Choice Of Model Research Articles

Sensitivity of global hydrological models to potential evapotranspiration estimation methods in the Senegal River Basin (West Africa)

Study regionSenegal River Basin in West Africa Study focusThis paper aims to evaluate the sensitivity of global hydrological models to potential evapotranspiration (PET) methods in the Senegal River Basin. Potential evapotranspiration is estimated using 21 methods and its influence on the performance of three GR models (GR4J, GR5J and GR6J) is investigated in five catchments. The data used are mean rainfall, discharge and observed and calculated PET over the period 1984-1995. PET is calculated based on observed climate data and those from NASA POWER reanalysis data. The methodology consists in: (i) comparing the consistency of reanalysis data with respect to the observed PET, (ii) assessing the robustness of GR models and their sensitivity to different PET estimation methods. The evaluation criteria used to assess the performance of the hydrological model are KGE and PBIAS. New hydrological insights for the regionGood consistency is obtained between PET calculated with observed and reanalysis data. The GR4J and GR5J are more efficient to simulate flows in the Senegal River sub-basins. The aerodynamic PET methods perform well with the three hydrological models. However, in this context where data are scarce, temperature methods such as Droogers and Allen are a good choice for hydrological modeling. The results also show that the GR models have the ability to adapt to poorly estimated PET.

Maintaining the validity of inference from linear mixed models in stepped-wedge cluster randomized trials under misspecified random-effects structures.

Linear mixed models are commonly used in analyzing stepped-wedge cluster randomized trials. A key consideration for analyzing a stepped-wedge cluster randomized trial is accounting for the potentially complex correlation structure, which can be achieved by specifying random-effects. The simplest random effects structure is random intercept but more complex structures such as random cluster-by-period, discrete-time decay, and more recently, the random intervention structure, have been proposed. Specifying appropriate random effects in practice can be challenging: assuming more complex correlation structures may be reasonable but they are vulnerable to computational challenges. To circumvent these challenges, robust variance estimators may be applied to linear mixed models to provide consistent estimators of standard errors of fixed effect parameters in the presence of random-effects misspecification. However, there has been no empirical investigation of robust variance estimators for stepped-wedge cluster randomized trials. In this article, we review six robust variance estimators (both standard and small-sample bias-corrected robust variance estimators) that are available for linear mixed models in R, and then describe a comprehensive simulation study to examine the performance of these robust variance estimators for stepped-wedge cluster randomized trials with a continuous outcome under different data generators. For each data generator, we investigate whether the use of a robust variance estimator with either the random intercept model or the random cluster-by-period model is sufficient to provide valid statistical inference for fixed effect parameters, when these working models are subject to random-effect misspecification. Our results indicate that the random intercept and random cluster-by-period models with robust variance estimators performed adequately. The CR3 robust variance estimator (approximate jackknife) estimator, coupled with the number of clusters minus two degrees of freedom correction, consistently gave the best coverage results, but could be slightly conservative when the number of clusters was below 16. We summarize the implications of our results for the linear mixed model analysis of stepped-wedge cluster randomized trials and offer some practical recommendations on the choice of the analytic model.

A model-based boosting approach to risk factors for physical intimate partner violence against women and girls in Mexico

AbstractThe goal of this study was to identify and describe the extent to which a comprehensive set of risk factors from the ecological model are associated with physical intimate partner violence (IPV) victimization in Mexico. To achieve this goal, a structured additive probit model is applied to a dataset of 35,000 observations and 42 theoretical correlates from 10 data sources. Due to the model's high dimensionality, the boosting algorithm is used for estimating and simultaneously performing variable selection and model choice. The findings indicate that age at sexual initiation and marriage, sexual and professional autonomy, social connectedness, household overcrowding, housework division, women's political participation, and geographical space are associated with physical IPV. The findings provide evidence of risk factors that were previously unknown in Mexico or were solely based on theoretical grounds without empirical testing. Specifically, this paper makes three key contributions. First, by examining the individual and relationship levels, it was possible to identify high-risk population subgroups that are often overlooked, such as women who experienced sexual initiation during childhood and women living in overcrowded families. Second, the inclusion of community factors enabled the identification of the importance of promoting women's political participation. Finally, the introduction of several emerging indicators allowed to examine the experiences faced by women in various aspects of life, such as decision-making power, social networks, and the division of housework.

Decoding cognitive health using machine learning: A comprehensive evaluation for diagnosis of significant memory concern

AbstractThe timely identification of significant memory concern (SMC) is crucial for proactive cognitive health management, especially in an aging population. Detecting SMC early enables timely intervention and personalized care, potentially slowing cognitive disorder progression. This study presents a state‐of‐the‐art review followed by a comprehensive evaluation of machine learning models within the randomized neural networks (RNNs) and hyperplane‐based classifiers (HbCs) family to investigate SMC diagnosis thoroughly. Utilizing the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset, 111 individuals with SMC and 111 healthy older adults are analyzed based on T1W magnetic resonance imaging (MRI) scans, extracting rich features. This analysis is based on baseline structural MRI (sMRI) scans, extracting rich features from gray matter (GM), white matter (WM), Jacobian determinant (JD), and cortical thickness (CT) measurements. In RNNs, deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) emerge as the best classifiers in terms of performance metrics in the identification of SMC. In HbCs, Kernelized pinball general twin support vector machine (Pin‐GTSVM‐K) excels in CT and WM features, whereas Linear Pin‐GTSVM (Pin‐GTSVM‐L) and Linear intuitionistic fuzzy TSVM (IFTSVM‐L) performs well in the JD and GM features sets, respectively. This comprehensive evaluation emphasizes the critical role of feature selection, feature based‐interpretability and model choice in attaining an effective classifier for SMC diagnosis. The inclusion of statistical analyses further reinforces the credibility of the results, affirming the rigor of this analysis. The performance measures exhibit the suitability of this framework in aiding researchers with the automated and accurate assessment of SMC. The source codes of the algorithms and datasets used in this study are available at https://github.com/mtanveer1/SMC.This article is categorized under: Technologies > Classification Technologies > Machine Learning Application Areas > Health Care

The Importance of Including Variable Exposure Concentrations When Assessing Toxicity of Sediment-Associated Pharmaceuticals to an Amphipod.

Pharmaceuticals have been classified as an environmental concern due to their increasing consumption globally and potential environmental impact. We examined the toxicity of sediment-associated diclofenac and citalopram administered as both single compounds and in a mixture to the sediment-living amphipod Corophium volutator. This laboratory-based study addressed the following research questions: (1) What is the toxicity of sediment-associated diclofenac and citalopram to C. volutator? (2) Can the mixture effect be described with either of the two mixture models: concentration addition (CA) or independent action (IA)? (3) What is the importance of the choice of (i) exposure measure (start concentration, time-weighted average [TWA], full exposure profile) and (ii) effect model (concentration-response vs. the toxicokinetic-toxicodynamic model general unified threshold model for survival in its reduced form [GUTS-RED]) for the derived effect concentration values? Diclofenac was more toxic than citalopram to C. volutator as a single compound (10-day exposure). Diclofenac exposure to C. volutator provided median lethal concentrations (LC50s) within the same range (11 µg g-1 dry wt sediment) using concentration-response based on TWA and both GUTS-RED models. However, concentration-response based on measured start concentrations provided an approximately 90% higher LC50 (21.6 ± 2.0 µg g-1 dry wt sediment). For citalopram, concentration-response parameters were similar regardless of model or concentration used (LC50 85-97 µg g-1 dry wt sediment), however, GUTS-RED with the assumption of individual tolerance resulted in a lower LC50 (64.9 [55.3-74.8] µg g-1 dry wt sediment). The mixture of diclofenac and citalopram followed the CA quite closely, whereas the result was synergistic when using the IA prediction. In summary, concentration-response based on TWA and GUTS-RED provided similar and reasonably good fits compared to the data set. The implications are that GUTS-RED will provide a more flexible model, which, in principle, can extend beyond the experimental period and make predictions based on variable exposure profiles (toxicity at different time frames and at different variable exposure scenarios) compared to concentration-response, which provides contaminant toxicity at one point in time. Environ Toxicol Chem 2024;00:1-11. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Parametric Study of the Deep Excavation Performance of Underground Pumping Station Based on Numerical Method

Environmental responses to deep excavations are combined results of numerous factors. The effects of some factors are relatively straightforward and can be considered carefully during the design. On the other hand, more features impact excavation-induced performances indirectly, making their influences difficult to be clearly understood. Unfortunately, the complexity and non-repeatability of practical projects make it impossible to thoroughly understand these issues through realistic deep excavation projects. Therefore, parametric studies based on repeatable laboratory and numerical tests are desired to investigate these issues further. This work examines the influence of several key features on excavation-induced displacements through a series of 3D numerical tests. The study includes the choice of soil constitutive models, the modeling method of the soil–wall interface, and the influences of various key soil parameters. The comparison shows that the MCC model can yield a displacement field similar to the HSS model, while its soil movement is greatly improved compared to the MC model. Both the soil–wall interface properties and soil parameters impact the excavation-induced displacement to a large extent. In addition, the influence mechanisms of these parameters are analyzed, and practical suggestions are given. The findings of this paper are expected to provide practical references to the design and construction of future deep excavation projects.

Using automated machine learning for the upscaling of gross primary productivity

Abstract. Estimating gross primary productivity (GPP) over space and time is fundamental for understanding the response of the terrestrial biosphere to climate change. Eddy covariance flux towers provide in situ estimates of GPP at the ecosystem scale, but their sparse geographical distribution limits larger-scale inference. Machine learning (ML) techniques have been used to address this problem by extrapolating local GPP measurements over space using satellite remote sensing data. However, the accuracy of the regression model can be affected by uncertainties introduced by model selection, parameterization, and choice of explanatory features, among others. Recent advances in automated ML (AutoML) provide a novel automated way to select and synthesize different ML models. In this work, we explore the potential of AutoML by training three major AutoML frameworks on eddy covariance measurements of GPP at 243 globally distributed sites. We compared their ability to predict GPP and its spatial and temporal variability based on different sets of remote sensing explanatory variables. Explanatory variables from only Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data and photosynthetically active radiation explained over 70 % of the monthly variability in GPP, while satellite-derived proxies for canopy structure, photosynthetic activity, environmental stressors, and meteorological variables from reanalysis (ERA5-Land) further improved the frameworks' predictive ability. We found that the AutoML framework Auto-sklearn consistently outperformed other AutoML frameworks as well as a classical random forest regressor in predicting GPP but with small performance differences, reaching an r2 of up to 0.75. We deployed the best-performing framework to generate global wall-to-wall maps highlighting GPP patterns in good agreement with satellite-derived reference data. This research benchmarks the application of AutoML in GPP estimation and assesses its potential and limitations in quantifying global photosynthetic activity.

Role modelling to support careers in general practice: a realist review protocol.

Role models encountered during undergraduate training play an important part in shaping future doctors. They can act as powerful attractants towards, and deterrents away from a career in general practice. Many general practitioner (GP) educators who act as role models are burnt-out and wish to leave the profession which may limit their ability to influence students positively, with consequent detrimental impact on recruitment to the specialty. A realist review will be undertaken, aiming to explore how, why and for whom role modelling in undergraduate medical education can support medical students towards careers in general practice. The realist review will follow Pawson's five steps, including: locating existing theories, searching for evidence, article selection, data extraction, synthesising evidence and drawing conclusions and will explore literature published in English language between 2013 and 2024. An initial explanatory framework (initial programme theory, IPT) will be developed guided by a stakeholder panel including medical undergraduates, GPs and patient/public representatives. Searches will be developed and conducted in electronic databases and grey literature. Studies will be included if they explore the relationship between GP role modelling and undergraduate career choice and relevant data will be extracted. Findings will refine the initial programme theory, unveiling key contexts, mechanisms and outcomes which influence role modelling in undergraduate GP medical education and support or deter students from careers in general practice. These findings will support recommendations and interventions to facilitate positive outcomes, including improved recruitment to general practice.

Public Health Impact and Cost-Effectiveness Analysis of 4-Component Meningococcal Serotype B Vaccination for Infants in France.

In France, meningococcal serogroup B (MenB) is the most common serogroup causing invasive meningococcal disease (IMD) in infants and young children. Our objective was to illustrate the impact of model choices on health outcomes and the cost-effectiveness of infant vaccination with the multicomponent meningococcal serogroup B vaccine (4CMenB) versus no vaccine in France. A previously published dynamic transmission-based cost-effectiveness model was adapted for the French context using updated, French-specific demographic, epidemiological, and cost data. IMD incidence and long-term sequelae were derived through analysis of French healthcare and surveillance databases. A collective perspective over a 100-year time horizon was adopted, with a discount rate of 2.5%, reduced to 1.5% after the first 30 years. Deterministic and probabilistic sensitivity and scenario analyses were performed. In the base case analysis, infant vaccination with 4CMenB avoided 3101 MenB IMD cases in infants aged <1 year (-54%) and 6845 cases in all age groups (-21%). The estimated incremental cost-effectiveness ratio was €316,272/quality-adjusted life-year (QALY) but was highly sensitive to the types of sequelae included, MenB incidence, vaccine effectiveness parameters, and consideration of life-expectancy in IMD survivors (range: €65,272/QALY to €493,218/QALY). Using economic models compliant with French methodology guidelines, 4CMenB does not seem cost-effective; however, results are sensitive to model choices and 4CMenB immunization is an effective strategy to prevent MenB IMD cases and to improve quality of life and economic burden associated with MenB IMD treatment, especially with regard to long-term sequelae.

Independent institution or cooperative institution? China’s deposit insurance institution model and the Honey Badger Algorithm

In the context of public deposit insurance organizational models, several interesting questions arise: Why does China's Deposit Insurance Corporation consistently lean toward the cooperative institution model, which is closely aligned with the central bank? Despite fervent advocacy for the independent institution model by the IADI and the U.S. Why does the unwavering stance exist? Is the choice of the cooperative institution model an "ignorant solution" or an "optimal solution" in China? Our work answers these questions for the first time, and we argue that it is the "optimal solution" that policymakers can choose after careful deliberation, not due to stupidity or inexperience. Based on the Honey Badger Algorithm, real options approach and expected loss pricing model, our work verifies the significant advantages of the cooperative institution model over the independent institution model in China. This pivotal distinction, primarily overlooked in the extant literature, suggests that universally accepted perspectives may not be ubiquitously relevant across all national contexts.

Electronic Couplings and Conversion Dynamics between Localized and Charge Transfer Excitations from Many-Body Green's Functions Theory.

We investigate the determination of electronic coupling between localized excitations (LEs) and charge-transfer (CT) excitations based on many-body Green's functions theory in the GW approximation with the Bethe-Salpeter equation (GW-BSE). Using a small molecule dimer system, we first study the influence of different diabatization methods, as well as different model choices within GW-BSE, such as the self-energy models or different levels of self-consistency, and find that these choices affect the LE-CT couplings only minimally. We then consider a large-scale low-donor morphology formed from rubrene and fullerene and evaluate the LE-CT couplings based on coupled GW-BSE-molecular mechanics calculations. For these disordered systems of bulky molecules, we observe differences in the couplings based on the Edmiston-Ruedenberg diabatization compared to the more approximate Generalize Mulliken-Hush and fragment charge difference diabatization formalisms. In a kinetic model for the conversion between LE and CT states, these differences affect the details of state populations in an intermediate time scale but not the final populations.

Advances in Studying Cancer Immunology in Mice.

The recent rise in effective immuno-oncology therapies has increased demand for experimental approaches to model anticancer immunity. A variety of mouse models have been developed and used to study cancer immunology. These include mutagen-induced, genetically engineered, syngeneic, and other models of cancer immunology. These models each have the potential to define mechanistic aspects of anticancer immune responses, identify potential therapeutic targets, and serve as preclinical models for further therapeutic development. Specific benefits and liabilities are characteristic of particular cancer immunology modeling approaches. The optimal choice and utilization of models depends on the cancer immunology scientific question being addressed and can serve to increase mechanistic understanding and development of human immuno-oncology therapies.

Theory for and from agent-based modelling: Insights from a virtual special issue and a vision

The Virtual Special Issue "Agents for Theory" discusses theory development using agent-based models (ABMs). The six contributions focus on how the word "theory" is used in the ABM literature, how reviews of ABMs should be conducted to gain general insights, how even the conceptualisation of ABMs can help to transform heuristic theories into scientific ones, how the context-dependent choice of decision models can be better justified, how reusable building blocks (RBBs) of ABMs could support theory development, and how a modular framework of RBBs can be used to identify general solutions. Overall, theories of decision making that go into ABMs and theories of system dynamics that come out of ABMs are interrelated, so the micro-macro perspective and the attempt to identify and reproduce patterns at both levels simultaneously is a way forward. Theory development requires clearer communication using a common language, reference to patterns, and more detailed model analysis and testing.

Comparative analysis of CFD models to simulate temperature non-uniformity during hydrogen tank refuelling

This study compares four computational fluid dynamics (CFD) models, i.e. k-ε, RSM, SAS, and LES, to simulate temperature non-uniformity during hydrogen tank refuelling. Three grids with a total number of control volumes of 64k, 363k, and 2.9 M were used. The maximum dimensionless wall distances, y+, were 80, 5 and 2.5, respectively. The predictive capability of the models was assessed by recommended statistical indicators using ten thermocouple locations during 620 s of the experiment. The comparative analysis demonstrated a better predictive capability of temperature non-uniformity and stratification by the LES model and a shorter simulation time. It is concluded that meshes with y+ larger than 80 would overpredict the average hydrogen temperature while meshes with y+<5 provide a good simulation accuracy. The results underline the importance of the turbulence model choice and the numerical grid resolution for proper prediction of temperature non-uniformity during hydrogen tank refuelling.

The Interaction Between Cumulative Culture and Gender

In a society characterized by rapid technological and social advancements, we inherit knowledge from our predecessors while simultaneously building upon it to forge new cultural developments, and such process contributes to the Cumulative culture, the continuous and interactive process of adding innovations to a socially heritable unity of knowledge. Considering the prevalence of gender inequality background in our cultures, this academic article explores the intricate relationship between gender equality and cumulative culture. This paper posits that gender, as defined within the constraints of male and female due to various limitations, plays a pivotal role in this cumulative cultural process. As the results, the examination reveals a clear and positive association between cumulative culture and gender equality. Cumulative culture wields a significant influence over children's choices of gender role models, consequently impacting knowledge transmission and children's labor allocation. Simultaneously, fostering gender egalitarianism offers a pivotal avenue for advancing the evolution of human cumulative culture. This paper aims to declare the benefits of gender equality towards cultural evolution and humanity and proposes the potential ways to foster cultural societal thriving.

Socio-Economic Status Classification for Poverty Mitigation

The concept of satellite images available from various sources using image processing technique provides an insight to predict or determine the poverty at various levels based on certain features like roads, buildings, schools and other factors. In the present scenario we are making use of Convolutional Neural Networks (CNN) mainly used for image recognition and processing to predict the poverty at various levels. The efficiency and accuracy of the model depends upon the number of pre-processing steps employed, the number of datasets used, the type of welfare indicator targeted and the choice of AI model. Leveraging advanced algorithms together with the ample amount of geographic information which satellites capture offers an appropriate approach for delicate socio-economic indicator evaluation. Leveraging advanced algorithms together with the ample amount of geographic information which satellites capture offers an appropriate approach for delicate socio-economic indicator evaluation. Key Words: Convolutional neural networks, ReLU architecture, Gaussian Blur

Determinants in Used Car Purchase Results

The automotive industry in Indonesia continues to experience rapid growth amidst intense competition among companies to create flagship products. This development is spurred by various factors, including lifestyle changes and the increased social status of the population. This research investigates the factors influencing the decision to purchase used cars in Jakarta, particularly among individuals aged 18-40. The automotive industry in Indonesia is undergoing rapid growth driven by lifestyle developments, increased community mobility, and enhanced social status. The purchase of used cars has become an intriguing phenomenon in the dynamics of the automotive industry, with consumers influenced by economic considerations, diversity in model and brand choices, and individual preferences. The respondents in this study are individuals aged 18-40 who are interested in purchasing used cars in Jakarta. A survey method is employed to collect data, with questionnaires distributed to randomly selected respondents. Data analysis is conducted to identify the key factors influencing the decision to purchase used cars. Purchasing used cars is an interesting phenomenon observed in the dynamics of the automotive industry. Consumers in this purchasing process are influenced by several factors, including economic considerations such as affordable prices and lower depreciation values compared to new cars. Additionally, the diversity of model and brand choices is also an important consideration for consumers when choosing used cars. The findings of this research are expected to provide deeper insights into the preferences and needs of consumers when buying used cars in Jakarta. These findings can serve as a guide for used car manufacturers and the automotive industry as a whole in designing more effective

Machine Learning and image analysis towards improved energy management in Industry 4.0: a practical case study on quality control

With the advent of Industry 4.0, Artificial Intelligence (AI) has created a favorable environment for the digitalization of manufacturing and processing, helping industries to automate and optimize operations. In this work, we focus on a practical case study of a brake caliper quality control operation, which is usually accomplished by human inspection and requires a dedicated handling system, with a slow production rate and thus inefficient energy usage. We report on a developed Machine Learning (ML) methodology, based on Deep Convolutional Neural Networks (D-CNNs), to automatically extract information from images, to automate the process. A complete workflow has been developed on the target industrial test case. In order to find the best compromise between accuracy and computational demand of the model, several D-CNNs architectures have been tested. The results show that, a judicious choice of the ML model with a proper training, allows a fast and accurate quality control; thus, the proposed workflow could be implemented for an ML-powered version of the considered problem. This would eventually enable a better management of the available resources, in terms of time consumption and energy usage.

Different receptor models show differences in ligand binding strength and location: a computational drug screening for the tick-borne encephalitis virus.

The tick-borne encephalitis virus (TBE) is a neurotrophic disease that has spread more rapidly throughout Europe and Asia in the past few years. At the same time, no cure or specific therapy is known to battle the illness apart from vaccination. To find a pharmacologically relevant drug, a computer-aided drug screening was initiated. Such a procedure probes a possible binding of a drug to the RNA Polymerase of TBE. The crystal structure of the receptor, however, includes missing and partially modeled regions, which rendered the structure incomplete and of questionable use for a thorough drug screening procedure. The quality of the receptor model was addressed by studying three putative structures created. We show that the choice of receptor models greatly influences the binding affinity of potential drug molecules and that the binding location could also be significantly impacted. We demonstrate that some drug candidates are unsuitable for one model but show decent results for another. Without any prejudice on the three employed receptor models, the study reveals the imperative need to investigate the receptor structure before drug binding is probed whether experimentally or computationally.

Comparison of De values from Late Pleistocene alluvial deposits on the coast of Galicia (NW Spain) using BayLum or Analyst-based procedures

The coastal sedimentary record of Galicia (NW Spain) is important to study sea-level oscillations during the Late Quaternary. The alluvial deposits preserved in the Ria de Coruña and Ria de Arousa are remnants of fluvial valleys flooded by the sea during the successive Pleistocene and Holocene transgressions. A first chronological study by Optical Stimulated Luminescence (OSL)- revealed a fast-component dominated, but dim OSL signal emitted by quartz from this area. Equivalent dose distributions, as obtained with the Analyst software, yielded large overdispersion values, which were negatively correlated to the potassium concentration in the sediment, thus suggesting the influence of beta dose rate heterogeneities. Nevertheless, for most samples many aliquots were considered in saturation, leading to a palaeodose and age underestimation – regardless of the model choice for age calculation. In this paper, we present new analyses using the open-source R package BayLum, as the so-called saturated aliquots may be incorporated in age calculation. Thus, we test this model performance and more commonly used models in retrieving large doses. Then, we discuss the implications for age calculation for these continental sediments formed during the Late Pleistocene.