Search Results (11,239)

Adolescents growing up in multilingual regions experience diverse educational contexts that may shape their daily routines and psychosocial environments, but their independent relevance for mental health remains unclear. South Tyrol, with its parallel German-, Italian-, and Ladin-language school systems, provides a unique setting to examine these associations. This study assessed whether school language and home–school language mismatch are associated with mental health, psychosomatic symptoms, and health-related behaviors among adolescents. We analyzed data from a population-based survey of 2005 adolescents aged 11–19 years who provided self-reported information on mental health, psychosomatic complaints, school stress, social support, digital behaviors, lifestyle, and sleep. Multivariable regression analyses examined the independent association of home–school language mismatch with mental health outcomes, adjusting for sociodemographic and educational factors and further incorporating sleep-related behaviors. Mental health outcomes, psychosomatic symptoms, and most health-related behaviors showed little variation by school language, with generally small effect sizes. Home–school language mismatch was associated with slightly higher depressive symptom scores in unadjusted analyses but was not independently associated with mental health outcomes after adjustment. In contrast, weekly sleep problems emerged as the strongest correlate of depressive symptoms, accounting for a substantial proportion of explained variance. These findings indicate that adolescent mental health in this multilingual context is associated less with the language of schooling itself than with broader behavioral and developmental factors, highlighting sleep-related behaviors as a central and modifiable target for prevention. Full article

Automated mapping of vertical traffic signs and horizontal road markings is essential for road safety and Intelligent Transportation Systems (ITS). Traditional methods are labor-intensive, while existing automated solutions often lack a unified approach or are proprietary, limiting research accessibility and reproducibility. This paper presents a comprehensive framework for identifying these assets using a low-cost, vehicle-mounted action camera. A distance-aware frame extraction strategy is introduced to minimize data redundancy and ensure high spatial diversity. Specific strategies address the class imbalance inherent in real-world driving, ensuring robust detection for infrequent sign categories. Deep learning models handle the distinct geometries of vertical and horizontal assets, employing segmentation-based annotation for irregular road markings. Experimental results show high performance, with leading YOLO-based architectures achieving an F1-score of 0.92 for vertical signage and 0.96 for horizontal markings. By transforming raw visual data into structured georeferenced information, this framework facilitates the generation of High-Definition (HD) maps and digital inventories, supporting road authorities in proactive maintenance planning and regional road safety assessments. Full article

The rapid advancement of internet technology has transformed the tourism industry from traditional offline services to digital networked, and intelligent platforms. WebGIS has become critical infrastructure for tourism information retrieval and spatial decision-making. However, the growing volume and heterogeneity of multi-source tourism data expose fundamental limitations in conventional relational database architectures, particularly in handling complex spatial semantic queries. To address this, the present study proposes a WebGIS design methodology that integrates knowledge graphs with relational databases through a dual-database collaborative architecture. Using tourist attraction data from China’s Xinjiang Uyghur Autonomous Region as a case study, a prototype Xinjiang Smart Tourism WebGIS system was constructed, which consists of an asynchronous synchronization mechanism based on Change Data Capture (CDC) to ensure data consistency across heterogeneous databases. Subsequently, tourism semantic queries of varying depths were constructed and comprehensively tested across different data scales. The experimental results indicate that the proposed methodology effectively decouples business transactions and supports complex relationship computations, achieving shorter cross-domain semantic query times and higher latency stability. These findings offer practical guidance for designing high-performance regional tourism information services. Full article

English proficiency is vital for non-native speakers’ career development, yet classroom instruction alone cannot meet practical demands, making informal digital learning of English (IDLE) increasingly important. Artificial intelligence (AI), with conversational and multimodal functions, offers new opportunities for IDLE. However, existing research on AI-mediated IDLE has predominantly focused on language majors and often relied on a single methodological lens, neglecting STEM undergraduates and the complex network dynamics among motivational factors. However, research has largely focused on language majors, leaving STEM majors underexplored. Guided by the Hedonic-Motivation System Adoption Model (HMSAM), this study analyzed data from 413 Chinese STEM majors using partial least squares structural equation modeling (PLS-SEM, SmartPLS 4.0) and psychological network analysis (PNA, R 4.5.3). PLS-SEM results showed that enjoyment was the strongest direct predictor of AI-IDLE, followed by focused immersion, perceived usefulness, and curiosity. Control contributed indirectly via focused immersion, while boredom was non-significant. Perceived ease of use influenced AI-IDLE only through cognitive and emotional pathways. The model explained 58.1% of the variance. PNA further identified enjoyment, focused immersion, and control as central nodes, while the link between perceived usefulness and AI-IDLE was non-significant. These findings suggest that Chinese STEM undergraduates’ AI-IDLE is primarily driven by intrinsic hedonic motivations rather than utilitarian evaluations. The study provides empirical support for designing AI tools that enhance enjoyment and control to foster STEM students’ extracurricular English engagement. Full article

Social media platforms and user-generated content (UGC) have become central to how travelers discover and evaluate cultural destinations, yet lesser-known second-tier heritage sites remain substantially underrepresented in digital tourism research. This study investigates how Chinese tourists perceive and engage with the intangible cultural heritage (ICH) of Lamphun, Thailand, through UGC collected from three major Chinese social media platforms (WeChat, Douyin, and Rednote) spanning the period from 2019 to 2023. A total of 642 relevant posts were analyzed using a mixed-methods analytical framework comprising SnowNLP-based Chinese-language sentiment analysis, rule-based tourism intention classification, and TF-IDF-driven K-means thematic clustering. Results indicate an overall predominance of positive sentiment, with sentiment score emerging as the strongest predictor of tourism intention. Thematic clustering revealed three distinct experiential dimensions, with culinary heritage and contemporary local lifestyle and cafe exploration generating the highest sentiment distribution and within-cluster tourism intention rate. These findings demonstrate the analytical value of integrated UGC data mining for underrepresented ICH destinations and offer empirical insights to support data-driven destination marketing strategies and destination management organization (DMO) decision-making for the promotion of secondary cultural heritage destinations. Full article

Background/Objectives: Emerging viral zoonoses represent a growing threat to global public health, with most newly emerging infectious diseases originating from animal reservoirs. Recent outbreaks of monkeypox, Ebola virus disease, Marburg virus disease, Rift Valley fever, and avian influenza highlight the capacity of zoonotic viruses to cross species barriers, spread internationally, and generate substantial health, social, and economic consequences. This review examines the ecological, epidemiological, and biological determinants of viral zoonotic emergence and transmission, with particular emphasis on vaccination and outbreak prevention strategies. Methods: A structured narrative review was conducted using a predefined literature search strategy across major scientific databases. Peer-reviewed epidemiological, clinical, and public health publications published between January 2000 and February 2026 were screened and selected according to predefined relevance criteria. Results: The emergence of viral zoonoses is driven by complex interactions among animal reservoirs, environmental and climatic changes, human behavior, and viral adaptation. Although transmission pathways and clinical outcomes differ among pathogens, common determinants of spillover and outbreak amplification were identified. Current evidence supports the importance of integrated surveillance, genomic monitoring, vaccination strategies, and community engagement as key components of preparedness and response. Emerging preventive approaches targeting pathogen transmission, including transmission-blocking strategies and vector-associated microbiota interventions, may provide additional opportunities for disease control. Conclusions: Strengthening preparedness for emerging viral zoonoses requires coordinated One Health approaches integrating human, animal, and environmental health. Future priorities include the development of next-generation vaccines, expansion of digital and genomic surveillance systems, improved equitable access to vaccines, and innovative interventions aimed at reducing zoonotic spillover and interrupting pathogen transmission. Full article

The article presents selected problems related to the modernization of large-panel buildings in the context of the requirements arising from the EU EPBD (Energy Performance of Buildings Directive). The study has an exploratory character and is based on qualitative case study analyses of selected large-panel residential buildings representing different prefabrication systems and modernization conditions. The characteristic features of prefabricated buildings are outlined, and the main modernization barriers are identified, including structural limitations, insufficient thermal performance of building envelopes, outdated technical systems, and organizational and legal challenges resulting from ownership structures. Particular attention is given to the EPBD requirements concerning energy efficiency improvement, CO₂ emission reduction, and the implementation of the zero-emission building (ZEB) standard. The analysis indicates that the modernization of large-panel buildings requires a systemic approach integrating technical, economic, and organizational measures. The importance of comprehensive thermal retrofitting and the integration of renewable energy sources is emphasized. The findings also suggest that digital tools such as BIM (Building Information Modeling) may support modernization planning and building information management. The conclusions of the article indicate that the effective implementation of the EPBD provisions for large-panel buildings will only be possible with simultaneous systemic support, including financial and regulatory instruments, as well as the development of technical and organizational competencies within the construction sector. Full article

Climate change, rapid urbanization, land-use changes, and the creation of a multi-layered risk environment by multiple disaster hazards have made interdisciplinary educational models—capable of integrating resilience knowledge into planning and design education—all the more essential. Nevertheless, the systematic and competency-based integration of scientific knowledge generated in the fields of ecological planning, nature-based solutions, multi-hazard analysis, and digital planning tools into higher education curricula remains limited. This study aims to develop a competency-based curriculum model for ecological planning and disaster-resilient cities by adapting the resilience literature into a modular educational model. Literature mapping, thematic clustering, gap identification, competence framework building, and curricular architecture development are the steps of the study’s design-based analytical framework. Studies published between 2015 and 2025 were examined in terms of disaster types, analytical tools, and planning approaches; they were then reorganized based on three competency areas: green skills, digital skills, and resilience skills. The findings have resulted in a modular curriculum comprising 35 modules and 105 topics, structured within a three-tiered framework consisting of conceptual content, practical application, and case-based learning. The original contribution of this study is its proposal of a structured educational model that bridges the gap between the production of scientific knowledge and curriculum design. The proposed framework provides a scalable and adaptable foundation for undergraduate, graduate, and professional education contexts; it also establishes a foundation for AI-supported personalized learning pathways in ecological planning and disaster resilience education. Full article

Cancer patients receiving systemic therapy experience substantial treatment-related symptoms. Nurse-led digital health interventions, e.g., interactive voice response systems, web platforms, mobile apps, and telehealth, have emerged as strategies to strengthen supportive care. To evaluate its effectiveness, this systematic review summarizes evidence exclusively from randomized controlled trials (RCTs). Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, four databases were searched from inception to January 2025 for eligible RCTs involving adults undergoing anticancer therapy; evaluating nurse-led or nurse-co-led interventions using digital or telecommunication technologies; reporting validated symptom or health-related quality of life (HRQoL) outcomes. Risk of bias was assessed. Nine RCTs (N = 3344) met criteria; seven had low risk of bias. Interventions using telephone systems, web portals, mobile apps, or videoconferencing reduced symptom burden and improved HRQoL. The Symptom Care at Home system reduced symptom burden by ~43%, with greatest effects from combined automated monitoring and nurse practitioner follow-up. Additional benefits included improved anxiety, self-efficacy, patient participation, fewer severe toxicities and hospitalization days. In conclusion, nurse-led digital interventions effectively reduce symptom burden and support HRQoL during systemic therapy. Multicomponent models integrating automated monitoring with structured nursing follow-up and decision support appear most beneficial. Full article

Beekeeping is highly affected by climate change, which alters environmental conditions and challenges colony stability. In this context, digital monitoring technologies can enhance apiary resilience. This study presents the development and field validation of a low-cost hive monitoring system based on a customizable Raspberry Pi architecture, integrating temperature and weight sensors with robust data continuity features. The system was evaluated over one year in Southern Europe (Serbia) against a commercial reference. Results show that correlation between systems depends on both the monitored parameter and the biological state of the colony. For weight, strong agreement was observed only during winter, when reduced biological activity allows reliable comparison, whereas correlations were weak in more active periods. Conversely, temperature monitoring exhibited the highest correlation over long-term datasets, indicating that extended time scales are required for reliable sensor validation. These findings highlight the importance of a context-aware validation approach in apiculture. The proposed system provides a cost-effective and reliable solution for continuous hive monitoring, supporting data-driven management and improved resilience under climate variability. Full article

The open sharing of data as a factor of production is an important institutional mechanism for promoting sustainable innovation in the digital economy. Using Chinese A-share listed firms as the research sample and exploiting the staggered rollout of government open data (GOD) platforms across prefecture-level cities as a quasi-natural experiment, this paper constructs a staggered difference-in-differences (DID) model to examine the effect of GOD on green collaborative innovation (GCI) and its underlying mechanisms. The results show that GOD significantly promotes GCI, indicating that open government data can help firms strengthen collaboration in green innovation and contribute to more sustainable development. Mechanism analysis shows that GOD promotes GCI through four channels: increasing government subsidies, reducing information asymmetry, raising public environmental awareness, and advancing corporate digital transformation. Heterogeneity analysis reveals that the innovation-promoting effect of GOD is more pronounced in large cities, non-resource-based cities, and southern cities, and is more salient among state-owned enterprises, capital-intensive firms, and mature firms. This paper provides empirical evidence on the microeconomic effects of market-oriented data allocation and highlights the role of GOD in supporting GCI, corporate sustainable transformation, and the sustainable development of the digital economy. Full article

Background: Gorlin–Goltz syndrome is a rare autosomal dominant condition with characteristic craniofacial and odontogenic anomalies. Orofacial alterations in childhood may precede dermatological findings, highlighting the relevance of early orthodontic and functional evaluation. Objective: This case describes a multidisciplinary orthodontic and Home Sleep Apnea Testing (HSAT)-based approach for the assessment of craniofacial morphology and sleep-disordered breathing (SDB) risk in a pediatric patient with Gorlin–Goltz syndrome. Methods: A 12-year-old male with a genetically confirmed PTCH1 mutation underwent digital intraoral scanning, orthodontic evaluation, and SDB screening using the Pediatric Sleep Questionnaire (PSQ). Following a positive screening score, HSAT with the Philips Alice NightOne^® system was performed under specialist supervision. Results: The patient showed recurrent odontogenic cysts, a lateral open bite, and unilateral Class II canine relationship. The PSQ score was 0.579, exceeding the validated cut-off of 0.33 and indicating an elevated SDB risk. HSAT findings were suggestive of mild obstructive sleep apnea based on Respiratory Event Index (REI) values (REI 4.7/h), with an isolated SpO₂ nadir of 77% and a maximum recorded apnea duration of 425 s, warranting cautious specialist interpretation and follow-up assessment. Conclusions: Integrating orthodontic assessment, digital documentation, validated screening tools, and objective HSAT-based evaluation may support the early recognition of functional compromise in syndromic pediatric patients. Positive screening results should prompt specialist referral and objective sleep assessment, while attended polysomnography remains indicated when comprehensive sleep architecture evaluation or definitive characterization is required. Full article

3D city models (3DCMs) are increasingly used in urban simulations, cadastral workflows, and digital building permit processes, and automated validation of these models has become a routine requirement. Existing validation services typically produce dense, frequently generated by SHACL engines, text-based reports that are difficult for users without a semantic-web background to interpret. This paper describes the CHEK Validation Results Viewer (CHEK VRV), a Flask-based web application that couples the OGC Data Completeness Validator with an interactive Three.js 3D viewer of the CityJSON input. SHACL violations and geometric invalidities are spatially explorable on a visual representation of the model, and the tool supports both predefined CHEK Building Block Profiles and user-uploaded custom profiles. We report a formative usability study with twelve domain experts (urban planners, data vendors, academics) and use it to identify the tool’s current strengths, its limitations, and a prioritized development roadmap. As reported by the participant experts, such a presentation is easier to interpret than the raw JSON-LD report. Full article

The digital documentation of existing buildings is particularly important when original construction drawings or reliable as-built records are unavailable. This study evaluates the feasibility and selected dimensional consistency of a locally registered Scan-to-BIM workflow integrating unmanned aerial vehicle (UAV) photogrammetry for exterior documentation and smartphone LiDAR for interior data capture. A two-storey reinforced-concrete building with unavailable original project documentation was selected as a single case study. Exterior images were acquired using a DJI Mavic 3E (DJI, Shenzhen, China), while interior spaces were scanned using an iPhone 16 Pro Max (Apple Inc., Cupertino, CA, USA) and Polycam v5.1.5 in LiDAR mode. The UAV images were processed in Agisoft Metashape Professional 2.2.0 to generate the exterior photogrammetric point cloud, and the smartphone LiDAR data were organised with this dataset in Autodesk ReCap Pro 2025. Both point clouds were then used as geometric references for creating a geometry-oriented as-is BIM model in Autodesk Revit 2025. To evaluate selected dimensional consistency, 32 independent field measurements collected using a steel tape measure and a laser distance meter were compared with corresponding BIM-derived dimensions. The dimensional comparison yielded a mean absolute error (MAE) of 29.56 mm, a root mean square error (RMSE) of 31.21 mm, a maximum absolute error (MaxAE) of 46.00 mm, and a mean signed error (MSE) of +29.56 mm. These results indicate centimetre-level dimensional consistency for the selected validation dimensions, with a small systematic positive offset in the BIM-derived dimensions. The workflow can support preliminary geometric documentation and general as-is BIM for a small existing building, but it does not demonstrate survey-grade georeferencing, full registration accuracy, modelling reproducibility, or general applicability without further testing. Full article

Soil organic carbon (SOC) plays a critical role in climate regulation, soil fertility, and ecosystem resilience, making its accurate spatial quantification essential for sustainable land management and greenhouse gas (GHG) reporting. However, mapping SOC in heterogeneous agroforestry systems remains challenging due to vegetation cover and landscape complexity. In this study, we develop and evaluate a hybrid bare soil modelling- Digital Soil Mapping supported by ML framework to generate high-resolution soil properties predictions in Mediterranean agroforestry systems (Extremadura, Spain). A dual modelling approach was implemented, combining (i) Bare Soil modelling using Sentinel-2 multi-temporal reflectance composites and (ii) Digital Soil Mapping (DSM) supported by environmental covariates (climate, terrain, vegetation) following the SCORPAN framework. Machine learning models, namely Quantile Regression Forests (QRF) and Extreme Gradient Boosting (XGBoost), were applied and optimised using automated hyperparameter tuning (FLAML). A total of 107 LUCAS topsoil samples and 36 complementary points from the Forest ICP Level I were used for calibration and validation, with a 70/30 train–test split. Results show that Sentinel-2-based modelling can effectively capture SOC spatial variability in bare soil conditions, while DSM improves predictions in vegetated areas. Model performance reached R² values up to 0.76 (QRF, pH) and RMSE as low as 0.03 (XGBoost, N), with uncertainty quantified using the Prediction Interval Ratio (PIR) and performance further supported by RPIQ values up to 3.15. However, prediction accuracy remains sensitive to vegetation structure and sample density. The proposed framework provides a scalable and uncertainty-aware approach for SOC mapping, supporting Tier-3 GHG inventories and emerging Monitoring, Reporting, and Verification (MRV) systems. The results highlight the importance of integrating multi-source datasets and hybrid modelling strategies for reliable SOC estimation in complex landscapes. Full article