Framework Of Model Research Articles

A Model-Based Approach to the Problem of Variable Choice

Abstract This article distinguishes causal modeling, metaphysical, epistemic, and modeling reasons for variable/parameter choice. I argue in favor of justifying variable/parameter choices by appealing to modeling reasons concerning the limitations of the available measurements, experimental data, modeling techniques, and modeling frameworks. I use this “tyranny of availability” to identify normative criteria for variable/parameter choice that apply across most scientific modeling contexts and investigate their metaphysical and epistemological implications.

Deep learning based hybrid POD-LSTM framework for laminar natural convection flow in a rectangular enclosure

Abstract Laminar natural convection in side-heated enclosures is characterized by transient phenomena of the working fluid till it reaches steady state. The side heating can done in several ways the most common way being heating one end at constant temperature and cooling the other end. One of the other ways is heating both sides of the enclosure at a constant heat flux. Mathematical modeling of such problems using Computational Fluid Dynamics (CFD) essentially involves considerable amount of computational time and power to predict the flow phenomena observed in actual experimentation. In the last few years, data driven model frameworks have proven to be anefficient way in saving both time and computational cost in several applications. In the present study, a data driven model framework using a combination of unsupervised machine learning (using Proper Orthogonal Decomposition [POD]) and supervised deep learning models (using Long Short Term Memory [LSTM]) has been developed and referred to as POD-LSTM framework. The selection of a few dominant spatial bases and accompanying temporal modes provides us with a reduced order model of the system. The flow is then reconstructed and compared with results of CFD simulations. The Rayleigh number (Ra) chosen for the study is 3.27 × 1010. The estimated time to reach stedy state for this Ra number is 15,000 s. The POD-LSTM framework is trained using data obtained from a validated CFD model for the first 1,000 s. The trained model was then tested to predict temporal dynamics for the entire 15,000 s. The predictions provided by POD-LSTM framework were found upto 98 % accurate compared to the ones predicted by CFD. The computational time and power was however an order of magnitude lower for the POD-LSTM framework than that required for the CFD model.

Investigating the e-learning choice under the learners’ perspective using demand driven learning model: insights from Vietnam

Over the past four decades, e-learning has experienced rapid global growth, revolutionizing higher education. In Vietnam, universities have increasingly turned their attention and resources toward e-learning development, adapting to specific contexts and needs. This study investigates the drivers of e-learning choice within the framework of the demand-driven learning model (DDLM), with a primary focus on three core factors: quality content, delivery, and service. Employing a quantitative approach and the PLS-SEM technique, this research uncovers significant findings. The results of the study highlight the pivotal role of quality content, which exerts the most potent influence on e-learning choice, evidenced by a robust path coefficient of 0.400. Service and delivery, with direct path coefficients of 0.183 and 0.173, respectively, also play substantial roles in shaping e-learning decisions. Moreover, mediate role of delivery in the the e-learning choice model has been confirmed. Quality content leads to delivery, and delivery, in turn, leads to e-learning choice. Similar pathway has been found with service. A higher level of service increases delivery, which positively impacts on e-learning choice. These findings hold critical implications for the formulation of e-learning development policies in Vietnam’s higher education institutions. Universities should prioritize the continuous development of high-quality, engaging, and up-to-date educational content that aligns with industry needs and student interests. Additionally, emphasis should be placed on providing a supportive e-learning experience, characterized by responsive customer service, accessible technical support, and efficient issue resolution mechanisms. Moreover, universities should consider the implementation of user-friendly and interactive content delivery platforms and methods that actively engage learners. In essence, this research serves as a guide for universities in Vietnam, enabling them to enhance their e-learning offerings by ensuring the content quality, support services, and delivery methods, ultimately fostering a dynamic and accessible learning environment that meets the evolving needs of their students and the demands of the modern educational landscape.

Estimation of joint kinetics during manual material handling using inertial motion capture: a follow-up study

Abstract Musculoskeletal models based on inertial motion capture and ground reaction force (GRF) prediction hold great potential for field-based risk assessment of manual material handling. However, previous evaluations have identified inaccuracies in the methodology?s estimation of spinal forces, while the accuracy of other key outcome variables is currently unclear. This study evaluated knee, shoulder, and L5-S1 joint reaction forces (JRFs) derived from a musculoskeletal model based on inertial motion capture and GRF prediction against a model based on simultaneously collected optical motion capture and force plate measurements. Data from 19 healthy subjects performing lifts with various horizontal locations, deposit heights and asymmetry angles were analyzed, and the consistency and absolute agreement of the model estimates statistically compared. Despite varying levels of agreement across tasks and variables, considerable absolute differences were identified for the L5-S1 axial compression (RMSE = 63.0-94.2%BW) and anteroposterior shear forces (RMSE = 40.9-80.6%BW) as well as the bilateral knee JRFs (RMSE = 78.9-117%BW). Glenohumeral JRFs and vertical GRFs exhibited the highest overall consistency (r = 0.33-0.91, median 0.78) and absolute agreement (RMSE = 7.63-34.9%BW), while the L5-S1 axial compression forces also showed decent consistency (r = 0.04-0.89, median 0.80). The findings generally align with prior evaluations, indicating persistent challenges with the accuracy of key outcome variables. While the modelling framework shows promise, further development of the methodology is encouraged to enhance its applicability in ergonomic evaluations.

Development of a recommendation engine for university study programme selection: A regression-based approach

This paper is the third in a series focused on bridging the gap between secondary and higher education. Our primary objective is to develop a robust theoretical framework for an innovative e-business model called the Undergraduate Study Programme Search System (USPSS). This system considers multiple criteria to reduce the likelihood of exam failure or the need for multiple retakes, while maximizing the chances of successful program completion. Testing of the proposed algorithm demonstrated that the Stochastic Gradient Boosted Regression Trees method outperforms the current method used in Lithuania for admitting applicants to 47 educational programs. Specifically, it is more accurate than the Probabilistic Neural Network for 25 programs, the Ensemble of Regression Trees for 24 programs, the Single Regression Tree for 18 programs, the Random Forest Regression for 16 programs, the Bayesian Additive Regression Trees for 13 programs, and the Regression by Discretization for 10 programs.

Some aspects to consider to build teachers' job satisfaction: A comparative study of Uzbek and Hungarian teacher appraisals

The initiative of the Hungarian government to introduce a new teacher appraisal system within the whole-scale teacher assessment made us review the operational teacher career models and teacher appraisal frameworks linked to salary schemes in the world and compare the planned Hungarian model with the Uzbek teacher career model and teacher appraisal framework. The comparison includes the evolution of appraisals, their procedures, criteria, and methodology. Our study proposes some ideas for decision-makers to consider when introducing or modifying teacher assessment frameworks so that they can better contribute to teachers' well-being and to improving the quality of work in education.

A normative framework dissociates need and motivation in hypothalamic neurons.

Physiological needs evoke motivational drives that produce natural behaviors for survival. In previous studies, the temporally intertwined dynamics of need and motivation have made it challenging to differentiate these two components. On the basis of classic homeostatic theories, we established a normative framework to derive computational models for need-encoding and motivation-encoding neurons. By combining the model-based predictions and naturalistic experimental paradigms, we demonstrated that agouti-related peptide (AgRP) and lateral hypothalamic leptin receptor (LHLepR) neuronal activities encode need and motivation, respectively. Our model further explains the difference in the dynamics of appetitive behaviors induced by optogenetic stimulation of AgRP or LHLepR neurons. Our study provides a normative modeling framework that explains how hypothalamic neurons separately encode need and motivation in the mammalian brain.

Quantitative pharmacology of dual-targeted bicistronic CAR-T-cell therapy using multiscale mechanistic modeling.

Despite the initial success of single-targeted chimeric-antigen receptor (CAR) T-cell therapy in hematological malignancies, its long-term effectiveness is often hindered by antigen heterogeneity and escape. As a result, there is a growing interest in cell therapies targeting multiple antigens (≥2). However, the dose-exposure-response relationship and specific factors influencing the pharmacology of dual-targeted CAR-T-cell therapy remain unclear. In this study, we have developed a multiscale cellular kinetic-pharmacodynamic (CK-PD) model using case studies from CD19/CD22 and GPRC5D/BCMA autologous CAR-Ts. Initially, an invitro tumor-killing model characterized the impact of individual binder affinities and their contribution to overall potency across varying (1) effector: target (ET) ratios and (2) tumor-associated antigen (TAA) expressing cell lines. Subsequently, an integrated CK-PD model was developed in pediatric acute lymphoblastic leukemia (ALL) patients, which accounted for CAR-T-cell product composition and relative antigen abundance in patients' tumor burden to characterize patient-level multiphasic cellular kinetics using multiple bioanalytical assays (e.g., flow and qPCR-based readouts). Global sensitivity analysis highlighted relative antigen expression, maximum killing rate constant, and CAR-T expansion rate constant as major determinants for observed exposure of dual-targeted CAR-T-cell therapy. This modeling framework could facilitate dose-optimization and construct refinement for dual-targeted bicistronic CAR-T-cell therapies, serving as a valuable tool for both forward and reverse translation in drug development.

Financial Toxicity Among Asian American Cancer Survivors

This Viewpoint highlights financial toxicity specific to Asian American cancer survivors and presents steps forward within the framework of the social ecological model of health, considering individual, interpersonal, community, and policy-level dimensions.

The Usage of Machine Learning Algorithms in Credit Evaluation Model

Credit scoring models are important tools for evaluating the credit status of individuals or businesses and play a significant role in the financial field. This study aims to suggest a prediction framework for credit evaluation model based on machine learning (ML) algorithms and explore the application principles and steps of combining credit scoring models, then provide an practical example to show the method for applying the model.In addition, the article also analyzes the issues that credit evaluation models should pay attention to in production practice at the end.

Racial disparity in mortality from tuberculosis in the US between states with and without a history of Jim-Crow laws: an analysis of the Global Burden of Disease (GBD) and risk factors study, 1990 to 2019.

While TB-related mortality in the US declined four-fold from 1990 to 2019, country-level estimates of TB burden obscure within-state racial heterogeneity and changes in TB burden over time. In sixteen US Southern States and Washington DC, the effects of health inequities engendered by Jim-Crow laws enacted from the late 1800s to the 1960s have not been evaluated for TB-related mortality. We, therefore, sought to compare TB mortality rates and annualized rate of change (AROC) between 1990 and 2019 in former Jim-Crow vs. non-Jim-Crow states to help guide response efforts and inform resource prioritization to improve racial equity. We evaluated whether TB-related mortality varied over time, from 1990 to 2019, between states that have a history of enacting Jim-Crow laws vs. states with no such history using estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019). TB mortality per 100,000 population and bootstrap 95% uncertainty intervals (UIs) were modeled using the Cause of Death Ensemble model (CODEm) framework with varying combinations of predictive covariates. For changes over time, we present age-standardized AROC as the percent difference in the natural logarithm of the rate in 1990 and 2019 divided by 30 (i.e., 100*[ln(2019 Rate/1990 Rate)/(30)) and the corresponding 95% UIs. TB-related mortality in all US states declined between 1990 and 2019. From 1990 to 2019, most former Jim-Crow states had higher mortality rates than states that did not enact Jim-Crow laws. The most significant decline in TB mortality was in Washington DC, with a six-fold decline from 2.69 (2.46-2.96) per 100,000 population in 1990 to 0.45(0.37-0.55) in 2019, corresponding to an AROC of -0.83% (-0.86;-0.79). The lowest decline was in Iowa, from 0.30 (0.27-0.33) to 0.09 (0.07-0.11) (AROC: -0.70% (-0.76; -0.63)). Eleven of the 16 states and Washington DC in the third tertile of TB mortality rate in 1990 (range 0.81-2.69) had a history of Jim-Crow laws, whereas none of the 17 states in the first tertile (range 0.30-0.51) had such history. Conversely, mortality decreased relatively slowly in former Jim-Crow states than in non-Jim-Crow states. Even though the 1964 Civil Rights Act dismantled Jim-Crow statutes, racial inequities in TB burden experienced by past generations may still be felt in subsequent generations. Understanding the role of structural racism at the intersection of science and medicine shows the complex ways historical laws, such as Jim-Crow laws, continue to negatively impact health outcomes and warn of future dangers, such as COVID-19, to avoid.

Strategic Considerations for Small Businesses: Selecting the Optimal E-commerce Platform

Choosing the right e-commerce platform is crucial for small businesses seeking to expand their market presence and streamline operations. This paper explores the key factors influencing platform selection, such as usability, scalability, flexibility, and cost, while applying the Technology Acceptance Model (TAM) and Resource-Based View (RBV) frameworks to analyze platform adoption. The study focuses on Shopify, WooCommerce, and Amazon Marketplace, comparing their impact on business growth, customer satisfaction, and operational efficiency. Additionally, the paper discusses emerging trends, including artificial intelligence (AI) and automation, offering practical insights for small businesses looking to optimize their digital strategies and gain a competitive edge

A machine learning assisted preliminary design methodology for bolted composite joints in large structures

Abstract Damage initiation hotspots around features, such as bolts and ply drops, must be investigated during the preliminary design phase of large composite structures, such as composite airframes. A global-local modelling approach is commonly employed to perform this investigation, whereby a global low-fidelity model is used to drive high-fidelity local models around the features of interest. However, this methodology is slow, repetitive and expert-dependent. In this investigation, we address these issues by applying machine learning techniques to this global-local modelling framework and demonstrate the time-saving benefit when predicting damage initiation of bolted composite joints. Feature engineering of model inputs and outputs, and appropriate customisation of machine learning methods enables damage initiation prediction. Special consideration is given to the boundary conditions that must be varied to simulate the response of the bolted composite joints. Results show over three orders of magnitude time-saving benefit and satisfactory accuracy of the proposed methodology. This indicates its potential to be developed further into a rapid design and optimisation tool.

Fitting Single- and Multiple-Indicator STARTS Models as Dynamic Structural Equation Models

The STARTS model is a structural equation model that decomposes individual differences in a longitudinally assessed variable into a time-invariant stable component, a time-varying autoregressive component, and a time-point specific state component. Typically, the model is estimated with Maximum Likelihood in standard SEM software. Here, we show how different STARTS models (the single-indicator univariate and multivariate and the multiple-indicator univariate model) can be incorporated into the Dynamic Structural Equation Model framework. We use the data of an experience sampling study to illustrate the expositions and we provide codes to fit the models in Mplus. As the DSEM parameters are estimated with a Bayesian approach, we also report the results of three simulation studies in which we examined the statistical properties of the parameter estimates obtained in the DSEM framework. Finally, we discuss how to estimate variants of the STARTS model and we make suggestions for future applied and methodological research.

Population pharmacokinetic modeling of paired plasma–breast milk lamivudine data for estimation of infant exposure in breastfeeding mother–infant pairs

AbstractAround 1.2 million women living with HIV give birth annually, majority of whom will breastfeed their infants while receiving antiretroviral therapy (ART). Lamivudine, a component of first‐line ART regimens crosses from maternal plasma to breast milk, with measurable concentrations in some breastfed infants. Wide variability in plasma‐to‐breast milk transfer has been reported within‐ or across studies, probably due to differences in sampling framework. This work sought to characterize the milk‐to‐plasma transfer of lamivudine, quantify inter‐patient variability and associated factors, and predict exposure of a breastfed infant. We explored data from an observational pharmacokinetic study that included 35 Ugandan mothers and their infants. Mothers received lamivudine doses of 150 mg twice daily or 300 mg once daily as part of their antiretroviral regimen. Pharmacokinetic sampling was undertaken across two visits approximately 8 weeks apart, providing 248 maternal plasma, 256 breast milk‐, and 151 infant blood concentrations, measured across a 24‐h sampling interval. A one‐compartmental model best described the plasma disposition of lamivudine, with first‐order absorption, interindividual variability on clearance and volume of distribution, and a proportional residual error model. A lag in time of plasma‐to‐breast milk drug accumulation was described using an effect compartment model with a milk‐to‐plasma ratio of 1.77. An estimated daily infant dose of 179.3 μg/kg (range: 125.8, 282.3) closely predicted the observed infant steady‐state concentrations and translated into 3.34% (2.13, 7.20) and 3.35% (1.10, 7.15) of the standard daily maternal dose in visits 1 and 2, respectively. The established modeling framework can be extended to other drugs.

Simulated sensitivity of the Amazon rainforest to extreme drought

Abstract The Amazon rainforest is highly biodiverse and the largest intact, tropical forest in the world. Undisturbed tropical forests act as a carbon sink by taking up about 15% of anthropogenic carbon emissions per year. In the past decades, a declining trend in the carbon sink capacity in the Amazon rainforest has been observed due to increased carbon losses and tree mortality. The causes are disputed, but increasing temperatures and more frequent severe droughts are potentially major drivers. We employ a novel modelling framework and hypothesize that previously rare, extreme droughts in the Amazon, such as the ones in 2005 and 2010, constitute the main cause behind the decline of the net carbon sink in aboveground biomass. Our dynamic vegetation model simulates process-based plant hydraulics and drought-induced mortality, and accounts for the diversity of strategies in plant responses to drought based on observed hydraulic vulnerability curves. The simulated impact of the 2005 drought event temporarily turned the annual Amazon net carbon sink to a carbon source of about 0.25 MgC ha-1. In contrast to other dynamic vegetation models our model simulated an increasing trend in carbon losses and a declining trend in the Amazon carbon sink over the past 25 years (net sink rate of -0.018 Mg C ha-1 year-1 or -0.22 Mg C ha-1 per decade) which corresponds well with long-term forest monitoring data (net sink rate of -0.016 Mg C ha-1 year-1). We show that this trend is entirely attributable to drought-induced forest mortality during extreme years. The simulations show a threshold-like behaviour between drought intensity and biomass loss, which is due to xylem vulnerability, indicating the potentially high sensitivity of Amazon forests to extreme drought. Further increases in the severity and frequency of droughts might thus lead to greater carbon release and tree mortality than previously assumed.

Fuzzy fractional shallow water wave equations: analysis, convergence of solutions, and comparative study with depth as triangular fuzzy number

Abstract We explore the integration of fuzzy fractional calculus into the modeling framework, recognizing its significance in capturing the inherent uncertainties and complexities present in Shallow Water Wave (ffSWW) dynamics. By incorporating fuzzy fractional calculus, we aim to enhance the accuracy and robustness of ffSWW equations, particularly in representing vague or imprecise parameters such as seabed topography, initial wave conditions, and material properties. In this article, we consider the time derivative as a fractional order instead of the traditional integer order, which allows us to interpret the behavior of the solution for different orders. Further, the sea depth has been considered as a Triangular Fuzzy Number (TFN). We employ the Homotopy Perturbation Transform Method (HPTM) to obtain the solution of ffSWW equations. The convergence of the obtained series solutions has been investigated theoretically and numerically. Also, the acquired results using the current method are validated through the comparison with pre-existing findings concerning integer order. Furthermore, simulation results for various fractional orders, as well as fuzzy lower and upper solutions of depth-averaged velocity and water surface elevation, are provided for triangular fuzzy numbers.

Small-sample Bayesian error estimation for ergodic, chaotic systems of ordinary differential equations

The discretization of chaotic systems introduces statistical errors in addition to discretization errors into the estimation of quantities of interest. In order to efficiently arrive at estimates of quantities of interest, these two forms of error should be balanced; however, simulations are run without knowledge of the true/asymptotic outputs of interest or their error behaviors. In this work, we develop a framework for error modeling and identification using small-sample Bayesian inference that allows approximation of the optimal balance between sampling time and discretization precision without the computation of high-cost libraries of reference solutions. The result enables the possibility of running chaotic and turbulent simulations in a way that minimizes the total error between sampling and discretization without prior knowledge of the error behavior of the system.

Development of digital literacy model for college students in Minzu colleges and universities in Southwest China

Constructing a digital literacy model for college students in Minzu Colleges and Universities in Southwest China is crucial for advancing the implementation of the "Action Plan for Improving National Digital Literacy and Skills" and effectively conducting digital literacy education. By researching digital literacy models from abroad and employing bibliometric methods, an initial conceptual framework for the digital literacy model for college students was developed. A mixed-methods research approach, combining quantitative and qualitative research, was used. Confirmatory Factor Analysis (CFA) was employed to test the validity and reliability of the initial model, which was then refined based on CFA results and expert feedback. In-depth interviews with experts were conducted to further optimize the model. The resulting digital literacy model for college students in Minzu Colleges and Universities in Southwest China not only holds significant implications for digital literacy education in this region but also provides a reference for constructing digital literacy models for other educational stages and in different regions. This model-building approach can serve as a valuable reference for similar research and practice.

The Influence of Group Identification and Morality in Support for Defunding the Police

There is very little research regarding what factors drive support for defunding the police. Using the theoretical framework of the Group Engagement Model and the Moral Foundations Theory, this study explores if and how race, group identification, and morality shape support for defunding the police. Results indicated that increased superordinate identification and binding moral foundations reduced support for defunding the police among White respondents. For Black respondents, increased subordinate identification explains their increased support for defunding the police. Attachment to one’s racial group and concerns for its welfare coupled with moral intuitions driven by care and fairness toward victims of police brutality who are disproportionately Black explains why Blacks Americans began and support calls to defund the police.