Search Results (10,530)

In today’s society, children are increasingly exposed to negative environmental information. How such exposure shapes pro-environmental behavioral intentions matters for the Sustainable Development Goals (SDGs). However, empirical evidence specific to Chinese children remains limited. An explanatory sequential mixed-methods study was conducted with Grade 4 to 6 students in N City, China (survey n = 253; focus groups n = 16). The survey assessed negative environmental information exposure, environmental worry, environmental self-efficacy, and behavioral intentions, and tested mediation and moderation models. Focus groups were analyzed thematically to refine the mechanisms. Quantitative results revealed a clear “double-edged” pattern: exposure to negative environmental information was positively associated with pro-environmental behavioral intentions via heightened environmental worry, yet negatively associated with intentions via reduced environmental self-efficacy. Moreover, environmental self-efficacy moderated the link between worry and intention. Qualitative findings further corroborated and specified these pathways, indicating that children interpret negative messages through crisis narratives, blame attribution, and scale comparison, whereas actionable scripts and positive feedback help sustain perceived control and support translating worry into intention. Sustainability communication and education should therefore pair risk information with efficacy cues, feasible actions, and meaningful feedback rather than relying solely on threat narratives. Full article

This study evaluates the swelling potential in clayey soils of the Paleogene Brebi, Mera, and Moigrad formations in the Transylvanian Basin (Romania) by integrating direct free-swelling tests (FS; STAS 1913/12-88) with indirect index-property diagrams and semi-quantitative X-ray diffraction (XRD; RIR method). The indirect analysis combines three swelling-susceptibility classification charts—Seed et al. (AI–clay), Van der Merwe (PI–clay), and Dakshanamurthy and Raman (LL–PI)—with mineralogical trends from the Casagrande plasticity chart, complemented by Holtz and Kovacs’s clay-mineral reference fields and Skempton’s activity concept (AI = PI/% < 2 µm). The geotechnical dataset comprises 88 Brebi, 46 Mera, and 263 Moigrad specimens (with parameter counts varying by test), an XRD was performed on a representative subset. The free swell (FS) results indicate that Brebi soils range from low to active behavior (50–135%) without reaching the very active class; most Brebi specimens fall in the medium-activity range. Moigrad spans the full FS spectrum (20–190%) but is predominantly in the medium-to-active range. In contrast, Mera soils exhibit predominantly active behavior, covering the full range of activity classes (30–170%). The empirical classification charts diverge systematically: clay-sensitive schemes tend to assign higher swell susceptibility than the LL–PI approach, especially in carbonate-influenced soils. XRD results corroborate these patterns: Brebi is calcite-rich (mean ≈ 53.5 wt% CaCO₃) with minor expandable minerals (mean ≈ 3.1 wt%); Mera is feldspathic (orthoclase mean ≈ 55.3 wt%) with variable expandable phases; and Moigrad has a higher clay-mineral content (mean ≈ 38.8 wt%). Overall, swelling is controlled by the combined effects of clay-fraction reactivity, clay volume continuity, and carbonate-related microstructural constraints. Full article

The digital transformation of education demands a comprehensive understanding of how leadership orientations and digital competencies intersect among educators. This exploratory cross-sectional study examined associations between self-reported leadership orientations, digital skills, and organizational readiness for innovation among 71 primary school teachers in Western Attica, Greece. Using the Multifactor Leadership Questionnaire (MLQ Form-5x) adapted for respondents without administrative roles, we measured leadership self-concept—teachers’ preferences and tendencies regarding leadership—rather than enacted behaviors. This distinction is critical given that 94.4% of participants lacked principal experience; thus, responses reflect aspirational orientations rather than observed behavioral patterns. Descriptive profiling approaches, including K-means clustering and multinomial logistic regression, identified three tentative response pattern groupings: Passive-Moderate (53.5%), Balanced-Active (33.8%), and High-Engagement (12.7%), with observed multivariate differences. After reverse-coding the passive-avoidant items, transformational leadership showed the highest mean score (M = 4.33), followed by passive-avoidant (M = 4.15; reflecting low endorsement of avoidant behaviors) and transactional (M = 3.91). Transformational leadership demonstrated acceptable internal consistency (α = 0.783), while transactional (α = 0.583) and passive-avoidant (α = 0.617) scales showed lower reliability, warranting cautious interpretation. Critical competency gaps emerged in professional digital domains—particularly web development (22.5% deficit) and administrative systems (18.3% deficit)—despite a surplus in consumer technologies such as social media (−29.6%), revealing an ‘aspirational gap’ between leadership self-concept and digital readiness—technology familiarity does not automatically translate to digital leadership capability. Digital skills showed the strongest association with profile membership, with each additional skill associated with a 32–67% increase in the odds of membership in more engaged profiles. These findings suggest digital competency development may be associated with leadership orientation patterns, though the cross-sectional design precludes causal inference. Methodological limitations—including lower scale reliability, weak cluster separation (silhouette = 0.150), and modest sample size—require that findings be interpreted as hypothesis-generating rather than definitive. This work offers preliminary insights relevant to SDG4 (Quality Education) regarding heterogeneity in leadership orientation among primary educators, while highlighting the need for culturally validated instruments and for replication with larger samples. Full article

Large language models (LLMs) are increasingly used to predict human behavior from plain-text descriptions of experimental tasks that range from judging disease severity to consequential medical decisions. While these methods promise quick insights without complex psychological theories, we reveal a critical flaw: they often latch onto accidental patterns in the data that seem predictive but collapse when faced with novel experimental conditions. Testing across multiple behavioral studies, we show these models can generate wildly inaccurate predictions, sometimes even reversing true relationships, when applied beyond their training context. Standard validation techniques miss this flaw, creating false confidence in their reliability. We introduce a simple diagnostic tool to spot these failures and urge researchers to prioritize theoretical grounding over statistical convenience. Without this, LLM-driven behavioral predictions risk being scientifically meaningless, despite impressive initial results. Full article

This study evaluates activity patterns and determines optimal observation periods for assessing the welfare of lowland tapirs (Tapirus terrestris L.) housed in the following two Danish zoological institutions: Aalborg Zoo and Randers Regnskov. The objectives were to identify the most efficient time window for welfare assessments, determine whether machine learning (ML) could support behavioral evaluations by providing automated estimates of activity, and examine whether automated pose-based tracking could serve as a proxy for manual ethogram observations. Behavioral data were collected using standardized ethograms from wildlife camera footage recorded over 72 h. Lowland tapirs were generally more active during daytime, with individuals at Aalborg Zoo showing peak activity between 07:00 and 14:00, while those at Randers Regnskov were most active between 12:00 and 18:00. Activity patterns differed between institutions, with Aalborg individuals displaying concentrated activity peaks and Randers individuals showing more evenly distributed activity. A preliminary ML analysis using the pose-estimation tool SLEAP demonstrated that movement-based activity estimates closely matched manually coded data, suggesting that automated tracking may offer an efficient and non-invasive tool for welfare monitoring. The findings highlight the potential for integrating automated analysis into routine welfare assessments of zoo-housed animals. Full article

In this work, we present a Temporal Graph Neural Network (TGNN) architecture specifically designed for link prediction in dynamic graphs. The proposed approach is evaluated on a dynamic social network constructed from internal email communication between employees of Wrocław University of Science and Technology that was collected over a continuous period of 605 days. To capture short-term fluctuations in communication behavior, we introduce the use of very short temporal aggregation windows, down to a single day, for constructing temporal graph snapshots. This fine-grained temporal resolution allows the model to accurately learn evolving interaction patterns and adapt to the dynamic nature of social communication networks. The TGNN model demonstrates consistently high predictive performance, achieving 99.28% ROC-AUC (Receiver Operating Characteristic—Area Under Curve) and 99.17% Average Precision in link prediction tasks. These results confirm that the model is able to distinguish between existing and emerging communication links with high reliability across temporal intervals. The architecture, optimized exclusively for temporal link prediction, effectively utilizes its representational capacity for modeling edge formation processes in time-dependent networks. The findings highlight the potential of focused TGNN architectures and short-time-window modeling in improving predictive accuracy and temporal resolution in link prediction applications involving evolving social or organizational structures. Full article

In recent years, Transformer-based methods have demonstrated proficiency in capturing complex patterns for time series forecasting. However, their quadratic complexity relative to input sequence length poses a significant bottleneck for scalability and real-world deployment. Recently, the Mamba architecture has emerged as a compelling alternative by mitigating the prohibitive computational overhead and latency inherent in Transformers. Nevertheless, a vanilla Mamba backbone often struggles to adequately characterize intricate temporal dynamics, particularly long-term trend shifts and non-stationary behaviors. To bridge the gap between Mamba’s global scanning and local dependency modeling, we propose C-T-Mamba, a hybrid framework that synergistically integrates a Mamba block, channel attention, and a temporal convolution block. Specifically, the Mamba block is leveraged to capture long-range temporal dependencies with linear scaling, the channel attention mechanism filters redundant information, and the temporal convolution block extracts multi-scale local and global features. Extensive experiments on five public benchmarks demonstrate that C-T-Mamba consistently outperforms state-of-the-art (SOTA) baselines (e.g., PatchTST and iTransformer), achieving average reductions of 4.3–18.5% in MSE and 3.9–16.2% in MAE compared to representative Transformer-based and CNN-based models. Inference scaling analysis reveals that C-T-Mamba effectively breaks the computational bottleneck; at a horizon of 1536, it achieves an $8.8\times$ reduction in GPU memory and over 10× speedup compared to standard Transformers. At 2048 steps, its latency remains as low as 8.9 ms, demonstrating superior linear scaling. These results underscore that C-T-Mamba achieves SOTA accuracy while maintaining a minimal computational footprint, making it highly effective for long-term multivariate time series forecasting. Full article

Background and Objective: Adolescent bipolar disorder (BD) has substantial symptom overlaps with other psychiatric disorders. Identifying its distinctive candidate neuroimaging markers may be helpful for exploratory early differentiation and to inform future translational studies after independent validation. Methods: This cross-sectional study enrolled adolescents with BD and age- and sex-matched healthy controls. Assessments included clinical/behavioral scales and an emotional Go/NoGo task-based fMRI (Go trials require a response; NoGo trials require response inhibition) acquired across three mood states (depression, mania, and remission) and matched controls. We applied several conventional machine learning classifiers to task-fMRI data to classify BD versus healthy controls and to identify the most relevant neuroimaging predictors. Results: A total of 43 adolescents with BD (15 in remission, 11 with depression, and 17 with mania) and 43 matched healthy controls were included. Under the Go-NoGo condition, activation-derived features in the remission state showed the strongest discrimination, with RF achieving the best performance (accuracy = 94.29%, AUC = 98.57%). These findings suggest that task-evoked functional alterations may remain detectable during remission. In addition, activation patterns in regions within the limbic system, prefrontal cortex, and default mode network were significantly correlated with clinical scales and behavioral measures implicating these regions in emotion regulation and cognitive functioning in adolescents with BD. Conclusion: This study showed that adolescents with BD during remission without manic and depressive symptoms may still have aberrant neural activity in the limbic system, prefrontal cortex, and default mode network, which may serve as a potential candidate neuroimaging signature of adolescent BD. Full article

Background/Objectives: Although tissue inhibitors of metalloproteinases (TIMPs) are key regulators in breast cancer, their differential expression, clinical relevance, and molecular roles remain unclear. This study aimed to compare the expression patterns of the four TIMPs in breast cancer and evaluate their molecular interactions and associated pathways through an integrated bioinformatic analysis. Methods: The expression of TIMPs and their correlations with MMPs were analyzed using the TCGA PanCancer, cBioPortal, and GEO datasets. Associations between TIMP expression and overall survival were assessed in the TCGA Breast Invasive Carcinoma PanCancer cohort. Pathway enrichment analysis was performed using GO, KEGG, and DAVID. The relationships between immune cell infiltration, stromal cells, and TIMP expression were assessed using the EPIC algorithm. Statistical analyses were performed using R. Results: TIMP1 was the only inhibitor overexpressed in breast tumors and showed significant associations with the Luminal B, HER2, TNBC, and normal-like subtypes, along with a modest increase across stages. TIMP2, TIMP3, and TIMP4 were downregulated in tumors. High expression of TIMP1 and TIMP4 correlated with better overall survival. TIMP1-associated genes were enriched in NF-kappa and PI3K–Akt signaling and actin cytoskeleton components. TIMP2 was linked to Hedgehog and MAPK pathways and actin-related elements. TIMP3 correlated with Hedgehog and PI3K–Akt signaling, DNA damage response, and membrane components. TIMP4 was associated with VEGF, MAPK, PI3K–Akt, DNA damage pathways, and actin organization. TIMP2 showed strong positive correlations with MMP2 and MMP14, while TIMP4 showed negative correlations with MMP1 and MMP9. Interestingly, we found a strong positive correlation between TIMP2 and TIMP3 with ADAM12, as well as between TIMP2 and TIMP3 with ADAM10, and negative correlations with ADAM15. The differential expression of TIMPs favors greater infiltration of immune cells related to tumor progression and poor prognosis in breast cancer patients. Conclusions: TIMPs display contrasting expression profiles and distinct pathway associations in breast cancer. TIMP1 emerges as the only consistently overexpressed inhibitor, while TIMP4 appears as a promising prognostic marker with unique MMP correlations that may influence tumor behaviors. Full article

This review synthetizes experimental evidence on collagen-related bioactivity and the biomaterial potential of plant species native to the Chihuahuan Desert, aiming to identify natural compounds that could enhance next-generation dermal bioinks for 3D bioprinting. A structured search across major databases included studies characterizing plant extracts or metabolites, with reported effects on collagen synthesis, fibroblast activity, inflammation, oxidative balance, or interactions with polymers commonly used in skin-engineering materials being developed. Evidence was organized thematically to reveal mechanistic patterns despite methodological heterogeneity. Several species, among them Larrea tridentata, Opuntia spp., Aloe spp., Matricaria chamomilla, Simmondsia chinensis, Prosopis glandulosa, and Artemisia ludoviciana, repeatedly demonstrated the presence of bioactive metabolites such as lignans, flavonoids, phenolic acids, terpenoids, and polysaccharides. These compounds support pathways central to extracellular matrix repair, including stimulation of fibroblast migration and collagen I/III expression, modulation of inflammatory cascades, antioxidant protection, and stabilization of ECM structures. Notably, several metabolites also influence viscoelastic and crosslinking behaviors, suggesting that they may enhance the printability, mechanical stability, and cell-supportive properties of collagen-, GelMA-, and hyaluronic acid-based bioinks. The review also reflects on the bioethical and sustainability considerations regarding endemic floral resources, highlighting the importance of responsible sourcing, conservation extraction practices, and alignment with international biodiversity and access to benefit/sharing frameworks. Taken together, these findings point to a promising, yet largely unexplored, opportunity: integrating regionally derived phytochemicals into bioinks to create biologically active, environmentally conscious, and clinically relevant materials capable of improving collagen remodeling and regenerative outcomes in 3D-printed skin. Full article

Background/Objectives: Although cyberbullying among adolescents has been widely studied, relatively little attention has been paid to the overlapping roles through which cyberbullying is experienced. This study reconceptualizes cyberbullying involvement by classifying perpetration, victimization, and witnessing into eight mutually exclusive involvement types, enabling systematic and non-overlapping comparison of adolescents’ experiences. The study further examines how engagement in individual-level cyber-risk indicators is associated with different patterns of cyberbullying involvement. Methods: The study analyzed nationally representative data from the 2022 Cyberbullying Survey conducted by the Korea National Information Society Agency, including 9693 students from elementary, middle, and high schools across South Korea. Individual-level cyber-risk indicators were assessed through multiple dimensions, including risky online behaviors, intensity of digital activity, peer environments, and awareness of harmful online behaviors. Multinomial logistic regression analyses were conducted to examine associations between individual-level cyber-risk indicators and the eight types of cyberbullying involvement. Results: Engagement in individual-level cyber-risk indicators was associated with increased odds of involvement in at least one cyberbullying type. Risky online behaviors and exposure to peers engaging in cyberbullying were linked to higher likelihood of both single and overlapping involvement patterns, whereas greater acceptance of harmful online behaviors was consistently associated with lower odds of victimization. Conclusions: These findings underscore cyberbullying as a relational and context-dependent phenomenon shaped by everyday digital practices and peer norms rather than isolated individual behavior. From a school social work perspective, the results support preventive, environment-focused interventions, including school-based media literacy education and institutionalized cyberbullying response systems, as promising strategies for reducing cyberbullying involvement among adolescents. Full article

The monitoring of perennial and annual crops requires different analytical approaches due to their contrasting phenological dynamics and management practices. This study investigates the temporal behavior of the Normalized Difference Vegetation Index (NDVI) derived from Harmonized Landsat and Sentinel-2 (HLS) imagery to characterize apple, grape, soybean, and maize crops in Vacaria, Southern Brazil, between January 2024 and April 2025. NDVI time series were extracted from cloud-free HLS observations and analyzed using raw, interpolated, and Savitzky–Golay, smoothed data, supported by field reference points collected with the AgroTag application. Distinct NDVI temporal patterns were observed, with apple and grape showing higher stability and soybean and maize exhibiting stronger seasonal variability. Descriptive statistics derived from 112 observation dates confirmed these differences, highlighting the ability of HLS-based NDVI time series to capture crop-specific phenological patterns at the municipal scale. Complementary analysis using the SATVeg platform demonstrated consistency in long-term vegetation trends while evidencing scale limitations of coarse-resolution data for small perennial plots. Overall, the findings demonstrate that the NDVI enables robust monitoring of mixed agricultural landscapes, with complementary spatial resolutions and analytical tools enhancing crop-specific phenological analysis. Full article

Due to the significant potential of crowd flow prediction in the domains of commercial activities and public management, numerous researchers have commenced investing in pertinent investigations. The majority of existing studies employ recurrent neural networks, long short-term memory, and similar models to achieve their objectives. Despite the advancements in predictive modeling, the objective of many existing studies remains in the minimization of distance errors. This focus, however, introduces three notable limitations in prediction outcomes: (1) the predicted location may represent an average of multiple points rather than a distinct target, (2) the results may fail to reflect actual user behavior patterns, and (3) the predictions may lack geographic plausibility. To address these challenges, we developed a Transformer-based model integrated with adversarial network architecture. The Transformer component has shown considerable effectiveness in forecasting individual movement trajectories, while the discriminator within the adversarial framework guides the generator in refining outputs to better reflect user habits and spatial rationality. Full article

In recent years, the expansion of network scale and the diversification of attack methods pose dual challenges to intrusion detection systems in extracting effective features and addressing class imbalance. To address these issues, the Spatial–Temporal Equilibrium Graph Convolutional Network (STEGCN) is proposed. This model integrates Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU), leveraging GCN to extract high-order spatial features from network traffic data while capturing complex topological relationships and latent patterns. Meanwhile, GRU efficiently models the dynamic evolution of network traffic over time, accurately depicting temporal trends and anomaly patterns. The synergy of these two components provides a comprehensive representation of network behavior. To mitigate class imbalance in intrusion detection, the Equalization Loss v2 (EQLv2) is introduced. By dynamically adjusting gradient contributions, this function reduces the dominance of majority classes, thereby enhancing the model’s sensitivity to minority-class attacks. Experimental results demonstrate that STEGCN achieves superior detection performance on the UNSW-NB15 and CICIDS2017 datasets. Compared with traditional deep learning models, STEGCN shows significant improvements in accuracy and recall, particularly in detecting minority-class intrusions. Full article

Metaverse presents significant opportunities for educational advancement by facilitating immersive, personalized, and interactive learning experiences through technologies such as virtual reality (VR), augmented reality (AR), extended reality (XR), and artificial intelligence (AI). However, this potential is compromised if digital environments fail to uphold individuals’ privacy, autonomy, and equity. Despite their widespread adoption, the privacy implications of these environments remain inadequately understood, both in terms of technical vulnerabilities and legislative challenges, particularly regarding user consent management. Contemporary Metaverse systems collect highly sensitive information, including biometric signals, spatial behavior, motion patterns, and interaction data, often surpassing the granularity captured by traditional social networks. The lack of privacy-by-design solutions, coupled with the complexity of underlying technologies such as VR/AR infrastructures, 3D tracking systems, and AI-driven personalization engines, makes these platforms vulnerable to security breaches, data misuse, and opaque processing practices. This study presents a structured literature review and comparative analysis of privacy risks, consent mechanisms, and digital boundaries in metaverse platforms, with particular attention to educational contexts. We argue that privacy-aware design is essential not only for ethical compliance but also for supporting the long-term sustainability goals of digital education. Our findings aim to inform and support the development of secure, inclusive, and ethically grounded immersive learning environments by providing insights into systemic privacy and policy shortcomings. Full article