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This study investigates the entrainment phenomenon, which is related to how speakers adapt to their partners in daily conversation. In contrast to previous fragmented studies, we propose a comprehensive approach that integrates various acoustic features and explores entrainment across multiple parameters, the comprehensived considered entrainment can be extracted using a linear model. We hypothesize that interlocutors utilize diverse strategies for modulating their speech features to reflect different dialogue scenarios. We calculate proximity, convergence, and synchrony metrics to differentiate conversation scenarios and propose a linear model to clarify how dialogue strategies modulate and integrate speech features. The results of evaluation with four types of conversations from the Golden Marriage corpus show that the proposed method outperforms principal component analysis in extracting integrated acoustic features and exploring entrainment across multiple parameters by the classification results. This research contributes to understanding how speakers adapt in conversations, offering insights into the intricate dynamics of entrainment and enhancing the accuracy of conversation scenario classification. [ABSTRACT FROM AUTHOR]

Dual language learners (DLLs) are a growing student population in the United States, especially in early childhood. However, DLL growth is uneven across the United States, and in many classrooms, DLLs will constitute a language minority—that is, they will be the sole or one of few students who speak a non-English home language. Given the importance of teacher perceptions for children's development, data are needed to understand how early childhood teachers view their language-minority DLL students. We use prekindergarten data collected in the American South and two-level hierarchical linear modeling to examine teacher perceptions of language-minority DLL students' end-of-year academic and social-emotional abilities. Findings indicate that teacher perceptions of language-minority DLL status were positively related to social-emotional abilities, but non-significantly related to academic abilities. Implications include collecting localized data in order to understand how early childhood teachers perceive their language-minority DLL students and using these data to enhance teacher training. [ABSTRACT FROM AUTHOR]

Variable selection methods with nonlocal priors have been widely studied in linear regression models, and their theoretical and empirical performances have been reported. However, the crucial model selection properties for hierarchical nonlocal priors in high-dimensional generalized linear regression have rarely been investigated. In this paper, we consider a hierarchical nonlocal prior for highdimensional logistic regression models and investigate theoretical properties of the posterior distribution. Specifically, a product moment (pMOM) nonlocal prior is imposed over the regression coefficients with an Inverse-Gamma prior on the tuning parameter. Under standard regularity assumptions, we establish strong model selection consistency in a high-dimensional setting, where the number of covariates is allowed to increase at a sub-exponential rate with the sample size. We implement the Laplace approximation for computing the posterior probabilities, and a modified shotgun stochastic search procedure is suggested for efficiently exploring the model space. We demonstrate the validity of the proposed method through simulation studies and an RNA-sequencing dataset for stratifying disease risk. [ABSTRACT FROM AUTHOR]

Since the inception of operations research, linear programming has received the attention of researchers in this field due to the many areas of its use. The focus was on the methods used to find the optimal solution for linear models. The direct simplex method, with its three basic stages, begins by writing the linear model in standard form and then finding a basic solution that is improved according to the simplex steps until We get the optimal solution, but we encounter many linear models that do not give us a basic solution after we put it in a standard form, and here we need to solve a rule through which we reach the optimal solution. For these models, researchers and scholars in the field of operations research introduced the simplex method with an artificial basis, which helped to Find the optimal solution for linear models, given the importance of this method and as a complement to the previous research we presented using the concepts of neutrosophic science. In this research, we will reformulate the simplex algorithm with an artificial basis using concepts of neutrosophic science. [ABSTRACT FROM AUTHOR]

Much of the literature on research-oriented teaching relies on the impressions of instructors who have experimented with such practices. Often authors are enthusiastic. This article shifts the focus to assess student satisfaction with such methods. We hypothesize that a student preference for research-based learning in Political Science would grow out of having a positive research experience during their studies, and that on average this retrospective satisfaction will depend on the nature of the collaboration. Using multicountry survey data spanning eleven institutions, we offer a series of linear models that distinguish three different research experiences: research with their professors, research with their peers, and solo research. Our results indicate that research experience does predicts student preference for research-based learning, but the direction of that pattern depends on whether they performed the research with their professors or other students. Whereas research experiences with other students positively predict a preference for research-oriented teaching, past research collaboration with professors generated a negative assessment, especially among students with no methodological training, and who did not use their involvement in such research projects to improve their methods skills. [ABSTRACT FROM AUTHOR]

Academic achievement is an important factor strongly related to positive educational experiences that facilitate subsequent learning. Therefore, identifying students who need support at an early stage and promptly providing appropriate intervention play a crucial role in preventing learning deficits. This study examined the longitudinal change in elementary school students' proficiency status to understand the nature of longitudinal transitions. It explored the feasibility of developing early predictive models for identifying students potentially facing academic difficulties based on information obtained from elementary school. Deep neural network and hierarchical generalized linear models were applied to large-scale national assessment panel data collected within the framework of an accountability system in South Korea. The results showed that although most students maintained their initial proficiency status, a substantial number of students made longitudinal transitions from non-proficiency to proficiency or vice-versa as they progressed from elementary to high school. The predictive models had an acceptable level of accuracy in predicting future academic performance, considering the degree of student mobility across proficiency levels over grades. The study concluded that the predictive models can serve as a starting point to identify students who need assistance and provide educators with a means to intervene promptly. [ABSTRACT FROM AUTHOR]

Bayesian model criticism is an important part of the practice of Bayesian statistics. Traditionally, model criticism methods have been based on the predictive check, an adaptation of goodness-of-fit testing to Bayesian modeling and an effective method to understand how well a model captures the distribution of the data. In modern practice, however, researchers iteratively build and develop many models, exploring a space of models to help solve the problem at hand. While classical predictive checks can help assess each one, they cannot help the researcher understand how the models relate to each other. This paper introduces the posterior predictive null check (PPN), a method for Bayesian model criticism that helps characterize the relationships between models. The idea behind the PPN is to check whether data from one model’s predictive distribution can pass a predictive check designed for another model. This form of criticism complements the classical predictive check by providing a comparative tool. A collection of PPNs, which we call a PPN study, can help us understand which models are equivalent and which models provide different perspectives on the data. With mixture models, we demonstrate how a PPN study, along with traditional predictive checks, can help select the number of components by the principle of parsimony. With probabilistic factor models, we demonstrate how a PPN study can help understand relationships between different classes of models, such as linear models and models based on neural networks. Finally, we analyze data from the literature on predictive checks to show how a PPN study can improve the practice of Bayesian model criticism. [ABSTRACT FROM AUTHOR]

Background: Faba bean (Vicia faba L.) is an important crop with high protein content. Tens of thousands of faba bean accessions are available in germplasm collections throughout the world. Morphological characterization of these materials can enrich these collections and aid in the selection of genotypes for use in breeding programs. Results: In this study, 26 morphological characters were analyzed for 61 faba bean landraces and 53 cultivars over two seasons in Izmir, Turkey. The genotypes had high diversity for several yield traits including number of pods per plant, dry seed yield, and 100‐seed weight. Association mapping was conducted for the morphological characters using 651 alleles from 100 simple sequence repeat (SSR) markers and a general linear model based on the Q matrix. A false discovery rate of 0.20 was used to test the significance of marker–trait associations resulting in 75 loci detected for 20 of the morphological characters (p ≤ 0.001). Conclusion: Overall, 44% of the quantitative trait loci (QTLs) were for seed traits, with 24%, 15%, and 17% of QTL identified for vegetative, inflorescence, and pod traits, respectively. The phenotypic data and marker–trait associations generated by this work can help breeding programs in the selection and improvement of faba bean. [ABSTRACT FROM AUTHOR]

Copyright of Revue Française de Géotechnique is the property of EDP Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Emerging data suggests the COVID-19 crisis exacerbated preexisting, long-documented gender inequities among U.S. faculty in higher education. During the initial Spring 2020 'lockdown' in the U.S., 80 students conveyed their experiences with faculty across 362 courses. We evaluated whether students' reports of faculty supportiveness, accommodations granted, and pandemic-impacted, anticipated grade outcomes differed according to faculty gender via mixed linear models (data on 362 courses were nested within 80 student reporters). Students perceived their women instructors as more supportive, accommodating, and anticipated lesser course grade decreases across the semester than in courses taught by men. Accordingly, we interpret that amidst the 'lockdown' crisis, women faculty earned higher perceived supportiveness and positive student outcomes than their male counterparts. Further, the data likely reflects women faculty's greater conscription into demonstrated care work, despite the coding of such labor as "feminine," thereby rendering such work devalued. To reframe, to the degree that students expect more 'intensive pedagogies,' which invites faculty and administrators to gender disparate demands, such pressures likely translate to 'hidden service' burdens, and correspondingly, less time for career-advancing activities (such as research). Broader implications are discussed, alongside women faculty's documented experiences of acceleration in career and work/family pressures in pandemic-times, which combine to exacerbate long-standing, yet now-amplified penalties, potentially driving a widening, gendered chasm in academic career outcomes. We conclude by offering constructive suggestions to mitigate any discriminatory impacts imposed by students' gendered assessment inputs and expectations. [ABSTRACT FROM AUTHOR]

The study of time and the response of individuals to contextual changes are crucial aspects in the social sciences, especially when analyzing educational inequalities and parental involvement during early stages of schooling. This work contrasts certain conclusions made by Alonso-Carmona and Martín-Criado (2022) in their critical examination of the general linear models applied to survey data, especially regarding the impact of parental practices and expectations on children's education. In response to the critique made by Andrew Abbott (2001), a detailed examination is offered of the critical postulates related to generalized linear models, while also examining the alternative model proposed by Alonso-Carmona and Martín-Criado: the processual and relational approach, based on the conclusions reached. [ABSTRACT FROM AUTHOR]

Although previous research has observed a facilitative influence of the first language (L1) on the acquisition and processing of gender agreement in a second language (L2), particularly in language pairs with similar gender agreement marking, the question of whether knowledge of two languages with gender can confer an additional advantage for L3/Ln (third or additional language) learners has not yet been addressed. The present study aimed to fill this research gap by examining the processing of gender agreement in intermediate and advanced L3/Ln Swedish among two groups of Polish native speakers: 30 L2 English / L3 Swedish learners, and 30 L2 English / L3 German / L4 Swedish learners. Participants were tested by means of a speeded grammaticality judgment task, in which they judged the correctness of indefinite noun phrases that either agreed or did not agree in gender. They also completed an untimed gender assignment task to control for their lexical knowledge of gender. Accuracy and response time data were submitted to Generalized Linear Models. The analysis shows that L4 Swedish learners process noun phrases faster than L3 Swedish learners, but only at the intermediate proficiency level; however, the groups do not differ in their judgment accuracy. This advantage is interpreted in terms of a surface transfer of similar gender agreement marking, which helps the learners automatize gender agreement processes earlier, but does not increase their sensitivity to gender-agreement violations. Moreover, the results accord with previous L2 studies in showing that learners of Swedish as L3/Ln develop sensitivity to ungrammaticality with advancing proficiency and benefit substantially from their gender assignment knowledge in processing gender agreement. Crucially, the present study provides preliminary evidence of a multilingual advantage in processing morphosyntactic features in L3/Ln. [ABSTRACT FROM AUTHOR]

Generalized linear models and generalized linear mixed models (GLMMs) are fundamental tools for predictive analyses. In insurance, GLMMs are particularly important, because they provide not only a tool for prediction but also a theoretical justification for setting premiums. Although thousands of resources are available for introducing GLMMs as a classical and fundamental tool in statistical analysis, few resources seem to be available for the insurance industry. This study targets insurance professionals already familiar with basic actuarial mathematics and explains GLMMs and their linkage with classical actuarial pricing tools, such as the B¨uhlmann premium method. Focus of the study is mainly on the modeling aspect of GLMMs and their application to pricing, while avoiding technical issues related to statistical estimation, which can be automatically handled by most statistical software. [ABSTRACT FROM AUTHOR]

The aim of this study is to prove that the dynamic behavior of tensegrity grids can be controlled. This possibility is very important, especially for deployable structures. The impact the support conditions of the structure on the existence of the immanent characteristics, such as self-stress states and infinitesimal mechanisms, and consequently on the dynamic control, is analyzed. Grids built with the modified Quartex modules are considered. A geometrically non-linear model is used, implemented in an original program written in the Mathematica environment. The results confirm the feasibility of controlling tensegrity structures characterized by the presence of the infinitesimal mechanisms. In the case that the mechanisms do not exist, structures are insensitive to the change of the initial prestress level. The occurrence of mechanisms can be controlled by changing the support conditions of the structure. The obtained results make tensegrity a very promising structural concept, applicable in many areas when conventional solutions are insufficient. [ABSTRACT FROM AUTHOR]

The study revisits the Menzerath's Law, which articulates the inverse relationship between the length of constructs and the mean length of their constituents. This relationship is famously modelled by Gabriel Altmann's model, which combines power and exponential relations. His formulas have been widely used to describe this relationship across linguistics and biology, however, there is no satisfactory explanation for his model. Therefore, the paper proposes shifting our perspective to examine directly the relationship between the number of constituents in a construct and the number of subconstituents in the same construct. This relationship may be explained by a simple model based on linear regression, which leads to a hyperbolic model of the Menzerath's Law. This approach is successful for several datasets, but insufficient for others. [ABSTRACT FROM AUTHOR]

Background/Aim: Ablating a spherical area during hepatocellular carcinoma ablation therapy is a very important issue. We aimed to determine the ablation area of bovine liver using various radiofrequency ablation (RFA) protocols. Materials and Methods: Bovine liver (1-2 kg) was placed in an aluminum tray, which was punctured with STARmed VIVA 2.0 17-gauge (G) and 15-G electrodes using a current-carrying tip. Under the step-up or linear method, with an ablation time up to one break and RFA output stop, the size of the color change area (representing the thermally coagulated area) of the bovine liver was measured along the vertical and horizontal axes, and the ablated volume and total heat generated were calculated. Results: 5-W per minute increases protocol resulted in greater horizontal and vertical diameters of the ablated area than 10-W per minute increases protocol under the step-up method. For 5-W and 10-W per minute increases under the step-up method, the aspect ratio was 0.81 and 0.67 with a 17-G electrode, and 0.73 and 0.69 with a 15-G electrode, respectively. For 5-W and 10-W increases under the linear method, the aspect ratio was 0.89 and 0.82, respectively. Sufficient ablation was obtained, with vertical and horizontal diameters of 50 mm and 43.50 mm, respectively. Although the ablation time was long, the watt output value at the break and average watt value were low. Conclusion: Gradual increase in output (5 W) using the step-up method yielded a more spherical ablation area, and longer ablation time in the linear method with a 15-G electrode could result in a more spherical ablation area in real clinical practice in humans. Future studies should examine concerns regarding long ablation times. [ABSTRACT FROM AUTHOR]

This paper proposes a simulation linear model created in the MATLAB environment, which provides a process for regulating blood sugar levels. The controller is built for the need for any type of diabetes to control and normalize the blood sugar content of the patient in order to eliminate the differences in the quality of life of a diabetic patient and a healthy person. The linearization of the nonlinear model was performed, and the adequacy of the linearized model was verified and confirmed using the MATLAB simulation. The choice of the PID controller and the CHR method for its adjustment was justified and MATLAB tools were used to show the implementation of these methods. The model of the patient with the controller has been built; the algorithm for the automatic adjustment of the PID controller parameters has been developed and realized. The directions for continuation of the work on this problem regarding regulation in the system under study are proposed. [ABSTRACT FROM AUTHOR]

Copyright of Afrika Statistika is the property of Statistics & Probability African Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Not only unemployment itself but also the reduced work intensity of a household has a major impact on the social exclusion of a person. The work intensity of households is currently being monitored in Europe mainly for purposes of identifying those people or households that are excluded from the labour market. The households' work intensity directly affects the inclusion or exclusion from the labour market, which is one of the three social exclusion dimensions. Moreover, it also, as confirmed by several studies, fundamentally affects the other two dimensions of social exclusion, namely income poverty and material deprivation. The aim of the paper was to assess which factors in interaction with the economic activity status of a person significantly affect the household's work intensity and, depending on these factors, to estimate the household's work intensity. For this purpose, the general linear model and the associated analysis of marginal means and the contrast analysis were used. The analyses are based on a database EU-SILC 2020 for the Slovak Republic and performed in the SAS Enterprise Guide and by means of PROC GLM in the SAS programming language using CONTRAST and ESTIMATE statements. The article examines between which levels of significant factors there is a significant difference in terms of a household's work intensity and in particular provides estimates of work intensity depending on the household type, educational attainment level and the age of a person. At the same time, in all three cases households are broken down by the economic activity status of the person. The presented analyses revealed categories of persons that are the most and the least threatened by labour market exclusion from the point of view of the considered factors. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ibero-Americana de Estratégia (RIAE) is the property of Revista Ibero-Americana de Estrategia/UNINOVE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An introduction is presented in which author discusses articles on topics including focuses on Africa which is not only a point of origin to explain diasporic performance traditions, Africa is also a place of return and reinvention disrupting linear models of influence.

Recently, several studies have been conducted using state space model. In this study, a dynamic linear model with state space model form is applied to stock data. The monthly returns for 135 Korean stocks are fitted to a dynamic linear model, to obtain an estimate of the time-varying β-coefficient time-series. The model formula used for the return is a capital asset pricing model formula explained in economics. In particular, the transition equation of the state space model form is appropriately modified to satisfy the assumptions of the error term. k-shape clustering is performed to classify the 135 estimated β time-series into several groups. As a result of the clustering, four clusters are obtained, each consisting of approximately 30 stocks. It is found that the distribution is different for each group, so that it is well grouped to have its own characteristics. In addition, a common pattern is observed for each group, which could be interpreted appropriately. [ABSTRACT FROM AUTHOR]

Variable selection is one of the most crucial tasks in supervised learning, such as regression and classification. The best subset selection is straightforward and optimal but not practically applicable unless the number of predictors is small. In this article, we propose directly solving the best subset selection via the genetic algorithm (GA), a popular stochastic optimization algorithm based on the principle of Darwinian evolution. To further improve the variable selection performance, we propose to run multiple GA to solve the best subset selection and then synthesize the results, which we call ensemble GA (EGA). The EGA significantly improves variable selection performance. In addition, the proposed method is essentially the best subset selection and hence applicable to a variety of models with different selection criteria. We compare the proposed EGA to existing variable selection methods under various models, including linear regression, Poisson regression, and Cox regression for survival data. Both simulation and real data analysis demonstrate the promising performance of the proposed method. [ABSTRACT FROM AUTHOR]

Background: Challenges in measuring dynamic scapular orientation limit assessment of scapulothoracic and glenohumeral contributions to shoulder function in children with brachial plexus birth injury (BPBI). Double calibration acromion marker cluster (D-AMC) and linear model approaches have been validated to estimate scapular motion in healthy adults, but neither has been evaluated in BPBI.Research Question: Are the linear model and D-AMC approaches able to accurately estimate scapular orientation in children with BPBI at functional arm postures?Methods: Seventeen children with BPBI positioned their affected limbs in 11 static positions while their segment orientations were measured with motion capture. Linear model and D-AMC estimates of scapular orientation were compared against palpation at six of the static positions with functional relevance to BPBI using a three-way repeat measures ANOVA and a comparison of root mean square errors (RMSE) against literature AMC values for healthy adults.Results: The D-AMC was similar to palpation across all positions and scapular axes while the linear model differed from palpation in a few instances. RMSEs of the D-AMC (3.7-14.8°) and particularly the linear model (4.6-24.8°) were generally at or beyond the upper range of past AMC analyses on healthy adults (1.6-14.2°), especially for more complex, multiplanar arm postures. Despite the D-AMC outperforming the linear model, this approach still produced clinically meaningful (>10°) errors for roughly (12.7-22.5%) of subjects.Significance: Current methods for estimating dynamic scapular orientation remain less than ideal for BPBI. Use of the D-AMC may be appropriate to gain broad insights into general dynamic scapulothoracic and glenohumeral function; however, given their potential for producing clinically meaningful errors, the D-AMC and linear model are not recommended for diagnostic purposes or outcomes assessment on an individual patient basis unless their patient-specific accuracy has been evaluated and confirmed prior to use. [ABSTRACT FROM AUTHOR]

The correct representation of wind speeds at hub height (e.g., 100m above ground) is becoming more and more important with respect to the expansion of renewable energy. In this study, a post-processing of the wind speed of the regional reanalysis COSMO-REA6 in Central Europe is performed based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of 8 compared to COSMOREA6 and considers different vertical atmospheric stabilities. In the second step, a statistical correction is performed using a neural network, as well as a generalized linear model based on different variables of the reanalysis. Although only a few measurements by masts or lidars are available at hub height, an improvement of the wind speed in the root-mean-squared error of almost 30% can be achieved. A final comparison with radiosonde observations confirms the added value of combining the physical and statistical approaches in post-processing the wind speed. [ABSTRACT FROM AUTHOR]

Inter-municipal cooperation in metropolitan areas has been previously shown to save costs, but can it also improve environmental outcomes? The existing empirical evidence is largely based on single case studies and does not allow to ascertain the net effect of cooperation. We develop a three-level mixed-effects linear model to conduct a systematic large-n study testing the impact of cooperation in transportation on CO2 transport emissions. We use a novel dataset covering over 200 metropolitan areas in 16 OECD countries. The findings demonstrate that both fragmented and consolidated metropolitan governance structures are equally inefficient in delivering a reduction in CO2 transport emissions. Further, without functional enforcement mechanisms, mitigation policies fail to have a positive effect on environmental outcomes. Inter-municipal cooperation in metropolitan areas facilitates coherence and widespread enforcement and emerges as a crucial factor explaining the reduction of CO2 transport emissions. Effects of metropolitan cooperation on transportation are magnified by the presence of national environmental mitigation policies. [ABSTRACT FROM AUTHOR]

Since World War II, value liberalization has been a worldwide trend, but more recently, there has been a resurgence of conservativism. Modernization and cultural theories have difficulty explaining the shift to liberal or illiberal values, but the political environment, an underestimated contextual factor, could shed light on the mechanisms driving it. This study used hierarchical linear modeling on all six waves of the World Values Survey data (1981–2014) to demonstrate that political freedom helps to explain the rise and fall of liberal attitudes when controlling for societal affluence, inequality, and cultural backgrounds. It finds that political freedom conditions the effects of education: although education is usually considered a liberalizing force, its effect is much weaker in non-free than free societies. The findings remind us of the importance of a free political environment for a robust democracy and point to the complex nature of educational effects, namely the ability of education to socialize people in liberal or illiberal directions depending on the political context and the regime's agenda. [ABSTRACT FROM AUTHOR]

This paper is concerned with the estimation problem in semiparametric linear mixed models when some of the covariates are measured with errors. The authors proposed the corrected score function estimators for the parametric and non parametric components. The resulting estimators are shown to be consistent and asymptotically normal. An iterative algorithm is proposed for estimating the parameters. Asymptotic normality of the estimators is also derived. Finite sample performance of the proposed estimators is assessed by Monte Carlo simulation studies. We further illustrate the proposed procedures by an application. [ABSTRACT FROM AUTHOR]

The paper shortly presents shock absorber design evolution and resulting achieved characteristics. The way in which suspension performance is evaluated is described giving information about models used for suspension parameter tuning during simulation testing of suspension transmissibility (FRF - Frequency Response Function) for most important suspension assessment criteria. More detailed information about models of shock absorber (damper) nonlinearities of characteristics allows for description of methods of linear and nonlinear suspension models FRF estimation. Testing linear suspension model is possible with the use of analytical transfer function formulas which were used to verify methods for estimation FRF using estimated power spectral density functions of excitation and response signals. Designing appropriate input signal allowing to get useful response signals was necessary for the success of this research. Proposed FRF estimation method was used for linear estimation of nonlinear suspension for a given range of working conditions. It was demonstrated that there is no single value of a damping coefficient which would make the linear model responses similar to the responses of the nonlinear one. Then the bilinear model was proposed, giving good damper static nonlinear characteristic. [ABSTRACT FROM AUTHOR]

A subdiffusion problem in which the diffusion term is related to a stable stochastic process is introduced. Linear models of these systems have been studied in a general way, but non-linear models require a more specific analysis. The model presented in this work corresponds to a non-linear evolution equation with fractional time derivative and a pseudo-differential operator acting on the spatial variable. This type of equations has a couple of fundamental solutions, whose estimates for the Lp-norm are found to obtain three main results concerning mild and global solutions. The existence and uniqueness of a mild solution is based on the conditions required in some parameters, one of which is the order of stability of the stochastic process. The existence and uniqueness of a global solution is found for the case of small initial conditions and another for non-negative initial conditions. The relationship between the Fourier analysis and Markov processes, together with the theory of fixed points in Banach spaces, is particularly exploited. In addition, the present work includes the asymptotic behavior of global solutions as a linear combination of the fundamental solutions with Lp-decay. [ABSTRACT FROM AUTHOR]

This paper presents injection‐shift‐factor‐based linear modelling for various types of series flexible ac transmission system (FACTS) devices within the DC power flow framework. The presented models allow FACTS devices to be properly integrated in current operation and planning software tools, which is key to harnessing the power flow capabilities provided by FACTS technology. Although recent literature has attempted to develop linear models for FACTS devices, the existing models do not accurately reflect the actual operating range for many FACTS devices. Compared to the existing models, the modelling approach presented here reflects the principle of operation of each type of series FACTS device in adjusting transmission line reactance. Through mathematical derivation, linear constraints for FACTS operation are formulated, which are used to formulate power system operation models. The formulated problems are then analysed through simulation studies on various test systems. The results highlight the significant computational efficiency improvements provided by linear FACTS modelling in DC‐based operation models. [ABSTRACT FROM AUTHOR]

A fin is traditionally thought of as an extension of a surface to facilitate the transfer of heat away from a larger body to which it is attached. In this paper, the authors study some mathematical properties of a nonlinear heat transfer model for a fin and its relation to an associated linear model. Specifically, they prove that the solution exists and is unique, and they determine bounds for the temperature. Further, they prove the monotonicity of the temperature distribution, and they obtain an estimate for the maximal difference between the temperatures as determined by the nonlinear and linear models. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to develop and illustrate an optimum variable selection approach using Multiple Linear Regression (MLR) and validation using Multi-Layer Feed Forward Neural Network (MLFF), also known as Multilayer Perceptron Neural Network (MLP) while taking bootstrapping into account. All of the factors specified will be evaluated to determine if they have a meaningful association. Objective:The goal of this study was to create a new method in making predictions for oral health data by combining a few statistical techniques. This would make the model more accurate. Material and Methods:A set of medical data that consists 30 observations was used to develop the methodology. The data descriptions of variables being used in this retrospective research including Creatinine, Fasting Blood Sugar, Haemoglobin A1C, and Urea were evaluated using advanced computational statistical modelling approaches. The medical data are used to test the R syntax that is developed in this study. The statistics for each sample are calculated using a model that incorporates bootstrapping and multiple linear regression techniques. Results:The statistical strategy which combines Bootstrap, MLFF and MLR is better than regular statistical method being used for this type of data. The hybrid model technique's accuracy was increased when the data was separated into training and testing dataset. Four variables are taken into account in this case: fasting blood sugar, creatinine, haemoglobin A1C, and urea. Fasting Blood Sugar (β1: 0.461889; p< 0.05), Creatinine (β3: 0.029761; p< 0.05) and Urea (β2:-0.454766; p< 0.05) are all significant, whereas MLFF and MLRhave the predicted mean square error (PMSE) values of 0.01226 and 0.3531 respectively. Conclusion: The value of PMSE is mainly used to diagnose the performance of MLR and MLFF. The MLFF is used to determine how close predicted values are to real data, while the model that has low PMSE value indicates that the model is accurate. The R syntax for combining Bootstrap, MLR, and MLFF is also included in this research article. In a word, this research establishes the superiority of the hybrid model method. [ABSTRACT FROM AUTHOR]

In this study, a non-linear version of a Cramer-Lundberg risk model is examined. The objective of this work is to evaluate the ruin probability of the non-linear risk model. Hie classical linear Cramer-Lundberg model has been widely studied in the literature. However, the linear model is not always realistic. Because an insurance company's premium income cannot always increase linearly. Therefore, it is recommended to adapt premium income as a function which increases monotonically and yet its rate of growth decreases over time. Thus, to account for this, a more realistic non-linear mathematical model has been constructed and investigated, when the premium income function is p(t) = cVt. Then Lundberg type upper bound was calculated for the ruin probability for the model under investigation. [ABSTRACT FROM AUTHOR]

We present an ensemble filtering method based on a linear model for the precision matrix (the inverse of the covariance) with the parameters determined by Score Matching Estimation. The method provides a rigorous covariance regularization when the underlying random field is Gaussian Markov. The parameters are found by solving a system of linear equations. The analysis step uses the inverse formulation of the Kalman update. Several filter versions, differing in the construction of the analysis ensemble, are proposed, as well as a Score matching version of the Extended Kalman Filter. [ABSTRACT FROM AUTHOR]

In this work, we present a framework for ensemble‐based (E) chance‐constrained (CC) model predictive control (MPC) in sewer systems. The framework considers the availability of ensemble forecasts and the difficulties with propagation of distributions; through distribution estimation. Utilizing a case study of the sewer network of the city of Aarhus in Denmark, the performance of the ECC‐MPC framework is evaluated through simulations. The evaluations were based on linear models of the case study and compare the ECC‐MPC performance with the performance of CC‐MPC. Based on the simulations, it was found that the ECC‐MPC performed comparable to the performance of the CC‐MPC, not only in the context of overflow and outflow but also with respect to behavior in response to changes in different aspects of forecast uncertainties. Regarding the aspects, it was found that expectation offset biases in the forecast were affecting the performance of the CC‐ and ECC‐MPC the most. While other aspects only had a reduced effect on the performances, within the ranges tested. With the comparable performances, it was found that ECC‐MPC would work as an alternative approach to CC‐MPC. [ABSTRACT FROM AUTHOR]

The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the effect of trader inventory on the price of VSTOXX futures through a combination of stochastic processes and machine learning methods. We formulate a simple and efficient pricing methodology for VSTOXX futures, which assumes a Heston-type stochastic process for the underlying EURO STOXX 50 market. Under these dynamics, approximate analytical formulas for the implied volatility smile and the VSTOXX index have recently been derived. We use the EURO STOXX 50 option implied volatilities and the VSTOXX index value to estimate the parameters of this Heston model. Following the calibration, we calculate theoretical VSTOXX futures prices and compare them to the actual market prices. While theoretical and market prices are usually in line, we also observe time periods, during which the market price does not agree with our Heston model. We collect a variety of market features that could potentially explain the price deviations and calibrate two machine learning models to the price difference: a regularized linear model and a random forest. We find that both models indicate a strong influence of accumulated trader positions on the VSTOXX futures price. [ABSTRACT FROM AUTHOR]

This study examines the Qur'ān's view towards gender and argues that all three masculine, feminine and egalitarian (gender-inclusive) discourses exist in its text, and that these discourses do not follow a simple and linear model but rather a nonlinear and complex one. It also provides evidence, showing that gender equality in the Qur'ān is achieved in two ways: firstly, through linguistic devices that are devoid of gender distinctions, and secondly, through concurrent use of masculine and feminine gender markers in one context. The masculine discourse is, however, more prominent in the verses of this book and manifests itself to varying degrees in three ways: (1) in some verses, only masculine indicators are used but it can be inferred based on the (Taghlīb) principle that both men and women are the intended addressees; (2) in some verses, rules regarding women are stipulated, whilst men are, in fact, the main addressees and (3) in verses that state rules on reciprocal issues such as divorce and marriage but these rules are addressed to only men. The feminine discourse is also present in the Qur'ān and can be seen in verses that address women. [ABSTRACT FROM AUTHOR]

Hyperspectral unmixing (HU) plays an important role in the hyperspectral image (HSI) processing and analysis. It aims to decompose mixed pixels into pure spectral signatures and their associated abundances. The autoencoder (AE) framework achieves good performance in HU by automatically learning low-dimensional embedding and reconstructing data. Most of the existing HU methods based on the AE framework can be divided into two categories, one is the pixelto-pixel network focusing on the spectral information of HSI, and the other is the 2DCNN network focusing on the spatial information of HSI. However, HSI contains both spatial and spectral features, it is helpful to add both spectral and spatial information into unmixing methods. Besides, most of the existing unmixing methods are based on linear mixed models (LMM). In LMM, the incident light is assumed to be reflected by each component present in the scene only once prior to collection by the camera sensor, and the observed spectrum is thus a linear combination of the endmembers. However, the real scene is often complex, and due to the existence of spectral variability (SV) and photon nonlinear interaction, the assumption of LMM is often not valid (Zhao et al., 2022). As a result, the LMM hardly makes an accurate unmixing in reality. The excellent potential of neural networks to solve nonlinear problems provides the possibility to establish nonlinear mixed models, which may solve this problem. [ABSTRACT FROM AUTHOR]