Search Results (2,069)

Public awareness and perception of human biomonitoring (HBM) and pesticide exposure are essential for informed decision-making and policy, yet understanding remains limited and often shaped by media and advocacy. This study combined three focus group discussions with Latvian citizens and an online content analysis of pesticide-related posts. Discussions explored understanding of HBM, attitudes toward chemical exposures, and support for related research, while content analysis identified commonly discussed pesticides and the role of non-governmental organisations (NGO) in shaping public opinion. Findings indicate low awareness and frequent misconceptions about HBM, often confused with wearable health technologies rather than a tool for assessing internal chemical exposure. Concerns were mainly linked to food additives and household chemicals, with less attention to pesticides. Glyphosate emerged as the most debated pesticide, largely driven by NGO activity and media coverage. Trust in government initiatives was mixed, with concerns about political influence, industry interests, and data privacy. Nevertheless, participants expressed strong support for further national research. Overall, the results highlight gaps in public understanding and the significant influence of media and advocacy. Strengthening risk communication, transparency, and public engagement is essential to build trust and support the development of Latvia’s HBM framework. Full article

Background: Mental health concerns are notably common among students attending universities in Saudi Arabia, and low engagement with psychological services has been widely documented in this population group. Telemental health has emerged as a promising alternative under Vision 2030’s digital transformation agenda, although the determinants of university students’ intentions to use these services have received limited empirical attention in Saudi Arabia. Objective: This study examined attitudes toward telemental health services, perceived barriers to seeking psychological help, and predictors of behavioral intentions to use telemental health services among university students in Saudi Arabia, based on the Technology Acceptance Model and Theory of Planned Behavior. Methods: A cross-sectional design was employed using an online, self-administered questionnaire. A total of 236 undergraduate students from three large universities in Riyadh were recruited using convenience sampling methods. We examined demographic variables, telemental health attitude variables (ease of use, usefulness, subjective norms, trust in telemental health, relative advantage, intentions, and attitudes), and barrier subscales (fear of stigma, trust in mental health professionals, difficulties in self-disclosure, perceived devaluation, and lack of knowledge) among university students. Descriptive statistics, Welch’s t-tests, and multiple linear regression analyses were conducted using SPSS (version 29). Results: Participants demonstrated moderately positive attitudes toward telemental health (M = 74.15, SD = 16.11) and reported moderate overall barriers (M = 50.76, SD = 14.44), with trust in mental health professionals being the most prominent barrier. The regression model explained 58.0% of the variance in behavioral intentions (F(19, 211) = 15.35, p < 0.001). Attitude was the strongest predictor (β = 0.534, p < 0.001), followed by trust in telemental health, sex, and difficulty in self-disclosure. Conclusions: Culturally tailored awareness campaigns, trust-building communication, and gender-sensitive service design are recommended to promote the adoption of telemental health by Saudi university students. These efforts align with Vision 2030’s digital health priorities and may support the equitable expansion of mental healthcare access in this population. Full article

Introduction: Problematic internet use has been increasingly associated with depression, anxiety and other psychiatric symptoms; however, its impact on attentional functioning has not been thoroughly researched. This cross-sectional study was conducted in Târgu Mureș, Romania, and aimed to examine the associations between problematic internet use, attentional control, and symptoms of depression and anxiety in adults, and to determine whether problematic internet use independently predicts attentional control after accounting for emotional symptoms. Methods: A cross-sectional study was conducted on 224 adults who completed an anonymous online survey between 1 January 2026 and 1 April 2026. Problematic internet use was assessed using the Compulsive Internet Use Scale-14 (CIUS-14), attentional control using the Attentional Control Scale (ACS), depressive symptoms using the Patient Health Questionnaire-9 (PHQ-9), anxiety symptoms using the Generalized Anxiety Disorder-7 scale (GAD-7), and eating disorder risk using the SCOFF questionnaire. Descriptive statistics, internal consistency analyses, Pearson correlations, group comparisons according to the CIUS-14 screening threshold, and multiple linear regression analyses were performed. Results: Problematic internet use was significantly associated with lower attentional control (r = −0.493, p < 0.001), higher depressive symptoms (r = 0.408, p < 0.001), and higher anxiety symptoms (r = 0.467, p < 0.001). In the regression model, problematic internet use remained the only significant independent predictor of attentional control (B = −0.597, p < 0.001), whereas depressive and anxiety symptoms were not significant after adjustment. Participants above the CIUS-14 screening threshold reported significantly lower attentional control and higher depression and anxiety scores than those below the threshold. Conclusions: Problematic internet use was associated with poorer attentional control and greater emotional symptom severity in Romanian adults. These findings suggest that problematic internet use may be linked to a broader cognitive–emotional vulnerability profile. However, because of the cross-sectional design, self-report measures, convenience sampling, and lack of detailed information on specific online activities, the findings should be interpreted cautiously. Longitudinal studies using objective cognitive measures and more detailed assessment of digital behaviors are needed. Full article

To address the detection challenges in X-ray weld seam images caused by weak contrast, slender structures, and multi-scale coexistence, we propose MGDR-YOLO, an industrially deployable detector with four coordinated designs. First, a MultiBackbone parallel heterogeneous backbone is designed to perform complementary direction–detail modeling and lightweight context modeling under a shared shallow stem, enhancing the joint representation of fine-grained features and global semantics. Second, Gated Attention Fusion Block (GAFB) is introduced to perform selective in-scale fusion via channel gating and local–global attention mechanisms, thereby suppressing channel redundancy and noise leakage induced by naive concatenation. Third, Directional Feature Convolution (DFConv) decouples standard 2D convolution into horizontal and vertical branches and fuses them using depthwise separable convolution, substantially reducing computational cost while preserving geometric alignment. Finally, Rep Shared Convolutional Detection Head (RSCD) improves detection head consistency and inference throughput through cross-scale shared convolutions and a training-to-deployment re-parameterization scheme. The experimental results show that MGDR-YOLO significantly outperforms YOLOv11n, increasing the mean average precision (mAP) from 92.9% to 95.2%. The performance gain is most pronounced for the LP class (slender and low-contrast defects), with an mAP improvement of 10.1 percentage points. Meanwhile, the proposed model achieves a 39.4% increase in frames per second (FPS) while reducing the number of parameters by 46.2%, demonstrating superior efficiency. These results indicate that MGDR-YOLO consistently improves the accuracy and robustness of X-ray weld defect detection while maintaining real-time performance, making it well suited for resource-constrained industrial online inspection scenarios. Full article

Accurate identification of weak nodes is a prerequisite for online security assessment, preventive control, and resilience enhancement in modern power systems. However, conventional single-layer graph-learning models mainly emphasize local neighborhood aggregation and are insufficient for characterizing vulnerability propagation from equipment-level disturbance to regional congestion and system-level transfer constraints. This paper proposes a mechanism-aware hierarchical graph-learning framework for weak-node identification in complex interconnected power grids. We emphasize that attention, fusion, and gating operations are standard neural-network mechanisms and are not claimed as new generic deep-learning blocks. The contribution of this paper is the power-system-specific formulation: constructing an electrically meaningful local-supernode hierarchy, defining reproducible mechanism-based node and branch-vulnerability proxies, and interpreting weak-node rankings through node–line–corridor coupling evidence. In the validated implementation, a local graph convolutional encoder and a supernode/global graph convolutional encoder generate 32-dimensional local embeddings and 16-dimensional global embeddings, which are concatenated and decoded by a 48 → 24 → 1 multilayer perceptron to obtain node vulnerability scores. Experiments are conducted on reproducible IEEE benchmark data generated from pandapower standard systems, with representative comparisons on the IEEE 57-bus, 145-bus, and 300-bus systems and a detailed structural interpretation on the IEEE 145-bus case. The present results validate the ability of the implemented local–global hierarchical model to reproduce the proposed mechanism-based vulnerability proxy on representative small- and medium-scale benchmarks. Full article

This paper presents two forecast-driven energy trading methodologies for a grid-connected photovoltaic-battery system participating in the day-ahead electricity market. Both methodologies use bidirectional long short-term memory neural networks with attention to forecast electricity prices, but they differ in the way the resulting forecasts are converted into operational decisions. The first method uses 24- to 48 h-ahead price forecasts within a mixed-integer linear programming rolling-horizon optimizer to compute the revenue-maximizing schedule for the following day. The second method uses an online twin delayed deep deterministic policy gradient controller that outputs a complete 24 h charge–discharge schedule once per day, using state information that includes battery state, recent price history, forecast prices, and forecast photovoltaic production. The control models are trained using historical data from 2019 to 2022, validated chronologically on 2023 data, and tested on the 2024 annual horizon, while the price forecaster is trained and validated on non-2024 data and evaluated on the held-out 2024 test period. In the realistic execution setting, schedules are planned using forecast photovoltaic production and implemented against actual photovoltaic production, while the day-ahead omniscience benchmark uses actual next-day prices and actual PV production as ideal scheduling inputs. The BiLSTM-MILP framework achieves EUR 10,928.7 over the 2024 test horizon, corresponding to 82.67% of the day-ahead omniscience benchmark. The online BiLSTM-TD3 controller achieves EUR 10,884.9, corresponding to 82.34% of the same benchmark and 99.60% of the BiLSTM-MILP revenue, while outperforming a rule-based baseline by 34.9%. These results show that online deep reinforcement learning can approach the performance of explicit mathematical optimization in day-ahead PV-battery trading while substantially improving over simple rule-based operation. Overall, the results indicate that BiLSTM-based forecasts can support both optimization-based and reinforcement-learning-based day-ahead control for the examined PV-battery system. Full article

Adopting a circular economy approach requires new business models, multi-stakeholder engagement, and tailored financial models and mechanisms as core pillars. This paper examines the conditions needed to scale circular economy initiatives in Europe by analysing insights collected from the DECISO project and conducting a comparative analysis of 38 European projects. The study adopts a mixed methods approach that integrates an online stakeholder survey with inputs generated through participatory workshops and discussions of selected use cases. This combined approach is used to identify the main structural barriers limiting the maturity and investment readiness of circular economy projects, such as regulatory complexity, difficulties in accessing funding, and weak stakeholder dialogue mechanisms. The approach was also used for enabling factors that can support development of circular economy. Particular attention is given to the role of project development assistance, modular financing strategies, and de-risking tools, which are highlighted as crucial elements for supporting the technical and economic credibility of projects and attracting public and private investors. The article also identifies and addresses seven unresolved research gaps in the literature, including the lack of interoperable policy instruments, the absence of business models capable of integrating investor expectations, the paucity of integrated methodologies for assessing technical and economic regulatory feasibility, and the need for trust-building procedures. The findings suggest that the transition to a regenerative economy requires a systemic approach based on coherent policies, de-risking financial instruments, collaborative governance, and strategic technical support throughout the project development cycle. Full article

Artificial intelligence (AI) is increasingly being deployed in various sectors of e-commerce. Consequently, it becomes necessary to identify the impact of algorithmic design parameters on buyer behaviour. This study examines the impact of algorithmic anthropomorphism (ANT), algorithmic transparency (TRAN) and perceived algorithmic fairness (FAIR) on consumer purchase intentions (PI) in the Turkish e-commerce market. In addition, this study examines technology acceptance—operationalised through the Technology Acceptance Model (TAM)—as a boundary condition, with particular attention to the differential moderating roles of perceived ease of use (PEOU) and perceived usefulness (PU). A structured questionnaire was distributed among 384 online consumers in Türkiye via Qualtrics. A confirmatory factor analysis (CFA) established the psychometric adequacy of the measurement model (all AVE > 0.50, all CR > 0.87; HTMT < 0.85 across theoretically distinct constructs). The proposed model was tested using the PROCESS macro for sequential mediation and moderation analyses, with bootstrap confidence intervals based on 5000 resamples. The results reveal that: (1) algorithmic anthropomorphism positively affects both algorithmic transparency and perceived algorithmic fairness; (2) algorithmic transparency has a significant positive effect on both perceived fairness and purchase intention; (3) perceived algorithmic fairness mediates the relationships between algorithmic anthropomorphism and purchase intention, as well as between algorithmic transparency and purchase intention; and (4) although the composite technology acceptance level (TAL) measure does not significantly moderate the anthropomorphism–purchase intention path (p = 0.075), disaggregating TAL into its sub-dimensions reveals that PEOU significantly moderates this relationship (p < 0.001), whereas PU does not (p = 0.199). The composite-TAL result is therefore not statistically supported, but the dimension-specific PEOU finding is robust. These findings offer theoretical contributions to AI-driven consumer behaviour research and practical implications for the design of algorithmic e-commerce systems in emerging digital markets. Full article

Wastewater membrane bioreactors (MBRs) have become an important advanced treatment technology due to their ability to produce high-quality effluent suitable for discharge and water reuse. However, their broader and more sustainable application remains constrained by membrane fouling, elevated energy demand, and the operational complexity of coupled biological and membrane separation processes. This comprehensive review critically evaluates the growing application of machine learning (ML), explainable artificial intelligence (XAI), and digital twin (DT) technologies in MBR systems. Published studies on fouling prediction, energy optimization, effluent quality estimation, and intelligent operational support are critically evaluated, with explicit attention to model performance, dataset limitations, and generalizability. The reviewed literature shows that ML models, particularly ensemble methods, support vector machines, and deep learning approaches, have demonstrated strong potential for predicting major MBR performance indicators, including transmembrane pressure, permeate flux, fouling resistance, and selected effluent-quality variables. In parallel, XAI methods such as SHAP, LIME, and Anchors are increasingly being used to enhance model transparency and to reveal the dominant factors controlling process performance. Digital twin frameworks further extend this potential by enabling the integration of mechanistic understanding, online sensor data, data-driven prediction, and interpretable decision support within real-time operational platforms. Nevertheless, several barriers continue to hinder practical implementation, including the limited number of full-scale studies, the scarcity of openly accessible and standardized datasets, insufficient consideration of uncertainty and model drift, and the early-stage maturity of DT deployment in operational plants. The evidence reviewed suggests that integrating ML, XAI, and DT can substantially improve the reliability, interpretability, and operational efficiency of MBR systems. Future research should therefore focus on full-scale validation, the development of benchmark datasets, uncertainty-aware modeling, and practical deployment strategies for interpretable intelligent MBR management. Full article

This study analyzed online review data to examine user requirements for audiovisual products and to compare requirement salience and satisfaction across traditional and emerging product contexts. We collected 86,213 Chinese-language reviews of Skyworth TVs, Xiaomi TVs, and Xiaomi projectors from JD.com. LDA topic modeling was used to identify major user requirement areas, and Logistic Regression, Random Forest, and Support Vector Machine (SVM) models were compared for sentiment classification, with the tuned SVM model retained for downstream analysis. The results show that user discussions primarily concern audiovisual experience, cost performance, service quality, design aesthetics, and intelligent operation. Skyworth TVs receive particularly strong evaluations for picture and sound quality (97.89% positive sentiment), whereas Xiaomi TVs are more strongly associated with cost-effectiveness and smart features (94.05% positive sentiment). Xiaomi projectors attract attention for portability but receive lower satisfaction ratings on core audiovisual performance and intelligent operation. These findings suggest that traditional manufacturers should continue strengthening core performance while improving service responsiveness, whereas emerging brands should build on their technological advantages while further enhancing their product reliability and user experience. Full article

With the advancement of wide-area measurement systems (WAMSs), response-driven methods for transient stability analysis have gained increasing attention in recent years. The maximum Lyapunov exponent (MLE)-based trajectory analysis technique enables online transient stability assessment by capturing the trend characteristics of system trajectories. Motivated by this capability, a rapid construction methodology for the practical dynamic security region (PDSR) is proposed based on the MLE criterion. Initially, through analyzing the dynamic characteristics of generator rotor angle trajectories after disturbances, the dynamic MLE characteristics of the generator’s angular velocity deviation trajectory are extracted to formulate the MLE-based stability criterion. Subsequently, a stability boundary function based on MLE trajectories is developed, and the linear relationship between the injection space parameters and the MLE stability boundary function is derived. Finally, leveraging the sensitivity of the stability boundary function to the variations in injection space parameters, the dynamic security region is constructed around the dominant instability critical point, thereby establishing a mapping function between transient stability and the injection space parameters. The effectiveness of the proposed method is demonstrated through simulations on the IEEE39 power system. Results show that the method exhibits promising performance in terms of speed and adaptability for transient stability analysis and boundary construction. Full article

This study examines the effects of digital sustainability orientation on consumers’ responses, with a focus on the roles of digital green innovation and consumer environmental consciousness in shaping green brand advocacy in social media marketing. Drawing on the Resource-Based View, Dynamic Capability perspective, and Signaling theory, the study proposes that sustainability-oriented digital strategies are more effective when translated into visible, credible forms of digital green innovation. Using the quantitative research design, data were collected from a sample of 300 Saudi Arabian consumers who interact with eco-friendly brands and sustainability-related content on digital platforms such as Facebook, WhatsApp, Instagram, and TikTok. The study used purposive and convenience sampling to ensure that participants were aware of sustainability communication online. Data analysis was performed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) to test the measurement and structural models and evaluate the hypotheses. The results show that the direct positive effect of digital sustainability orientation on digital green innovation is high, but there is no direct effect on green brand advocacy. However, digital green innovation fully mediates this relationship, making the importance of tangible innovation even greater in turning sustainability intentions into consumer support. Moreover, consumer environmental consciousness plays a significant moderating role in the relationship between digital sustainability orientation and green brand advocacy, suggesting that the more environmentally conscious consumers are, the more responsive they are to sustainability-driven digital strategies. The study contributes to the available literature on digital sustainability and green marketing by showing that being sustainability-oriented is not enough to encourage consumer advocacy without having credible innovation. Practically speaking, the findings show that organizations must pay attention to innovation-based sustainability initiatives and develop genuine digital communication strategies to attract environmentally conscious consumers. Ultimately, the research serves as a great reminder of the importance of integrating digital innovation, sustainability practices, and consumer engagement as key drivers of strong green brand advocacy. Full article

Attention deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition frequently associated with psychiatric comorbidity, including disordered eating. Adult women remain under-recognized and underrepresented in ADHD research, and emerging evidence suggests that symptom expression may be shaped by gendered social factors, ovarian hormone fluctuations, and metabolic health. In this manuscript, we provide a gender-focused theoretical overview of the literature linking ADHD to binge eating symptoms in adult women, with attention to underdiagnosis, menstrual cycle-related symptom variability, and obesity-related metabolic risk, and empirically test the association between a self-reported ADHD diagnosis and binge eating symptoms in an online cross-sectional sample of adult women. Women reporting an ADHD diagnosis (n = 140) were compared with a random subsample of n = 140 women without ADHD drawn from the same survey; comparability between groups on age, education, and employment was formally verified; and binge eating symptoms were assessed with the Binge Eating Scale (BES) as a continuous outcome and as an ordered three-category variable. Women reporting an ADHD diagnosis showed significantly higher BES scores than controls (rank-biserial r = 0.28, 95% CI 0.15–0.41), and a higher proportion of severe binge eating symptomatology (BES ≥ 27; 22.1% vs. 11.4%; OR = 2.20, 95% CI 1.14–4.25) than controls. The association remained significant in a sensitivity analysis adjusting for age and BMI. Taken together, our findings support the need for routine, gender-sensitive screening for binge eating symptoms in women with ADHD, as well as ADHD screening in women presenting with binge eating and obesity. Full article

Within the contemporary debate on tourism, sustainability and well-being, increasing attention has been devoted to the role of cultural and nature-based tourism experiences on pathways for human transformation and well-being. This study contributes to this emerging area of research by analyzing the emotional well-being associated with travellers’ experiences on Italian pathways through an automated text-mining approach based on their online reviews on TripAdvisor. Through an analysis of reviews relating to 10 pathways, it improves the understanding of how emotional well-being emerges from reviews, their linguistic structure and how the main experiential factors are associated with positive and negative emotions experienced by travellers. The findings provide evidence of the association of these experiences with emotional well-being, show how well-being emerges in relation to the dimensions of challenge, community and escape during the journey on the pathway, and identify distinct linguistic styles in reviews associated with well-being. Full article

As a novel urban leisure activity, Citywalk is reshaping the spatial organization of urban tourism resources and spatial experience patterns. This phenomenon provides a crucial entry point for understanding new tourist–destination relationships from the perspective of spatial behavior. This paper takes Harbin, an Internet-Famous City (IFC), as a case study and integrates multi-source data, including pedestrian trajectories, social media texts, and urban infrastructure. A cross-modal analytical framework for Citywalk networks is constructed to examine the structural evolution of Citywalk networks and the relationship between digital-space and physical-space in the context of IFCs. The results indicate that: (1) During its rise as an IFC, Harbin’s Citywalk network transformed from a single-core agglomeration structure to a multi-nodal radial structure, exhibiting a pattern of core reinforcement and outward expansion. (2) Online visibility was associated with the emergence of new nodes and network expansion, but a structural misalignment was observed between digital-space association and physical-space linkage. (3) Emotional differentiation among newly visible nodes further reflected the uneven development of the Citywalk network, while concentrated digital attention was accompanied by persistent structural imbalance. This study highlights the digital–physical misalignment in urban tourism networks, suggests the important role of social media in shaping tourists’ route imagination and emotional evaluation, and provides references for the spatial optimization and sustainable management of urban tourism resources in the new development stage. Full article