Search Results (370)

Selecting the appropriate academic track is a critical decision for students, as misalignment between program requirements and individual cognitive, personality, and competency profiles can significantly impact academic performance, persistence, and overall educational outcomes. Traditional educational recommender systems often rely solely on skill matching or on the correlation of interests, failing to account for the dimension of competency that is required for success in specific academic tracks. This paper introduces a novel Multi-Dimensional Psychometric Alignment (MDPA) algorithm that moves beyond simple rank-order correlation between skills and programs by jointly integrating multiple psychometric perspectives and evaluating both preference similarity and competency sufficiency. Based on a structured synthesis of Cognitive Preferences (MBTI), Cognitive Modalities (Gardner’s Multiple Intelligences), and Personality Stability (Big Five), the proposed profile captures complementary dimensions of student readiness that are usually examined separately in prior educational recommender systems. Then applies an alignment algorithm-which is based on a hybrid similarity metric that fuses Spearman’s Rank Correlation (Interest Shape) with Weighted Euclidean Distance (Competency Magnitude), enforced by non-linear threshold penalties for critical traits- in order to find the best options for students. This approach constitutes a deterministic, explainable recommender system whose novelty lies in combining heterogeneous psychometric evidence with an explicit magnitude–shape matching mechanism and threshold-based academic viability constraints. Our approach is validated through a case study of university students in Kazakhstan, and the results demonstrate how “academic fit” is better modeled as a function of both interest pattern and trait sufficiency, offering a robust alternative to “black-box” skill-based recommenders. Full article

In this study, a functional surface coating composed of Fe-Mn binary oxide (FM) and poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS, PP) was applied to a PVDF membrane (PP-FM-PVDF) for efficient arsenate (As(V)) removal. PP acts as a dispersant and hydrophilic modifier, ensuring uniform FM distribution and reducing the water contact angle to 50.1°. The PP-FM-PVDF membrane achieves a maximum As(V) adsorption capacity of 30.43 mg/g, outperforming pristine and singly modified membranes. The batch adsorption data fit the Langmuir isotherm (R² = 0.999) and pseudo-second-order kinetic model (R² = 0.99), indicating monolayer chemisorption. The coating increases the specific surface area to 27.33 m²/g and the tensile strength to 6.41 MPa. Dynamic filtration shows that 2.70 L (2149.7 L/m²) of 100 μg/L As(V) solution can be treated before the permeate concentration exceeds the WHO guideline of 10 μg/L. After alkaline regeneration (pH 11), 62.9% of the initial capacity is retained. Complementary surface-sensitive analyses (zeta potential, XPS, and EXAFS) reveal that arsenate adsorption occurs primarily through ligand exchange between arsenate oxyanions and Fe/Mn surface hydroxyl groups on the coating, forming inner-sphere bidentate complexes (Fe–O–As and Mn–O–As), while electrostatic interactions play a secondary, pH-dependent role. This surface engineering strategy—synergistically integrating a conductive hydrophilic polymer with a metal oxide as a functional coating on PVDF—offers a reusable, high-performance platform for arsenate remediation, underscoring the critical role of interface design in environmental membrane applications. Full article

Accurate corrosion-rate prediction for buried pipelines is fundamental to sustainable integrity management, yet industrial corrosion datasets are typically small and heterogeneous, making reliable model training challenging. This study proposes CARE-Boost (Context-Aware Restrained-Ensemble Boosting), a compact method designed for exactly this setting. The algorithm fuses three complementary components: a practical-variable gradient-boosting branch trained on directly measurable pipeline predictors; a spatial-neighborhood context branch that encodes short-range continuity from adjacent stake-point predictors; and a restrained regime-focused augmentation scheme stabilized by fixed convex blending. The engineering dataset was collected from a natural-gas pipeline in Central Asia and organized as a one-dimensional spatial sequence. Under repeated $5\times 2$ cross-validation, CARE-Boost achieves RMSE $=0.0577\mathrm{mm}/\mathrm{year}$, MAE $=0.0314\mathrm{mm}/\mathrm{year}$, and $R^2=0.472$, outperforming XGBoost ($0.0599$, $0.0320$, $0.432$) and LightGBM ($0.0618$, $0.0333$, $0.385$); the improvement over XGBoost is statistically significant ($p=0.0068$, splitwise Wilcoxon). Split-conformal intervals achieve $95.0\%$ empirical coverage at the nominal $90\%$ level. SHAP attribution identifies soil aggressiveness, pH, water content, and bicarbonate as the dominant corrosion drivers, and the mean fit–predict cycle completes in $1.80$ s, supporting deployment in routine integrity workflows. These findings position CARE-Boost as a practically viable uncertainty-aware corrosion predictor for sustainable integrity management under small-sample conditions, with its primary evidence lying in improved point prediction, calibrated uncertainty, and interpretable spatially informed inference. Full article

In Brazil, approximately 16.4 million people (8.1% of the population) live in informal settlements (favelas), with Rio de Janeiro among the most heavily affected. This situation results from rapid rural–urban migration and unplanned urbanization, leading to persistent land tenure conflicts, exemplified by the decades-long struggle in the Santa Luzia favela. This study demonstrates how participatory geospatial methodologies can support land regularization while preventing displacement. Unlike conventional participatory mapping studies that often prioritize community empowerment over technical precision or, conversely, state-led cadastres that prioritize accuracy over local participation, this study integrates two complementary frameworks: counter-cartographies (to redress power asymmetries) and fit-for-purpose land administration (to ensure minimal technical standards for tenure security). Through a university–community collaboration, a low-cost cadastral survey of Santa Luzia was conducted using remotely piloted aircraft photogrammetry to generate high-resolution orthoimagery (2 cm ground sample distance), GIS vectorization integrated with resident interviews and local knowledge, and spatial analysis compliant with local technical standards. The findings demonstrate three specific innovations: (1) methodological: volunteer students and community residents co-produced cartography achieving 2 cm precision, meeting legal requirements for land regularization without expensive professional surveys; (2) participatory: unlike purely community-led mapping that may lack legal enforceability or top-down systems that exclude local knowledge, this model embeds participatory data collection within Brazil’s Social Interest Regularization (REURB-S) framework, ensuring both grassroots legitimacy and state recognition; and (3) policy-making: the project operationalizes counter-cartographies not as symbolic resistance but as a legally compliant pathway to tenure security, offering a transferable model for democratizing land administration in informal settlements while challenging exclusionary urban planning. Full article

Colorectal cancer (CRC) is responsible for a high cancer burden and a high number of deaths all over the world, although effective screening can make it preventable to a significant extent. Stool-based tests, such as the fecal immunochemical test (FIT) and multitarget stool DNA (mt-sDNA) testing, are gaining considerable popularity as non-invasive procedures that can be a replacement for colonoscopies for people at an average risk for colon cancer. Despite evidence from several randomized controlled trials supporting the use of these tests for colorectal cancer screening, their external validity in a real-world setting is influenced by many factors such as adherence, timely follow-up post testing, the healthcare cost burden, accessibility and the capacity of the health system. In this article, we have performed an extensive narrative literature review of research published between 2020 and 2025 comparing FIT and the mt-sDNA test with reference to diagnostic accuracy, cost-effectiveness, adherence and outcomes of implementation. We discuss the issues of sensitivity and specificity, look at post-test requirements for colonoscopy and check if there is any discrimination in healthcare. These findings suggest that FIT and mt-sDNA tests should not be considered competing technologies but rather complementary screening methods, with their overall effectiveness contingent upon appropriate patient selection and widespread system-level implementation. It is crucial to combine strategic test selection with a robust follow-up infrastructure to ensure that the entire population benefits from the CRC prevention program. Full article

Urban street canyons exert a critical influence on local microclimates; however, the dynamics of mixed convective airflow under unsteady wind and thermal forcing remain poorly quantified. This study systematically investigates the spatiotemporal characteristics of airflow within symmetric and asymmetric street canyons through integrated long-term field measurements and complementary CFD simulations. Field data collected over 120 monitoring days at the Weishui Campus of Chang’an University were analyzed using the Levenberg–Marquardt nonlinear curve-fitting algorithm. The analysis demonstrates that sine functions accurately represent diurnal surface temperature variations during consecutive clear sky periods, whereas polynomial functions of varying orders are required to characterize meteorologically complex episodes, including cold-wave cooling and seasonal transitions. Ambient wind patterns outside the canyon were further classified into two characteristic variation modes: stepwise and gradual. Complementary unsteady RANS simulations, with wall boundary conditions derived directly from the fitted field data, reveal that canyon geometry and meteorological forcing jointly govern the evolution of airflow structures and thermal distributions across seasons. In the symmetric canyon, the flow transitions from complex multi-vortex activity in spring and summer to a more stable regime in autumn, with two well-defined counter-rotating vortices emerging during winter cold-wave events. In the asymmetric canyon, strong summer solar heating sustains a dominant leeward vortex with a strengthening secondary structure, whereas winter cold wave intrusion generates a hierarchically nested vortex system in which secondary and tertiary vortices progressively develop and detach. By coupling empirical surface temperature functions with CFD boundary conditions, this study advances the precision of predictive microclimate models and provides an evidence-based framework for optimizing street canyon geometry to enhance ventilation performance, energy efficiency, and outdoor thermal comfort. Full article

Background/Objectives: Regular physical exercise is strongly recommended for individuals with type 1 diabetes mellitus (T1DM) because of its beneficial effects on cardiovascular fitness, insulin sensitivity, metabolic control, and overall health. Nevertheless, participation in physical activity remains limited, largely due to the fear of exercise-induced hypoglycemia and glycemic instability. Glycemic responses to exercise in T1DM are influenced by the interaction between exercise modality, circulating insulin levels, nutritional status, and diabetes technologies. Continuous aerobic exercise, resistance training, high-intensity interval exercise, and mixed intermittent activities elicit distinct metabolic and hormonal responses, resulting in heterogeneous glycemic trajectories. This narrative review aimed to provide a clinically oriented synthesis of the physiological mechanisms underlying exercise-related glycemic fluctuations in T1DM and to discuss integrated insulin- and carbohydrate-based strategies to support safer participation in physical activity in the context of modern diabetes technologies. Methods: A structured narrative review was conducted using PubMed/MEDLINE, Scopus, and complementary searches in Google Scholar to identify experimental studies, observational studies, systematic reviews, consensus statements, and clinical guidelines focused on exercise-related glycemic responses in individuals with T1DM. Only articles published in English were considered. Evidence was selected and synthesized according to relevance to exercise modality, insulin therapy strategies, carbohydrate management, and diabetes technologies, including continuous glucose monitoring, continuous subcutaneous insulin infusion, and automated insulin delivery systems. The final narrative synthesis was based on 44 selected studies, reviews, consensus statements, and guidance documents considered most relevant to the objectives of this narrative review. Results: Available evidence indicates that continuous moderate-intensity aerobic exercise is most consistently associated with progressive glucose declines and increased risk of hypoglycemia, particularly when performed in the presence of elevated insulin on board. In contrast, resistance exercise and short-duration high-intensity or anaerobic exercise more frequently induce stable glycemia or transient hyperglycemia through adrenergic stimulation and increased hepatic glucose output. Mixed and intermittent exercise modalities often produce more variable responses depending on exercise sequencing, nutritional status, and insulin exposure. Across studies, integrated adjustment of basal and prandial insulin doses together with individualized carbohydrate supplementation emerged as the most effective strategy to reduce exercise-related glycemic instability. Continuous glucose monitoring and insulin pump technologies improved glucose trend awareness and management flexibility; however, physical exercise remains a challenging condition for current automated insulin delivery algorithms and still requires active user-driven decision-making. Conclusions: Exercise management in T1DM should be based on an individualized interpretation of exercise modality, glucose trends, insulin exposure, and nutritional context rather than on fixed glucose thresholds alone. Combining anticipatory insulin adjustments, tailored carbohydrate strategies, and appropriate use of diabetes technologies may substantially reduce glycemic variability and improve confidence toward physical activity participation. Structured education and individualized clinical guidance remain essential to translate physiological knowledge into effective real-world exercise management. Full article

This paper investigates the estimation of the stress–strength reliability parameter $R=P(Y<X)$ when both stress and strength follow independent Garhy distributions under progressive Type-II censoring schemes. A closed-form expression for $\mathit{R}$ is explicitly derived, enabling effective and precise calculation without numerical integration. The Garhy distribution, a flexible one-parameter lifetime model with an increasing hazard function, is confirmed by full-scale goodness-of-fit diagnostics. A Bayesian estimation model is trained on non-informative priors (normal and extended Jeffreys priors) under squared error loss. The posterior expectations are analytically intractable; we adopt two complementary methods of computation: (i) Markov Chain Monte Carlo (MCMC) using the Metropolis–Hastings algorithm and (ii) the Tierney–Kadane (TK) approximation, which provides extremely precise analytical estimates with significantly reduced computational burden. Monte Carlo simulations are large-scale and compare the proposed estimators under different censoring schemes, sample sizes, and parameter configurations in terms of bias and mean squared error (MSE). The methodology is further applied to a real medical dataset comprising kidney dialysis patient survival times, demonstrating its practical relevance in clinical reliability assessment. Results consistently indicate that Bayesian methods, particularly with the extended Jeffreys prior, outperform classical MLEs in terms of stability and accuracy, especially under heavy censoring. Moreover, the TK approximation yields estimates virtually identical to MCMC while requiring only a fraction of the computational effort. We further extend the TK framework to approximate the posterior variance of R and the expected log-likelihood, providing a fully analytical alternative to MCMC for comprehensive Bayesian inference. Full article

Monitoring performance in football often combines physical testing and GPS-derived external-load measures, although their relationships remain unclear. This study examined the relationships between physical-test outcomes and GPS-derived external-load variables during the pre-season in professional female football players and whether these measures appear to capture distinct dimensions of performance. This observational study monitored 24 outfield players from a Brazilian Women’s First Division team during a 6-week pre-season. Players performed the countermovement jump, 10 m and 30 m sprints, change-of-direction test, and 30–15 intermittent fitness test while external load was recorded across field sessions. Associations were examined using Pearson’s or Spearman’s correlations, and principal component analysis (PCA) was applied. Significant correlations were more frequent within than between domains. Total distance correlated with accelerations (ρ = 0.740, p < 0.001), decelerations (ρ = 0.684, p < 0.001), Z3 distance (ρ = 0.595, p = 0.003), and Z4 distance (ρ = 0.584, p = 0.003), while sprint count correlated with sprint distance (r = 0.950, p < 0.001). Estimated VO₂max correlated positively with CMJ (r = 0.533, p = 0.007) and negatively with 10 m (r = −0.445, p = 0.029) and 30 m sprint times (r = −0.476, p = 0.019). PCA identified two components explaining 61.4% of the total variance: external load (40.6%) and physical performance (20.8%). These findings indicate that both approaches capture distinct and complementary aspects of performance. Full article

Alzheimer’s disease (AD) is increasingly recognized as a biological continuum characterized by early neuropathological and molecular changes that precede the onset of clinical symptoms. Fluid biomarkers have transformed the diagnostic landscape by enabling the in vivo detection of core AD pathologies, particularly amyloid-β deposition and tau-related neurodegeneration. Despite the rapid expansion of candidate biomarkers, however, only a limited number have successfully translated into clinical practice. Discovery-phase approaches, primarily driven by mass spectrometry-based proteomics, enable the unbiased identification of novel biomarker candidates across multiple biological pathways. Research-phase methods, including immunoassays such as enzyme-linked immunosorbent assay (ELISA), electrochemiluminescence immunoassays (ECLIA), microfluidic platforms, and ultrasensitive technologies such as single-molecule array (SIMOA), support analytical and clinical validation in well-characterized cohorts. Clinical implementation has been advanced by fully automated platforms, including Lumipulse and Elecsys, which have obtained regulatory approval for cerebrospinal fluid biomarkers and, more recently, blood-based biomarkers. These developments represent a paradigm shift toward minimally invasive and scalable diagnostic strategies that may reduce dependence on neuroimaging techniques. Nevertheless, major challenges remain, including assay standardization, inter-platform variability, demonstration of clinical utility, and barriers to widespread clinical adoption. This review provides a comprehensive overview of analytical methods used to measure AD fluid biomarkers in cerebrospinal fluid and plasma, structured according to the biomarker development pipeline from discovery to clinical implementation. Overall, the review highlights a fit-for-purpose approach to biomarker development and emphasizes the complementary roles of diverse analytical technologies across the different phases of biomarker translation. Full article

Background/Objectives: Sepsis is clinically dynamic, and isolated admission biomarker values may insufficiently capture early biological evolution after treatment initiation. This study evaluated whether routine biomarker reassessment at approximately 48 h provides incremental prognostic information beyond admission Sequential Organ Failure Assessment (SOFA) score for 28-day mortality in sepsis. The analysis was framed as an exploratory 48 h landmark prognostic assessment among patients who were alive and had complete biomarker reassessment data at 48 ± 6 h. Methods: We conducted a prospective single-center observational cohort study including adult patients with sepsis. Clinical and laboratory data were collected at baseline (M1) and repeated 48 ± 6 h later (M2). The primary outcome was 28-day mortality. Candidate biomarkers included C-reactive protein (CRP), procalcitonin (PCT), lactate (LAC), and neutrophil-to-lymphocyte ratio (NLR). PCT clearance and NLR change were calculated as relative changes between M1 and M2, whereas 48 h CRP and 48 h lactate were evaluated as early reassessment values. Exploratory logistic regression models were constructed using admission SOFA as the clinical reference model. Model discrimination and fit were summarized using receiver operating characteristic analysis, likelihood-ratio testing, and Nagelkerke R²; the models were not intended as validated individual-level risk calculators. Results: The 48 h landmark analytical cohort included 126 patients, of whom 44 (34.9%) died within 28 days. Admission biomarker values showed limited prognostic signal. SOFA alone showed fair discrimination (AUC 0.740). Among the primary SOFA-augmented models, SOFA plus PCT clearance showed the highest discrimination and explanatory performance (AUC 0.810; Nagelkerke R² 0.332) and significantly improved model fit compared with SOFA alone. SOFA plus NLR change and SOFA plus 48 h lactate also provided incremental prognostic information, although their gains were more modest. In exploratory combined modeling, SOFA plus PCT clearance and NLR change provided the most coherent additional signal, with all predictors retaining independent associations with 28-day mortality. Conclusions: In this exploratory single-center 48 h landmark analysis, selected routine biomarker reassessment measures were associated with 28-day mortality beyond admission SOFA. PCT clearance provided the clearest incremental prognostic signal, while NLR change offered complementary information. Persistent 48 h lactate elevation was also informative, whereas lactate clearance was not. These findings should be interpreted as hypothesis-generating and require validation in larger cohorts, ideally including serial organ dysfunction measures such as 48 h SOFA or SOFA change. Full article

This conceptual article introduces knowledge-centric recruitment (KCR) as a distinct dynamic capability that reframes recruitment and post-hire socialization as strategic knowledge-development activities. (1) Background: Unlike conventional vacancy-driven approaches, KCR is a proactive process through which firms deliberately access and import external organizational capabilities embodied in senior professionals—termed knowledge-hires—from rival organizations. These knowledge-hires embody tacit, socio-cognitive building blocks of capabilities developed through involvement in their prior employers’ routines and practices. (2) Methods: This article develops a micro-foundational model of KCR comprising four interrelated processes: external capability scanning and prioritization, identification of target capabilities and knowledge-hires, evaluation through the novel lens of contextual capability fit, and expectations of adaptation during onboarding. (3) Results: Contextual capability fit integrates complementary and supplementary quality with knowledge distance to enable firms to forecast both the strategic value of inbound capabilities and the hire’s expected socialization difficulty. (4) Conclusions: The primary theoretical contribution lies in advancing the learning-by-hiring literature by shifting the focus from passive knowledge diffusion to deliberate, calculative capability acquisition. By integrating insights from the knowledge-based view, person–organization fit, absorptive capacity, and strategic recruitment, the KCR model offers a coherent micro-foundational framework for transforming employee mobility into a source of sustained competitive advantage. Full article

Constructing age-friendly residential environments is essential for supporting aging in place among the growing population of urban empty-nest older adults in China. Grounded in person–environment fit theory, this study developed and validated a multidimensional Aging-Suitability Index (ASI) to examine how residential environmental factors shape housing suitability and perceived independence. In this study, “aging suitability” refers to the degree of fit between residential environments and older adults’ needs for safety, functionality, accessibility, social support, and technological support, with the central aim of enabling aging in place and independent living. Questionnaire data were collected from 753 urban empty-nest older adults across 19 provinces in China and analyzed using partial least squares structural equation modeling (PLS-SEM). The structural model showed strong explanatory power (R² = 0.754). The results revealed a clear hierarchy of environmental influences. Safety facilities and physical design were the strongest direct predictors of residential aging suitability, indicating that risk reduction and ergonomically appropriate spatial design constitute the foundation of age-friendly housing. Although accessibility showed a smaller direct effect, it exerted a significant indirect effect through perceived independence, with 67.35% of its total effect mediated through this pathway, highlighting the importance of barrier-free design in maintaining autonomy. Social support and smart technology also contributed positively as complementary resources that strengthened person–environment fit. These findings suggest that age-friendly housing interventions should move beyond fragmented modifications toward integrated residential renewal strategies that prioritize safety and physical design, improve accessibility to support independent living, and combine community support with age-friendly technologies. This study provides empirical evidence to inform built-environment decision-making in the design and renewal of housing for older adults in rapidly aging urban contexts. Full article

Accurately predicting traffic crash severity is critical for intelligent transportation systems, where outcomes emerge from the interaction of infrastructure, environment, traffic control, and human behavior. However, existing approaches face three key challenges: severe class imbalance, computational inefficiency, and limited support for system-level risk understanding. To address these issues, this study proposes a unified and system-aware framework integrating Conditional Tabular Generative Adversarial Network (CTGAN), Tabular Prior-data Fitted Network (TabPFN), and eXplainable Artificial Intelligence (XAI) methods for data augmentation, efficient prediction, and interpretable analysis. CTGAN enhances rare but critical crash states while preserving feature dependencies; TabPFN enables accurate multi-class prediction with limited dataset-specific tuning; and XAI methods quantify the influence of key factors and their interactions. Experiments on a real-world crash dataset from Boston show that the proposed framework achieves competitive predictive performance with less reliance on dataset-specific hyperparameter tuning, while also providing complementary interpretability results from multiple perspectives. The results further reveal that crash severity is jointly shaped by visibility, traffic control, roadside features, and temporal dynamics, highlighting the interconnected nature of risk within the transportation system. By integrating predictive modeling with complementary interpretability analysis, the framework provides a systems-oriented basis for examining how environmental, infrastructural, and temporal conditions jointly relate to crash severity in the studied urban crash data, while offering a methodological reference for broader safety applications that require further validation. Full article

This study addresses pressure regulation in an induction-motor-driven centrifugal fan and introduces two complementary novelties: a Softsign-PI controller that shapes the tracking error via a Softsign nonlinearity before PI regulation and a hybrid starfish optimization with a differential evolution (hSFOA-DE) scheme for automatically tuning the controller parameters. The approach is evaluated on an experimentally validated nonlinear fan–motor model and benchmarked against modern metaheuristics—starfish optimization algorithm (SFOA), animated oat optimization (AOO), electric eel foraging optimization (EEFO), differential evolution (DE), particle swarm optimization (PSO)—as well as classical tunings—Murrill-based 2-DOF PID, Tyreus–Luyben PID and Ziegler–Nichols PI. Statistical summaries and boxplots indicate superior central tendency with reduced run-to-run variability; fitness–evolution curves show faster convergence; and time-domain performance metrics confirm improved transient and steady-state behaviour. Objective function comparisons further show the lowest values of both the Zwe-Lee Gaing (ZLG) and integral of absolute error (IAE), supporting advantages in robustness and tracking accuracy of the proposed approach. These gains reduce overshoot and cumulative error, which can lessen throttling losses and actuator duty in fan/pump service, suggesting potential energy and maintenance benefits. Full article