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Дивидендные выплаты российских компаний и необходимые данные для построения модели. Часть
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The models of doing for school mathematics varies and mainly based on etymological sense of meaning according to words or phrases in a sentence. The students positioned their thinking based on word understanding of a proof problem. They also relate between presented information and formula in doing proof. When facing visual representations, the students tend to prove by algebra. The visual representations to be imagined merely as a tool for arranging a formal proof, i.e. when so many algebra symbols in proving, including formula and the procedures. For algebraic or analytic or symbolic-illustrative representation, the mathematics proof is just symbolic manipulation than the meaning. In doing proof, a meaning of word or the combination construct a pattern of proof changed to mathematics statement. For example, a word 'to test' has multi-example pattern. While, 'to judge' shows logical pattern. The etymological sense is a learning understanding of doing proof. There s an intersection of the etymological and the understanding. Data presented in Etymological-Understanding axis. The intersections moved from information (or data) toward pattern in proof problem. There are two other intersection, i.e. meaning and experience. That is an easy way to prove much of mathematics problems. When facing to the difficult proof problem, a set of the intersection have an action role in doing proof to an intervention of the etymological sense of meaning. That is a real learning activities, specifically in doing proof. . There is also an intersection of doing proof between word meaning and logic. That is a new culture in doing mathematics proof, a belief of the learning. The data embedded in a four quadrant of Monophonic-Context axis and Velocity-Viscosity axes (Fig. 2). The students' experience also appear in thinking, drawing (a step to a proof), testing, and developing their performances. The etymological sense of word or phrase meaning brings the students to a broader or more rational (not common sense) ways to prove. The proof trajectories growth cross mathematics content knowledge. That is psychological context addressed by students' belief and determined using the empirical proof. The fitted of the empirical data mapping its model as an entropy value indicated 'creative proof', i.e. in illustrating, describing mathematics representations, making a rational (or consistence) relation, and generating a proof. The models of proof show the differences representation. When the students ask to 'try', then they elaborate a proof by cases. . But, to 'determine' made more algebra thinking. Finally, there are performance levels in doing proof etymologically. Nine performances aroused as variables of etymological sense of meaning. That is a measure of teaching and learning of doing proof. That is an exploring of the etymological toward more proof representation. That is way to assess doing proof through meaning of word. .
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Данные для эконометрического моделирования премии за поглощение
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Data associated with paper "Improved Interface State Density by Low Temperature Epitaxy Grown AlN for AlGaN/GaN Metal-Insulator-Semiconductor Diodes"
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This dataset contains records of an experimental research project on energetic expenditure during walking and carrying burdens in a sample of humans, of both sexes, aged from 23 to 50 years old. The dataset also contains some anthropometrical and body composition data of the subjects. Metabolic rates were also recorded during the different tests, performed at the BioEnergy and Motion Lab (LabBioEM) of National Research Center on Human Evolution (CENIEH, Spain) in 2015. Main outcomes and findings of the experimental project were published in several works (Prado-Nóvoa et al., 2019; Vidal-Cordasco et al., 2017; Zorrilla-Revilla et al., 2017). The list of references and the description of recorded variables are included in the data and document files.
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There is a detailed Readme.pdf in the files for the informations about the dataset. The main purpose is providing a dataset for the vibration behavior of a robot manipulator system under the control input of model-associative vibration control (MAVC) prodecure. Velocity profile is shown as [∗,𝑡𝑐𝑜𝑛,𝑡𝑑𝑒𝑐,𝑡𝑚] in study. In the case studies for both simulations and experiments, the parameters are varied as follows; 𝑡𝑐𝑜𝑛 can be valued as 0, 𝑡1ℎ or 2𝑡1ℎ, 𝑡𝑑𝑒𝑐 can be valued as 𝑡1ℎ,2𝑡1ℎ,3𝑡1ℎ,4𝑡1ℎ or 5𝑡1ℎ and 𝑡𝑚 can be valued as 1 or 1.5 seconds for corresponded 90 or 135 angular displacements. Thus thirty different velocity profiles are produced with aim to performed on system. Cases are invastigated with and without performing the MAVC procedure. Than the robot manipulator is examined for both unloaded and loaded cases, therefore total one hundred twenty cases are occured. More details can be found in related study.
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Butterfly larvae were sampled in field margins at maize anthesis. Larvae were sampled at peak maize anthesis, in maize field margins in Lleida in 2013; two separate anthesis periods were contemplated according to planting date of the maize crops. The first maize crop planted around 15-March to 15-April flowers in July (anthesis I), the second maize crop planted around 15-May to 15-June following harvest of winter cereal, flowers in August (anthesis II). Ten and twelve sites were sampled at each anthesis period, selecting 100 m of two different field margins. All potential larval host plants of butterflies were sampled. The preferred sampling methodology was visual inspection, but in some cases beating sampling was applied. Plant dimensions, sampling method and sampling time were recorded. All Lepidoptera larvae were collected, reared at the laboratory to adult, and identified to species if possible following literature. The excel files show the mean number of butterfly and moth larvae recorded at each sampling site (per 100 m margin). Other information given includes the total plant area sampled and the time spent searching the larval host plants per site.
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Primary and raw data from the manuscript "CACNA1S haploinsufficiency confers resistance to New World arenavirus infection"
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This data set contains the derived data products for the published manuscript Orgel et al. (2020) "Re-examination of the population, stratigraphy, and sequence of mercurian basins: Implications for Mercury´s early impact history and comparison with the Moon" at Journal of Geophysical Research Planets. This data package includes GIS-ready shapefiles of Mercurian basins, crater counting measurements, and Craterstats files.
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The Kälin and Kochenov Quality of Nationality Index (QNI) ranks the objective quality of nationalities worldwide. It explores three internal factors (economic strength, human development, and peace and stability) and four external factors (diversity and weight of travel freedom and diversity and weight of settlement freedom) which are used to measure the value of virtually all nationalities worldwide. Peace and stability counts for 10% of aggregate value, all other six factors count for 15% each. The QNI has been created by Dr. Christian H. Kälin, Chairman of Henley & Partners, and Prof. Dimitry Kochenov, Professor of European Constitutional Law and Citizenship at the University of Groningen. This dataset is the basis of the Kälin and Kochenov Quality of Nationality Index, edited by Dimitry Kochenov and Justin Lindeboom (Hart Publishing 2019). Measurement and sources: 1) Economic Strength of the country conferring the nationality is measured by GDP, excluding NRR, with power purchasing parity (PPP). GDP with PPP and NRR have been collected from the World Bank. All figures are normalized to a 0-15% scale. 2) Peace and Stability of the country conferring the nationality is measured by reference to the Global Peace Index. All figures are normalized to a 0-10% scale. 3) Human Development of the country conferring the nationality is measured by reference to the UN Human Development Index. All figures are normalized to a 0-15% scale. 4) Diversity of Settlement Freedom refers to the number of foreign countries in which a nationality's holders can freely settle (including the right to work there) without having to obtain a visa or with visa-on-arrival. All figures are normalized to a 0-15% scale. Data is gathered through extensive research with the assistance of regional experts. 5) Weight of Settlement Freedom measures the qualitative value of the foreign countries in which a nationality's holder is allowed to settle freely. Each settlement destination is valued by reference to its Economic Strength and Human Development. The aggregate value of all settlement destinations determines a nationality's weight of settlement freedom. All figures are normalized to a 0-15% scale. 6) Diversity of Travel Freedom measures the number of destinations to which a nationality's holder can travel to visa-free or with visa-on-arrival. All figures are normalized to a 0-15% scale. This data is provided by the International Air Transport Association (IATA). 7) Weight of Travel Freedom measures the qualitative value of visa-free and visa-on-arrival travel destinations, and also relies on data provided by IATA. Each travel destination is valued by reference to its Economic strength and Human Development. The aggregate value of all travel destinations determines a nationality's weight of travel freedom. All figures are normalized to a 0-15% scale. This dataset contains metadata collected for the purpose of the QNI from 2011 to 2018, as well as the resulting rankings.
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