text stringlengths 9 3.55k | source stringlengths 31 280 |
|---|---|
Every two years, it publishes a select list known as the Watch List of 100 Most Endangered Sites that is in urgent need of preservation funding and protection. The sites are nominated by governments, conservation professionals, site caretakers, non-government organizations (NGOs), concerned individuals, and others working in the field. An independent panel of international experts then select 100 candidates from these entries to be part of the Watch List, based on the significance of the sites, the urgency of the threat, and the viability of both advocacy and conservation solutions. For the succeeding two-year period until a new Watch List is published, these 100 sites can avail grants and funds from the WMF, as well as from other foundations, private donors, and corporations by capitalizing on the publicity and attention gained from the inclusion on the Watch List. | https://en.wikipedia.org/wiki/2006_World_Monuments_Watch |
The 2006 World Monuments Watch List of 100 Most Endangered Sites was launched on June 21, 2005, by WMF President Bonnie Burnham. It marked the first time that an entire country was placed on the Watch List. Iraq, long considered as the "cradle of human civilization" and within whose borders lie an estimated 10,000 archaeological sites, has been left vulnerable to widespread looting, vandalism, and other acts of violence in the wake of the 2003 military invasion. The World Monuments Watch provides a valuable barometer of the state of heritage preservation worldwide… The biennial Watch list tells us not only which sites are in peril, but also what kinds of threats—natural disaster, war, pollution, neglect, or other issues—are endangering the world's heritage. On October 6, 2005, nearly four months after the publication of the 2006 Watch List and more than a month after the significant devastation brought about by Hurricane Katrina on America's Gulf Coast, the WMF, together with partners American Express Foundation and National Trust for Historic Preservation, decided to place the Gulf Coast and New Orleans as the 101st endangered site on the 2006 Watch List. | https://en.wikipedia.org/wiki/2006_World_Monuments_Watch |
The following countries/territories have multiple sites entered on the 2006 Watch List, listed by the number of sites: | https://en.wikipedia.org/wiki/2006_World_Monuments_Watch |
Data preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues. Analyzing data that has not been carefully screened for such problems can produce misleading results. | https://en.wikipedia.org/wiki/Data_Preprocessing |
Thus, representation and quality of data is necessary before running any analysis. Often, data preprocessing is the most important phase of a machine learning project, especially in computational biology. If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of methods used in data preprocessing include cleaning, instance selection, normalization, one-hot encoding, data transformation, feature extraction and feature selection. | https://en.wikipedia.org/wiki/Data_Preprocessing |
The origins of data preprocessing are located in data mining. The idea is to aggregate existing information and search in the content. Later it was recognized, that for machine learning and neural networks a data preprocessing step is needed too. So it has become to a universal technique which is used in computing in general. | https://en.wikipedia.org/wiki/Data_Preprocessing |
Data preprocessing allows for the removal of unwanted data with the use of data cleaning, this allows the user to have a dataset to contain more valuable information after the preprocessing stage for data manipulation later in the data mining process. Editing such dataset to either correct data corruption or human error is a crucial step to get accurate quantifiers like true positives, true negatives, false positives and false negatives found in a confusion matrix that are commonly used for a medical diagnosis. Users are able to join data files together and use preprocessing to filter any unnecessary noise from the data which can allow for higher accuracy. | https://en.wikipedia.org/wiki/Data_Preprocessing |
Users use Python programming scripts accompanied by the pandas library which gives them the ability to import data from a comma-separated values as a data-frame. The data-frame is then used to manipulate data that can be challenging otherwise to do in Excel. pandas (software) which is a powerful tool that allows for data analysis and manipulation; which makes data visualizations, statistical operations and much more, a lot easier. | https://en.wikipedia.org/wiki/Data_Preprocessing |
Many also use the R programming language to do such tasks as well. The reason why a user transforms existing files into a new one is because of many reasons. Data preprocessing has the objective to add missing values, aggregate information, label data with categories (data binning) and smooth a trajectory. More advanced techniques like principal component analysis and feature selection are working with statistical formulas and are applied to complex datasets which are recorded by GPS trackers and motion capture devices. | https://en.wikipedia.org/wiki/Data_Preprocessing |
Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process. Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing phase. Domain knowledge also works as constraint. | https://en.wikipedia.org/wiki/Data_Preprocessing |
It does this by using working as set of prior knowledge to reduce the space required for searching and acting as a guide to the data. Simply put, semantic preprocessing seeks to filter data using the original environment of said data more correctly and efficiently. There are increasingly complex problems which are asking to be solved by more elaborate techniques to better analyze existing information. | https://en.wikipedia.org/wiki/Data_Preprocessing |
Instead of creating a simple script for aggregating different numerical values into a single value, it make sense to focus on semantic based data preprocessing. The idea is to build a dedicated ontology, which explains on a higher level what the problem is about. In regards to semantic data mining and semantic pre-processing, ontologies are a way to conceptualize and formally define semantic knowledge and data. | https://en.wikipedia.org/wiki/Data_Preprocessing |
The Protégé (software) is the standard tool for constructing an ontology. In general, the use of ontologies bridges the gaps between data, applications, algorithms, and results that occur from semantic mismatches. As a result, semantic data mining combined with ontology has many applications where semantic ambiguity can impact the usefulness and efficiency of data systems. | https://en.wikipedia.org/wiki/Data_Preprocessing |
Applications include the medical field, language processing, banking, and even tutoring, among many more. There are various strengths to using a semantic data mining and ontological based approach. | https://en.wikipedia.org/wiki/Data_Preprocessing |
As previously mentioned, these tools can help during the per-processing phase by filtering out non-desirable data from the data set. Additionally, well-structured formal semantics integrated into well designed ontologies can return powerful data that can be easily read and processed by machines. A specifically useful example of this exists in the medical use of semantic data processing. | https://en.wikipedia.org/wiki/Data_Preprocessing |
As an example, a patient is having a medical emergency and is being rushed to hospital. The emergency responders are trying to figure out the best medicine to administer to help the patient. Under normal data processing, scouring all the patient’s medical data to ensure they are getting the best treatment could take too long and risk the patients’ health or even life. | https://en.wikipedia.org/wiki/Data_Preprocessing |
However, using semantically processed ontologies, the first responders could save the patient’s life. Tools like a semantic reasoner can use ontology to infer the what best medicine to administer to the patient is based on their medical history, such as if they have a certain cancer or other conditions, simply by examining the natural language used in the patient's medical records. This would allow the first responders to quickly and efficiently search for medicine without having worry about the patient’s medical history themselves, as the semantic reasoner would already have analyzed this data and found solutions. | https://en.wikipedia.org/wiki/Data_Preprocessing |
In general, this illustrates the incredible strength of using semantic data mining and ontologies. They allow for quicker and more efficient data extraction on the user side, as the user has fewer variables to account for, since the semantically pre-processed data and ontology built for the data have already accounted for many of these variables. | https://en.wikipedia.org/wiki/Data_Preprocessing |
However, there are some drawbacks to this approach. Namely, it requires a high amount of computational power and complexity, even with relatively small data sets. This could result in higher costs and increased difficulties in building and maintaining semantic data processing systems. | https://en.wikipedia.org/wiki/Data_Preprocessing |
This can be mitigated somewhat if the data set is already well organized and formatted, but even then, the complexity is still higher when compared to standard data processing.Below is a simple a diagram combining some of the processes, in particular semantic data mining and their use in ontology. The diagram depicts a data set being broken up into two parts: the characteristics of its domain, or domain knowledge, and then the actual acquired data. The domain characteristics are then processed to become user understood domain knowledge that can be applied to the data. | https://en.wikipedia.org/wiki/Data_Preprocessing |
Meanwhile, the data set is processed and stored so that the domain knowledge can applied to it, so that the process may continue. This application forms the ontology. From there, the ontology can be used to analyze data and process results. | https://en.wikipedia.org/wiki/Data_Preprocessing |
Fuzzy preprocessing is another, more advanced technique for solving complex problems. Fuzzy preprocessing and fuzzy data mining make use of fuzzy sets. These data sets are composed of two elements: a set and a membership function for the set which comprises 0 and 1. | https://en.wikipedia.org/wiki/Data_Preprocessing |
Fuzzy preprocessing uses this fuzzy data set to ground numerical values with linguistic information. Raw data is then transformed into natural language. Ultimately, fuzzy data mining's goal is to help deal with inexact information, such as an incomplete database. Currently fuzzy preprocessing, as well as other fuzzy based data mining techniques see frequent use with neural networks and artificial intelligence. | https://en.wikipedia.org/wiki/Data_Preprocessing |
Karu Treasure is the name given to a collection of 363 valuable Lydian artifacts dating from the 7th century BC and originating from Uşak Province in western Turkey, which were the subject of a legal battle between Turkey and New York Metropolitan Museum of Art between 1987 and 1993, which were returned to Turkey in 1993 after the Museum admitted it had known the objects were stolen when they had purchased them. The collection is alternatively known as the Lydian Hoard. The items are exhibited in the Uşak Museum of Archaeology. The collection made sensational news once again in May 2006 when a key piece, a golden hippocamp, on display in Uşak Museum along with the rest of the collection, was discovered to have been replaced by a fake, probably between March and August 2005.Yet another term used for the collection is "Croesus Treasure". | https://en.wikipedia.org/wiki/Karun_Treasure |
Although the artifacts were closely contemporary to Croesus, whether they should be directly associated with the legendary Lydian king or not remains debatable. Croesus' wealth had repercussions on a number of Asian cultures in a vein similar to his fame in the western cultures, and is referred to either as Qaru (Arabic, Persian) or Karu (Turkish), or Korah, with the mythical proportions of his fortune also echoed in various ways, parallel to the English language expression "as rich as Croesus". This explains why the term "Karu Treasure" took hold, and in any case, the king Croesus' Treasure consisted of more than 363 pieces and the tomb chamber tumulus where most artifacts were discovered (they originate from close but different sites) was that of a woman. | https://en.wikipedia.org/wiki/Karun_Treasure |
The main and the most precious part of the treasure comes from a tomb chamber of a Lydian princess reached through illegal excavations carried out by three fortune-seekers from Uşak's depending Güre village, at the proximity of which the tomb was located, at the locality called Toptepe. After having dug for days and unable to break through the marble masonry of the chamber door, they had dynamited the roof of the tomb in the night of 6 June 1966, to be the first to see the breathtaking sight of the buried Lydian noblewoman and her treasures after 2600 years. The treasure looted from this particular tomb was enriched by further finds by the same men in other tumuli of the locality during 1966-1967. The collection was smuggled outside Turkey in separate dispatches through İzmir and Amsterdam, to be bought by the Metropolitan Museum of Art between 1967–1968, at an invoiced cost of $1.2 million for 200 of the pieces within the collection. | https://en.wikipedia.org/wiki/Karun_Treasure |
The efforts made by successive Turkish governments to retrieve the collection were incited since the very beginning and followed until conclusion by the journalist Özgen Acar. Acar had chanced upon some pieces of the collection for the first time in 1984 in a Met Museum catalogue and had informed Turkey's Ministry of Culture of their clear provenance, while he also wrote several articles and pursued the bureaucratic channels within Turkey with insistence throughout the affair. He acted as a voluntary envoy of the Ministry within the frame of the judicial case launched in New York City in 1987 and brought to conclusion in 1993, at the same time as he was named consultant in the larger framework of the Turkey's participation in the work carried out by UNIDROIT regarding the protection of historic, cultural and religious heritage. Acar's name is also synonymous in Turkey for the retrieval of another set of smuggled archaeological goods, termed "Elmalı Treasure" in reference to their site of origin, the town of Elmalı in southwestern Turkey, and involving this time Lydian coins and extremely rare decadrachms dating from the period of the Delian League, with the Boston Museum of Fine Arts as his opposite party. | https://en.wikipedia.org/wiki/Karun_Treasure |
The clear need for a museum worthy of the treasure was being voiced ever since the artifacts had returned to Turkey. With the seizure by the authorities of ten other illegally excavated artifacts in 1998, further archaeological discoveries and the known presence of eight gold pieces that had appeared in 2000 during an exhibition in a Paris private gallery for which attempts for retrieval were yet to be made, a handsome collection of base consisting of a total of 375 pieces was already accumulated. But the small museum in Uşak where the collection was placed, more focused on storage of Ushak carpets and operating under conditions of budgetary and staff restraints, did not fully meet the requirements for the preservation of Karu Treasure. Doubts about the site's suitability were reinforced by the filing of legal action against museum staff regarding the 2007 theft. Ten people were initially accused in relation to the hippocamp's replacement with a fake; the museum's former director was the only one kept in custody. | https://en.wikipedia.org/wiki/Karun_Treasure |
Some in Uşak and beyond associate the treasure with a curse. Legend has it that the seven men who took part in the illegal digs "died violent deaths or suffered great misfortune". | https://en.wikipedia.org/wiki/Karun_Treasure |
The NRS process (New Regeneration System) is a process to reduce calcium from beet-root thin-juice. It is used in beet-sugar factories to improve the capacity and operating time of evaporators and to produce soft molasses that can be further de-sugarised with chromatography. The original technology was invented by AKZO for a different application. It was first used in French sugar factories, starting in the 1970s. | https://en.wikipedia.org/wiki/NRS_process |
Plants may have a capacity of 100-1000 m3/h or greater. The system is often used in the US, France, and Britain. German sugar makers traditionally prefer to invest into bigger evaporation capacity. | https://en.wikipedia.org/wiki/NRS_process |
The NRS-installation will consist of a number of columns filled with strong-acid-cationic resin. It is installed after carbonatation and filtration and before evaporation. The resin is loaded with sodium Na+ ions, that are exchanged for calcium Ca++. | https://en.wikipedia.org/wiki/NRS_process |
The softened juice will then be evaporated. For the regeneration of the resin softened juice will be mixed with caustic-soda (NaOH) and will be sent to the columns to transform the resin back into Na-form. The calcium-rich juice with high pH is sent in several fractions to the beginning of the clarification process, where alkalinisation is needed and the calcium is absorbed by the solid organic matter. | https://en.wikipedia.org/wiki/NRS_process |
It is effluent free and uses the product for regeneration. Chemicals are consumed in about the same amount as in traditional technology. The resin is robust, stable and not expensive. The control technology is simple. | https://en.wikipedia.org/wiki/NRS_process |
Reduces the calcium content. Less soda needed. Reduces possible turbidity. | https://en.wikipedia.org/wiki/NRS_process |
Academic discourse socialization is defined as one's growing process to realize the academic discourse and reach the expectation of the academic community. Academic discourse socialization is a form of language socialization through which newcomers or novices gain knowledge of the academic discourses by socializing and interacting with peers, experts, or more knowledgeable people in their community and social network. A dynamic and complex process, academic discourse socialization requires negotiation of both knowledge and one's identity. This kind of interaction is defined as a bidirectional process in which both novice learners and experts learn from one another. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
Over the last two decades, the field of applied linguistics has given renewed attention to academic discourse socialization, especially the disciplinary socialization of second language students. A growing body of research has explored socialization experiences of both first and second language learners through oral discourses, such as academic presentations, small group discussions and student-teacher individual conferences for feedback on writing. To understand the complex processes that academic discourse socialization entails, some studies have also explored students' out of class interactions. For instance, Seloni's micro ethnography investigated the role of both in-class and out-of-class collaboration of first-year doctoral students in facilitating their socialization into their respective academic communities. She also noted that in these social spaces (classroom and informal interactions) doctoral students accepted and resisted literacy practices and thus created "hybrid forms of literacy practices". While some studies revealed that out of class collaborations are effective and have a positive effect on socialization experiences, others demonstrated that these collaborations are not always favorable.Recently, written interactions in the form of feedback have also gained some attention in the field and increased our understanding on the impact of feedback (as a social practice) on second language students' socialization into their academic discourses and communities.Technology-mediated academic discourse socialization have also become more common with the increasing use of digital tools, such as discussion forums, google docs, blogs and applications of Wikipedia-based assignments in higher education. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
Collaboration is central to academic discourse socialization. Shifting from individual to collaborative work and building a social network expands understanding of the textbook and discourses. Not only interactions that take place in formal settings (classrooms) but also collaborating with others, especially peers beyond the classroom, help learners socialize into their desired academic communities. Academic Discourse Socialization is an investment in which learners get academic and emotional support as a return and peers play an important role in providing this support. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
Peers are also called literacy brokers and could be someone who is going through the same process, they don't necessarily have to be experts. Peer support both inside (formal contexts) and outside (informal contexts) of the classroom help learners gain knowledge of academic discourses and enhances students' understanding of their trajectories, identities and capabilities.Academic presentations also provide a good context to socialize into oral discourses and culture of discourse communities. Socialization through small group discussions allows learners to draw ideas from their prior and existing knowledge and understand a new concept. Given the affordances of digital tools, asynchronous discussions are also considered a productive for academic discourse socialization and literacy development, provided these discussions are graded, carefully designed, and pay attention to learners' agency. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
"Academic discourse refers to the ways of thinking and using language which exist in the academy." Discourse is not just "language" itself; discourse is language use that represents a person's existence in the world. Thus, what one has said and written are significant to academic community, which also shows that the institution cannot exist without academic discourse. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
Academic discourse does not only function as a tool to convey one's thoughts but also influences one's formation of social identity, values, and world knowledge. The common ways to present academic discourse are through textbooks, conference presentations, dissertations, lectures, and research articles. Students in the institution learn to display their thoughts through different types of academic discourse, such as classroom and conference presentations, assignments, and dissertations. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
In this way, they acquire social practice in the different academic fields, get to the heart of the academic enterprise, and finally become a member of a social group. Discourse conventions in a particular academic field are shaped by the ways of thinking of community members and the values they believe in. Written works and speeches are widely accepted if they are composed and delivered in a suitable way in terms of discourse conventions. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
The recognition of a publication from an academic community is regarded as the accomplishment of one's academic life and the realization of academic discourse. It is highly motivating when one's published paper was cited or further developed by community members because it is evidence of acceptance. In order to get a reputation of the academic community, people make some contributions through publication to receive compliments. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
From the mid-1960s, the issues of academic discourse have caught researchers’ and scholars’ eyes and grown massively. The first reason why academic discourse has become popular is because the number of students in higher education has dramatically increased, resulting in greater diversity of students. “This more culturally, socially and linguistically heterogeneous student population means that learners bring different identities, understandings and habits of meaning-making to a more diverse range of subjects.” Therefore, it leads to the problem that it is more difficult for teachers to know whether students acquire the required ability of the principle or not. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
With the popularity of the concept of academic discourse, teachers can clearly define students’ learning achievement through their performance on different types of academic discourse. The second reason concerns the transformation of education system. Nowadays, schools do not solely rely on government funding; instead, students’ fees are thought of as a major source of income. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
Universities are more competitive because students as customers choose prestigious schools which are highly evaluated on the aspect of academic discourse, including the publication of dissertations and lectures in conferences. The last reason, and also the most important factor affecting the development of academic discourse is the spread of English. English becomes a lingua franca for oral and written communication. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
Even academic journals, as a representative type of academic discourse, are most in English. Moreover, “the global status of English has come to influence both the lives of scholars throughout the globe and the production and exchange of academic knowledge in the twenty-first century.” As a result, the learning of academic discourse is especially meaningful for second language learners. Novice learners first enter into legitimate peripheral participation and then move to the center of the academic community. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
That is, beginners first acquire the conventions of academic discourse peripherally and imitate discourse activities from experienced learners or experts. After a period of time, learners can also complete academic oral presentations and academic essays, and in the end, the publication of dissertations and participation in international conferences just as what former experts do in the academic community. Students in the institution learn to display their thoughts through different types of academic discourse, such as classroom and conference presentations, assignments, and dissertations. In this way, they acquire social practice in the different academic fields, get to the heart of the academic enterprise, and finally become a member of a social group, which can be seen as a process of academic discourse socialization. | https://en.wikipedia.org/wiki/Academic_discourse_socialization |
Externships are experiential learning opportunities, similar to internships, provided by partnerships between educational institutions and employers to give students practical experiences in their field of study. In medicine, it may refer to a visiting physician who is not part of the regular staff. In law, it usually refers to rigorous legal work opportunities undertaken by law students for law school credit, similar to that of a junior attorney. It is derived from Latin externus and from English -ship. The term externship has a first known use date of 1945 in the Merriam-Webster dictionary. | https://en.wikipedia.org/wiki/Externship |
Externships are often viewed as job shadowing since externs are closely supervised by employee volunteers who agree to walk them through day-to-day routines at the company or organization. They can be viewed as external studies which combine classroom knowledge with real-world experience. This knowledge prepares students for the transition from school to career. The experience obtained through externships allows students to apply their coursework learning to real-life settings, and to observe and ask questions within that context. | https://en.wikipedia.org/wiki/Externship |
Externships may lead to opportunities after students complete their studies. They can help pre-graduates get their foot in the door for possible job openings or even make them better candidates for aggressive internship opportunities, and to allow externs to become familiar with new professions and job fields. Externships are also a source of networking contacts once a profession is chosen. | https://en.wikipedia.org/wiki/Externship |
Externships are not only conducted for the benefit of the extern, but for the host as well. Both parties get a chance to observe one another. Successful externships could lead to recruitment possibilities which would be based on a thoroughly informed decision. | https://en.wikipedia.org/wiki/Externship |
Legal externships, like internships, can be taken for law school credit. Internships and externships offered by law schools accredited by the Council of the ABA Section on Legal Education and Admission to the Bar are called "law clinic" and "field placement" courses, respectively, by (Accreditation) Standard 304. Experiential Courses: Simulation Courses, Law Clinics, and Field Placements. Standards 304(b) and (c) address the Council's expectations for simulation courses in clinics/internships and placements/externships, respectively. | https://en.wikipedia.org/wiki/Externship |
Standard 304(d) applies to field placements (externships), defining them as follows for purposes of law school accreditation: "A field placement course provides substantial lawyering experience that (1) is reasonably similar to the experience of a lawyer advising or representing a client or engaging in other lawyering tasks in a setting outside a law clinic under the supervision of a licensed attorney or an individual otherwise qualified to supervise..."Law schools accredited by state government agencies, such as the State Bar of California, in addition to or in place of the ABA Council, must comply with the accreditation standards of those agencies, in order to maintain those accreditations. In California's case, Rule 4.102 in Division 2. Accredited Law School Rules of Title 4. | https://en.wikipedia.org/wiki/Externship |
Admissions and Educational Standards provides: "A law school provisionally or fully approved by the American Bar Association is deemed accredited by the Committee and exempt from these rules, unless the American Bar Association withdraws its approval." (Inset added.) Other states that accredit law schools within their boundaries include Alabama, Connecticut, Massachusetts, and Tennessee. | https://en.wikipedia.org/wiki/Externship |
No university or free-standing law school allows students to receive academic credit in simulation, clinic (internship) or field placement (externship) courses for making coffee, taking inventory, or other tasks unrelated to practical experience to develop lawyering skills. Students can make the coffee if they wish - but the time they spend doing it cannot be counted as part of their experiental course time commitment. Neither can they receive academic credit for performing a paid job. | https://en.wikipedia.org/wiki/Externship |
The History of the Museums Association is the history of the UK based Museums Association (MA), which is the oldest museum association in the world. The concept was first proposed by Elijah Howarth of the Weston Park Museum, Sheffield in 1877. | https://en.wikipedia.org/wiki/History_of_the_Museums_Association |
The objects of the MA were stated as follows:: 88 The object of the Association shall be the promotion of better and more systematic working of Museums throughout the Kingdom. In order to promote a better knowledge of Museums, the Association shall meet in a different town each succeeding year. That each Museum contributing not less than one guinea a year be a Member of the Association, and that individuals interested in scientific work be admitted as Associates on payment of 10s. | https://en.wikipedia.org/wiki/History_of_the_Museums_Association |
6d. annually. That each Museum be represented by three delegates, each having one vote. | https://en.wikipedia.org/wiki/History_of_the_Museums_Association |
Each Associate to have one vote. That each Museum belonging to the Association and each Associate receive one copy of the publications of the Association. That a General Meeting of the Association be held annually, for the transaction of business, the reading of papers, and the discussion of matters relating to Museums | https://en.wikipedia.org/wiki/History_of_the_Museums_Association |
The Museums Association held annual conferences incorporating the Annual General Meeting, hosted by different museums: == References == | https://en.wikipedia.org/wiki/History_of_the_Museums_Association |
In classical differential geometry, Clairaut's relation, named after Alexis Claude de Clairaut, is a formula that characterizes the great circle paths on the unit sphere. The formula states that if γ is a parametrization of a great circle then ρ ( γ ( t ) ) sin ψ ( γ ( t ) ) = constant , {\displaystyle \rho (\gamma (t))\sin \psi (\gamma (t))={\text{constant}},\,} where ρ(P) is the distance from a point P on the great circle to the z-axis, and ψ(P) is the angle between the great circle and the meridian through the point P. The relation remains valid for a geodesic on an arbitrary surface of revolution. A statement of the general version of Clairaut's relation is: Let γ be a geodesic on a surface of revolution S, let ρ be the distance of a point of S from the axis of rotation, and let ψ be the angle between γ and the meridian of S. Then ρ sin ψ is constant along γ. Conversely, if ρ sin ψ is constant along some curve γ in the surface, and if no part of γ is part of some parallel of S, then γ is a geodesic. Pressley (p. 185) explains this theorem as an expression of conservation of angular momentum about the axis of revolution when a particle moves along a geodesic under no forces other than those that keep it on the surface. | https://en.wikipedia.org/wiki/Clairaut's_relation_(differential_geometry) |
The expression military–industrial complex (MIC) describes the relationship between a country's military and the defense industry that supplies it, seen together as a vested interest which influences public policy. A driving factor behind the relationship between the military and the defense-minded corporations is that both sides benefit—one side from obtaining war weapons, and the other from being paid to supply them. The term is most often used in reference to the system behind the armed forces of the United States, where the relationship is most prevalent due to close links among defense contractors, the Pentagon, and politicians. The expression gained popularity after a warning of the relationship's detrimental effects, in the farewell address of President Dwight D. Eisenhower on January 17, 1961.In the context of the United States, the appellation is sometimes extended to military–industrial–congressional complex (MICC), adding the U.S. Congress to form a three-sided relationship termed an "iron triangle". Its three legs include political contributions, political approval for military spending, lobbying to support bureaucracies, and oversight of the industry; or more broadly, the entire network of contracts and flows of money and resources among individuals as well as corporations and institutions of the defense contractors, private military contractors, the Pentagon, Congress, and the executive branch. | https://en.wikipedia.org/wiki/Military–industrial_complex |
President of the United States (and five-star general since World War II) Dwight D. Eisenhower used the term in his Farewell Address to the Nation on January 17, 1961: A vital element in keeping the peace is our military establishment. Our arms must be mighty, ready for instant action, so that no potential aggressor may be tempted to risk his own destruction... This conjunction of an immense military establishment and a large arms industry is new in the American experience. The total influence—economic, political, even spiritual—is felt in every city, every statehouse, every office of the federal government. | https://en.wikipedia.org/wiki/Military–industrial_complex |
We recognize the imperative need for this development. Yet we must not fail to comprehend its grave implications. Our toil, resources and livelihood are all involved; so is the very structure of our society. | https://en.wikipedia.org/wiki/Military–industrial_complex |
In the councils of government, we must guard against the acquisition of unwarranted influence, whether sought or unsought, by the military–industrial complex. The potential for the disastrous rise of misplaced power exists, and will persist. We must never let the weight of this combination endanger our liberties or democratic processes. | https://en.wikipedia.org/wiki/Military–industrial_complex |
We should take nothing for granted. Only an alert and knowledgeable citizenry can compel the proper meshing of the huge industrial and military machinery of defense with our peaceful methods and goals so that security and liberty may prosper together. The phrase was thought to have been "war-based" industrial complex before becoming "military" in later drafts of Eisenhower's speech, a claim passed on only by oral history. | https://en.wikipedia.org/wiki/Military–industrial_complex |
Geoffrey Perret, in his biography of Eisenhower, claims that, in one draft of the speech, the phrase was "military–industrial–congressional complex", indicating the essential role that the United States Congress plays in the propagation of the military industry, but the word "congressional" was dropped from the final version to appease the then-currently elected officials. James Ledbetter calls this a "stubborn misconception" not supported by any evidence; likewise a claim by Douglas Brinkley that it was originally "military–industrial–scientific complex". Additionally, Henry Giroux claims that it was originally "military–industrial–academic complex". | https://en.wikipedia.org/wiki/Military–industrial_complex |
The actual authors of the speech were Eisenhower's speechwriters Ralph E. Williams and Malcolm Moos. Attempts to conceptualize something similar to a modern "military–industrial complex" existed before Eisenhower's address. Ledbetter finds the precise term used in 1947 in close to its later meaning in an article in Foreign Affairs by Winfield W. Riefler. | https://en.wikipedia.org/wiki/Military–industrial_complex |
In 1956, sociologist C. Wright Mills had claimed in his book The Power Elite that a class of military, business, and political leaders, driven by mutual interests, were the real leaders of the state, and were effectively beyond democratic control. Friedrich Hayek mentions in his 1944 book The Road to Serfdom the danger of a support of monopolistic organization of industry from World War II political remnants: Another element which after this war is likely to strengthen the tendencies in this direction will be some of the men who during the war have tasted the powers of coercive control and will find it difficult to reconcile themselves with the humbler roles they will then have to play . Vietnam War–era activists, such as Seymour Melman, referred frequently to the concept, and use continued throughout the Cold War: George F. Kennan wrote in his preface to Norman Cousins's 1987 book The Pathology of Power, "Were the Soviet Union to sink tomorrow under the waters of the ocean, the American military–industrial complex would have to remain, substantially unchanged, until some other adversary could be invented. | https://en.wikipedia.org/wiki/Military–industrial_complex |
Anything else would be an unacceptable shock to the American economy." In the late 1990s James Kurth asserted, "By the mid-1980s... the term had largely fallen out of public discussion." He went on to argue that "hatever the power of arguments about the influence of the military–industrial complex on weapons procurement during the Cold War, they are much less relevant to the current era".Contemporary students and critics of U.S. | https://en.wikipedia.org/wiki/Military–industrial_complex |
militarism continue to refer to and employ the term, however. For example, historian Chalmers Johnson uses words from the second, third, and fourth paragraphs quoted above from Eisenhower's address as an epigraph to Chapter Two ("The Roots of American Militarism") of a 2004 volume on this subject. P. W. Singer's book concerning private military companies illustrates contemporary ways in which industry, particularly an information-based one, still interacts with the U.S. | https://en.wikipedia.org/wiki/Military–industrial_complex |
federal and the Pentagon.The expressions permanent war economy and war corporatism are related concepts that have also been used in association with this term. The term is also used to describe comparable collusion in other political entities such as the German Empire (prior to and through the first world war), Britain, France, and (post-Soviet) Russia.Linguist and anarchist theorist Noam Chomsky has suggested that "military–industrial complex" is a misnomer because (as he considers it) the phenomenon in question "is not specifically military". He asserts, "There is no military–industrial complex: it's just the industrial system operating under one or another pretext (defense was a pretext for a long time)." | https://en.wikipedia.org/wiki/Military–industrial_complex |
At the end of the Cold War, American defense contractors bewailed what they called declining government weapons spending. They saw escalation of tensions, such as with Russia over Ukraine, as new opportunities for increased weapons sales, and have pushed the political system, both directly and through industry groups such as the National Defense Industrial Association, to spend more on military hardware. Pentagon contractor-funded American think tanks such as the Lexington Institute and the Atlantic Council have also demanded increased spending in view of the perceived Russian threat. Independent Western observers such as William Huntzberger, director of the Arms & Security Project at the Center for International Policy, noted that "Russian saber-rattling has additional benefits for weapons makers because it has become a standard part of the argument for higher Pentagon spending—even though the Pentagon already has more than enough money to address any actual threat to the United States." | https://en.wikipedia.org/wiki/Military–industrial_complex |
Some sources divide the history of the military–industrial complex into three distinct eras. | https://en.wikipedia.org/wiki/Military–industrial_complex |
From 1797 to 1941, the government only relied on civilian industries while the country was actually at war. The government owned their own shipyards and weapons manufacturing facilities which they relied on through World War I. With World War II came a massive shift in the way that the American government armed the military. With the onset of World War II President Franklin D. Roosevelt established the War Production Board to coordinate civilian industries and shift them into wartime production. | https://en.wikipedia.org/wiki/Military–industrial_complex |
Throughout World War II arms production in the United States went from around one percent of the annual GDP to 40 percent of the GDP. Various American companies, such as Boeing and General Motors, maintained and expanded their defense divisions. These companies have gone on to develop various technologies that have improved civilian life as well, such as night-vision goggles and GPS. | https://en.wikipedia.org/wiki/Military–industrial_complex |
The second era is identified as beginning with the coining of the term by President Dwight D. Eisenhower. This era continued through the Cold War period, up to the end of the Warsaw Pact and the collapse of the Soviet Union. A 1965 article written by Marc Pilisuk and Thomas Hayden says benefits of the Military Industrial Complex of the United States include the advancement of the civilian technology market as civilian companies benefit from innovations from the MIC and vice versa. In 1993 the Pentagon urged defense contractors to consolidate due to the collapse of communism and shrinking defense budget. | https://en.wikipedia.org/wiki/Military–industrial_complex |
In the third era, defense contractors either consolidated or shifted their focus to civilian innovation. From 1992 to 1997 there was a total of US$55 billion worth of mergers in the defense industry, with major defense companies purchasing smaller competitors.In the current era, the military–industrial complex is seen as a core part of American policy-making. The American domestic economy is now tied directly to the success of the MIC which has led to concerns of repression as Cold War-era attitudes are still prevalent among the American public.Shifts in values and the collapse of communism have ushered in a new era for the military–industrial complex. The Department of Defense works in coordination with traditional military–industrial complex aligned companies such as Lockheed Martin and Northrop Grumman. Many former defense contractors have shifted operations to the civilian market and sold off their defense departments. | https://en.wikipedia.org/wiki/Military–industrial_complex |
According to the military subsidy theory, the Cold War–era mass production of aircraft benefited the civilian aircraft industry. The theory asserts that the technologies developed during the Cold War along with the financial backing of the military led to the dominance of American aviation companies. There is also strong evidence that the United States federal government intentionally paid a higher price for these innovations to serve as a subsidy for civilian aircraft advancement. | https://en.wikipedia.org/wiki/Military–industrial_complex |
According to the Stockholm International Peace Research Institute (SIPRI), total world spending on military expenses in 2022 was $2,240 billion. 39% of this total, or $837 billion, was spent by the United States. China was the second largest spender, with $292 billion and 13% of the global share. The privatization of the production and invention of military technology also leads to a complicated relationship with significant research and development of many technologies. | https://en.wikipedia.org/wiki/Military–industrial_complex |
In 2011, the United States spent more (in absolute numbers) on its military than the next 13 countries combined.The military budget of the United States for the 2009 fiscal year was $515.4 billion. Adding emergency discretionary spending and supplemental spending brings the sum to $651.2 billion. This does not include many military-related items that are outside of the Defense Department budget. | https://en.wikipedia.org/wiki/Military–industrial_complex |
Overall the U.S. federal government is spending about $1 trillion annually on defense-related purposes.In a 2012 story, Salon reported, "Despite a decline in global arms sales in 2010 due to recessionary pressures, the United States increased its market share, accounting for a whopping 53 percent of the trade that year. Last year saw the United States on pace to deliver more than $46 billion in foreign arms sales." The defense industry also tends to contribute heavily to incumbent members of Congress. | https://en.wikipedia.org/wiki/Military–industrial_complex |
A thesis similar to the military–industrial complex was originally expressed by Daniel Guérin, in his 1936 book Fascism and Big Business, about the fascist government ties to heavy industry. It can be defined as, "an informal and changing coalition of groups with vested psychological, moral, and material interests in the continuous development and maintenance of high levels of weaponry, in preservation of colonial markets and in military-strategic conceptions of internal affairs." An exhibit of the trend was made in Franz Leopold Neumann's book Behemoth: The Structure and Practice of National Socialism in 1942, a study of how Nazism came into a position of power in a democratic state. Within decades of its inception, the idea of the military–industrial complex gave rise to other similar industrial complexes, including the animal–industrial complex, prison–industrial complex, pharmaceutical–industrial complex, entertainment-industrial complex, and medical–industrial complex. | https://en.wikipedia.org/wiki/Military–industrial_complex |
: ix–xxv Virtually all institutions in sectors ranging from agriculture, medicine, entertainment, and media, to education, criminal justice, security, and transportation, began reconceiving and reconstructing in accordance with capitalist, industrial, and bureaucratic models with the aim of realizing profit, growth, and other imperatives. According to Steven Best, all these systems interrelate and reinforce one another.The concept of the military–industrial complex has been also expanded to include the entertainment and creative industries as well. For an example in practice, Matthew Brummer describes Japan's Manga Military and how the Ministry of Defense uses popular culture and the moe that it engenders to shape domestic and international perceptions.An alternative term to describe the interdependence between the military-industrial complex and the entertainment industry is coined by James Der Derian as "Military-Industrial-Media-Entertainment-Network".Ray McGovern extended this appellation to Military-Industrial-Congressional-Intelligence-Media-Academia-Think-Tank complex, MICIMATT. | https://en.wikipedia.org/wiki/Military–industrial_complex |
Mode choice analysis is the third step in the conventional four-step transportation forecasting model of transportation planning, following trip distribution and preceding route assignment. From origin-destination table inputs provided by trip distribution, mode choice analysis allows the modeler to determine probabilities that travelers will use a certain mode of transport. These probabilities are called the modal share, and can be used to produce an estimate of the amount of trips taken using each feasible mode. | https://en.wikipedia.org/wiki/Mode_choice |
The early transportation planning model developed by the Chicago Area Transportation Study (CATS) focused on transit. It wanted to know how much travel would continue by transit. The CATS divided transit trips into two classes: trips to the Central Business District, or CBD (mainly by subway/elevated transit, express buses, and commuter trains) and other (mainly on the local bus system). | https://en.wikipedia.org/wiki/Mode_choice |
For the latter, increases in auto ownership and use were a trade-off against bus use; trend data were used. CBD travel was analyzed using historic mode choice data together with projections of CBD land uses. Somewhat similar techniques were used in many studies. Two decades after CATS, for example, the London study followed essentially the same procedure, but in this case, researchers first divided trips into those made in the inner part of the city and those in the outer part. This procedure was followed because it was thought that income (resulting in the purchase and use of automobiles) drove mode choice. | https://en.wikipedia.org/wiki/Mode_choice |
The CATS had diversion curve techniques available and used them for some tasks. At first, the CATS studied the diversion of auto traffic from streets and arterial roads to proposed expressways. Diversion curves were also used for bypasses built around cities to find out what percent of traffic would use the bypass. The mode choice version of diversion curve analysis proceeds this way: one forms a ratio, say: c transit c auto = R {\displaystyle {\frac {c_{\text{transit}}}{c_{\text{auto}}}}=R} where: cm = travel time by mode m and R is empirical data in the form:Given the R that we have calculated, the graph tells us the percent of users in the market that will choose transit. | https://en.wikipedia.org/wiki/Mode_choice |
A variation on the technique is to use costs rather than time in the diversion ratio. The decision to use a time or cost ratio turns on the problem at hand. Transit agencies developed diversion curves for different kinds of situations, so variables like income and population density entered implicitly. | https://en.wikipedia.org/wiki/Mode_choice |
Diversion curves are based on empirical observations, and their improvement has resulted from better (more and more pointed) data. Curves are available for many markets. It is not difficult to obtain data and array results. | https://en.wikipedia.org/wiki/Mode_choice |
Expansion of transit has motivated data development by operators and planners. Yacov Zahavi’s UMOT studies, discussed earlier, contain many examples of diversion curves. In a sense, diversion curve analysis is expert system analysis. Planners could "eyeball" neighborhoods and estimate transit ridership by routes and time of day. Instead, diversion is observed empirically and charts drawn. | https://en.wikipedia.org/wiki/Mode_choice |
Travel demand theory was introduced in the appendix on traffic generation. The core of the field is the set of models developed following work by Stan Warner in 1962 (Strategic Choice of Mode in Urban Travel: A Study of Binary Choice). Using data from the CATS, Warner investigated classification techniques using models from biology and psychology. Building from Warner and other early investigators, disaggregate demand models emerged. | https://en.wikipedia.org/wiki/Mode_choice |
Analysis is disaggregate in that individuals are the basic units of observation, yet aggregate because models yield a single set of parameters describing the choice behavior of the population. Behavior enters because the theory made use of consumer behavior concepts from economics and parts of choice behavior concepts from psychology. Researchers at the University of California, Berkeley (especially Daniel McFadden, who won a Nobel Prize in Economics for his efforts) and the Massachusetts Institute of Technology (Moshe Ben-Akiva) (and in MIT associated consulting firms, especially Cambridge Systematics) developed what has become known as choice models, direct demand models (DDM), Random Utility Models (RUM) or, in its most used form, the multinomial logit model (MNL). | https://en.wikipedia.org/wiki/Mode_choice |
Choice models have attracted a lot of attention and work; the Proceedings of the International Association for Travel Behavior Research chronicles the evolution of the models. The models are treated in modern transportation planning and transportation engineering textbooks. One reason for rapid model development was a felt need. | https://en.wikipedia.org/wiki/Mode_choice |
Systems were being proposed (especially transit systems) where no empirical experience of the type used in diversion curves was available. Choice models permit comparison of more than two alternatives and the importance of attributes of alternatives. There was the general desire for an analysis technique that depended less on aggregate analysis and with a greater behavioral content. And there was attraction, too, because choice models have logical and behavioral roots extended back to the 1920s as well as roots in Kelvin Lancaster’s consumer behavior theory, in utility theory, and in modern statistical methods. | https://en.wikipedia.org/wiki/Mode_choice |
Early psychology work involved the typical experiment: Here are two objects with weights, w1 and w2, which is heavier? The finding from such an experiment would be that the greater the difference in weight, the greater the probability of choosing correctly. Graphs similar to the one on the right result. Louis Leon Thurstone proposed (in the 1920s) that perceived weight, w = v + e,where v is the true weight and e is random with E(e) = 0.The assumption that e is normally and identically distributed (NID) yields the binary probit model. | https://en.wikipedia.org/wiki/Mode_choice |
Economists deal with utility rather than physical weights, and say that observed utility = mean utility + random term.The characteristics of the object, x, must be considered, so we have u(x) = v(x) + e(x).If we follow Thurston's assumption, we again have a probit model. An alternative is to assume that the error terms are independently and identically distributed with a Weibull, Gumbel Type I, or double exponential distribution. (They are much the same, and differ slightly in their tails (thicker) from the normal distribution). This yields the multinomial logit model (MNL). | https://en.wikipedia.org/wiki/Mode_choice |
Daniel McFadden argued that the Weibull had desirable properties compared to other distributions that might be used. Among other things, the error terms are normally and identically distributed. The logit model is simply a log ratio of the probability of choosing a mode to the probability of not choosing a mode. | https://en.wikipedia.org/wiki/Mode_choice |
log ( P i 1 − P i ) = v ( x i ) {\displaystyle \log \left({\frac {P_{i}}{1-P_{i}}}\right)=v(x_{i})} Observe the mathematical similarity between the logit model and the S-curves we estimated earlier, although here share increases with utility rather than time. With a choice model we are explaining the share of travelers using a mode (or the probability that an individual traveler uses a mode multiplied by the number of travelers). The comparison with S-curves is suggestive that modes (or technologies) get adopted as their utility increases, which happens over time for several reasons. | https://en.wikipedia.org/wiki/Mode_choice |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.