetzer & Du Preez, 2017 Polystomoides terrapenis (Harwood, 1932) Chaabane, Du Preez, Johnston & Verneau, 2022 Uteropolystomoides multifalx (Stunkard, 1924) Chaabane, Du Preez, Johnston & Verneau, 2022 == Awards == 1994: W.O. Neitz medal for the best dissertation in parasitology by the Parasitological Association of Southern Africa. == References == == External links == Profile on Namibiana Publications by Louis du Preez at ResearchGate
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Premature oxidation, (sometimes shortened to premox, or POx) is a flaw that occurs in white wines, when the presumably ageworthy wine is expected to be in good condition yet is found to be oxidised and often undrinkable. In particular the affliction has received attention in connection to incidents of whites produced in Burgundy. The afflicted vintages are predominantly from the late 1990s, and in particular those of 96, 97 and 98, until 2002. There have also been reports of premature oxidation occurring in wines from Australia, Alsace, Germany, and Bordeaux. == Hypotheses == Clive Coates, MW has stated that "Poorly-performing corks are the main culprits behind prematurely aged white Burgundy", while Pierre Rovani of The Wine Advocate has stated the contrary, "corks are not the issue". Allen Meadows has speculated that "based on what we know today, the most likely source of the problem is cork-related, though it appears this has been exacerbated by generally lower levels of SO2", while Steve Tanzer believes it to be a combination of several factors that involve corks, global warming resulting in overripe fruit, excessive stirring of the lees, and insufficient use of sulfur dioxide. Roger Boulton, professor of UC Davis, agreed with the probability of multifactorial causality, stating, "there are likely to be both closure issues and wine chemistry issues, so looking for the [single] answer will be like missing the bus". The French oenologists Denis Dubourdieu and Valérie Lavigne-Cruege launched a theory that with the recent trends of abstaining from the use of herbicides and letting grass grow freely in the vineyards of Burgundy, the grass competing with vines for water in conjunction with a warm vintage may cause the vines to endure extreme stress. As a result, grapes grown on highly stressed vines may have insufficient quantities of glutathione, a compound
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that functions as an essential antioxidant during the fermentation process. According to Michel Bettane, Burgundy producers reacted by taking steps to address the possible causes by heightened scrutiny of cork quality, more awareness of possible sulfur dioxide insufficiency, and a decrease of the practice of batonnage, the stirring of the lees that adds richness to the wines but also increases oxygen contact. In December 2006, Jamie Goode published an analysis of the problem of premature oxidation in The World of Fine Wine and explores some possible solutions. == Organic winemaking == Premature oxidation is a risk in organic winemaking, in which a lowered sulfite content is stipulated. Use of bâtonnage is known to increase premature oxidation. == References == == External links == Oxidized Burgundies Wiki Site internet community project
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In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm, and is typically used in the machine learning and natural language processing domains. == Algorithm == The ID3 algorithm begins with the original set S {\displaystyle S} as the root node. On each iteration of the algorithm, it iterates through every unused attribute of the set S {\displaystyle S} and calculates the entropy H ( S ) {\displaystyle \mathrm {H} {(S)}} or the information gain I G ( S ) {\displaystyle IG(S)} of that attribute. It then selects the attribute which has the smallest entropy (or largest information gain) value. The set S {\displaystyle S} is then split or partitioned by the selected attribute to produce subsets of the data. (For example, a node can be split into child nodes based upon the subsets of the population whose ages are less than 50, between 50 and 100, and greater than 100.) The algorithm continues to recurse on each subset, considering only attributes never selected before. Recursion on a subset may stop in one of these cases: every element in the subset belongs to the same class; in which case the node is turned into a leaf node and labelled with the class of the examples. there are no more attributes to be selected, but the examples still do not belong to the same class. In this case, the node is made a leaf node and labelled with the most common class of the examples in the subset. there are no examples in the subset, which happens when no example in the parent set was found to match a specific value of the selected attribute. An example could be
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the absence of a person among the population with age over 100 years. Then a leaf node is created and labelled with the most common class of the examples in the parent node's set. Throughout the algorithm, the decision tree is constructed with each non-terminal node (internal node) representing the selected attribute on which the data was split, and terminal nodes (leaf nodes) representing the class label of the final subset of this branch. === Summary === Calculate the entropy of every attribute a {\displaystyle a} of the data set S {\displaystyle S} . Partition ("split") the set S {\displaystyle S} into subsets using the attribute for which the resulting entropy after splitting is minimized; or, equivalently, information gain is maximum. Make a decision tree node containing that attribute. Recurse on subsets using the remaining attributes. === Properties === ID3 does not guarantee an optimal solution. It can converge upon local optima. It uses a greedy strategy by selecting the locally best attribute to split the dataset on each iteration. The algorithm's optimality can be improved by using backtracking during the search for the optimal decision tree at the cost of possibly taking longer. ID3 can overfit the training data. To avoid overfitting, smaller decision trees should be preferred over larger ones. This algorithm usually produces small trees, but it does not always produce the smallest possible decision tree. ID3 is harder to use on continuous data than on factored data (factored data has a discrete number of possible values, thus reducing the possible branch points). If the values of any given attribute are continuous, then there are many more places to split the data on this attribute, and searching for the best value to split by can be time-consuming. === Usage === The ID3 algorithm is used by training
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on a data set S {\displaystyle S} to produce a decision tree which is stored in memory. At runtime, this decision tree is used to classify new test cases (feature vectors) by traversing the decision tree using the features of the datum to arrive at a leaf node. == The ID3 metrics == === Entropy === Entropy H ( S ) {\displaystyle \mathrm {H} {(S)}} is a measure of the amount of uncertainty in the (data) set S {\displaystyle S} (i.e. entropy characterizes the (data) set S {\displaystyle S} ). H ( S ) = ∑ x ∈ X − p ( x ) log 2 p ( x ) {\displaystyle \mathrm {H} {(S)}=\sum _{x\in X}{-p(x)\log _{2}p(x)}} Where, S {\displaystyle S} – The current dataset for which entropy is being calculated This changes at each step of the ID3 algorithm, either to a subset of the previous set in the case of splitting on an attribute or to a "sibling" partition of the parent in case the recursion terminated previously. X {\displaystyle X} – The set of classes in S {\displaystyle S} p ( x ) {\displaystyle p(x)} – The proportion of the number of elements in class x {\displaystyle x} to the number of elements in set S {\displaystyle S} When H ( S ) = 0 {\displaystyle \mathrm {H} {(S)}=0} , the set S {\displaystyle S} is perfectly classified (i.e. all elements in S {\displaystyle S} are of the same class). In ID3, entropy is calculated for each remaining attribute. The attribute with the smallest entropy is used to split the set S {\displaystyle S} on this iteration. Entropy in information theory measures how much information is expected to be gained upon measuring a random variable; as such, it can also be used to quantify the amount
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to which the distribution of the quantity's values is unknown. A constant quantity has zero entropy, as its distribution is perfectly known. In contrast, a uniformly distributed random variable (discretely or continuously uniform) maximizes entropy. Therefore, the greater the entropy at a node, the less information is known about the classification of data at this stage of the tree; and therefore, the greater the potential to improve the classification here. As such, ID3 is a greedy heuristic performing a best-first search for locally optimal entropy values. Its accuracy can be improved by preprocessing the data. === Information gain === Information gain I G ( A ) {\displaystyle IG(A)} is the measure of the difference in entropy from before to after the set S {\displaystyle S} is split on an attribute A {\displaystyle A} . In other words, how much uncertainty in S {\displaystyle S} was reduced after splitting set S {\displaystyle S} on attribute A {\displaystyle A} . I G ( S , A ) = H ( S ) − ∑ t ∈ T p ( t ) H ( t ) = H ( S ) − H ( S | A ) . {\displaystyle IG(S,A)=\mathrm {H} {(S)}-\sum _{t\in T}p(t)\mathrm {H} {(t)}=\mathrm {H} {(S)}-\mathrm {H} {(S|A)}.} Where, H ( S ) {\displaystyle \mathrm {H} (S)} – Entropy of set S {\displaystyle S} T {\displaystyle T} – The subsets created from splitting set S {\displaystyle S} by attribute A {\displaystyle A} such that S = ⋃ t ∈ T t {\displaystyle S=\bigcup _{t\in T}t} p ( t ) {\displaystyle p(t)} – The proportion of the number of elements in t {\displaystyle t} to the number of elements in set S {\displaystyle S} H ( t ) {\displaystyle \mathrm {H} (t)} – Entropy of subset t {\displaystyle t} In ID3,
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information gain can be calculated (instead of entropy) for each remaining attribute. The attribute with the largest information gain is used to split the set S {\displaystyle S} on this iteration. == See also == Classification and regression tree (CART) C4.5 algorithm Decision tree learning Decision tree model == References == == Further reading == Mitchell, Tom Michael (1997). Machine Learning. New York, NY: McGraw-Hill. pp. 55–58. ISBN 0070428077. OCLC 36417892. Grzymala-Busse, Jerzy W. (February 1993). "Selected Algorithms of Machine Learning from Examples" (PDF). Fundamenta Informaticae. 18 (2): 193–207 – via ResearchGate. == External links == Seminars – http://www2.cs.uregina.ca/ Description and examples – http://www.cise.ufl.edu/ Description and examples – http://www.cis.temple.edu/ Decision Trees and Political Party Classification
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Drinking is the act of ingesting water or other liquids into the body through the mouth, proboscis, or elsewhere. Humans drink by swallowing, completed by peristalsis in the esophagus. The physiological processes of drinking vary widely among other animals. Most animals drink water to maintain bodily hydration, although many can survive on the water gained from their food. Water is required for many physiological processes. Both inadequate and (less commonly) excessive water intake are associated with health problems. == Methods of drinking == === In humans === When a liquid enters a human mouth, the swallowing process is completed by peristalsis which delivers the liquid through the esophagus to the stomach; much of the activity is assisted by gravity. The liquid may be poured from the hands or drinkware may be used as vessels. Drinking can also be by sipping or sucking, typically when imbibing hot liquids or drinking from a spoon. Infants employ a method of suction wherein the lips are pressed tight around a source, as in breastfeeding: a combination of breath and tongue movement creates a vacuum which draws in liquid. === In other land mammals === By necessity, terrestrial animals in captivity become accustomed to drinking water, but most free-roaming animals stay hydrated through the fluids and moisture in fresh food, and learn to actively seek foods with high fluid content. When conditions impel them to drink from bodies of water, the methods and motions differ greatly among species. Cats, canines, and ruminants all lower the neck and lap in water with their powerful tongues. Cats and canines lap up water with the tongue in a spoon-like shape. Canines lap water by scooping it into their mouth with a tongue which has taken the shape of a ladle. However, with cats, only the tip of their
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tongue (which is smooth) touches the water, and then the cat quickly pulls its tongue back into its mouth which soon closes; this results in a column of liquid being pulled into the cat's mouth, which is then secured by its mouth closing. Ruminants and most other herbivores partially submerge the tip of the mouth in order to draw in water by means of a plunging action with the tongue held straight. Cats drink at a significantly slower pace than ruminants, who face greater natural predation hazards. Many desert animals do not drink even if water becomes available, but rely on eating succulent plants. In cold and frozen environments, some animals like hares, tree squirrels, and bighorn sheep resort to consuming snow and icicles. In savannas, the drinking method of giraffes has been a source of speculation for its apparent defiance of gravity; the most recent theory contemplates the animal's long neck functions like a plunger pump. Uniquely, elephants draw water into their trunks and squirt it into their mouths. === In birds === Most birds scoop or draw water into the buccal areas of their bills, raising and tilting their heads back to drink. An exception is the common pigeon, which can suck in water directly by inhalation. === In insects === Most insects obtain adequate water from their food: When dehydrated from a lack of moist food, however, many species will drink from standing water. Additionally, all terrestrial insects constantly absorb a certain amount of the air's humidity through their cuticles. Some desert insects, such as Onymacris unguicularis, have evolved to drink substantially from nighttime fog. === In marine life === Amphibians and aquatic animals which live in freshwater do not need to drink: they absorb water steadily through the skin by osmosis. Saltwater fish, however, drink through
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"page_id": 66254,
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the mouth as they swim, and purge the excess salt through the gills. Saltwater fishes do drink plenty of water and excrete a small volume of concentrated urine. == Hydration and dehydration == Like nearly all other life forms, humans require water for tissue hydration. Lack of hydration causes thirst, a desire to drink which is regulated by the hypothalamus in response to subtle changes in the body's electrolyte levels and blood volume. A decline in total body water is called dehydration and will eventually lead to death by hypernatremia. Methods used in the management of dehydration include assisted drinking or oral rehydration therapy. An overconsumption of water can lead to water intoxication, which can dangerously dilute the concentration of salts in the body. Overhydration sometimes occurs among athletes and outdoor laborers, but it can also be a sign of disease or damage to the hypothalamus. A persistent desire to drink inordinate quantities of water is a psychological condition termed polydipsia. It is often accompanied by polyuria and may itself be a symptom of diabetes mellitus or diabetes insipidus. === Human water requirements === A daily intake of water is required for the normal physiological functioning of the human body. The USDA recommends a daily intake of total water: not necessarily by drinking but by consumption of water contained in other beverages and foods. The recommended intake is 3.7 liters (appx. 1 gallon) per day for an adult male, and 2.7 liters (appx. 0.75 gallon) for an adult female. Other sources, however, claim that a high intake of fresh drinking water, separate and distinct from other sources of moisture, is necessary for good health – eight servings per day of eight fluid ounces (1.8 liters, or 0.5 gallon) is the amount recommended by many nutritionists, although there is no scientific
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"page_id": 66254,
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"title": "Drinking"
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evidence supporting this recommendation. Evidence-based hydration experts say that the amount of drinking water needed depends on ambient temperature, activity level, body size, and sweat rate. Research shows drinking when thirsty will maintain hydration to within about 2% of the needed level. Drinking beyond thirst might be beneficial for people who need to perform tasks that require intense concentration, and those with kidney disease, kidney stones, urinary tract infections, and people with a weak sense of thirst (which may include more older people). == Alcoholic beverages == The term "drinking" is often used metonymically for the consumption of alcoholic beverages. Most cultures throughout history have incorporated some number of the wide variety of "strong drinks" into their meals, celebrations, ceremonies, toasts and other occasions. Evidence of fermented drinks in human culture goes back as early as the Neolithic Period, and the first pictorial evidence can be found in Egypt around 4,000 BC. Alcohol consumption has developed into a variety of well-established drinking cultures around the world. Despite its popularity, alcohol consumption poses significant health risks. Alcohol abuse and the addiction of alcoholism are common maladies in developed countries worldwide. A high rate of consumption can also lead to cirrhosis, gastritis, gout, pancreatitis, hypertension, various forms of cancer, and numerous other illnesses. == See also == Eating Hydration (disambiguation) == References == === Bibliography === Broom, Donald M. (1981). Biology of Behaviour: Mechanisms, Functions and Applications. Cambridge: Cambridge University Press. ISBN 0-521-29906-3. Retrieved 31 August 2013. Curtis, Helena; Barnes, N. Sue (1994). Invitation to Biology. Macmillan. ISBN 0879016795. Retrieved 31 August 2013. Fiebach, Nicholas H., ed. (2007). Principles of Ambulatory Medicine. Lippincott Williams & Wilkins. ISBN 978-0-7817-6227-4. Retrieved 31 August 2013. Flint, Austin (1875). The Physiology of Man. New York: D. Appleton and Co. OCLC 5357686. Retrieved 31 August 2013. Gately,
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{
"page_id": 66254,
"source": null,
"title": "Drinking"
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Iain (2008). Drink: A Cultural History of Alcohol. New York: Penguin. pp. 1–14. ISBN 978-1-59240-464-3. Retrieved 31 August 2013. Mayer, William (2012). Physiological Mammalogy. Vol. II. Elsevier. ISBN 9780323155250. Retrieved 31 August 2013. Provan, Drew (2010). Oxford Handbook of Clinical and Laboratory Investigation. Oxford: Oxford University Press. ISBN 978-0-19-923371-7. Retrieved 31 August 2013. Smith, Robert Meade (1890). The Physiology of the Domestic Animals. Philadelphia, London: F.A. Davis. Retrieved 31 August 2013. == External links == "Are You Drinking Enough?", recommendations by the European Hydration Institute (Madrid)
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{
"page_id": 66254,
"source": null,
"title": "Drinking"
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A prill is a small aggregate or globule of a material, most often a dry sphere, formed from a melted liquid through spray crystallization. Prilled is a term used in mining and manufacturing to refer to a product that has been pelletized. ANFO explosive typically comprises ammonium nitrate prills mixed with #2 fuel oil. The pellets are a neater, simpler form for handling, with reduced dust. The material to be prilled must be in a solid state at room temperature and a low-viscosity liquid when melted. Prills are formed by allowing drops of the melted prill substance to congeal or freeze in mid-air after being dripped from the top of a tall prilling tower. Certain agrochemicals such as urea are often supplied in prilled form. Fertilizers (ammonium nitrate, urea, NPK fertilizer) and some detergent powders are commonly manufactured as prills. However prilling of ammonium nitrate and urea has in recent years been replaced by fluid bed granulation as this gives strong and more abrasion-resistant granules. Melted material may also be atomized and then allowed to form smaller prills that are useful in cosmetics, food, and animal feed. == See also == Shot (pellet) == References ==
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"page_id": 262863,
"source": null,
"title": "Prill"
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Debora M. Kane is an Honorary Professor at the Department of Electronic Materials Engineering at the Australian National University since 2022. Her research interests are in non-linear optics and laser physics. She is a Fellow of The Optical Society and has edited four books on nanotechnology, nanomaterials and semiconductor lasers. == Early life and education == Kane obtained a bachelor's degree from the University of Otago in 1979. In 1983, she received her PhD from the University of St Andrews. Her thesis used optical spectroscopy techniques to study atomic transitions in various materials for applications in laser physics. == Research and career == Kane began her postdoctoral career as a research fellow at the University of Southampton in 1984, working on developing techniques to improve the operation of dye lasers. In 1986, she moved to Massey University, where she became a lecturer in physics. Kane was a faculty member at the Department of Physics at Macquarie University between 1989 and 2021, serving as Head of Department from 2003 to 2006, and later held a personal chair in Physics until the end of her tenure. Her current research spans various aspects of laser physics, particularly non-linear optics and dynamics in semiconductor lasers, how laser technologies can be used for applications in surface science studies and nanomaterial processing, and the development of new visible and ultraviolet light sources. == Awards and honours == Fellow of The Optical Society, 2017 Chair of the IUPAC Commission on Laser Physics and Photonics, 2015-17 Australian Institute of Physics Women in Physics Lecturer and Medallist, 2006 == Selected publications == Kane has co-authored over 200 academic publications and nine book chapters on laser physics. She has also edited four books: Nanomaterials: Science and Applications (2016), Nanotechnology in Australia: Showcase of Early Career Research (2011), Laser Cleaning II
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"page_id": 66323151,
"source": null,
"title": "Deborah M Kane"
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(2007), and Unlocking Dynamic Diversity: Optical Feedback Effects on Semiconductor Lasers (2005). == References ==
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"page_id": 66323151,
"source": null,
"title": "Deborah M Kane"
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Gerhard Theodor Materlik (born 16 January 1945) is a German physicist and science manager. He has made significant contributions to X-ray physics, notably improvements in the real-world application of synchrotron radiation. He is a Professor of Facilities Science at the University College London since 2013. == Education and early career == Materlik completed his undergraduate education in physics in Münster and Munich in 1970. He earned his doctorate from the University of Dortmund in 1975. After postdoctoral appointments at Cornell University (1975–1977) and Bell Laboratories, he took a job at the German Electron Synchrotron (DESY) in Hamburg. == Work == From 2001–2013, Materlik was Chief Executive of the Diamond Light Source, the United Kingdom's synchrotron facility. He was the leader of the team that constructed the accelerators, which speed up electrons to near the speed of light, and also the instrumentation installed to apply this radiation in experiments covering a spectral range from infrared radiation up to X-rays. His discoveries have become widely used experimental methods. He has published more than 200 papers. He assisted in the development of synchrotron sources worldwide. == Awards and honours == In 2007, Materlik was awarded a Commander of the Most Excellent Order of the British Empire and became a Fellow of the Institute of Physics. He was elected a Fellow of the Royal Society (FRS) in 2011. His certificate of election reads: Gerhard Materlik has made important discoveries in the science and application of Synchroton Radiation and is the leader of the team that constructed and now operates the world leading Diamond Light Source facility. He has contributed to the many fields in the application of synchrotron x-rays (SXR) most of which are now widely used experimental methods with SXR. He has made notable contributions to the improvement of SXR sources, notably the
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{
"page_id": 49873615,
"source": null,
"title": "Gerhard Materlik"
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soft X-ray FEL, FLASH at DESY, the newly commissioned hard X-ray FEL, LCLS at SLAC and the hard X-ray FEL, E-XFEL, currently been built at DESY. In 2014 he was awarded the Glazebrook Medal by the Institute of Physics for his leadership in establishing a world-leading laboratory at the Diamond Light Source. == References == == External links == Diamond CEO Prof. Gerd Materlik elected Fellow of the Royal Society
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"page_id": 49873615,
"source": null,
"title": "Gerhard Materlik"
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SynBio is a long-term project started in 2011 with the goal of creating innovative medicines, including what are known as Biobetters. This project is a collaborative effort of several Russian and international pharmaceutical companies. The largest private participant of SynBio is the Human Stem Cells Institute (HSCI), a leading Russian biotech company, and Rusnano is a key investor. The project is a significant example of international cooperation between researchers in Russia, England, and Germany. Special project company SynBio LLC is headquartered in Moscow. Currently, SynBio LLC is developing nine drugs based on three biotechnology platforms (Histone, PolyXen and Gemacell) for the treatment of liver disease, cardiovascular disease, acute leukemia, growth hormone deficiency and diabetes mellitus. The SynBio project also entails the creation of modern production facilities. These facilities will be dedicated to the manufacturing of the company's pharmaceutical substances and market-ready medicines once they have successfully undergone clinical testing. == Research and Development Centers == Xenetic Biosciences (London, Great Britain) is a leading biopharmaceutical company operating from the UK that develops high-value, differentiated pharmaceutical products in the fields of protein drugs, vaccines and anti-cancer drugs. SymBioTec GmbH (Saarbrücken, Germany) is a company working to develop next generation medicines for the treatment of cancer and infectious diseases. SymBioTec has a state-of-the-art biotech laboratory equipped for proteomics, cell and molecular biology as well as biotechnology. HSCI (Moscow, Russia) is one of the leading biotech companies of Russia that is engaged in development of cell- and gene-based technologies with promising medical applications. == References == == External links == Official website
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{
"page_id": 33161940,
"source": null,
"title": "SynBio"
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The molecular formula C17H36 (molar mass: 240.27 g/mol, exact mass: 240.2817 u) may refer to: 3,3-Di-tert-butyl-2,2,4,4-tetramethylpentane Heptadecane
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{
"page_id": 20775637,
"source": null,
"title": "C17H36"
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A Chemical safety assessment (CSA) is an analysis used in many situations where chemical are used and where there is a possibility that they may present a risk to life, health or the environment. The European Union has adopted this phrase to describe a process that has to be performed by registrants for substances manufactured and imported in quantities starting at 10 tonnes per year and by downstream users if their uses are not addressed by their supplier," according to REACH (Registration, Evaluation, Authorisation and Restriction of Chemicals) legislation. The CSA includes the evaluation of all available relevant information in order to assess risks arising from the manufacture and/or use of a substance. The process needs to be documented adequately and the results have to be documented in a chemical safety report (CSR), which is to be submitted to the European Chemicals Agency as part of the respective registration dossier. The purpose is to ensure that the risks related to the substance are controlled. The chemical safety assessment of a substance comprises the following steps: Assessment of the human health hazard Human health hazard assessment of physicochemical properties Assessment of the environmental hazard Persistent, bioaccumulative and toxic (PBT) and very persistent and very bioaccumulative (vPvB) assessment The European Chemicals Agency (ECHA) has developed a software tool to support industry in preparing a Chemical Safety Assessment (CSA) and Chemical Safety Report (CSR). == Chemical Safety Report == Chemical Safety Reports are the main end point for data assessment under REACH (the European Community Regulation on chemicals and their safe use, concerning the Registration, Evaluation, Authorisation and Restriction of Chemical substances) in which hazard and exposure data are considered together to assess the risk of a substance. == See also == Chemical safety Msdsonline Occupational exposure banding Control banding == References ==
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"page_id": 25625302,
"source": null,
"title": "Chemical safety assessment"
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In physical chemistry and chemical engineering, extent of reaction is a quantity that measures the extent to which the reaction has proceeded. Often, it refers specifically to the value of the extent of reaction when equilibrium has been reached. It is usually denoted by the Greek letter ξ. The extent of reaction is usually defined so that it has units of amount (moles). It was introduced by the Belgian scientist Théophile de Donder. == Definition == Consider the reaction A ⇌ 2 B + 3 C Suppose an infinitesimal amount d n i {\displaystyle dn_{i}} of the reactant A changes into B and C. This requires that all three mole numbers change according to the stoichiometry of the reaction, but they will not change by the same amounts. However, the extent of reaction ξ {\displaystyle \xi } can be used to describe the changes on a common footing as needed. The change of the number of moles of A can be represented by the equation d n A = − d ξ {\displaystyle dn_{A}=-d\xi } , the change of B is d n B = + 2 d ξ {\displaystyle dn_{B}=+2d\xi } , and the change of C is d n C = + 3 d ξ {\displaystyle dn_{C}=+3d\xi } . The change in the extent of reaction is then defined as d ξ = d n i ν i {\displaystyle d\xi ={\frac {dn_{i}}{\nu _{i}}}} where n i {\displaystyle n_{i}} denotes the number of moles of the i t h {\displaystyle i^{th}} reactant or product and ν i {\displaystyle \nu _{i}} is the stoichiometric number of the i t h {\displaystyle i^{th}} reactant or product. Although less common, we see from this expression that since the stoichiometric number can either be considered to be dimensionless or to have units of
|
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"page_id": 3670743,
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moles, conversely the extent of reaction can either be considered to have units of moles or to be a unitless mole fraction. The extent of reaction represents the amount of progress made towards equilibrium in a chemical reaction. Considering finite changes instead of infinitesimal changes, one can write the equation for the extent of a reaction as Δ ξ = Δ n i ν i {\displaystyle \Delta \xi ={\frac {\Delta n_{i}}{\nu _{i}}}} The extent of a reaction is generally defined as zero at the beginning of the reaction. Thus the change of ξ {\displaystyle \xi } is the extent itself. Assuming that the system has come to equilibrium, ξ e q u i = n e q u i , i − n i n i t i a l , i ν i {\displaystyle \xi _{equi}={\frac {n_{equi,i}-n_{initial,i}}{\nu _{i}}}} Although in the example above the extent of reaction was positive since the system shifted in the forward direction, this usage implies that in general the extent of reaction can be positive or negative, depending on the direction that the system shifts from its initial composition. == Relations == The relation between the change in Gibbs reaction energy and Gibbs energy can be defined as the slope of the Gibbs energy plotted against the extent of reaction at constant pressure and temperature. Δ r G = ( ∂ G ∂ ξ ) p , T {\displaystyle \Delta _{r}G=\left({\frac {\partial G}{\partial \xi }}\right)_{p,T}} This formula leads to the Nernst equation when applied to the oxidation-reduction reaction which generates the voltage of a voltaic cell. Analogously, the relation between the change in reaction enthalpy and enthalpy can be defined. For example, Δ r H = ( ∂ H ∂ ξ ) p , T {\displaystyle \Delta _{r}H=\left({\frac {\partial H}{\partial \xi }}\right)_{p,T}} == Example
|
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"page_id": 3670743,
"source": null,
"title": "Extent of reaction"
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== The extent of reaction is a useful quantity in computations with equilibrium reactions. Consider the reaction 2 A ⇌ B + 3 C where the initial amounts are n A = 2 mol , n B = 1 mol , n C = 0 mol {\displaystyle n_{A}=2\ {\text{mol}},n_{B}=1\ {\text{mol}},n_{C}=0\ {\text{mol}}} , and the equilibrium amount of A is 0.5 mol. We can calculate the extent of reaction in equilibrium from its definition ξ e q u i = Δ n A ν A = 0.5 mol − 2 mol − 2 = 0.75 mol {\displaystyle \xi _{equi}={\frac {\Delta n_{A}}{\nu _{A}}}={\frac {0.5\ {\text{mol}}-2\ {\text{mol}}}{-2}}=0.75\ {\text{mol}}} In the above, we note that the stoichiometric number of a reactant is negative. Now when we know the extent, we can rearrange the equation and calculate the equilibrium amounts of B and C. n e q u i , i = ξ e q u i ν i + n i n i t i a l , i {\displaystyle n_{equi,i}=\xi _{equi}\nu _{i}+n_{initial,i}} n B = 0.75 mol × 1 + 1 mol = 1.75 mol {\displaystyle n_{B}=0.75\ {\text{mol}}\times 1+1\ {\text{mol}}=1.75\ {\text{mol}}} n C = 0.75 mol × 3 + 0 mol = 2.25 mol {\displaystyle n_{C}=0.75\ {\text{mol}}\times 3+0\ {\text{mol}}=2.25\ {\text{mol}}} == References ==
|
{
"page_id": 3670743,
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"title": "Extent of reaction"
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Ecophagy is a term coined by Robert Freitas that means the consumption of an ecosystem. It derives from Greek οἶκος (oikos) 'house, household' and φαγεῖν (phagein) 'to eat'. Freitas used the term to describe a scenario involving molecular nanotechnology gone awry. In this situation (called the grey goo scenario) out-of-control self-replicating nanorobots consume entire ecosystems, resulting in global ecophagy. == Etymology == However, the word "ecophagy" is now applied more generally in reference to any event—nuclear war, the spread of monoculture, massive species extinctions—that might fundamentally alter the planet. Scholars suggest that these events might result in ecocide in that they would undermine the capacity of the Earth's biological population to repair itself. Others suggest that more mundane and less spectacular events—the unrelenting growth of the human population, the steady transformation of the natural world by human beings—will eventually result in a planet that is considerably less vibrant, and one that is, apart from humans, essentially lifeless. These people believe that the current human trajectory puts us on a path that will eventually lead to ecophagy. In the paper in which Freitas coined the term he wrote: Perhaps the earliest-recognized and best-known danger of molecular nanotechnology is the risk that self-replicating nanorobots capable of functioning autonomously in the natural environment could quickly convert that natural environment (e.g., "biomass") into replicas of themselves (e.g., "nanomass") on a global basis, a scenario usually referred to as the "grey goo problem" but perhaps more properly termed "global ecophagy". == See also == Ecocide Grey goo Molecular assembler == References == == Further reading == Philip Ball, The Robot Within Archived 2016-09-23 at the Wayback Machine, New Scientist, 15 March 2003. == External links == Some Limits to Global Ecophagy by Biovorous Nanoreplicators, with Public Policy Recommendations critical review of the Freitas article in
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"page_id": 197337,
"source": null,
"title": "Ecophagy"
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biosafety group Green Goo - Life In The Era Of Humane Genocide by Nick Szabo Human Global Ecophagy (Or, How Quickly Can Humans Consume the Earth?) "Intentional Ecophagy" references "Nanotechnology Daily News"
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{
"page_id": 197337,
"source": null,
"title": "Ecophagy"
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The Physics Instructional Association (PIRA) is an American association of physics education professionals and enthusiasts. Members are physics teachers, physics administrators, physics educational support staff and physics students. Interests cover all aspects of physics education with an emphasis on demonstrations, laboratories and outreach. The association is also responsible for maintaining the Demonstration Classification Scheme (DCS), a standardized scheme for categorization of physics demonstrations. == Affiliations == PIRA holds annual meetings during the summer meeting of the American Association of Physics Teachers. It is sponsored by the Apparatus Committee and annually hosts the Lecture Demonstration Workshop. PIRA assists or hosts the Physics Demonstrations Show at each summer meeting when the hosting institution requests. == Demonstration bibliography == PIRA has continually updated the Demonstration Bibliography since its inception in the 1980s. It is based on a unique numbering system called the Demonstration Classification Scheme (DCS). The scheme originated from the demonstrations catalog used at the University of Minnesota. PIRA has also generated a subset of this list called the PIRA 200. These 200 demonstrations are the recommended basic collection for any physics department. == See also == American Association of Physics Teachers Scientific demonstration == References == == External links == Official PIRA website Archived 2012-06-15 at the Wayback Machine 2011 APS Excellence in Education Award
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{
"page_id": 26018522,
"source": null,
"title": "Physics Instructional Resource Association"
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For a small country, Albania is characterised by a considerable wealth of terrestrial and marine ecosystems and habitats with contrasting floral, faunal, and fungal species, defined in an area of 28,748 square kilometres. Most of the country is predominantly of Mediterranean character, comprehending the country's center and south, while the alpine affinity is more visible in the northeast. Apart the diversity of topography and climate, the direct proximity of Albania to the Mediterranean Sea and the significant location within the European continent have created favorable conditions for appearance of a vast array of flora, fauna and funga with an immense quality, which led the country to be recognised as an important biodiversity hotspot in the continent. The number of globally threatened faunal species in Albania is high with an integral part of more than 181 species, ranking seventh in the Mediterranean Basin. Albania is predominantly mountainous and hilly with the rapid landscape change from marine to alpine within a short distance. Only one-third of the country consists of lowlands that sprawl across the west of the country facing the Mediterranean Sea with a coastline length of about 476 km (296 mi). The mountain chains consequently cross the length of the country from north to south, featuring the Albanian Alps in the north, the Sharr Mountains in the northeast, the Skanderbeg Mountains in the center, the Korab Mountains in the east, the Pindus Mountains in the southeast and the Ceraunian Mountains in the southwest stretching alongside the Albanian Riviera. The hydrographic network of Albania is composed of lakes, rivers, wetlands, seas and groundwaters. There are about 250 lakes of different origins, including tectonic, glacial and fluvial lakes. Among the most important is the lake of Shkodër, the largest lake in Southern Europe, followed by Ohrid, which is considered one of the
|
{
"page_id": 53805787,
"source": null,
"title": "Biodiversity of Albania"
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most ancient lakes in the world. The rivers also have a valuable effect on the local coastal biodiversity (or wildlife). There are 152 rivers in the country, most notable amongst them Drin, Vjosa, Shkumbin, Mat, Ishëm and Osum. The coasts along the Mediterranean Sea are home to various lagoons including Karavasta and Narta. Protected areas belong to the most essential instruments of nature conservation. 799 types of protected areas are designated in Albania, spanning 5.216,96 square kilometres. Amongst them are 14 national parks, 1 marine park, 4 Ramsar sites, 3 World Heritage Sites, 45 important plant areas, 16 important bird areas and 786 protected areas of various categories. == Ecoregions == The country of Albania is part of the Boreal Kingdom and stretches specifically within the Illyrian province of the Circumboreal Region. Its territory can be conventionally subdivided into four terrestrial ecoregions of the Palearctic realm. The Illyrian deciduous forests stretch along the Albanian Adriatic and Ionian Sea Coast in the west across the Mediterranean Basin, while the Pindus Mountains mixed forests occur in the Eastern and Southeastern Mountain Ranges in the east. The Dinaric Mountains mixed forests cover most of the Albanian Alps in the north, while the Balkan mixed forests extend across the eastern end of the range. == Ecosystems == === Forests === Forests are the most widespread terrestrial ecosystem in Albania. They represent an essential functional and aesthetic component on 36% of the landscapes in the country. The forests of northern Albania are similar to those of Continental Europe, in contrast, the forests of southern Albania share similarities with those of the Mediterranean Basin. Forests can take many forms, depending on their latitude, soil, rainfall and prevailing temperatures. In Albania forest cover is around 29.% of the total land area, equivalent to 788,900 hectares (ha) of
|
{
"page_id": 53805787,
"source": null,
"title": "Biodiversity of Albania"
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forest in 2020, up from 788,800 hectares (ha) in 1990. Of the naturally regenerating forest 11% was reported to be primary forest (consisting of native tree species with no clearly visible indications of human activity) and around 0% of the forest area was found within protected areas. For the year 2015, 97% of the forest area was reported to be under public ownership, 3% private ownership and 0% with ownership listed as other or unknown. The concentration of deciduous trees dominates in the country's forests, ranging from almost 56.8% or 6,093 square kilometres of the forested territory. Oak represents an important natural forest resource in Albania with 32.1% followed by beech with 18.4%. There are 12 oak species found in Albania distributed all across the country's territory from north to south, and east to west. The coniferous forests cover 1,756 square kilometres which constitutes 16.4% of the country's forested total area. Although black pine dominates and is among the most significant tree species in the country, occupying a surface area of roughly 10.2%. It is primary found in the central mountain range but also scattered in the northern and southern mountain range. Silver fir accounts 1.4% of the conifers with 152 square kilometres, commonly found in the slopes and valleys of the mountains and alongside the Albanian Adriatic and Ionian Sea coasts in the west. === Wetlands === Albania possesses a wealth of wetland ecosystems supporting diverse and unique habitats. These wetlands contain respectively numerous ecological commodities and services but are under an important charge due to the rapid urbanization and industrialization. Marshes, reed beds and lakes are found in all regions, along with rivers and deltas while wetlands are distributed from the high internally mountainous zone in the southeast to the coastline in the west. The richest wetland regions
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{
"page_id": 53805787,
"source": null,
"title": "Biodiversity of Albania"
}
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are more particularly in the coastal plain along the entire west border of Albania that is shaped by the Adriatic and Ionian Sea. The wetland complex of Butrint, Karavasta and Narta represents one of the most important coastal wetland sites of Albania. The lagoons are separated from the sea by rather narrow sandy bars, which continuously change in size and shape. Other important lagoons include the Patoku Lagoon, Kune-Vain Lagoon, Viluni Lagoon and many others. Albania is home to several of the most important lakes in Southern Europe. Four lakes are apportioned with its neighbouring countries for instance Lake Shkodër with Montenegro, Lake Ohrid with North Macedonia, Small Lake Prespa with Greece and Lake Prespa with North Macedonia and Greece. All of them are nevertheless of international importance not least for the limnology and biodiversity. Moreover, Lake Shkodër and Lake Prespa have been recognised as a wetland of international importance by official designation under the Ramsar Convention. === Estuaries === An estuary is a partly enclosed coastal body of water that form at river mouths and provide unique habitats for migratory bird populations, invertebrates, as well as marine fish, including those that visit to breed. The main characteristics of estuarine life are the variability in salinity and sedimentation. They are determined by a region's geology, and influenced by topographical, chemical and climatic conditions. Although small in size, Albania has many rivers that flow through its expanses. The major rivers of Albania are the Drin, Vjosa, Mat, Ishëm, Erzen, Shkumbin and Seman that discharges into the eastern Adriatic Sea. River flows are highly variable with high flows in winter and early spring and dramatically lower flows in the late summer. In addition, the rivers have received little scientific attention from biologists and little is known about the status of biodiversity they
|
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contain, however, the river basin of Drin is one of the most important biodiversity hotspots in Europe. == Flora == Albania features contrasting and different vegetation types, determined mainly by topography, hydrology, climate and geology. It enjoys a diversity of temperate ecologies, incorporating both deciduous and coniferous forests, wetlands, river deltas, alpine and subalpine pastures and meadows, evergreen and broadleaf bushes, marine and coastal landscapes. Strategically located on the northern shore of the Mediterranean Sea, Albania appertain to one of the planet's biodiversity hotspots due to the elevated level of endemism within the Mediterranean Basin. The flora of Albania consists of more than 3,200 vascular and 2,350 non-vascular plants and a lesser known number of fungi. The chief elements of the country's flora are 24% mediterranean, 22% balkanic, 18% european and 14% eurasian. Phytogeographically, the country straddles the Illyrian province of the Circumboreal Region within the Boreal Kingdom. According to the World Wide Fund for Nature and the European Environment Agency, it falls within four terrestrial ecoregions of the Palearctic realm, including the Illyrian deciduous forests, Balkan mixed forests, Pindus Mountains mixed forests and Dinaric Mountains mixed forests. About 3,000 different species of plants grow in Albania, many of which are used for medicinal purposes. Coastal regions and lowlands have typical Mediterranean macchia vegetation, whereas oak forests and vegetation are found on higher elevations. Vast forests of black pine, beech and fir are found on higher mountains and alpine grasslands grow at elevations above 1800 meters. The genus with the most species in Albania is Trifolium (clover) with a total of 63 species. This is principally due to the Mediterranean climate along the coast. The country is also home to over 20 species of Verbascum, which is due to the proximity to Anatolia, the centre of diversity of Mulleins. ==
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"page_id": 53805787,
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Fauna == === Birds === The geographical location of Albania in combination with its variable and complex climate is responsible for the diverse bird population in the country. Over 353 species of bird have been recorded in Albania with 11 globally threatened species and a species introduced by humans. The country is home to favorable wetlands, lagoons, lakes, estuaries and deltas together with the corresponding habitats. These habitats serve as feeding ground for thousands of migrating birds that travels between Northern Africa and Europe through the Adriatic flyway. There are numerous raptor species found in Albania, some of which are the eagles, hawks, falcons and vultures. The eagles are widespread over the country while different species inhabit different habitats. The golden eagle is the largest bird of prey and especially found in mountainous areas, cliffs and remote areas of Albania. The white-tailed eagle is found wherever there are large bodies of water and takes mainly fish and occasionally other vertebrates. The short-toed snake eagle is a forest species and takes mostly snakes but also some lizards. There is a great plenty of hawk species found across the country including the Eurasian sparrowhawk, the Levant sparrowhawk and the northern goshawk. The falcons that occur in the country are well represented by a number of species. They are represented by the eleonora's falcon, eurasian hobby, lanner falcon, peregrine falcon, saker falcon and merlin. A dozen species of vultures can be found living in the country mainly in certain parts of gorges, on cliffs, rocks and caves. Among the most important and prominent species is the globally threatened egyptian vulture. These birds inhabit mainly the southern of Albania but can be found in very few territories in the north. Located in the Mediterranean Sea, Albania has a rich marine avifauna with many large
|
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"page_id": 53805787,
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"title": "Biodiversity of Albania"
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and various seabird colonies dotted around its pristine coastline in the west. Pelicans and flamingos are more commonly found in the coastal areas. The extremely rare Dalmatian pelican is the most common pelican in the country and very heavy for a flying bird. The greater flamingo, which is out of the six species of flamingos on the planet, can be found along warm, watery regions especially in lagoons such as in Karavasta Lagoon and Narta Lagoon. === Mammals === Albania is home to a wide range of mammals that are closely associated with its geographical location and climatic conditions. Approximately 58 species of mammals have been recorded to occur in the country. The protected areas, including national parks, nature reserves and biosphere reserves, provide protection to the mammals and are the most likely locations where these animals can be seen. For a small country, Albania challenges an important role in maintaining and ensuring the long-term survival of the large carnivores of the western and southern Balkan Peninsula. The carnivores seem to be primarily distributed in the last remaining forests throughout the country especially in the areas around the Albanian Alps in the north, the Korab Mountains in the east and the scattered elevated areas in the south, such as in the Karaburun Peninsula, Valamara Mountains and Nemërçka Mountains. Small terrestrial mammals (STM) are made up of 31 different species. Although the majority of the STM species found in the country have a wide global distribution, six species are known to be endemic to the Balkans and two others to Europe. They have a significant portion of their global distribution range within Albanian territory. The country's cat species include the Eurasian lynx and European wildcat. All of them are critically endangered, threatened and protected. The country is host to at present
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the largest distribution area of the critically endangered Balkan lynx, which is considered to be the largest cat in the Balkans, with an estimated population of less than 100 individuals. The family Canidae has several members in Albania including the gray wolf, Eurasian wolf, red fox and golden jackal. The distribution range of the gray and Eurasian wolf encompasses most of the country's territory. The red fox, which is native, is the largest fox species and appears in every corner of Albania. However, the range of the golden jackal extends across the Western Lowlands along the Albanian Adriatic and Ionian Sea Coast. The brown bear, perhaps Albania's most famous wildlife species, is one of the most valuable elements of the biodiversity and plays as well as an important role in biodiversity maintenance. They are found across much of the country, including the Northern, Central and Southern Mountain Range of Albania, and are part of the Dinaric-Pindus population, which is the second largest population in Europe. The brown bear populations in both Albania and North Macedonia are of significant and important biological and genetic value, as they constitute the connecting populations between the bears of the countries of Serbia, Croatia and Slovenia in the northern of the Balkans and the bears of Greece in the south. The largest family of carnivorous mammals belongs to the otters, badgers, weasels and martens, all of which are found in the country. All of these are short, furry animals with short, rounded ears and thick fur, but they differ markedly in size, habit and habitat. The Eurasian otter is found throughout much of the country and healthy populations were localised in rivers and marshes in the northwest and the south. The European badger is the most common badger in Albania and found across much of
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"page_id": 53805787,
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"title": "Biodiversity of Albania"
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the country's territory. Classified as carnivores, pinnipeds are divided between earless seals and eared seals. Earless seals do not have ears and cannot get their hind flippers underneath their bodies to crawl. In contrast, eared seals have protruding ears and can walk with all four limbs on land. Nevertheless, the Mediterranean monk seal, among the world's rarest pinniped species, is the only seal species that can be found in Albania. It is primarily home in the rocky coastal regions of southern Albania such as in Karaburun Peninsula, Sazan Island and Ksamil Islands that provide good habitats for the endangered species. Considering the great availability of water, the country's coast is estimated to be 381 kilometres (237 mi) long. The Mediterranean Sea, which includes the Adriatic Sea and the Ionian Sea that makes up the entire west border of Albania, is home to increasingly rare populations of cetaceans. Nonetheless, the country has several cetacean species that live in the Albanian Mediterranean Sea. The short-beaked common dolphin is known to inhabit coastal waters. The common bottlenose dolphin is abundant along the Albanian Adriatic Sea Coast especially in winter and spring seasons where they come to coastal areas to breed. Areas to protect the dolphin species were established in Buna River-Velipoja, Karaburun-Sazan, Ksamil Islands, Vjosa-Narta and other places. Therefore, the Cuvier's beaked whale has been recorded several times in Albanian waters. The even-toed ungulates are represented by species such as the roe deer and chamois. Although found in the other nearby Balkan countries, red deer have been locally extinct in Albania for the better part of the 20th century, whereas fallow deer are only present in captivity. === Reptiles === Despite the fact that there are no exact studies, Albania ranks among the most important regions in the Balkan Peninsula in terms of
|
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"page_id": 53805787,
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"title": "Biodiversity of Albania"
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reptiles with over thirty-seven species being recorded. Many of them are widespread particularly along the Albanian coasts that contains a wide diversity of habitats and ecosystems. There are several species of sea turtle that nest on the country's beaches. The loggerhead turtle is a large oceanic turtle with flippers and a reddish-brown shell. The green sea turtle is another important species in the Mediterranean Sea and occasionally found in the Bay of Drin in the north and Bay of Vlorë in the south of Albania. The hawksbill sea turtle is one of the world's most endangered sea turtles and basically found in tropical waters around the world but also occasionally in Albania. The territory of Albania is populated by two important species of freshwater turtles such as the European pond turtle and the Balkan pond turtle. One of the best-known turtles of Albania is the Hermann's tortoise which is relatively abundant throughout the country. Lizards are also found in the country. Large lizards such as the European green lizard, Balkan green lizard, Mediterranean house gecko and blue-throated keeled lizard are probably the country's most regularly encountered reptiles. === Fish === Albania has approximately 249 fish species in its coastal waters and 64 freshwater species in its rivers and lakes. Even though fish of marine and freshwaters can be found in various parts of waters throughout the country. The Adriatic and Ionian Sea inside the Mediterranean Sea are home of salt water fish, while fresh water fish occurs on Lake Butrint, Lake Shkodër, Lake Ohrid, Lake Prespa as well as in Karavasta Lagoon, Narta Lagoon and Patos Lagoon. Lake Ohrid, Europe's oldest lake, is located between Albania and North Macedonia. As one of the world's few ancient lakes, it is the lake which contains the largest number of endemic species in
|
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"page_id": 53805787,
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"title": "Biodiversity of Albania"
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the world, with 212 species of animals and plants. It is the habitat for many rare fish species such as the endangered Ohrid trout, one of the most ancient trout in the entire Balkan Peninsula. With more than 28 species identified, out of 38 species which were recorded for the entire Adriatic Sea, the diversity of sharks in Albania is among the most abundant in the Balkans. Among the most important and common species are the small-spotted catshark, nursehound, common smooth-hound, longnose spurdog, spiny dogfish, angelshark and common thresher. == Protected areas == Numerous parts of Albania are protected in accordance with a number of national and international designations due to their natural, historical or cultural value. Protected areas belong to the most important instruments of conservation which in turn contributes effectively to the maintenance of species, habitats and ecosystems. The country has currently fifteen designated national parks, whereby one is specified as a marine park. Ranging from the Adriatic Sea and the Ionian Sea to the Albanian Alps and the Ceraunian Mountains, they possesses outstanding landscapes constituting habitats to thousands of plant and animal species. Butrint, Divjakë-Karavasta, Karaburun-Sazan, Llogara, Prespa, Shebenik-Jabllanicë, Theth and Valbonë are among the most spectacular national parks of the country. == See also == Geography of Albania Protected areas of Albania Albanian Adriatic and Ionian Sea Coast == References ==
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"page_id": 53805787,
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Zita Carla Torrão Pinto Martins (born 1979), OSE, is a Portuguese astrobiologist, and an associate professor at Instituto Superior Técnico. She was a Royal Society University Research Fellow (URF) at Imperial College London. Her research explores how life may have begun on Earth by looking for organic compounds in meteorite samples. == Early life and education == As a child, Zita Martins studied classical ballet from the age of four and was encouraged by her teacher to progress to the National Ballet School in Portugal, which would have put her on track to become a professional dancer. Instead, at the age of 15, she decided she wanted to pursue science, gave up ballet and taught herself Russian. At secondary school, she filled in a careers test, which advised her strengths were in science and art, which Zita Martins says was not very helpful. As an undergraduate studying chemistry, at Instituto Superior Técnico, Martins was unsure how to direct her education towards a career in space science. She says, "I emailed NASA and asked them what I should do. They told me to do an internship in the Netherlands. I did an internship there, and did a really cool project analysing samples from space (i.e. meteorites). I thought: ‘this is cool; I want to do this for the rest of my life’. She was awarded a PhD in 2007 for Chemical analysis of organic molecules in carbonaceous meteorites from Leiden University supervised by Pascale Ehrenfreund. While completing her PhD, she gave a talk which was led to an invitation to be an Invited Scientist at NASA. == Research and career == In 2013, Zita Martins, working with colleagues from the University of Kent shot steel projectiles at ice samples, which simulated the composition of comets to find out if their impact
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"page_id": 43516635,
"source": null,
"title": "Zita Martins"
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is responsible for the production of complex organic molecules. The experiment found that the impact-shock of a comet produces a number of amino acids, which are the building blocks of proteins. This has implications for the origin of life on Earth but also potentially in the icy moons of Jupiter and Saturn. Zita Martins is Co-Investigator of two European Space Agency missions, OREOcube and EXOcube, which will be installed on the International Space Station in the future. Zita Martins has an active involvement with the international media. She is a BBC Expert Women Scientist. === Awards and honours === ==== Honours ==== Officer of the Military Order of Saint James of the Sword (25 February 2019) - For exceptional and outstanding merits in science. ==== Academic Awards ==== Royal Society University Research Fellowship (URF) in 2009. == References ==
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"page_id": 43516635,
"source": null,
"title": "Zita Martins"
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C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. C4.5 is an extension of Quinlan's earlier ID3 algorithm. The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier. In 2011, authors of the Weka machine learning software described the C4.5 algorithm as "a landmark decision tree program that is probably the machine learning workhorse most widely used in practice to date". It became quite popular after ranking #1 in the Top 10 Algorithms in Data Mining pre-eminent paper published by Springer LNCS in 2008. == Algorithm == C4.5 builds decision trees from a set of training data in the same way as ID3, using the concept of information entropy. The training data is a set S = s 1 , s 2 , . . . {\displaystyle S={s_{1},s_{2},...}} of already classified samples. Each sample s i {\displaystyle s_{i}} consists of a p-dimensional vector ( x 1 , i , x 2 , i , . . . , x p , i ) {\displaystyle (x_{1,i},x_{2,i},...,x_{p,i})} , where the x j {\displaystyle x_{j}} represent attribute values or features of the sample, as well as the class in which s i {\displaystyle s_{i}} falls. At each node of the tree, C4.5 chooses the attribute of the data that most effectively splits its set of samples into subsets enriched in one class or the other. The splitting criterion is the normalized information gain (difference in entropy). The attribute with the highest normalized information gain is chosen to make the decision. The C4.5 algorithm then recurses on the partitioned sublists. This algorithm has a few base cases. All the samples in the list belong to the same class. When this happens, it simply creates a
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"page_id": 1966814,
"source": null,
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leaf node for the decision tree saying to choose that class. None of the features provide any information gain. In this case, C4.5 creates a decision node higher up the tree using the expected value of the class. Instance of previously unseen class encountered. Again, C4.5 creates a decision node higher up the tree using the expected value. === Pseudocode === In pseudocode, the general algorithm for building decision trees is: Check for the above base cases. For each attribute a, find the normalized information gain ratio from splitting on a. Let a_best be the attribute with the highest normalized information gain. Create a decision node that splits on a_best. Recurse on the sublists obtained by splitting on a_best, and add those nodes as children of node. == Implementations == J48 is an open source Java implementation of the C4.5 algorithm in the Weka data mining tool. == Improvements from ID3 algorithm == C4.5 made a number of improvements to ID3. Some of these are: Handling both continuous and discrete attributes - In order to handle continuous attributes, C4.5 creates a threshold and then splits the list into those whose attribute value is above the threshold and those that are less than or equal to it. Handling training data with missing attribute values - C4.5 allows attribute values to be marked as ? for missing. Missing attribute values are simply not used in gain and entropy calculations. Handling attributes with differing costs. Pruning trees after creation - C4.5 goes back through the tree once it's been created and attempts to remove branches that do not help by replacing them with leaf nodes. == Improvements in C5.0/See5 algorithm == Quinlan went on to create C5.0 and See5 (C5.0 for Unix/Linux, See5 for Windows) which he markets commercially. C5.0 offers a number
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{
"page_id": 1966814,
"source": null,
"title": "C4.5 algorithm"
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of improvements on C4.5. Some of these are: Speed - C5.0 is significantly faster than C4.5 (several orders of magnitude) Memory usage - C5.0 is more memory efficient than C4.5 Smaller decision trees - C5.0 gets similar results to C4.5 with considerably smaller decision trees. Support for boosting - Boosting improves the trees and gives them more accuracy. Weighting - C5.0 allows you to weight different cases and misclassification types. Winnowing - a C5.0 option automatically winnows the attributes to remove those that may be unhelpful. Source for a single-threaded Linux version of C5.0 is available under the GNU General Public License (GPL). == See also == ID3 algorithm Modifying C4.5 to generate temporal and causal rules == References == == External links == Original implementation on Ross Quinlan's homepage: http://www.rulequest.com/Personal/ See5 and C5.0
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{
"page_id": 1966814,
"source": null,
"title": "C4.5 algorithm"
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Gonzalo Javier Trancho Gayo (Madrid, 8 February 1955) is a Spanish anthropologist. He obtained doctor and bachelor degrees in Biological Sciences at the Universidad Complutense de Madrid, where he is also a professor in the Zoology and Anthropology Department. His thesis dealt with a cell biology study of populations of Nilotides and he has taken part in several researches in Spain and more countries (for instance, El hombre arcaico costero: su biodiversidad y bioadaptación, Chile). He's a member of the Asociación Española de Paleopatología. == Partial bibliography == Paleodieta de la población ibérica de Villasviejas del Tamuja : análisis de la necrópolis de el Mercadillo (Botija, Cáceres), 1998. Dieta, indicadores de salud y caracterización biomorfológica de la población medieval musulmana de Xarea (Vélez Rubio, Almería), 1998. Investigaciones antropológicas en España, 1997. == External links == Asociación Española de Paleopatología Boletín
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{
"page_id": 6554334,
"source": null,
"title": "Gonzalo Trancho"
}
|
In molecular biology mir-186 microRNA is a short RNA molecule. MicroRNAs function to regulate the expression levels of other genes by several mechanisms. == See also == MicroRNA == References == == Further reading == == External links == Page for mir-186 microRNA precursor family at Rfam
|
{
"page_id": 36373215,
"source": null,
"title": "Mir-186 microRNA precursor family"
}
|
2-Acetyl-1-pyrroline (2AP) is an aroma compound and flavor that gives freshly baked bread, jasmine rice and basmati rice, the herb pandan (Pandanus amaryllifolius), and bread flowers (Vallaris glabra) their customary smell. Many observers describe the smell as similar to "hot, buttered popcorn", and it is credited for lending this odor to the scent of binturong (bearcat) urine. Fresh marking fluid (MF) and urine of the tiger (Indian, Amur or Siberian) and Indian leopard also have a strong aroma due to 2AP. 2AP and its structural homolog, 6-acetyl-2,3,4,5-tetrahydropyridine of similar smell, can be formed by Maillard reactions during heating of food such as the baking of bread dough. Both compounds have odor thresholds below 0.06 ng/L. == Structure and properties == 2AP is a substituted pyrroline and a cyclic imine as well as a ketone. == References ==
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{
"page_id": 15008481,
"source": null,
"title": "2-Acetyl-1-pyrroline"
}
|
In organic chemistry, the Jocic reaction, also called the Jocic–Reeve reaction (named after Zivojin Jocic and Wilkins Reeve) is a name reaction that generates α-substituted carboxylic acids from trichloromethylcarbinols and corresponding nucleophiles in the presence of sodium hydroxide. The reaction involves nucleophilic displacement of the hydroxyl group in a 1,1,1-trichloro-2-hydroxyalkyl structure with concomitant conversion of the trichloromethyl portion to a carboxylic acid or other acyl group. The key stages of the reaction involve an SN2 reaction, where the nucleophile displaces the oxygen with geometric inversion. == Mechanism == The reaction mechanism involves an epoxide intermediate that undergoes an SN2 reaction by the nucleophile. As a result of this mechanistic aspect, the reaction can easily occur on secondary or tertiary positions, and chiral products can be made by using chiral alcohol substrates. The reaction is one stage of the Corey–Link reaction, the Bargellini reaction, and other processes for synthesizing α-amino acids and related structures. Using hydride as the nucleophile, which also reduces the carbonyl of the product, allows this sequence to be used as a homologation reaction for primary alcohols. == Scope == Examples of this reaction include: Generation of α-azidocarboxylic acids with the use of sodium azide as the nucleophile in DME with the presence of sodium hydroxide.Conversion of aldehydes to homoelongated carboxylic acids, by first reacting with trichloromethide to form a trichloromethylcarbinol, then undergoing a Jocic reaction with either sodium borohydride or sodium phenylseleno(triethoxy)borate as the nucleophile in sodium hydroxide. This reaction can be followed by the introduction of an amine, to form the corresponding homoelongated amides. == References ==
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{
"page_id": 64946914,
"source": null,
"title": "Jocic reaction"
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Thevetins are a group of poisonous cardiac glycosides. They are obtained especially from the seeds of a West Indian shrub or small tree (Cascabela thevetia syn. Thevetia nereifolia) of the dogbane family (Apocynaceae). Hydrolysis products include glucose, digitalose, and a sterol. == References ==
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{
"page_id": 32375525,
"source": null,
"title": "Thevetin"
}
|
J. H. Wilkerson & Son Brickworks was a historic abandoned brickworks and national historic district located at Milford, Kent County, Delaware. The district includes the sites of three contributing buildings and one contributing site at the brickworks that operated from 1912 to 1957. The sheds, machinery, kiln, and other structures which housed the machinery remain standing, others have deteriorated or collapsed. Last standing were the storage shed, the shed over the brick-making machine, and one of the drying sheds. All of the machinery was in place as were other pieces of equipment used in the brick-making process. The walls of the kiln remain standing, just as they would have been left after the fired bricks are removed. It was listed on the National Register of Historic Places in 1978. It is listed on the Delaware Cultural and Historic Resources GIS system as destroyed or demolished. == References == == External links == Historic American Engineering Record (HAER) No. DE-5, "J. H. Wilkerson & Sons Brick Works, Front Street (Road 409), Milford, Sussex County, DE", 35 photos, 3 measured drawings, 13 data pages Brick Making Machine website
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"page_id": 38339304,
"source": null,
"title": "J. H. Wilkerson & Son Brickworks"
}
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Food history is an interdisciplinary field that examines the history and the cultural, economic, environmental, and sociological impacts of food and human nutrition. It is considered distinct from the more traditional field of culinary history, which focuses on the origin and recreation of specific recipes. The first journal in the field, Petits Propos Culinaires, was launched in 1979 and the first conference on the subject was the 1981 Oxford Food Symposium. == See also == Early impact of Mesoamerican goods in Iberian society Food studies List of ancient dishes List of historical cuisines List of food and beverage museums Timeline of food == References == == Further reading == Olver, Lynne. The Secret Ingredient. (2024) The Food Timeline. (Illustrated Edition) Block, Stephen. "Food History". The Kitchen Project. Collingham, Lizzie. Taste of War: World War II and the Battle for Food (2013) Cumo, Christopher, ed. Foods That Changed History: How Foods Shaped Civilization from the Ancient World to the Present (Facts on File, 2015) online Gremillion, Kristen J. Ancestral Appetites: Food in Prehistory (Cambridge UP, 2011) 188 pages; explores the processes of dietary adaptation in prehistory that contributed to the diversity of global foodways. Grew, Raymond. Food in Global History, Westview Press, 2000 Heiser Charles B. Seed to civilisation. The story of food (Harvard UP, 1990) Johnson, Sylvia A. Tomatoes, Potatoes, Corn, and Beans: How the Foods of the Americas Changed Eating around the World (Atheneum Books, 1997). online Kiple, Kenneth F. and Kriemhild Coneè Ornelas, eds. The Cambridge World History of Food, (2 vol, 2000). Katz, Solomon ed. The Encyclopedia of Food and Culture (Scribner, 2003) Lacey, Richard. Hard to swallow: a brief history of food (1994) online free Le, Stephen (2018). 100 Million Years of Food: What Our Ancestors Ate and Why It Matters Today. Picador. ISBN 978-1250117885. Mintz,
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{
"page_id": 21955305,
"source": null,
"title": "Food history"
}
|
Sidney. Tasting Food, Tasting Freedom: Excursions into Eating, Power, and the Past, (1997). Nestle, Marion. Food Politics: How the Food Industry Influences Nutrition and Health (2nd ed 2007). Olver, Lynne. "Food Timeline: food history research service". The Food Timeline. Parasecoli, Fabio & Peter Scholliers, eds. A Cultural History of Food, 6 volumes (Berg Publishers, 2012) Pilcher, Jeffrey M. ed. The Oxford Handbook of Food History (2017). Online review Pilcher, Jeffrey M. Food in World History (2017) advanced survey Ritchie, Carson I.A. Food in civilization: how history has been affected by human tastes (1981) online free Snodgrass, Mary Ellen, ed. World Food: An Encyclopedia of History, Culture and Social Influence from Hunter Gatherers to the Age of Globalization (Routledge, 2012) Vernon, James. Hunger: A Modern History (Harvard UP, 2007). === Foods and meals === Abbott, Elizabeth. Sugar: A Bittersweet History (2015) 464pp. Albala, Ken. Beans: A History (2007). Anderson, Heather Arndt. Breakfast: A History (2014) 238pp Atkins, Peter. Liquid Materialities: A History of Milk, Science and the Law (Ashgate, 2010). Blake, Michael. Maize for the Gods: Unearthing the 9,000-Year History of Corn (2015). Collingham, Lizzie. Curry: A Tale of Cooks and Conquerors (2007) Elias, Megan. Lunch: A History (2014) 204pp Foster, Nelson and Linda S. Cordell. Chilies to Chocolate: Food the Americas Gave the World (1992) Kindstedt, Paul. Cheese and Culture: A History of Cheese and its Place in Western Civilization (2012) Kurlansky, Mark. Milk!: A 10,000-Year Food Fracas (2018). excerpt Kurlansky, Mark. Salt: A World History (2003) excerpt Martin, Laura C. A History of Tea: The Life and Times of the World's Favorite Beverage (2018) excerpt Mintz, Sidney. Sweetness and Power: The Place of Sugar in Modern History (1986) Morris, Jonathan. Coffee: A Global History (2019) excerpt Pettigrew, Jane, and Bruce Richardson. A Social History of Tea: Tea's Influence on
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{
"page_id": 21955305,
"source": null,
"title": "Food history"
}
|
Commerce, Culture & Community (2015). Piatti-Farnell, Lorna. Beef: A Global History (2013) excerpt Reader, John. Propitious Esculent: The Potato in World History (2008), 315pp a standard scholarly history Salaman, R.N. The history and social influence of the potato (1949) Smith, Andrew F. Sugar: A Global History (2015) excerpt Valenze, Deborah,. Milk: A Local and Global History (Yale UP, 2012) === Historiography === Claflin, Kyri and Peter Scholliers, eds. Writing Food History, a Global Perspective (Berg, 2012) De La Peña, Carolyn, and Benjamin N. Lawrance. "Introduction: Traversing the local/global and food/culture divides." Food and Foodways 19.1-2 (2011): 1–10. Duffett, Rachel, and Ina Zweiniger-Bargielowska, eds. Food and War in Twentieth Century Europe (2011) excerpt Otter, Chris. "The British Nutrition Transition and its Histories", History Compass 10/11 (2012): pp. 812–825, [DOI]: 10.1111/hic3.12001 Peters Kernan, Sarah. "Recent Trends in Food History Research in the United States: 2017-19." Food & History (Jan 2021), Vol. 18 Issue 1/2, pp 233–240. Pilcher, Jeffrey M. "The embodied imagination in recent writings on food history." American Historical Review 121#3 (2016): 861–887. Pilcher, Jeffrey M., ed. Food History: Critical and Primary Sources (2015) 4 vol; reprints 76 primary and secondary sources. Scholliers, Peter. " Twenty-five Years of Studying un Phénomène Social Total: Food History Writing on Europe in the Nineteenth and Twentieth Centuries," Food, Culture & Society: An International Journal of Multidisciplinary Research (2007) 10#3 pp 449–471 https://doi.org/10.2752/155280107X239881 Woolgar, Christopher M. "Food and the middle ages." Journal of Medieval History 36.1 (2010): 1–19. === Asia === Achaya, Kongandra Thammu. A historical dictionary of Indian food (New Delhi: Oxford UP, 1998). Cheung, Sidney, and David Y.H. Wu. The globalisation of Chinese food (Routledge, 2014). Chung, Hae Kyung, et al. "Understanding Korean food culture from Korean paintings." Journal of Ethnic Foods 3#1 (2016): 42–50. Cwiertka, Katarzyna Joanna. Modern Japanese cuisine: Food, power
|
{
"page_id": 21955305,
"source": null,
"title": "Food history"
}
|
and national identity (Reaktion Books, 2006). Kim, Soon Hee, et al. "Korean diet: characteristics and historical background." Journal of Ethnic Foods 3.1 (2016): 26–31. Kushner, Barak. Slurp! a Social and Culinary History of Ramen: Japan's Favorite Noodle Soup (2014) a scholarly cultural history over 1000 years Simoons, Frederick J. Food in China: a cultural and historical inquiry (2014). === Europe === Gentilcore, David. Food and Health in Early Modern Europe: Diet, Medicine and Society, 1450–1800 (Bloomsbury, 2016) Goldman, Wendy Z. and Donald Filtzer, eds. Food Provisioning in the Soviet Union during World War II (2015) Roll, Eric. The Combined Food Board. A study in wartime international planning (1956), on World War II Rosen, William. The Third Horseman: Climate change and the great famine of the 14th century (Penguin, 2014). Scarpellini, Emanuela. Food and Foodways in Italy from 1861 to the Present (2014) ==== Great Britain ==== Addyman, Mary et al. eds. Food, Drink, and the Written Word in Britain, 1820–1945 (Taylor & Francis, 2017). Barnett, Margaret. British Food Policy During the First World War (Routledge, 2014). Beveridge, W. H. British Food Control (1928), in World War I Brears, P. Cooking and Dining in Medieval England (2008) Burnett, John. Plenty and want: a social history of diet in England from 1815 to the present day (2nd ed. 1979). A standard scholarly history. Collins, E. J. T. "Dietary change and cereal consumption in Britain in the nineteenth century." Agricultural History Review (1975) 23#2, 97–115. Gautier, Alban. "Cooking and cuisine in late Anglo-Saxon England." Anglo-Saxon England 41 (2012): 373–406. Gazeley, I. and Newell, A. "Urban working-class food consumption and nutrition in Britain in 1904" Economic History Review. (2014). http://onlinelibrary.wiley.com/doi/10.1111/ehr.12065/pdf. Harris, Bernard, Roderick Floud, and Sok Chul Hong. "How many calories? Food availability in England and Wales in the eighteenth and nineteenth centuries". Research
|
{
"page_id": 21955305,
"source": null,
"title": "Food history"
}
|
in economic history. (2015). 111–191. Hartley, Dorothy. Food In England: A complete guide to the food that makes us who we are (Hachette UK, 2014). Mennell, Stephen. All Manners of Food: Eating and Taste in England and France from the Middle Ages to the Present (2nd ed U of Illinois Press, 1996) Meredith, D. and Oxley, D. "Food and fodder: feeding England, 1700-1900." Past and Present (2014). (2014). 222:163-214. Oddy, Derek. From Plain Fare to Fusion Food: British Diet from the 1890s to the 1990s (Boydell Press, 2003). Oddy, D. " Food, drink and nutrition" in F.M.L. Thompson, ed., The Cambridge social history of Britain, 1750–1950. Volume 2. People and their environment (1990). pp. 2:251-278. Otter, Chris. "The British Nutrition Transition and its Histories", History Compass 10#11 (2012): pp. 812–825, [DOI]: 10.1111/hic3.12001 Panayi, Panikos. Spicing Up Britain: The Multicultural History of British Food (2010) Spencer, Colin. British Food: An Extraordinary Thousand Years of History (2007). Woolgar. C. N. The Culture of Food in England, 1200–1500 (2016). 260 pp., === United States === Pendergrast, Mark. For God, Country, and Coca-Cola: The Definitive History of the Great American Soft Drink and the Company That Makes It (2013) Shapiro, Laura. Something From the Oven: Reinventing Dinner in 1950s America, Viking Adult 2004, ISBN 0-670-87154-0 Smith, Andrew F. ed. The Oxford companion to American food and drink (2007) Veit, Helen Zoe, ed. Food in the Civil War Era: The North (Michigan State University Press, 2014) Veit, Helen Zoe. Modern Food, Moral Food: Self-Control, Science, and the Rise of Modern American Eating in the Early Twentieth Century (University of North Carolina Press, 2013) Wallach, Jennifer Jensen. How America Eats: A social history of U.S. food and culture (2014) 256256pp Williams, Elizabeth M. New Orleans: A Food Biography (AltaMira Press, 2012). === Journals === Petits Propos
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{
"page_id": 21955305,
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"title": "Food history"
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Culinaires, first journal in the field Food and Foodways: Explorations in the History and Culture of Human Nourishment Food, Culture and Society: An International Journal of Multidisciplinary Research Food & History: multilingual scientific journal about the history and culture of food published by the (IEHCA) === Other languages === Montanari, Massimo, Il mondo in cucina (The world in the kitchen). Laterza, 2002 ASIN: B0055J686G Mintalová - Zubercová, Zora: Všetko okolo stola I. (All around the table I.), Vydavateľstvo Matice slovenskej, 2009, ISBN 978-80-89208-94-4
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{
"page_id": 21955305,
"source": null,
"title": "Food history"
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Surface-enhanced ellipsometric contrast microscopy (SEEC) uses an upright or inverted optical microscope in a crossed polarization configuration and specific supporting plates called surfs on which the sample is deposited for observation. It is described as an optical nanoscopy technique. SEEC relies on precise control of the reflection properties of polarized light on a surface, improving the axial sensitivity of an optical microscope by two orders of magnitude without reducing its lateral resolution. Applications could include real-time visualization of films as thin as 0.3 micrometers and isolated nano-objects in air and in water. == Principles == A 2006 study on polarized light coherence led to the development of new supports (the surfs) having contrast amplification properties for standard optical microscopy in cross-polarizer mode. Made of optical layers on an opaque or transparent substrate, these supports do not modify the light polarization after reflection even if the numerical aperture of the incident source is significant. This property is modified when a sample is present on a surf; a non-null light component is then detected after it has been analyzed, rendering the sample visible. The performance of these supports is evaluated by measuring the contrast (C) of the sample defined as: C = (I1-I0)/(I0+I1) where I0 and I1 represent the intensities reflected by the bare surf and by the analyzed sample on the surf, respectively. For a one nanometer-film thickness, the surfs display a contrast 200 times higher than on silicon wafer. This high contrast increase allows the visualization with standard optical microscope of films with thicknesses down to 0.3 nanometers, as well as nano-objects (down to a 2 nanometer diameter) and this, without any kind of sample labeling (neither fluorescence, nor a radioactive marker). An illustration of the contrast enhancement is in the Figure for optical microscopy between cross polarizers of a
|
{
"page_id": 24117996,
"source": null,
"title": "SEEC microscopy"
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|
Langmuir-Blodgett structure on a silicon wafer and on a surf. == Applications == === Life sciences === Biological films Biochips Soft lithography === Thin films and surface treatment === Langmuir-Blodgett films === Nano-materials === Nanoparticles Graphene == Commercial applications == Nanolane's Sarfus Mapping Station is based on surface-enhanced ellipsometric contrast microscopy. == References ==
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{
"page_id": 24117996,
"source": null,
"title": "SEEC microscopy"
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Chromosomal landing is a genetic technique used to identify and isolate clones in a genetic library. Chromosomal landing reduces the problem of analyzing large, and/or highly repetitive genomes by minimizing the need for chromosome walking. It is based on the principle that the expected average between-marker distances can be smaller than the average insert length of a clone library containing the gene of interest. From the abstract of PMID 7716809: The strategy of chromosome walking is based on the assumption that it is difficult and time consuming to find DNA markers that are physically close to a gene of interest. Recent technological developments invalidate this assumption for many species. As a result, the mapping paradigm has now changed such that one first isolates one or more DNA marker(s) at a physical distance from the targeted gene that is less than the average insert size of the genomic library being used for clone isolation. The DNA marker is then used to screen the library and isolate (or 'land' on) the clone containing the gene, without any need for chromosome walking and its associated problems. Chromosome landing, together with the technology that has made it possible, is likely to become the main strategy by which map-based cloning is applied to isolate both major genes and genes underlying quantitative traits in plant species. == See also == Primer walking == References ==
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{
"page_id": 18744044,
"source": null,
"title": "Chromosome landing"
}
|
Wolfgang Ketter (born Traben-Trarbach, Germany, 1972) is Chaired Professor of Information Systems for a Sustainable Society at the University of Cologne. and a prominent scientist in the application of artificial intelligence, machine learning and intelligent agents in the design of smart markets, including demand response mechanisms and in particular automated auctions. He is a co-founder of the open energy system platform Power TAC, an automated retail electricity trading platform that simulates the performance of retail markets in an increasingly prosumer- and renewable-energy-influenced electricity landscape. == Career == === Advisory roles === Ketter is an advisor on the energy transition to the German government, in particular, the energy-intensive German state of North Rhine-Westphalia. He is also a fellow of the World Economic Forum and member of the WEF Global Council on Future Mobility and the Global New Mobility Coalition, contributing on the use of AI and machine learning to address issues arising from growth in electrification of energy such as the use of batteries as virtual power plants, the management of electric vehicle charging to prevent grid congestion, or the potential for peer-to-peer electricity trading. Ketter has also been an advisor for over a decade to the Port of Rotterdam on the design of energy cooperatives and energy trading platforms as well as one of the largest auction companies in the world, Royal FloraHolland, where his initial research led to a redesign of auction mechanisms and decision support systems. The cumulative research project team received the Association for Information Systems Impact Award in 2020 === Research === Ketter’s research is multidisciplinary, addressing the overlap of AI and ML in the economics of retail energy and mobility markets. The industry and policy applications of his research interconnect in large-scale projects such as the EU Smart city development project Ruggedised, for which the
|
{
"page_id": 63111921,
"source": null,
"title": "Wolfgang Ketter"
}
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Erasmus University-based team's publication on the optimization of the City of Rotterdam's electric transit bus network was recognized with the Institute for Operations Research and the Management Sciences Daniel H. Wagner runner-up award. His research focuses on the use of competitive benchmarking and intelligent agents in virtual world simulations of retail energy markets as part of a smart grid. A small-scale version of the Power TAC project led to a publication on demand side management, 'A simulation of household behavior under variable prices' that has several hundred citations in publications representing a variety of scientific disciplines. Two of his publications in the Management Information Systems Quarterly journal and one in Energy Economics form the foundation for the current Power TAC platform. In 2016 and 2019 he was Chair of the Workshop on Information Technologies and Systems. Ketter is Coordinator of the Key Research Initiative Sustainable Smart Energy & Mobility at the University of Cologne, where he is a chaired Professor of Information Systems for a Sustainable Society. At the Rotterdam School of Management, Erasmus University, he is Professor of Next Generation Information Systems as well as Director of the Erasmus Centre for Future Energy Business and Academic Director of Smart Cities and Smart Energy at the Erasmus Centre of Data Analytics. He has been a visiting professor at the Haas School of Business and Berkeley Institute of Data Science, University of California at Berkeley in 2016 to 2017. == References ==
|
{
"page_id": 63111921,
"source": null,
"title": "Wolfgang Ketter"
}
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Siegfried Adolf Wouthuysen (17 August 1916 – 14 July 1996) was a Dutch physicist who made contributions to quantum mechanics. He collaborated with Leslie Lawrance Foldy to develop the Foldy–Wouthuysen transformation, and with George B. Field to develop the Wouthuysen–Field coupling. == Early life == Wouthuysen was born in Amsterdam in 1916. He obtained his bachelor's degree in Chemistry at Ghent University in 1936 and his master's degree in Mathematics and Physics at Leiden University in 1939. == Research == Wouthuysen gained his Ph.D. from the University of California, Berkeley, 1948 with a dissertation on self-energy and relativistic covariance in field theory, under his advisor J. Robert Oppenheimer. In 1949 he became assistant professor and in 1955 full professor of Physics at the University of Amsterdam, which position he held until his retirement in 1984. == References == == External links == "Database Joods Biografisch Woordenboek". Retrieved 2013-07-21.
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{
"page_id": 39912177,
"source": null,
"title": "Siegfried Adolf Wouthuysen"
}
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Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input-output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead, the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge) with the goal of maximizing the cumulative reward (the feedback of which might be incomplete or delayed). The search for this balance is known as the exploration–exploitation dilemma. The environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dynamic programming techniques. The main difference between classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the Markov decision process, and they target large MDPs where exact methods become infeasible. == Principles == Due to its generality, reinforcement learning is studied in many disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics. In the operations research and control literature, RL is called approximate dynamic programming, or neuro-dynamic programming. The problems of interest in RL have also been studied in the theory of optimal control, which is concerned mostly with the existence and characterization of optimal solutions, and algorithms for their exact computation, and less with learning or approximation (particularly in the absence of a mathematical model of the environment). Basic reinforcement learning is modeled as a Markov decision process: A set of environment and agent states
|
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"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
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(the state space), S {\displaystyle {\mathcal {S}}} ; A set of actions (the action space), A {\displaystyle {\mathcal {A}}} , of the agent; P a ( s , s ′ ) = Pr ( S t + 1 = s ′ ∣ S t = s , A t = a ) {\displaystyle P_{a}(s,s')=\Pr(S_{t+1}=s'\mid S_{t}=s,A_{t}=a)} , the transition probability (at time t {\displaystyle t} ) from state s {\displaystyle s} to state s ′ {\displaystyle s'} under action a {\displaystyle a} . R a ( s , s ′ ) {\displaystyle R_{a}(s,s')} , the immediate reward after transition from s {\displaystyle s} to s ′ {\displaystyle s'} under action a {\displaystyle a} . The purpose of reinforcement learning is for the agent to learn an optimal (or near-optimal) policy that maximizes the reward function or other user-provided reinforcement signal that accumulates from immediate rewards. This is similar to processes that appear to occur in animal psychology. For example, biological brains are hardwired to interpret signals such as pain and hunger as negative reinforcements, and interpret pleasure and food intake as positive reinforcements. In some circumstances, animals learn to adopt behaviors that optimize these rewards. This suggests that animals are capable of reinforcement learning. A basic reinforcement learning agent interacts with its environment in discrete time steps. At each time step t, the agent receives the current state S t {\displaystyle S_{t}} and reward R t {\displaystyle R_{t}} . It then chooses an action A t {\displaystyle A_{t}} from the set of available actions, which is subsequently sent to the environment. The environment moves to a new state S t + 1 {\displaystyle S_{t+1}} and the reward R t + 1 {\displaystyle R_{t+1}} associated with the transition ( S t , A t , S t + 1 ) {\displaystyle (S_{t},A_{t},S_{t+1})}
|
{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
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is determined. The goal of a reinforcement learning agent is to learn a policy: π : S × A → [ 0 , 1 ] {\displaystyle \pi :{\mathcal {S}}\times {\mathcal {A}}\rightarrow [0,1]} , π ( s , a ) = Pr ( A t = a ∣ S t = s ) {\displaystyle \pi (s,a)=\Pr(A_{t}=a\mid S_{t}=s)} that maximizes the expected cumulative reward. Formulating the problem as a Markov decision process assumes the agent directly observes the current environmental state; in this case, the problem is said to have full observability. If the agent only has access to a subset of states, or if the observed states are corrupted by noise, the agent is said to have partial observability, and formally the problem must be formulated as a partially observable Markov decision process. In both cases, the set of actions available to the agent can be restricted. For example, the state of an account balance could be restricted to be positive; if the current value of the state is 3 and the state transition attempts to reduce the value by 4, the transition will not be allowed. When the agent's performance is compared to that of an agent that acts optimally, the difference in performance yields the notion of regret. In order to act near optimally, the agent must reason about long-term consequences of its actions (i.e., maximize future rewards), although the immediate reward associated with this might be negative. Thus, reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off. It has been applied successfully to various problems, including energy storage, robot control, photovoltaic generators, backgammon, checkers, Go (AlphaGo), and autonomous driving systems. Two elements make reinforcement learning powerful: the use of samples to optimize performance, and the use of function approximation to deal with
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{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
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large environments. Thanks to these two key components, RL can be used in large environments in the following situations: A model of the environment is known, but an analytic solution is not available; Only a simulation model of the environment is given (the subject of simulation-based optimization); The only way to collect information about the environment is to interact with it. The first two of these problems could be considered planning problems (since some form of model is available), while the last one could be considered to be a genuine learning problem. However, reinforcement learning converts both planning problems to machine learning problems. == Exploration == The exploration vs. exploitation trade-off has been most thoroughly studied through the multi-armed bandit problem and for finite state space Markov decision processes in Burnetas and Katehakis (1997). Reinforcement learning requires clever exploration mechanisms; randomly selecting actions, without reference to an estimated probability distribution, shows poor performance. The case of (small) finite Markov decision processes is relatively well understood. However, due to the lack of algorithms that scale well with the number of states (or scale to problems with infinite state spaces), simple exploration methods are the most practical. One such method is ε {\displaystyle \varepsilon } -greedy, where 0 < ε < 1 {\displaystyle 0<\varepsilon <1} is a parameter controlling the amount of exploration vs. exploitation. With probability 1 − ε {\displaystyle 1-\varepsilon } , exploitation is chosen, and the agent chooses the action that it believes has the best long-term effect (ties between actions are broken uniformly at random). Alternatively, with probability ε {\displaystyle \varepsilon } , exploration is chosen, and the action is chosen uniformly at random. ε {\displaystyle \varepsilon } is usually a fixed parameter but can be adjusted either according to a schedule (making the agent explore progressively less),
|
{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
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or adaptively based on heuristics. == Algorithms for control learning == Even if the issue of exploration is disregarded and even if the state was observable (assumed hereafter), the problem remains to use past experience to find out which actions lead to higher cumulative rewards. === Criterion of optimality === ==== Policy ==== The agent's action selection is modeled as a map called policy: π : A × S → [ 0 , 1 ] {\displaystyle \pi :{\mathcal {A}}\times {\mathcal {S}}\rightarrow [0,1]} π ( a , s ) = Pr ( A t = a ∣ S t = s ) {\displaystyle \pi (a,s)=\Pr(A_{t}=a\mid S_{t}=s)} The policy map gives the probability of taking action a {\displaystyle a} when in state s {\displaystyle s} .: 61 There are also deterministic policies π {\displaystyle \pi } for which π ( s ) {\displaystyle \pi (s)} denotes the action that should be played at state s {\displaystyle s} . ==== State-value function ==== The state-value function V π ( s ) {\displaystyle V_{\pi }(s)} is defined as, expected discounted return starting with state s {\displaystyle s} , i.e. S 0 = s {\displaystyle S_{0}=s} , and successively following policy π {\displaystyle \pi } . Hence, roughly speaking, the value function estimates "how good" it is to be in a given state.: 60 V π ( s ) = E [ G ∣ S 0 = s ] = E [ ∑ t = 0 ∞ γ t R t + 1 ∣ S 0 = s ] , {\displaystyle V_{\pi }(s)=\operatorname {\mathbb {E} } [G\mid S_{0}=s]=\operatorname {\mathbb {E} } \left[\sum _{t=0}^{\infty }\gamma ^{t}R_{t+1}\mid S_{0}=s\right],} where the random variable G {\displaystyle G} denotes the discounted return, and is defined as the sum of future discounted rewards: G = ∑ t = 0
|
{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
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∞ γ t R t + 1 = R 1 + γ R 2 + γ 2 R 3 + … , {\displaystyle G=\sum _{t=0}^{\infty }\gamma ^{t}R_{t+1}=R_{1}+\gamma R_{2}+\gamma ^{2}R_{3}+\dots ,} where R t + 1 {\displaystyle R_{t+1}} is the reward for transitioning from state S t {\displaystyle S_{t}} to S t + 1 {\displaystyle S_{t+1}} , 0 ≤ γ < 1 {\displaystyle 0\leq \gamma <1} is the discount rate. γ {\displaystyle \gamma } is less than 1, so rewards in the distant future are weighted less than rewards in the immediate future. The algorithm must find a policy with maximum expected discounted return. From the theory of Markov decision processes it is known that, without loss of generality, the search can be restricted to the set of so-called stationary policies. A policy is stationary if the action-distribution returned by it depends only on the last state visited (from the observation agent's history). The search can be further restricted to deterministic stationary policies. A deterministic stationary policy deterministically selects actions based on the current state. Since any such policy can be identified with a mapping from the set of states to the set of actions, these policies can be identified with such mappings with no loss of generality. === Brute force === The brute force approach entails two steps: For each possible policy, sample returns while following it Choose the policy with the largest expected discounted return One problem with this is that the number of policies can be large, or even infinite. Another is that the variance of the returns may be large, which requires many samples to accurately estimate the discounted return of each policy. These problems can be ameliorated if we assume some structure and allow samples generated from one policy to influence the estimates made for others.
|
{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
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The two main approaches for achieving this are value function estimation and direct policy search. === Value function === Value function approaches attempt to find a policy that maximizes the discounted return by maintaining a set of estimates of expected discounted returns E [ G ] {\displaystyle \operatorname {\mathbb {E} } [G]} for some policy (usually either the "current" [on-policy] or the optimal [off-policy] one). These methods rely on the theory of Markov decision processes, where optimality is defined in a sense stronger than the one above: A policy is optimal if it achieves the best-expected discounted return from any initial state (i.e., initial distributions play no role in this definition). Again, an optimal policy can always be found among stationary policies. To define optimality in a formal manner, define the state-value of a policy π {\displaystyle \pi } by V π ( s ) = E [ G ∣ s , π ] , {\displaystyle V^{\pi }(s)=\operatorname {\mathbb {E} } [G\mid s,\pi ],} where G {\displaystyle G} stands for the discounted return associated with following π {\displaystyle \pi } from the initial state s {\displaystyle s} . Defining V ∗ ( s ) {\displaystyle V^{*}(s)} as the maximum possible state-value of V π ( s ) {\displaystyle V^{\pi }(s)} , where π {\displaystyle \pi } is allowed to change, V ∗ ( s ) = max π V π ( s ) . {\displaystyle V^{*}(s)=\max _{\pi }V^{\pi }(s).} A policy that achieves these optimal state-values in each state is called optimal. Clearly, a policy that is optimal in this sense is also optimal in the sense that it maximizes the expected discounted return, since V ∗ ( s ) = max π E [ G ∣ s , π ] {\displaystyle V^{*}(s)=\max _{\pi }\mathbb {E} [G\mid s,\pi
|
{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
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]} , where s {\displaystyle s} is a state randomly sampled from the distribution μ {\displaystyle \mu } of initial states (so μ ( s ) = Pr ( S 0 = s ) {\displaystyle \mu (s)=\Pr(S_{0}=s)} ). Although state-values suffice to define optimality, it is useful to define action-values. Given a state s {\displaystyle s} , an action a {\displaystyle a} and a policy π {\displaystyle \pi } , the action-value of the pair ( s , a ) {\displaystyle (s,a)} under π {\displaystyle \pi } is defined by Q π ( s , a ) = E [ G ∣ s , a , π ] , {\displaystyle Q^{\pi }(s,a)=\operatorname {\mathbb {E} } [G\mid s,a,\pi ],\,} where G {\displaystyle G} now stands for the random discounted return associated with first taking action a {\displaystyle a} in state s {\displaystyle s} and following π {\displaystyle \pi } , thereafter. The theory of Markov decision processes states that if π ∗ {\displaystyle \pi ^{*}} is an optimal policy, we act optimally (take the optimal action) by choosing the action from Q π ∗ ( s , ⋅ ) {\displaystyle Q^{\pi ^{*}}(s,\cdot )} with the highest action-value at each state, s {\displaystyle s} . The action-value function of such an optimal policy ( Q π ∗ {\displaystyle Q^{\pi ^{*}}} ) is called the optimal action-value function and is commonly denoted by Q ∗ {\displaystyle Q^{*}} . In summary, the knowledge of the optimal action-value function alone suffices to know how to act optimally. Assuming full knowledge of the Markov decision process, the two basic approaches to compute the optimal action-value function are value iteration and policy iteration. Both algorithms compute a sequence of functions Q k {\displaystyle Q_{k}} ( k = 0 , 1 , 2 , … {\displaystyle k=0,1,2,\ldots
|
{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
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} ) that converge to Q ∗ {\displaystyle Q^{*}} . Computing these functions involves computing expectations over the whole state-space, which is impractical for all but the smallest (finite) Markov decision processes. In reinforcement learning methods, expectations are approximated by averaging over samples and using function approximation techniques to cope with the need to represent value functions over large state-action spaces. ==== Monte Carlo methods ==== Monte Carlo methods are used to solve reinforcement learning problems by averaging sample returns. Unlike methods that require full knowledge of the environment's dynamics, Monte Carlo methods rely solely on actual or simulated experience—sequences of states, actions, and rewards obtained from interaction with an environment. This makes them applicable in situations where the complete dynamics are unknown. Learning from actual experience does not require prior knowledge of the environment and can still lead to optimal behavior. When using simulated experience, only a model capable of generating sample transitions is required, rather than a full specification of transition probabilities, which is necessary for dynamic programming methods. Monte Carlo methods apply to episodic tasks, where experience is divided into episodes that eventually terminate. Policy and value function updates occur only after the completion of an episode, making these methods incremental on an episode-by-episode basis, though not on a step-by-step (online) basis. The term "Monte Carlo" generally refers to any method involving random sampling; however, in this context, it specifically refers to methods that compute averages from complete returns, rather than partial returns. These methods function similarly to the bandit algorithms, in which returns are averaged for each state-action pair. The key difference is that actions taken in one state affect the returns of subsequent states within the same episode, making the problem non-stationary. To address this non-stationarity, Monte Carlo methods use the framework of general policy
|
{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
}
|
iteration (GPI). While dynamic programming computes value functions using full knowledge of the Markov decision process (MDP), Monte Carlo methods learn these functions through sample returns. The value functions and policies interact similarly to dynamic programming to achieve optimality, first addressing the prediction problem and then extending to policy improvement and control, all based on sampled experience. ==== Temporal difference methods ==== The first problem is corrected by allowing the procedure to change the policy (at some or all states) before the values settle. This too may be problematic as it might prevent convergence. Most current algorithms do this, giving rise to the class of generalized policy iteration algorithms. Many actor-critic methods belong to this category. The second issue can be corrected by allowing trajectories to contribute to any state-action pair in them. This may also help to some extent with the third problem, although a better solution when returns have high variance is Sutton's temporal difference (TD) methods that are based on the recursive Bellman equation. The computation in TD methods can be incremental (when after each transition the memory is changed and the transition is thrown away), or batch (when the transitions are batched and the estimates are computed once based on the batch). Batch methods, such as the least-squares temporal difference method, may use the information in the samples better, while incremental methods are the only choice when batch methods are infeasible due to their high computational or memory complexity. Some methods try to combine the two approaches. Methods based on temporal differences also overcome the fourth issue. Another problem specific to TD comes from their reliance on the recursive Bellman equation. Most TD methods have a so-called λ {\displaystyle \lambda } parameter ( 0 ≤ λ ≤ 1 ) {\displaystyle (0\leq \lambda \leq 1)} that can
|
{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
}
|
continuously interpolate between Monte Carlo methods that do not rely on the Bellman equations and the basic TD methods that rely entirely on the Bellman equations. This can be effective in palliating this issue. ==== Function approximation methods ==== In order to address the fifth issue, function approximation methods are used. Linear function approximation starts with a mapping ϕ {\displaystyle \phi } that assigns a finite-dimensional vector to each state-action pair. Then, the action values of a state-action pair ( s , a ) {\displaystyle (s,a)} are obtained by linearly combining the components of ϕ ( s , a ) {\displaystyle \phi (s,a)} with some weights θ {\displaystyle \theta } : Q ( s , a ) = ∑ i = 1 d θ i ϕ i ( s , a ) . {\displaystyle Q(s,a)=\sum _{i=1}^{d}\theta _{i}\phi _{i}(s,a).} The algorithms then adjust the weights, instead of adjusting the values associated with the individual state-action pairs. Methods based on ideas from nonparametric statistics (which can be seen to construct their own features) have been explored. Value iteration can also be used as a starting point, giving rise to the Q-learning algorithm and its many variants. Including Deep Q-learning methods when a neural network is used to represent Q, with various applications in stochastic search problems. The problem with using action-values is that they may need highly precise estimates of the competing action values that can be hard to obtain when the returns are noisy, though this problem is mitigated to some extent by temporal difference methods. Using the so-called compatible function approximation method compromises generality and efficiency. === Direct policy search === An alternative method is to search directly in (some subset of) the policy space, in which case the problem becomes a case of stochastic optimization. The two approaches available
|
{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
}
|
are gradient-based and gradient-free methods. Gradient-based methods (policy gradient methods) start with a mapping from a finite-dimensional (parameter) space to the space of policies: given the parameter vector θ {\displaystyle \theta } , let π θ {\displaystyle \pi _{\theta }} denote the policy associated to θ {\displaystyle \theta } . Defining the performance function by ρ ( θ ) = ρ π θ {\displaystyle \rho (\theta )=\rho ^{\pi _{\theta }}} under mild conditions this function will be differentiable as a function of the parameter vector θ {\displaystyle \theta } . If the gradient of ρ {\displaystyle \rho } was known, one could use gradient ascent. Since an analytic expression for the gradient is not available, only a noisy estimate is available. Such an estimate can be constructed in many ways, giving rise to algorithms such as Williams's REINFORCE method (which is known as the likelihood ratio method in the simulation-based optimization literature). A large class of methods avoids relying on gradient information. These include simulated annealing, cross-entropy search or methods of evolutionary computation. Many gradient-free methods can achieve (in theory and in the limit) a global optimum. Policy search methods may converge slowly given noisy data. For example, this happens in episodic problems when the trajectories are long and the variance of the returns is large. Value-function based methods that rely on temporal differences might help in this case. In recent years, actor–critic methods have been proposed and performed well on various problems. Policy search methods have been used in the robotics context. Many policy search methods may get stuck in local optima (as they are based on local search). === Model-based algorithms === Finally, all of the above methods can be combined with algorithms that first learn a model of the Markov decision process, the probability of each next
|
{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
}
|
state given an action taken from an existing state. For instance, the Dyna algorithm learns a model from experience, and uses that to provide more modelled transitions for a value function, in addition to the real transitions. Such methods can sometimes be extended to use of non-parametric models, such as when the transitions are simply stored and "replayed" to the learning algorithm. Model-based methods can be more computationally intensive than model-free approaches, and their utility can be limited by the extent to which the Markov decision process can be learnt. There are other ways to use models than to update a value function. For instance, in model predictive control the model is used to update the behavior directly. == Theory == Both the asymptotic and finite-sample behaviors of most algorithms are well understood. Algorithms with provably good online performance (addressing the exploration issue) are known. Efficient exploration of Markov decision processes is given in Burnetas and Katehakis (1997). Finite-time performance bounds have also appeared for many algorithms, but these bounds are expected to be rather loose and thus more work is needed to better understand the relative advantages and limitations. For incremental algorithms, asymptotic convergence issues have been settled. Temporal-difference-based algorithms converge under a wider set of conditions than was previously possible (for example, when used with arbitrary, smooth function approximation). == Research == Research topics include: actor-critic architecture actor-critic-scenery architecture adaptive methods that work with fewer (or no) parameters under a large number of conditions bug detection in software projects continuous learning combinations with logic-based frameworks exploration in large Markov decision processes entity-based reinforcement learning human feedback interaction between implicit and explicit learning in skill acquisition intrinsic motivation which differentiates information-seeking, curiosity-type behaviours from task-dependent goal-directed behaviours large-scale empirical evaluations large (or continuous) action spaces modular and hierarchical reinforcement
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{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
}
|
learning multiagent/distributed reinforcement learning is a topic of interest. Applications are expanding. occupant-centric control optimization of computing resources partial information (e.g., using predictive state representation) reward function based on maximising novel information sample-based planning (e.g., based on Monte Carlo tree search). securities trading transfer learning TD learning modeling dopamine-based learning in the brain. Dopaminergic projections from the substantia nigra to the basal ganglia function are the prediction error. value-function and policy search methods == Comparison of key algorithms == The following table lists the key algorithms for learning a policy depending on several criteria: The algorithm can be on-policy (it performs policy updates using trajectories sampled via the current policy) or off-policy. The action space may be discrete (e.g. the action space could be "going up", "going left", "going right", "going down", "stay") or continuous (e.g. moving the arm with a given angle). The state space may be discrete (e.g. the agent could be in a cell in a grid) or continuous (e.g. the agent could be located at a given position in the plane). === Associative reinforcement learning === Associative reinforcement learning tasks combine facets of stochastic learning automata tasks and supervised learning pattern classification tasks. In associative reinforcement learning tasks, the learning system interacts in a closed loop with its environment. === Deep reinforcement learning === This approach extends reinforcement learning by using a deep neural network and without explicitly designing the state space. The work on learning ATARI games by Google DeepMind increased attention to deep reinforcement learning or end-to-end reinforcement learning. === Adversarial deep reinforcement learning === Adversarial deep reinforcement learning is an active area of research in reinforcement learning focusing on vulnerabilities of learned policies. In this research area some studies initially showed that reinforcement learning policies are susceptible to imperceptible adversarial manipulations. While some
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{
"page_id": 66294,
"source": null,
"title": "Reinforcement learning"
}
|
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