Search Results (1,415)

The rapid growth of Earth science observation and simulation data has made efficient data classification increasingly challenging, particularly under conditions of limited annotation resources and continuously evolving data semantics. Conventional classification methods rely heavily on large-scale labeled datasets, which are costly to construct and difficult to adapt to dynamic classification systems. This paper proposes a hierarchical classification framework for Earth science data that leverages large language models (LLMs) and explicitly incorporates hierarchical label relationships to constrain model inference and enhance classification consistency across complex, domain-specific semantic spaces. The framework further integrates retrieval-augmented generation (RAG) and knowledge graph (KG) techniques to introduce external domain knowledge and explicit semantic constraints, enhancing contextual understanding, interpretability, and adaptability to semantic evolution. A benchmark dataset with a two-level hierarchical label structure is constructed based on official NASA metadata. Experimental results demonstrate that by integrating few-shot learning and label space optimization strategies, the proposed framework steadily outperforms various baseline methods in hierarchical classification tasks. Compared with the Bert-BiLSTM model, it achieves an absolute improvement of 8.68% in Micro-F1 and 29.92% in Macro-F1 on the overall hierarchical paths. The framework demonstrates clear advantages in long-tailed data distributions, particularly for minority classes, highlighting its potential for scalable annotation and efficient management of large-scale Earth science datasets. Full article

Modeling contextual relationships among key features is crucial for improving object detection, yet existing relation-based methods rely on fixed feature selection and shared transformations, limiting their ability to capture diverse feature interactions in complex scenes. To address this, we propose MoE-RelationNet++, a relation enhancement framework based on a Mixture-of-Experts (MoE) mechanism. Unlike fixed selection and shared transformations, the proposed MoE enhancement module adopts a conditional computation paradigm: a dynamic router adaptively assigns features to specialized experts, enabling heterogeneous relationship modeling and overcoming the representational bottlenecks inherent in traditional shared mappings. Furthermore, to alleviate its computational burden and reduce redundant inputs, a lightweight key selector using depthwise separable convolution is introduced to adaptively identify informative features. To ensure robust relation modeling and prevent noisy or unreliable feature interactions from degrading the experts, an energy verification mechanism is employed to evaluate feature reliability and refine the overall process. Extensive experiments on MS COCO show consistent improvements across multiple detectors, increasing AP by 4.2, 3.2, 3.3, and 3.1 points for RetinaNet, FCOS, ATSS, and Faster R-CNN, respectively. Additionally, the method achieves a 1.5 AP gain on the VisDrone-DET2019 benchmark. These results demonstrate that MoE-RelationNet++ effectively captures heterogeneous relations via conditional expert routing, overcoming the representational limitations of fixed transformations. Moreover, it can be seamlessly integrated into various detection frameworks as an add-on enhancement module, consistently improving their performance without modifying the base architecture. Full article

Accurate road segmentation is a core perceptual technology for autonomous driving, but faces two challenges: (1) ambiguous road boundaries caused by insufficient modeling of contextual information relationships in CNN-based networks and (2) inadequate LiDAR-camera fusion due to modality gaps between heterogeneous sensors. To mitigate these limitations, this paper proposes a novel approach, named LiDAR-Camera High-Resolution Feature Fusion Network (LC-HR2FNet), a multi-cross-stage fusion model designed for road segmentation. Firstly, a new type of pseudo-LiDAR-Image representation is generated via an early-level fusion strategy and data complementation. Sparse point clouds are transformed into dense LiDAR-Image data and then concatenated with RGB channel maps to form complementary multi-modal data inputs. Subsequently, a modified HRNet backbone integrated with cross-stage feature fusion is constructed to strengthen information interaction across different branches and enhance the modeling of contextual relationships. Additionally, a dilated feature collection model is designed to collect multi-scale confidence scores for pixel-wise class determination. Experiments on the KITTI road benchmark demonstrate that the proposed method achieves a MaxF of 97.39% on UMM_ROAD and an average of 96.28% across all urban scenarios, demonstrating superior performance and robustness. Full article

Background: Short sleep and formula feeding during infancy are associated with increased risk of childhood obesity. Feeding practices and sleep arrangements vary during infancy and may also be dynamic, yet their impact on infant night sleep duration remains unclear. Understanding these relationships is crucial for formulating recommendations to support breastfeeding and address sleep concerns. Objective: We examined the association between feeding mode and parent-reported infant night sleep duration during the first postnatal year, while additionally evaluating night-weaning and bedsharing as contextual sleep-related practices. Methods: Infants in the Phoenix Metropolitan Area (n = 193) were followed up at 3, 8, 13, 26, 39, and 52 weeks post-birth. Sleep and feeding questionnaires were answered at each visit. A multilevel growth model estimated infant night sleep duration trajectories by feeding mode (ordinal: exclusive formula, mixed, exclusive breastfeeding), night-weaning, and bedsharing as time-variant predictors. Maternal education and household income were covariates to account for differences in study attrition. Results: Infant night sleep duration followed a curvilinear trajectory, starting at 7.92 h (95% CI: 5.78, 10.06) and increasing by 0.40 h/month (95% CI: 0.21, 0.60), with a deceleration over time (0.02 h/month², p < 0.001). Each increase in levels of breast milk consumption was associated with an increase in infant night sleep duration (B = 0.87 h, p < 0.001), but the association weakened as the infant aged (B = −0.07 h/month, p < 0.001). Despite 59.7% of bedsharing infants being exclusively breastfed, bedsharing was not significantly associated with infant night sleep duration. Similarly, night-weaning was not significantly associated with infant night sleep duration. Conclusions: Breastfeeding is associated with longer infant night sleep duration, whereas bedsharing showed no association despite its correlation with breastfeeding. This research highlights the importance of breastfeeding in early life, not only for its developmental benefits but also for its relationship with infant night sleep duration, an essential component of healthy infant growth. Full article

Alexithymia has been associated with deficits in social cognition, although findings are inconsistent and often limited by methodological constraints. This study aimed to clarify this relationship using ecologically valid and traditional standardized measures across multiple social-cognitive domains. A total of 163 adults from the general population completed a series of measures, including the Toronto Alexithymia Scale (TAS-20), Questionnaire of Cognitive and Affective Empathy (QCAE), Reading the Mind in the Eyes Test (RMET), Movies for the Assessment of Social Cognition (MASC), and Amsterdam Dynamic Facial Expression Set—Bath Intensity Variations (ADFES-BIV). Results of hierarchical regression analyses revealed that alexithymia facets significantly predicted performance on affective and cognitive empathy (QCAE), and Theory of Mind (MASC total and “No ToM” scores). The only exceptions were affective Theory of Mind (RMET) and recognition of others’ emotions (ADFES-BIV), for which none of the alexithymia facets emerged as significant predictors. The findings suggest that alexithymia is associated with poorer performance in cognitive and affective empathy and contextual Theory of Mind, whereas no significant association emerged for emotion recognition. The results suggest that integrating dynamic and context-rich tasks may be useful for detecting subtle social-cognitive difficulties in individuals with alexithymic traits. Full article

Non-timber forest products (NTFPs) constitute an important component of forest-based production systems and biomass supply chains in Türkiye. Despite their growing economic and ecological significance, the long-term structural dynamics of NTFP production remain insufficiently understood. This study examines temporal and structural changes in NTFP production in Türkiye during the period 1988–2024 using official production statistics and production support data. The analysis applies a quantitative framework that combines linear trend analysis, Shannon diversity and Herfindahl–Hirschman concentration indices, volatility measures based on the coefficient of variation, and regression models to evaluate production trends, structural transformations, stabilization patterns, and the effectiveness of production support mechanisms. The findings reveal a non-linear and multi-phase development pattern characterized by diversification and production growth after 2000, followed by increasing concentration and greater production volatility after 2018. Although total production volume increased substantially, portfolio diversity declined over time, and dependence on a limited number of high-volume products intensified, indicating growing structural vulnerability within the system. In addition, production support mechanisms showed a weak and heterogeneous relationship with production outcomes. A limited contextual comparison with Lithuania’s multifunctional NTFP system is also included to position the findings within a broader European context. Overall, the results suggest that increasing production alone is insufficient to ensure long-term system stability. Instead, diversification-oriented and risk-sensitive resource management strategies that account for production risks, regional disparities, and product heterogeneity are essential for developing sustainable and resilient NTFP production systems. Full article

The rapid integration of artificial intelligence (AI) into workplace ecosystems is intensifying adaptation pressure for new employees. This study examines how Workplace Fear of Missing Out (WFMO) influences adaptive performance in this context. Methods: Drawing on Conservation of Resources Theory and the Emotion Regulation Process Model, a dual-path mediating model was tested using survey data from 442 new employees. Hierarchical regression, the Bootstrap method, and fuzzy-set qualitative comparative analysis (fsQCA) were employed. Results: WFMO is positively associated with adaptive performance. Role stress and cognitive reappraisal function as independent mediators in this relationship. Leader empathy positively moderates both direct relationships and indirect mediating pathways. Fuzzy-set qualitative comparative analysis reveals two distinct configurational paths to high adaptive performance. Conclusion: Workplace Fear of Missing Out can be transformed into adaptive behavior through resource mobilization and cognitive reappraisal mechanisms, with leader empathy serving as a critical contextual amplifier. These findings challenge the traditional view of workplace anxiety as uniformly detrimental and provide actionable insights for organizational management in technology-driven environments. Full article

Background: Autism spectrum disorder (ASD) is consistently associated with a high prevalence of sleep disturbances across the lifespan, with reported rates ranging from 60% to 86% depending on age and clinical characteristics. Although this issue has been widely described in the international literature, Spain currently lacks large-scale data to estimate the prevalence of sleep disturbances or to examine their relationship with factors such as age, intellectual disability, and co-occurring conditions. This study aims to estimate the prevalence and severity of sleep disturbances in individuals with autism spectrum disorder in Spain and to examine their associations with developmental stage, intellectual disability, affective symptoms, and contextual factors. Methods: This is a cross-sectional observational survey with nationwide dissemination approved by the Ethics Committee of the Universidad Católica San Antonio de Murcia. Data will be collected through an online survey (SurveyMonkey) including validated instruments: the Children’s Sleep Habits Questionnaire–Autism (CSHQ-Autism) and the Sleep Disturbance Scale for Children (SDSC) for pediatric participants; the Pittsburgh Sleep Quality Index (PSQI) for adolescents and adults without intellectual disability; and the Diagnostic Assessment for the Severely Handicapped–II (DASH-II) for adults with intellectual disability. Anxiety and depressive symptoms will be assessed using the Child Behavior Checklist (CBCL) in children and adolescents and the Hospital Anxiety and Depression Scale (HADS) and DASH-II. Statistical analyses will be conducted using SPSS v22 by applying parametric or non-parametric tests according to data distribution. Conclusions: This study represents one of the first survey protocols with nationwide dissemination designed to assess sleep disturbances in individuals with ASD in Spain. The resulting findings are expected to help identify vulnerability profiles, inform public health strategies, and support the development of multidisciplinary interventions aimed at improving sleep and, consequently, the quality of life of individuals with autism and their families. Full article

The advent of social media has transformed brand communication to put storytelling at the center of building engagement and awareness. But the role of long-term brand value in virality is an essential challenge. This paper conducts a bibliometric and thematic analysis from the fields of marketing, psychology, and media studies published between 2015 and 2025, examining the correlation between narrative design and audience response, separating short-term popularity and long-term consumer appeal. The analysis was based on a structured literature review and qualitative methodological framework, using the literature sourced through Scopus, Web of Science, PsycINFO, and Google Scholar published between 2015 and 2025. Thematic coding searched for emotional tones, devices used in the narration, types of metrics, and contextual factors in inclusion and exclusion criteria. The findings indicate a divide in quantitative measures, such as likes and shares, and qualitative measures, such as sentiment and resonance stories. Story elements such as authenticity, the depth of the characters, and video-based content had a major effect on the two types of engagement. Storytelling effectiveness was also mediated by influencer participation, algorithmic interactions, and audience demographics. The results confirm that meaningful storytelling with hybrid metrics contributes to stronger brand–consumer relationships. Future studies ought to shift to predictive modeling and focus on the ability of AI to dictate personalized brand stories in diverse cultures. Full article

Increasing stakeholder pressures and environmental uncertainty require organizations to move beyond short-term financial outcomes and pursue sustainable performance. Despite the growing interest in sustainable leadership, the mechanisms and conditions under which this leadership approach is associated with sustainable performance have not yet been sufficiently clarified. This study examines the relationships between sustainable leadership and sustainable performance within a framework that incorporates the mediating role of organizational commitment and the moderating role of intra-organizational knowledge sharing. The research model was tested using data obtained from 399 employees working in SMEs operating in Türkiye through a convenience sampling approach, and the hypotheses were examined using PROCESS Macro Model 14 with a bootstrapping procedure (5000 resamples). The findings indicate that sustainable leadership is positively associated with sustainable performance and organizational commitment. Furthermore, organizational commitment is positively associated with sustainable performance and partially mediates the relationship between sustainable leadership and performance. In addition, intra-organizational knowledge sharing strengthens this indirect relationship, such that higher levels of knowledge sharing are associated with a stronger indirect effect. These findings suggest that sustainable performance is not solely a direct outcome of leadership behaviors but is associated with the interaction between employees’ relational bonds with the organization and knowledge-based processes. By integrating the natural resource-based view, social exchange theory, and the knowledge-based view, the study offers a conditional process perspective that is consistent with the observed relationships and contributes to the literature by providing a more integrated and contextually grounded understanding. Full article

LiDAR point clouds provide accurate and direct representations of spatial locations and geometric structures of objects in 3D space, making them essential for applications such as target recognition in autonomous driving and 3D reconstruction in smart cities. However, large-scale point clouds pose challenges, including massive data volume, uneven density distribution, and complex object structures. Existing point-based and graph-based semantic segmentation networks often suffer from limitations such as loss of local contextual information, over-reliance on local graph construction, and insufficient modeling of relationships between neighboring points. To address these issues, we propose PG-Net, a novel network that integrates discrete point distribution features with local graph structural information. The framework includes: (1) a point branch equipped with a Local Adaptive Feature Augmentation (LAFA) module to extract efficient local features; (2) a graph branch featuring a Dynamic Graph Feature Aggregation (DGFA) module, which explicitly models relationships among points in local graphs and adaptively balances a point’s intrinsic features with its neighborhood context; and (3) fuses local features from both branches, allowing their complementary strengths to enhance feature representation, a process further promoted by a New Aggregation Loss Function. Experiments on the Toronto3D and S3DIS datasets show that PG-Net achieves overall accuracy (OA) of 97.69% and 89.87%, and mean Intersection-over-Union (mIoU) of 83.51% and 73.22%, respectively. Comparative and ablation studies against advanced methods such as RandLA-Net, BAAF-Net, and LACV-Net demonstrate the effectiveness and robustness of our approach. By jointly exploiting discrete point distribution and local graph structural relationships, PG-Net effectively leverages the complementary strengths of its dual-branch design, offering a reliable solution for efficient and accurate large-scale point cloud semantic segmentation. Full article

The contemporary cyber-threat landscape is becoming increasingly diverse and complex, creating a persistent gap between situational awareness and operational response. This study presents a framework designed to bridge this gap by transforming up-to-date cyber-threat intelligence (CTI) into standardized knowledge structures and actionable defense measures. First, the proposed framework integrates the threat data collected from OpenCTI and normalizes them based on the MITRE ATT&CK tactics and techniques matrix. It then leverages a large language model to automatically generate diverse threat scenarios based on the analyzed intelligence. Each scenario is organized as a tactic sequence, and individual techniques are mapped to MITRE D3FEND defensive categories based on official ATT&CK–D3FEND relationships and structured contextual interpretation. Finally, the framework produces outputs in the form of a Defense Description that includes the corresponding technique IDs, recommended defense strategies, supporting rationales, and prerequisites. An evaluation using several recent cases demonstrates that the proposed framework effectively connects current threat intelligence with practical defense strategies. In summary, the proposed framework strengthens proactive cyber defense by directly linking structured attack flows to actionable context-aware defensive techniques. In addition, this framework provides a structured pipeline that systematizes and automates steps conventionally performed manually, thereby reducing repetitive analyst effort. Full article

Urban park use is a key indicator of sustainable urban development, reflecting the accessibility and social value of urban green infrastructure. However, existing studies often struggle to distinguish stable spatial differences from short-term temporal dynamics. Using monthly data for 125 urban parks in Las Vegas from 2022 to 2024, this study examines how park visitation is shaped by spatial, temporal, and contextual factors. It addresses three objectives: identifying cross-park determinants of visitation, examining within-park monthly dynamics, and assessing spatial variation in key relationships. Park visitation is measured using observed visit counts, with dwell time and travel distance used as alternative behavioral outcomes for robustness tests. To address these research questions, this study asks: (1) what structural and contextual factors explain cross-park differences in park visitation; (2) how park visitation responds to changing contextual conditions within parks over time at the monthly scale; and (3) whether the relationships between park visitation and its key determinants vary across space. To answer these questions, the analysis combines annual cross-sectional ordinary least squares (OLS) regression, monthly panel models, Random Forest analysis, robustness tests, and geographically weighted regression. This study employs a triangulated analytical framework combining cross-sectional ordinary least squares (OLS) regression monthly fixed-effects (FE) panel models, and Random Forest (RF) analysis. These factors function as stable support for sustainable park use. Crime exposure shows no stable global linear effect, but its association with visitation appears conditional on temporal and spatial context. Overall, the findings suggest that park visitation is shaped by the interaction of physical design, safety conditions, and urban context. By explicitly separating cross-sectional spatial and economic inequalities from within-park temporal dynamics, this study offers policy-relevant evidence for urban planners and park managers seeking to promote more inclusive, efficient, and sustainable urban park systems through integrated design, economic activation, and safety-oriented interventions. Full article

In this study, we propose a quantum-probability-inspired complex-valued model for multilingual stance detection. The model brings together ideas from granular computing and quantum theory to better capture semantic meaning across different languages. The proposed model combines contextual embeddings, graph convolutional networks, and a quantum-inspired feature interaction module (QFIM) to capture complex, high-order, and non-linear relationships in multilingual data. The QFIM models quantum amplitude-like interactions to represent overlapping semantic patterns in the latent space. This design helps the system better distinguish subtle differences in stance expressions. To strengthen the representation, a granulation mechanism based on contextual embedding similarity is employed to extract semantically coherent text granules. These granules expand the feature space and help align different elements of the stance structure more accurately. Experimental results on standard benchmark datasets show that the proposed model consistently achieves better performance than existing state-of-the-art methods. Full article

Background/Objectives: This study aimed to examine whether psychological resilience mediates the relationship between religious coping behaviors and menopausal symptoms among postmenopausal women. Methods: A cross-sectional study was conducted in Türkiye between July 2024 and July 2025 with women aged 45–60 years in the natural menopausal period (n = 190). Data were collected using a sociodemographic questionnaire, the Menopause Rating Scale (MRS), the Religious Coping Styles Scale (RCSS), and the 10-item Connor–Davidson Resilience Scale (CD-RISC-10). Descriptive statistics, Spearman correlation analysis, and structural equation modeling (SEM) with robust estimation were performed. The potential mediating role of psychological resilience was examined using SEM. Results: Negative religious coping was significantly associated with lower psychological resilience (β = −0.17, p = 0.050). However, psychological resilience did not show a significant association with menopausal symptoms in the structural model (β = −0.11, p = 0.134). Positive religious coping was not significantly related to resilience (β = −0.04, p = 0.649). The overall model explained a low proportion of variance in menopausal symptoms (R² ≈ 0.05). No evidence of a mediating effect of psychological resilience was found. Bootstrapped indirect effects indicated that the mediating role of psychological resilience was not statistically significant, as the confidence interval included zero. Conclusions: Although psychological resilience and religious coping were associated at the correlational level, no evidence of a mediating effect was found. The low explanatory power of the model suggests that menopausal symptoms are influenced by broader biological and contextual factors. The findings should be interpreted cautiously, and further longitudinal research is needed. Full article