Search Results (11,174)

This study addresses national shortages in school-based mental health (SBMH) providers, particularly in rural and high-needs areas, by examining two innovative training models designed to expand the school psychology workforce. The Grow Your Own (GYO) program respecializes practicing educators in rural communities to become school psychologists, while the Dual-Credentialing Clinical Training (DCT) model integrates school psychology training with supervised clinical experiences, leading toward educational certification and state mental health licensure. Program evaluation data were used to assess early implementation, feasibility, and success of both programs. In the GYO program, nine educators completed training, with eight employed in rural schools one to two years post-graduation, and average supervisor ratings meeting or exceeding the program’s competency expectations across all ten domains. In the DCT program, five trainees completed internship, four earned provisional mental health licenses, two progressed to independent licensure, and four became certified school psychologists. Together, findings indicate that place-based respecialization can strengthen rural retention, while dual-credentialing can expand clinical capacity and funding flexibility, creating complementary training models to help grow the SBMH workforce. Continued scaling and evaluation may enhance access to comprehensive SBMH services for students in under-resourced settings. Full article

Diet quality, nutrition knowledge, and psychosomatic literacy—defined as the understanding of the interactions between diet, gut microbiota, and mental well-being—may shape weight-related behaviours in youth. This study used a cross-sectional design to integrate these domains with digital information pathways in Central–Eastern Europe. This study assessed diet quality, nutrition, and psychosomatic knowledge, supplement use, and health-information sources among Polish adolescents and young adults, with emphasis on age-related differences and the role of social media. A cross-sectional, anonymous online survey (October 2025–January 2026) was conducted in Poland (final analytical sample: n = 478; adolescents 15–19 years vs. young adults 20–30 years). Of 591 individuals who accessed the survey, 478 were included in the final analytical sample. Diet quality was estimated from FFQ data using KomPAN-derived indices (pHDI-10, nHDI-14, DQI). Nutrition knowledge (0–25 points), psychosomatic/gut–brain indicators, supplementation, and information sources were analysed using χ²/Fisher tests and Mann–Whitney U tests with effect sizes. The primary outcomes measured were dietary supplement use and excess body weight (BMI ≥ 25 kg/m²). Multivariable logistic regression examined predictors of supplement use and BMI ≥ 25 kg/m². Overall diet quality was low to moderate, with limited intake of whole grains, legumes, and fish, and common nutrition misconceptions. Social media was the most frequently indicated source of diet/supplement information and was independently associated with more frequent supplement use (OR = 2.29; 95% CI: 1.43–3.64). Adolescents reported lower whole-grain intake and more misconceptions than young adults. Predictors of BMI ≥ 25 kg/m² included male sex (OR = 2.46; 95% CI: 1.46–4.15), lower education, and lower nutrition knowledge, while age showed a non-linear positive association with excess body weight. Polish adolescents and young adults show gaps between declared pro-health attitudes and actual diet quality/competencies. Social media reliance appears particularly linked to product-oriented behaviours (supplementation). Prevention should strengthen nutrition and food safety education, digital health literacy, and professional guidance on supplementation, especially in adolescents. Our findings suggest that social media is a primary driver for dietary supplementation among Polish youth, more so than objective nutrition knowledge. While diet quality is linked to weight status, the relationship is complex. These results may inform future public health interventions targeting digital health literacy to promote balanced nutrition and safe supplementation practices. 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

Earthquake-induced landslide reactivation poses a sustained hazard for years following major seismic events, yet operational prediction remains constrained by heterogeneous multi-modal data, sparse supervision, and the absence of uncertainty-aware frameworks. This paper presents PIMSEL, a physically guided multi-modal semi-supervised framework for post-seismic landslide reactivation risk assessment. PIMSEL integrates satellite-derived morphological features, precipitation time series, and seismic hazard attributes through four components: entropy-regularized optimal transport for cross-modal semantic alignment without paired supervision; causally constrained hierarchical fusion enforcing domain-consistent modal weighting; scenario-based prototype mutation for semi-supervised learning from sparse expert annotations; and prototype-anchored variational graph clustering that simultaneously stratifies landslides into HIGH, MEDIUM, and LOW risk tiers and produces decomposed aleatoric and epistemic uncertainty estimates for operational triage. The HIGH risk tier operationally corresponds to predicted reactivation, validated against 598 documented reactivation events across 7482 co-seismic landslides from three Sichuan Province earthquake sequences: the 2013 Lushan (Mw 7.0), 2017 Jiuzhaigou (Mw 7.0), and 2022 Luding (Mw 6.8) events. PIMSEL achieves 82.5% reactivation recall and 66.4% precision, outperforming twelve baselines across clustering quality, classification, and uncertainty calibration metrics. Ablation studies confirm that optimal transport alignment contributes the largest individual performance gain. Current limitations include quarterly assessment frequency and dependence on optical imagery under cloud cover, which future integration of real-time meteorological triggers and SAR data should address. Full article

Rural communities face significant healthcare access barriers that contribute to persistent health disparities. Mobile health clinics (MHCs) have emerged as a promising strategy for expanding healthcare access, yet their effectiveness in rural settings remains understudied. The aim of this review was to examine the literature to determine what is known about access, health outcomes, and the cost-effectiveness of rural MHCs, specifically with regard to their impact on patient access and outcomes, return on investment (ROI)/financial, and program sustainability. We conducted a comprehensive search of peer-reviewed and grey literature sources. Systematic screening yielded 34 documents for full analysis. Thematic analysis was conducted across three domains: patient access, patient outcomes, and ROI/sustainability. All 34 documents provided data on patient access, with common themes including expanded service utilization, multi-service integration, overcoming geographic and transportation barriers, and improved healthcare affordability. Thirty-two documents addressed patient outcomes, reporting improvements in preventive care delivery, chronic disease management, and high patient satisfaction. Twenty-eight documents included ROI/sustainability information, with evidence suggesting cost-effectiveness particularly through emergency department visit avoidance and multi-service integration. Across the literature reviewed, the quality of evidence varied considerably, yet we concluded mobile health clinics demonstrate promise for expanding healthcare access and improving outcomes in rural populations. Key success factors include multi-service integration, diverse funding partnerships, technological integration, and strong community engagement. More rigorous research with longitudinal clinical outcome measures and robust economic analyses is needed. Full article

Construction projects contributing to smart city (SC) development largely consist of megaprojects due to their complex and multidisciplinary nature and their high costs. Effective project management (PM) is essential for the implementation of these projects in the Construction 5.0 era. This study aims to systematically analyze the research trends and identify FRDs in construction PM for megaprojects, which are essential for the development of SCs in the Construction 5.0 era. With this aim, a systematic literature review based on the PRISMA 2020 checklist was performed through a bibliometric analysis using VOSviewer version 1.6 Studies are gathered under five main clusters (i.e., the PM cluster, the smart construction and data security cluster, the SC and technology cluster, the spatial data integration cluster, and the lifecycle cluster). It has been determined that two main nodes (i.e., SC and digital twin) are located at the center of all these clusters. As a result of the analysis, two future research directions are determined (i.e., the relationship between megaprojects and SCs and the relationship between construction project management and SCs). As the identified clusters, nodes, and future research directions are interrelated and comply with the PMBOK 7th edition performance domains, focusing on them to support construction PM performance complies with efforts to facilitate the successful implementation of megaprojects integrated with SCs. The findings demonstrate the lack of a PM model within the SC ecosystem that synchronizes all phases of megaproject construction with SCs. Thus, this study can contribute to the development of smart, sustainable, and resilient cities. Full article

Additive manufacturing (AM) has transformed traditional manufacturing by enabling the fabrication of complex geometries and functional components that are difficult or impossible to produce using conventional techniques. Recent advancements have expanded AM capabilities through the integration of multi-material systems, allowing for enhanced performance, customisation, and functionality of manufactured parts. Despite rapid development, there is a limited consolidated understanding of the processes, material combinations, and practical implications of multi-material additive manufacturing (MMAM) across different application domains. This study aims to provide a comprehensive overview of general additive manufacturing processes, with a particular focus on the evolution and implementation of multi-material fabrication techniques. The review draws upon publicly available scientific literature to analyse various AM technologies, material pairing strategies, and process parameters. Comparative analysis is conducted between the additive and conventional manufacturing approaches to highlight advantages and limitations. The findings reveal significant progress in material compatibility, interface bonding, and process integration, enabling the production of multifunctional and performance-optimised components. Diverse applications are identified across aerospace, biomedical, and industrial sectors. MMAM represents a critical advancement in modern manufacturing, offering expanded design freedom and functional integration. Continued research is essential to address the remaining challenges in material compatibility, scalability, and process standardisation. Full article

Clear cell renal cell carcinoma (ccRCC) is characterized by marked biological heterogeneity, which limits the prognostic accuracy of conventional clinicopathological models. Increasing attention has therefore focused on identification of biomarkers that can enhance risk stratification throughout all stages of the disease. Starting from the current state of the art, this narrative review summarizes and critically appraises the evidence published over the past decade regarding prognostic biomarkers in ccRCC. The analysis is structured into four overarching domains: (i) genomic biomarkers, covering somatic alterations and transcriptomic signatures; (ii) tissue-based biomarkers, including immunohistochemical surrogates and immune microenvironment features; (iii) circulating biomarkers, such as systemic inflammation parameters and indices; and (iv) integrated predictive models, represented by emerging multi-omic approaches. Going through the broad framework of potential prognostic biomarkers, emphasis is placed on their individual and integrative value in relation to classic clinical-pathological factors and survival parameters. At the tissue level, chromosome 3p-related alterations constitute a central molecular feature of ccRCC. Among these, BAP1 loss has emerged as one of the most consistently validated indicators of aggressive tumor behavior. Disruption of the SETD2/H3K36me3 axis and immune-related biomarkers, including PD-L1 expression, have demonstrated prognostic associations in selected settings, although with variable and context-dependent performance. In the circulating compartment, plasma KIM-1 has shown prognostic relevance following nephrectomy, while postoperative detection of circulating tumor DNA (ctDNA) may identify patients at increased risk of recurrence. However, limited analytical sensitivity and methodological heterogeneity currently restrict the broader clinical applicability of ctDNA-based strategies. Systemic inflammatory indices, such as the neutrophil-to-lymphocyte ratio, show reproducible associations with outcomes but largely reflect host inflammatory status rather than tumor-specific biology. However, no single biomarker currently supports routine prognostic implementation in ccRCC. Future progress will likely depend on integrative models combining genomic, tissue-based, immune, and circulating parameters with established clinical variables. Prospective validation and clear demonstration of incremental clinical utility will be essential before such strategies can meaningfully inform therapeutic decision-making. Full article

The widespread adoption of encryption protocols such as TLS 1.3 has significantly reduced the visibility of packet payloads, limiting the effectiveness of traditional traffic analysis methods. Recent deep learning approaches attempt to learn representations directly from raw byte sequences; however, in encrypted environments, byte-level patterns often exhibit high entropy and unstable ordering, raising concerns about their reliability. In this work, we revisit the roles of content and structural information in traffic classification and argue that effective modeling should move beyond content-only representations. We propose a structure-aware framework that models hierarchical relationships across fields, layers, and sessions while representing byte information using compact, permutation-invariant summaries. In addition, we introduce a hierarchical shuffle pretraining strategy to capture relational dependencies and an adaptive inter-level gating mechanism to dynamically integrate multi-level representations. Extensive experiments on multiple datasets with varying levels of encryption demonstrate that byte-level sequential patterns are not always essential, while structural information provides consistent complementary cues. Furthermore, the importance of different structural levels varies across datasets, highlighting the need for adaptive multi-level modeling. The proposed method achieves strong performance across diverse datasets, including highly encrypted traffic, while maintaining robustness under domain shifts and limited data scenarios. These results suggest that combining compact content representations with structural context and adaptive integration is a promising direction for encrypted traffic analysis. Full article

The rapid expansion of renewable energy has raised the demand for accurate, long-term wind power forecasting. However, wind power series are strongly affected by meteorological factors and exhibit pronounced volatility, making long-term prediction challenging. To model these characteristics more comprehensively, we propose STFNet, a dual-branch neural architecture that integrates time-domain and frequency-domain modeling. STFNet contains two key modules: (1) an MLFE module, which explicitly captures lag effects and non-stationary transitions through parallel multi-scale convolutions and a difference-convolution branch and further enhances multivariate dependency learning via cross-variable interaction modeling, and (2) an FGFE module, which applies DCT to capture long-cycle trends and uses a learnable low-pass filter for noise suppression. Experiments on two real-world wind farm datasets (LY and HG) show that STFNet consistently outperforms strong baselines, achieving average MSE reductions of 15.9–26.6% while maintaining a high computational efficiency. Ablation studies further confirm the effectiveness of each module, indicating the strong practical potential of STFNet for wind farm operation and management. Full article

Digital management of modern railway bridges, a substantial part of high-speed railway networks, is often hindered by manual interpretation of construction drawings for Building Information Modeling (BIM). While individual technologies like optical character recognition (OCR) and neural networks are well-established, their generic application often fails on complex engineering documents. To address this, a domain-adaptive automatic recognition and semantic interpretation framework is proposed for railway bridge construction drawings. The novelty of this work lies in a specialized hybrid data fusion strategy that intelligently merges vector CAD file parsing with morphology-denoised OCR, resolving spatial and semantic conflicts. Furthermore, a back-propagation (BP) neural network is explicitly adapted to classify the extracted text into specific engineering categories, overcoming the challenges of dense layouts and overlapping symbols. Finally, the framework achieves end-to-end integration by transforming these semantic entities directly into structured, IFC-compatible BIM parameters. Evaluated on 250 real-world drawings, the framework achieved an average F1-score of 91.0% in semantic classification and improved processing efficiency by 6.5 times compared to manual methods. Moreover, 93.8% of the extracted entities achieved strict BIM parameter correctness, defined as seamless mapping to Revit IFC attributes without manual intervention. Full article

Improving the prediction accuracy of renewable energy power generation units is an important goal of the “source-storage integration” approach. However, the abundance of anomalous data and indistinct features in renewable energy station data seriously affects the health status prediction of these generator sets. To effectively enhance the performance of renewable energy generation prediction, this paper proposes an efficient data cleaning method for renewable energy stations based on anomaly detection and feature enhancement. First, anomaly detection is achieved by calculating a baseline power curve and partitioning data, utilizing the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Subsequently, considering that current models often learn low-frequency features while ignoring high-frequency features when processing time-series data, a data feature enhancement method is proposed. The proposed method integrates high-/low-frequency data decomposition, time–frequency domain conversion, and an improved attention mechanism to effectively enhance the high-frequency features of renewable energy station data, and reduces the RMSE of mainstream forecasting models significantly. Finally, using data from a renewable energy station in a region of China, the effectiveness and superiority of the anomaly detection and feature enhancement methods are analyzed. The results show that for renewable energy generation data, the proposed method reduces the RMSE of LSTM and Transformer models by 15.12%, 16.67% and 16.24%, 18.32% respectively, significantly improving prediction accuracy. Full article

In modern intelligent food processing, the unpredictable variability in pomelo orientation on high-speed conveyors poses a significant challenge to automated grading and precision peeling operations. To address this, a deep learning-based method is proposed for the real-time detection of pomelo posture. Firstly, a pomelo posture dataset was constructed to support model training and validation. Secondly, to balance the extraction of posture features from uniform fruits with the low-power constraints of edge deployment, a domain-specific architectural optimization is presented. Building on the YOLOv8n framework, the proposed model synergistically integrates specialized modules. A lightweight GhostHGNetV2 foundation is utilized to significantly reduce computational redundancy while maintaining the resolution required to detect key anatomical landmarks. To overcome spatial confusion and capture multi-scale global appearance information, a multi-path coordinate attention (MPCA) module is introduced. Furthermore, the SlimNeck architecture and VoVGSCSP module streamline multi-scale feature fusion via one-time aggregation, effectively preventing computational bottlenecks. This design optimizes the computational efficiency of the model while maintaining detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv8n model, the proposed method increased the mAP50 accuracy by 3.67% while reducing parameter count and computational load by 17.5% and 23.3%, respectively. Additionally, it achieved a processing speed of 19.3 FPS on the Jetson Orin Nano 6G edge platform. This research provides a critical technical foundation for the recognition of pomelo posture, enabling subsequent orientation rectification and fostering the development of streamlined, automated pomelo processing lines. Full article

Indoor Environmental Quality (IEQ) strongly influences health, comfort, and learning performance in academic buildings, yet assessment practices remain fragmented and rarely aligned with sustainability goals. This study conducted a PRISMA 2020-guided systematic literature review to identify, screen, and map IEQ indicators for educational facilities and to develop a sustainability-aligned framework for classroom evaluation. Searches of Google Scholar, Scopus, and Web of Science (2010–2025) yielded 365 records; after de-duplication and eligibility screening, 142 peer-reviewed studies were included. From these, 118 unique IEQ indicators were extracted and classified into six domains: thermal comfort, indoor air quality, acoustic quality, visual comfort, environmental quality, and spatial quality. Using sustainability-oriented screening criteria (measurability, relevance, reliability, data accessibility, understandability, and long-term applicability), 50 indicators (42%) were retained as methodologically robust, while 68 (58%) were excluded due to weak standardization or limited practical applicability. The retained indicators were systematically mapped to the environmental, social, and economic pillars and aligned with key SDGs (3, 4, 7, 11, and 13). The resulting Sustainability-Aligned IEQ Indicator Framework integrates quality-screened indicators with pillar/SDG alignment and a mixed-method pathway that combines objective monitoring and occupant perception, supporting context-sensitive evaluation, particularly for naturally ventilated and tropical learning environments. Full article

To address the polarization fading noise in coherent detection phase-sensitive optical time-domain reflectometry (Φ-OTDR) for distributed low-frequency vibration sensing, a Φ-OTDR sensing scheme integrating polarization diversity reception and the variational mode decomposition (VMD) algorithm is proposed. The mechanism of polarization fading induced by fiber birefringence and external perturbations is systematically analyzed. A signal–noise mathematical model for polarization diversity reception is established, and the adaptive decomposition capability of the VMD algorithm for non-stationary phase signals is elaborated. This scheme can accurately separate the additional noise introduced by polarization diversity reception from the target low-frequency vibration signals. Experimental results demonstrate that, compared with the single-path detection scheme, the proposed method eliminates the amplitude attenuation of beat frequency signals caused by polarization mismatch at the optical path level. Meanwhile, it effectively suppresses both the additional noise introduced by polarization diversity and the low-frequency phase drift resulting from unstable laser frequency. It achieves precise phase restoration of vibration signals excited at 50 Hz under three typical sensing distances of 5 km, 10 km, and 30 km. Additionally, it successfully restores low-frequency vibration signals as low as 0.6 Hz at the sensing distance of 30 km. Full article