Search Results (4,259)

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder whose clinical assessment relies mainly on behavioral and neuropsychological evaluation. This study evaluates a subject-wise machine learning framework for distinguishing children with ADHD from healthy controls using multichannel EEG-derived features. The public dataset comprised 121 participants (61 ADHD and 60 controls), with 19-channel EEG recordings sampled at 128 Hz. Signals were segmented into 4-s windows with 50% overlap, and statistical and spectral features were extracted, including mean, standard deviation, and theta-, alpha-, and beta-band power. Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), and Logistic Regression (LR) were evaluated using strict subject-wise separation. RF achieved the highest Accuracy (0.8099), F1-score (0.8160), Balanced Accuracy (0.8097), and MCC (0.6204), whereas SVM obtained the highest Sensitivity (0.8525) and ROC-AUC (0.8527). An additional subject-specific analysis based on individual alpha frequency (IAF) was performed to account for inter-individual spectral variability; mean IAF values were 8.8320 Hz for ADHD and 8.8833 Hz for controls, and the individualized-band analysis did not improve classification performance. Bootstrap confidence intervals and non-parametric tests indicated comparable performance among RF, SVM, and GB. Frontal and fronto-central channels, particularly Fz, showed the greatest model-derived contribution. Overall, the framework provides a reproducible subject-wise EEG classification approach, although external validation on independent cohorts remains necessary before clinical application. Full article

Mulberry (Morus spp.) leaves, fruits, branches, and root bark are rich in bioactive compounds, including 1-deoxynojirimycin (1-DNJ) and γ-aminobutyric acid (GABA), supporting their potential use in food, medicinal, and feed applications. Their high moisture content, however, makes fresh materials highly susceptible to postharvest quality deterioration, making drying essential for stabilization and high-value utilization. Drying technologies involve trade-offs among efficiency, energy consumption, sensory quality, rehydration, and bioactive-compound retention. This review provides a comprehensive overview of pretreatment and drying technologies for mulberry materials, with particular attention to differences in raw-material characteristics, processing conditions, analytical methods, and reporting bases that limit direct comparisons among studies. Current evidence suggests that low-temperature, low-oxygen, or short-duration technologies, including vacuum freeze-drying, microwave drying, and microwave-vacuum drying, may better preserve quality in certain thermosensitive products, although their benefits remain product- and process-dependent. Hot-air, solar, infrared, heat-pump, and hybrid drying remain practical options for bulk products but require optimization to balance quality, energy efficiency, and scalability. For juice and functional powders, carrier selection, powder properties, and bioaccessibility require further study. Overall, the available evidence is heterogeneous, and some conclusions rely on limited mulberry-specific data or extrapolation from related plant matrices. Future research should emphasize standardized quality evaluation, harmonized reporting, kinetic modeling, multi-objective optimization, online monitoring, energy and carbon-footprint assessment, and industrial-scale validation. Full article

Cultural heritage holds great value, yet its sustainability faces numerous pressures. Technological advances and the COVID-19 pandemic have promoted the development of virtual tourism, bringing new opportunities and challenges to the cultural heritage sector. However, research on the impacts of virtual tourism on cultural heritage sites has mainly focused on socio-cultural factors, with limited attention to environmental and economic aspects. Moreover, most studies have examined online rather than on-site modes of virtual tourism. To address these gaps, this study draws on a heritage sustainability framework based on the triple bottom line and investigates users’ perceptions of the implications of virtual tourism for the overall sustainability of cultural heritage sites, encompassing both online and on-site contexts. Qualitative questionnaire surveys, observations, and visual documentation were conducted at two virtual heritage tourism sites in Sichuan, China. The findings show that the virtual tourism applications at both sites were perceived as enhancing the socio-cultural values of the associated heritage sites in various ways. Virtual tourism also showed potential to expand the economic utility of heritage at these sites and support their environmental sustainability, despite involving substantial investment costs and possible environmental harm associated with its development. Overall, virtual tourism was perceived as having the potential to support heritage sustainability, but its development should balance socio-cultural, environmental, and economic dimensions. Full article

Rest intolerance—the difficulty of truly relaxing during rest, accompanied by guilt and anxiety—is increasingly prevalent among university students, yet its attentional antecedents remain poorly understood. Drawing on motivational conflict theory, this research examined temporal focus as a correlate of rest intolerance and perceived involution as a boundary condition. Study 1, a cross-sectional survey of 290 Chinese university students, found that trait future temporal focus was positively associated with rest intolerance, whereas present temporal focus was negatively associated with it, with perceived involution moderating both associations. An exploratory latent profile analysis identified three profiles—balanced (80.7%), future-focused (10.7%), and present-focused (8.6%)—with the future-focused profile showing the highest rest intolerance. Study 2, a 2 (temporal focus: future vs. present) × 2 (perceived involution: high vs. low) experiment with 131 participants, found that induced future focus was related to higher reported rest intolerance than induced present focus, particularly under low involution; under high involution, this difference was reduced. These findings identify temporal focus as a systematic attentional factor relevant to rest intolerance and suggest the potential applicability of motivational conflict theory to the rest domain. Full article

Fiscal cyclicality occupies a central position in macroeconomic stabilization, but existing empirical studies have focused predominantly on determining whether fiscal behaviour is procyclical or countercyclical, with relatively limited attention being paid to the dynamic evolution and intensity of fiscal response over time. The purpose of this study is to develop a dynamic framework for assessing the direction, intensity and temporal evolution of fiscal cyclicality in the European Union. This research analyses 27 Member States during the period 2001–2025. The fiscal cyclicality coefficient is estimated using five-year rolling ordinary least squares (OLS) regressions of the budget balance on the output gap, allowing fiscal cyclicality to vary across countries and over time. In order to enhance the reliability of the estimates, the computed coefficients are winsorised and used to construct a continuous measure of fiscal cyclicality intensity and a normalized Score index. The findings reveal substantial heterogeneity in fiscal cyclicality across EU Member States and over time, indicating a predominant countercyclical behaviour during significant macroeconomic shocks, while also demonstrating significant differences in the strength and stability of fiscal responses. Countercyclical observations account for 83.07% of the rolling-window estimates, compared with 11.82% procyclical and 5.11% acyclical observations. The additional robustness tests indicate that the main structure of the estimated coefficient series is preserved under alternative treatments of extreme observations, the exclusion of individual countries and the use of an earlier information set for the output gap. Analysing fiscal cyclicality solely through its direction therefore provides an incomplete representation of fiscal behaviour. The proposed framework extends the existing literature by simultaneously considering the direction, intensity and dynamics of fiscal cyclicality over time, providing a more comprehensive basis for comparative analysis of fiscal policy and future assessments of fiscal sustainability. For policymakers, the resulting Score provides an additional quantitative indicator for monitoring changes in the intensity of fiscal behaviour, and when considered jointly with the estimated coefficient, changes in its direction may complement existing assessments of fiscal policy within the European Semester and the European economic governance framework. Full article

Cryopreservation plays a critical role in the long-term preservation of biological materials and has become an indispensable technology in biomedicine, agriculture, animal breeding, and biodiversity conservation. Despite substantial advances in cryobiology, the preservation of cells and tissues at ultra-low temperatures remains limited by cryoinjuries associated with ice crystal formation, osmotic stress, oxidative damage, and cellular dehydration. This review provides an overview of the historical development and fundamental principles of cryopreservation and critically summarizes the key factors affecting cryopreservation efficiency, including cryoprotective agents, antioxidants, osmotic balance, freezing and thawing protocols, and emerging ice-controlling materials. In addition, recent advances in the cryopreservation of diverse biological materials—such as sperm, oocytes, embryos, stem cells, and gonadal tissues from different animal species—are critically discussed, with particular attention to species-specific differences and the distinction between post-thaw survival and functional recovery. Finally, the current limitations of existing cryopreservation technologies and future research directions are highlighted to facilitate the development of safer and more effective preservation strategies for animal genetic resource conservation and biomedical applications. Full article

Vibration damper detection in unmanned aerial vehicle (UAV)-based transmission line inspection presents distinctive task-specific challenges: the targets are not only small and weakly textured, but also characterized by slender structures. Their effective identification therefore depends on the preservation of local contour cues and the appropriate organization of deep contextual responses. To address the limitations of conventional lightweight detectors in structural feature representation, cross-scale semantic consistency, and bounding-box localization, this paper proposes CARE-Net (Cascaded Attention and Refinement Enhanced Network), a compact detection framework for vibration damper detection. CARE-Net adopts an asymmetric design consisting of front-end structural enhancement and back-end contextual refinement. Specifically, the Cascaded Residual Attention Block (CRAB) is deployed in the backbone to strengthen the representation of slender contours and local structural features of vibration damper targets. The Dynamic Context Refinement Network (DCRN) is introduced at the backbone–neck transition to improve the contextual organization of deep features and the quality of cross-scale feature fusion. Meanwhile, an Adaptive Focal Complete IoU Loss (AF-CIoU) is proposed to optimize bounding-box regression for difficult samples without altering the inference architecture. A UAV-based vibration damper dataset covering three condition categories, namely normal, rusted, and dilapidated, is constructed in this study. Experimental results show that CARE-Net achieves an mAP^@0.5 of 0.951 and an mAP^@0.5:0.95 of 0.628 with 2.44 M parameters and 6.2 GFLOPs. Further configuration experiments indicate that, compared with repeatedly introducing attention enhancement into high-level features, stage-specific feature modeling is better suited to the slender small-object detection task investigated in this study. The proposed method provides a solution for intelligent vibration damper inspection of transmission lines that balances detection accuracy, model compactness, and potential for terminal-side application. Full article

Cross-condition bearing fault diagnosis suffers from severe performance degradation due to domain shift across different operating conditions, especially when only a few labeled target-domain samples are available. To address this challenge, this paper proposes a progressive attention-guided two-stage transfer learning framework for few-shot bearing fault diagnosis across different fixed operating points. First, the raw time-domain vibration signals are fused with frequency-domain representations extracted by short-time Fourier transform (STFT) to enhance fault feature representation. Then, a progressive attention-guided feature learning strategy is developed by integrating dual efficient channel attention (ECA) modules into a deep one-dimensional convolutional neural network (1D-CNN), enabling the network to adaptively emphasize fault-sensitive features while suppressing redundant information. Subsequently, a two-stage transfer learning strategy is designed, consisting of transferable feature learning from the source domain and few-shot adaptation to the target domain. During target-domain adaptation, key feature extraction layers are frozen, and a sample-balanced optimization mechanism is introduced to alleviate the dominance of source-domain samples during joint training. Experimental results on the Case Western Reserve University (CWRU) bearing dataset demonstrate that the proposed method achieves an average accuracy of 99.96% across three cross-condition transfer tasks. Furthermore, experiments conducted on a self-built shaft system dataset show that the proposed method achieves an average accuracy of 87.11% under three representative transfer scenarios. The results verify that the proposed framework effectively mitigates domain shift and enables accurate bearing fault diagnosis with limited labeled target-domain samples. Full article

Ecotourism, as one of the most important forms of sustainable tourism, plays a significant role in the conservation of natural resources, the economic development of local communities, and the achievement of sustainable development goals. However, the sustainable development of this sector requires the accurate identification of suitable areas and the simultaneous assessment of the ecological capacities and limitations arising from anthropogenic activities, an issue that has received less attention at the national scale, particularly in countries with high environmental diversity such as Iran. Therefore, the present study was conducted with the aim of assessing the potential for ecotourism development in Iran based on Geographic Information System (GIS) and Spatial Multi-Criteria Decision Analysis (SMCDA). In line with the scope of the sub-factors evaluated, the assessed construct is referred to throughout this study as Environmental–Anthropogenic Ecotourism Suitability (EAES), reflecting an environmental-quality and anthropogenic-pressure perspective rather than a comprehensive assessment of ecotourism sustainability. The main innovation of this study lies in the simultaneous integration of natural and anthropogenic factors at the national scale and the sensitivity assessment of the results through the design of different development scenarios. In this study, a set of natural sub-factors including protected areas, vegetation cover, slope, precipitation, land use, and natural attraction density, as well as anthropogenic sub-factors including settlements, roads, accommodations, mines, industrial parks, dams, power transmission lines, dust, and the Global Human Modification (GHM) index were used. The weighting of the sub-factors was carried out using the Best–Worst Method (BWM), and spatial suitability maps were subsequently generated through the Weighted Linear Combination (WLC) method in the GIS environment. To evaluate uncertainty and examine the effect of the relative importance of sub-factors, three scenarios including natural factor dominance, anthropogenic factor dominance, and a balanced scenario were designed and analyzed. The results showed that protected areas (0.19), vegetation cover (0.17), and natural attraction density (0.16) were the most important natural sub-factors, while the GHM index (0.16), roads (0.15), and settlements (0.14) were the most important anthropogenic sub-factors affecting ecotourism development. The spatial pattern of the results indicated the concentration of areas with good potential in the Alborz and Zagros mountain ranges, the Hyrcanian forests, and parts of the protected areas of Iran. In the balanced scenario, approximately 24.8% of Iran’s area was classified within the suitable and highly suitable classes, while 46.3% was classified within the moderately suitable class. Furthermore, comparison of the scenarios showed that the use of one-dimensional approaches may lead to unrealistic estimates of ecotourism capacity. Overall, the results of this study indicate that the sustainable development of ecotourism in Iran requires the adoption of an integrated approach in which the conservation of natural assets and the management of anthropogenic interventions are simultaneously considered. The proposed framework can serve as a decision-support tool for spatial planning, investment prioritization, and sustainable ecotourism development policymaking in Iran and other similar regions. Full article

This study examines whether investor attention and the market information environment transmit environmental, social, and governance (ESG) information into stock returns. The analysis employs a balanced panel of 31 mining companies listed on the Indonesia Stock Exchange from 2019 to 2023, comprising 155 firm-year observations. ESG performance is measured using an external ESG score, investor attention is proxied by the Google Search Volume Index, and the information environment is captured inversely by the relative bid–ask spread. Firm fixed-effects models with heteroskedasticity-robust standard errors clustered at the firm level are estimated for the investor-attention, spread, and stock-return equations. Indirect effects are assessed using 5000 firm-level cluster-bootstrap replications. The results show that ESG performance is not significantly associated with contemporaneous stock returns. ESG is positively but only marginally associated with investor attention and significantly associated with a narrower relative bid–ask spread, indicating a more favourable information environment. Investor attention and the relative spread are significantly associated with stock returns. However, neither the attention-mediated effect nor the spread-mediated effect is statistically significant. These findings distinguish ESG signal recognition from signal pricing, showing that ESG information can influence investor attention and the market information environment without forming a statistically significant transmission mechanism to contemporaneous stock returns. Full article

Co-phase traction power supply system (CTPSS) integrated with photovoltaic (PV) and hybrid energy storage system (HESS) can enhance power supply capacity while facilitating the on-site consumption of renewable energy. This integration provides a pathway toward green development and the achievement of the “carbon peaking and carbon neutrality” goal. However, the stochastic nature of PV poses challenges to safe and economical operation of the system. Existing energy management methods remain limited in simultaneously balancing operational economy and extreme risks caused by PV uncertainty, making it difficult to achieve an effective trade-off between operating cost and constraint violation risk. To address this issue, a risk-sensitive optimal scheduling framework integrating probabilistic PV forecasting, correlated scenario generation, and risk-aware optimization is developed. First, a PV probabilistic prediction model based on parallel TCN-BiLSTM-Attention is proposed, which extracts multiscale local features and long-range temporal features from PV for accurate uncertainty quantification. Second, a t-Copula PV scenario generation method driven by weather classification and temporal correlation is proposed. On this basis, a day-ahead optimal scheduling strategy integrating chance constraints programming (CCP) and conditional value at risk (CVaR) is established to simultaneously control constraint violation risk and extreme economic risk. The proposed prediction model can accurately quantify uncertainty, achieving an R² value of 0.9979. When the allowable power supply loss probability is 0.5%, the proposed scheme’s operating cost is reduced by 1.36% compared with the baseline scheme. The results demonstrate that the proposed framework can effectively coordinate operating economy and extreme-risk control, thereby improving the risk-sensitive operational performance of CTPSS with PV and HESS. The proposed strategy balances economy and extreme risk, providing a reference for safe, low-carbon and economical operation of CTPSS with PV and HESS. Full article

Agricultural pest and disease images often lose diagnostically important textures during transmission and storage, while conventional codecs struggle to balance high compression ratios and information fidelity. This study proposes YOLOv11n-SWAQ, a lightweight adaptive compression framework integrating shared feature encoding and critical-region-aware quantization. The YOLOv11n backbone and feature fusion network are reused as a shared encoder to reduce redundant feature extraction. A lightweight window-based attention module enhances lesion boundaries, insect contours, and local textures. Detection boxes and attention weights are combined to construct a three-level quantization map for background, ordinary critical, and high-importance texture regions, while a hyperprior entropy model improves latent probability estimation. At an actual complete-stream bitrate of approximately 0.31 bpp, the proposed method achieved an ROI-PSNR of 36.970 dB and an ROI-MS-SSIM of 0.9652. Compared with the YOLOv11n+CAE cascaded baseline, the reconstruction error in complex-texture regions decreased by 4.8%, indicating preferential bitrate allocation to diagnostically important regions. The proposed method achieved an end-to-end processing time of 13.44 ms, representing an average reduction of approximately 72% compared with representative advanced learned image compression models. Overall, YOLOv11n-SWAQ provides a favorable trade-off between critical-region fidelity and computational efficiency for high-ratio agricultural image compression, with its principal advantage lying in lightweight task-oriented processing rather than a large absolute gain in global reconstruction quality. Full article

Automatic classroom student behavior recognition faces three major challenges: the coexistence of small targets and complex backgrounds, the conflict between details and semantics, and the interference from low-quality samples. To address these issues, this paper proposes a scene-driven integrated optimization framework, termed Hyper-YOLO-G. In the backbone network, a Squeeze-and-Excitation (SE) attention module is embedded to purify features and thereby enhance the channels of key behaviors. In the neck, a bidirectional weighted feature pyramid network (BiFPN) is introduced to perform scale-balanced multi-scale fusion. Moreover, the Wise-IoU v3 (WIoU v3) loss function is adopted to calibrate gradients and reduce the negative impact of low-quality samples. Experimental results on a public classroom behavior dataset show that Hyper-YOLO-G achieves 73.7% mAP@50 (mean average precision at IoU threshold 0.5), which is 4.9% higher than the baseline Hyper-YOLO. Ablation studies and generalization experiments on an independent multi-class dataset further demonstrate the combined effectiveness of each module and the generalization ability of the framework. This study provides a reliable technical path for high-precision, lightweight behavior recognition in complex classroom environments. Full article

Automated bridge surface defect detection is essential for improving the efficiency and objectivity of infrastructure inspection under complex field imaging conditions. This study proposes a Multi-Scale Detection Transformer (MS-DETR), an RT-DETR-based detector that integrates HeteroFusionNet, a multi-objective scale-aware integration network (MOSAIN), and a global attention two-dimensional module (GATM). HeteroFusionNet combines shared shallow feature extraction with heterogeneous dual-branch deep modelling to reduce redundant computation and enhance complementary local and global representations. MOSAIN injects high-resolution shallow details into the P3 feature path to improve small-defect recognition, whereas GATM adapts high-level attention encoding to dense two-dimensional visual features. On the self-built bridge defect dataset, MS-DETR achieved a mAP50 of 65.4% and an F1-score of 0.64, with 14.33 M parameters, 39.1 GFLOPs, and 62.3 FPS on an RTX 4090 GPU. On the RDD (China) road defect dataset and the VisDrone2019 dataset, MS-DETR achieved mAP50 values of 88.9% and an AP50 of 0.487, respectively. These results demonstrate that MS-DETR achieves competitive detection accuracy while maintaining a favorable balance between model complexity and inference speed under the evaluated experimental settings. Full article

Taekwondo is a high-intensity, intermittent, weight-category combat sport requiring repeated explosive actions, rapid decision-making, and effective recovery between bouts. These demands, combined with congested competition schedules and weight-management requirements, make nutrition important for both performance and athlete health. This narrative review provides an evidence-informed synthesis of nutritional strategies relevant to taekwondo performance, recovery, and weight management by integrating available taekwondo-specific evidence with findings from comparable combat sports and established sports nutrition guidelines. Particular attention is given to energy and carbohydrate availability, protein intake, hydration and electrolyte balance, selected ergogenic aids, nutritional periodization, and safe weight management. The review also proposes an applied integrative framework illustrating how nutrition may influence interacting physiological, neuromuscular, cognitive, and recovery-related processes. A key limitation is the scarcity of taekwondo-specific intervention studies, meaning that several practical recommendations are necessarily extrapolated from comparable combat sports and broader athletic populations. From an applied perspective, nutritional priorities should include maintaining adequate energy and carbohydrate availability, supporting recovery through appropriate protein and fluid–electrolyte intake, and adopting gradual, individualized weight-management strategies that minimize dehydration, low energy availability, and Relative Energy Deficiency in Sport (RED-S) risk. Overall, nutritional strategies in taekwondo should be individualized and aligned with training load, competition demands, recovery needs, and weight-management goals. Further taekwondo-specific experimental studies are needed to refine these recommendations and strengthen sport-specific nutritional guidance. Full article