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Статті в журналах з теми "SyR modellyng"

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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

Nakamura, G., S. Plaszczynski, B. Grammaticos, and M. Badoual. "Modelling the Effect of Virulent Variants with SIR." Nelineinaya Dinamika 17, no. 4 (2021): 475–90. http://dx.doi.org/10.20537/nd210409.

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We study the effect of an emerging virus mutation on the evolution of an epidemic, inspired by the appearance of the delta variant of SARS-CoV-2. We show that if the new variant is markedly more infective than the existing ones the epidemic can resurge immediately. The vaccination of the population plays a crucial role in the evolution of the epidemic. When the older (and more vulnerable) layers of the population are protected, the new infections concern mainly younger people, resulting in fewer hospitalisations and a reduced stress on the health system. We study also the effects of vacations, partially effective vaccines and vaccination strategies based on epidemic-awareness. An important finding concerns vaccination deniers: their attitude may lead to a prolonged wave of epidemic and an increased number of hospital admissions.

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2

Máca, P., and P. Torfs. "The influence of temporal rainfall distribution in the flood runoff modelling." Soil and Water Research 4, Special Issue 2 (March 19, 2010): S102—S110. http://dx.doi.org/10.17221/471-swr.

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The rainfall input is one of the main factors influencing the magnitude of the runoff response during a flood event. Its temporal and spatial distribution significantly contributes to the formation of hydrograph shape, peak discharge and flood volume. A novel approach to the evaluation of the role of the temporal rainfall pattern of hydrograph is presented in this contribution. The methodology shown is based on the coupling of the deterministic event based runoff model with the stochastic rainfall disaggregation model. The rainfall model simulates the hyetograph ensemble, which is the direct input to the calibrated event based runoff model. The event based runoff model calibration is based on the evaluation of real flood events. The rainfall ensemble is simulated according to the preservation of important statistical properties, which are estimated from the real rainfall data inputs. The proposed combination of two simulation techniques enables to generate the hydrograph ensemble upon a single flood event. The evaluation of the temporal rainfall distribution impact on the flood runoff response is performed through the determination of the selected rainfall runoff characteristics of the simulated hydrograph ensemble. The main result confirms the importance of the rainfall volume inputs and its temporal distribution on the flood runoff generation. The methodology shown enables to evaluate the potential of the real flood event to generate the flood event within the conditions of the small catchment scale.

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3

Jones, C. "Software enhancement modelling." Journal of Software Maintenance: Research and Practice 1, no. 2 (December 1989): 91–100. http://dx.doi.org/10.1002/smr.4360010203.

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4

De La Torre Cruz, Juan, Francisco Jesús Cañadas Quesada, Nicolás Ruiz Reyes, Pedro Vera Candeas, and Julio José Carabias Orti. "Wheezing Sound Separation Based on Informed Inter-Segment Non-Negative Matrix Partial Co-Factorization." Sensors 20, no. 9 (May 8, 2020): 2679. http://dx.doi.org/10.3390/s20092679.

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Wheezing reveals important cues that can be useful in alerting about respiratory disorders, such as Chronic Obstructive Pulmonary Disease. Early detection of wheezing through auscultation will allow the physician to be aware of the existence of the respiratory disorder in its early stage, thus minimizing the damage the disorder can cause to the subject, especially in low-income and middle-income countries. The proposed method presents an extended version of Non-negative Matrix Partial Co-Factorization (NMPCF) that eliminates most of the acoustic interference caused by normal respiratory sounds while preserving the wheezing content needed by the physician to make a reliable diagnosis of the subject’s airway status. This extension, called Informed Inter-Segment NMPCF (IIS-NMPCF), attempts to overcome the drawback of the conventional NMPCF that treats all segments of the spectrogram equally, adding greater importance for signal reconstruction of repetitive sound events to those segments where wheezing sounds have not been detected. Specifically, IIS-NMPCF is based on a bases sharing process in which inter-segment information, informed by a wheezing detection system, is incorporated into the factorization to reconstruct a more accurate modelling of normal respiratory sounds. Results demonstrate the significant improvement obtained in the wheezing sound quality by IIS-NMPCF compared to the conventional NMPCF for all the Signal-to-Noise Ratio (SNR) scenarios evaluated, specifically, an SDR, SIR and SAR improvement equals 5.8 dB, 4.9 dB and 7.5 dB evaluating a noisy scenario with SNR = −5 dB.

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5

Mousavi, S. F., and M. J. Amiri. "Modelling nitrate concentration of groundwater using adaptive neural-based fuzzy inference system." Soil and Water Research 7, No. 2 (May 18, 2012): 73–83. http://dx.doi.org/10.17221/46/2010-swr.

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High nitrate concentration in groundwater is a major problem in agricultural areas in Iran. Nitrate pollution in groundwater of the particular regions in Isfahan province of Iran has been investigated. The objective of this study was to evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) for estimating the nitrate concentration. In this research, 175 observation wells were selected and nitrate, potassium, magnesium, sodium, chloride, bicarbonate, sulphate, calcium and hardness were determined in groundwater samples for five consecutive months. Electrical conductivity (EC) and pH were also measured and the sodium absorption ratio (SAR) was calculated. The five-month average of bicarbonate, hardness, EC, calcium and magnesium are taken as the input data and the nitrate concentration as the output data. Based on the obtained structures, four ANFIS models were tested against the measured nitrate concentrations to assess the accuracy of each model. The results showed that ANFIS1 was the most accurate (RMSE = 1.17 and R<sup>2</sup> = 0.93) and ANFIS4 was the worst (RMSE = 2.94 and R<sup>2</sup> = 0.68) for estimating the nitrate concentration. In ranking the models, ANFIS2 and ANFIS3 ranked the second and third, respectively. The results showed that all ANFIS models underestimated the nitrate concentration. In general, the ANFIS1 model is recommendable for prediction of nitrate level in groundwater of the studied region.

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6

Jeulin, D. "Morphological modelling of surfaces." Surface Engineering 14, no. 3 (January 1998): 199–204. http://dx.doi.org/10.1179/sur.1998.14.3.199.

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7

Zádorová, T., L. Brodský, and M. Rohošková. "Soil-terrain modelling and erosion analysis at field scale level, a case study." Soil and Water Research 4, No. 2 (June 16, 2009): 84–90. http://dx.doi.org/10.17221/45/2008-swr.

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Pedometrical methods and digital soil mapping represent a progressive approach to the evaluation of various terrain-related soil processes. A detailed digital terrain model was used for the analysis of erosion – sedimentation situation and description of specific soil properties at meso-relief scale. A study plot in Central Bohemia (area 4 ha) with highly diversified geological conditions was chosen for the study combining digital modelling with a detailed terrain survey and laboratory analysis. The curvature and topographic wetness index values were the main features for the accumulation and soil loss areas evaluation. The terrain survey and DTM results corresponded in the case of the soil cover structure and soil depth, showing a vast impact of accelerated water erosion and direct tillage erosion on the soil redistribution. In the concave and base slope positions, colluvial soils were identified, while Regosols with A–C profile stratigraphy form in the upper convex parts of the study plot. Particular soil properties distribution (pH, organic carbon content, texture) is, besides erosion, significantly influenced by parent rock variation and the relation to the terrain characteristics is less distinctive. The study showed a significant impact of the terrain properties on the general material redistribution and the possibility of soil cover structure prediction in areas with complicated geological conditions.

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8

Votrubová, J., M. Dohnal, T. Vogel, and M. Tesař. "On parameterization of heat conduction in coupled soil water and heat flow modelling." Soil and Water Research 7, No. 4 (November 9, 2012): 125–37. http://dx.doi.org/10.17221/21/2012-swr.

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Soil water and heat transport plays an important role in various hydrologic, agricultural, and industrial applications. Accordingly, an increasing attention is paid to relevant simulation models. In the present study, soil thermal conditions at a mountain meadow during the vegetation season were simulated. A dual-continuum model of coupled water and heat transport was employed to account for preferential flow effects. Data collected at an experimental site in the &Scaron;umava Mountains, southern Bohemia, during the vegetation season 2009 were employed. Soil hydraulic properties (retention curve and hydraulic conductivity) determined by independent soil tests were used. Unavailable hydraulic parameters were adjusted to obtain satisfactory hydraulic model performance. Soil thermal properties were estimated based on values found in literature without further optimization. Three different approaches were used to approximate the soil thermal conductivity function, λ(θ): (i) relationships provided by Chung and Horton (ii) linear estimates as described by Loukili, Woodbury and Snelgrove, (iii) methodology proposed by Côté and Konrad. The simulated thermal conditions were compared to those observed. The impact of different soil thermal conductivity approximations on the heat transport simulation results was analysed. The differences between the simulation results in terms of the soil temperature were small. Regarding the surface soil heat flux, these differences became substantial. More realistic simulations were obtained using λ(θ) estimates based on the soil texture and composition. The differences between these two, related to neglecting vs. considering λ(θ) non-linearity, were found negligible.

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9

Price, A. T. "MATHEMATICAL MODELLING OF WELD PHENOMENA." Surface Engineering 9, no. 2 (January 1993): 124–25. http://dx.doi.org/10.1179/sur.1993.9.2.124.

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10

Moradi, G., and B. Mehdinejadiani. "Modelling solute transport in homogeneous and heterogeneous porous media using spatial fractional advection-dispersion equation." Soil and Water Research 13, No. 1 (January 24, 2018): 18–28. http://dx.doi.org/10.17221/245/2016-swr.

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This paper compared the abilities of advection-dispersion equation (ADE) and spatial fractional advection-dispersion equation (sFADE) to describe the migration of a non-reactive contaminant in homogeneous and heterogeneous soils. To this end, laboratory tests were conducted in a sandbox sizing 2.5 × 0.1 × 0.6 m (length × width × height). After performing a parametric sensitivity analysis, parameters of sFADE and ADE were individually estimated using the inverse problem method at each distance. The dependency of estimated parameters on distance was examined. The estimated parameters at 30 cm were used to predict breakthrough curves (BTCs) at subsequent distances. The results of sensitivity analysis indicated that average pore-water velocity and dispersion coefficient were, respectively, the most and least sensitive parameters in both mathematical models. The values of fractional differentiation orders (α) for sFADE were smaller than 2 in both soils. The scale-dependency of the dispersion coefficients of ADE and sFADE was observed in both soils. However, the application of sFADE to describe solute transport reduced the scale effect on the dispersion coefficient, especially in the heterogeneous soil. For the homogeneous soil, the predicting results of ADE and sFADE were nearly similar, while for the heterogeneous soil, the predicting results of sFADE were more satisfactory in comparison with those of ADE, especially when the transport distance increased. Compared to ADE, the sFADE simulated somewhat better the tailing parts of BTCs and showed the earlier arrival of tracer. Overall, the solute transport, especially in the heterogeneous soil, was non-Fickian and the sFADE somewhat better described non-Fickian transport.

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11

Potęga, Paulina. "Frame Based Modelling of Specialist Languages." Studia Anglica Resoviensia 14 (2017): 121–31. http://dx.doi.org/10.15584/sar.2017.14.10.

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12

Eatock Taylor, Rodney. "On Modelling the Diffraction of Water Waves." Ship Technology Research 54, no. 2 (April 2007): 54–80. http://dx.doi.org/10.1179/str.2007.54.2.002.

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13

Caiti, Andrea, Vincenzo Calabró, Sergio Grammatico, Andrea Munafó, and Mirko Stifani. "Lagrangian Modelling of an Underwater Wave Glider." Ship Technology Research 59, no. 1 (January 2012): 6–12. http://dx.doi.org/10.1179/str.2012.59.1.001.

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14

Rödel, J., and H. J. Spies. "Modelling of austenite formation during rapid heating." Surface Engineering 12, no. 4 (January 1996): 313–18. http://dx.doi.org/10.1179/sur.1996.12.4.313.

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15

Terdpaopong, Kanitsorn, and Dessalegn Getie Mihret. "Modelling SME credit risk: Thai empirical evidence." Small Enterprise Research 18, no. 1 (September 2011): 63–79. http://dx.doi.org/10.5172/ser.18.1.63.

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16

Poolman, M. G. "ScrumPy: metabolic modelling with Python." IEE Proceedings - Systems Biology 153, no. 5 (2006): 375. http://dx.doi.org/10.1049/ip-syb:20060010.

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17

Mandel, J. J., W. Dubitzky, and N. M. Palfreyman. "Modelling codependence in biological systems." IET Systems Biology 1, no. 1 (January 1, 2007): 18–32. http://dx.doi.org/10.1049/iet-syb:20060002.

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18

Burrows, R. B., G. R. Warnes, and R. C. Hanumara. "Statistical modelling of biochemical pathways." IET Systems Biology 1, no. 6 (November 1, 2007): 353–60. http://dx.doi.org/10.1049/iet-syb:20060074.

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19

Cooling, M. T., E. J. Crampin, and P. Hunter. "Modelling biological modularity with CellML." IET Systems Biology 2, no. 2 (March 1, 2008): 73–79. http://dx.doi.org/10.1049/iet-syb:20070020.

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20

Kim, Doohwan, Chulsu Jo, and Jae Woo Lee. "A Study on SIR-based Unemployment Modelling from the System Dynamics Perspective." Korean System Dynamics Review 23, no. 3 (September 30, 2022): 85–108. http://dx.doi.org/10.32588/ksds.23.3.4.

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21

SUARD, E., Y. FAYOLLE, R. CLÉMENT, V. ROCHER, M. ALLIET, C. ALBASI, and S. GILLOT. "Limitation du colmatage dans un bioréacteur à membranes à l’échelle semi-industrielle : modélisation et caractérisation de l’hydrodynamique." Techniques Sciences Méthodes, no. 5 (May 20, 2020): 47–57. http://dx.doi.org/10.36904/tsm/202005047.

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Анотація:

Les bioréacteurs à membranes se sont largement développés ces dernières années en assainissement des eaux résiduaires urbaines. Malgré les avantages du procédé, le colmatage des membranes engendre des contraintes économiques liées aux coûts énergétiques et au remplacement prématuré des membranes. Différentes stratégies de limitation du colmatage sont mises en place, dont l’aération séquencée des membranes à l’aide de grosses bulles. Cependant, les mécanismes d’action de cette aération et l’hydrodynamique des réacteurs restent relativement méconnus, en particulier à l’échelle industrielle et en présence de boues. Un pilote semi-industriel de filtration membranaire a été conçu et installé sur l’unité de traitement des jus de Seine Aval. Ce pilote a permis d’étudier l’impact de l’aération sur les performances de filtration en couplant une analyse statistique (systèmes d’inférence floue) et une caractérisation de l’hydrodynamique du réacteur (tomographie de résistivité électrique). Des chemins préférentiels de passage des bulles ont été observés à faible débit d’air et forte concentration en matières en suspension à l’aide de la tomographie de résistivité électrique. Ces observations fournissent des éléments d’explication aux résultats de l’arbre de décision flou focalisé sur l’analyse de l’impact du débit d’air sur la dérive journalière de perméabilité. La concentration en matières en suspension des boues et la différence de demande chimique en oxygène entre le surnageant des boues et le perméat s’avèrent être les facteurs les plus impactants pour les conditions opératoires testées. Ils traduisent l’efficacité de l’aération, que ce soit en matière de répartition du champ de bulles et/ou de relargage d’éléments colmatants. Ces résultats invitent à considérer la régulation de l’aération en fonction des propriétés des boues dans les procédés industriels.

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22

El‐Zaart, A., and D. Ziou. "Statistical modelling of multimodal SAR images." International Journal of Remote Sensing 28, no. 10 (May 2007): 2277–94. http://dx.doi.org/10.1080/01431160600933997.

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23

Delignon, Y., R. Garello, and A. Hillion. "Statistical modelling of ocean SAR images." IEE Proceedings - Radar, Sonar and Navigation 144, no. 6 (1997): 348. http://dx.doi.org/10.1049/ip-rsn:19971497.

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24

Majumder, Jayanta, Dracos Vassalos, Luis Guarin, and Guro C. Vassalos. "Modelling and Simulation of Shipboard Environment and Operations." Ship Technology Research 52, no. 4 (October 2005): 159–71. http://dx.doi.org/10.1179/str.2005.52.4.003.

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25

Bao, Y., and D. T. Gawne. "Process modelling of thermal spray deposition of thermosets." Surface Engineering 11, no. 3 (January 1995): 215–22. http://dx.doi.org/10.1179/sur.1995.11.3.215.

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26

Chover, Joshua. "Modelling afferent connectivity, postsynaptic plasticity, and signal discrimination." Synapse 3, no. 2 (1989): 101–16. http://dx.doi.org/10.1002/syn.890030202.

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27

Fei, Yuan, Huibiao Zhu, Xi Wu, Huixing Fang, and Shengchao Qin. "Comparative modelling and verification of Pthreads and Dthreads." Journal of Software: Evolution and Process 30, no. 3 (November 17, 2017): e1919. http://dx.doi.org/10.1002/smr.1919.

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28

Buckley, Jim, Andrew P. LeGear, Chris Exton, Ross Cadogan, Trevor Johnston, Bill Looby, and Rainer Koschke. "Encapsulating targeted component abstractions using software Reflexion Modelling." Journal of Software Maintenance and Evolution: Research and Practice 20, no. 2 (2008): 107–34. http://dx.doi.org/10.1002/smr.364.

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29

Araújo, Jussara de Loiola, and Petrina Rúbria Nogueira Avelar. "Modelagem Matemática e o Desenvolvimento do Pensamento Integral." Bolema: Boletim de Educação Matemática 36, no. 72 (April 2022): 239–61. http://dx.doi.org/10.1590/1980-4415v36n72a11.

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Resumo Inspiradas por um projeto de modelagem matemática e pelas discussões teóricas que subsidiam os conceitos de pensamento numérico, pensamento geométrico e pensamento algébrico, nosso objetivo, neste artigo, é propor e caracterizar uma primeira versão de um constructo teórico denominado pensamento integral . O projeto de modelagem foi realizado por estudantes do 6º ano do Ensino Fundamental e um de seus propósitos era calcular a área superficial da lama que cobriu uma região do município de Brumadinho, em Minas Gerais, Brasil, após o rompimento da barragem de rejeitos de minério de ferro. Trata-se, portanto, de um ensaio empírico-teórico, por fundamentar-se em uma prática escolar de modelagem na educação matemática colocada em diálogo com referenciais teóricos. Mobilizamos problematizações sobre o ensino de integral, em especial a ideia de aproximação, e conceitos próprios da educação matemática na Educação Básica para, a partir da experiência no projeto de modelagem, descrever o conhecimento envolvido no cálculo de áreas de regiões com quaisquer formatos e que pode ser desenvolvido por estudantes do Ensino Fundamental.

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30

Koelman, Herbert J. "Application of the H-rep Ship Hull Modelling Concept." Ship Technology Research 50, no. 4 (October 2003): 172–81. http://dx.doi.org/10.1179/str.2003.50.4.005.

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31

Karakostas, V. "Modelling and maintenance software systems at the teleological level." Journal of Software Maintenance: Research and Practice 2, no. 1 (March 1990): 47–59. http://dx.doi.org/10.1002/smr.4360020105.

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32

Wolstenholme, Eric. "Using generic system archetypes to support thinking and modelling." System Dynamics Review 20, no. 4 (2004): 341–56. http://dx.doi.org/10.1002/sdr.302.

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33

Uys, L., J. H. S. Hofmeyr, J. L. Snoep, and J. M. Rohwer. "Software tools that facilitate kinetic modelling with large data sets: an example using growth modelling in sugarcane." IEE Proceedings - Systems Biology 153, no. 5 (2006): 385. http://dx.doi.org/10.1049/ip-syb:20060050.

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34

Grima, R., and J. Kim. "Editorial: Modelling noise in biochemical reaction networks." IET Systems Biology 6, no. 4 (August 1, 2012): 101. http://dx.doi.org/10.1049/iet-syb.2012.0039.

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35

Gawthrop, Peter J., Ivo Siekmann, Tatiana Kameneva, Susmita Saha, Michael R. Ibbotson, and Edmund J. Crampin. "Bond graph modelling of chemoelectrical energy transduction." IET Systems Biology 11, no. 5 (October 2017): 127–38. http://dx.doi.org/10.1049/iet-syb.2017.0006.

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36

Moraru, I. I., F. Morgan, Y. Li, L. M. Loew, J. C. Schaff, A. Lakshminarayana, B. M. Slepchenko, F. Gao, and M. L. Blinov. "Virtual Cell modelling and simulation software environment." IET Systems Biology 2, no. 5 (September 1, 2008): 352–62. http://dx.doi.org/10.1049/iet-syb:20080102.

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37

Wang, G., Q. Chen, and Z. Ren. "Modelling of time-varying discrete-time systems." IET Signal Processing 5, no. 1 (2011): 104. http://dx.doi.org/10.1049/iet-spr.2008.0071.

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38

Barančíková, G., J. Halás, M. Gutteková, J. Makovníková, M. Nováková, R. Skalský, and Z. Tarasovičová. "Application of RothC model to predict soil organic carbon stock on agricultural soils of Slovakia." Soil and Water Research 5, No. 1 (February 26, 2010): 1–9. http://dx.doi.org/10.17221/23/2009-swr.

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Анотація:

Soil organic matter (SOM) takes part in many environmental functions and, depending on the conditions, it can be a source or a sink of the greenhouse gases. Presently, the changes in soil organic carbon (SOC) stock can arise because of the climatic changes or changes in the land use and land management. A promising method in the estimation of SOC changes is modelling, one of the most used models for the prediction of changes in soil organic carbon stock on agricultural land being the RothC model. Because of its simplicity and availability of the input data, RothC was used for testing the efficiency to predict the development of SOC stock during 35-year period on agricultural land of Slovakia. The received data show an increase of SOC stock during the first (20 years) phase and no significant changes in the course of the second part of modelling. The increase of SOC stock in the first phase can be explained by a high carbon input of plant residues and manure and a lower temperature in comparison with the second modelling part.

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39

Golański, D., P. Bieliński, and T. Wierzchoń. "NUMERICAL MODELLING OF RESIDUAL STRESSES IN BORIDE lAYERS ON STEEL." Surface Engineering 13, no. 2 (January 1997): 145–48. http://dx.doi.org/10.1179/sur.1997.13.2.145.

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40

Peter L Sholberg. "Modelling the development of postharvest diseases in fruits and vegetables." Stewart Postharvest Review 4, no. 4 (2008): 1–6. http://dx.doi.org/10.2212/spr.2008.4.2.

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41

Lavery, Janet, Cornelia Boldyreff, Bin Ling, and Colin Allison. "Modelling the evolution of legacy systems to Web-based systems." Journal of Software Maintenance and Evolution: Research and Practice 16, no. 12 (January 2004): 5–30. http://dx.doi.org/10.1002/smr.282.

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42

Sagar, Surendra Kumar. "SIR-SI Mathematical Model for Zika Virus Progression Dynamics in India: A Case Study." Journal of Communicable Diseases 53, no. 02 (June 30, 2021): 100–104. http://dx.doi.org/10.24321/0019.5138.202132.

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Анотація:

Viral diseases are very hazardous for humanity because in the case of most viral diseases, drugs are not effective. At present, the whole world is living with the fear of COVID-19. From time to time, several viral diseases have been troubling human life. In this article, we have tried to capture the progression dynamics of Zika Virus (ZIKV) infection in the Indian scenario. A constructed model is based on compartment modelling. In the model, Susceptible-Infected-Recovered (SIR) structure is used for the human population and Susceptible-Infected (SI) structure is used for mosquito population. The value of the basic reproduction number (R0) is computed 0.36 at baseline values of parameters involved in the model. The lower value of R0 suggests that infection was unable to spread in the human population. Sensitive analysis for R0 revealed that the most accountable parameter in the spread of infection was mosquito biting rate. The modelling technique might be useful for other diseases also.

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43

Chitroub, Salim, Amrane Houacine, and Boualem Sansal. "Statistical characterisation and modelling of SAR images." Signal Processing 82, no. 1 (January 2002): 69–92. http://dx.doi.org/10.1016/s0165-1684(01)00158-x.

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44

Lemos, José V. "Discontinuum Modelling in Rock Engineering." Soils and Rocks 36, no. 2 (May 1, 2013): 137–56. http://dx.doi.org/10.28927/sr.362137.

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The evolution of numerical modelling in rock mechanics has made possible a more realistic representation of the behaviour of rock masses, and a more reliable prediction of the response of rock engineering structures. Discontinuum modelling techniques, namely the discrete element method, based on the explicit representation of the rock mass discontinuous structure, have progressively acquired a broader role. In this lecture, the essential concepts and main features of these numerical techniques are discussed, with reference to their historical development. The use of these idealizations is exemplified by two areas in which they proved very effective. On one hand, the analysis of practical rock engineering problems, intended, for example, for safety assessment or monitoring interpretation. This type of application is illustrated herein by dam foundation analyses based on deformable block models. A second level of discontinuum modelling involves research aimed at the understanding of the fundamental behaviour of rock and rock masses. An example of this approach is the analysis of fracture phenomena with particle models, primarily of laboratory specimens, but being extended to larger scales. Critical issues identified in the effective application of these modelling tools are examined, as well as some foreseeable evolution trends.

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Hassan, Ahamad, Pak Sham, and Hugh Markus. "Modelling Feasibility of Genetic Approaches to Human Stroke." Stroke 32, suppl_1 (January 2001): 322. http://dx.doi.org/10.1161/str.32.suppl_1.322.

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33 Background Twin, population and animal studies suggest that genetic factors are important in stroke. To date all research in humans has used case control (CC) methods to determine association between candidate genes and stroke. Alternative approaches include linkage analysis with the advantage of allowing detection of novel genes and transmission disequilibrium testing, which uses internal family controls e.g.parents(TDT) or unaffected sibs (S-TDT)to reduce the risk of population stratification which is a problem in case control studies. Despite theoretical benefits, the feasiblity of these methods remains unknown. Methods Family history was prospectively determined in 727 consecutive cerebrovascular patients and 623 age matched controls. Patients with suspected Mendelian syndromes were excluded. Parent and sibling status (alive or dead and the presence of stroke)was documented. The data was used to model the number of stroke patients required for the different study designs. Results Family history of young stroke (≤65)was an independent risk factor for ischaemic stroke OR 1.45 (95% CI 1.01–2.09) p=0.05, early onset proband stroke (≤65) OR 2.22(1.42–3.50)p=0.0005 and stroke due to large vessel disease OR 1.75 (1.09–2.83) p=0.02. In the age group <65 (n=305), 14 affected sibs, 19 parent offspring trios and 626 discordant sibling pairs were identified. The adjusted sibling relative risk ratio was estimated to be 1.78. If 3 genetic loci play an important role in stroke, this translates into 1000 affected sibling pairs being required for an affected sib pair linkage study. Over 100 000 patients would require screening based on a 50% recruitment rate in relatives. For candidate gene studies, assuming an odds ratio of 2 and allele frequency of 0.1, 986 (CC),31681 (TDT) and 3635 (S-TDT) patients would need to be screened to achieve the necessary sample sizes. Conclusion This study supports the role of independent genetic risk factors in stroke which have greater influence in young individuals. Novel approaches are potentially feasible, although linkage studies and TDT would require large scale multicentre collaborations.

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Meades, G., N. K. Thalji, M. de Queiroz, X. Cai, and G. L. Waldrop. "Mathematical modelling of negative feedback regulation by carboxyltransferase." IET Systems Biology 5, no. 3 (May 1, 2011): 220–28. http://dx.doi.org/10.1049/iet-syb.2010.0071.

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47

Lim, K. M., E. B. Shim, and S. H. Yang. "Systemic modelling of human bioenergetics and blood circulation." IET Systems Biology 6, no. 5 (October 1, 2012): 187–95. http://dx.doi.org/10.1049/iet-syb.2011.0035.

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Ye, P., S. A. Smolka, E. Entcheva, and R. Grosu. "Modelling excitable cells using cycle-linear hybrid automata." IET Systems Biology 2, no. 1 (January 1, 2008): 24–32. http://dx.doi.org/10.1049/iet-syb:20070001.

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Weißenborn, Christian, Wolfgang Fricke, and Stephen J. Kennedy. "Bond Strength of SPS Panels - Failure Criterion and Finite-Element Modelling." Ship Technology Research 55, no. 3 (July 2008): 98–114. http://dx.doi.org/10.1179/str.2008.55.3.002.

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Ali, Mohamed Shaaban. "Simulation and Modelling of near Infrared Spectroscopy (NIRS) as Brain Monitor." Spectral Analysis Review 02, no. 02 (2014): 3–5. http://dx.doi.org/10.4236/sar.2014.22002.

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