Search Results (3,174)

This study proposes a hierarchical Archimedean copula (HAC) framework to model the complex dependence structures in hydrodynamic landslide deformations, with a focus on the Donglinxin (DLX) landslide. Hierarchical Archimedean copulas, compared to elliptical copulas, offer greater flexibility by requiring fewer parameters while maintaining broader applicability. The HAC model, combined with pseudo-maximum likelihood estimation (PMLE), is applied to analyze the interdependencies among the landslide-related variables, such as monthly displacement increments, reservoir water level fluctuations, groundwater variations, and precipitation. A case study of the DLX landslide demonstrates the model’s ability to quantify the critical aspects of landslide deformation, including variable correlations, risk thresholds, conditional probabilities, and return periods. The analysis reveals a strong hierarchical dependence between monthly displacement increments and reservoir water level drops. The model also provides valuable insights into the potential risk factors, helping to optimize landslide monitoring and early-warning systems for more effective disaster mitigation. Full article

Efficient contract management is essential for ensuring sustainable and reliable supply chains; yet, traditional methods remain manual, error-prone, and inefficient, leading to delays, financial risks, and compliance challenges. AI and blockchain technology offer a transformative alternative, enabling the establishment of automated, transparent, and self-executing smart contracts that enhance efficiency and sustainability. As part of AI-driven smart contract automation, we previously implemented contractual clause extraction using question answering (QA) and named entity recognition (NER). This paper presents the next step in the information extraction process, relation extraction (RE), which aims to identify relationships between key legal entities and convert them into structured business rules for smart contract execution. To address RE in legal contracts, we present a novel hierarchical transformer model that captures sentence- and document-level dependencies. It incorporates global and segment-based attention mechanisms to extract complex legal relationships spanning multiple sentences. Given the scarcity of publicly available contractual datasets, we also introduce the contractual relation extraction (ContRE) dataset, specifically curated to support relation extraction tasks in legal contracts, that we use to evaluate the proposed model. Together, these contributions enable the structured automation of legal rules from unstructured contract text, advancing the development of AI-powered smart contracts. Full article

Accurate water body detection is essential for autonomous navigation and operational planning of unmanned surface vehicles (USVs). To address model adaptability to ambiguous boundaries caused by diverse scenarios and climatic conditions, this study proposes GFANet (Global–Local Feature Attention Network) for the real-time water surface semantic segmentation of camera-captured images. First, a Global–Local Feature (GLF) extraction module is proposed, integrating a self-attention-based local feature extractor and a multi-scale global feature extractor for parallel feature learning, thereby enhancing hierarchical feature representation. Second, a Gated Attention (GA) module is designed with a dual-branch gating mechanism to implement noise suppression and efficient low-level feature utilization. The method was validated on three publicly available datasets in relevant domains. The experimental results on the Riwa dataset show that GFANet achieves state-of-the-art segmentation performance (4.41 M parameters, 7.15 GFLOPs) with an mIoU of 82.29% and an mPA of 89.49%. Comparable performance metrics were obtained on the USVInland and WaterSeg datasets. Additionally, GFANet achieves a 154.98 FPS processing speed, meeting real-time segmentation requirements. The experimental results verify that GFANet achieves an optimal balance between high segmentation accuracy and real-time processing efficiency. Full article

Viewpoint planning is crucial to ensure both inspection efficiency and observation precision in UAV inspection tasks. To address the issues of excessive waypoints and inadequate observation precision in traditional methods, this paper proposes a hierarchical reinforcement learning-based viewpoint planning method. The proposed method decomposes the viewpoint planning task into a high-level waypoint planning strategy and a low-level pose and zoom planning strategy. Additionally, a reward function is designed to enhance inspection precision, enabling collaborative optimization of waypoint positions, viewpoint poses, and focal lengths. Experimental results show that, compared with the classic coverage path planning method and non-hierarchical reinforcement learning approaches, the proposed method reduces the number of waypoints by at least 70% across multiple inspection objects. Furthermore, experiments with viewpoint planning at different precision levels demonstrate that the proposed method achieves scalable precision during inspection, with the observation resolution improving to 1.51 pixels/mm. Finally, a qualitative comparison is made between the proposed method in this paper and other representative methods in viewpoint planning. These results effectively demonstrate the validity and superiority of the proposed method in improving both inspection task efficiency and observation precision. Full article

Background/Objective: Emotional eating is a significant health problem associated with increased obesity and mental health among children and adolescents. Investigating emotional eating and its associated factors is critical, as it coincides with key developmental periods during which eating patterns are formed. This study assessed the contribution of anxiety and sleep quality to emotional eating among 232 Hispanic girls (n = 124, with a mean age of 10.23 years, SD = 1.40) and boys (n = 108, with a mean age of 10.36 years, SD = 1.57). Methods: This study used a correctional research design. Participants completed a series of surveys including demographics, acculturation, McKnight Risk Factor Survey-IV emotional eating subscale, Multidimensional Anxiety Scale for Children, and Pittsburgh Sleep Quality Index. Participants also had their objective body height and weight measured. Results: Descriptive analyses showed that most girls (84%) and boys (87%) were born in the United States and were either overweight (n = 24, 19% girls; n = 18, 17% boys) or with obesity (n = 61, 49% girls; n = 61, 56% boys). The hierarchical regression analyses revealed that, for girls, poor sleep quality was the sole significant factor associated with EE (β = 350, p < 0.001), controlling for age and BMI. For boys, poor sleep quality (β = 0.302, p < 0.01) and anxiety (β = 0.247, p < 0.05) were significant. Conclusions: The study’s findings suggest that emotional eating interventions may need to focus on reducing anxiety levels and improving sleep quality in Hispanic children and early adolescents Full article

The difference in chicken egg production is closely related to the efficiency of follicle selection, which is marked by granulosa cell differentiation and progesterone production with cholesterol as the substrate. The conversion of 7-dehydrocholesterol to cholesterol catalyzed by 7-Dehydrocholesterol reductase (DHCR7) is the rate-limiting step in cholesterol synthesis. Our previous study revealed that estrogen enhanced the mRNA expression of three DHCR7 transcript variants (T1, T3, and T4) in a dose-dependent manner in the granulosa cells of chicken pre-hierarchical follicles (Pre-GCs). This study investigates the molecular mechanisms through which estrogen regulates DHCR7 in chicken Pre-GCs. At the transcriptional level, through CUT&RUN-qPCR, we found that under basal conditions, sterol-regulatory element binding protein 2 (SREBP2) bound to the promoters of three DHCR7 transcript variants to promote cholesterol synthesis in Pre-GCs to maintain low cholesterol levels; meanwhile upon estrogen treatment, estrogen receptors α and β bound to the regulatory regions of three chicken DHCR7 transcript variants, leading to a reduction in the interaction between SREBP2 and DHCR7. At the translational level, the upstream open reading frames (uORFs) and N6-methyladenosine (m⁶A) modification in the 5′UTR of different DHCR7 transcripts differentially regulate the expression of T3 and T4, as detected by dual-luciferase reporter assays, but this regulation is not affected by estrogen. This study systematically explores the molecular mechanisms through which estrogen upregulates DHCR7 expression in chicken Pre-GCs and provides a clue for understanding the molecular mechanisms underlying cholesterol synthesis in chicken ovarian follicles. Full article

Multi-object tracking still faces significant challenges in complex conditions such as dense scenes, occlusion environments, and non-linear motion, especially regarding the detection and identity maintenance of small objects. To tackle these issues, this paper proposes a multi-modal fusion tracking framework that realizes high-precision tracking in complex scenarios by collaboratively optimizing feature enhancement and motion prediction. Firstly, a multi-scale feature adaptive enhancement (MS-FAE) module is designed, integrating multi-level features and introducing a small object adaptive attention mechanism to enhance the representation ability for small objects. Secondly, a cross-frame feature association module (CFAM) is put forward, constructing a global semantic association network via grouped cross-attention and a memory recall mechanism to solve the matching difficulties in occlusion and dense scenes. Thirdly, a Dynamic Motion Model (DMM) is developed, enabling the robust prediction of non-linear motion based on an improved Kalman filter framework. Finally, a Bi-modal dynamic decision method (BDDM) is devised to fuse appearance and motion information for hierarchical decision making. Experiments conducted on multiple public datasets, including MOT17, MOT20, and VisDrone-MOT, demonstrate that this method remarkably improves tracking accuracy while maintaining real-time performance. On the MOT17 test set, it achieves 63.7% in HOTA, 61.4 FPS in processing speed, and 79.4% in IDF1, outperforming current state-of-the-art tracking algorithms. Full article

Objectives: This study aimed to assess the social support level among perimenopausal women and explore its key influencing factors. Methods: From November 2022 to March 2023, a stratified multistage random sampling method was used to recruit 647 perimenopausal women from three communities in Tianjin, China. The participants completed the Social Support Rating Scale (SSRS), the Kupperman Menopausal Index (KMI), and a sociodemographic questionnaire. Nonparametric tests, correlation analysis, and stepwise regression analysis were conducted to explore key factors influencing social support. Robustness checks were performed using hierarchical regression analysis. Results: The overall social support level of perimenopausal women was moderately low (34.190 ± 10.007), with the lowest scores observed in the 46–50 age group (33.000 ± 9.666). Stepwise regression analysis showed that, compared to married women, single women reported significantly lower social support levels (β = −0.242, p < 0.001). Using public sector employees as the reference group, women in all other occupational categories (including self-employed, corporate employees, farmers, freelancers, and other professions) had significantly lower social support scores (β range: −0.196 to −0.232, all p < 0.05). Compared to those with good family relationships, women with average (β = −0.420, p < 0.001) and poor (β = −0.349, p < 0.001) family relationships reported significantly lower social support levels. In terms of menopausal symptoms, greater severity of palpitations (β = −0.140, p < 0.05) and dyspareunia (β = −0.143, p < 0.05) was associated with lower social support, while higher levels of neuroticism (β = 0.102, p < 0.05) and joint/muscle pain (β = 0.158, p < 0.05) were linked to greater social support. Conclusions: Social support levels among perimenopausal women were generally low, particularly among those aged 46–50 years. Marital status, occupational type, and family relationships were key influencing factors, and certain menopausal symptoms were closely related to social support, especially those that are difficult to discuss, such as palpitations and dyspareunia. These findings highlight the necessity of strengthening social support networks for perimenopausal women and provide scientific evidence for the development of targeted interventions and public health policies to enhance their well-being and promote healthy aging. Full article

Maize plays a crucial role in global nutrition and food security, with Mexico making a significant contribution through its diverse native corn genotypes. However, research on flours derived from these native maize genotypes remains limited, hindering their potential applications in food manufacturing. This study aimed to determine the nutritional, physicochemical, techno-functional, structural, and antioxidant properties of corn kernel flours from nine native Mexican maize accessions cultivated in the highlands of Jalisco. Enough cobs for each maize accession were randomly selected to yield 1000 g of corn kernels. Data analysis was conducted by analysis of variance and Kruskal–Wallis tests (α = 0.05). Moreover, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were performed. Native corn kernel flour (NCKF) demonstrated higher protein and fat content compared to white hybrid corn flour (WHF). While both flours showed similar pH, titratable acidity, and water activity levels, NCKF exhibited higher total soluble solids. Additionally, NCKF showed superior techno-functional properties, including water solubility, water absorption index, swelling power, emulsifying capacity, and foaming capacity, while its oil absorption index was comparable to that of WHF. Moreover, NCKF contained higher levels of bioactive compounds, such as soluble phenols, condensed tannins, flavonoids, anthocyanins, and carotenoids, along with enhanced antioxidant properties, as measured by FRAP, DPPH, and ABTS assays. FTIR analysis revealed that all NCKF samples exhibited patterns similar to those of WHF with differences in transmittance intensities. Notably, spectral differences were identified by PCA, while HCA demonstrated that corn flours exhibited similitudes and differences among them, which can be categorized into four groups based on their nutritional, physicochemical, and technological–functional properties, as well as antioxidant compound contents. Overall, the evaluated corn flours displayed nutritional, physicochemical, techno-functional, and antioxidant properties for the potential development of functional or nutraceutical food and beverage products. Full article

The spatial navigation of filamentous fungi was compared for three species, namely Pycnoporus cinnabarinus, Neurospora crassa wild type and ro-1 mutant, and Armillaria mellea, in microfluidic structures. The analysis of the navigation of these filamentous fungi in open and especially confining environments suggests that they perform space exploration using a hierarchical, three-layered system of information processing. The output of the space navigation of a single hypha is the result of coordination and competition between three programs with their corresponding subroutines: (i) the sensing of narrow passages (remote- or contact-based); (ii) directional memory; and (iii) branching (collision-induced or stochastic). One information-processing level up, the spatial distribution of multiple, closely collocated hyphae is the result of a combination of (i) negative autotropism and (ii) cytoplasm reallocation between closely related branches (with anastomosis as an alternative subroutine to increase robustness). Finally, the mycelium is the result of the sum of quasi-autonomous sub-populations of hyphae performing distribution in space in parallel based on the different spatial conditions and constraints found locally. The efficiency of space exploration by filamentous fungi appears to be the result of the synergy of various biological algorithms integrated into a hierarchical architecture of information processing, balancing complexity with specialization. Full article

To address the challenge of precise picking point localization in morphologically diverse safflower plants, this study proposes PointSafNet—a novel three-stage 3D point cloud analysis framework with distinct architectural and methodological innovations. In Stage I, we introduce a multi-view reconstruction pipeline integrating Structure from Motion (SfM) and Multi-View Stereo (MVS) to generate high-fidelity 3D plant point clouds. Stage II develops a dual-branch architecture employing Star modules for multi-scale hierarchical geometric feature extraction at the organ level (filaments and frui balls), complemented by a Context-Anchored Attention (CAA) mechanism to capture long-range contextual information. This synergistic feature learning approach addresses morphological variations, achieving 86.83% segmentation accuracy (surpassing PointNet++ by 7.37%) and outperforming conventional point cloud models. Stage III proposes an optimized geometric analysis pipeline combining dual-centroid spatial vectorization with Oriented Bounding Box (OBB)-based proximity analysis, resolving picking coordinate localization across diverse plants with 90% positioning accuracy and 68.82% mean IoU (13.71% improvement). The experiments demonstrate that PointSafNet systematically integrates 3D reconstruction, hierarchical feature learning, and geometric reasoning to provide visual guidance for robotic harvesting systems in complex plant canopies. The framework’s dual emphasis on architectural innovation and geometric modeling offers a generalizable solution for precision agriculture tasks involving morphologically diverse safflowers. Full article

In China’s rapidly digitizing IT industry, empowering leadership has become a crucial catalyst for workplace proactivity; however, the mechanisms linking leadership practices to individual proactive behaviors remain underexplored. This study addresses this gap by proposing a multi-level framework that integrates team processes and technological contexts. Based on the job demands–resources theory, the research examines the mechanisms of empowering leadership through parallel team-level pathways and the influence of digital infrastructure on these dynamics. Data were gathered in three phases from 510 employees across 74 teams in seven IT firms. Hierarchical analyses with SPSS 27.0, AMOS 28.0, and HLM 6.08 revealed three pathways: empowering leadership significantly enhances workplace proactivity, with team job crafting and psychological safety serving as sequential mediators. Moreover, access to knowledge via ICT moderates the relationship between team job crafting and workplace proactivity. This study theoretically contests sequential mediation assumptions by demonstrating parallel, non-overlapping mechanisms and redefines ICT’s role as a contextual enhancer in digital workplaces. Practically, it offers organizations a modular strategy: leaders can prioritize either job crafting systems or psychological safety climates to foster proactivity, depending on their team’s technological readiness. These insights offer practical recommendations for optimizing leadership practices in high-pressure IT environments, where digital tools and team dynamics influence employee initiative. Full article

This study investigates the transformation of Acmella oleracea with the Agrobacterium rhizogenes rolC gene and evaluates its impact on phytochemical composition and biological activity. A total of 480 plant nodes were subjected to Agrobacterium−mediated transformation, leading to the regeneration of 35 putative transgenic plants. Molecular analysis confirmed the presence of the rolC transgene in 17 clones, of which four (C123, C127, C129, and C132) exhibited rolC mRNA expression. Phytochemical profiling of hydroalcoholic extracts of aerial parts (AP) and roots (R) revealed significant differences (p ≤ 0.05) between transgenic and non-transgenic plants (CTR). Compared to non−transgenic plants, transgenic AP exhibited lower total phenolic content but retained or increased flavonoid concentrations, particularly flavan−3−ols, whereas R extracts consistently showed reduced secondary metabolite levels. LC−DAD−ESI−MS analysis identified a diverse metabolite profile, with AP being notably rich in flavonoids (48.65%) and alkylamides (32.43%), including spilanthol. Functional assessments across antioxidant and anti−inflammatory assays demonstrated that R extracts exhibited stronger bioactivity compared to AP extracts, as indicated by lower IC₅₀ values (0.004–2.18 mg/mL for R vs. 0.007–7.24 mg/mL for AP). However, iron−chelating capacity was higher in AP extracts, correlating with flavonoid concentration. Hierarchical clustering confirmed that transgenic lines C123 and C127 most closely resembled the control, while C129 and C132 displayed distinct metabolic profiles. These findings highlight rolC’s role in modulating secondary metabolite synthesis, influencing both the phytochemical composition and functional properties of A. oleracea extracts. Full article

This study aims to analyze the spatial effects triggered by dollar liquidity by constructing a multidimensional spatial matrix that modifies the traditional monetary spatial framework. We utilized a three-level spatial econometric model (Spatial Lag, Durbin, and Generalized Nested Space) to measure Gross Domestic Product (GDP), Consumer Price Index (CPI), and Asset Price Bubbles (BBL) through five spillover channels (geography, linguistics, politics, war, and economy). Our aim is to establish a systematic relationship between the conduction mechanism, means, economic indicators, and dollar externalities to examine liquidity spillover effects at varying distances in the global monetary space. We find that the spatial effects induced by the global circulation of the US dollar behave significantly differently in a single matrix space compared to in a multidimensional space. While the model verifies the existence of a positive correlation between the complexity of a single space and the spillover effect from a conduction mechanism perspective, the measure of the multidimensional matrix shows that the significance of the spillover effect weakens with an increase in abstraction level from a conduction means perspective. It suggests that spatial matrices of different dimensions reflect different economic realities. The former shows hierarchical multivariate details in independent matrices, while the variation in the level of abstraction of matrices of different dimensions in the latter enhances their interactivity and complexity. Full article

With the vast quantity of GPS data that have been collected from location-based social networks, Point-of-Interest (POI) recommendation aims to predict users’ next locations by learning from their historical check-in trajectories. While Graph Neural Network (GNN)-based models have shown promising results in this field, they typically construct single-layer graphs that fail to capture the hierarchical nature of human mobility patterns. To address this limitation, we propose a novel Hierarchical Graph Learning (HGL) framework that models POI relationships at multiple scales. Specifically, we construct a three-level graph structure: a base-level graph capturing direct POI transitions, a region-level graph modeling area-based interactions through spatio-temporal clustering, and a global-level graph representing category-based patterns. To effectively utilize this hierarchical structure, we design a cross-layer information propagation mechanism that enables bidirectional message passing between different levels, allowing the model to capture both fine-grained POI interactions and coarse-grained mobility patterns. Compared to traditional models, our hierarchical structure improves cold-start robustness and achieves superior performance on real-world datasets. While the incorporation of multi-layer attention and clustering introduces moderate computational overhead, the cost remains acceptable for offline recommendation contexts. Full article