Search Results (2,865)

Self-rated health is a comprehensive indicator reflecting an individual’s subjective assessment of their overall health status. The health condition of students in elite universities is directly related to the quality of talent reserves and the long-term development of the country. However, the multiple challenges they face make them prone to subhealth issues. To understand and effectively intervene in the health dilemmas of this group from a psychological perspective, this study constructed a cross-lagged model to examine the potential bidirectional relationship between anxiety and self-rated health. We utilized two-wave longitudinal data from a sample of 896 undergraduate students (mean age 21.37 years, 60.27% male, 92.08% Han nationality) from five elite universities in Beijing, China. Anxiety was measured using the Depression Anxiety Stress Scales, while self-rated health was assessed via a single-item score. The study revealed that during the two survey periods, the anxiety levels of elite university students decreased (7.682/7.462), whereas their self-rated health scores increased (81.781/83.255). Higher levels of anxiety were significantly associated with lower levels of self-rated health in both the concurrent and cross-lagged analyses (r = −0.299~−0.173, p < 0.01). Prior anxiety could predict later self-rated health (β = −0.081, p < 0.05), but the reverse path from self-rated health to anxiety was not confirmed. Our findings indicate that anxiety among elite university students has a unidirectional prospective effect on self-rated health. On the basis of these findings, universities should integrate mental health services into their routine work systems, and students should also increase their sense of personal responsibility for their own health, actively seeking effective pathways to improve their physical and mental well-being. Full article

Objective: We aimed to examine the longitudinal associations between lifestyle behaviors, academic achievement, and physical diseases in adolescents. Study Design: Longitudinal cohort study. Methods: We recruited participants (n = 4330; mean age of 14.0 (SD = 1.51) years at the first time point and 16.0 (1.51) years at the second time point) from 16 districts in Shanghai, China, who completed a survey in 2021 (T1) and 2023 (T2). We employed a cross-lagged panel network model to explore the interconnected relationships among lifestyle behaviors, academic achievement, and physical condition (i.e., obesity, high blood pressure, high myopia, depressive symptoms). Results: Among the cross-lagged associations, the predictive effects of T1 obesity on T2 high blood pressure (OR = 2.39), T1 breakfast skipping on T2 TV screen time (OR = 1.49), (in cross-domain relationships) T1 symptoms of depression on T2 low fruit and vegetable consumption (OR = 2.43), T1 obesity on T2 TV screen time (OR = 1.53), and T1 computer time on T2 high BP (OR = 1.31) were particularly prominent. Nonetheless, the observed cross-lagged effect sizes were small. Based on the sum of expected influence on their connecting nodes, obesity, depressive symptoms, and breakfast skipping demonstrated their paramount roles in the network metrics. We found breakfast skipping showed the strongest bridging effect among all factors in association with coexisting conditions and academic performance in children. Conclusions: Our findings identified breakfast skipping as the pivotal bridge node with the highest centrality within the network of modifiable lifestyle factors. Although this does not imply direct causality, its prominent bridge effect highlights its essential role in maintaining network stability and mediating interactions across distinct variable clusters. Full article

Background: Patients with inflammatory rheumatic disease (IRD) are susceptible to influenza infections and complications yet avoid vaccination for fear of exacerbation. This study evaluates the 2023/24 influenza vaccine in IRD patients, aiming to provide recommendations for this group in the upcoming season. Methods: In this prospective, longitudinal study, we assessed the self-reported impact of influenza vaccination on patients with IRD. Participants were recruited nationwide between October and December 2023 and completed an online questionnaire after vaccination as well as at three and six months of follow-up. Results: Among 633 patients, 87.5% were female, with a median age of 50.4 (18–84) years. Post-vaccination, 50% experienced injection site pain; 41% reported no side effects. IRD flares occurred in 5%, with 1% requiring changes to immunomodulation. Among 428 patients with follow-up, influenza infections were reported in 38 patients (8.9%), including 10 (2.3%) with reinfections. No severe cases requiring hospitalization were reported. Spondyloarthritis patients had higher susceptibility to influenza (p = 0.002), accounting for 55.3% of infections. IRD flare-ups in the 12 months before vaccination predicted infections (p = 0.002). Conclusions: The 2023/24 vaccine was well tolerated by IRD patients, with no impact on the course of the disease in 95% of cases. Only 9% of patients reported influenza infections, none of which were severe. In light of these findings, physicians are advised to recommend vaccination to eligible IRD patients during the upcoming season. Full article

Smart devices and multimodal biosignal systems, including electroencephalography (EEG/MEG), ECG-derived heart rate variability (HRV), and electromyography (EMG), increasingly supported by artificial intelligence (AI), are being explored to improve the assessment and longitudinal monitoring of mental health conditions. Despite rapid growth, the available evidence remains heterogeneous, and clinical translation is limited by variability in acquisition protocols, analytical pipelines, and validation quality. This systematic review synthesizes current applications, signal-processing approaches, and methodological limitations of biosignal-based smart systems for mental health monitoring. Methods: A PRISMA 2020-guided systematic review was conducted across PubMed/MEDLINE, Scopus, the Web of Science Core Collection, IEEE Xplore, and the ACM Digital Library for studies published between 2013 and 2026. Eligible records reported human applications of wearable/smart devices or multimodal biosignals (e.g., EEG/MEG, ECG/HRV, EMG, EDA/GSR, and sleep/activity) for the detection, monitoring, or management of mental health outcomes. The reviewed literature after predefined inclusion/exclusion criteria clustered into six themes: depression detection and monitoring (37%), stress/anxiety management (18%), post-traumatic stress disorder (PTSD)/trauma (5%), technological innovations for monitoring (25%), brain-state-dependent stimulation/interventions (3%), and socioeconomic context (7%). Across modalities, common analytical pipelines included artifact suppression, feature extraction (time/frequency/nonlinear indices such as entropy and complexity), and machine learning/deep learning models (e.g., SVM, random forests, CNNs, and transformers) for classification or prediction. However, 67% of studies involved sample sizes below 100 participants, limited ecological validity, and lacked external validation; heterogeneity in protocols and outcomes constrained comparability. Conclusions: Overall, multimodal systems demonstrate strong potential to augment conventional mental health assessment, particularly via wearable cardiac metrics and passive sensing approaches, but current evidence is dominated by proof-of-concept studies. Future work should prioritize standardized reporting, rigorous validation in diverse real-world cohorts, transparent model evaluations, and ethics-by-design principles (privacy, fairness, and clinical governance) to support translation into practice. Full article

This article presents an academic literature analysis of 75 Industry 4.0 (I4.0) and Industry 5.0 (I5.0) maturity models published between 2020 and 2024, examining their empirical validation, sectoral scope, geographical origin, and stated applicability to developing-country contexts. The study combines descriptive profiling, contingency-table analyses with exact tests and effect sizes, and a large-scale synthesis of 562 research gaps reported by model authors. Knowledge production is highly concentrated in single-country studies (77.3%) and in developed economies, while most models do not explicitly or implicitly document applicability to developing-country settings (approximately 83%). Empirical validation practices are uneven, with multiple-case studies (33.3%) and surveys (24.0%) dominating, and sectoral coverage is strongly skewed toward manufacturing, limiting transferability to other sectors relevant for emerging economies. A statistically detectable association is observed between the development level of the model’s country of origin and the presence of applicability statements (${\chi }^2$ = 17.13, $p<0.05$, moderate effect size), whereas authorship configuration shows no substantive association. Thematic analysis of reported gaps highlights persistent deficits in empirical rigor, sectoral breadth, SME orientation, operationalization of human-centric and sustainability dimensions associated with Industry 5.0, availability of implementation tools, and longitudinal or predictive evidence. The article concludes by outlining a research agenda focused on context-aware validation designs, broader sectoral grounding, and greater transparency and reproducibility, supported by open access to all underlying data, codebooks, and taxonomies. Full article

Objectives: This study aimed to develop and validate an explainable machine learning (ML) model to predict the risk of sarcopenia in older European adults with arthritis, providing a practical tool for early and precise screening in clinical settings. Methods: We analyzed data from the English Longitudinal Study of Aging (ELSA) and the Survey of Health, Aging and Retirement in Europe (SHARE). The final analysis included 1959 participants aged ≥65 years. The ELSA dataset was divided into a training set (n = 1371) and an internal validation set (n = 588), while the SHARE dataset (n = 1001) served as an independent external test cohort. From an initial pool of 33 variables, nine core predictors were identified using ensemble feature selection techniques. Six ML algorithms were compared, with model performance evaluated using the Area Under the Curve (AUC) and calibration analysis. Model interpretability was enhanced via SHapley Additive exPlanations (SHAP). Results: The Decision Tree model demonstrated the optimal balance between performance and interpretability. It achieved an AUC of 0.921 (95% CI: 0.848–0.988) in the internal validation set and maintained robust generalizability in the external SHARE cohort with an AUC of 0.958 (95% CI: 0.931–0.985). The nine key predictors identified were stroke history, BMI, HDL, loneliness, walking speed, disease duration, age, recall summary score, and total cholesterol. SHAP analysis visualized the specific contribution of these features to individual risk. Conclusions: This study successfully developed a high-performance, explainable, lightweight ML model for sarcopenia risk prediction. By inputting only nine readily available clinical indicators via an online tool, individualized risk assessment can be generated. This facilitates early identification and risk stratification of sarcopenia in older European arthritis patients, thereby providing valuable decision support for implementing precision interventions. Full article

Depression is a heterogeneous neuropsychiatric disorder with variable clinical presentation and response to treatment. This variability has motivated interest in neuroimaging biomarkers capable of disease characterization and therapeutic prediction. Positron emission tomography (PET) enables in vivo assessment of cerebral glucose utilization, neurochemical targets, inflammatory markers, and cerebral blood flow. This narrative review synthesizes PET studies conducted predominantly in adults with major depressive disorder diagnosed using DSM-based criteria, with bipolar disorder included only when imaging was performed during a depressive episode. Studies were identified through a structured, non-systematic literature search of major databases. Depression is consistently associated with regionally specific PET alterations within cortico-limbic and cortico-striatal circuits; studies most frequently report reduced glucose-derived PET measures in prefrontal and anterior cingulate regions at baseline, with treatment responders showing relative increases or redistribution of these measures following interventions. Neurochemical PET studies demonstrate altered receptor, transporter, or enzyme-related binding in serotonergic, dopaminergic, and noradrenergic systems, while neuroinflammatory and perfusion studies reveal regionally increased PET signals in subsets of patients. Overall, PET findings indicate convergent, region-specific and neurochemical alterations associated with depressive episodes and treatment response. Interpretation is constrained by methodological and clinical heterogeneity, underscoring the need for harmonized, longitudinal PET studies. Full article

Circulating tumor cells (CTCs) are shed from the primary tumor into the bloodstream and represent dynamic molecular biomarkers for monitoring the progression of cancer. While profiling tumor tissues with over expression of cell surface markers, such as PD-L1 or HER2, is standard in guiding therapy, tissue samples are often inaccessible and inadequate, especially post-surgery or in cases of recurrence. Emerging clinical evidence indicates that CTC counts and biomarker surface expression can predict prognosis and therapeutic resistance more accurately than imaging or tissue-based approaches. Recent advancements in the CTC detection methods, based on physical properties or surface markers (e.g., EpCAM), coupled with next-generation sequencing (NGS) have enabled the isolation of these rare cells and their molecular characterization. Consequently, CTCs provide a real-time alternative, enabling repeated, longitudinal assessment of tumor phenotype and therapeutic response. This review emphasizes the translational potential of surface protein biomarkers on CTCs for profiling, namely PD-L1, HER2, and EGFR, as a clinically actionable approach to stratify patients, guide immunotherapy decisions, and monitor minimal residual disease (MRD), especially when longitudinal tissue biopsies are not feasible. Full article

Hot-forming, as a typical representative forming technology of high-strength steel (HSS), is one of the most effective ways to manufacture structural components for achieving automotive lightweighting goal. In this paper, a newly-developed commercial microalloyed hot-formed steel is selected and its hot-forming is studied by experiments and simulations. The new steel has a wide undercooled austenite region, providing more suitable condition for the manufacturing of one-piece large-sized integrated parts. The high-temperature mechanical behaviors of the investigated steel show that the flow stress obviously decreases with the increase in deformation temperature, and it increases with the increasing strain rate. An integrated component assembly of the rear floor and longitudinal beam is selected as a typical one-piece integrated part when performing the hot-forming simulation to evaluate the formability. The influences of the key process parameters, namely forming velocity and frictional coefficient, on formability are further analyzed. Finally, the Latin Hypercube Sampling (LHS) method is used to generate the parameter combination and the Response Surface Method (RSM) is adopted in optimization. As a result, an optimal process parameter combination is obtained and its predicted result matches the simulated one very well, with a relative error of only 2.57%. The research results of this paper are favorable for understanding the mechanical behaviors of the hot-formed steel at elevated temperatures, improving the formability and providing a reference for the development of large-sized integrated hot-formed parts. Full article

Introduction: Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease of the central nervous system, frequently associated with visual and oculomotor disturbances. Quantitative analysis of eye movements represents a non-invasive method for assessing central nervous system dysfunction beyond conventional imaging; however, the diagnostic and predictive value of oculomotor metrics remains insufficiently defined. Objectives: The aims of this study were to compare smooth pursuit gain and reflexive saccade parameters (latency, velocity, and precision) between individuals with MS and healthy controls, and to evaluate their ability to discriminate disease status. Methods: This cross-sectional study included 46 clinically stable patients with MS (EDSS ≤ 6.5) and 46 age- and sex-matched healthy controls. Oculomotor function was assessed using videonystagmography under standardized conditions. Group differences across horizontal and vertical gaze directions were analyzed using linear mixed-effects models. Random forest models were applied to assess the discriminative performance of oculomotor parameters, with permutation-based feature importance and receiver operating characteristic (ROC) curve analysis. Results: Patients with MS showed significantly reduced smooth pursuit gain across most horizontal and vertical directions compared with controls. Saccadic latency was significantly prolonged in all tested movement directions. Saccadic velocity exhibited selective directional impairment consistent with subtle medial longitudinal fasciculus involvement, whereas saccadic precision did not differ significantly between groups. A random forest model combining pursuit and saccadic parameters demonstrated only moderate discriminative performance between MS patients and controls (AUC = 0.694), with saccadic latency contributing most strongly to classification. Conclusions: Quantitative eye-movement assessment revealed widespread oculomotor abnormalities in MS, particularly reduced smooth pursuit gain and prolonged saccadic latency. Although the overall discriminative accuracy of oculomotor parameters was limited, these findings support their potential role as complementary markers of central nervous system dysfunction. Further longitudinal and multimodal studies are required to clarify their clinical relevance and prognostic value. Full article

Although previous studies have established a link between perceived discrimination and negative adolescent outcomes, potential mediating and moderating factors—specifically, the mediating role of self-esteem and the distinct moderating roles of positive and negative coping styles—remain underexplored. This longitudinal study aimed to examine whether adolescents’ perceptions of discrimination directed toward themselves or their classmates predict their prosocial behaviors through the mediating role of self-esteem and whether positive and negative coping styles moderate this pathway. A total of 531 junior high school students (M_age = 15.73, SD_age = 0.67, 47.83% males) from Changchun, Jilin Province, China, completed measures of perceived discrimination, self-esteem, prosocial behaviors, and coping styles across three time points. Higher levels of perceived discrimination at T1 were associated with fewer prosocial behaviors at T3, and this relationship was mediated by reduced self-esteem at T2. Moreover, both positive and negative coping styles at T1 served as moderators. Positive coping moderated the negative effects of perceived discrimination on both self-esteem and prosocial behaviors, while negative coping moderated the positive association between self-esteem and prosocial behaviors. These findings underscore the distinct role of perceived discrimination, self-esteem, and coping styles in shaping adolescent prosocial development and offer valuable implications for educational interventions aimed at fostering prosociality. Full article

Background and Objectives: Dyspnea is a common chief complaint in the emergency department. While global longitudinal strain and biplane ejection fraction are reliable markers of left ventricular systolic function, their assessment requires advanced echocardiographic tools and expertise. Simple point-of-care ultrasound parameters, such as E-point septal separation and fractional shortening may serve as practical alternatives for rapid bedside evaluation. Materials and Methods: EPSS and FS were measured by emergency physicians using POCUS, while reference EF and GLS were obtained by cardiologists via transthoracic echocardiography. Correlation analyses, receiver operating characteristic curves, and agreement statistics were used to evaluate the diagnostic accuracy of EPSS and FS for predicting reduced EF (<50%) and GLS (<16%). Results: Reduced EF was present in 54.0% and reduced GLS in 55.6% of patients. EPSS showed strong negative correlations with EF (ρ = −0.834) and GLS (ρ = −0.782), while FS correlated positively with EF (ρ = 0.773) and GLS (ρ = 0.714), all p < 0.001. ROC analysis demonstrated excellent diagnostic accuracy of EPSS (AUC = 0.922 for EF; 0.949 for GLS) and good accuracy of FS (AUC = 0.874 for EF; 0.865 for GLS). Optimal cut-off values were EPSS ≥ 7.0 mm and FS ≤ 25%. Agreement with reference TTE was good for EPSS (κ = 0.676 for EF; κ = 0.738 for GLS) and moderate for FS (κ ≈ 0.56). Conclusions: Both EPSS and FS measured by POCUS provide reliable estimates of left ventricular systolic function in dyspneic ED patients, with EPSS demonstrating superior diagnostic performance. Full article

Accurate numerical simulation of debris flows is essential for hazard assessment and early-warning design, yet high-fidelity solvers remain computationally expensive, especially when large ensembles must be explored under epistemic uncertainty in rheology, initial conditions, and topography. At the same time, field observations are typically sparse and heterogeneous, limiting purely data-driven approaches. In this work, we develop a deep-learning Fourier Neural Operator (FNO) as a fast, physics-consistent surrogate for one-dimensional shallow-water debris-flow simulations and demonstrate its application to the Rendinara–Morino system in central Italy. A validated finite-volume solver, equipped with HLLC and Rusanov fluxes, hydrostatic reconstruction, Voellmy-type basal friction, and robust wet–dry treatment, is used to generate a large ensemble of synthetic simulations over longitudinal profiles representative of the study area. The parameter space of bulk density, initial flow thickness, and Voellmy friction coefficients is systematically sampled, and the resulting space–time fields of flow depth and velocity form the training dataset. A two-dimensional FNO in the $(x,t)$ domain is trained to learn the full solution operator, mapping topography, rheological parameters, and initial conditions directly to $h(x,t)$ and $u(x,t)$, thereby acting as a site-specific digital twin of the numerical solver. On a held-out validation set, the surrogate achieves mean relative $L^2$ errors of about 6–$7\%$ for flow depth and 10–$15\%$ for velocity, and it generalizes to an unseen longitudinal profile with comparable accuracy. We further show that targeted reweighting of the training objective significantly improves the prediction of the velocity field without degrading depth accuracy, reducing the velocity error on the unseen profile by more than a factor of two. Finally, the FNO provides speed-ups of approximately $36\times$ with respect to the reference solver at inference time. These results demonstrate that combining physics-based synthetic data with operator-learning architectures enables the construction of accurate, computationally efficient, and site-adapted surrogates for debris-flow hazard analysis in data-scarce environments. Full article

Background: People with Parkinson’s disease suffer from gait impairments. Clinical scales provide a limited and rater-dependent assessment of gait. Wearable sensors allow an objective characterization by capturing rhythm, pace, and signature patterns. This study investigated if sensor-derived gait parameters have prognostic value for short-term progression of gait impairments. Methods: A total of 111 longitudinal visit pairs were analyzed, where participants underwent clinical evaluation and a 4 × 10 m walking test instrumented with wearable sensors. Changes in the UPDRSIII gait score between baseline and follow-up were used to classify participants as Improvers, Stables, or Deteriorators. Baseline group differences were assessed statistically. Machine-learning classifiers were trained to predict group membership using clinical variables alone, sensor-derived gait features alone, or a combination of both. Results: Significant between-group differences emerged. In participants with UPDRSIII gait score = 1, Improvers showed higher median gait velocity ($0.81 \mathrm{m}/\mathrm{s}$) and stride length ($0.80 \mathrm{m}$) than Stables ($0.68 \mathrm{m}/\mathrm{s}$; $0.70 \mathrm{m}$) and Deteriorators ($0.59 \mathrm{m}/\mathrm{s}$; $0.68 \mathrm{m}$), along with lower stance time variability (3.10% vs. 4.49% and 3.75%; all $p<0.05$). The combined sensor-based and clinical model showed the best performance (AUC $0.82$). Conclusions: Integrating sensor-derived gait parameters with clinical score can support the identification of patients at risk of gait deterioration in the near future. Full article

Considering the variability of driving conditions in mining areas, existing control strategies are difficult to meet the comprehensive performance requirements of mining dump trucks in the longitudinal, lateral, and vertical directions. Longitudinal, lateral, and vertical (LLV) coordinated control of active hydro-pneumatic suspension system based on model predictive control (MPC) is constructed in this paper. The vehicle dynamic response under random road surface input based on wheelbase characteristics is established, and the rationality of the active hydro-pneumatic suspension LLV coordinated control strategy based on MPC is analyzed. Handling stability is taken as the overall control objective for active hydro-pneumatic suspension on C-class road surfaces. The dynamic tire loads of the six wheels of the mining dump truck are reduced by 25.8%, 29.1%, 30.6%, 27.6%, 29.9%, and 28.1%, respectively, in the unloaded state, while the longitudinal, lateral, and vertical body accelerations have not deteriorated. Under the E-class road surface, the overall control objective of the mining dump truck is comfort, and the longitudinal, lateral, and vertical accelerations in the unloaded state have been optimized by 34.6%, 31.4%, and 34.1%, respectively. Full article