Journal articles on the topic 'Model selection curves'
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Müller, Samuel, and Alan H. Welsh. "On Model Selection Curves." International Statistical Review 78, no. 2 (May 5, 2010): 240–56. http://dx.doi.org/10.1111/j.1751-5823.2010.00108.x.
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Gonzales Martínez, Rolando. "The Wage Curve, Once More with Feeling: Bayesian Model Averaging of Heckit Models." Econometric Research in Finance 3, no. 2 (October 15, 2018): 79–92. http://dx.doi.org/10.33119/erfin.2018.3.2.1.
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The sensitivity of the wage curve to sample-selection and model uncertainty was evaluated with Bayesian methods. More than 8000 Heckit wage curves were estimated using data from the 2017 household survey of Bolivia. After averaging the estimates with the posterior probability of each model being true, the wage curve elasticity in Bolivia is close to -0.01. This result suggests that in this country the wage curve is inelastic and does not follow the international statistical regularity of wage curves.
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Emmert-Streib, Frank, and Matthias Dehmer. "Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error." Machine Learning and Knowledge Extraction 1, no. 1 (March 22, 2019): 521–51. http://dx.doi.org/10.3390/make1010032.
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When performing a regression or classification analysis, one needs to specify a statistical model. This model should avoid the overfitting and underfitting of data, and achieve a low generalization error that characterizes its prediction performance. In order to identify such a model, one needs to decide which model to select from candidate model families based on performance evaluations. In this paper, we review the theoretical framework of model selection and model assessment, including error-complexity curves, the bias-variance tradeoff, and learning curves for evaluating statistical models. We discuss criterion-based, step-wise selection procedures and resampling methods for model selection, whereas cross-validation provides the most simple and generic means for computationally estimating all required entities. To make the theoretical concepts transparent, we present worked examples for linear regression models. However, our conceptual presentation is extensible to more general models, as well as classification problems.
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Young, Peg, and J. Keith Ord. "Model selection and estimation for technological growth curves." International Journal of Forecasting 5, no. 4 (January 1989): 501–13. http://dx.doi.org/10.1016/0169-2070(89)90005-8.
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Fryer, R. J., A. F. Zuur, and N. Graham. "Using mixed models to combine smooth size-selection and catch-comparison curves over hauls." Canadian Journal of Fisheries and Aquatic Sciences 60, no. 4 (April 1, 2003): 448–59. http://dx.doi.org/10.1139/f03-029.
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Parametric size-selection curves are often combined over hauls to estimate a mean selection curve using a mixed model in which between-haul variation in selection is treated as a random effect. This paper shows how the mixed model can be extended to estimate a mean selection curve when smooth nonparametric size-selection curves are used. The method also estimates the between-haul variation in selection at each length and can model fixed effects in the form of the different levels of a categorical variable. Data obtained to estimate the size-selection of dab by a Nordmøre grid are used for illustration. The method can also be used to provide a length-based analysis of catch-comparison data, either to compare a test net with a standard net or to calibrate two research survey vessels. Haddock data from an intercalibration exercise are used for illustration.
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Wang, Ziming, Zexi Yang, Xiuzhen Wang, Qiang Yue, Zhendong Xia, and Hong Xiao. "Residence Time Distribution (RTD) Applications in Continuous Casting Tundish: A Review and New Perspectives." Metals 12, no. 8 (August 17, 2022): 1366. http://dx.doi.org/10.3390/met12081366.
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The continuous casting tundish is a very important metallurgical reactor in continuous casting production. The flow characteristics of tundishes are usually evaluated by residence time distribution (RTD) curves. At present, the analysis model of RTD curves still has limitations. In this study, we reviewed RTD curve analysis models of the single flow and multi-flow tundish. We compared the mixing model and modified combination model for RTD curves of single flow tundish. At the same time, multi-strand tundish flow characteristics analysis models for RTD curves were analyzed. Based on the RTD curves obtained from a tundish water experiment, the applicability of various models is discussed, providing a reference for the selection of RTD analysis models. Finally, we proposed a flow characteristics analysis of multi-strand tundish based on a cumulative time distribution curve (F-curve). The F-curve and intensity curve can be used to analyze and compare the flow characteristics of multi-strand tundishes. The modified dead zone calculation method is also more reasonable. This method provides a new perspective for the study of multi-strand tundishes or other reactor flow characteristics analysis models.
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KULASEKERA, K. B., and JAVIER OLAYA. "VARIABLE SELECTION IN NONPARAMETRIC REGRESSION MODEL." International Journal of Reliability, Quality and Safety Engineering 11, no. 02 (June 2004): 141–61. http://dx.doi.org/10.1142/s0218539304001415.
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A new procedure is proposed for deciding whether a candidate variable is significant in a general nonparametric regression model with independent covariates. A forward selection process is conducted using a formal test of equality of regression curves at each stage. The proposed procedure does not require multidimensional smoothing at any intermediate step. Asymptotic properties are given. Some simulation results and a real application are given.
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Ralston, Stephen. "Size Selection of Snappers (Lutjanidae) by Hook and Line Gear." Canadian Journal of Fisheries and Aquatic Sciences 47, no. 4 (April 1, 1990): 696–700. http://dx.doi.org/10.1139/f90-078.
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The most commonly used theoretical models of gear selection have been the logistic and normal curves. These are usually applied to trawls and gillnets, respectively. In contrast, little critical work has been completed concerning the selective properties of fish hooks, although both types of selectivity curves have arbitrarily been applied to hook catch data in the literature. No study has clearly demonstrated the actual form of a selection curve for hooks. To determine which type of curve (logistic or normal) best describes the selective sampling characteristics of fish hooks, an experiment was conducted in the Marianas Islands during 1982–84. During all fishing operations two different sizes (#20 and #28) of circle fish hooks were fished simultaneously and in equal number. Under these conditions, the length specific ratios of snapper (Lutjanidae) catches taken by the two hook sizes provided a basis for distinguishing which model was most appropriate. Results showed that neither model in its simplest form depicted hook selectivity well. While small hooks caught substantially more small fish, large hooks were somewhat more effective in capturing the larger size classes.
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Krenek, Sascha, Thomas U. Berendonk, and Thomas Petzoldt. "Thermal performance curves of Paramecium caudatum: A model selection approach." European Journal of Protistology 47, no. 2 (May 2011): 124–37. http://dx.doi.org/10.1016/j.ejop.2010.12.001.
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Winters, G. H., and J. P. Wheeler. "Direct and Indirect Estimation of Gillnet Selection Curves of Atlantic Herring (Clupea harengus harengus)." Canadian Journal of Fisheries and Aquatic Sciences 47, no. 3 (March 1, 1990): 460–70. http://dx.doi.org/10.1139/f90-050.
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Length-specific selection curves for Atlantic herring (Clupea harengus) were calculated for a series of gillnets ranging in mesh size from 50.8 to 76.2 mm (stretched measure) using Holt's (1963) model (ICNAF Spec. Publ. 5: 106–115). These curves were than compared with direct estimates of length-specific selectivity obtained from a comparison of gillnet catch length frequencies with population length composition data as determined from acoustic surveys. Selection curves calculated indirectly using the Holt model were unimodal and congruent. The empirical selection curves however were multimodal and fishing power varied with mesh size. These differences in selectivities were due to the fact that herring were caught not only by wedging at the maximum girth but also at other body positions such as the gills and snout. Each of these modes of capture have different length-specific selectivity characteristics and, since the relative contributions of the different modes of capture varied both between nets and annually, the selection curve of herring for a particular mesh size is not unique. It can however be reasonably approximated when girth is used as the selection criterion. Direct empirical selectivities are therefore recommended when interpreting population parameters from herring gillnet catch data.
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Do, Duy Ngoc, and Younes Miar. "Evaluation of Growth Curve Models for Body Weight in American Mink." Animals 10, no. 1 (December 20, 2019): 22. http://dx.doi.org/10.3390/ani10010022.
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Modelling the growth curves of animals is important for optimizing the management and efficiency of animal production; however, little is known about the growth curves in American mink (Neovison vison). The study evaluated the performances of four three-parameter (Logistic, Gompertz, von Bertalanffy, and Brody), four four-parameter (Richards, Weibull, Bridges, and Janoscheck) and two polynomial models for describing the growth curves in mink. Body weights were collected from the third week of life to the week 31 in 738 black mink (373 males and 365 females). Models were fitted using the nls and nlsLM functions in stats and minpack.lm packages in R software, respectively. The Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) were used for model comparison. Based on these criteria, Logistic and Richards were the best models for males and females, respectively. Four-parameter models had better performance compared to the other models except for Logistic model. The estimated maximum weight and mature growth rate varied among the models and differed between males and females. The results indicated that males and females had different growth curves as males grew faster and reached to the maximum body weight later compared to females. Further studies on genetic parameters and selection response for growth curve parameters are required for development of selection programs based on the shape of growth curves in mink.
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Araujo Neto, F. R., D. P. Oliveira, R. R. Aspilcueta-Borquis, D. A. Vieira, K. C. Guimarães, H. N. Oliveira, and H. Tonhati. "Selection of nonlinear mixed models for growth curves of dairy buffaloes (Bubalus bubalis)." Journal of Agricultural Science 158, no. 3 (April 2020): 218–24. http://dx.doi.org/10.1017/s0021859620000325.
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AbstractThe determination of livestock growth patterns is important for meat or milk production systems, and nonlinear models are used to summarize and interpret the information. The aim of this study was to more accurately estimate growth curve parameters in buffalo cows by evaluating and selecting nonlinear mixed models that employ different types of residuals and include or not contemporary groups (CG) as a covariate. Weight records from 720 animals obtained over a period of 60 months were used. The growth curves were fit using nonlinear mixed-effects models. The Bertalanffy, Gompertz and Logistic models were evaluated. Modelling residuals using four structures (constant, combined, exponential and proportional) and the inclusion or not of CG in the models were also evaluated. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to select the model. In addition to estimating the parameters of the nonlinear growth models and their correlations, the instantaneous growth rate and inflection point were obtained. The Bertalanffy model with a combined residual structure and CG exhibited the lowest AIC and BIC values. Asymptotic weight (A) estimates ranged from 621.8 to 742.1 kg, and the maturity rate (k) ranged from 0.068 to 0.115 kg/month. The correlation between A and k ranged from −0.32 to −0.82 among the models evaluated. The selection criteria indicated that the Bertalanffy model was the most suitable for growth curve analysis in buffaloes.
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Hovgård, Holger, Hans Lassen, Niels Madsen, Thomas Moth Poulsen, and David Wileman. "Gillnet selectivity for North Sea Atlantic cod (Gadus morhua): model ambiguity and data quality are related." Canadian Journal of Fisheries and Aquatic Sciences 56, no. 7 (July 1, 1999): 1307–16. http://dx.doi.org/10.1139/f99-070.
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Gillnet selectivity curves for North Sea Atlantic cod (Gadus morhua) were fitted to catch data obtained with six different mesh sizes. The selectivity curves investigated included frequently used selectivity models following the normal, lognormal, and gamma distributions. Another group of selectivity models that take the method of capture (gilled, maxillae, or "randomly" enmeshed) into consideration was also included. The best description of the selection data was found for the latter models. Therefore, the capture processes and girth measurements should be recorded as a matter of routine and such data used when constructing and evaluating gillnet selectivity models. The shape of the selectivity curve for those size intervals where there were satisfactory catch information was well defined, while the selection curve was ambiguous outside the interval with adequate data. Ambiguities in the shape of the selectivity curve can be diminished by choosing an appropriate range in mesh sizes and ensuring that the mesh sizes match the size distribution of the population fished. It is furthermore suggested that the estimated length distribution of the fish encountering the nets be robust to misspecification of the selectivity model.
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Kalliojärvi, Heidi, Kari Lappalainen, and Seppo Valkealahti. "Feasibility of Photovoltaic Module Single-Diode Model Fitting to the Current–Voltage Curves Measured in the Vicinity of the Maximum Power Point for Online Condition Monitoring Purposes." Energies 15, no. 23 (November 30, 2022): 9079. http://dx.doi.org/10.3390/en15239079.
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Photovoltaic system condition monitoring can be performed via single-diode model fitting to measured current–voltage curves. Model parameters can reveal cell aging and degradation. Conventional parameter identification methods require the measurement of entire current–voltage curves, causing interruptions in energy production. Instead, partial curves measured near the maximum power point offer a promising option for online condition monitoring. Unfortunately, measurement data reduction affects fitting and diagnosis accuracy. Thus, the optimal selection of maximum power point neighbourhoods used for fitting requires a systematic analysis of the effect of data selection on the fitted parameters. To date, only one published article has addressed this issue with a small number of measured curves using symmetrically chosen neighbourhoods with respect to the maximum power. Moreover, no study has determined single-diode fit quality to partial curves constructed via other principles, e.g., as a percentage of the maximum power point voltage. Such investigation is justified since the voltage is typically the inverter reference quantity. Our work takes the study of this topic to a whole new scientific level by systematically examining how limiting the current–voltage curve measuring range to maximum power point proximity based on both power and voltage affects single-diode model parameters. An extensive dataset with 2400 measured curves was analysed, and statistically credible results were obtained for the first time. We fitted the single-diode model directly to experimental curves without measuring outdoor conditions or using approximations. Our results provide clear guidance on how the choices of partial curves affect the fitting accuracy. A significant finding is that the correct selection of maximum power point neighbourhoods provides promising real-case online aging detection opportunities.
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Silva, Luana Cristina R. da, Alcinei M. Azevedo, Carlos E. Pedrosa, Valter C. Andrade Júnior, Nermy R. Valadares, Vanessa V. de Araújo, and Evander A. Ferreira. "Selection of kale accesses to dehydration post-harvest by model identity test." Horticultura Brasileira 38, no. 4 (December 2020): 378–81. http://dx.doi.org/10.1590/s0102-053620200406.
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ABSTRACT The selection of kale genotypes more resistant to dehydration is important, since this product is marketed fresh and characterized as perishable. For the post-harvest study, the adjustment of regression models is useful. However, when there are many treatments, it is difficult to identify the superior one through the graphical representation of the curves. In this sense, the model identity test groups the curves establishing genotypes that have statistically similar behavior. Thus, we aimed to select kale accesses for post-harvest dehydration using the model identity test. The accumulated loss of fresh matter of 22 kale genotypaes was evaluated, being 19 of the germplasm bank of the UFVJM and three commercial cultivars (COM). The model identity test was used for the statistical grouping of the regression curves. The UFVJM-19 and UFVJM-32 accessions had lower rates of dehydration as a function of time. The test facilitated the interpretation of the results, with a reduction of 22 to six regression curves, helping to select the best genotypes. The UFVJM-19 and UFVJM-32 accessions are the most indicated because they present lower post-harvest dehydration, being the most recommended for commercialization.
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Meneghetti, Alessio, Massimiliano Sala, and Daniele Taufer. "A New ECDLP-Based PoW Model." Mathematics 8, no. 8 (August 12, 2020): 1344. http://dx.doi.org/10.3390/math8081344.
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Blockchain technology has attracted a lot of research interest in the last few years. Originally, their consensus algorithm was Hashcash, which is an instance of the so-called Proof-of-Work. Nowadays, there are several competing consensus algorithms, not necessarily PoW. In this paper, we propose an alternative proof of work algorithm which is based on the solution of consecutive discrete logarithm problems over the point group of elliptic curves. At the same time, we sketch a blockchain scheme, whose consensus is reached via our algorithm. In the considered architecture, the curves are pseudorandomly determined by block creators, chosen to be cryptographically secure and changed every epoch. Given the current state of the chain and a prescribed set of transactions, the curve selection is fully rigid, therefore trust is needed neither in miners nor in the scheme proposers.
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Cankaya, S., A. Unalan, and E. Soydan. "Selection of a mathematical model to describe the lactation curves of Jersey cattle." Archives Animal Breeding 54, no. 1 (October 10, 2011): 27–35. http://dx.doi.org/10.5194/aab-54-27-2011.
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Abstract. The extent of the usefulness of a lactation model depends on how well it succeeds in imitating the biological lactation process and how well it adjust for environmental and other factors that could influence production. Therefore, the objective of this study was to compare five different lactation curve models (Wood, Cobby and Le Du, Wilmink, Exponential and Parabolic Exponential model), and to find the best model that provided a good description of the lactation curve of Jersey cattle herd. Data used in this study were the first to seventh lactation official milk yield records from monthly recording of 3 630 lactations between 1984 and 2008 in the farm. The results showed that Wood model which has minimum residual standard deviation (3.562), maximum adjusted R2 value (91.6 %) and maximum persistency value (93.3 %) performed the best fit to the data and allowed a suitable description of the lactation curve. It was concluded that the Wood model provided accurate estimates of milk yield for all lactation numbers because this model was found to be more superior to the other models. Consequently, the usage of Wood model would provide some useful information on genetic improvement of the Jersey breed under pasture-based dry seasonal production systems.
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BATRA, T. R. "COMPARISON OF TWO MATHEMATICAL MODELS IN FITTING LACTATION CURVES FOR PURELINE AND CROSSLINE DAIRY COWS." Canadian Journal of Animal Science 66, no. 2 (June 1, 1986): 405–14. http://dx.doi.org/10.4141/cjas86-042.
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Weekly milk yield of 2066 first, 1407 second, and 755 third lactation pureline and crossline cows was used to study environmental and genetic effects on the coefficients of the lactation curves derived by modified gamma and inverse polynomial functions. The inverse polynomial function provided a better fit than the modified gamma function based on comparison of R2 values. Coefficients of the two models were analyzed to evaluate environmental and genetic effects on the shape of the lactation curve. The model included station, year of calving, month of calving, age at calving, days open, line of sire, line of dam, interaction of line of sire with line of dam, and sires within line of sire. In addition, coefficients of the lactation curve were analyzed by another model which included station, year of calving, month of calving, age at calving, days open, breed additive, maternal, and heterosis effects. Effects of station, year of calving, month of calving, and days open were mostly significant (P < 0.05); however the effect of age at calving was not significant on the coefficients of the lactation curves. Significant (P < 0.05) line of sire and line of dam effects on the level of initial yield suggest that genetic improvement of this trait could be achieved through selection. Breed additive and maternal effects were mostly nonsignificant on the coefficients of the lactation curves indicating shape of the lactation curve could not be changed by selection. There was very little evidence of nonadditive genetic variation associated with the coefficients of lactation curves. Key words: Lactation curves, pureline, crossline, dairy cows
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Perrone, J., and C. A. Madramootoo. "Improved curve number selection for runoff prediction." Canadian Journal of Civil Engineering 25, no. 4 (August 1, 1998): 728–34. http://dx.doi.org/10.1139/l98-007.
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The three antecedent moisture conditions used in the SCS (Soil Conservation Service) curve number method of surface runoff volume prediction have been shown to be inapplicable in humid regions such as the Ottawa - St. Lawrence Lowlands. The antecedent precipitation index is an alternative indicator of soil moisture. Using a hydrologic database, calibration curves were developed to correlate antecedent precipitation index to the SCS curve number. Curve numbers were then input to the AGNPS hydrologic model. When compared to the three antecedent moisture conditions in the SCS curve number method, use of antecedent precipitation index as a soil moisture indicator considerably improved surface runoff volume simulations. However, peak flow was generally overpredicted by the AGNPS model.Key words: AGNPS, antecedent moisture, curve number, peak flow, surface runoff, hydrologic modeling, precipitation.
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Gallacher, Daniel, Peter Auguste, and Martin Connock. "How Do Pharmaceutical Companies Model Survival of Cancer Patients? A Review of NICE Single Technology Appraisals in 2017." International Journal of Technology Assessment in Health Care 35, no. 2 (2019): 160–67. http://dx.doi.org/10.1017/s0266462319000175.
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AbstractObjectivesBefore an intervention is publicly funded within the United Kingdom, the cost-effectiveness is assessed by the National Institute of Health and Care Excellence (NICE). The efficacy of an intervention across the patients’ lifetime is often influential of the cost-effectiveness analyses, but is associated with large uncertainties. We reviewed committee documents containing company submissions and evidence review group (ERG) reports to establish the methods used when extrapolating survival data, whether these adhered to NICE Technical Support Document (TSD) 14, and how uncertainty was addressed.MethodsA systematic search was completed on the NHS Evidence Search webpage limited to single technology appraisals of cancer interventions published in 2017, with information obtained from the NICE Web site.ResultsTwenty-eight appraisals were identified, covering twenty-two interventions across eighteen diseases. Every economic model used parametric curves to model survival. All submissions used goodness-of-fit statistics and plausibility of extrapolations when selecting a parametric curve. Twenty-five submissions considered alternate parametric curves in scenario analyses. Six submissions reported including the parameters of the survival curves in the probabilistic sensitivity analysis. ERGs agreed with the company's choice of parametric curve in nine appraisals, and agreed with all major survival-related assumptions in two appraisals.ConclusionsTSD 14 on survival extrapolation was followed in all appraisals. Despite this, the choice of parametric curve remains subjective. Recent developments in Bayesian approaches to extrapolation are not implemented. More precise guidance on the selection of curves and modelling of uncertainty may reduce subjectivity, accelerating the appraisal process.
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Cunha, Daniel de Noronha Figueiredo Vieira da, José Carlos Pereira, Fabyano Fonseca e. Silva, Oriel Fajardo de Campos, José Luis Braga, and Janaina Azevedo Martuscello. "Selection of models of lactation curves to use in milk production simulation systems." Revista Brasileira de Zootecnia 39, no. 4 (April 2010): 891–902. http://dx.doi.org/10.1590/s1516-35982010000400026.
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The objective of this study was to select models of lactation curves with a better adjustment to the observed data in models of milk production simulation systems. A data base on 6,459 recordings of daily milk production was used. These data were obtained from monthly and fortnightly controls of milk between 2004 and 2007, from 472 lactations of animals from ten different milking cow herd farms. Based on rolling averages of milk production (MP-L/day) per cow, the ten herd farms were divided into low (L < 15), medium (15 <M < 20) and high (H > 20). Data were also divided according to the lactation numbers in first, second, third or greater. Eight lactation curve models commonly used in literature were compared. The models were individually adjusted for each lactation. The goodness of fit used for comparison of those models was the coefficient of determination, mean square error, mean square prediction error and the Bayesian information criterion. The values for the goodness of fit obtained in each model were compared by using 95% probability confidence interval. Wilmink (1987) model showed a better adjustment for cows of the first lactation numbers, whereas the Wood (1967) model showed a better adjustment for cows of the third or greater lactations numbers for the low milk production groups. Wood model showed a better adjustment for all the lactation numbers for the medium milk production group. Dijkstra (1997) model showed a better adjustment for all lactation numbers for the high milk production group. Despite of being more recent, the model by Pollott (2000), mechanist based and with a higher number of parameters, showed a good convergence for the used data.
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Machado, Sebastião do Amaral, Ronan Felipe de Souza, Eldemar Jaskiu, and Ricardo Cavalheiro. "Construction of site curves for native Mimosa scabrella stands in the metropolitan region of Curitiba." CERNE 17, no. 4 (December 2011): 489–97. http://dx.doi.org/10.1590/s0104-77602011000400007.
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This study aims to construct definitive sites curves for native bracatinga stands (Mimosa scabrella Benth) in the metropolitan region of Curitiba. Data used was extracted from 648 pairs of values of dominant height and age. Ten mathematical models were tested for guide curve fitting. Selection of the best performing model was based on adjusted coefficient of determination(Raj²), standard error of estimate in percentage (Syx%) and graphic analysis of residuals. Models were tested and the Chapman-Richards model was selected for construction of limit curves of site classes due to the biological significance of its coefficients, statistical performance and good distribution of residuals. Curve anamorphism and model validity were verified using the test proposed by Kirby (1975). Curve stability was demonstrated based on stem analysis data composing part of the database. Site classification for bracatinga stands was thus considered suitable and can potentially be applied to development of growth and yield equations for this particular species.
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Zhang, Wei, Weiting Zhang, Xiang Li, Xiaoming Cao, Guoqiang Yang, and Hui Zhang. "Predicting Tumor Perineural Invasion Status in High-Grade Prostate Cancer Based on a Clinical–Radiomics Model Incorporating T2-Weighted and Diffusion-Weighted Magnetic Resonance Images." Cancers 15, no. 1 (December 23, 2022): 86. http://dx.doi.org/10.3390/cancers15010086.
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Purpose: To explore the role of bi-parametric MRI radiomics features in identifying PNI in high-grade PCa and to further develop a combined nomogram with clinical information. Methods: 183 high-grade PCa patients were included in this retrospective study. Tumor regions of interest (ROIs) were manually delineated on T2WI and DWI images. Radiomics features were extracted from lesion area segmented images obtained. Univariate logistic regression analysis and the least absolute shrinkage and selection operator (LASSO) method were used for feature selection. A clinical model, a radiomics model, and a combined model were developed to predict PNI positive. Predictive performance was estimated using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: The differential diagnostic efficiency of the clinical model had no statistical difference compared with the radiomics model (area under the curve (AUC) values were 0.766 and 0.823 in the train and test group, respectively). The radiomics model showed better discrimination in both the train cohort and test cohort (train AUC: 0.879 and test AUC: 0.908) than each subcategory image (T2WI train AUC: 0.813 and test AUC: 0.827; DWI train AUC: 0.749 and test AUC: 0.734). The discrimination efficiency improved when combining the radiomics and clinical models (train AUC: 0.906 and test AUC: 0.947). Conclusion: The model including radiomics signatures and clinical factors can accurately predict PNI positive in high-grade PCa patients.
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Mehtätalo, Lauri, Sergio de-Miguel, and Timothy G. Gregoire. "Modeling height-diameter curves for prediction." Canadian Journal of Forest Research 45, no. 7 (July 2015): 826–37. http://dx.doi.org/10.1139/cjfr-2015-0054.
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Individual tree heights are needed in many situations, including estimation of tree volume, dominant height, and simulation of tree growth. However, height measurements are tedious compared to tree diameter measurements, and therefore height–diameter (H–D) models are commonly used for prediction of tree height. Previous studies have fitted H–D models using approaches that include plot-specific predictors in the models and those that do not include them. In both these approaches, aggregation of the observations to sample plots has usually been taken into account through random effects, but this has not always been done. In this paper, we discuss four alternative model formulations and report an extensive comparison of 16 nonlinear functions in this context using a total of 28 datasets. The datasets represent a wide range of tree species, regions, and ecological zones, consisting of about 126 000 measured trees from 3717 sample plots. Specific R-functions for model fitting and prediction were developed to enable such an extensive model fitting and comparison. Suggestions on model selection, model fitting procedures, and prediction are given and interpretation of the predictions from different models are discussed. No uniformly best function, model formulation, or model fitting procedure was found. However, a 2-parameter Näslund and Curtis function provided satisfactory fit in most datasets for the plot-specific H–D relationship. Model fitting and height imputation procedures developed for this study are provided in an R-package for later use.
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Gönen, Mithat, and Glenn Heller. "Lehmann Family of ROC Curves." Medical Decision Making 30, no. 4 (March 30, 2010): 509–17. http://dx.doi.org/10.1177/0272989x09360067.
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Receiver operating characteristic (ROC) curves evaluate the discriminatory power of a continuous marker to predict a binary outcome. The most popular parametric model for an ROC curve is the binormal model, which assumes that the marker, after a monotone transformation, is normally distributed conditional on the outcome. Here, the authors present an alternative to the binormal model based on the Lehmann family, also known as the proportional hazards specification. The resulting ROC curve and its functionals (such as the area under the curve and the sensitivity at a given level of specificity) have simple analytic forms. Closed-form expressions for the functional estimates and their corresponding asymptotic variances are derived. This family accommodates the comparison of multiple markers, covariate adjustments, and clustered data through a regression formulation. Evaluation of the underlying assumptions, model fitting, and model selection can be performed using any off-the-shelf proportional hazards statistical software package.
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Nagy, Tibor, János Tóth, and Tamás Ladics. "Automatic kinetic model generation and selection based on concentration versus time curves." International Journal of Chemical Kinetics 52, no. 2 (November 29, 2019): 109–23. http://dx.doi.org/10.1002/kin.21335.
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Mostaghimzadeh, Ehsan, Seyed Mohammad Ashrafi, Arash Adib, and Zong Woo Geem. "Investigation of Forecast Accuracy and its Impact on the Efficiency of Data-Driven Forecast-Based Reservoir Operating Rules." Water 13, no. 19 (October 2, 2021): 2737. http://dx.doi.org/10.3390/w13192737.
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Today, variable flow pattern, which uses static rule curves, is considered one of the challenges of reservoir operation. One way to overcome this problem is to develop forecast-based rule curves. However, managers must have an estimate of the influence of forecast accuracy on operation performance due to the intrinsic limitations of forecast models. This study attempts to develop a forecast model and investigate the effects of the corresponding accuracy on the operation performance of two conventional rule curves. To develop a forecast model, two methods according to autocorrelation and wrapper-based feature selection models are introduced to deal with the wavelet components of inflow. Finally, the operation performances of two polynomial and hedging rule curves are investigated using forecasted and actual inflows. The results of applying the model to the Dez reservoir in Iran visualized that a 4% improvement in the correlation coefficient of the coupled forecast model could reduce the relative deficit of the polynomial rule curve by 8.1%. Moreover, with 2% and 10% improvement in the Willmott and Nash—Sutcliffe indices, the same 8.1% reduction in the relative deficit can be expected. Similar results are observed for hedging rules where increasing forecast accuracy decreased the relative deficit by 15.5%. In general, it was concluded that hedging rule curves are more sensitive to forecast accuracy than polynomial rule curves are.
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van der Steen, H. A. M., and P. W. Knap. "Effect of selection for growth, FCR and lean % on feed intake corves in pigs." Proceedings of the British Society of Animal Production (1972) 1994 (March 1994): 17. http://dx.doi.org/10.1017/s030822960002571x.
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Available technology allows pig breeding companies to automate feed intake recording during performance test. This provides data on ‘average daily feed intake’ as recorded with more traditional manual systems. It also results in feed intake curves, i.e. the relationship between ‘days on test’ and ‘daily feed intake’. This information can be used in different ways. The feed intake curve may be described using sophisticated linear or non-linear models; these may describe the feed intake curve accurately, but model parameters cannot be used easily in genetic/economic evaluation in the context of a breeding programme. A simple method to describe feed intake curves is used in this paper, allowing for easy interpretation of the results. The objective is to study the impact of existing selection procedures on the feed intake curve and the utilisation of variation in its shape in pig breeding.Performance test data of 1331 boars of a Large White based line, collected from November 1990 to March 1993 were analysed. Boars are tested over a 12 week period, starting at approximately 30 kg. Feed intake data are recorded with the Hunday FIRE system.
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Liu, Jin Sheng. "Numerical Simulation and Optimization of Small Low-Speed Wind Tunnel Contraction Flow." Applied Mechanics and Materials 733 (February 2015): 595–98. http://dx.doi.org/10.4028/www.scientific.net/amm.733.595.
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The principle of the design of the wind-tunnel contraction is introduced; several typical contraction curves provided by former researchers are collected and compared. Using commercial Computational Fluid Dynamics (CFD) software-Fluent: Fluent. The flied quality of different contraction curves using a 3-D axial symmetric k-ε model is compared and some suggestions on the selection of the curves to different requirement are made. Witozinsky curve and Batchelor-Shaw curve prone to counter pressure at the entrance area, serious energy loss, the speed of export poor uniformity. The uniformity of the flow field is a little better of the improved 3-power curve (Xm = 0.5).5-power curve (Xm=0.5, n=7) shows low turbulence degrees, a certain advantage in energy consumption and strong stability.
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Nesvidomin, A. V. "Generalized method for forming plane isotropic curves." Energy and Automation, no. 4 (September 23, 2020): 109–15. http://dx.doi.org/10.31548/energiya2020.04.109.
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The process of modeling the temperature distribution on surfaces, applying an image to curved areas with minimal distortion requires the formation of isometric grids on the plane and on the surface. One of the common ways to form planar isometric networks is to use the functions of a complex variable and planar isotropic curves, followed by separation of the real and imaginary parts. The development of computer models for the interactive search and analysis of isometric networks according to various initial geometric conditions provides a generalized method for their formation with the possibility of varying their shape and position. It is proposed to use an isotropic vector for the formation of flat isotropic curves, which ensured a single sequence of analytical calculations according to the following initial conditions: 1) selection of an arbitrary function of a real argument; 2) a given parametric equation of a plane curve; 3) a given polar equation of a plane curve. Since the analytical calculations of the derivation of the parametric equation of a plane isotropic curve and the corresponding isometric grid are rather laborious, their execution is carried out in the environment of the Maple symbolic algebra. To this end, the corresponding software has been created, which interactively allows you to select the function of a real argument, a parametric or polar equation of a plane guide curve. All subsequent stages of analytical transformations to form an isotropic curve and the corresponding isometric grid are carried out automatically. An interactive model for the formation and analysis of plane isotropic curves with various initial conditions has been created, which has shown its effectiveness, which is confirmed by the given examples of plane isometric grids for specific functions of the real parameter, plane curves in the parametric and polar form of their job.
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Moodie, Nathan, William Ampomah, Wei Jia, and Brian McPherson. "Relative Permeability: A Critical Parameter in Numerical Simulations of Multiphase Flow in Porous Media." Energies 14, no. 9 (April 22, 2021): 2370. http://dx.doi.org/10.3390/en14092370.
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Effective multiphase flow and transport simulations are a critical tool for screening, selection, and operation of geological CO2 storage sites. The relative permeability curve assumed for these simulations can introduce a large source of uncertainty. It significantly impacts forecasts of all aspects of the reservoir simulation, from CO2 trapping efficiency and phase behavior to volumes of oil, water, and gas produced. Careful consideration must be given to this relationship, so a primary goal of this study is to evaluate the impacts on CO2-EOR model forecasts of a wide range of relevant relative permeability curves, from near linear to highly curved. The Farnsworth Unit (FWU) is an active CO2-EOR operation in the Texas Panhandle and the location of our study site. The Morrow ‘B’ Sandstone, a clastic formation composed of medium to coarse sands, is the target storage formation. Results indicate that uncertainty in the relative permeability curve can impart a significant impact on model predictions. Therefore, selecting an appropriate relative permeability curve for the reservoir of interest is critical for CO2-EOR model design. If measured laboratory relative permeability data are not available, it must be considered as a significant source of uncertainty.
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Ghettini, Simone, Alessandro Sorce, and Roberto Sacile. "Data-Driven Air-Cooled Condenser Performance Assessment: Model and Input Variable Selection Comparison." E3S Web of Conferences 197 (2020): 10003. http://dx.doi.org/10.1051/e3sconf/202019710003.
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This paper presents a data–driven model for the estimation of the performance of an aircooled steam condenser (ACC) with the aim to develop an efficient online monitoring, summarized by the condenser pressure (or vacuum) as Key Performance Indicator. The estimation of the ACC performance model was based on different dataset from three different combined cycle power plants with a gross power of above 380 MWe each, focusing on stationary condition of the steam turbine. The datasets include both boundary (e.g. Ambient Temperature, Wind Speed) and operative parameters (e.g. steam mass flow rate, Steam turbine power, electrical load of the ACC fans) acquired from the power plants and some derived variable as the incondensable fraction, which calculation is here proposed as additional parameter. After a preliminary sensitivity analysis on data correlation, the paper focuses on the evaluation of different ACC Condenser models: Semi-Empirical model is described trough curves typically based on steam mass flow rate (or condenser load) and the ambient temperature as main parameters. Since monitoring based on ACC design curves Semi-Empirical models, provides biased poor results, with an error of about 15%, the curves parameters were estimated basing on training data set. Other two data driven models were presented, basing on a neural network modelling and multi linear regression technique and compared on the base of the reduced number of input at first and then including aldo the other process variables in the prediction of the condenser back pressure. Estimate the parameters of the Semi-Empirical model, results in a better prediction if just steam mass flow rate and ambient temperature are available, with an error of the 7%, thanks to the knowledge contained within the “curves shapes”, with respect to linear regression (8.3%) and Neural Network models (7.6%). Higher accuracy can be then obtained by considering a larger number of operative parameters and exploiting more complex data-driven model. With a higher number of features, the neural network model has proved a higher accuracy than the linear regression model. In fact, the mean percentage error of the NN model (2.6%), in all plant operating conditions, is slightly lower than the error of the linear regression model, but presents and much lower than the mean error of the Semi-Empirical model thanks to the additional data-based knowledge.
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Mallick, Javed, Swapan Talukdar, Nabil Ben Kahla, Mohd Ahmed, Majed Alsubih, Mohammed K. Almesfer, and Abu Reza Md Towfiqul Islam. "A Novel Hybrid Model for Developing Groundwater Potentiality Model Using High Resolution Digital Elevation Model (DEM) Derived Factors." Water 13, no. 19 (September 25, 2021): 2632. http://dx.doi.org/10.3390/w13192632.
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The present work aims to build a unique hybrid model by combining six fuzzy operator feature selection-based techniques with logistic regression (LR) for producing groundwater potential models (GPMs) utilising high resolution DEM-derived parameters in Saudi Arabia’s Bisha area. The current work focuses exclusively on the influence of DEM-derived parameters on GPMs modelling, without considering other variables. AND, OR, GAMMA 0.75, GAMMA 0.8, GAMMA 0.85, and GAMMA 0.9 are six hybrid models based on fuzzy feature selection. The GPMs were validated by using empirical and binormal receiver operating characteristic curves (ROC). An RF-based sensitivity analysis was performed in order to examine the influence of GPM settings. Six hybrid algorithms and one unique hybrid model have predicted 1835–2149 km2 as very high and 3235–4585 km2 as high groundwater potential regions. The AND model (ROCe-AUC: 0.81; ROCb-AUC: 0.804) outperformed the other models based on ROC’s area under curve (AUC). A novel hybrid model was constructed by combining six GPMs (considering as variables) with the LR model. The AUC of ROCe and ROCb revealed that the novel hybrid model outperformed existing fuzzy-based GPMs (ROCe: 0.866; ROCb: 0.892). With DEM-derived parameters, the present work will help to improve the effectiveness of GPMs for developing sustainable groundwater management plans.
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Akeju, Oluwatosin Victor, Kostas Senetakis, and Yu Wang. "Bayesian Parameter Identification and Model Selection for Normalized Modulus Reduction Curves of Soils." Journal of Earthquake Engineering 23, no. 2 (September 11, 2017): 305–33. http://dx.doi.org/10.1080/13632469.2017.1323051.
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Denuit, Michel, Dominik Sznajder, and Julien Trufin. "Model selection based on Lorenz and concentration curves, Gini indices and convex order." Insurance: Mathematics and Economics 89 (November 2019): 128–39. http://dx.doi.org/10.1016/j.insmatheco.2019.09.001.
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Pscheidt, Jay W., and Stephanie Heckert. "Progression of Kernel Mold on Hazelnut." Plant Disease 105, no. 5 (May 1, 2021): 1320–27. http://dx.doi.org/10.1094/pdis-05-20-1088-re.
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Hazelnut kernel mold, caused by a number of fungal species, has been a chronic problem in Pacific Northwest hazelnut production areas for many years. Two highly susceptible breeding selections and two commercial cultivars were used to investigate kernel mold development over time and possible correlations with rainfall. Nuts were allowed to naturally fall onto orchard soil, regularly collected, cracked open, and evaluated for kernel mold. Disease progress for each selection or cultivar was evaluated each year with both linear and exponential models. The general progression of kernel mold was similar for the two breeding selections and cultivars Ennis and Lewis, where kernel mold increased slowly during the nut dropping period but more rapidly after normal harvest. An exponential model described disease progress better than a linear model for 8 of the 10 significant disease progress curves examined. Although some years had significantly higher estimated rates of disease increase, this parameter was inversely related to the area under the disease progress curve (AUDPC). The incidence of kernel mold did not significantly increase over time for 8 of the 18 disease progress curves examined, including 6 of 8 curves for commercial cultivars. The relationship between initial kernel mold incidence and AUDPC was described well with a simple linear model indicating that initial disease incidence appeared to be a good predictor of AUDPC. The longer nuts remained on the ground, especially after harvest, the higher the incidence of kernel mold. Kernel mold incidence was not significantly correlated with rainfall totals for any period of time from flowering to harvest. Multiple harvests ending shortly after all nuts have fallen should result in lower incidence of kernel mold for growers.
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Chen, Xiaohong, Changqing Ye, Jiaming Zhang, Chongyu Xu, Lijuan Zhang, and Yihan Tang. "Selection of an Optimal Distribution Curve for Non-Stationary Flood Series." Atmosphere 10, no. 1 (January 15, 2019): 31. http://dx.doi.org/10.3390/atmos10010031.
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The stationarity assumption of hydrological processes has long been compromised by human disturbances in river basins. The traditional hydrological extreme-value analysis method, i.e., “extreme value theory” which assumes stationarity of the time series, needs to be amended in order to adapt to these changes. In this paper, taking the East River basin, south China as a case study, a framework was put forward for selection of a suitable distribution curve for non-stationary flood series by using the time-varying moments (TVM). Data used for this study are the annual maximum daily flow of 1954–2009 at the Longchuan, Heyuan and Boluo Stations in the study basin. Five types of distribution curves and eight kinds of trend models, for a combination of 40 models, were evaluated and compared. The results showed that the flood series and optimal distribution curves in the East River basin have been significantly impacted by a continuously changing environment. With the increase of the degree of human influence, the thinner tails of distributions are more suitable for fitting the observed flow data, and the trend models are changed from CP (mean and standard deviation fitted by parabolic trend model) to CL (mean and standard deviation fitted by linear trend model) from upstream to downstream of the catchment. The design flood flow corresponding to a return period of more than 10 years at the Longchuan, Heyuan and Boluo Stations was overestimated by more than 28.36%, 53.24% and 26.06%, respectively if the non-stationarity of series is not considered and the traditional method is still used for calculation. The study reveals that in a changing environment, more advanced statistical methods that explicitly account for the non-stationarity of extreme flood characteristics are required.
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Sampson, David B., and Robert D. Scott. "A spatial model for fishery age-selection at the population level." Canadian Journal of Fisheries and Aquatic Sciences 68, no. 6 (June 2011): 1077–86. http://dx.doi.org/10.1139/f2011-044.
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Different age classes do not generally experience the same rates of fishing mortality. The processes resulting in age- (or length-) selection operate at several scales. At the broadest scale, population-selection measures the age-specific probability of capture, while at the finest scale contact-selection describes the vulnerability of fish that encounter the fishing gear. Population-selectivity is the process most relevant to fish population dynamics and stock assessment, but it has received far less attention than processes operating at gear-specific scales. Despite wide recognition of the diverse shapes possible for population-selectivity, the processes determining these shapes are poorly understood. This paper develops a reasonably simple model of population-selectivity from a set of survival equations, coupled to allow movement between subpopulations, and explores the conditions necessary to produce different shaped population-selection curves. Important factors influencing the population-selectivity model are the gear-specific selection characteristics of the fleets, their effort levels relative to one another, the spatial distribution of fishing mortality, and the movement of fish between subpopulations. The model can generate quite complicated curves and has surprising properties. For example, under a wide variety of conditions, even though the same asymptotic gear-selectivity applies in all subpopulations, the overall population-selectivity will be dome-shaped unless fishing mortality is uniform across all subpopulations.
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Geng, Xiaoxiao, Shuize Wang, Asad Ullah, Guilin Wu, and Hao Wang. "Prediction of Hardenability Curves for Non-Boron Steels via a Combined Machine Learning Model." Materials 15, no. 9 (April 26, 2022): 3127. http://dx.doi.org/10.3390/ma15093127.
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Hardenability is one of the most basic criteria influencing the formulation of the heat treatment process and steel selection. Therefore, it is of great engineering value to calculate the hardenability curves rapidly and accurately without resorting to any laborious and costly experiments. However, generating a high-precision computational model for steels with different hardenability remains a challenge. In this study, a combined machine learning (CML) model including k-nearest neighbor and random forest is established to predict the hardenability curves of non-boron steels solely on the basis of chemical compositions: (i) random forest is first applied to classify steel into low- and high-hardenability steel; (ii) k-nearest neighbor and random forest models are then developed to predict the hardenability of low- and high-hardenability steel. Model validation is carried out by calculating and comparing the hardenability curves of five steels using different models. The results reveal that the CML model works well for its distinguished prediction performance with precise classification accuracy (100%), high correlation coefficient (≥0.981), and low mean absolute errors (≤3.6 HRC) and root-mean-square errors (≤3.9 HRC); it performs better than JMatPro and empirical formulas including the ideal critical diameter method and modified nonlinear equation. Therefore, this study demonstrates that the CML model combining material informatics and data-driven machine learning can rapidly and efficiently predict the hardenability curves of non-boron steel, with high prediction accuracy and a wide application range. It can guide process design and machine part selection, reducing the cost of trial and error and accelerating the development of new materials.
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Ologhadien, I., and G. O. Esebene. "Model Selection and Design of Storm Water Drainage Systems at Oleh, Delta State, Nigeria." European Journal of Engineering and Technology Research 7, no. 1 (February 22, 2022): 70–77. http://dx.doi.org/10.24018/ejeng.2022.7.1.2728.
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Rainfall Intensity-Duration-Frequency curves and equations present the exceedance probability of a given rainfall intensity and storm duration expected to occur at a particular location, for the design of storm water drainage systems and hydraulic structures. The aim of this study was to develop IDF curves/equations aim for Oleh, a municipality situated in the low-lying and flood prone region of Nigeria. The study was conducted using annual maxima rainfall series (ARMS) of 28 years obtained from Nigeria Meteorological Agency (NiMet). The AMRS were extracted into 15 Nos. storm durations between 10minutes and 600 minutes. The AMRS of each duration was fitted to Gumbel (EV1), LN2 and Normal (N) distributions using the K-S and A-D goodness-of-fit module of Easyfit software, Version 5.6. The graphical plots, development of IDF models and computations of performance measures of R2 NSE and RSR, were executed in Microsoft Excel, 2010. The results of GOF tests show that for K-S tests, Gumbel (EVI) is best-fit distribution in four durations, LN2 was best –fit in Nine out of fifteen durations, and Normal distribution durations scored 2. Similarly, for A-D GOF test, Gumbel (EVI) scored three (3), LN2 scored eleven (11), while Normal distribution scored one (1). Consequently, LN2 is the best-fit distribution, seconded by Gumbel (EVI). Accordingly, LN2 and Gumbel (EVI) distributions have been adopted in the development of the IDF models for Oleh municipality, while the performances of the IDF models may be presented the inequality; 0.984 ? R2 ? 0.998; 0.990 ? NSE ? 0.998 and 0.045 ? RSR ? 0.096. The graphical plots, IDF curves and models exhibited the peculiar attributes of IDF curves/equations reported in literature. The performance measures show that the equations are robust for practical application in the design of storm water drainage systems and hydraulic structures.
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Kervrann, Charles. "BAYESIAN IMAGE SEGMENTATION THROUGH LEVEL LINES SELECTION." Image Analysis & Stereology 20, no. 3 (May 3, 2011): 163. http://dx.doi.org/10.5566/ias.v20.p163-168.
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Bayesian statistical theory is a convenient way of taking a priori information into consideration when inference is made from images. In Bayesian image segmentation, the a priori distribution should capture the knowledge about objects. Taking inspiration from (Alvarez et al., 1999), we design a prior density that penalizes the area of homogeneous parts in images. The segmentation problem is further formulated as the estimation of the set of curves that maximizes the posterior distribution. In this paper, we explore a posterior distribution model for which its maximal mode is given by a subset of level curves, that is the boundaries of image level sets. For the completeness of the paper, we present a stepwise greedy algorithm for computing partitions with connected components.
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Gibson, Neale P. "Reliable inference of light curve parameters in the presence of systematics." Proceedings of the International Astronomical Union 11, A29A (August 2015): 202–4. http://dx.doi.org/10.1017/s1743921316002805.
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AbstractTime-series photometry and spectroscopy of transiting exoplanets allow us to study their atmospheres. Unfortunately, the required precision to extract atmospheric information surpasses the design specifications of most general purpose instrumentation. This results in instrumental systematics in the light curves that are typically larger than the target precision. Systematics must therefore be modelled, leaving the inference of light-curve parameters conditioned on the subjective choice of systematics models and model-selection criteria. Here, I briefly review the use of systematics models commonly used for transmission and emission spectroscopy, including model selection, marginalisation over models, and stochastic processes. These form a hierarchy of models with increasing degree of objectivity. I argue that marginalisation over many systematics models is a minimal requirement for robust inference. Stochastic models provide even more flexibility and objectivity, and therefore produce the most reliable results. However, no systematics models are perfect, and the best strategy is to compare multiple methods and repeat observations where possible.
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Abasova Inara Afrail. "PROCESSING OF OIL WELL PRESSURE RECOVERY CURVES." Science Review, no. 1(18) (January 31, 2019): 18–20. http://dx.doi.org/10.31435/rsglobal_sr/31012019/6336.
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In the article the development of a mathematical model describing the PRC is studied on the base of pressure recovery curve method.Detailed processing of the pressure recovery curve made it possible to determine the deterioration of reservoir permeability in many wells. Here two methods are considered - stationary (steady conditions of selection) and non- stationary.The article proves that the use of these methods allows to develop a mathematical model to increase the determination of this task.On the base of numerical simulation, the following facts had impact on the results of the pressure recovery curve: well shutdown time, taking into account the initial transition section, taking into account curve change section before well shutdown.The study of variable factors impact on the results is carried out by interval estimation.The mathematical model describing the pressure recovery curve is local and changes its structures. This model can be used in industry conditions.
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Piccardi, Monica, Raúl Macchiavelli, Ariel Capitaine Funes, Gabriel A. Bó, and Mónica Balzarini. "Fitting milk production curves through nonlinear mixed models." Journal of Dairy Research 84, no. 2 (March 28, 2017): 146–53. http://dx.doi.org/10.1017/s0022029917000085.
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The aim of this work was to fit and compare three non-linear models (Wood, Milkbot and diphasic) to model lactation curves from two approaches: with and without cow random effect. Knowing the behaviour of lactation curves is critical for decision-making in a dairy farm. Knowledge of the model of milk production progress along each lactation is necessary not only at the mean population level (dairy farm), but also at individual level (cow-lactation). The fits were made in a group of high production and reproduction dairy farms; in first and third lactations in cool seasons. A total of 2167 complete lactations were involved, of which 984 were first-lactations and the remaining ones, third lactations (19 382 milk yield tests). PROC NLMIXED in SAS was used to make the fits and estimate the model parameters. The diphasic model resulted to be computationally complex and barely practical. Regarding the classical Wood and MilkBot models, although the information criteria suggest the selection of MilkBot, the differences in the estimation of production indicators did not show a significant improvement. The Wood model was found to be a good option for fitting the expected value of lactation curves. Furthermore, the three models fitted better when the subject (cow) random effect was considered, which is related to magnitude of production. The random effect improved the predictive potential of the models, but it did not have a significant effect on the production indicators derived from the lactation curves, such as milk yield and days in milk to peak.
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Wang, Zhong Yi, Jia Han, Tao Sun, and Yun Liang Yu. "The Effect and Application of Different Turbulence Models on the Design of Inertial Stage." Advanced Materials Research 230-232 (May 2011): 405–9. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.405.
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The numerical simulation of the resistance characteristics of the inertial stage had been researched with different turbulence models, and the resistance characteristic computation curves of the inertial stage with different turbulence models have been obtained. Then curves contrasted with the actual value from experiments. The results show that the error of the Standard model is a little larger, but results of the RNG and Reynolds Stress are comparatively accurate. It has provided the reference frame of the turbulence model selection when the numerical simulation of the inertial stage is conducted.
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CHEN, YAN-GUANG. "LOGISTIC MODELS OF FRACTAL DIMENSION GROWTH OF URBAN MORPHOLOGY." Fractals 26, no. 03 (June 2018): 1850033. http://dx.doi.org/10.1142/s0218348x18500330.
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Urban form can be described with fractal dimension, which is a measurement of space filling of urban evolution. However, how to model and understand the fractal dimension growth of urban morphology are still pending questions. This paper is devoted to the research on the fractal dimension curves of urban growth. The principle of squashing function and empirical evidences are employed to demonstrate the following inference: the fractal dimension time series of a city’s spatial form take on a sigmoid curve. Among various sigmoid functions, the logistic function is the most probable selection. The observational data of fractal dimension of different cities from different sources support this logic judgment. A further discovery is that the fractal dimension curves of cities in the developed countries differ from those in the developing countries. A generalized logistic function is thus proposed to model the fractal dimension curves of different types of cities. The general logistic models can be used to predict the missing values and estimate the growth rates of fractal dimension of city development. Moreover, these models can be utilized to analyze when and where there is a fractal of urban form.
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Kurnaz, Burcu, Hasan Önder, Dariusz Piwczyński, Magdalena Kolenda, and Beata Sitkowska. "Determination of the Best Model to Predict Milk Dry Matter in High Milk Yielding Dairy Cattle." Acta Scientiarum Polonorum Zootechnica 20, no. 3 (March 14, 2022): 41–44. http://dx.doi.org/10.21005/asp.2021.20.3.05.
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This study was aimed to determinate the best model to predict milk dry matter in high milk yielding dairy cattle. Level of milk dry matter (MDM) (%) is of great importance. The material of this study consisted of 2208 milking records of dairy cattle yielding more than 40 l per day from Polish Holstein Friesian population. In this study to estimate the milk dry matter, regression of daily milk yield (MY) (l), milk urea (MU), milk protein (MP) (%) and milk fat (MF) (%) as explanatory variables were used. To estimate the best fitting, curve estimation was used. Estimation of the curves showed that milk urea was cubic, milk yield, milk protein and milk fat were quadratic. To avoid multicollinearity where VIF value greater than 10, stepwise variable selection procedure was used. After variable selection the regression equation was obtained as MDM=2.879+1.290*MF+2.395*MP-0.039*MF^2–0.225*MP^2 with 0.946 coefficient of determination. Our results showed that milk fat (%) and milk protein (%) can be used to estimate the milk dry matter (%) with a great achievement in high milk yielding dairy cattle.
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BAÍLLO, AMPARO, LAURA MARTÍNEZ-MUÑOZ, and MARIO MELLADO. "HOMOGENEITY TESTS FOR MICHAELIS–MENTEN CURVES WITH APPLICATION TO FLUORESCENCE RESONANCE ENERGY TRANSFER DATA." Journal of Biological Systems 21, no. 03 (September 2013): 1350017. http://dx.doi.org/10.1142/s0218339013500174.
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Resonance energy transfer methods are widely used for evaluating protein–protein interactions and protein conformational changes. Sensitized emission fluorescence resonance energy transfer (FRET) measures energy transfer as a function of the acceptor-to-donor ratio, generating FRET saturation curves. To reduce sampling variability effects, several replications (statistical samples) of the saturation curve are generated in the same biological conditions. Here we study procedures to determine whether these statistical samples are homogeneous, in the sense that they are extracted from the same underlying regression model (Michaelis–Menten kinetics). We used three methods to test the homogeneity of the samples: two hypothesis testing procedures (an F-test and bootstrap resampling) and model selection. The performance of the three methods was compared in a Monte Carlo study and through analysis in living cells of FRET saturation curves for dimeric CXCR4 complexes. This analysis shows that the F-test, the bootstrap procedure and the model selection method lead in general to similar conclusions, although the latter gave the best results when sample sizes were small, whereas the F-test and the bootstrap method were more appropriate for large samples. In practice, all three methods are easy to use simultaneously and show consistency.
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Mansouri, Babak, Mohsen Ghafory-Ashtiany, Kambod Amini-Hosseini, Reza Nourjou, and Mehdi Mousavi. "Building Seismic Loss Model for Tehran." Earthquake Spectra 26, no. 1 (February 2010): 153–68. http://dx.doi.org/10.1193/1.3280377.
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In order to model the building seismic loss for Tehran, urban databases have been compiled and processed considering different census zones, city blocks, and parcel records. Aerial photos, together with stereo image processing and ground survey data, have provided parcel level geospatial information. These data sets include urban features, land uses, and building inventory with height information. This research also focuses on the selection and the development of structural vulnerability functions and risk algorithms. The damage curves are selected or modified according to some regional data, the ATC-13 report, and the functions obtained for Costa Rica. Also, analytical fragility curves are derived and adopted for the area of study after the HAZUS-FEMA methodology. Finally, an upgradeable seismic risk model is developed in GIS using all compiled input data and structural vulnerability functions.
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Buza, Gergely, Shobhit Jain, and George Haller. "Using spectral submanifolds for optimal mode selection in nonlinear model reduction." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 477, no. 2246 (February 2021): 20200725. http://dx.doi.org/10.1098/rspa.2020.0725.
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Model reduction of large nonlinear systems often involves the projection of the governing equations onto linear subspaces spanned by carefully selected modes. The criteria to select the modes relevant for reduction are usually problem-specific and heuristic. In this work, we propose a rigorous mode-selection criterion based on the recent theory of spectral submanifolds (SSMs), which facilitates a reliable projection of the governing nonlinear equations onto modal subspaces. SSMs are exact invariant manifolds in the phase space that act as nonlinear continuations of linear normal modes. Our criterion identifies critical linear normal modes whose associated SSMs have locally the largest curvature. These modes should then be included in any projection-based model reduction as they are the most sensitive to nonlinearities. To make this mode selection automatic, we develop explicit formulae for the scalar curvature of an SSM and provide an open-source numerical implementation of our mode-selection procedure. We illustrate the power of this procedure by accurately reproducing the forced-response curves on three examples of varying complexity, including high-dimensional finite-element models.