Search Results (4,308)

Stance detection has emerged as an essential tool in natural language processing for understanding how individuals express agreement, disagreement, or neutrality toward specific targets in social and online discourse. It plays a crucial role in bilingual and multilingual environments, including English-Arabic social media ecosystems, where differences in language structure, discourse style, and data availability pose significant challenges for reliable stance modelling. Existing approaches often struggle with target awareness, cross-lingual generalization, robustness to noisy user-generated text, and the interpretability of model decisions. This study aims to build a reliable, explainable target-aware bilingual stance-detection framework that generalizes across heterogeneous stance formats and languages without retraining on a dataset specific to the target language. Thus, a unified dual-encoder architecture based on mDeBERTa-v3 is proposed. Cross-language contrastive learning offers an auxiliary training objective to align English and Arabic stance representations in a common semantic space. Robustness-oriented regularization is used to mitigate the effects of informal language, vocabulary variation, and adversarial noise. To promote transparency and trustworthiness, the framework incorporates token-level rationale extraction, enables fine-grained interpretability, and supports analysis of hallucination. The proposed model is tested on a combined bilingual test set and two structurally distinct zero-shot benchmarks: MT-CSD and AraStance. Experimental results show consistent performance, with accuracies of 85.0% and 86.8% and F1-scores of 84.7% and 86.8% on the zero-shot benchmarks, confirming stable performance and realistic generalization. Ultimately, these findings reveal that effective bilingual stance detection can be achieved via explicit target conditioning, cross-lingual alignment, and explainability-driven design. Full article

Background/Objectives: Adolescence is pivotal for establishing dietary habits; however, school-based nutritional education remains focused on information dissemination, with minimal effects on behavior modification. Evidence from neuroscience, education, and food literacy indicates that attention, engagement, sensory experiences, and social contexts are integral to effective learning in nutrition education. This article conceptualizes a framework for adolescent food education beyond knowledge transmission, aiming to cultivate taste competence using the Mediterranean Diet as a pedagogical ecosystem. Methods: This study employed a conceptual methodology, utilizing interdisciplinary literature from food literacy, sensory education, developmental neuroscience, educational theory, and public health nutrition. It synthesizes empirical findings and theoretical models to develop the Teaching Autonomous Sensory Taste in the Mediterranean Diet (TASTE–MED) framework. Results: This study introduces taste competence as a multifaceted educational outcome, encompassing sensory, relational, cultural, and reflective dimensions. The TASTE–MED framework outlines how experiential, multisensory, and socially embedded learning processes can be implemented in schools, facilitated by the Mediterranean Diet, which provides a sensory-rich and culturally significant context. The educational implications are discussed in terms of curriculum design, teacher training, family involvement and digital tools. Conclusions: The TASTE–MED framework redefines food literacy as an embodied and socially situated competence rather than a cognitive construct. This framework provides a theoretical foundation for informing the design, evaluation, and research of future interventions, advocating for the transition from information-based nutrition education to competence-oriented food education during adolescence. Full article

Community-oriented policing (COP) has long been hailed as an approach to enhancing community trust in police, addressing community problems proactively rather than reactively, and fostering police-community engagement. However, some law enforcement agencies experience difficulty in translating the broad philosophy of COP into a practical and actionable strategy. Agencies cannot expect to fulfill COP objectives without a structured plan and accountability for achieving this goal. Therefore, drawing on existing themes for operationalizing proactive community engagement, this paper brings current research and best practices together to present a set of evidence-based strategies for COP foot and bike patrols. We highlight relevant considerations and tips that may be underscored in the minutiae of implementation within the COP foot and bike patrol process, yet remain critical to success. By operationalizing COP foot and bike patrols at several stages, the authors aim to assist agencies in striving for more than just symbolic or temporary approaches to COP. Full article

The rapid growth of artificial intelligence-generated content (AIGC) has created serious challenges for image copyright protection, since semantic edits and deep-fake manipulations can easily erase or distort embedded watermarks. Traditional robust watermarking methods, which are mainly designed to resist pixel-level distortions such as noise, compression or filtering, often fail when faced with content-level transformations generated by AIGC models. This paper presents PatchSeal, a robust and intangible image watermarking framework that combines multi-targeted and attention-oriented embedding with a focus-oriented masking. The proposed framework introduces a segmentation-assisted embedding strategy that distributes watermark bits across several prominent regions to improve resilience to semantic changes. An attention-based module, composed of a subject extraction branch and a channel weighting branch, adapts to the encoder towards texture-rich and semantically stable regions, improving both invisibility and robustness. Experiments conducted in three public object data sets show that PatchSeal achieves an average PSNR of 43.13 dB and a bit precision of 92.98 percent under various AIGC editing conditions, surpassing representative methods such as MBRS and FIN. These results demonstrate the effectiveness of the proposed method in resisting AIGC-driven manipulations and provide new practical paths and methodological insights for the design of robust watermarks in the AIGC era. Full article

Background/Objectives: Accurate three-dimensional (3D) facial scanning is increasingly important in digital dentistry for diagnosis, treatment planning, and virtual patient creation. Multiple facial scanning technologies are available; however, their metric reliability varies depending on acquisition principles and anatomical orientation. This study aimed to evaluate the trueness, orientation-dependent performance (vertical midline versus horizontal facial measurements), and scanning time of four facial scanning technologies using calibrated manual anthropometry as the reference standard. Methods: Thirty dentate adult participants received adhesive fiducial markers on five predefined facial landmarks. Four linear facial distances were measured clinically using a digital caliper and compared with corresponding measurements obtained from standardized 3D facial scans. Digital measurements were extracted following uniform metric normalization. Inter-examiner reliability, measurement trueness, orientation-related differences, and scanning time were analyzed. Results: Inter-examiner reliability was excellent for both clinical and digital measurements (ICC > 0.93). All facial scanning technologies significantly overestimated manual distances (p < 0.001). The structured-light scanning system showed the smallest deviations (typically <1 mm) and the highest overall accuracy, followed by the depth-fusion system, while photogrammetry-based and NeRF-based approaches demonstrated larger errors, frequently exceeding 2–3 mm. Horizontal facial distances consistently showed greater deviations than vertical midline measurements across all systems. Scanning time differed significantly between technologies, with passive image-based approaches being the fastest and NeRF-based acquisition requiring the longest capture time. Conclusions: Active structured-light facial scanning demonstrated the highest trueness for linear facial anthropometry, whereas passive photogrammetry and NeRF-based approaches showed lower metric trueness and are currently more suitable for educational applications. Full article

The continued growth of electric vehicle (EV) deployment has placed increasing emphasis on the development of charging infrastructure that is efficient, reliable, and compliant with safety requirements over a wide range of power levels. In EV charging systems, DC–DC converters work as a key interface for voltage adaptation, power regulation, and battery protection, making the choice of converter topology a crucial design consideration. This study provides a comparative and application-focused review of commonly employed isolated and non-isolated DC–DC converter topologies used in EV charging architectures. The comparison is carried out by examining voltage gain behavior, efficiency tendencies, switching and thermal stress, soft-switching capability, component utilization, control complexity, cost-related aspects, and practical deployment constraints. Fundamental operating principles and representative time-domain simulations are used to highlight relative performance trends of PWM-based and resonant isolated converters under typical charging conditions. Rather than introducing new converter structures or control methods, the objective of this work is to offer practical, design-oriented insights that support informed topology selection. Based on the comparative analysis, non-isolated converters are found to be well suited for low- to medium-power onboard charging applications, whereas isolated resonant converters are more appropriate for high-power and fast-charging systems when safety, scalability, efficiency trends, and system-level implementation factors are considered together. Full article

Developments in electric vehicle (EV) technology are pushing on-board chargers (OBCs) toward higher power density and efficiency, making high-frequency transformer loss prediction a critical design bottleneck. However, the accuracy of commonly used analytical winding-loss models varies strongly with frequency, conductor type (Litz/solid), window fill factor, and winding layout (e.g., interleaved), which can render single-model-based optimization unreliable. In this study, six analytical copper-loss models from the literature were independently reimplemented in a unified Python 3.11.5 workflow with a standardized interface to enable fair comparison under identical geometry and operating conditions. The models were benchmarked against 2D finite-element simulations on test scenarios with increasing physical complexity, including high fill-factor Litz windings and interleaved arrangements. The results confirm a regime-dependent behavior: no single model consistently outperforms others across the full design space, and model dispersion increases in geometrically stressed and higher-frequency regions. To manage this uncertainty, variance maps were generated and model disagreement was quantified using the coefficient of variation (CV). Finally, a reliability-oriented multi-objective optimization framework based on NSGA-II was developed, where a SmartTransformerRouter selects a reference loss estimate per candidate and CV is incorporated via constraints/penalties, with optional FEM triggering in high-uncertainty regions. Full article

Background/Objectives: Commitment to self-tracking refers to the extent to which individuals are dedicated to the practice of wearable- and app-based self-monitoring. This commitment is behaviorally grounded and captures users’ sustained investment in wearable and app-based self-monitoring. The objective of this study was to validate the Commitment to Self-Tracking (C2ST) scale in Czechia by examining its dimensionality, confirmatory model fit, reliability, and known-groups evidence among self-tracking device users. Methods: The results were obtained from a face-to-face survey of a sample of 502 self-tracking device users who were recruited from the Czech general population using address-based sampling. The sample was randomly split into two subsamples for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Item- and scale-level descriptive statistics and internal consistency (Cronbach’s α, McDonald’s ω) were calculated. The EFA was utilized to evaluate the factorability and latent structure of the model, and the CFA was employed to assess the model’s fit. The known-groups validity was examined using nonparametric group comparisons (Kruskal–Wallis H and Mann–Whitney U tests) with theoretically relevant external indicators, such as social comparison orientation, willingness to share data, perceived usefulness of tracking, and self-rated health. Results: The C2ST score demonstrated a full range of theoretical variation, exhibiting minimal floor (7.2%) and ceiling (2.0%) effects and a nearly symmetrical distribution. The internal consistency of the scale was found to be high (α = 0.968; ω = 0.968), and the corrected item-total correlations were uniformly high. The EFA supported a single-factor solution that explained 74.4% of the variance. The CFA model demonstrated a unidimensional structure, indicating that the observed variables were best explained by a single factor. An improved model attained an adequate-to-excellent fit (RMSEA = 0.051; SRMR = 0.018; CFI = 0.991; TLI = 0.986) and accounted for substantial item variance (R² = 0.60–0.82). The known-groups validity was supported by pronounced differences in C2ST scores across social comparison and data-sharing orientations, as well as perceived usefulness of tracking for health and training goals (all p < 0.001). Conclusions: The Czech C2ST has been demonstrated to exhibit high reliability and a clear, unidimensional structure. Additionally, it exhibited robust CFA support and theory-consistent known-groups validity among self-tracking device users. The scale is appropriate for research on self-tracking commitment and persistence. Full article

Background/Objectives: Orthorexia nervosa (ON) is an emerging condition marked by a preoccupation with healthy eating that is linked to diminished well-being and social functioning. While research on ON extends across countries, no studies about ON have been found in Lithuania. This study aimed to investigate dietary patterns, socio-demographic correlates, and the prevalence of orthorexic tendencies in a Lithuanian adult sample. Methods: A cross-sectional online survey was conducted using the ORTO-R 6-item scale, a Food Frequency Questionnaire (FFQ), and socio-demographic and dietary behavior measures. Principal component analysis (PCA) was applied to the FFQ to identify dietary patterns, and stepwise multiple linear regression was used to examine predictors of orthorexic tendencies. Results: Approximately 15% of the Lithuanian adult sample exhibited elevated orthorexic tendencies, while three dietary factors were extracted, including Balanced-Traditional, Processed-Dense and Protein-Rich patterns. Both Balanced-Traditional and Protein-Rich dietary patterns were positively associated with orthorexic tendencies, although only the Balanced-Traditional pattern remained a significant predictor in the fully adjusted regression model, which explained 16.2% of the variance in ORTO-R scores (F(7,468) = 12.97, p < 0.001). Higher orthorexic tendencies were associated with following a dietary plan, adherence to the Healthy-Traditional pattern, being female, younger age, higher meal frequency, employment status, and being married. Conclusions: Orthorexic behaviors were more prevalent among younger women, individuals following structured diets, and those adhering to health-oriented eating patterns. These findings highlight the interplay between demographic and dietary factors in shaping orthorexic tendencies in the Lithuanian population. Full article

An integrated methodology was employed, incorporating spray pyrolysis synthesis, gamma irradiation post-treatment, and Box–Behnken statistical optimization. This approach was designed to systematically refine the structural and optical properties of CZTS thin films, with the objective of enhancing their photocatalytic degradation efficiency. At a dose of 5 kGy, gamma irradiation resulted in an approximately 300% increase in crystallite size and improved crystallinity relative to non-irradiated samples. As the irradiation increases, the films exhibited a stronger preferential orientation along the (112) plane, which peaked at 20 kGy. Analysis using the Williamson–Hall method revealed complex microstructural evolution, showing crystallite sizes varying from ~12.48 nm to ~71.27 nm based on the irradiation dose applied. The photocatalytic activity was assessed through the UV-driven degradation of Brun Sella Solid dye, employing H₂O₂ as a co-reactant. The optimization process, guided by the Box–Behnken design which tested parameters such as pH (2 to 14), gamma dose (0 to 20 kGy), and H₂O₂ volume (100 to 500 μL), achieved a remarkable maximum degradation efficiency of 98% under optimal conditions. This study highlights the synergistic combination of controlled defect engineering through gamma irradiation and meticulous parameter optimization establishing a robust framework for the development of high-performance, earth-abundant photocatalysts suitable for environmental remediation applications. Full article

With the global environmental challenges and accelerating digitalization, promoting the continuous use of digital low-carbon applications (DLCAs) constitutes a critical pathway for China to achieve green transformation objectives. DLCAs represent innovative products and services that leverage digital technologies—through substitution, sharing, and intelligent control mechanisms—to deliver energy-saving and emission-reduction benefits to consumers. Drawing on construal level theory (CLT), this study investigates how reward strategies influence DLCA continuance intention. Findings from three experiments targeting Chinese consumers demonstrate that material rewards (compared to immaterial rewards) significantly increase DLCA continuance intention, with attitude serving as a mediating mechanism. Furthermore, reward timing (delayed vs. immediate) moderates this relationship: under delayed (vs. immediate) conditions, immaterial (vs. material) rewards generate a more favorable attitude, thereby strengthening continuance intention. Additionally, reward orientation (altruistic vs. self-oriented) serves as a boundary condition for the moderating effect of reward timing. Specifically, under a self-oriented framing, the construal fit between immaterial–delayed and material–immediate rewards proves most effective in fostering positive attitudes and continuance intention. Under altruistic framing, however, immaterial rewards consistently outperform material rewards in enhancing consumer attitudes and continuance intention. This research not only extends CLT within the domain of reward strategy design but also offers actionable insights for firms seeking to develop effective incentive mechanisms that promote sustained customer engagement. Full article

This paper presents an intelligent UAV-based parking occupancy monitoring system using a lightweight DJI Mini 3 UAV platform and the YOLOv11 object-detection model. A proprietary aerial dataset was collected from a university parking lot and augmented to address data scarcity, defining two task-oriented classes: vehicle and parking. The proposed framework integrates UAV data acquisition, annotation, data augmentation, training, real-time inference, and occupancy computation into a deployable end-to-end pipeline. Experimental results demonstrate strong detection performance and stable real-time inference, achieving competitive precision, recall, and mAP (mean Average Precision) metrics while maintaining high frame rates suitable for real-time deployment. Comparative evaluation against YOLOv8 and YOLOv9 highlights deployment-oriented advantages rather than architectural novelty. The study confirms that UAV-based vision systems can provide a scalable, low-infrastructure solution for real-time parking monitoring and urban mobility applications, contributing an applied, system-level framework focused on integration and deployment feasibility. Full article

Background: Acute abdominal emergencies represent a major diagnostic challenge in emergency medicine, requiring rapid and accurate integration of clinical, laboratory, and imaging data. Although laboratory parameters play a central role in real-world diagnostic workflows, the extent to which they are systematically represented and integrated within radiology research publications remains unclear. Objective: To evaluate how laboratory data are represented, contextualized, and functionally utilized in radiology publications focusing on computed tomography (CT)–based evaluation of acute abdominal emergencies. Methods: A methodological content analysis was conducted on 72 original radiology research articles published between 2020 and 2024. Eligible studies focused on CT-based imaging of acute abdominal emergency conditions. Publications were screened and analyzed at the title and abstract level using a predefined coding framework to assess the presence of laboratory data, types of laboratory parameters reported, their contextual role (background information, imaging trigger, diagnostic modifier, or prognostic indicator), degree of laboratory–imaging integration, and presence of decision-oriented reporting. Descriptive statistics were used to summarize reporting patterns. Results: Laboratory data were reported in 61.1% of all included studies (n = 44/72). However, their functional utilization varied substantially. Laboratory parameters were most frequently presented as background clinical information, whereas explicit use as imaging triggers (26.4%, n = 19/72), diagnostic modifiers (19.4%, n = 14/72), or components of explicit laboratory–imaging integration (15.3%, n = 11/72) was less common. Decision-oriented reporting was present in 23.6% of all studies (n = 17/72). Explicit integration was described in publications addressing diagnostically complex and time-sensitive conditions, such as acute bowel ischemia and severe acute pancreatitis. Conclusion: Laboratory data are commonly reported in CT-based radiology publications addressing acute abdominal emergencies; however, the manner in which these data are incorporated into imaging-centered diagnostic narratives varies across studies. Differences are observed in how laboratory–imaging relationships are described, with some publications presenting integrated discussion and others reporting imaging findings independently of laboratory context. These observations characterize reporting practices within the analyzed literature and do not imply statistical associations or causal effects. Full article

Engineering education is increasingly expected to prepare graduates capable of addressing sustainability challenges, public safety concerns, and ethical responsibilities. However, in many civil and environmental engineering curricula, sustainability and ethics are still treated as supplementary topics rather than being systematically embedded in core technical courses. This study reports a sustainability-oriented curriculum reform implemented in a Building Water Supply and Drainage Engineering course, integrating Education for Sustainable Development (ESD) principles into CDIO-aligned project-based learning activities. A single-group pre–post quasi-experimental design was adopted with 100 undergraduate students. Quantitative data were collected using a competency-based questionnaire, and paired-sample t-tests, effect sizes, and 95% confidence intervals were applied to examine changes in students’ self-reported competencies. Qualitative data were obtained from reflective learning reports and analyzed through thematic analysis. The results indicate statistically significant improvements in sustainability awareness, ethical and professional responsibility, human-centered design, and systems thinking, with large effect sizes. These findings provide context-specific descriptive evidence supporting the feasibility of embedding sustainability and ethical responsibility within discipline-specific technical engineering courses. Nevertheless, the absence of a control group and the reliance on self-reported measures limit causal interpretation. Future research is recommended to adopt comparative or longitudinal designs and incorporate more objective performance-based assessments. Full article

Post-combustion CO₂ capture using monoethanolamine (MEA) is a mature mitigation technology, yet its high energy demand and complex dynamics remain major challenges. This study presents a unified dynamic modeling and control framework for an MEA-based absorption–regeneration system, focusing on a comparative evaluation of PID, fuzzy logic control (FLC), and model predictive control (MPC) under realistic operating disturbances. A control-oriented surrogate model was developed in MATLAB R2024b/Simulink and validated against published benchmark trends. The control objective was to maintain CO₂ capture efficiency above 90% while minimizing reboiler energy consumption under ±10% inlet CO₂ concentration and flue gas flow disturbances. Simulation results showed that PID control ensures basic stability but exhibits slow recovery and high energy usage, while FLC improves robustness with limited dynamic improvement. MPC consistently maintained capture efficiency above the target value, reduced the settling time by approximately 37%, and achieved a 12.4% reduction in average reboiler duty compared to PID control. The results demonstrate that a unified, implementation-oriented modeling framework enables the effective assessment of advanced control strategies and supports the energy-efficient operation of industrial MEA-based CO₂ capture systems. Full article