Modeling Approach Research Articles

Investigating the Origin of Automatic Rhodopsin Modeling Outliers Using the Microbial Gloeobacter Rhodopsin as Testbed.

The automatic rhodopsin modeling (ARM) approach is a computational workflow devised for the automatic buildup of hybrid quantum mechanics/molecular mechanics (QM/MM) models of wild-type rhodopsins and mutants, with the purpose of establishing trends in their photophysical and photochemical properties. Despite the success of ARM in accurately describing the visible light absorption maxima of many rhodopsins, for a few cases, called outliers, it might lead to large deviations with respect to experiments. Applying ARM toGloeobacter rhodopsin (GR), a microbial rhodopsin with important applications in optogenetics, we analyze the origin of such outliers in the absorption energies obtained for GR wild-type and mutants at neutral pH, with a total root-mean-square deviation (RMSD) of 0.42 eV with respect to the experimental GR excitation energies. Having discussed the importance and the uncertainty of one particular amino-acid pKa, namely histidine at position 87, we propose and test several modifications to the standard ARM protocol: (i) improved pKa predictions along with the consideration of several protonation microstates, (ii) attenuation of the opsin electrostatic potential at short-range, (iii) substitution of the state-average complete active space (CAS) electronic structure method by its state-specific approach, and (iv) complete replacement of CAS with mixed-reference spin-flip time-dependent density functional theory (MRSF-TDDFT). The best RMSD result we obtain is 0.2 eV combining the protonation of H87 and using MRSF/CAMH-B3LYP.

Prediction of manhole-cover skid-resistance performance using decision trees and regression analysis

This study conducts skid-resistance coefficient tests on 3,600 manhole covers and collects data on various influencing factors, such as surface conditions, pattern types, starting zones, turning zones, usage duration, wheel track locations, and surrounding traffic volume. A preliminary classification analysis of the data is performed using decision trees to identify key factors affecting manhole-cover skid resistance. Subsequently, a multivariate nonlinear regression model is applied to examine the impact of these factors on the target variables. By integrating decision trees and nonlinear regression in a hybrid modeling approach, the study aims to reveal complex patterns in the data and generate reliable predictive results. The decision-tree analysis using non-continuous variables reveals that manhole covers located at turns and starting zones, and those with worn surfaces are more likely to fail to meet safety standards for skid resistance. The multivariate nonlinear regression model, which compares the predicted values against the actual British Pendulum Number (BPN) test results, achieves an accuracy of 82.9%.

Reconciling Conflicting Data Curation Actions: Transparency Through Argumentation

We propose a new approach for modeling and reconciling conflicting data cleaning actions. Such conflicts arise naturally in collaborative data curation settings where multiple experts work independently and then aim to put their efforts together to improve and accelerate data cleaning. The key idea of our approach is to model conflicting updates as a formal argumentation framework (AF). Such argumentation frameworks can be automatically analyzed and solved by translating them to a logic program PAF whose declarative semantics yield a transparent solution with many desirable properties, e.g., uncontroversial updates are accepted, unjustified ones are rejected, and the remaining ambiguities are exposed and presented to users for further analysis. After motivating the problem, we introduce our approach and illustrate it with a detailed running example introducing both well-founded and stable semantics to help understand the AF solutions. We have begun to develop open source tools and Jupyter notebooks that demonstrate the practicality of our approach. In future work we plan to develop a toolkit for conflict resolution that can be used in conjunction with OpenRefine, a popular interactive data cleaning tool.

Integrated Simulation of Groundwater Flow and Nitrate Transport in an Alluvial Aquifer Using MODFLOW and MT3D: Insights into Pollution Dynamics and Management Strategies

This study employs an integrated numerical modeling approach using MODFLOW and MT3D to simulate groundwater flow and nitrate transport in the alluvial aquifer of Hennaya plain. The groundwater flow model was calibrated and validated against observed hydraulic heads, showing excellent agreement in both steady-state and transient conditions, with a correlation coefficients (R2) of 0.99 and 0.987, respectively. Meticulous calibration yielded adjusted hydraulic conductivity values between 10−1 and 10−11 m/s, with effective porosity ranging from 0.03 to 0.34 and total porosity values varying from 0.29 to 0.38 across the aquifer. Water budget analysis revealed that the aquifer’s primary recharge occurs from the southern side. Nitrate transport modeling indicated that advection is the dominant process, with contaminants migration predominantly occurring from south to north, following the groundwater flow direction. Pollution levels were found to decrease gradually with distance from sources, confirming agricultural activities and sewage disposal as primary contributors to nitrate contamination. Predictive scenarios over a 40-year period explored various management strategies, which suggest that maintaining current nitrogen input rates will lead to continued increases in nitrate pollution, while a 50% reduction in agricultural inputs could significantly improve groundwater quality. However, even with substantial reductions, nitrate concentrations are not expected to reach levels safe for drinking within the simulation timeframe. This study underscores the need for immediate and sustained action to address nitrate pollution in the Hennaya Plain aquifer, emphasizing the importance of stringent nitrogen management practices, particularly in the agricultural sector.

A statistical modelling approach for determining the cause of reported respiratory syndromes from internet-based participatory surveillance when influenza virus and SARS-CoV-2 are co-circulating.

Symptom-only case definitions are insufficient to discriminate COVID-like illness from acute respiratory infection (ARI) or influenza-like illness (ILI), due to the overlap in case definitions. Our objective was to develop a statistical method that does not rely on case definitions to determine the contribution of influenza virus and SARS-CoV-2 to the ARI burden during periods when both viruses are circulating. Data sources used for testing the approach were weekly ARI syndrome reports from the Infectieradar participatory syndromic surveillance system during the analysis period (the first 25 weeks of 2022, in which SARS-CoV-2 and influenza virus co-circulated in the Netherlands) and data from virologically tested ARI (including ILI) patients who consulted a general practitioner in the same period. Estimation of the proportions of ARI attributable to influenza virus, SARS-CoV-2, or another cause was framed as an inference problem, through which all data sources are combined within a Bayesian framework to infer the weekly numbers of ARI reports attributable to each cause. Posterior distributions for the attribution proportions were obtained using Markov Chain Monte-Carlo methods. Application of the approach to the example data sources indicated that, of the total ARI reports (total of 11,312; weekly mean of 452) during the analysis period, the model attributed 35.4% (95% CrI: 29.2-40.0%) and 27.0% (95% CrI: 19.3-35.2%) to influenza virus and SARS-CoV-2, respectively. The proposed statistical model allows the attribution of respiratory syndrome reports from participatory surveillance to either influenza virus or SARS-CoV-2 infection in periods when both viruses are circulating, but comparability of the participatory surveillance and virologically tested populations is important. Portability for use by other countries with established participatory respiratory surveillance systems is an asset.

A minimal physiologically-based pharmacokinetic modeling platform to predict intratumor exposure and receptor occupancy of an anti-LAG-3 monoclonal antibody.

In oncology drug development, measuring drug concentrations at the tumor site and at the targeted receptor remains an ongoing challenge. Positron emission tomography (PET)-imaging is a promising noninvasive method to quantify intratumor exposure of a radiolabeled drug (biodistribution data) and target saturation by treatment doses invivo. Here, we present the development and application of a minimal physiologically-based pharmacokinetic (mPBPK) modeling approach to integrate biodistribution data in a quantitative platform to characterize and predict intratumor exposure and receptor occupancy (RO) of BI 754111, an IgG-based anti-lymphocyte-activation gene 3 (LAG-3) monoclonal antibody (mAb). Specifically, calibration and qualification of the predictions were performed using 89Zr-labeled BI 754111 biodistribution data, that is, PET-derived intratumor drug concentration data, tumor-to-plasma ratios, and data from Patlak analyses. The model predictions were refined iteratively by the inclusion of additional biological processes into the model structure and the use of sensitivity analyses to assess the impact of model assumptions and parameter uncertainty on the predictions and model robustness. The developed mPBPK model allowed an adequate description of observed tumor radioactivity concentrations and tumor-to-plasma ratios leading to subsequent adequate prediction of LAG-3 RO at different dose levels. In the future, the developed model could be used as a platform for the prediction and analysis of biodistribution data for other mAbs and may ultimately support dose optimization by identifying dosages resulting in saturated RO.

Seeing through the gray box: an integrated approach to physiological modeling of phytoplankton stoichiometry

The ‘black boxes’ of ecological stoichiometry, planktonic microbes, have long been recognized to have considerable effects on global biogeochemical cycles. Significant progress has been made in studying these effects and expanding our understanding of microbial stoichiometry. However, the ‘black box’ has not been completely cracked open; there remain gaps in our knowledge of the fate of elements within the phytoplankton cell, and the effect of external processes on nutrient fluxes through their metabolism and into macromolecules and biomass - the eponymous ‘gray box’. In this review paper, we describe the development of an integrative modeling approach that involves a stoichiometrically explicit model of Macromolecular Allocation and Genome-scale Metabolic Analysis (MAGMA) to gain insights into the intra- and extracellular fluxes of nutrients using the cyanobacterium Parasynechococcus marenigrum WH8102 as a target model organism. We then describe an example of the genome-scale resources for P. marenigrum that can be used to build such an integrated modeling tool to see through the gray box of phytoplankton stoichiometry and improve our understanding of the effects of resource supplies and other environmental drivers, especially temperature, on C:N:P demand, acquisition, and allocation at the cellular level.

Patent Trend Analysis in Construction Materials for Carbon Emission Reduction: A Topic Modeling Approach

The increasing concern over carbon emissions and climate change has led to great interest in research and innovation for construction materials to reduce environmental impact. This study employs topic modelling techniques to analyze patent trends in the field of advanced construction materials focusing on carbon emission reduction. The methodology involves the application of topic modelling algorithms, which is Latent Dirichlet Allocation (LDA), to categorize the latent thematic clusters within the patent corpus and to cluster patent technologies. The resulting topics identify key areas of technological advancement, providing insights into emerging trends and innovations. In addition, this study explores the interconnectivity between topics that will help develop solutions for carbon emission reduction. The analysis highlights the advanced materials technologies and their development to achieve sustainable solutions. This patent trend analysis can guide researchers and industry professionals and contribute to the understanding of global efforts to reduce carbon emissions in advanced construction materials.

Retraction notice: Investigating sustainable development for the COVID-19 vaccine supply chain: a structural equation modelling approach

Retraction notice: Investigating sustainable development for the COVID-19 vaccine supply chain: a structural equation modelling approach

Understanding the Underlying Knowledge Structure in Business Ethics, Governance and Sustainability Research

Given their significant role, this study aims to comprehensively understand the linkages between business ethics, governance, and Sustainability research. The study is focused on knowing the knowledge structure that connects these concepts, identifying the critical areas of focus, and tracking their evolution over time. A comprehensive analysis was conducted on a data set covering 2003 to 2024. Two worldwide repute databases, namely, Scopus and Web of Science (WoS), were used to search scientific publications (Sharma et al. 2023; Sharma et al. 2024). The selection process adhered to the inclusion and exclusion criteria outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The Structural Topic Modeling (STM) approach was employed to group related data segments, revealing underlying patterns and significant content. Specific latent topics such as Experiential learning, corporate governance structure and behavior, Sustainability through energy policy, and Environmental protection acts were identified, which depicted the underlying knowledge areas in this field of study. Future research in business ethics and corporate Sustainability must pay attention to the emerging contextual issues in business contexts and make the future study of business ethics and corporate sustainability move beyond traditional totems and taboos in social ventures, family businesses, start-ups, and SMEs.

Analysing heterogeneity in factors affecting adoption of ride-hailing services: a stepwise LCCA-MCDM modelling approach

AbstractThe study investigates the latent heterogeneity in travel behaviour among urban travellers, including ride-hailing service (RHS) users and non-users, by incorporating attitudes so as to reinforce conventional user-segmentation approaches. Simultaneously, prioritisation of ride-hailing specific attributes was carried out to assess how RHS will operate in a sustainable way. The study initially examines latent heterogeneity in travellers through a Latent Class Cluster Analysis (LCCA) model. Subsequently, it prioritises key RHS-specific attributes for each cluster using three established Multi Criteria Decision Making (MCDM) techniques. Three clusters were identified based on individuals’ attitudes and covariates (socio-demographics, travel habits, and built environment attributes). The largest cluster is the Tech-savvy ride-hailing-ready individuals (48%) with higher technological literacy, showing maximum acceptance towards ride-hailing. The second largest cluster comprises the Traditional active-mobility individuals (28%) who display the least proclivity towards RHS, probably due to their technological inhibition coupled with greater attachment to traditional travel alternatives. Lastly, the PV-loving multimodal individuals (24%) are primarily vehicle owners but prefer RHS for occasional trips. The final ranking obtained from the analysis has revealed that travel time, reliability, and flexibility are the motivators, while travel cost and waiting time are the deterrents, as perceived by the users, that influence RHS in the Indian context.

Development of a Generalized Partition Curve Model for Hydrocyclones

ABSTRACT Partition curves associated with hydrocyclone classification processes have been known to deviate from the typical S-shape by exhibiting asymmetric and non-monotonic behaviors, making their mathematical modeling challenging. In addition to the well-documented phenomenon of fish-hooks at the finer end, the partition curves can also sometimes exhibit fat-tailed behavior at the coarser end of the size spectrum, which has not been extensively highlighted in the literature. This study aimed to develop a parametric model for the partition curves which can systematically and effectively accommodate both these irregularities at the coarser and finer ends. Instead of adopting a piece-wise modeling approach for the task, a continuous model was successfully developed in the form of the addition of three component functions, each affecting different regions and features of the partition curve with a total of 7 fitting parameters. Two of the component functions were modified versions of the cumulative density functions of the Weibull distribution while the third was the modified solution of a single hyperbolic branch having a horizontal and a slant asymptote to accommodate the observed fat-tailed behavior. The model exhibited remarkable fitting performance for both experimentally generated and literature data. An alternative method of defining the separation cut size has also been proposed in this study to overcome the limitations of the classical concept of the d50. It was also shown how the developed model can be utilized for quantitative analysis of the critical aspects of the partition curve such as the cut size, bypass, and the dip point.

Phosphorus transport in a hotter and drier climate: in-channel release of legacy phosphorus during summer low-flow conditions

Abstract. “Legacy phosphorus” is the historical accumulation of phosphorus (P) in soils and sediments due to past human inputs. River networks represent a potential sink and/or source of legacy P, with many in-channel processes potentially governing the storage and mobilization of P over time. The objective of this study was to evaluate the potential contribution of the in-channel release of legacy P to bioavailable P transport in streams during summer low-flow conditions across a land use gradient in Minnesota, USA. We addressed this objective through the synthesis of (1) water quality and streamflow (Q) data collected for 143 gauged watersheds across the state of Minnesota between 2007–2021 (22 750 total samples); (2) water quality data from 33 additional ditch, stream, and river sites in Minnesota sampled under low-flow conditions in the summer of 2014; and (3) water quality data collected from tile drainage outlets for 10 monitored farm fields between 2011–2021. We used geospatial data and a random forest modeling approach to identify possible drivers of bioavailable P concentrations during summer low flows for gauged watersheds. During low flows in late summer, between one-third to one-half of the gauged watersheds we studied exhibited soluble reactive phosphorus (SRP) concentrations that were above previously identified thresholds for eutrophication of 0.02–0.04 mg L−1. For many of these watersheds, stream SRP concentrations in late summer were above those observed in tile drainage outlets. Elevated SRP concentrations during late-summer low flows weakened concentration–discharge relationships that would otherwise appear to indicate more strongly mobilizing SRP–Q responses across other seasons and flow conditions. While wastewater discharge likely contributed to elevated P concentrations for watersheds with high densities of treatment plants, many watersheds did not have substantial wastewater impacts. The most important variables for predicting bioavailable P concentrations during late-summer low-flow conditions in a random forest model were land use in riparian areas (particularly crop cover); soil characteristics including soil erodibility, soil permeability, and soil clay content; agricultural intensity (reflected via higher pesticide use, higher phosphorus uptake by crops, and higher fertilizer application rates); watershed precipitation; and stream temperature. These findings suggest that, for stream and river sites heavily impacted by past and current P inputs associated with agriculture and urbanization, biogeochemical processes mediated by climate and geology can result in the release of legacy P from in-channel stores during late-summer low-flow conditions. As summers become hotter and, at times, drier – which are predicted changes in this region – conditions for the release of legacy P stored in stream and river channels will likely become more prolonged and/or more acute, increasing eutrophication risk.

Analyzing the Effect of Factors on Individuals’ Subjective Well-Being with Quantile Bayesian Structural Equation Modeling Approach

This study investigates factors influencing individuals’ levels of subjective well-being by examining fundamental variables, called life domains, such as satisfaction with health, education, marriage, housing district, work life, social life, relationships, and public services. The aim is to understand how these domains affect various quantile values of subjective well-being. To achieve this, the Life Satisfaction Survey dataset for 2020, obtained from the Turkish Statistical Institute, was utilized, and Bayesian Quantile Structural Equation Modeling and Bayesian Structural Equation Modeling methods were applied. The empirical study indicates that all life domains were found to have a positive impact on subjective well-being, except for housing-district satisfaction and education satisfaction. In particular, for individuals with low subjective well-being, satisfaction with work life, relationships, and public services has a stronger effect on subjective well-being compared to those in higher quantiles. Conversely, marriage satisfaction has a stronger effect on subjective well-being among individuals in the higher quantiles. This study’s unique contribution is that examining subjective well-being according to quantile values provides detailed information on how the factors influencing individuals’ subjective well-being vary across different levels.

An enthalpy-based model for the physics of ice-crystal icing

Ice-crystal icing (ICI) in aircraft engines is a major threat to flight safety. Due to the complex thermodynamic and phase-change conditions involved in ICI, rigorous modelling of the accretion process remains limited. The present study proposes a novel modelling approach based on the physically observed mixed-phase nature of the accretion layers. The mathematical model, which is derived from the enthalpy change after accretion (the enthalpy model), is compared with an existing pure-phase layer model (the three-layer model). Scaling laws and asymptotic solutions are developed for both models. The onset of ice accretion, the icing layer thickness and solid ice fraction within the layer are determined by a set of non-dimensional parameters including the Péclet number, the Stefan number, the Biot number, the melt ratio and the evaporative rate. Thresholds for freezing and non-freezing conditions are developed. The asymptotic solutions present good agreement with numerical solutions at low Péclet numbers. Both the asymptotic and numerical solutions show that, when compared with the three-layer model, the enthalpy model presents a thicker icing layer and a thicker water layer above the substrate due to mixed-phased features and modified Stefan conditions. Modelling in terms of the enthalpy poses significant advantages in the development of numerical methods to complex three-dimensional geometrical and flow configurations. These results improve understanding of the accretion process and provide a novel, rigorous mathematical framework for accurate modelling of ICI.

Advancements and challenges in Arabic question answering systems: a comprehensive survey

This paper explores the development and challenges of Arabic Question Answering Systems (QAS), with a particular focus on addressing the linguistic nuances and data scarcity unique to Arabic. It categorizes Arabic QAS by domain (open or closed), question type (factoid, causal, complex), and modeling approach (rule-based, machine learning, deep learning). Key recent systems are reviewed, including their methodologies, datasets, and performance metrics, highlighting the growing role of deep learning techniques. State-of-the-art NLP methods, especially transformers, have greatly advanced QAS, although these gains are more substantial for English. Despite recent progress, Arabic QAS research remains limited by resource constraints, including a reliance on translated datasets and the absence of comprehensive Arabic benchmarks.

Reliability analysis of phased mission systems based on generalized stochastic Petri nets under mixed uncertainty

Phased mission systems (PMS) are widely applied across diverse industrial systems. Technological advancements in these systems have markedly improved their performance. However, fault characteristics such as uncertainty and common cause failure (CCF) frequently emerge during failures, which significantly raises new challenges in reliability analysis. A novel system reliability analysis framework is proposed to address the mixed uncertainty and CCF in PMS. Specifically, a hybrid modeling approach that combines dynamic fault trees and generalized stochastic Petri nets (GSPN) is employed to accurately model PMS containing CCF. This approach integrates predicates and assertions from GSPN to define four reusable sub-Petri net models at different hierarchical levels, including module, phase, CCF, and overall mission levels. Furthermore, interval theory is incorporated into the framework to manage imprecise component lifetime data. For uncertainty quantification, a synergistic approach combining genetic algorithm and Monte Carlo simulation is utilized. Additionally, a simplified approach is introduced to reduce the complexity of computing the reliability boundary values of PMS. Finally, the proposed method is applied to conduct reliability analysis of a phased altitude and orbit control system. The analysis results validate its effectiveness and applicability in addressing the mixed uncertainty and CCF of PMS.

Cultural intelligence and cooperation in the construction industry: the mediating role of trust

Purpose This study aims to investigate the mediating role of trust in the relationship between cultural intelligence (CQ) and cooperation among construction professionals. Furthermore, this study assesses perceived differences in CQ, trust and cooperation between individuals with and without experience working with foreigners in the construction industry. Design/methodology/approach Data were gathered from a cross-sectional survey of 408 engineers in Myanmar’s construction industry. A confirmatory factor analysis validated structural equation modeling approach was used to address research hypotheses, and an independent samples t-test was performed to identify the perceived differences between two categories of respondents. Findings The structural equation modeling results identified CQ as a positive direct predictor of cooperation, affect-based trust and cognition-based trust. Both affect-based trust and cognition-based trust directly and positively influenced cooperation. The relationship between CQ and cooperation was partially mediated by affect-based trust and cognition-based trust. The findings of the independent samples t-test revealed that construction employees with prior experience working with foreigners tend to exhibit a higher level of CQ, trust and cooperation than their counterparts. Originality/value The present study added the mediating role of trust in CQ and cooperation linkage, an area that has received limited attention in the literature.

Factors predicting the mathematics anxiety of adolescents: a structural equation modeling approach

IntroductionMathematics anxiety (MA) is a distinct negative emotional state or trait that individuals experience when confronted with mathematical problems in everyday life and academic contexts. This study aims to identify the key predictors of MA among secondary-level students in Bangladesh.MethodsUtilizing a quantitative cross-sectional research design, data were collected from 486 students across 89 institutions. Later, the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).ResultsThe findings revealed that math related negative past experiences (β = 0.241, t = 4.914, p < 0.001) and a perceived lack of teacher support (β = 0.234, t = 5.440, p < 0.001) significantly contribute to students’ low self-efficacy in mathematics. This low self-efficacy is further influenced by negative attitudes and test anxiety, ultimately leading to increased MA (β = 0.694, t = 22.695, p < 0.001). Additionally, cognitive challenges, particularly working memory difficulties, directly affect MA (β = 0.110, t = 2.659, p = 0.008). The study also found that negative attitudes (β = 0.347, t = 9.063, p < 0.001) and test anxiety (β = 0.251, t = 5.913, p < 0.001) independently exacerbate MA. Moreover, a lack of motivation in learning mathematics is directly influenced by this elevated level of MA (β = 0.384, t = 9.939, p < 0.001).DiscussionTaken together, the study proposes several key recommendations and policy implications to inform the development of synchronized policies by educational authorities aimed at combatting, reducing MA among secondary-level students in Bangladesh and similar contexts.

Emergence of temporal noise hierarchy in co-regulated genes of multi-output feed-forward loop

Natural variations in gene expression, called noise, are fundamental to biological systems. The expression noise can be beneficial or detrimental to cellular functions. While the impact of noise on individual genes is well-established, our understanding of how noise behaves when multiple genes are co-expressed by shared regulatory elements within transcription networks remains elusive. This lack of understanding extends to how the architecture and regulatory features of these networks influence noise. To address this gap, we study the multi-output feed-forward loop motif. The motif is prevalent in bacteria and yeast and influences co-expression of multiple genes by shared transcription factors (TFs). Focusing on a two-output variant of the motif, the present study explores the interplay between its architecture, co-expression (symmetric and asymmetric) patterns of the two genes, and the associated noise dynamics. We employ a stochastic modeling approach to investigate how the binding affinities of the TFs influence symmetric and asymmetric expression patterns and the resulting noise dynamics in the co-expressed genes. This knowledge could guide the development of strategies for manipulating gene expression patterns through targeted modulation of TF binding affinities.