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Journal articles on the topic 'Models'

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Author: Grafiati
Published: 4 June 2021
Last updated: 31 July 2024

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1

Wheeler, Gregory. "Models, Models, and Models." Metaphilosophy 44, no. 3 (April 2013): 293–300. http://dx.doi.org/10.1111/meta.12036.

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2

Mohammed, Mohammed Ameen, Zheng Han, and Yange Li. "Exploring the Detection Accuracy of Concrete Cracks Using Various CNN Models." Advances in Materials Science and Engineering 2021 (September 9, 2021): 1–11. http://dx.doi.org/10.1155/2021/9923704.

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Automatic crack detection with the least amount of workforce has become a crucial task in the inspection and evaluation of the performances of concrete structure in civil engineering. Recently, although many concrete crack detection models based on convolutional neural networks (CNNs) have been developed, the accuracy of the proposed models varies. Up-to-date, the issue regarding the convolutional neural network architecture with best performance for detecting concrete cracks is still debated in many previous studies. In this paper, we choose three established open-source CNN models (Model1, Model2, and Model3) which have been well-illustrated and verified in previous studies and test them for the purpose of crack detection of concrete structures. The chosen three models are trained using a concrete crack dataset containing 40,000 images those with 227 × 227-pixel in size. The performance of three different convolutional neural network (CNN) models was then evaluated. The comprehensive comparison result indicates that Model2 which used batch normalization is capable of the best performance amongst the three models as selected for concrete cracks detection, with recording the highest classification accuracy and low loss. In a conclusion, we recommend Model2 for a concrete crack detection task.
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3

Lei, Y., and S. Y Zhang. "Comparison and selection of growth models using the Schnute model." Journal of Forest Science 52, No. 4 (January 9, 2012): 188–96. http://dx.doi.org/10.17221/4501-jfs.

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Forestmodellers have long faced the problem of selecting an appropriate mathematical model to describe tree ontogenetic or size-shape empirical relationships for tree species. A common practice is to develop many models (or a model pool) that include different functional forms, and then to select the most appropriate one for a given data set. However, this process may impose subjective restrictions on the functional form. In this process, little attention is paid to the features (e.g. asymptote and inflection point rather than asymptote and nonasymptote) of different functional forms, and to the intrinsic curve of a given data set. In order to find a better way of comparing and selecting the growth models, this paper describes and analyses the characteristics of the Schnute model. This model has both flexibility and versatility that have not been used in forestry. In this study, the Schnute model was applied to different data sets of selected forest species to determine their functional forms. The results indicate that the model shows some desirable properties for the examined data sets, and allows for discerning the different intrinsic curve shapes such as sigmoid, concave and other curve shapes. Since no suitable functional form for a given data set is usually known prior to the comparison of candidate models, it is recommended that the Schnute model be used as the first step to determine an appropriate functional form of the data set under investigation in order to avoid using a functional form a priori.
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4

Perrin, C., V. Andréassian, and C. Michel. "Simple benchmark models as a basis for model efficiency criteria." River Systems 17, no. 1-2 (July 28, 2006): 221–44. http://dx.doi.org/10.1127/lr/17/2006/221.

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5

Corballis, Michael C. "Postscript: Models, models...." Psychological Review 108, no. 4 (2001): 809–10. http://dx.doi.org/10.1037/h0092889.

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6

Cwojdziński, Leszek, Jerzy Lewitowicz, and Andrzej Żyluk. "Models of Air Missions / Modele Misji Lotniczych." Journal of KONBiN 22, no. 1 (June 1, 2012): 35–44. http://dx.doi.org/10.2478/jok-2013-0020.

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Abstract Combat missions of both a single multi-role aircraft and a group of aircraft. Compatibility and readiness - properties determining accomplishment of an air operation. Scenarios of air operations. Models of air-to-air and air-to-surface operations (AAO and ASO, respectively). Computer modelling of air missions.
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7

Müller, Marcelo Dias, Thiago Taglialegna Salles, Domingos Sávio Campos Paciullo, Alexandre Magno Brighenti, and Carlos Renato Tavares de Castro. "EQUAÇÕES DE ALTURA, VOLUME E AFILAMENTO PARA EUCALIPTO E ACÁCIA ESTABELECIDOS EM SISTEMA SILVIPASTORIL." FLORESTA 44, no. 3 (March 21, 2014): 473. http://dx.doi.org/10.5380/rf.v44i3.33149.

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O objetivo deste trabalho foi selecionar modelos alométricos para estimativa de altura, volume e afilamento do tronco de duas espécies florestais estabelecidas em um sistema silvipastoril com 10 anos de idade. Para a variável volume, foram testados modelos de simples e dupla entrada e para o afilamento do tronco foram testados modelos segmentados e não segmentados. Para estimativa da altura das árvores de eucalipto, foi selecionado o modelo semilogarítmico e para a acácia o modelo de melhor desempenho foi o exponencial. Entre os modelos volumétricos, aqueles de dupla entrada apresentaram melhores ajustes, sendo que o modelo de Schumacher & Hall se destacou com estatísticas ligeiramente superiores aos demais. Para o afilamento de tronco, os modelos segmentados apresentaram melhor desempenho. Para árvores de eucalipto, o modelo de Max & Burkhart se mostrou mais adequado, e para acácia, o modelo de Parresol foi o que apresentou o melhor ajuste. De forma geral, os modelos se ajustaram melhor aos dados das árvores de eucalipto.Palavras-chave: Agrossilvicultura; modelagem; programa ABC. AbstractHeight, volume and taper equations for eucalipt and acacia mangium established in a silvopastoral system. The objective of this work was to select allometric models for height, volume and taper estimation of two forestry species established in a ten years old silvopastoral system. Single (breast height diameter as the independent variable) and double entry (breast height diameter and total height as independent variables) models were evaluated for the volume estimation. We evaluated segmented and non-segmented models for the taper equations. The semilogaritmic model and the exponential model fitted better for the estimation of eucalyptus and acacia trees height, respectively. In general, the double entry volumetric models presented the best adjustments and Schumacher & Hall`s model presented slightly higher statistics among the models. In the case of taper models, we observed that the segmented models fitted better to the data for both species. Max & Burkhar´s model and the Parresol´s model were selected for eucalyptus trees and acacia trees, respectively. In general, all tested models fitted better to eucalyptus trees.Keywords: Agroforestry; Modeling; Eucalyptus; Acacia.
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8

ten Cate, Jacob M. "Models and Role Models." Caries Research 49, Suppl. 1 (2015): 3–10. http://dx.doi.org/10.1159/000380870.

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Developing experimental models to understand dental caries has been the theme in our research group. Our first, the pH-cycling model, was developed to investigate the chemical reactions in enamel or dentine, which lead to dental caries. It aimed to leverage our understanding of the fluoride mode of action and was also utilized for the formulation of oral care products. In addition, we made use of intra-oral (in situ) models to study other features of the oral environment that drive the de/remineralization balance in individual patients. This model addressed basic questions, such as how enamel and dentine are affected by challenges in the oral cavity, as well as practical issues related to fluoride toothpaste efficacy. The observation that perhaps fluoride is not sufficiently potent to reduce dental caries in the present-day society triggered us to expand our knowledge in the bacterial aetiology of dental caries. For this we developed the Amsterdam Active Attachment biofilm model. Different from studies on planktonic (‘single') bacteria, this biofilm model captures bacteria in a habitat similar to dental plaque. With data from the combination of these models, it should be possible to study separate processes which together may lead to dental caries. Also products and novel agents could be evaluated that interfere with either of the processes. Having these separate models in place, a suggestion is made to design computer models to encompass the available information. Models but also role models are of the utmost importance in bringing and guiding research and researchers.
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9

Baden-Fuller, Charles, and Mary S. Morgan. "Business Models as Models." Long Range Planning 43, no. 2-3 (April 2010): 156–71. http://dx.doi.org/10.1016/j.lrp.2010.02.005.

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10

Salis, Fiora. "Models and exploratory models." Studies in History and Philosophy of Science Part A 63 (June 2017): 58–61. http://dx.doi.org/10.1016/j.shpsa.2017.04.004.

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11

Hall, Randolph W. "Discrete models/continuous models." Omega 14, no. 3 (January 1986): 213–20. http://dx.doi.org/10.1016/0305-0483(86)90040-x.

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12

Dorado, Rubén. "Statistical models for languaje representation." Revista Ontare 1, no. 1 (September 16, 2015): 29. http://dx.doi.org/10.21158/23823399.v1.n1.2013.1208.

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ONTARE. REVISTA DE INVESTIGACIÓN DE LA FACULTAD DE INGENIERÍAThis paper discuses several models for the computational representation of language. First, some n-gram models that are based on Markov models are introduced. Second, a family of models known as the exponential models is taken into account. This family in particular allows the incorporation of several features to model. Third, a recent current of research, the probabilistic Bayesian approach, is discussed. In this kind of models, language is modeled as a probabilistic distribution. Several distributions and probabilistic processes, such as the Dirichlet distribution and the Pitman- Yor process, are used to approximate the linguistic phenomena. Finally, the problem of sparseness of the language and its common solution known as smoothing is discussed. RESUMENEste documento discute varios modelos para la representación computacional del lenguaje. En primer lugar, se introducen los modelos de n-gramas que son basados en los modelos Markov. Luego, se toma en cuenta una familia de modelos conocido como el modelo exponencial. Esta familia en particular permite la incorporación de varias funciones para modelar. Como tercer punto, se discute una corriente reciente de la investigación, el enfoque probabilístico Bayesiano. En este tipo de modelos, el lenguaje es modelado como una distribución probabilística. Se utilizan varias distribuciones y procesos probabilísticos para aproximar los fenómenos lingüísticos, tales como la distribución de Dirichlet y el proceso de Pitman-Yor. Finalmente, se discute el problema de la escasez del lenguaje y su solución más común conocida como smoothing o redistribución.
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13

Babel, L. V., and D. Karssenberg. "Hydrological models are mediating models." Hydrology and Earth System Sciences Discussions 10, no. 8 (August 16, 2013): 10535–63. http://dx.doi.org/10.5194/hessd-10-10535-2013.

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Abstract. Despite the increasing role of models in hydrological research and decision-making processes, only few accounts of the nature and function of models exist in hydrology. Earlier considerations have traditionally been conducted while making a clear distinction between physically-based and conceptual models. A new philosophical account, primarily based on the fields of physics and economics, transcends classes of models and scientific disciplines by considering models as "mediators" between theory and observations. The core of this approach lies in identifying models as (1) being only partially dependent on theory and observations, (2) integrating non-deductive elements in their construction, and (3) carrying the role of instruments of scientific enquiry about both theory and the world. The applicability of this approach to hydrology is evaluated in the present article. Three widely used hydrological models, each showing a different degree of apparent physicality, are confronted to the main characteristics of the "mediating models" concept. We argue that irrespective of their kind, hydrological models depend on both theory and observations, rather than merely on one of these two domains. Their construction is additionally involving a large number of miscellaneous, external ingredients, such as past experiences, model objectives, knowledge and preferences of the modeller, as well as hardware and software resources. We show that hydrological models convey the role of instruments in scientific practice by mediating between theory and the world. It results from these considerations that the traditional distinction between physically-based and conceptual models is necessarily too simplistic and refers at best to the stage at which theory and observations are steering model construction. The large variety of ingredients involved in model construction would deserve closer attention, for being rarely explicitly presented in peer-reviewed literature. We believe that devoting more importance to identifying and communicating on the many factors involved in model development might increase transparency of model building.
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14

NUKAGA, Yoshio. "Medicalization Models and Biomedicalization Models." Japanese Sociological Review 56, no. 4 (2006): 815–29. http://dx.doi.org/10.4057/jsr.56.815.

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15

Dixion-Gough, Robert. "Theoretical Models and Simulation Models." Géographes associés 18, no. 1 (1996): 51–53. http://dx.doi.org/10.3406/geoas.1996.2006.

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16

Grewal, Jasleen K., Martin Krzywinski, and Naomi Altman. "Markov models — hidden Markov models." Nature Methods 16, no. 9 (August 30, 2019): 795–96. http://dx.doi.org/10.1038/s41592-019-0532-6.

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17

Ehrenberg, Andrew S. C., Neil R. Barnard, and Byron Sharp. "Decision models or descriptive models?" International Journal of Research in Marketing 17, no. 2-3 (September 2000): 147–58. http://dx.doi.org/10.1016/s0167-8116(00)00018-5.

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18

Gouvea, Julia, and Cynthia Passmore. "‘Models of’ versus ‘Models for’." Science & Education 26, no. 1-2 (March 2017): 49–63. http://dx.doi.org/10.1007/s11191-017-9884-4.

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19

Richelle, MarcN. "Animal models: Models of what?" Behavioural Processes 29, no. 1-2 (April 1993): 114. http://dx.doi.org/10.1016/0376-6357(93)90032-m.

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20

Ladzani, Watson. "Benchmarking the South African Excellence Model against worldclass best practice business Excellence Models." Environmental Economics 7, no. 4 (December 9, 2016): 8–19. http://dx.doi.org/10.21511/ee.07(4).2016.01.

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This study benchmarks South African Excellence Model (SAEM) against selected similar models in developed economies. The primary research question is: Why are South African small and medium enterprises (SMEs) not performing well in comparison to similar businesses in developed economies? The objectives of the study are to compare SAEM against worldclass best practice business excellence models, measure and rate SMEs management performance criteria. Random sampling was used to select SMEs in the construction industry. The findings revealed SAEM as equally a good model as worldclass best practice models. Empirical results revealed low management performance scores in comparison with those of worldclass best practice. Recommendations included aggressive marketing of SAEM and introducing continuous performance management improvement strategies on poorly rated criteria. Keywords: benchmarking, business excellence models, developed economies, management performance, small and medium enterprises. JEL Classification: L26, O1, L25
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21

Rotello, Caren M., Evan Heit, and Laura J. Kelly. "Do modals identify better models? A comparison of signal detection and probabilistic models of inductive reasoning." Cognitive Psychology 112 (August 2019): 1–24. http://dx.doi.org/10.1016/j.cogpsych.2019.03.004.

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22

Fenton, Steve, John Carter, and Tariq Modood. "Ethnicity and Academia: Closure Models, Racism Models and Market Models." Sociological Research Online 5, no. 2 (September 2000): 116–34. http://dx.doi.org/10.5153/sro.497.

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The paper addresses racism, discrimination, equal opportunities policies, institutional cultures, and the pressures of markets in influencing the position of minority ethnic groups in academia. The representation and position of minority ethnic groups among academic staff in UK higher education has previously been little studied. Data from the Higher Education Statistical Agency records and from new surveys are presented and analysed. Representation is low especially among some groups, but is growing among younger sections of academic staff, and is much higher in some academic subject areas than others. Analysis of terms of contract and of seniority by ethnic groups suggests that minorities are significantly less well placed within the profession. An important distinction is between British and non- British nationality in assessing ethnicity and academic posts; non- British staff may be seen as part of a global labour market, especially in fixed term contract research work. The evidence is evaluated alongside a re-exploration of principal models for explaining ethnic disadvantage in labour markets: closure, discrimination, equal opportunities, institutional racism and markets. The authors conclude that a combination of the last two models offers the best prospect of a full explanation.
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23

Moraes, Marcelo Botelho da Costa, and Marcelo Seido Nagano. "Cash Management Policies By Evolutionary Models: A Comparison Using The MILLER-ORR Model." Journal of Information Systems and Technology Management 10, no. 3 (December 30, 2013): 561–76. http://dx.doi.org/10.4301/s1807-17752013000300006.

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24

Derman, Emanuel. "Models." Financial Analysts Journal 65, no. 1 (January 2009): 28–33. http://dx.doi.org/10.2469/faj.v65.n1.5.

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Derman, Emanuel. "Models." CFA Digest 39, no. 2 (May 2009): 92–94. http://dx.doi.org/10.2469/dig.v39.n2.33.

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26

Stacey, A. M., and G. R. Grimmett. "models." Annals of Probability 26, no. 4 (October 1998): 1788–812. http://dx.doi.org/10.1214/aop/1022855883.

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27

Trindade, A. Alexandre, Richard A. Davis, and F. Jay Breidt. "models." Annals of Statistics 29, no. 4 (August 2001): 919–46. http://dx.doi.org/10.1214/aos/1013699987.

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28

Skouras, K. "models." Annals of Statistics 28, no. 3 (May 2000): 871–79. http://dx.doi.org/10.1214/aos/1015952002.

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29

Akritas, Michael G., and Ingrid Van Keilegom. "models." Annals of Statistics 27, no. 5 (October 1999): 1745–84. http://dx.doi.org/10.1214/aos/1017939150.

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30

Seely, Justus F., David S. Birkes, and Dawn M. Vanleeuwen. "models." Annals of Statistics 27, no. 6 (December 1999): 1927–47. http://dx.doi.org/10.1214/aos/1017939245.

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31

Jiang, Jiming. "models." Annals of Statistics 27, no. 6 (December 1999): 1974–2007. http://dx.doi.org/10.1214/aos/1017939247.

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32

Brombacher, Aarnout. "Models…" Quality and Reliability Engineering International 33, no. 1 (January 17, 2017): 5. http://dx.doi.org/10.1002/qre.2131.

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33

Kozhevnikov, Dmitrii Nikolaevich. "From training models to assimilation models." Moscow University Pedagogical Education Bulletin, no. 4 (December 29, 2013): 50–60. http://dx.doi.org/10.51314/2073-2635-2013-4-50-60.

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In article training process which has to lead the trainee to outlook formation through outlook is considered. On the example of archetype model of training process of learning, transfer of experience, check of its assimilation and formation of knowledge of the studied phenomenon is considered. It is offered to form an image model by means of the various model representations integrated into didactic complexes of tutorials. According to current trends the main demands made to system of means of model presentation are allocated.
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34

Bykov, D. V. "Sigma models as Gross–Neveu models." Theoretical and Mathematical Physics 208, no. 2 (August 2021): 993–1003. http://dx.doi.org/10.1134/s0040577921080018.

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35

Mäki, U. "Models Are Experiments, Experiments Are Models." Voprosy Ekonomiki, no. 11 (November 20, 2008): 81–89. http://dx.doi.org/10.32609/0042-8736-2008-11-81-89.

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The article shows that a model is a representation of something beyond itself in the sense of being used as a representative of that something, and in prompting questions of resemblance between the model and that something. Models are substitute systems that are directly examined in order to indirectly acquire information about their target systems. An economic experiment is an arrangement seeking to isolate a fragment of the world by controlling for causally relevant things outside that fragment. It is suggested that many theoretical models are (‘thought’) experiments, and that many ordinary experiments are (‘material’) models. The major difference between the two is that the controls effecting the required isolation are based on material manipulations in one case, and on assumptions in the other.
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36

Shen, Liang-Tsen, Sheng-Jie Luo, Chun-Kai Huang, and Bing-Yu Chen. "SD Models: Super-Deformed Character Models." Computer Graphics Forum 31, no. 7 (September 2012): 2067–75. http://dx.doi.org/10.1111/j.1467-8659.2012.03199.x.

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37

ABDUSSATTAR. "COSMOLOGICAL MODELS GENERALIZING ROBERTSON–WALKER MODELS." International Journal of Modern Physics D 12, no. 09 (October 2003): 1603–13. http://dx.doi.org/10.1142/s021827180300433x.

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Considering the physical 3-space t= constant of the space–time metrics as spheroidal and pseudo-spheroidal, cosmological models which are generalizations of Robertson–Walker models are obtained. Specific forms of these general models as solutions of Einstein's field equations are also discussed in the radiation and the matter dominated era of the universe.
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38

Greca, Ileana Maria, and Marco Antonio Moreira. "Mental models, conceptual models, and modelling." International Journal of Science Education 22, no. 1 (January 2000): 1–11. http://dx.doi.org/10.1080/095006900289976.

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39

Sorensen, E. V., J. Nordahl, and N. H. Hansen. "From CSP models to Markov models." IEEE Transactions on Software Engineering 19, no. 6 (June 1993): 554–70. http://dx.doi.org/10.1109/32.232021.

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40

JOHANSEN, TOR A., and BJARNE FOSS. "Constructing NARMAX models using ARMAX models." International Journal of Control 58, no. 5 (November 1993): 1125–53. http://dx.doi.org/10.1080/00207179308923046.

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41

ROSSINI, G. L., and F. A. SCHAPOSNIK. "FERMIONIC COSET MODELS AS TOPOLOGICAL MODELS." Modern Physics Letters A 09, no. 03 (January 30, 1994): 193–203. http://dx.doi.org/10.1142/s0217732394000216.

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By considering the fermionic realization of G/H coset models, we show that the partition function for the U(1)/U(1) model defines a topological quantum field theory and coincides with that of a two-dimensional Abelian BF system. In the non-Abelian case, we prove the topological character of G/G coset models by explicit computation, also finding a natural extension of two-dimensional BF systems with non-Abelian symmetry.
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42

Solis, Carlos, Jose H. Canos, Manuel Llavador, and Ma Carmen Penades. "From Process Models to Navigational Models." IEEE Latin America Transactions 5, no. 4 (July 2007): 238–44. http://dx.doi.org/10.1109/tla.2007.4378512.

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43

Banasiak, J. "Kinetic models – mathematical models of everything?" Physics of Life Reviews 16 (March 2016): 140–41. http://dx.doi.org/10.1016/j.plrev.2016.01.005.

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44

Lerner, David, Peter Grathwohl, Al Valocchi, and Emil Frind. "Models AND data; data AND models." Journal of Contaminant Hydrology 65, no. 3-4 (September 2003): 159–60. http://dx.doi.org/10.1016/s0169-7722(03)00127-x.

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45

Sullivan, Roger J., and Henry F. Lyle, III. "Economic models are not evolutionary models." Behavioral and Brain Sciences 28, no. 6 (December 2005): 836. http://dx.doi.org/10.1017/s0140525x05430149.

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Henrich et al. reject the “selfishness axiom” within a narrowly-defined economic model, and are premature in claiming that they have demonstrated cross-cultural variability in “selfishness” as defined in broader evolutionary theory. We also question whether a key experimental condition, anonymity, can be maintained in the small, cohesive, social groupings employed in the study.
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Molenaar, Peter C. M. "Latent variable models are network models." Behavioral and Brain Sciences 33, no. 2-3 (June 2010): 166. http://dx.doi.org/10.1017/s0140525x10000798.

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AbstractCramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.
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47

Currie, Gregory. "Models As Fictions, Fictions As Models." Monist 99, no. 3 (July 2016): 296–310. http://dx.doi.org/10.1093/monist/onw006.

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48

Ehrig, Karsten, Jochen Malte Küster, and Gabriele Taentzer. "Generating instance models from meta models." Software & Systems Modeling 8, no. 4 (July 1, 2008): 479–500. http://dx.doi.org/10.1007/s10270-008-0095-y.

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49

Hammond, Scott C., and J. B. Ritchie. "Models of Learning, Models of Life." Journal of Management Inquiry 2, no. 4 (December 1993): 326–30. http://dx.doi.org/10.1177/105649269324004.

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50

Mäki, Uskali. "Models are experiments, experiments are models." Journal of Economic Methodology 12, no. 2 (June 2005): 303–15. http://dx.doi.org/10.1080/13501780500086255.

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