Statistical Model Research Articles

Understanding the role of driver behaviors and performance in safety-critical events: Application of machine learning

Human factors contribute in some way to almost 93% of road crashes. Because the unavailability of good pre-crash data, the contribution of human factors to safety-critical events (SCEs) and the prediction of crashes using real-world data is lightly researched. This study provides predictive accuracy by harnessing unique real-world naturalistic driving study (NDS) data, which includes dynamic pre-crash information about driving behavior and performance. After cleaning and preprocessing, a final subsample (N = 9,237) was used and split into training and test samples. For consistent comparison of variables’ importance in statistical and machine learning (ML) models, the dominance analysis uncovered the most important predictors used by the ordered Probit model. Next, three non-parametric supervised ML methods, because promising prediction performance and cost-effectiveness, including Naïve Bayes, K-Nearest Neighbors, and Gradient Boosting Tree (GBT) were used. The overall out-of-sample prediction accuracy for the ordered Probit model was 85.75% which was lower than all three ML methods. The GBT showed the highest (91.23%) out-of-sample prediction accuracy. The availability of pre-crash naturalistic data helps significantly improve the prediction accuracy of SCEs as cumulative importance for all available human factors in the GBT classifier was 94%. For practical applications, refer to the article.

Ramadhan effect towards Nifty Shariah indices in India

PurposeThis paper aims to analyse the weekday effect on the Nifty Shariah indices as per the Islamic calendar. The study is intended to know about the return and volatility of these indices during Ramadhan and non-Ramadhan days.Design/methodology/approachThe study focuses on analysing the Nifty Shariah indices and Sensex daily returns collected from NSE India and BSE India, respectively, during the period of 1 August 2016 to 31 July 2022. Descriptive statistics are used to analyse the data, while the Ordinary Least Square method is used to determine the impact of weekdays on the Nifty Shariah indices. Additionally, the study applies the GARCH statistical model to examine the influence of Ramadhan on the returns and volatility of the Nifty Shariah indices.FindingsAll of the Nifty Shariah indices produced positive returns during the overall sample period. According to the study, Tuesday index returns outperform other weekdays. The GARCH model indicated that the coefficient values for the Nifty 50 Shariah and Nifty 500 Shariah indices were negative. Ramadhan has a strong negative effect on volatility, according to this study.Originality/valueThe outcomes of the research are beneficial for investors aiming to exploit daily or weekly price fluctuations, rather than pursuing extended investment periods. Furthermore, fund managers can employ these findings to shape trading strategies, and academics can examine the performance of Shariah indices in the Indian context. This enables devout investors to make significant financial choices, thus advancing ethical values in society and upholding standards of both public and private morality.

Insights into nearshore sandbar dynamics through process-based numerical and logistic regression modeling

Process-based nearshore morphodynamic models are commonly used tools by coastal engineers and planners to predict the nearshore morphology change of sandy beaches across various spatiotemporal scales. Accurate modeling of the morphological response on medium and long time scales is imperative for quantitative assessments of coastal infrastructure over a project’s intended life-span. However, most previous modeling applications have focused on single/sub-seasonal storm events and are often limited to an assessment of the subaerial beach (i.e. berm and dune). This not only leaves uncertainty concerning the quality of morphology predictions on extended (> weeks) time scales, but also the capacity of process-based models to emulate realistic nearshore sandbar dynamics and the corresponding exchange of sediment between the nearshore-beach system. To shed light on these meso-scale dynamics, CSHORE, a 1D phase-averaged, process-based nearshore morphodynamic model, was applied on an annual scale to a multi-barred, dissipative beach in Oysterville, WA, USA. Thousands of unique sediment transport and hydrodynamic parameter combinations were executed during model calibration. A large portion of these simulations displayed physically realistic sandbar dynamics, including the growth, decay, and migration of intertidal and subtidal sandbars. To explore the model mechanisms enabling realistic bar behavior, the binary and multinomial logistic regression model were used to quantify the relationship between model parameter selection and the probability of various categorical bar configurations occurring in the final predicted profile. The results indicate the most sensitive parameters associated with barred morphology, in this study, and support the use of separate sediment transport parameters for low and high wave energy conditions. The co-utilization of numerical and statistical modeling outlined in this publication is generalizable to future exploratory modeling and/or calibration routines concerned with categorical outcomes.

Characteristics of Nontyphoid Salmonella Isolated from Human, Environmental, Animal, and Food Samples in Burkina Faso: A Systematic Review and Meta-Analysis.

Salmonella is one of the world's leading causes of zoonotic and foodborne illnesses. Recently, antimicrobial resistance (AMR) has become one of the most critical challenges to public health and food safety. Herein, we employed a meta-analysis to determine the pooled prevalence and spatiotemporal distribution of serovars and antimicrobial resistance in NTS in Burkina Faso. To find eligible articles, a comprehensive literature search of PubMed, African Journals Online, ScienceDirect, Google Scholar, and the gray literature (university libraries) in Burkina was conducted for the period from 2008 to 2020. Studies meeting the inclusion criteria were selected and assessed for risk of bias. To assess the temporal and spatial relationships between serotypes and resistant strains from humans, animals, food, and the environment, a random-effects statistical model meta-analysis was carried out using the Comprehensive Meta-Analysis Version 3.0 program. The NTS prevalence rates were 4.6% (95% CI: 3-7) and 20.1% (95% CI: 6.6-47.4) in humans and animals, respectively, and 16.8% (95% CI: 10.5-25.8) and 15.6% (95% CI: 8.2-27.5) in food and the environment, respectively. Most NTS serovars were S. Derby, reported both in food and animals, and S. Typhimurium, reported in humans, while S. Croft II, S. Jodpur II, and S. Kentucky were the most prevalent in the environment. NTS isolates were highly resistant to erythromycin, amoxicillin, cefixime, and cephalothin, with a pooled prevalence of multidrug resistance of 29% (95% CI: 14.5-49.5). The results of this review show a high diversity of Salmonella serotypes, as well as high antibiotic resistance in Salmonella isolates from animal, human, food, and environmental samples in Burkina, calling for a consolidated "One Health" approach to better understand the drivers of pathogen emergence, spread, and antimicrobial resistance, as well as the formulation of intervention measures needed to limit the risk associated with the disease.

Lack of association of impaired upper airway sensation with the presence or absence of obstructive sleep apnoea or chronic rhinosinusitis in World Trade Center responders

ObjectiveExamine sensory function of the upper airway in four groups of subjects recruited from the World Trade Centre General Responder Cohort (WTCGRC), with/without obstructive sleep apnoea (OSA), and with/without chronic...

Are Forecasts of the Tropical Cyclone Radius of Maximum Wind Skillful?

AbstractThe radius of maximum wind (RMW) defines the location of the maximum winds in a tropical cyclone and is critical to understanding intensity change as well as hazard impacts. A comparison between the Hurricane Analysis and Forecast System (HAFS) models and two statistical models based off the National Hurricane Center official forecast is conducted relative to a new baseline climatology to better understand whether models have skill in forecasting the RMW of North Atlantic tropical cyclones. On average, the HAFS models are less skillful than the climatology and persistence baseline and two statistically derived RMW estimates. The performance of the HAFS models is dependent on intensity with better skill for stronger tropical cyclones compared to weaker tropical cyclones. To further improve guidance of tropical cyclone hazards, more work needs to be done to improve forecasts of tropical cyclone structure.

A dataset of human capital-weighted population estimates for 185 countries from 1970 to 2100

We provide a novel dataset of human capital-weighted population size (HCWP) for 185 countries from 1970 to 2100. HCWP summarizes a population’s productive capacity and human capital heterogeneity in a single metric, enabling comparisons across countries and over time. The weights are derived from Mincerian earnings functions applied to multi-country census data on educational attainment. The model used to compute the returns to schooling accounts for the diminishing positive relative relationship between education and wages as the overall education of populations rises. The population weights are adjusted by a skills assessment factor representing differences in education quality across countries and years. HCWP is calculated by applying these adjusted human capital weights to population estimates and projections disaggregated by age, sex and education, spanning the period 1970–2020 and 2020–2100 for five Shared Socioeconomic Pathway scenarios. Validation analyses demonstrate the utility of the new HCWP data in explaining national income trends. As a more comprehensive population measure than basic size and age-sex indicators, HCWP enhances the power of statistical models aimed at the assessment of socioeconomic change impacts and forecasting.

Transcriptome analysis of the Japanese eel (Anguilla japonica) during larval metamorphosis

BackgroundAnguillid eels spend their larval period as leptocephalus larvae that have a unique and specialized body form with leaf-like and transparent features, and they undergo drastic metamorphosis to juvenile glass eels. Less is known about the transition of leptocephali to the glass eel stage, because it is difficult to catch the metamorphosing larvae in the open ocean. However, recent advances in rearing techniques for the Japanese eel have made it possible to study the larval metamorphosis of anguillid eels. In the present study, we investigated the dynamics of gene expression during the metamorphosis of Japanese eel leptocephali using RNA sequencing.ResultsDuring metamorphosis, Japanese eels were classified into 7 developmental stages according to their morphological characteristics, and RNA sequencing was used to collect gene expression data from each stage. A total of 354.8 million clean reads were generated from the body and 365.5 million from the head, after the processing of raw reads. For filtering of genes that characterize developmental stages, a classification model created by a Random Forest algorithm was built. Using the importance of explanatory variables feature obtained from the created model, we identified 46 genes selected in the body and 169 genes selected in the head that were defined as the “most characteristic genes” during eel metamorphosis. Next, network analysis and subsequently gene clustering were conducted using the most characteristic genes and their correlated genes, and then 6 clusters in the body and 5 clusters in the head were constructed. Then, the characteristics of the clusters were revealed by Gene Ontology (GO) enrichment analysis. The expression patterns and GO terms of each stage were consistent with previous observations and experiments during the larval metamorphosis of the Japanese eel.ConclusionGenome and transcriptome resources have been generated for metamorphosing Japanese eels. Genes that characterized metamorphosis of the Japanese eel were identified through statistical modeling by a Random Forest algorithm. The functions of these genes were consistent with previous observations and experiments during the metamorphosis of anguillid eels.

Competency model development: The backbone of successful stealth assessments

AbstractBackgroundStealth assessment is a learning analytics method, which leverages the collection and analysis of learners' interaction data to make real‐time inferences about their learning. Employed in digital learning environments, stealth assessment helps researchers, educators, and teachers evaluate learners' competencies and customize the learning experience to their specific needs. This adaptability is closely intertwined with theories related to learning, engagement, and motivation. The foundation of stealth assessment rests on evidence‐cantered design (ECD), consisting of four core models: the Competency Model (CM), Evidence Model, Task Model, and Assembly Model.ObjectiveThe first step in designing a stealth assessment entails producing operational definitions of the constructs to be assessed. The CM establishes a framework of latent variables representing the target constructs, as well as their interrelations. When developing the CM, assessment designers must produce clear descriptions of the claims associated with the latent variables and their states, as well as sketch out how the competencies can be measured using assessment tasks. As the designers elaborate on the assessment model, the CM definitions need to be revisited to make sure they work with the scope and constraints of the assessment. Although this is the first step, problems at this stage may result in an assessment that does not meet the intended purpose. The objective of this paper is to elucidate the necessary steps for CM development and to highlight potential challenges in the process, along with strategies for addressing them, particularly for designers without much formal assessment experience.MethodThis paper is a methodological exposition, showcasing five examples of CM development. Specifically, we conducted a qualitative retrospective analysis of the CM development procedure, wherein participants unfamiliar with ECD applied the framework and showcased their work. In a stealth assessment course, four groups of students (novice stealth assessment designers) engaged in developing stealth assessments for challenging‐to‐measure constructs across four distinct projects. During their CM development process, we observed various activities to pinpoint areas of difficulty.ResultsThis paper presents five illustrative examples, including one for assessing physics understanding and four for the development of CMs for four complex competencies: (1) systems thinking, (2) online information credibility evaluation, (3) computational thinking, and (4) collaborative creativity. Each example represents a case in CM development, offering valuable insights.ConclusionThe paper concludes by discussing several guidelines derived from the examples discussed. Emphasizing the importance of dedicating ample time to fine‐tune CMs can significantly enhance the accuracy of assessments related to learners' knowledge and skills. It underscores the significance of qualitative phases in crafting comprehensive stealth assessments, such as CMs, alongside the quantitative statistical modeling and technical aspects of these assessments.

A statistical model on heat transportation of hybrid magnetic nanoparticles with slip constraints on a heated shrinking sensor device using analysis of variance: Sensitivity analysis

A statistical model finds application across various engineering and scientific contexts, aiding in the analysis and enhancement of heat transfer processes. Its utility extends to supporting scientists and engineers in gaining deeper insights into the impact of diverse factors on heat transfer and optimizing procedures for enhanced efficiency. Notably, the heat transfer efficiency of hybrid nanofluids containing ferromagnetic nanoparticles outperforms that of conventional fluids. In this study, a statistical model is utilized to predict the increased heat transfer rate in a heated shrinking sensor employing hybrid magnetic nanoparticles with slip restrictions. Incorporating radiative heat flux and heat source/sink components contributes novelty to the study. The model is converted into a non-dimensional form using appropriate similarity rules, and the resulting problem is solved computationally using bvp5c, a built-in function in MATLAB. Using advanced mathematical modeling and simulations, this research evaluates system performance in various scenarios and identifies optimal conditions for maximizing heat transfer rates. The study's main outcomes are; that including the thermal radiation and Prandtl number enhances the heat transfer rate, whereas the heat source decreases it. Further, the increased modified Hartmann number and suction parameter raise the shear rate coefficient.

Uncertainty components in profile likelihood fits

When a measurement of a physical quantity is reported, the total uncertainty is usually decomposed into statistical and systematic uncertainties. This decomposition is not only useful for understanding the contributions to the total uncertainty, but is also required to propagate these contributions in subsequent analyses, such as combinations or interpretation fits including results from other measurements or experiments. In profile likelihood fits, widely applied in high-energy physics analyses, contributions of systematic uncertainties are routinely quantified using “impacts,” which are not adequate for such applications. We discuss the difference between impacts and actual uncertainty components, and establish methods to determine the latter in a wide range of statistical models.

Weather Extremes Shock Maize Production: Current Approaches and Future Research Directions in Africa.

Extreme weather events have led to widespread yield losses and significant global economic damage in recent decades. African agriculture is particularly vulnerable due to its harsh environments and limited adaptation capacity. This systematic review analyzes 96 articles from Web of Science, Science Direct, and Google Scholar, focusing on biophysical studies related to maize in Africa and worldwide. We investigated the observed and projected extreme weather events in Africa, their impacts on maize production, and the approaches used to assess these effects. Our analysis reveals that drought, heatwaves, and floods are major threats to African maize production, impacting yields, suitable cultivation areas, and farmers' livelihoods. While studies have employed various methods, including field experiments, statistical models, and process-based modeling, African research is often limited by data gaps and technological constraints. We identify three main gaps: (i) lack of reliable long-term experimental and empirical data, (ii) limited access to advanced climate change adaptation technologies, and (iii) insufficient knowledge about specific extreme weather patterns and their interactions with management regimes. This review highlights the urgent need for targeted research in Africa to improve understanding of extreme weather impacts and formulate effective adaptation strategies. We advocate for focused research on data collection, technology transfer, and integration of local knowledge with new technologies to bolster maize resilience and food security in Africa.

Kriging-based surrogate data-enriching artificial neural network prediction of strength and permeability of permeable cement-stabilized base

Limited test data hinder the accurate prediction of mechanical strength and permeability of permeable cement-stabilized base materials (PCBM). Here we show a kriging-based surrogate model assisted artificial neural network (KS-ANN) framework that integrates laboratory testing, mathematical modeling, and machine learning. A statistical distribution model was established from limited test data to enrich the dataset through the combination of markov chain monte carlo simulation and kriging-based surrogate modeling. Subsequently, an artificial neural network (ANN) model was trained using the enriched dataset. The results demonstrate that the well-trained KS-ANN model effectively captures the actual data distribution characteristics. The accurate prediction of the mechanical strength and permeability of PCBM under the constraint of limited data validates the effectiveness of the proposed framework. As compared to traditional ANN models, the KS-ANN model improves the prediction accuracy of PCBM’s mechanical strength by 21%. Based on the accurate prediction of PCBM’s mechanical strength and permeability by the KS-ANN model, an optimization function was developed to determine the optimal cement content and compaction force range of PCBM, enabling it to concurrently satisfy the requirements of mechanical strength and permeability. This study provides a cost-effective and rapid solution for evaluating the performance and optimizing the design of PCBM and similar materials.

Clinical Manifestations’ Spectrum of Smartphone Addiction: Moving from an Addiction toward a Clinical Syndrome

Background Smartphone addiction is an emerging type of addiction in the digital era, characterized by smartphone dependence that negatively affects human health with a wide range of psychological and physical manifestations. Objective This study aimed to evaluate the detailed clinical manifestations of smartphone addiction as a delineated clinical syndrome. Methods A cross-sectional study design was employed to assess smartphone addiction prevalence and its health impacts among Syrian undergraduates using the Smartphone Addiction Scale-Short Version (SAS-SV 2013), the Kessler psychological distress scale (K-6), and a comprehensive assessment of the clinical manifestations frequently linked to smartphone addiction in the literature. Different statistical modeling techniques were applied; a P value of < .05 was considered statistically significant. Results Of 1532 invited undergraduates, 1401 (91.45%) completed the assessment adequately. Most participants were females (59.7%) and below 23 years of age (73.2%). The prevalence of smartphone addiction was 67.80%; statistically significant smartphone addiction associations were revealed with psychological distress (P < .0001) with odds ratios of 3.308. Most screened physical manifestations also showed a significant association with smartphone addiction. Conclusion A high prevalence of smartphone addiction was observed with a broad spectrum of associated mental and physical manifestations. As smart device addiction becomes a global health concern, combining the clinical findings reported in the related literature into one clinical identity is necessary to develop a holistic management approach for the delineated clinical syndrome.

Factors affecting the duration of initial medical care seeking among older rural patients diagnosed with rheumatoid arthritis: a retrospective cohort study

BackgroundEarly diagnosis and treatment of rheumatoid arthritis (RA) are essential to prevent progressive joint destruction and improve the quality of life (QOL) of patients. This study aimed to identify the factors associated with the duration from symptom onset to seeking initial medical care among older rural patients diagnosed with RA.MethodsThis retrospective cohort study was conducted in Unnan City, Japan, using electronic patient records. Data from patients aged > 65 years, who were admitted to the Unnan City Hospital between April 2016 and March 2021, were analyzed. The primary outcome was the duration from symptom onset to the initial visit to the medical institution. Demographic factors, laboratory data, and data on symptoms were collected and analyzed using statistical tests and regression models.ResultsIn total, 221 participants were included in this study. The longer duration from symptom onset to medical care usage was significantly associated with age (adjusted odds ratio [OR]: 1.09, 95% confidence interval [CI]: 1.03–1.15), isolated conditions (adjusted OR: 4.45, 95% CI: 1.85–10.70), and wrist symptoms (adjusted OR: 3.22, 95% CI: 1.44–7.17). Higher education level and alcohol consumption were also associated with the duration from symptom onset to medical care usage.ConclusionsOlder age, isolated conditions, and specific joint symptoms were significant factors influencing delays in seeking medical care among older rural patients with RA. Interventions to improve health literacy, increase social support, and raise awareness of RA symptoms are essential for expediting diagnosis and improving patient QOL. Further research is needed to explore additional psychosocial factors and beliefs that affect health-seeking behaviors in patients with RA.

Computer-aided design and fabrication of nasal prostheses: a semi-automated algorithm using statistical shape modeling

Computer-aided design and fabrication of nasal prostheses: a semi-automated algorithm using statistical shape modeling

Development and external validation of a logistic and a penalized logistic model using machine-learning techniques to predict suicide attempts: A multicenter prospective cohort study in Korea

Despite previous efforts to build statistical models for predicting the risk of suicidal behavior using machine-learning analysis, a high-accuracy model can lead to overfitting. Furthermore, internal validation cannot completely address this problem. In this study, we created models for predicting the occurrence of suicide attempts among Koreans at high risk of suicide, and we verified these models in an independent cohort. We performed logistic and penalized regression for suicide attempts within 6 months among suicidal ideators and attempters in The Korean Cohort for the Model Predicting a Suicide and Suicide-related Behavior (K-COMPASS). We then validated the models in a test cohort. Our findings indicated that several factors significantly predicted suicide attempts in the models, including young age, suicidal ideation, previous suicidal attempts, anxiety, alcohol abuse, stress, and impulsivity. The area under the curve and positive predictive values were 0.941 and 0.484 after variable selection and 0.751 and 0.084 in the test cohort. The corresponding values for the penalized regression model were 0.943 and 0.524 in the original training cohort and 0.794 and 0.115 in the test cohort. The prediction model constructed through a prospective cohort study of the suicide high-risk group showed satisfactory accuracy even in the test cohort. The accuracy with penalized regression was greater than that with the “classical” logistic model.

Using the AIEM and Radarsat-2 SAR to Retrieve Bare Surface Soil Moisture

Taking the Jiefangzha irrigation area of the Inner Mongolia Autonomous Region as the research area, the response relationships between the backscattering coefficient and radar frequency, radar incidence angle, root-mean-square height, correlation length, and soil water content under different conditions were simulated using advanced integral equations. The backscattering characteristics of exposed surfaces in cold and dry irrigation areas were discussed, and the reasons for the different effects were analyzed. Based on this, surface roughness models and statistical regression moisture inversion models were constructed through co-polarized backscatter coefficients and combined surface roughness. The correlation between the inverted surface roughness values and the measured values was R2 = 0.7569. The correlation between the soil moisture simulation values and the measured values was R2 = 0.8501, with an RMSE of 0.04. The findings showed a strong correlation between the values from the regression simulation and the measured data, indicating that the model can be applied to soil moisture inversion and has a good inversion accuracy. Compared with previous studies in the same area, the inversion model proposed in this paper has a higher accuracy and is more suitable for the inversion of soil moisture in the Jiefangzha irrigation area. These findings can support research on the water cycle and water environment assessment in the region.

Modeling the impact of an ISO 9001 certified quality management system on the organizational performance of Moroccan services firms

Statistical modeling serves as a valuable tool for analyzing and resolving complex issues through the application of mathematical and statistical techniques. In the Moroccan context, where the services sector contributes more than 50% to the national wealth, there is a noticeable absence of such applications to gauge the impact of the ISO 9001 certified quality management systems on the organizational performance of companies. To address this gap, we have constructed a causal model to quantify the strength of causeand- effect relationships among the following key elements: • processes within a quality management system and their impact on organizational performance, • the influence of management responsibility process on all aspects of the quality management system, and • the relationship between the organizational and financial performance. This model is developed using Structural Equation Modeling (SEM) and estimated through the Partial Least Squares (PLS) approach, utilizing the XL-Stat software. The research involved a sample of 55 Moroccan service firms certified with ISO 9001 across various sectors. Face-to-face surveys were the primary data collection method, engaging multiple executives within the organizations. The findings of this research validate our conceptual model, revealing that: • certain processes within the quality management system positively and weakly correlate with the organizational performance, • the 'Management Responsibility' process has a positive influence on all aspects of the quality management system, and the organizational performance strongly and positively impacts the financial performance. Moreover, our model facilitates the computation of direct and indirect effects among its components, transforming cause-and-effect relationships into structural equations. Our research contributes to enhancing the understanding of the dynamics between quality management systems and the organizational performance in the Moroccan service sector.

The Added Value of Statistical Seasonal Forecasts

Seasonal climate predictions can assist with timely preparations for extreme episodes, such as dry or wet periods that have associated additional risks of droughts, fires and challenges for water management. Timely warnings for extreme warm summers or cold winters can aid in preparing for increased energy demand. We analyse seasonal forecasts produced by three different methods: (1) a multi-linear statistical forecasting system based on observations only; (2) a non-linear random forest model based on observations only; and (3) process-based dynamical forecast models. The statistical model is an empirical system based on multiple linear regression that is extended to include the trend over the previous 3 months in the predictors, and overfitting is further reduced by using an intermediate multiple linear regression model. This results in a significantly improved El Niño forecast skill, specifically in spring. Also, the Indian Ocean dipole (IOD) index forecast skill shows improvements, specifically in the summer and autumn months. A hybrid multi-model ensemble is constructed by combining the three forecasting methods. The different methods are used to produce seasonal forecasts (three-month means) for near-surface air temperature and monthly accumulated precipitation seasonal forecast with a lead time of one month. We find numerous regions with added value compared with multi-model ensembles based on dynamical models only. For instance, for June, July and August temperatures, added value is observed in extensive parts of both Northern and Southern America, as well as Europe.