Search Results (1,021)

Background/Objectives: School violence represents a significant concern for educational communities worldwide, affecting student well-being and academic development. While prior research has documented prevalence rates and risk factors, limited studies have examined social–cognitive factors associated with antisocial behavior specifically within vocational education contexts using integrated analytical approaches. This exploratory cross-sectional study examined social–cognitive factors—specifically self-reported attitudes about aggression norms, prosocial attitudes, and school climate perceptions—associated with violence-supportive attitudes among Greek vocational students. Methods: A cross-sectional design employed validated self-report instruments and traditional statistical methods. The sample comprised 76 vocational high school students (38.2% male; ages 14–18; response rate 75.2%) from one school in Patras, Greece. Validated instruments assessed attitudes toward interpersonal peer violence (α = 0.87), peer aggression norms across four subscales (α = 0.83–0.90), and school climate dimensions (α = 0.70–0.75). Analyses included descriptive statistics, Pearson correlations with bootstrapped confidence intervals, MANOVA for multivariate group comparisons, independent samples t-tests, propensity score matching for urban–rural comparisons, polynomial regression for developmental patterns, and path analysis for theoretical model testing. Results: Strong associations emerged between perceived school-level and individual-level aggression norms (r = 0.80, p < 0.001, 95% CI [0.71, 0.87]), representing one of the strongest relationships documented in school violence research. Violence-supportive attitudes demonstrated inverse associations with prosocial alternative norms (r = −0.37, p < 0.001, 95% CI [−0.55, −0.16]). Significant gender differences emerged for teacher–student relationships (d = −0.78, p = 0.002), with females reporting substantially more positive perceptions. Propensity-matched urban students demonstrated higher aggression norm endorsement compared to rural students across multiple indicators (d = 0.61–0.78, all p < 0.020). Polynomial regression revealed curvilinear developmental patterns with optimal teacher relationship quality during mid-adolescence (ages 15–16). Path analysis supported a sequential association model wherein school-level norms related to individual attitudes through prosocial alternative beliefs (indirect effect β = −0.22, p = 0.002, 95% CI [−0.34, −0.11]). Conclusions: This preliminary investigation identified social–cognitive factors—particularly normative beliefs about aggression at both individual and environmental levels—as strongly associated with violence-supportive attitudes in Greek vocational education. The exceptionally strong alignment between school-level and individual-level aggression norms (r = 0.80) suggests that environmental normative contexts may play a more substantial role in attitude formation than previously recognized in this educational setting. Gender and urban–rural differences indicate meaningful heterogeneity requiring differentiated approaches. Future research should employ longitudinal designs with multi-informant assessment and larger multi-site samples to establish temporal precedence, reduce method variance, and test causal hypotheses regarding relationships between normative beliefs and behavioral outcomes. Full article

Wearable devices are becoming widely applied in healthcare to enable continuous, noninvasive monitoring, but their use in pediatric populations remains relatively underexplored. This review synthesizes 36 clinical studies focused on pediatric hospital and outpatient wearables published between 2014 and 2025. Devices included wrist-worn trackers, adhesive biosensors, and more, capturing electrocardiography, photoplethysmography, accelerometry, and other signals. Clinical applications spanned a variety of care settings. Artificial intelligence (AI) partially enhanced interpretation for the early detection of conditions such as postoperative complications and sepsis. Despite their promising accuracy, most studies remain small, single-center pilots focused on feasibility and signal validity rather than outcomes such as mortality, readmission, or long-term recovery. Key barriers include pediatric-specific device design, motion-robust signal quality, regulatory clearance, workflow integration, and equitable adoption in low-resource settings. Ethical concerns such as privacy, consent, and incidental findings and regulatory constraints, particularly the lack of pediatric labeling and approval for consumer and AI-driven devices, further limit translation into practice. Future work should prioritize multi-center studies, multimodal analytics, explainable AI, and seamless integration into clinical pathways. With these advances, wearables can move beyond feasibility to become reliable, personalized tools that improve pediatric monitoring and care. Full article

To address issues such as attitude instability, insufficient adaptability, and poor operational quality of precision operation equipment caused by complex terrain conditions in hilly orchards, this study designed an electric carrier Self-Leveling Platform based on the 3-RRS parallel configuration. Focusing on the stability requirements of the operation plane, an automatic leveling control strategy was proposed with the constant center height of the moving platform as an additional constraint condition. Based on the inverse kinematics solution of the 3-RRS Parallel Mechanism, the analytical mapping relationship between the fuselage attitude and the compensation angle of the leveling leg crank was derived, and based on this, the working space of the Self-Leveling Platform and the maximum compensation angles of the moving platform in the pitch and roll directions were calculated. Key structural parameters were optimized using a multi-objective genetic algorithm, followed by the completion of a 3D model design and modal simulation analysis to verify the effectiveness of the structural design. Finally, leveling performance tests were conducted on a prototype. The results showed that the platform can achieve omnidirectional automatic leveling, with a maximum leveling time of 1.593 s and a maximum steady-state error of 0.62° under typical slope and load conditions. Analysis of variance results further indicated that there are significant differences in the leveling performance of the 3-RRS parallel configuration of the Self-Leveling Platform in the pitch and roll directions, demonstrating anisotropic characteristics. This study provides an effective solution for attitude stability control of orchard operation equipment in hilly areas and offers theoretical reference and technical support for the application of the 3-RRS parallel configuration in the agricultural equipment field. Full article

Early detection of Autism Spectrum Disorder (ASD), a neurodevelopmental condition characterized by social communication challenges, is essential for timely intervention. Naturalistic home videos collected via mobile applications offer scalable opportunities for digital diagnostics. We leveraged GuessWhat, a mobile game designed to engage parents and children, which has generated over 3000 structured videos from 382 children. From this collection, we curated a final analytic sample of 688 feature-rich videos centered on a single dyad, enabling more consistent modeling. We developed a two-step pipeline: (1) filtering to isolate high-quality videos, and (2) feature engineering to extract interpretable behavioral signals. Unimodal LSTM-based models trained on eye gaze, head position, and facial expression achieved test AUCs of 86% (95% CI: 0.79–0.92), 78% (95% CI: 0.69–0.86), and 67% (95% CI: 0.55–0.78), respectively. Late-stage fusion of unimodal outputs significantly improved predictive performance, yielding a test AUC of 90% (95% CI: 0.84–0.95). Our findings demonstrate the complementary value of distinct behavioral channels and support the feasibility of using mobile-captured videos for detecting clinically relevant signals. While further work is needed to improve generalizability and inclusivity, this study highlights the promise of real-time, scalable autism phenotyping for early interventions. Full article

As global urbanization accelerates, many regions—including Chinese cities—are continuously advancing their urban development. Urban renewal, as a component of urbanization, has increasingly become a focal point of attention in recent years. Achieving high-quality urban renewal requires an in-depth study of the interaction between the built environment and public space. Everyday public space, a subset of public space, represents residents’ everyday practices in the urban environment based on their own needs and is a recurring challenge in contemporary urban renewal and management. This study takes three distinct urban zones in Tianjin as case examples and employs quantitative analysis. From the perspectives of land-use patterns, residential indicators, urban design, and transportation systems, we compare the relationships between built-environment elements (both at the sub-regional and city-wide levels) and the distribution of everyday public spaces. In addition, we construct an evaluation model by integrating Structural Equation Modeling (SEM) with the Analytic Hierarchy Process (AHP) to assess everyday public spaces. The results show that the spatial distribution of everyday public spaces is significantly correlated with various urban elements, and these relationships differ across regions. Overall, the strongest correlations are found with large markets, residential area, population density, building density, and the 160 m integration index. Regarding evaluation, users and designers differ markedly in their focal points: users prioritize concrete elements such as the presence of specific facilities, whereas designers emphasize aspects that can be directly addressed through design interventions. In summary, we propose a complete research pathway—identification and evaluation based on the built environment—that can provide systematic, actionable, technical support for formulating and implementing urban-renewal policies, offering substantial practical value. Full article

Blockchain-based financial ecosystems generate unprecedented volumes of multi-temporal data streams requiring sophisticated analytical frameworks that leverage both on-chain transaction patterns and off-chain market microstructure dynamics. This study presents an empirical evaluation of a two-class confidence-threshold framework for cryptocurrency direction prediction, systematically integrating macro momentum indicators with microstructure dynamics through unified feature engineering. Building on established selective classification principles, the framework separates directional prediction from execution decisions through confidence-based thresholds, enabling explicit optimization of precision–recall trade-offs for decentralized financial applications. Unlike traditional three-class approaches that simultaneously learn direction and execution timing, our framework uses post-hoc confidence thresholds to separate these decisions. This enables systematic optimization of the accuracy-coverage trade-off for blockchain-integrated trading systems. We conduct comprehensive experiments across 11 major cryptocurrency pairs representing diverse blockchain protocols, evaluating prediction horizons from 10 to 600 min, deadband thresholds from 2 to 20 basis points, and confidence levels of 0.6 and 0.8. The experimental design employs rigorous temporal validation with symbol-wise splitting to prevent data leakage while maintaining realistic conditions for blockchain-integrated trading systems. High confidence regimes achieve peak profits of 167.64 basis points per trade with directional accuracies of 82–95% on executed trades, suggesting potential applicability for automated decentralized finance (DeFi) protocols and smart contract-based trading strategies on similar liquid cryptocurrency pairs. The systematic parameter optimization reveals fundamental trade-offs between trading frequency and signal quality in blockchain financial ecosystems, with high confidence strategies reducing median coverage while substantially improving per-trade profitability suitable for gas-optimized on-chain execution. Full article

The cosmetics industry faces mounting environmental pressure due to significant carbon emissions and pollution from daily product consumption, necessitating the systematic integration of sustainability into product development processes. This study develops an integrated decision-support framework combining Analytic Network Process (ANP) and Quality Function Deployment (QFD) with sustainability dimensions to guide cosmetics companies toward environmentally responsible operations. Using facial moisturizer development as a case study, the methodology transforms customer ecological expectations and technical requirements into prioritized design requirements through interdependent matrices (W_C and W_A) and integrated weighting, incorporating both classical ANP priorities (α = 0.70) and sustainability E-Vector scores (β = 0.30). Statistical analysis confirms the independence of technical and sustainability dimensions (r = 0.127, p = 0.743), validating the additive integration approach. Results reveal that hybrid criteria combining regulatory compliance with environmental performance achieve top priority rankings, with the integrated model demonstrating 75–80% concordance with industry R&D priorities from leading cosmetic companies and parametric robustness across realistic weighting scenarios. The framework enables the systematic translation of consumer sustainability demands into operational strategies while preserving safety primacy. This ANP-QFD approach provides cosmetics managers with a quantitative tool for balancing environmental responsibility with market competitiveness, positioning sustainability as a strategic advantage in an evolving regulatory landscape. Full article

Subways play an irreplaceable role in alleviating urban traffic congestion and showcasing a city’s historical and cultural heritage. Their speed and environmental benefits make them a vital component of sustainable urban development. Historical and cultural expression has become a focal point of subway spatial design and a core component of station planning. Building on this, the present study develops an evaluation system for metro station space that integrates history and culture and is grounded in the theory of genius loci (spirit of place). The Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) are used to derive indicator weights and conduct quantitative assessment. AHP results indicate that visual design, auditory elements, and cultural identity are the core priorities within the Xi’an metro station evaluation system. Design strategies integrate visual elements with historical and cultural contexts to create multisensory experiences encompassing form, color, sound, and touch. FCE further analyzes the indicators and shows that the overall design quality of the sampled Xi’an metro stations is generally high: auditory and visual elements are dominant, spiritual (psychological) experience and cultural identity approach excellence, and tactile elements show somewhat weaker performance. These findings suggest that metro space design requires deeper consideration across multiple dimensions. The proposed methodology can be applied to the design and evaluation of metro stations, providing practical guidance for culturally integrated metro spaces. Full article

In this work, we analyze how the coupling prism governs the performance of reduced-graphene-oxide (rGO)-assisted surface plasmon resonance (SPR) sensors for trace heavy-metal detection in water. A Kretschmann multilayer at 633 nm with a fixed Cu/Si₃N₄/rGO stack (45.0/5.00/1.41 nm) is modeled by transfer-matrix methods while varying the prism material among CaF₂, BK7, SiO₂, and SF₆. Performance optimization is carried out using angular sensitivity, full width at half maximum (FWHM), figure of merit (FoM), detection accuracy (DA), quality factor (QF), and a practical limit of detection (LoD). The analyte is represented by refractive-index typical of clean and contaminated water (n = 1.330 and 1.340). SF₆ yields the narrowest angular resonances but compresses analyte-induced angle spacing; CaF₂ provides larger analyte separations and consequently higher FoM and lower LoD under angle-encoded readout. The rGO interlayer enhances surface interaction across all prisms when co-tuned with the Cu and Si₃N₄ thicknesses. The sensitivity peaks around 310–320°·RIU⁻¹ for CaF₂. These results highlight the prism as a primary design variable in rGO-enhanced SPR sensing and position CaF₂-coupled architectures as promising for compact water-quality monitoring. Full article

Background: Clear aligners have become a common alternative to fixed appliances for tooth movement, and thermoplastic retainers hold the outcome. The prolonged intraoral contact of these devices has made the materials a focus of biocompatibility research. Objectives: This paper aims to summarize laboratory evidence on the biocompatibility of clear aligners and thermoplastic retainers. Materials included thermoformed polyethylene terephthalate glycol-modified (PETG), multilayer polyurethane, and directly printed resins. Primary outcomes were cytotoxicity, endocrine activity, and chemical or particle release. Methods: We systematically searched PubMed, the Cochrane Library, and Google Scholar through 31 May 2025, and we followed the PRISMA 2020 statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We applied predefined eligibility criteria. Two reviewers screened records and extracted data in duplicate, including study design, extraction conditions, surface-area-to-volume ratio (SA/V), cell models, endpoints, and analytical sensitivity as the limit of detection (LOD) and limit of quantification (LOQ). We assessed the risk of bias across seven domains and graded certainty by outcome. We did not register a protocol prospectively. Results: Seventeen studies met the inclusion criteria. Materials spanned multilayer polyurethanes (SmartTrack, Clarity), PETG sheets (Essix ACE, Duran), and directly printed resins (Graphy TC-85DAC); a subset tested zinc-oxide (ZnO) nanoparticle coatings. Typical extractions immersed 0.1–1 g of material in cell-culture medium or artificial saliva at 37 °C for 24 h to 30 days. Cell viability usually remained ≥80%. Mild cytotoxicity (about 60–70% viability) appeared with harsher extractions, extended soaks, or an inadequate post-curing of printed parts. The estrogen-sensitive proliferation assay (E-Screen) returned negative results. In saliva-like media, bisphenol A (BPA) and related leachables were undetectable or in the low ng/mL range. In printed resins, urethane dimethacrylate (UDMA) sometimes appeared in water extracts, and amounts varied with curing quality. Evidence for chemical leaching and endocrine outcomes is sparse. We found no eligible in vitro study that quantified particle or microplastic release while also measuring a biological endpoint; we discuss particle findings from mechanical wear simulations only as the external context. Limitations: The evidence base is limited to in vitro studies. Many reports incompletely described extraction ratios and processing parameters. Risk of bias and certainty: Most studies used appropriate cell models and controls, but the reporting of surface-area-to-volume ratios, LOD/LOQ, and detailed post-processing parameters was often incomplete. Sample sizes were small, and dynamic wear or enzymatic conditions were uncommon. The overall risk of bias was moderate, and the certainty of evidence was low to moderate due to heterogeneity and in vitro indirectness. Conclusions: Under standard laboratory conditions, clear aligners and thermoplastic retainers show a favorable biocompatibility profile. For printed resins, outcomes depend mainly on processing quality, especially thorough washing and appropriate light-curing parameters. To improve comparability and support clinical translation, we recommend harmonized test protocols, transparent reporting, interlaboratory ring trials, and targeted clinical biomonitoring. Full article

To address the issues of privacy-utility imbalance, insufficient incentives, and lack of verifiable computation in current medical data sharing, this paper proposes a blockchain-based fair verification and adaptive differential privacy mechanism. The mechanism adopts an integrated design that systematically tackles three core challenges: privacy protection, fair incentives, and verifiability. Instead of using a traditional fixed privacy budget allocation, it introduces a reputation-aware adaptive strategy that dynamically adjusts the privacy budget based on the contributors’ historical behavior and data quality, thereby improving aggregation performance under the same privacy constraints. Meanwhile, a fair incentive verification layer is established via smart contracts to quantify and confirm data contributions on-chain, automatically executing reciprocal rewards and mitigating the trust and motivation deficiencies in collaboration. To ensure enforceable privacy guarantees, the mechanism integrates lightweight zero-knowledge proof (zk-SNARK) technology to publicly verify off-chain differential privacy computations, proving correctness without revealing private data and achieving auditable privacy protection. Experimental results on multiple real-world medical datasets demonstrate that the proposed mechanism significantly improves analytical accuracy and fairness in budget allocation compared with baseline approaches, while maintaining controllable system overhead. The innovation lies in the organic integration of adaptive differential privacy, blockchain, fair incentives, and zero-knowledge proofs, establishing a trustworthy, efficient, and fair framework for medical data sharing. Full article

Background/Objectives: Children with visual impairments (VI) experience lower Quality of Life (QoL), higher sedentary time, and reduced motor competence as compared to their sighted peers, posing challenges to their health, well-being, and psychosocial development. While several QoL instruments have been developed internationally for children/youth with VI, none have been validated for use with U.S. pediatric populations. The purpose of this study was to evaluate the psychometric properties of the VISIONS QL assessment tool tailored for children/youth with VI, with a primary aim of variable/item reduction to develop a streamlined version of the instrument. Methods: This study featured a cross-sectional, descriptive analytic design with convenience sampling. Participants were children and youth with VI, aged 9–19 years, (N = 148; Boys = 71, Girls = 77; M_age = 14.49, SD = 3.36 years). A principal components analysis (PCA) with orthogonal varimax rotation was conducted to reduce dimensionality and identify components. Results: Results of the PCA yielded three components explaining 46% of the variance: Educational Opportunities = 7 items; Social and Familial Implications = 8 items; Communication = 5 items. Overall, the VISIONS QL Brief had a high level of internal consistency reliability (α = 0.857; Ω = 0.858) and test–retest reliability (ICC = 0.89, 95% CI = 0.84–0.92). The original 63-item version showed concurrent validity with the 20-item brief scale (r = 0.92, p < 0.0001). Conclusions: Findings affirm the multidimensional nature of QoL and support the usage of the VISIONS QL Brief and its utility in settings where time, accessibility, and cognitive load are critical considerations. Full article

Microplastics (MPs) are increasingly reported as contaminants in sewage sludge, with wastewater treatment plants retaining approximately 10³–10⁶ particles kg⁻¹ of dry sludge. Anaerobic digestion (AD), widely applied for sludge stabilization and energy recovery, does not consistently remove these particles; MPs frequently persist and, at elevated or sensitive loadings, have been shown to affect methane production, microbial communities and sludge quality. In parallel, thermal hydrolysis and related pretreatments are being implemented at full scale to enhance sludge biodegradability, exposing embedded MPs to high temperature and pressure prior to AD. This review compiles and analyzes experimental studies on MPs in sludge pretreatment and AD systems, with an emphasis on how pretreatment severity, MP type, particle size and concentration influence MP transformation and process performance. Reported data indicate that intensified pretreatment accelerates MP aging, causing fragmentation, oxidative surface modification and additive release, while subsequent AD generally induces limited further MP degradation but can be negatively affected through reduced methane yields, shifts in microbial consortia and altered behavior of co-contaminants. Mechanisms implicated include leaching of plastic additives, enhanced oxidative and physiological stress, and formation of plastisphere biofilms that perturb syntrophic interactions. Mitigation approaches, including optimized thermal hydrolysis–AD configurations and the use of carbonaceous sorbents, are assessed with regard to their effects on MP-associated inhibition and their practical constraints. Analytical limitations, uncertainties in MP mass balances and environmental fate, and key research needs for evaluating MP risks and designing MP-resilient sludge treatment and biosolid management strategies are identified. Full article

Educational equity and access to quality learning opportunities represent fundamental pillars of sustainable societal development, directly aligned with the United Nations Sustainable Development Goal 4 (Quality Education). Student retention remains a critical challenge in higher education, with early disengagement strongly predicting eventual failure and limiting opportunities for social mobility. While machine learning models have demonstrated impressive predictive accuracy for identifying at-risk students, most systems prioritize performance metrics over practical deployment constraints, creating a gap between research demonstrations and real-world impact for social good. We present an accountable and interpretable decision support system that balances three competing objectives essential for responsible AI deployment: ultra-early prediction timing (day 14 of semester), manageable instructor workload (flagging 15% of students), and model transparency (multiple explanation mechanisms). Using the Open University Learning Analytics Dataset (OULAD) containing 22,437 students across seven modules, we develop predictive models from activity patterns, assessment performance, and demographics observable within two weeks. We compare threshold-based rules, logistic regression (interpretable linear modeling), and gradient boosting (ensemble modeling) using temporal validation where early course presentations train models tested on later cohorts. Results show gradient boosting achieves AUC (Area Under the ROC Curve, measuring discrimination ability) of 0.789 and average precision of 0.722, with logistic regression performing nearly identically (AUC 0.783, AP 0.713), revealing that linear modeling captures most predictive signal and makes interpretability essentially free. At our recommended threshold of 0.607, the predictive model flags 15% of students with 84% precision and 35% recall, creating actionable alert lists instructors can manage within normal teaching duties while maintaining accountability for false positives. Calibration analysis confirms that predicted probabilities match observed failure rates, ensuring trustworthy risk estimates. Feature importance modeling reveals that assessment completion and activity patterns dominate demographic factors, providing transparent evidence that behavioral engagement matters more than student background. We implement a complete decision support system generating instructor reports, explainable natural language justifications for each alert, and personalized intervention templates. Our contribution advances responsible AI for social good by demonstrating that interpretable predictive modeling can support equitable educational outcomes when designed with explicit attention to timing, workload, and transparency—core principles of accountable artificial intelligence. Full article

The global drive toward sustainability and energy efficiency has accelerated the development of smart buildings integrating the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies optimise energy use, enhance occupant comfort, and advance building management systems. This study examines the integration of IoT and AI in energy-efficient smart buildings, emphasising applications and challenges. A qualitative methodology, combining systematic literature review, case study analysis, and systems analysis, underpins the research. Findings indicate that IoT enables smart metering, real-time energy monitoring, automated lighting and HVAC, occupancy-based energy optimisation, and renewable energy integration. AI complements these functions through predictive maintenance, energy forecasting, demand-side management, intelligent climate control, indoor air quality automation, and behaviour-driven analytics. Together, they reduce carbon emissions, lower operational costs, and improve occupant well-being. However, challenges remain, including data security and privacy risks, interoperability gaps, scalability and cost constraints, and retrofitting difficulties. To address these, the paper proposes a systems thinking-enabled conceptual framework structured around three pillars: adopting IoT and AI as enabling technologies, overcoming integration barriers, and identifying application areas that advance sustainability in smart buildings. This framework supports strategic decision-making toward net-zero and resilient building design. Full article