Near midair collisions (NMACs) remain a complex aviation safety challenge shaped by operational conditions and human factors. Aviation Safety Reporting System (ASRS) narratives provide useful information about communication, visual acquisition, workl...
Exploratory Graph Analysis (EGA) has emerged as a popular approach for estimating the dimensionality of multivariate data using psychometric networks. Sampling variability, however, has made reproducibility and generalizability a key issue in network...
Data uncertainty and limited model generalization remain critical bottlenecks in large-scale remote sensing of soil salinization. Although the integration of multi-source data has improved predictive potential, conventional deterministic feature sele...
Model validation is essential for evaluating how reliably a machine learning model performs on unseen data, especially in imbalanced classification where performance estimates can be affected by an underrepresentation of the minority class. Cross-val...
This paper presents a Multi-Strategy Market Dynamics Analysis (MSMDA) framework for agent-based economic modeling with reinforcement learning. The primary methodological contribution is an integrated strategy–stability–macro inference pip...
Background/Objectives: Clinical prediction models may support staged triage in acute pelvic pain, but simplified integer scores can lose information and destabilise point assignments. We evaluated a tiered clinical–ultrasound prediction pathway...
The increasing deployment of resource-constrained Internet of Things (IoT) devices requires security mechanisms that preserve confidentiality without compromising energy efficiency or responsiveness. Although Transport Layer Security (TLS) provides s...
Fully differential amplifier circuits are well suited for instrumentation front ends and signal-conditioning applications. They offer high common-mode rejection ratios (CMRRs) regardless of the passive component tolerances but remain sensitive to imb...
This paper introduces BCenetTucker, a novel bootstrap-enhanced extension of the CenetTucker model designed to address the instability of sparse support recovery in high-dimensional tensor settings. By integrating mode-specific resampling directly int...
(1) Background: Reference-interval flags read a lipid–glucose panel one analyte at a time. We asked whether a routine five-analyte panel supports reproducible, transportable, auditable descriptive grouping of laboratory profiles. (2) Methods: R...
Background/Objectives: Emotional stability is a personality trait associated with emotion regulation and adaptation to stress. Low emotional stability has been associated with anxiety, depressive symptoms, and emotional eating, but the mechanisms und...
LiDAR inventories were carried out to estimate the mean volume and variance in Eucalyptus globulus and Eucalyptus nitens stands. Uncertainty of the population estimates was examined using approximations based on the bootstrap method. Three methods we...
Regional yield comparisons can complement vineyard experiments but cannot identify causal irrigation responses. We linked Spanish agricultural and hydrological statistics for 70 community-year pairs in 14 communities during 2019–2023. Equal-wei...
Background: Atrial fibrillation (AF) is associated with oxidative stress and inflammation. Glutathione reductase (GR) and microRNA-144 (miR-144) may participate in redox-related pathways. This study aimed to evaluate relative GSR mRNA and miR-144 exp...
Road intersection crashes remain a major contributor to injuries due to complex conflict patterns and multimodal interactions. Among the factors influencing intersection safety, inadequate intersection sight distance (ISD) attributed to roadside sigh...
This study reanalyzes data from a previous investigation on critical thinking, metacognition, empathy, and psychological well-being by incorporating academic performance into a graphical-causal model. The aim was to compare two explanatory structures...
Background/Objectives: Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery with cardiopulmonary bypass (CPB), increasing morbidity and prolonging hospitalization. This study aimed to develop and validate an explora...
Background and Objective: Reliable medical research depends on data integrity, yet clinical datasets often contain real and systematically missing values. This study aimed to develop a robust, clinically realistic imputation framework for a raw Multi...
Background: Implementing effective interventions for specific cognitive symptoms is critical to reducing the disease burden of dementia. Previous studies have identified associations between overall cognitive function and dietary patterns in older ad...
Purpose: To develop an exploratory prognostic model integrating multiparametric MRI radiomics and clinical features for recurrence risk stratification in young-age breast cancer (YABC). Materials and Methods: In this prospective single-institution co...
Regional classification of dominant discontinuity sets supports open-pit slope characterization, but dip direction is circular and should not be analyzed as a linear variable. We reanalyzed 73 area-level records from 72 nominal survey areas at the Da...
The rapid growth of high-dimensional predictive models in science and industry has intensified the need for statistically rigorous interpretability tools. Although model-agnostic feature importance methods are widely used to explain black-box models,...
In Morocco, the increasing public expenditure on essential sectors, such as education, does not always lead to improved outcomes, highlighting a significant gap between resource allocation and quality enhancement. This study examines the efficiency o...
Myocardial infarction (MI) remains one of the most severe acute cardiac events, despite significant progress in diagnostics and therapy. Early identification of patients at risk within the broader cardiovascular disease (CVD) population is crucial fo...
Background/Objectives: This study examined annual probabilistic network structures among dietary habits, health-related attitudes, lifestyle behaviors, and body mass index (BMI) during implementation of the PETICA school-based program. Methods: This...
Naive Bayes (NB) classifiers are widely used for their simplicity, computational efficiency, and interpretability. However, their predictive performance can degrade significantly in real-world settings where the conditional independence assumption is...
A drug dissolution profile is one of the most critical dosage form characteristics with immediate and controlled drug release. Comparing the dissolution profiles of different pharmaceutical products plays a key role before starting the bioequivalence...
This study proposes an integrated short-term photovoltaic (PV) power forecasting framework, termed MSD-STB-OptFormer, to jointly address multiscale power fluctuations, unstable meteorological relevance, and model-parameter sensitivity. Successive var...
Type 1 diabetes mellitus (T1DM) is a chronic, non-preventable, and incurable disease that requires lifelong insulin administration. The principal challenge is calculating the prandial insulin bolus to avoid hypoglycemia and hyperglycemia. Traditional...
Background and Objectives: COPD is a heterogeneous disease in which conventional clinical classifications may not fully capture the complexity of patient profiles. This exploratory study aimed to examine whether multidimensional COPD phenotypes could...
Agricultural credit rationing remains a persistent systemic friction driven by information opacity and limited collateral. This study develops a credit risk early-warning system by fusing multi-source institutional digital footprints (tax compliance...
Multi-stage sampling designs are often used in household surveys because a sampling frame of elements may not be available or for cost considerations when data collection involves face-to-face interviews. In this context, variance estimation is a com...
This revised manuscript presents a real-calendar-anchored SEIR simulation benchmark for comparing manifold-constrained Gaussian process inference (MAGI) and physics-informed neural networks (PINNs). The study is explicitly positioned as an empirical...
Parametric regression methods are efficient when correctly specified but are sensitive to model misspecification and outliers. Nonparametric regression offers greater flexibility at the cost of reduced interpretability and susceptibility to the curse...
20 April 2025
This study investigates the application of large language models (LLMs) in sentiment analysis of financial news and their use in developing effective investment strategies. We conducted sentiment analysis on news articles related to the top 30 compan...
Uniform geochemical baselines can obscure natural variation among geological units, whereas local baselines may become unstable when sample sizes are limited. This study analyzed 209 agricultural topsoil samples from a geologically heterogeneous coun...
Complex thinking is increasingly relevant in international postgraduate education, yet limited evidence exists on how its components are internally articulated within multicultural graduate cohorts. This study examined the relational organization of...
We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single ne...
Objectives: To derive sex-specific Relative Body Mass Index (rBMI) cut-offs for classifying adolescents into four adiposity categories defined by dual-energy X-ray absorptiometry (DXA)-based body fat percentage (BF%), quantify their stability, and ev...
Legal certainty is a central condition of commercial exchange, yet its relationship with contractual digitalization remains insufficiently specified in business-to-business settings. This study examined how the degree of contractual digitalization wa...
Against the backdrop of the expansion of sustainable finance and the growing relevance of ESG-related information, disclosure and regulation, this paper examines the dynamic relationship between sustainability-related uncertainty and ESG equity marke...
Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should...
Growing evidence suggests that urban greenery is associated with improved mental health, yet how eye-level exposure functions within specific socio-environmental contexts remains underexplored. This study presents an exploratory case study utilizing...
Propofol and fentanyl are commonly used agents for the induction of anesthesia, and are often associated with hemodynamic disturbances. Understanding pharmacodynamic impacts is vital for parasympathetic and sympathetic tones during the anesthesia ind...
Regional geothermal screening often collapses prospectivity, territorial constraints, institutional support, and data maturity into a compensatory score, obscuring why an area ranks highly. We develop an evidence-aware, non-compensatory framework for...
This study examines the current account implications of sustainable rural tourism in Türkiye by measuring rural tourism intensity through tourist arrivals in locally embedded and small-scale accommodation structures—including mountain lodg...
When measurement invariance (MI) is violated in multi-group structural equation models, group-specific measurement artifacts inflate the between-group variance of structural parameters beyond their true values. Existing remedies—partial invaria...
Providing a historical overview of 50 years of fusion research, a review of the fundamentals and concepts of fusion and research efforts towards the implementation of a steady state tokamak reactor is presented. In 1990, a steady-state tokamak reacto...
This paper explores the fractal properties of token embedding spaces in GPT-2 language models by analyzing the stability of the correlation dimension, a measure of geometric complexity. Token embeddings represent words or subwords as vectors in a hig...
Background/Objectives: First NIBUT, Average NIBUT, and tear meniscus height (TMH) are routinely used to characterize tear film stability and tear volume at individual visits, whereas their longitudinal behavior across the hospital-attending populatio...
of 8