Search Results (7,418)

Background: Prostate cancer is one of the most commonly diagnosed cancers in men. Robot-assisted radical prostatectomy (RARP) is one of the recommended surgical treatment options; however, knowledge regarding changes in illness acceptance, psychological adjustment, and quality of life after surgery remains limited. Objective: To evaluate changes in illness acceptance, psychological adjustment, and quality of life in patients undergoing RARP. Materials and Methods: A total of 150 patients diagnosed with prostate cancer and scheduled for RARP were enrolled. Longitudinal analysis was performed in 93 patients who completed both study assessments. Evaluations were conducted before surgery and 3–4 months postoperatively using a self-developed questionnaire, the Acceptance of Illness Scale (AIS), the Mini-Mental Adjustment to Cancer (Mini-MAC), the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30), and the prostate cancer-specific module (EORTC QLQ-PR25). Results: Following RARP, illness acceptance did not change significantly after Holm adjustment (p_Holm = 0.465). Psychological adjustment showed an adverse postoperative pattern, characterised by increased anxious preoccupation, helplessness–hopelessness, and destructive style, together with reduced fighting spirit and constructive style. After adjustment across 25 paired outcome comparisons, significant increases remained in emotional and cognitive functioning scores, while constipation and diarrhoea decreased significantly. Sexual activity also decreased significantly after surgery. Among participants who completed both assessments, incontinence-aid use increased from 15/93 (16.1%) before surgery to 38/93 (40.9%) at follow-up (exact two-sided McNemar test, p < 0.001). Conclusions: During the early postoperative period following RARP, favourable changes were observed in selected quality-of-life scores, whereas illness acceptance did not change significantly after adjustment for multiple comparisons. At the same time, psychological adjustment showed an adverse pattern, characterised by reduced fighting spirit and constructive style together with increased anxious preoccupation, helplessness–hopelessness, and destructive style. Decreased sexual activity and increased incontinence-aid use further illustrate the multidimensional nature of early postoperative recovery. These exploratory findings indicate that favourable changes in selected quality-of-life outcomes do not necessarily coincide with improved psychological adaptation and support the inclusion of psycho-oncological assessment and support in postoperative care. Full article

Brief campus-based interventions for university students with elevated internet gaming disorder (IGD) symptoms have received little study. This randomized trial evaluated a five-session group program focused on gaming motives, self-regulation, and alternative sources of competence and self-worth. Sixty-three adult students at a Chinese vocational college who scored at least 20 on an adapted Gaming Disorder Scale for Adolescents were randomized to the program (n = 31) or an information-only control condition (n = 32). Assessments were scheduled at baseline, immediately after the intervention, and one month later. Because post-randomization outcome data were unavailable for 19 participants, the comparative analyses were restricted to 44 completers. The originally planned baseline-adjusted ANCOVA showed a lower adjusted T1 IGD score in the intervention group than in the control group (adjusted difference = −4.62, 95% CI −7.33 to −1.91, p = 0.0013). In a post hoc linear mixed-effects model, the between-group difference in change from baseline to post-intervention was −5.16 points (95% CI −7.81 to −2.51; Hedges g = −1.03). At one month, the difference was smaller and not statistically significant (−1.67 points, 95% CI −4.31 to 0.98). None of the secondary outcomes showed a significant interaction. Overall attrition was 30.2%. These preliminary findings concern selected completers rather than an intention-to-treat effect, and the between-group difference was not evident at one month. Retrospective registration, unavailable outcomes for non-completers, and post hoc analyses limit the strength of the inference. The trial was retrospectively registered in UMIN-CTR (UMIN000055209) on 9 August 2024. Full article

Soft magnetic materials determine the efficiency, size, thermal burden, and reliability of transformers, inductors, electrical machines, electromagnetic interference (EMI) components, and magnetic sensors. This review differs from property-by-property surveys by using a condition-aware, application-driven framework: magnetic performance is compared only together with frequency, peak magnetic flux density, temperature, waveform, direct current (DC) bias, geometry, and processing route. After a concise treatment of coercivity, permeability, saturation polarization, magnetostriction, and loss mechanisms, the major material families are quantitatively compared in terms of magnetic performance, processing, cost, and industrial maturity. The review then maps these families onto grid transformers, high-speed electrical machines, wide-bandgap power converters, integrated magnetics, wireless power transfer, aerospace electrical systems, and radiofrequency components. Particular attention is given to the trade-offs among saturation polarization, permeability, core loss, mechanical strength, thermal stability, manufacturability, and sustainability. Recent advances in strong and ductile soft magnets, wide-temperature ferrites, vortex and easy-plane composites, mixed-powder soft magnetic composites, nanocrystalline flake-ribbon cores, and additive manufacturing are assessed by technology maturity. A prioritized roadmap identifies near-term needs for standardized condition-specific data and manufacturing control, medium-term opportunities in magnetic–thermal co-design and digital twins, and longer-term prospects for adaptive, self-healing, and GHz magnetic architectures. The resulting framework is intended to support both material development and defensible industrial material selection. Full article

Background/Objectives: Heart transplantation is a life-saving treatment for advanced cardiac disease, but post-transplant adaptation may involve psychosocial, identity-related, and existential change. Some recipients interpret these changes as corresponding to donor characteristics, although the nature and evidence base of these reports remain unclear. This review mapped post-transplant changes reported by adult heart transplant recipients, perceived donor–recipient correspondences, and the explanatory frameworks proposed for these phenomena. Methods: A scoping review was conducted following the Arksey and O’Malley framework, Joanna Briggs Institute guidance, and PRISMA-ScR reporting standards. PubMed, Web of Science, Scopus, CINAHL, and PsychINFO were searched in August 2026. Quantitative, qualitative, mixed-methods, conceptual, opinion, and review articles were eligible. Findings were synthesized descriptively. Results: Sixteen sources published between 1992 and 2025 were included. Empirical studies reported changes in preferences, emotional and personality-related experiences, social and physical functioning, sexuality, identity and embodiment, sensory and memory-like experiences, and spirituality. Some changes were attributed by recipients to donor characteristics, whereas correspondences supported by independently obtained donor information were reported in a limited subset of studies. Proposed explanations ranged from psychological, pharmacological, physiological, and sociocultural processes to cellular, molecular, neurocardiac, energetic, and transpersonal hypotheses. Donor-transfer hypotheses were developed in the secondary literature and were not empirically established. Methodological quality varied substantially across sources. Conclusions: Post-transplant changes and perceived donor–recipient correspondences are meaningful phenomena, but current evidence does not support the transfer of donor memory, personality, or identity through the transplanted heart. Future prospective, longitudinal, and methodologically rigorous studies should distinguish reported experience, donor attribution, and proposed explanations. Full article

The rapid integration of solar photovoltaic (PV) generation, distributed energy resources, and advanced communication infrastructures is transforming conventional power systems into highly interconnected cyber–physical smart grids. Although this transition improves sustainability and operational flexibility, it also increases grid-management complexity and introduces cyber–physical vulnerabilities, including false data injection attacks, communication failures, equipment degradation, and renewable-induced operational instabilities. This paper presents the Digital-Twin-Enabled Resilience Framework (DTERF), a conceptual reference architecture for anomaly detection in high-PV cyber–physical smart grids. DTERF integrates heterogeneous cyber–physical data acquisition, Digital Twin-based contextual representation, machine-learning analytics, explainable decision support, adaptive operational response, continuous learning, and self-healing capabilities within a unified resilience cycle. The framework is grounded in a structured review and comparative assessment of contemporary machine-learning approaches and recent integrated smart-grid research. Its architecture is conceptually evaluated through requirements-to-architecture traceability, examining functional coverage and internal consistency across the complete operational cycle. The analysis shows that DTERF provides explicit architectural mechanisms addressing the principal requirements identified in the literature, including contextual anomaly analysis, interpretability, cybersecurity robustness, resilience support, and operational integration. Rather than proposing a new anomaly detection algorithm or claiming empirical performance superiority, DTERF provides a technology-agnostic architectural foundation for coordinating complementary capabilities required for resilient anomaly management. Future work should empirically validate the framework using Digital Twin simulation environments, representative high-PV distribution systems, cyber–physical anomaly scenarios, and real or utility-derived operational data. Full article

Background: Although social interaction anxiety, resilience, and self-esteem have each been investigated among nursing students, limited research has examined whether resilience contributes to the association between social interaction anxiety and self-esteem through an indirect pathway during clinical practice. Addressing this gap may improve the understanding of the psychological factors associated with students’ adaptation in clinical settings. Therefore, this study aimed to examine the relationships among social interaction anxiety, resilience, and self-esteem and the indirect role of resilience among nursing students. Methods: Using a cross-sectional descriptive research design, the study sampled 880 nursing students from two Egyptian universities through stratified random sampling. Participants completed the Connor–Davidson Resilience Scale, the Rosenberg Self-Esteem Scale, and the Social Interaction Anxiety Scale. An observed-score path analysis was used to examine the direct paths and the indirect pathway through resilience. Results: Social interaction anxiety was negatively correlated with self-esteem (r = −0.409, p < 0.001) and resilience (r = −0.134, p < 0.001), whereas resilience was positively correlated with self-esteem (r = 0.597, p < 0.001). In the observed-score path model, social interaction anxiety was negatively associated with resilience (B = −0.13, p < 0.001) and self-esteem (B = −0.18, p < 0.001), while resilience was positively associated with self-esteem (B = 0.32, p < 0.001). The product of the two component paths was small (approximately −0.04) and is reported descriptively as an indirect pathway. Conclusions: Resilience was positively associated with self-esteem and accounted for a small indirect component of the cross-sectional association between social interaction anxiety and self-esteem. This finding should not be interpreted as evidence of statistically significant or causal mediation. Full article

This study meta-analyzed the incremental contribution of artificial intelligence (AI) to flipped learning on students’ academic achievement, relative to flipped learning without AI. Following a protocol registered on the Open Science Framework (OSF), systematic searches were conducted in Web of Science and Scopus, supplemented by an ERIC search, with coverage through 25 July 2026; eligible studies used experimental or quasi-experimental designs comparing an AI-supported flipped condition with a matched non-AI flipped comparator on an academic-achievement outcome. Applying the comparator criterion strictly, ten studies (two randomized controlled trials and eight quasi-experimental studies; N = 937 learners) met all criteria; one further study whose comparator was conventional, non-flipped instruction was excluded from the primary analysis and retained only as a sensitivity check. Using Hedges’ g in a random-effects model (REML with the Knapp–Hartung adjustment), the pooled effect was positive, moderate and statistically significant: g = 0.67, 95% CI [0.32, 1.02], t(9) = 4.36, p = 0.002. Heterogeneity was substantial (I² = 64.6%, τ² = 0.086) and the 95% prediction interval ranged from −0.10 to 1.43, indicating that the true effect in a new setting could plausibly be large or close to negligible. Egger’s test was statistically significant (t = 3.25, p = 0.01), consistent with a small-study effect; excluding studies with fewer than 15 participants per arm attenuated the estimate to g = 0.53. Because the comparator was itself flipped learning, this estimate approximates the incremental contribution of the AI component rather than the combined effect of AI and the flipped model. Overall certainty of the evidence (GRADE) was low. AI can add value to flipped learning, but the benefit is conditional on implementation quality and context rather than an inherent property of the technology, and the findings should be read with caution given a small, heterogeneous and partly small-study-influenced evidence base. Full article

Accurate anomalous localization is a core challenge in industrial visual quality inspection. Current diffusion-based methods typically rely on single-scale reconstruction at a fixed resolution, often exhibiting limited performance on subtle or low-contrast anomalies due to the lack of hierarchical feature collaboration. We propose AdaNMD, a nested adaptive multi-resolution diffusion model that jointly optimizes accuracy and efficiency. AdaNMD constructs a three-branch nested decoding architecture. It utilizes Adaptive Group Normalization (AdaGN) to embed diffusion time steps and a top-down feature fusion module to generate semantically rich pyramid representations. Crucially, a resolution decision module dynamically evaluates image complexity, activating only the most suitable branch during inference to reduce redundant computation. To bridge the capability gap between branches, we introduce a cross-scale self-distillation mechanism where the high-resolution branch acts as a teacher for lighter branches. A unified multi-task loss function further guides the model toward robust inference policies. Experiments on the VisA and MVTec AD benchmarks demonstrate that AdaNMD achieves Area Under the Per-Region Overlap Curve (AUPRO) scores of 95.0% and 94.4%, respectively. Furthermore, compared to a baseline always using the high-resolution branch, our method improves inference speed by approximately 21%, confirming the architecture’s advantage in achieving high-precision anomaly localization and efficient inference simultaneously. Full article

Intrinsically self-sensing cementitious composites offer a promising basis for continuous structural health monitoring. Their electrical response, however, is strongly affected by reversible moisture change and freeze–thaw exposure. This study presents a computational proof-of-concept for the AI-driven design of a federated digital-twin architecture for damage identification and adaptive sensing in sustainable self-sensing cementitious infrastructure. The framework is developed and evaluated entirely in software on a physics-based synthetic testbed. At its present maturity, it is therefore a digital-twin precursor rather than an operational digital twin: it has no calibrated physical counterpart and no live, two-way data coupling, and no experimental validation is claimed. A transparent, physics-based signal generator produces fractional-change-in-resistance signals for twelve virtual CNT/biochar-functionalized LC3 and geopolymer specimens. Each passes through four progressive damage stages interleaved with wet–dry and freeze–thaw conditioning. The framework integrates a CNN-LSTM damage classifier, unsupervised domain adaptation, federated learning, reinforcement-learning-based active sensing, and quantum-inspired aggregation optimization. On three unseen virtual specimens (654 evaluation windows), the CNN-LSTM achieved 70.3% four-stage accuracy (95% Wilson confidence interval 66.7–73.7%) and a macro-F1 score of 0.650, with per-specimen accuracy ranging from 63.8% to 77.1%. It reached 85.2% (95% CI 82.3–87.7%) for the damaged-versus-undamaged decision and reduced environment-induced false alarms by 74.7% (95% CI 61.7–83.4%) relative to a calibrated threshold detector. Federated averaging was less accurate and less stable than centralized training; the 5.2 percentage-point gain from quantum-inspired aggregation lies within the resolution of the evaluation set and is not established as a real improvement. The active-sensing controller reduced measurement cost by 98.9% but detected only four of 27 damage-progression events. All sensing data are synthetic, and every interval reported here is recomputed from the evaluation counts already reported rather than obtained from additional experiments. The results therefore establish algorithmic feasibility only and identify the components requiring refinement before experimental validation. Full article

Continual learning has made significant progress toward enabling adaptive machine learning under evolving environments, yet real-world deployment increasingly exposes systems to persistent harsh conditions, including distributional shifts, feature evolution, delayed or scarce supervision, imbalance, noise, and recurring or novel classes. While prior research has largely addressed these challenges in isolation, growing environmental complexity motivates a broader rethinking of continual adaptation as a self-regulating process rather than solely a parameter update problem. Building upon the emerging framework of Self-Adaptive Learning (SAL), this perspective explores how learning systems may progress beyond reactive adaptation toward autonomous recognition, policy selection, and context-sensitive regulation of learning behavior under persistent uncertainty. Rather than proposing a specific algorithmic solution, we position SAL as a conceptual systems framework for organizing future research on resilient, long-lived machine learning systems. We discuss key implications for deployment robustness, evaluation, safety, and adaptive governance, while outlining major open challenges in developing practical self-regulating learners. By strengthening SAL as a forward-looking framework, this work aims to advance the broader conversation on machine learning systems capable of sustained autonomy in dynamic real-world environments. Full article

Background: Resilience is an important resource in nursing education, supporting students in adapting to demanding academic and clinical environments. However, evidence regarding the factors associated with resilience among nursing students remains inconsistent. This study aimed to examine the factors associated with resilience among undergraduate nursing students, with particular emphasis on gender, age, academic progression, institutional context, and selected sociodemographic characteristics. Methods: A cross-sectional quantitative study was conducted with 263 undergraduate nursing students from two higher education institutions in Portugal. Data were collected using the Takviriyanun Resilience Factors Scale, adapted and validated for the Portuguese population. Descriptive and inferential statistical analyses were performed to examine the two dimensions of the scale—Personal and Social Support Skills and Problem-Solving Skills—and their associations with sociodemographic and academic variables. Results: Students reported generally favourable levels of resilience-related factors, with higher scores observed in items reflecting personal responsibility and perceived social support. Comparatively lower scores were observed for items related to calmness and patience, confidence, optimism, and perceived social acceptance. A statistically significant gender difference was found in Problem-Solving Skills, with male students showing higher mean ranks than female students (p = 0.038); however, the effect size was small (r = 0.13). A moderate positive correlation was observed between Problem-Solving Skills and Personal and Social Support Skills (ρ = 0.394, p < 0.001). No statistically significant associations were found between the resilience dimensions and age, year of study, higher education institution, displacement status, place of residence, or religious affiliation. Conclusions: Undergraduate nursing students demonstrated generally favourable levels of resilience-related factors. Personal and Social Support Skills and Problem-Solving Skills were positively associated, suggesting a meaningful relationship between interpersonal resources and problem-solving competencies. Although a small gender difference was observed in Problem-Solving Skills, the findings do not indicate substantial gender-related differences in resilience-related factors. Educational strategies aimed at strengthening problem-solving, adaptive coping, emotional self-regulation, and social support may be relevant throughout undergraduate nursing education. Longitudinal and multicentre studies are warranted to further investigate the factors associated with resilience and to evaluate the effectiveness of resilience-focused educational interventions. Full article

Kawasaki disease (KD) is an acute, self-limiting systemic vasculitis that mainly affects young children. It is the leading cause of acquired heart disease in the developed world and is unique among childhood vasculitides for its predilection toward coronary artery involvement. This review describes the clinical spectrum of KD, highlighting diagnostic difficulties, especially in atypical presentations, and the challenges of managing treatment resistance. While intravenous immunoglobulin and aspirin remain the standard of care, especially in reducing coronary inflammation, early recognition and prompt therapy are crucial to preventing lifelong cardiac complications. To support this, various clinical risk scores have been developed over the decades to predict coronary artery lesions and IVIG resistance, enabling earlier intensification of treatment to prevent cardiac complications. Although the etiopathogenesis of KD remains unknown, extensive and robust research is ongoing to identify biomarkers, including epigenetic markers, innate and adaptive immunity activation markers, cytokine profiles, and to explore the influence of external triggers and the gut microbiome–immunity interplay. This review attempts to provide a concise overview of KD, aiming to assist clinicians in understanding the complexity of the problem rather than addressing all issues in detail. Full article

Hybrid CNN–Transformer (HCT) synthesis for automated neurodevelopmental diagnostics is an effective approach to constructing intelligent detection systems that are not merely oriented toward feature classification but primarily toward solving spatiotemporal pattern recognition problems in motor disorder assessment. In neurodevelopmental diagnostics, existing automated methods rely on fixed, single-model architectures that process spatial or temporal motion features independently, failing to adapt to the heterogeneous motor irregularities characteristic of developmental coordination disorder and degrading detection sensitivity and generalization across diverse patient populations. There is therefore a pressing need for models capable of simultaneously capturing intra-frame spatial coordination patterns and inter-frame temporal movement dependencies against interrelated diagnostic criteria including accuracy, sensitivity, and motor irregularity specificity. To address this challenge, this paper proposes HCT, a novel framework that integrates ResNet-based spatial feature extraction from optical flow maps and pose estimation skeletons with multi-head self-attention Transformer encoding for modeling long-range temporal dependencies across multi-frame motion sequences. Unlike conventional single-stream approaches, where spatial and temporal processing remain confined to independent architectures, HCT decouples spatiotemporal feature learning through a cross-modal fusion pipeline, constructing a unified discriminative architecture that captures motor coordination dependencies between motion imaging inputs and multiple diagnostic criteria simultaneously. The convolutional encoder generates diverse joint displacement features, which are consolidated through cross-modal attention fusion into a robust, unified embedding with enhanced generalization and resilience to inter-individual motor variability. Integration within neurodevelopmental assessment frameworks facilitates reliable developmental coordination disorder classification, motor irregularity prediction, and interpretable diagnostic decision support, advancing the accuracy, flexibility, and clinical validity of intelligent motor disorder diagnostic systems. Full article

Spatio-temporal epidemic forecasting aims to predict future outbreak trajectories across interconnected regions from historical epidemiological observations and meteorological covariates. However, existing approaches often fail to preserve historically salient epidemic states or to fully exploit delayed and region-varying meteorological associations, leading to unstable temporal representations and insufficient meteorological-context-aware spatio-temporal context for prediction at later forecast horizons. In this paper, we propose CASRL, a Climate-Aware Self-Retrospective Representation Learning network for stable and meteorological-context-aware spatio-temporal epidemic forecasting. CASRL first employs a Self-Retrospective Epidemic Encoder (SREE) to retrospectively aggregate historically salient epidemic states through query-guided weighting and adaptive gating, thereby preserving informative historical epidemic states within the look-back window. It then introduces a Climate-Adaptive Graph Message Passing (CAGMP) module that breaks away from traditional passive feature concatenation. Instead, it constructs a separate meteorological-view predictive graph conditioned on the static spatial prior and adaptively fuses it with the incidence-associated topology to model complex cross-regional predictive associations. By integrating self-retrospective epidemic representations with meteorological-view spatio-temporal interactions, CASRL produces forecasts with improved predictive stability at later forecast horizons. Extensive experiments on two public influenza benchmarks show that CASRL is competitive at shorter forecast horizons and provides clearer advantages at later forecast horizons, particularly in phase-alignment-related evaluation and 15-week-ahead forecasting. Full article

Safe rail transit operation is essential for socioeconomic development and the protection of public life and property. Traditional railway foreign object intrusion detection methods suffer from insufficient detection accuracy and poor real-time performance under complex scene conditions. While deep learning has achieved remarkable performance in general object detection tasks, existing lightweight detectors still face prominent challenges in railway scenarios, including severe background clutter, drastic variations in target scales, and constrained edge computing resources. To tackle the above issues, this paper proposes YOLOv11n-LSL, an improved lightweight and high-precision detector based on YOLOv11n. Specifically, a C2PSA-SWSA shifted-window self-attention module is designed to suppress background interference and improve the feature representation of small targets; an SPPF-LSKA large-kernel attention module is introduced to construct a large and adaptive receptive field, thereby improving the detection capability for foreign objects of different scales; in addition, the lightweight adaptive decoupled head (LADH) is introduced to alleviate feature conflicts between the classification and regression branches and reduce network parameter redundancy. Comparative experiments on a self-built railway foreign object intrusion dataset show that the proposed YOLOv11n-LSL outperforms the original YOLOv11n, achieving 84.6% mAP@0.5, 88.1% precision, and 79.5% recall, which are 3.0, 6.0, and 7.8 percentage points higher than the baseline, respectively. The model only contains 2.522 M parameters and achieves a single-frame latency of 12.3 ms. Compared with the original YOLOv11n with 2.583 M parameters, the parameter count is reduced by 2.4%, and the inference latency is decreased by 41.1%. The experimental results show that the proposed method effectively improves detection accuracy in complex railway scenes while maintaining lightweight inference performance. Full article