Search Results (2,507)

Rising non-performing assets (NPAs) remain a persistent threat to banking stability in emerging economies, including India. This study examines the role of conventional macroeconomic determinants in shaping NPA dynamics using annual panel data from 30 Indian banks over the period 2003–2022. Employing Robust Least Squares and dynamic modelling techniques, the analysis evaluates the impact of GDP growth, inflation, exchange rate movements, and repo rates, while addressing heteroscedasticity, autocorrelation, and bank-level heterogeneity. The findings indicate that currency depreciation significantly increases NPAs, whereas inflation and tighter monetary policy exert a moderating effect. GDP, however, does not exhibit a significant influence, suggesting limited macroeconomic transmission to banking asset quality. To ensure appropriate model specification, stationarity tests are conducted, guiding the inclusion of dynamic elements in the analysis. Once the model is adjusted accordingly, the results consistently highlight the relative importance of macroeconomic factors without yielding conflicting interpretations. While broader theoretical perspectives such as institutional memory and balance-sheet effects are acknowledged for contextual relevance, they are not empirically tested in this study. Overall, the findings emphasize that conventional macroeconomic variables play a meaningful, though selective, role in explaining NPA behaviour, offering clearer and more consistent insights for policy and banking practice. Full article

Background: Wearable sensors have gained increasing popularity as an objective method for remotely monitoring infant movement in naturalistic settings. Over the first year of life, infants generate a wide range of motions, from goal-directed to spontaneous movement. These include linear movements, such as kicks, and orientation changes, such as postural transitions. Many sensor processing pipelines emphasize capturing linear movements through movement-generated acceleration while focusing less on information about orientation embedded in the gravitational part of the data. Here, we introduce a complementary gravity-referenced approach that extracts the gravitational component of accelerometer signals to estimate limb orientation, extending the reliable quantification of rich and detailed aspects of infant movement. Infant orientation has demonstrated clinical relevance, including associations with later neuromotor outcomes, and it can be used to chart infant motor development, motivating the development of objective methods to quantify orientation from sensor data. Methods: Wearable sensors (Opal APDM) were used to longitudinally evaluate infant motor activity recorded in sessions conducted at 3, 6, 9, and 12 months of age. We extracted data from a 5 min segment that has simultaneous video recordings. From these datasets, applying the gravity-referenced method, we computed pitch, roll, and yaw, angles that collectively describe limb orientation. We then quantified orientation variability using axis-specific circular standard deviations (SDs) for pitch, roll, and yaw and a multi-axis composite measure based on generalized variance. Results: Axis-specific circular SDs for pitch, roll, and yaw, as well as the composite generalized variance, increased significantly from 3 to 12 months (p ≤ 0.01 for each metric). Composite variability was strongly associated with Mullen gross motor outcomes at 9 and 12 months of age (r = 0.55, p < 0.001). Conclusions: Overall, gravity-referenced pitch, roll, and yaw provide rich orientation features that increased as infants develop more postural transitions. Furthermore, the orientation features correlated with standardized measures of infant motor function. These orientation metrics can complement traditional linear kinematic measures and improve our ability to granularly track infant motor development in the first year of life. Full article

Understanding and monitoring driver mental workload is essential for improving road safety. This study proposes a multimodal machine learning framework to classify drivers’ mental workload using eye movement metrics, physiological signals, and driving behavior features. A driving simulator experiment was conducted with 26 participants under two workload levels induced by a secondary auditory task. Seven feature combinations and six classification algorithms were evaluated. The results showed that eye metrics were the most informative modality, and that feature selection had a greater impact on classification performance than algorithm choice. A support vector machine with optimized features was selected as the final model based on performance and stability, achieving an accuracy of 87.8% and an AUC of 0.95. To improve model transparency, SHapley Additive exPlanations (SHAP) was applied, highlighting key predictors such as blink rate and heart rate, and uncovering synergistic effects between visual and physiological variables. The model was further validated in a tunnel entrance scenario, where it identified increased workload associated with steeper longitudinal slopes. These findings emphasize the importance of multimodal data integration—particularly eye movements—for assessing mental workload. Future applications should prioritize feature diversity over algorithm complexity to enhance real-world implementation in workload monitoring systems. Full article

Background and Objectives: Postoperative pain after posterior spinal fusion (PSF) for adolescent idiopathic scoliosis (AIS) shows substantial interindividual variability, particularly during early mobilization. Although preoperative psychological vulnerability has been associated with less favorable pain trajectories in prior AIS research, evidence focused on the acute postoperative phase remains limited. This preliminary study evaluated whether preoperative psychological factors are associated with acute postoperative pain intensity, with separate assessment of resting and standing pain. Materials and Methods: A single-center retrospective cohort study included consecutive adolescents with AIS (<18 years) who underwent primary elective posterior instrumented spinal fusion between 1 January 2024 and 31 December 2025. Preoperative psychological variables were collected using validated instruments (STAIC-State, STAIC-Trait, Pain Catastrophizing Scale, HAQ/FDI, and inverted SRS-22). Pain intensity (VAS 0–10) was recorded at postoperative day (POD) 1, POD2, POD3, discharge, and 2-week follow-up in supine and standing positions. Derived endpoints included peak in-hospital standing pain, in-hospital standing pain burden (AUC), and standing–rest pain gaps. The prespecified inferential analysis used a linear mixed-effects model with fixed effects for time, position, preoperative STAIC-State, and position × STAIC-State interaction, with a patient-level random intercept. Results: Thirty-five patients were analyzed (mean age 15.2 ± 3.4 years; 62.9% female), with complete pain data at all timepoints. During hospitalization, standing pain was descriptively higher than resting pain (largest mean difference at POD2: 0.73 VAS points), with convergence at week 2 (both 1.52). In mixed-model analysis, pain significantly decreased at week 2 versus POD1 (β = −1.261, 95% CI −1.853 to −0.669; p < 0.001). Preoperative STAIC-State was not independently associated with postoperative pain (β = 0.030, 95% CI −0.065 to 0.124; p = 0.545), and no significant position × STAIC-State interaction was found (β = −0.008, 95% CI −0.079 to 0.064; p = 0.836). Conclusions: In this retrospective preliminary AIS cohort, postoperative pain improved significantly over time, while movement-evoked pain remained relevant during early recovery. In this preliminary cohort, no clear association was detected between preoperative state anxiety and acute postoperative pain intensity, supporting the need for broader multidimensional prognostic models in future prospective multicenter studies. Full article

This study examines selected factors influencing white maize price movements in South Africa over the period 1994–2024. Given the importance of white maize for food security, understanding the drivers of producer price dynamics is essential for effective policy formulation and managing price stability. Annual time-series data are analysed using an Autoregressive Distributed Lag (ARDL) modelling framework, complemented by bounds testing, an error-correction model, Toda–Yamamoto causality and structural break tests. The bounds test confirms the existence of a stable long-run cointegrating relationship between maize prices and the selected explanatory variables. In the short run, imports and fuel prices exert significant upward pressure on maize producer prices, while lagged fuel prices and rainfall reduce prices. In the long run, imports and fuel prices remain statistically significant determinants, whereas maize production, exports, the exchange rate, and rainfall are insignificant. Complemented with the structural break tests that identify regime shifts in the early 2000s, 2012, and 2021, causality results indicate that imports, rainfall and fuel prices lead to Granger causality in maize producer prices. Collectively the findings reinforce the conclusion that white maize prices in South Africa are governed by long-run structural relationships, while short-run price movements reflect temporary adjustments rather than permanent shifts in market fundamentals. An integrated, long-horizon analysis that jointly incorporates climatic, trade, and macroeconomic determinants within an ARDL framework is provided by the study. Therefore, the findings have important implications for climate-risk management, transport cost containment, trade and price-stabilisation policies. Full article

Accurate, non-contact monitoring of rumination behavior is essential for assessing dairy cow health and welfare, as well as for optimizing feeding strategies and herd management in modern precision livestock farming. However, practical deployment in commercial barns faces challenges such as occlusions, variable lighting, and dynamic cow movements. To address this, we developed a robust, automated vision-based framework for continuous rumination monitoring. The core of our system integrates an enhanced object detection algorithm with a robust tracking module, specifically improved to capture subtle behavioral features and maintain identity under complex conditions. Evaluated on a comprehensive dataset collected from commercial settings under various lighting and occlusion scenarios, our framework achieved high detection accuracy (mAP of 96.26%) and reliable tracking performance (multi-object tracking accuracy of 99.2%). This demonstrates its suitability for real-time, on-farm deployment. The study provides a practical, end-to-end solution for fine-grained behavioral analysis in complex environments, offering a tool that can enhance welfare assessment and support decision-making in dairy farm management. The methodological approach is also adaptable to other precision livestock monitoring tasks. Full article

Windsurfing (WS) and kitesurfing (KS) share the same environment but differ in biomechanics and equipment demands. This cross-sectional study compared physical performance between WS and KS athletes. Twenty-five male recreational athletes participated (WS n = 13, age 27.7 ± 7.0 years; KS n = 12, age 29.0 ± 7.5 years). Body composition, isometric strength (handgrip and back-and-leg dynamometer), dynamic balance (Y-Balance Test: YBT), functional movement quality (FMS), and drop-jump performance (ground contact time, reactive strength index, jump height, take-off time) were assessed. Groups were compared using the Mann–Whitney U test (p < 0.05), and Cliff’s δ was calculated for significant outcomes. Participant characteristics were similar, although surfing experience was greater in WS. KS showed higher leg strength (p = 0.041; δ = 0.481) with no difference in handgrip strength. KS also demonstrated higher FMS shoulder mobility (p = 0.022; δ = 0.532) and total FMS score (p = 0.014; δ = 0.577). No between-group differences were found for YBT metrics or drop-jump variables (p > 0.05). These findings indicate that KS athletes exhibit greater isometric pulling strength and movement proficiency, whereas balance and reactive jump performance are comparable, supporting discipline-specific conditioning priorities. Full article

Street vegetation is an important component of urban green space which plays a crucial role in promoting human well-being. To examine the impact of different types of street vegetation on individuals’ mental health, we presented two types of four street vegetation scenes in the real environment, concerning the form and species. One type consisted of random shrubs and regular shrubs. The other type consists of trees with single species and trees with diverse species. Forty participants took part in an experimental design to evaluate psychological and physiological changes before and after exposure to the street vegetation using the measures of EEG, HRV and eye movement. Our results identified that exposure to street vegetation enhanced alpha brain activity and reduced the HRV. In addition, eye movement was used to enhance restorative effects. The effect of different types of street vegetation varied significantly. It indicated that regular shrubs had a more positive effect on measures of relaxation compared with the random shrubs. The type of street vegetation of trees with diverse species had a more positive effect on measures of relaxation than the type of single species. The POMS scores of the regular shrubs decreased compared to the random shrubs and the diverse species decreased compared to the single species. The ROS scores of the regular and diverse types are higher than the random and single. The study suggests that the type manual-pruned street vegetation and the type of trees combined with plant diversity are generally more favorable in enhancing subjective comfort in the street vegetation. These findings underscore the importance of form and species in landscape planning and design to promote relaxation and comfort in the urban street environment. Full article

Work-related shoulder disorders during overhead assembly represent a persistent occupational challenge. We evaluated a quasi-direct-drive (QDD) active upper-limb exoskeleton during simulated overhead work, providing simultaneous metabolic, electromyographic, and kinematic assessment of QDD actuation under static and dynamic conditions. Seven healthy males completed within-subject comparisons of without-exoskeleton (WO) and with-exoskeleton (WE) conditions during dynamic screwing (5 min) and static holding (2.5 min, 3 kg). During static holding, the exoskeleton achieved substantial shoulder offloading (Upper Trapezius: −68.2%, 6/6 participants, p = 0.031, d = 3.61; Anterior Deltoid: −43.6%) and improved postural stability (32–41% variability reduction). However, metabolic cost increased during both static (+57.2%) and dynamic (+30.6%) tasks, while movement smoothness degraded. These findings extend prior task-dependent exoskeleton observations to QDD actuation, revealing that intrinsic backdrivability does not eliminate whole-body energy penalties from device mass. The exoskeleton exhibits task-dependent effectiveness: potentially suitable for prolonged static overhead holding but not currently recommended for dynamic assembly without mass reduction and control refinement. Full article

(1) Background: Military personnel are required to perform high-intensity actions and tactical tasks under external load, which increases system weight and alters movement mechanics. Understanding how these loaded conditions influence neuromuscular performance is essential for informing physical preparation and readiness monitoring. This study quantified the effects of tactical equipment on countermovement jump (CMJ) and countermovement rebound jump (CMRJ) force–time characteristics in active military personnel and evaluated the within-session reliability of these metrics under loaded and unloaded conditions; (2) Methods: Eighteen male soldiers performed CMJ and CMRJ assessments on dual force plates (1000 Hz) under unloaded and loaded conditions (standardized tactical equipment: 10.6 ± 1.18 kg). Force–time variables were categorized as strategy (phase durations, countermovement depth), driver (mean braking and propulsive force), and outcome (jump height, jump momentum, and modified reactive strength index; mRSI) metrics; (3) Results: CMJ outcome and driver metrics demonstrated good to excellent reliability under load (ICC ≥ 0.87; CV ≤ 8.4%), whereas CMRJ outcome variables showed reduced reliability and greater variability. Loaded conditions reduced jump height and mRSI in both CMJ and CMRJ (p < 0.05), while jump momentum and absolute mean force production increased, whereas force production relative to body mass decreased. During the CMJ (slow-SSC), participants exhibited longer braking and propulsive phase durations, indicating a temporal change in movement strategy under load, whereas CMRJ (fast-SSC) force–time characteristics showed increased contact time and reduced rebound metrics; (4) Conclusions: Overall, fast stretch–shortening cycle tasks appear more sensitive to loading conditions, whereas the CMJ provides a more robust and reliable assessment for monitoring neuromuscular performance in military personnel, particularly when considering both absolute and relative force responses. Full article

Accurate estimation of soil hydraulic parameters under drip irrigation is essential for improving water flow simulations and optimizing irrigation management; however, field measurements in aeolian sandy soils are often expensive and time-consuming. This study focused on typical aeolian sandy soils in the Kubuqi Desert. Field drip irrigation experiments were conducted to obtain temporal variations in soil water content and wetting front advancement, which were used to inversely estimate and calibrate hydraulic parameters for different soil layers. Soil pore space characteristics were quantified using nitrogen adsorption, and their relationships with hydraulic parameters were analyzed through correlation and redundancy analyses. On this basis, the combined effects of particle-size distribution and pore space structure on parameter prediction were evaluated, and soil water movement under drip irrigation was simulated and validated using HYDRUS-2D/3D. The results indicated pronounced spatial variability in soil hydraulic parameters. Residual water content, saturated hydraulic conductivity, and pore-size distribution index were significantly correlated with specific surface area, total pore volume, mean pore diameter, micropore volume fraction, and pore fractal dimension. Compared with approaches based solely on particle-size distribution, incorporating pore space structure effectively reduced the prediction errors of both hydraulic parameters and wetting front migration, thereby improving simulation accuracy. These findings demonstrate that integrating particle-size distribution and pore space characteristics provides a feasible approach for the rapid estimation of hydraulic parameters and the analysis of water movement in aeolian sandy soils under drip irrigation. Full article

Vigilance is used to detect distant threats in many species of birds. Allocation of time to vigilance is shaped by the social and physical environment of individuals, but little research has focused on how weather variables affect vigilance. Wind speed, in particular, can influence vigilance by decreasing manoeuvrability during escape, increasing energy costs or by decreasing the ability to communicate and assess risk. We examined how wind speed influenced vigilance in sentinels of a cooperative breeder, the Florida Scrub-Jay (Aphelocoma coerulescens). Sentinels in this species occupy vantage points to monitor their surroundings and can devote all their time to vigilance during sentinel bouts. We found that head turns in sentinels, which allow individuals to monitor different areas, became more frequent under windier conditions. Wind speed is not likely to affect manoeuvrability in sentinels that are already close to cover. Energy costs during high wind likely are not as relevant to sentinels as they may be to other group members, since sentinels do not forage. We conclude that the ability to assess risk visually was probably compromised by movements in the vegetation caused by wind. The study highlights behavioural adjustments to weather-related environmental variability. Full article

Global Navigation Satellite Systems (GNSS) offer precise movement analyses based on distance and speed in open-water sports. Despite the influence of swimming in triathlon, its performance analysis remains underdeveloped due to methodological limitations in capturing continuous data in aquatic environments. This review aimed to: (1) systematically analyse and compare the sensor-based technologies applied to open-water movement analysis, and (2) propose a framework for continuous GNSS-based assessment of triathlon swim performance. A systematic search was conducted prior to the 14 August 2025 across four databases (Web of Science, SPORTDiscus, PubMed, and SPONET). Studies were eligible if they analysed open-water sports using GNSS-based technologies for continuous movement or performance analysis. Studies limited to indoor swimming, inertial sensors, or non-sporting applications were excluded. Methodological quality and potential sources of bias were evaluated using a custom scheme based on GNSS reporting guidelines, as methodological heterogeneity precluded the application of standardised tools. Following screening and eligibility assessment, articles were analysed qualitatively. In total, 20 articles were included and focused on surfing, sailing, water skiing, windsurfing, kitesurfing, stand-up paddling (SUP), and swimming. Most studies focused on board- and sail-based sports, employed sampling frequencies between 1 and 15 Hz, and demonstrated substantial variability in device specifications and reporting quality. Different sensors and GNSS-derived variables were central to discipline-specific performance analysis. The strength of evidence is limited by the heterogeneous methodologies, and variable reporting quality. The proposed framework provides methodological guidance for implementing high-resolution GNSS-based monitoring in triathlon swimming to improve pacing analysis and race strategy development. Full article

Background: Obstructive sleep apnea (OSA) is a complex and diverse disorder affecting almost one billion individuals worldwide. Severity of untreated OSA, measured by the apnea–hypopnea index (AHI), is noted to be associated with an increased all-cause and cardiovascular mortality. Although widely used, AHI insufficiently captures disease variability as there is a poor correlation of symptoms with the AHI. There lies individual susceptibility to the effects of OSA and that parameter alone poorly predicts cardiovascular outcomes without considering intermittent hypoxia and the hemodynamic effects of OSA. Recognition of clinical, polysomnographic, and neurophysiological phenotypes offers an opportunity to refine diagnosis, prognosis, and management strategies. Methods: We conducted a narrative synthesis of the literature involving 70 articles, focusing on quantitative and qualitative (Q2) clinical traits, polysomnographic parameters, and mechanistic insights that enable subclassification of OSA beyond AHI. Evidence from large cohorts, animal models, and pathophysiological studies were reviewed. Results: Phenotyping based on a Q2 analysis of polysomnographic respiratory event predominance, event duration, positional and REM dependence, hypoxic burden, and arousal characteristics reveals significant heterogeneity in risk profiles and therapeutic response. Apnea-predominant OSA correlates with a higher oxygen desaturation index and Epworth sleepiness scale. Hypopnea-predominant OSA correlates with a cardiometabolic disease burden and may show a more favorable response to surgical therapies. The duration of respiratory events is related to cardiovascular risk, and REM-predominant OSA independently predicts hypertension and adverse cardiovascular outcomes. Supine-predominant OSA demonstrates treatment responsiveness to auto-positive airway pressure and positional therapy. Respiratory effort–related arousals (RERAs), RERA-predominant OSA and the broader respiratory disturbance index (RDI) provide neurophysiological insight often missed by AHI-based classifications. Hypoxic burden, rather than AHI, emerged as a superior predictor of cardiovascular events and mortality. Finally, arousal frequency and periodic limb movements independently predict cardiovascular morbidity. Conclusions: Employing Q2-based phenotyping that incorporates clinical, polysomnographic, and neurophysiological markers improves risk stratification, prognosis, and individualized management of OSA. Future investigations should prioritize integrating phenotypic subclassification into diagnostic criteria and treatment planning to advance precision medicine in sleep apnea care. Full article

After neurological injury, individuals often undergo physical therapy to regain motor function, which can be supplemented with use of virtual reality (VR). Rehabilitation commonly employs methods that encourage movement variability to promote functional gains, such as perturbations. Rehabilitation also commonly integrates additional sensory modalities for guidance and cognitive engagement to the protocol. In this exploratory, proof-of-concept study, neurotypical participants were trained on a custom tracing task with targeted dynamic shifting to induce movement variability, under both expected (proactive) and unexpected (reactive) conditions, with and without added auditory feedback. Participants significantly (p < 0.05) improved performance (tracing accuracy) after training with audio feedback. Participants trained without audio feedback showed decreased electrodermal activity (EDA), a measure of physiological engagement. Audio feedback during reach training with complex objectives (e.g., dynamic shifting) can promote performance improvements and cognitive engagement. Full article