Search Results (21,752)

Cognitive models of depression posit a core role for attentional biases, though empirical evidence remains inconsistent, likely due to variations in task demands. This study utilized eye-tracking to assess attentional patterns in individuals with depressive symptoms during a goal-directed visual search task, specifically dissociating early orienting and late disengagement. Seventy-seven participants, classified into high (HD) and low (LD) depressive-symptom groups based on PHQ-9 scores, completed a “face-in-the-crowd” (FITC) task. The set size (4, 8, or 12 faces) was varied to examine the role of perceptual load. The task involved searching for a single emotional target among neutral distractors (assessing early orienting) and searching for a single neutral target among emotional distractors (assessing late disengagement). Contrary to the negativity-bias hypothesis, the HD group demonstrated what might be interpreted as adaptive attentional regulation. During early orienting (8-face condition), the HD group showed reduced total dwell time on happy targets, suggesting accelerated identification. An attentional bias index (sad minus happy dwell time) correlated positively with depression severity. During late disengagement (8-face condition), the HD group exhibited shorter target fixation latency specifically with sad distractors, indicating facilitated disengagement from negative information. The corresponding bias index correlated negatively with depression levels. Under explicit goal-directed demands, individuals with high depressive symptoms displayed facilitated processing of happy faces and accelerated disengagement from sad faces, rather than an enhanced negativity bias. This pattern tentatively suggests a possible adaptive attentional regulatory mechanism in early depression, although the findings were limited to the 8-face condition and no significant group differences emerged at set sizes 4 or 12. Replication is required before firm conclusions can be drawn. The result underscores the critical influence of task demands and highlights the value of early identification and targeted intervention. Full article

The impact of wind on disdrometer measurements has not yet been demonstrated through controlled reproducible physical experiments. This study aims to provide quantitative evidence of the deviation in raindrop trajectories approaching the sensing area of an optical disdrometer (the Thies Clima LPM) when immersed in a wind flow with a known velocity and direction relative to the sensor orientation. To this end, water drops with diameters between 0.9 mm and 1 mm were released in a wind tunnel and directed towards the instrument’s sensing area. Their trajectories were measured using a high-speed camera and compared with those expected in undisturbed conditions, as well as with the airflow field around the instrument body as measured in previous studies. This experiment provided the first direct measurement of the deviation in individual drop trajectories induced by wind near the Thies Clima LPM, a disdrometer commonly used in hydrological studies and applications. The effect of the non-radially symmetric geometry of the instrument on wind direction was observed, identifying the configuration most affected (parallel to the laser beam). The repeatability of the drop releasing system was checked by releasing multiple drops from the same position. This allowed attributing differences in the observed trajectories to a variation in the drop diameter. The collected dataset can be used to validate numerical models of the wind-induced bias of disdrometers and to develop adjustment functions for field measurements. Full article

Hand range of motion (ROM) measurement is crucial for diagnosing joint limitations, tracking rehabilitation progress, and creating personalized treatment plans. In recent years, exergames combined with dedicated game controllers have emerged as promising tools to complement traditional hand rehabilitation; however, their validity as motor function assessment tools remains insufficiently explored. This study evaluates the validity of the eJamar game controller as a tool for measuring hand ROM and hand grip strength (HGS), by comparing its outputs with standard goniometry and dynamometry. In a prior technical validation using a robotic arm under controlled conditions, the device showed a mean error of approximately 1.5°, indicating high measurement precision under ideal conditions. In the clinical validation with 32 patients undergoing hand rehabilitation, performance was movement-dependent. Pronation and supination showed strong agreement (MAE < 3°) and higher agreement compared with other movements, whereas flexion, extension, and radial-ulnar deviation exhibited weaker correlations and substantially higher errors (around 20°). In contrast, grip strength measurements for more and less affected hands, respectively, showed high correlation (0.88–0.91) and moderate agreement (ICC 0.81–0.66) with MAE values around 4 kg-f. Overall, results suggest that the eJamar shows preliminary suitability for assessing HGS and forearm pronation and supination in clinical settings. However, for HGS, agreement should be interpreted with caution due to the observed bias and error levels, indicating that further validation and calibration are required before stronger clinical claims can be made. For wrist flexion, extension, and radial-ulnar deviation, the device currently shows limited accuracy and requires further improvement. Full article

Writing development in the early grades depends critically on transcription skills, yet little is known about how components of orthographic knowledge support children’s written composition in European Portuguese. This study examined whether lexical and sublexical orthographic knowledge assessed at the beginning of Grade 2 predict written composition at the end of the school year, and whether these effects are direct or mediated by word spelling. Eighty Grade 2 children completed measures of lexical orthographic knowledge (orthographic choice), sublexical orthographic knowledge (orthographic awareness), and word spelling at the beginning of the year, and a written composition task scored for lexical diversity at year’s end. Path analyses with maximum likelihood estimation and bias-corrected bootstrapping showed that orthographic knowledge explained 44% of the variance in word spelling and up to 18% in written composition. Lexical orthographic knowledge was a significant direct predictor of written composition (β = 0.38, p < 0.01), whereas sublexical orthographic knowledge showed a small but significant indirect effect through spelling (β = 0.08, p < 0.05) in the full mediation model. These findings highlight the central role of orthographic knowledge, particularly its lexical component, in supporting early writing in an orthography of intermediate depth. Full article

Introduction: Breast cancer-related lymphedema (BCRL) affects quality of life (QoL) and increases healthcare costs. Resistance training (RT) is proposed as a preventive strategy, although its safety and effectiveness remain uncertain. Objective: To evaluate the effectiveness and safety of RT in preventing BCRL in women at risk. Methods: MEDLINE, Embase, CENTRAL, PEDro, and LILACS databases were searched from their inception to January 2025, along with the gray literature, trial registries, and preprints. Risk of bias was assessed using RoB 2, and certainty of the evidence (CoE) was assessed using GRADE. Primary outcomes were the occurrence of lymphedema and overall QoL; secondary outcomes included pain, upper limb function, range of motion (ROM), grip strength, and adverse events. Results: Eight RCTs (n = 1131) were included. The effects of RT on lymphedema and arm volume are very uncertain (very low CoE). For QoL, pain, ROM, and grip strength, the findings were inconsistent and uncertain (low to very low CoE). Adverse events were mild and transient, with no serious complications. Conclusion: RT is probably safe in women at risk of developing BCRL. Its preventive effectiveness is highly uncertain. Well-designed RCTs with standardized diagnostic criteria, patient-centered outcomes, and long-term follow-up are needed to establish their role in BCRL prevention with greater certainty. Ethics and dissemination: This study did not require ethical approval. The results will be disseminated through publications in peer-reviewed journals and academic presentations. Registration: PROSPERO (CRD42023455720). Full article

The Ganges–Brahmaputra–Meghna (GBM) delta is one of the most densely populated and flood-prone regions in the world. Identifying the exposure patterns of settlement expansion under different flood hazard levels in the GBM delta is of significant importance for enhancing the delta’s regional resilience. This research regionally screens settlement flood exposure by overlaying the Global Urban Expansion Simulation Dataset and the Aqueduct Floods Hazard Map at a 1 km spatial resolution. To account for inter-model variability, this study utilized the ensemble mean of five global climate models for future projections in 2030 and 2050 under SSP2-4.5 and SSP5-8.5 scenarios. Flood hazards were categorized into four specific levels based on inundation depth, namely low-hazard (0–0.15 m), medium-hazard (0.15–0.5 m), high-hazard (0.5–1.5 m), and highest-hazard (≥1.5 m). The study employed spatial overlay analysis and excluded missing pixels to avoid statistical bias from incomplete data. The findings indicate that under historical and future socioeconomic scenarios, both high- and highest-hazard zones exhibit significant settlement expansion, and the expansion rate within highest-hazard zones (270.9–357.1%) is expected to increase substantially compared to the historical baseline, reaching 1.57–1.85 times the expansion rate of flood-safe zones. Within the high- and highest-hazard categories, the contribution rate of fluvial and coastal flood coincidence zones reaches 21% to 22%. Furthermore, approximately 87% of the settlements within these fluvial–coastal coincidence zones are exposed to high-hazard levels or above. This study characterizes the variation characteristics of settlement exposure within fluvial–coastal flood coincidence zones under future socioeconomic scenarios. These results provide a first-order regional screening and macro-scale support for identifying broad exposure trends and establishing a baseline for future high-resolution assessments in the GBM delta. Full article

The mathematical characterization of non-stationary signals remains a significant challenge, particularly when impulsive components are obscured by high-dimensional noise and structural coupling. This paper proposes an application-driven mathematical methodology for a learnable discrete wavelet transform (LDWT) that combines classical multi-resolution analysis with task-optimized data-driven adaptivity. Rather than introducing entirely new foundational theory, our approach strategically relaxes constraints from orthogonal wavelet theory within the non-perfect reconstruction filter bank framework, enabling controlled spectral decomposition optimized for supervised fault diagnosis. We introduce a specialized regularization term based on the half-band property to ensure spectral complementarity and minimize cross-band correlation, while a Jacobian-based stabilization approach is formulated to ensure the convergence of filter coefficients during optimization. The proposed algorithmic architecture, LDBRFnet, features a dual-branch encoder system designed to capture the mathematical synergy between sub-band-level global statistics and time-domain local morphology. This dual-view representation effectively mitigates feature leakage and overconfidence in classification. Theoretical analysis and numerical experiments demonstrate that the learned filters satisfy the frequency-shift property and maintain robust spectral partitioning even under low signal-to-noise ratios. Validation on complex vibration datasets confirms that the framework achieves superior diagnostic accuracy (over 95.5%) and computational efficiency, reducing model parameters by 96.7% compared to state-of-the-art baselines. This work provides a generalizable mathematical approach for adaptive signal decomposition and robust pattern recognition in interdisciplinary applications. Full article

This paper addresses the problem of nonparametric regression for functional time series, a setting complicated by the infinite-dimensional nature of the covariates, temporal dependence, and potential for outliers. We propose a new robust estimator that combines three powerful ideas: (i) k-nearest neighbors (kNN) for adaptive localization in the functional space; (ii) local linear smoothing to reduce bias; and (iii) $\mathcal{M}$-estimation to ensure resilience against atypical observations. The key theoretical contribution establishes the almost-complete convergence of the proposed estimator under mild conditions that account for the functional geometry, weak dependence (via quasi-association), and robustness constraints. The obtained rate of convergence explicitly reveals the interplay between the functional concentration, dependence strength, and local smoothness of the model. A simulation study demonstrates that this method offers superior stability and predictive accuracy compared to classical alternatives, particularly under heavy-tailed errors and data contamination. The practical relevance of the approach is further illustrated through a one-step-ahead prediction application to a real-world environmental dataset of hourly NOx measurements. Full article

The goal of laser polishing (LP) is to improve the surface quality of functional parts, components, and assemblies. LP is a complex nonlinear thermophysical process, in which laser radiation induces localized melting of a material with an initially rough surface topography. During LP, the thermodynamic state evolves dynamically due to transient melt flow, heat transfer, and rapid solidification within the laser–material interaction zone. A smooth surface is formed through the interplay between surface tension-driven flow, which promotes energy minimization, and nonequilibrium effects associated with melting and solidification. From the perspective of self-organization, LP can be interpreted as an open system driven by energy input, where complex material redistribution leads to the evolution of surface topography. In this work, the self-organization of molten material is analyzed using chaos-based descriptors, including the Lyapunov exponent, phase portrait, approximate entropy, and the Hurst exponent, calculated from measured surface topographies before and after laser polishing. The results show that LP acts as a spatial low-pass filter, reducing high-frequency surface components associated with micromilling marks, and exhibits a directional bias in material redistribution relative to the laser scanning direction. Among the evaluated descriptors, the Lyapunov and Hurst exponents demonstrate consistent behaviors, indicating their suitability as robust indicators of surface state in post-process analysis. For the investigated conditions (Inconel 718), a laser fluence of 158.3 mJ/cm² provided the best-achieved surface quality, corresponding to an improvement in surface roughness (Ra) of approximately 70% and the lowest Lyapunov exponent of 1.966 and highest Hurst exponent of 0.859. This study demonstrates that chaos-based analysis of surface topography provides a phenomenological framework for assessing process stability and surface evolution, offering a basis for thermophysics-informed development of LP in applications such as mold and die manufacturing. Full article

Liuchun Lake area, located in the high-elevation and topographically complex western region of Zhejiang Province, exhibits temperature variability strongly influenced by terrain-induced dynamics and local microclimates. The Zhejiang Operational Consensus Forecasts (ZJOCF) model shows pronounced systematic biases in this area, making it difficult to meet the demand for short-term, fine-scale forecasts in cultural-tourism applications. Using observational data from four stations at different elevations, this study analyzes how ZJOCF temperature forecast errors vary with altitude, develops a station-level machine-learning temperature bias-correction model, and evaluates its performance in terms of accuracy, mean absolute error (MAE), error distribution, and control of extreme errors. Results show that the accuracy of the raw forecasts decreases significantly with increasing elevation, with high-altitude sites exhibiting distinct warm biases and strong fluctuations. After correction, the 72 h forecast accuracy at the four stations increases to 69–71% (up to 40.8% at the mountaintop station), MAE is reduced by more than 60% on average, extreme-error cases decrease by 40–60%, and the error distribution shifts from a scattered multi-peak pattern to a concentrated single-peak structure. These findings demonstrate that station-level machine-learning correction can effectively mitigate structural errors in ZJOCF temperature forecasts over complex terrain, providing a reliable technical pathway for refined meteorological services in mountainous regions. Full article

Traditional leaf area index (LAI) measurement methods are destructive, time-consuming, and labor-intensive. In this study, 282 four-year-old central-leader apple trees were used as research subjects. Canopy reflectance spectra in the range of 4000−10,000 cm⁻¹ were collected, and the corresponding true LAI values were measured destructively by harvesting all leaves from a representative branch of each tree using a leaf area meter. The dataset was randomly divided into training (70%) and testing (30%) sets. Eight spectral pretreatment methods were compared. The Competitive Adaptive Reweighted Sampling (CARS) algorithm was employed to extract characteristic wavelengths. Subsequently, both a BP neural network and a Support Vector Machine (SVM) model for LAI prediction were constructed. The optimal model was selected based on evaluation metrics including the coefficient of determination (R²), mean absolute error (MAE), mean bias error (MBE), and mean absolute percentage error (MAPE). The combined preprocessing of MSC and SD yielded the optimal results, screening out 26 characteristic wavelengths. The SVM linear kernel model (c = 5, g = 0.3) constructed based on MSC + SD preprocessing performed best, achieving a validation set R² of 0.90, MAE of 0.2117, MBE of −0.1214, and MAPE of 16.09%. The performance on the training set and validation set was comparable, with no overfitting observed. The MSC + SD preprocessing combined with CARS feature screening and SVM linear kernel modeling enables rapid, non-destructive estimation of apple canopy LAI, providing an effective technical tool for precision orchard management. Full article

Plant-derived sweet proteins are promising low-calorie natural sweeteners that may reduce dietary sugar intake and prevent non-communicable diseases. Although seven have been identified—thaumatin, miraculin, monellin, mabinlin, brazzein, pentadin, and curculin (neoculin)—only thaumatin is currently approved as a food additive. The development of others requires comprehensive safety assessments, particularly regarding allergenicity. This systematic review aims to investigate and synthesize allergenicity assessment methods (in silico, in vitro, in vivo, and clinical) applied to these seven sweet proteins. The literature searches were conducted following PRISMA guidelines across Scopus, PubMed, and Wiley Online Library databases, up to 30 November 2025, with no time restrictions. The risk of bias in selected studies was evaluated using GRADE. After the selection process, 14 out of 2634 studies met the inclusion criteria. Thaumatin, miraculin, monellin, and brazzein emerged as the most extensively studied proteins. In silico approaches (sequence and structural homology) and in vitro assays (digestibility and cell-based methods) were the most commonly employed methods. In contrast, in vivo studies (animal models) and clinical evaluations (skin prick tests, oral food challenges) were rarely reported. Allergenicity studies on pentadin, mabinlin, and curculin (neoculin) are limited, indicating a research gap that requires further study to support regulatory approval and consumer acceptance. Full article

Introduction: Distress during pediatric magnetic resonance imaging (MRI) neuroimaging can compromise scan quality and negatively impact children’s experiences. This review aimed to systematically synthesize biomarkers and psychological factors associated with distress in children, adolescents, and young adults undergoing neuroimaging. Methods: This systematic review was conducted according to PRISMA and AMSTAR-2 guidelines and preregistered in OSF. A systematic search was performed in six electronic databases, including observational articles published between 2000 and 2025 that assessed distress during MRI and functional MRI (fMRI). Data extraction and risk of bias assessment (QUIPS tool) were performed independently by two reviewers. Results: Ten studies (n = 558) examining distress during neuroimaging were included in this review. Distress was assessed through subjective self- and parent-reports, objective physiological measures, and qualitative interviews. Overall, distress levels were low to moderate; most participants tolerated scans well, though younger age, male sex, parental anxiety, procedure length, and chronic illness were associated with greater discomfort. Noise, immobility, and boredom emerged as the most frequent triggers, while strategies such as distraction, age-appropriate information, and reducing waiting times were perceived as helpful. Among participants with cancer, scan-related anxiety was closely linked to fear of recurrence and perceived stress. Risk of bias across studies was moderate to high, particularly in domains of attrition and statistical reporting. Conclusions: Distress during scanning is driven by anticipatory and parental anxiety, procedure length, and chronic illness. Biomarkers (e.g., cortisol, blood pressure) showed inconsistent links with subjective distress, highlighting the need for integrated measures. Full article

The third-quadrant operation of silicon carbide (SiC) MOSFETs is investigated from the perspective of carrier transport, focusing on the interaction between two parallel conduction paths. Through experimental characterization and TCAD simulation, the conduction behavior of the PiN body diode and MOS channel under various gate-source bias conditions is examined. Results reveal that body-effect-induced threshold voltage (Vth) reduction enables channel conduction even under negative gate bias. Based on this mechanism, a transfer-characteristic-based method is developed to identify gate-voltage boundaries between conduction modes. The impact of negative gate bias on reverse recovery parameters, peak current (I_rr), charge (Q_rr), and time (t_rr), is quantitatively evaluated. At the unit-cell level, current sharing between the two paths is analyzed, clarifying the physical mechanism governing their redistribution. Full article

Artificial intelligence (AI) has become increasingly embedded in technology-enhanced learning environments, where peer assessment now serves both instructional and analytic purposes. Beyond allocating feedback and grades, it also produces data that is later interpreted through learning analytics systems. In practice, visible indicators such as students’ fairness perceptions and the degree of agreement among peer raters are often treated as signs that the assessment process is functioning effectively. However, these indicators do not necessarily correspond to grading validity. Students may regard a peer assessment process as fair even when peer-generated ratings remain weakly aligned with expert judgement. This study, therefore, examines whether the socio-technical configurations associated with high perceived fairness in a digitally mediated peer assessment environment also correspond to criterion-referenced grading validity. Data were collected from 215 undergraduate students enrolled in an Artificial Intelligence Foundations course over two consecutive semesters at a university in Taiwan, with instructor ratings serving as an external expert reference within the course context, rather than as a universal ground truth. Because anonymity conditions and semester were fully confounded in the study design, differences linked to anonymity should not be interpreted as isolated causal effects. A two-stage fuzzy-set Qualitative Comparative Analysis (fsQCA) was used. In the first stage, three equifinal configurations associated with high perceived fairness were identified. In the second stage, these configurations were examined against four grading objectivity outcomes: peer–instructor alignment, peer convergence, familiarity bias, and leniency bias. The findings show that fairness perception and grading validity are only partially aligned. Configurations anchored in explicit criterion transparency consistently supported both experiential legitimacy and evaluative accuracy. By contrast, one configuration was associated with high peer convergence while remaining weakly aligned with instructor standards, a pattern described here as false objectivity; this context-dependent configurational finding warrants further investigation across other settings. The study contributes to research on digitally enhanced assessment and learning analytics by showing that fairness perception, peer convergence, and grading validity should be treated as analytically distinct dimensions of assessment quality. Full article