Search Results (10,491)

Information popularity prediction is a critical problem in social network analysis. With the increasing prevalence of social platforms, accurate prediction of the diffusion process has become increasingly important. Existing methods mainly rely on graph neural networks to model structural relationships, but they are often insufficient in capturing the complex interplay between temporal evolution and local cascade structures, especially in real-world scenarios involving sparse or rapidly changing cascades. To address this issue, we propose the Cascading Dynamic attention-calibrated Graph Convolutional Network, named CasDacGCN. It enhances prediction performance through spatiotemporal feature fusion and adaptive representation learning. The model integrates snapshot-level local encoding, global temporal modeling, cross-attention mechanisms, and a hypernetwork-based sample-wise calibration strategy, enabling flexible modeling of multi-scale diffusion patterns. Results from experiments demonstrate that the proposed model consistently surpasses existing approaches on two real-world datasets, validating its effectiveness in popularity prediction tasks. Full article

With the continuous scaling-down of integrated circuit feature sizes, inverse lithography technology (ILT), as the most groundbreaking resolution enhancement technique (RET), has become crucial in advanced semiconductor manufacturing. By directly optimizing mask patterns through inverse computation rather than rule-based local corrections, ILT can more accurately approximate target design patterns while extending the process window. However, current mainstream ILT approaches—whether machine learning-based or gradient descent-based—all face the challenge of balancing mask optimization quality and computational time. Moreover, ILT often faces a trade-off between imaging fidelity and manufacturability; fidelity-prioritized optimization leads to explosive growth in mask complexity, whereas manufacturability constraints require compromising fidelity. To address these challenges, we propose an iterative deep learning-based ILT framework incorporating a lightweight model, ghost and adaptive attention U-net (EAAUnet) to accelerate runtime and reduce computational overhead while progressively improving mask quality through multiple iterations based on the pre-trained network model. Compared to recent state-of-the-art (SOTA) ILT solutions, our approach achieves up to a 39% improvement in mask quality metrics. Additionally, we introduce a mask constraint scheme to regulate complex SRAF (sub-resolution assist feature) patterns on the mask, effectively reducing manufacturing complexity. Full article

Background: NHS England has an ageing workforce. Approximately 30 percent of the NHS England workforce are aged 50 years and over, and the British Medical Association has argued that it is important that employers meet the needs of their ageing workforce and retain their skills and expertise. Objective: This sought to explore how NHS England Trusts support employees with fluctuating long-term health conditions, investigating systemic workforce challenges to providing adequate support and identifying opportunities for more inclusive and sustainable employment practices. Methods: Qualitative interviews were conducted with staff working in human resources, occupational health staff and clinical line managers involved in the support and management of staff with fluctuating long-term health conditions (n = 17). Results: The research found a misalignment between clinical managerial practices, human resource procedures, and the overarching NHS human resource policy framework, which was often seen as rigid and poorly suited to the fluctuating nature of some long-term conditions. These tensions were exacerbated by high staff turnover and limited organisational capacity. Nonetheless, instances of effective, person-centred support were also reported, typically occurring where cross-departmental collaboration and flexible, locally adapted approaches were in place. Conclusions: Findings suggest that targeted, flexible interventions for NHS employees with fluctuating long-term health conditions could enhance staff retention, reduce absenteeism, and promote more resilient workforce strategies. Identifying and scaling examples of good practice may be key to fostering a more inclusive and adaptive NHS employment model. Full article

This work presents a task-oriented framework for optimizing robotic surface finishing to improve efficiency and ensure feasibility under realistic kinematic and geometric constraints. The approach combines surface subdivision, optimal placement of the workpiece, and region-based toolpath planning to adapt machining strategies to local surface characteristics. A novel time evaluation criterion is introduced that improves our previous kinematic approach by incorporating dynamic aspects. This advancement enables a more realistic estimation of machining time, providing a more reliable basis for optimization and path planning. The framework determines both the optimal position of the workpiece and the subdivision of its surface into regions systematically, enabling machining directions and speeds to be adapted to the geometry of each region. The methodology was validated on several semi-complex surfaces through simulation and experimental trials with collaborative robotic manipulators. The results demonstrate that improved region-based optimization leads to machining time reductions of 9–26% compared to conventional single-direction machining strategies. The most significant improvements were achieved for larger, more complex geometries and denser machining paths, confirming the method’s industrial relevance. These findings establish the framework as a practical solution for reducing cycle time in specific robotic surface finishing tasks. Full article

This study investigates how trauma-informed and healing-centred architectural design is associated with rehabilitation and reintegration outcomes in young people’s correctional facilities. Drawing on international case studies, the analysis demonstrates that architecture is not a neutral backdrop but a contributing determinant within broader justice ecosystems. Trauma-informed environments are consistently linked to reductions in re-traumatisation and improvements in emotional regulation, while small-scale, community-oriented facilities are associated with enhanced skill development, autonomy, and reintegration potential. Culturally responsive designs that incorporate Indigenous practices and symbolic architecture are observed to support identity, resilience, and community belonging, underscoring the importance of cultural continuity in rehabilitation processes. In parallel, sustainable features such as biophilic design, renewable energy systems, and natural light are correlated with improvements in ecological performance and psychosocial well-being, indicating that sustainability and rehabilitation may be mutually reinforcing goals. Notably, the analysis highlights that supportive environments are also associated with staff well-being and institutional stability, underscoring the broader organisational benefits of healing architecture. The findings suggest that young people’s correctional facilities should not replicate adult prisons but instead provide safe, developmental, and culturally grounded spaces that respond to adolescents’ unique needs. This study contributes a novel conceptual model—the Trauma-Informed Healing Architecture (TIHA) framework—that integrates trauma-informed, cultural, and ecological design strategies within the Sustainable Development Goals (SDGs). The framework defines global standards as universal principles—safety, dignity, cultural responsiveness, and natural light—while remaining adaptable to local resources and justice systems. In this way, it provides internationally relevant yet context-sensitive guidance for young people’s correctional reform. Full article

Unlocking the potential for adaptability across different conditions or environments of under-characterized local bread wheat from Serbia and Bulgaria remains critical for resilient breeding. This study aimed to assess the diversity and potential for adaptability of 76 accessions (35 from Serbia and 41 from Bulgaria) by integrating molecular and agro-morphological approaches. Plant height ranged from 101.1 cm in Bulgarian cultivars to 130.1 cm in Serbian landraces, while the flowering time varied from 134.9 days in Bulgarian cultivars to 139.7 days in Serbian landraces. SSR markers detected 446 alleles (mean 9.49 per locus; PIC = 0.646), with Serbian landraces exhibiting the highest allelic richness (7.23 alleles per locus) and 106 private alleles. Bayesian clustering and UPGMA analyses revealed clear genetic differentiation between Serbian and Bulgarian accessions, with Serbian landraces showing higher admixture and substructure. Principal coordinate analysis confirmed these patterns and highlighted the intermediate positions of pre-Green Revolution Serbian cultivars. The local wheat germplasm harbors high genetic diversity and potential for adaptability, particularly for traits critical to environmental resilience. These findings underline the value of local wheat germplasm as a resource for developing sustainable breeding and conservation strategies. Full article

The Mediterranean fig (Ficus carica L.) is a dioecious fruit tree of high nutritional and economic value in the Mediterranean basin. In northern Morocco, phenological desynchronization between male and female fig trees limits pollination and production. This study aimed to characterize the phenological stages of indigenous fig and caprifig varieties using the BBCH scale and to evaluate the predictive capacity of artificial neural networks (ANNs). This study was conducted in the Bni Ahmed region over two consecutive years (2021 and 2022) at two sites. At each site, a total of 80 female fig trees were selected. Caprifig trees were selected in accordance with their availability (37 trees/site 1; 24 trees/site 2). Local meteorological data were incorporated into the analysis to evaluate the influence of climatic conditions on phenological stages. Our results revealed significant effects of temperature, humidity, and rainfall on phenological dynamics, along with a clear inter-varietal variability and pronounced desynchronization between male and female fig trees. Early-ripening caprifig varieties showed limited pollination efficiency, whereas late-ripening varieties were better synchronized with the longer receptivity period of female fig trees. Importantly, the ANN model demonstrated exceptional predictive performance (R² up to 0.985, RMSE < 1 day), serving as a robust and practical tool for forecasting key phenological stages and minimizing potential yield losses. These findings demonstrate the value of combining phenological monitoring with AI-based modeling to improve adaptive management of fig orchards under Mediterranean climate change. This is the first study in Morocco to implement such an integrated approach to fig and caprifig trees. Full article

Accurate spatial information of cropland is crucial for precision agricultural management and ensuring national food security. High-resolution remote sensing imagery combined with deep learning algorithms provides a promising approach for extracting detailed cropland information. However, due to the diverse morphological characteristics of croplands across different agricultural landscapes, existing deep learning methods encounter challenges in precise boundary localization. The advancement of large-scale vision models has led to the emergence of the Segment Anything Model (SAM), which has demonstrated remarkable performance on natural images and attracted considerable attention in the field of remote sensing image segmentation. However, when applied to high-resolution cropland extraction, SAM faces limitations in semantic expressiveness and cross-domain adaptability. To address these issues, this study proposes a dual-branch framework integrating SAM and a semantically aware network (SAM-SANet) for high-resolution cropland extraction. Specifically, a semantically aware branch based on a semantic segmentation network is applied to identify cropland areas, complemented by a boundary-constrained SAM branch that directs the model’s attention to boundary information and enhances cropland extraction performance. Additionally, a boundary-aware feature fusion module and a prompt generation and selection module are incorporated into the SAM branch for precise cropland boundary localization. The former aggregates multi-scale edge information to enhance boundary representation, while the latter generates prompts with high relevance to the boundary. To evaluate the effectiveness of the proposed approach, we construct three cropland datasets named GID-CD, JY-CD and QX-CD. Experimental results on these datasets demonstrated that SAM-SANet achieved mIoU scores of 87.58%, 91.17% and 71.39%, along with mF1 scores of 93.54%, 95.35% and 82.21%, respectively. Comparative experiments with mainstream semantic segmentation models further confirmed the superior performance of SAM-SANet in high-resolution cropland extraction. Full article

Unmanned aerial vehicle (UAV)-based object detection shows promising potential in intelligent transportation and disaster response. However, detecting small targets remains challenging due to inherent limitations (long-distance and low-resolution imaging) and environmental interference (complex backgrounds and occlusions). To address these issues, this paper proposes an enhanced small target detection model, DDF-YOLO, which achieves higher detection performance. First, a dynamic feature extraction module (C2f-DCNv4) employs deformable convolutions to effectively capture features from irregularly shaped objects. In addition, a dynamic upsampling module (DySample) optimizes multi-scale feature fusion by combining shallow spatial details with deep semantic features, preserving critical low-level information while enhancing generalization across scales. Finally, to balance rapid convergence with precise localization, an adaptive Focaler-ECIoU loss function dynamically adjusts training weights based on sample quality during bounding box regression. Extensive experiments on VisDrone2019 and UAVDT benchmarks demonstrate DDF-YOLO’s superiority. Compared to YOLOv8n, our model achieves gains of 8.6% and 4.8% in mAP50, along with improvements of 5.0% and 3.3% in mAP50-95, respectively. Furthermore, it exhibits superior efficiency, requiring only 7.3 GFLOPs and attaining an inference speed of 179 FPS. These results validate the model’s robustness for UAV-based detection, particularly in small-object scenarios. Full article

Community-Based Tourism (CBT) is increasingly pivotal for sustainable rural development in emerging economies, particularly in culturally rich nations like Indonesia. The vulnerability of tourism-dependent communities, starkly exposed by the COVID-19 pandemic, underscores the urgent need to understand how CBT can foster socio-economic resilience—the capacity to withstand, adapt to, and recover from shocks. This study aims to investigate the relationship between CBT governance models and socio-economic resilience in rural Indonesia, identifying the critical factors that enable communities to thrive amidst adversity. A comparative qualitative case study design was employed, focusing on three tourism villages in Yogyakarta (Nglanggeran) and Bali (Penglipuran, Jasri). Data were collected through semi-structured interviews, focus group discussions, and participant observation conducted from June to August 2024. The findings reveal that villages with inclusive participation, strong local leadership, and equitable benefit-sharing mechanisms (e.g., Nglanggeran) demonstrate higher resilience, characterized by economic diversification, robust social capital, and strong adaptive capacity. In contrast, top-down governance (Penglipuran) or entrepreneurial but fragmented initiatives (Jasri) can limit inclusivity and adaptability, constraining resilience. This research contributes to the CBT literature by providing a comparative analysis of resilience outcomes across different governance contexts in Indonesia. It offers a refined framework for understanding how local institutions and community agency interact to build resilience. The study provides practical insights for policymakers and community leaders, highlighting the importance of fostering inclusive governance, strategic partnerships, and economic diversification to enhance the long-term sustainability and resilience of tourism-dependent communities. Full article

In response to growing pressures for sustainability in tourism, this paper examines the techno-economic evaluation of green innovations in small and medium-sized tourism enterprises (SMEs). Focusing on a single case study of a hotel in Greece, the research investigates how and why specific sustainability interventions were implemented and assesses their operational and economic impacts. The study adopts an interpretivist approach, combining process tracing with thematic analysis. The analysis is guided by innovation diffusion theory, supported by organizational learning perspectives, to explain the stepwise adoption of sustainability practices and the internal adaptation processes that enabled them. The techno-economic evaluation draws on quantitative indicators and qualitative assessments of perceived benefits and implementation challenges, offering a broader view of value beyond purely financial metrics. Data were collected through semi-structured interviews, on-site observations, and internal documentation. The findings reveal a gradual, non-linear path to innovation, shaped by adoption dynamics and organizational learning, reinforced by leadership commitment, contextual adaptation, supply chain decisions, and external incentives. Key interventions, including solar energy adoption, composting, and the formation of zero-waste partnerships, resulted in measurable reductions in energy use and landfill waste, along with improvements in guest satisfaction, operational efficiency, and local collaboration. Although it is subject to limitations typical of single-case designs, the study demonstrates how even modest sustainability efforts, when integrated into daily operations, can generate multiple types of outcomes (economic, environmental, and operational). The paper offers practical implications for tourism SMEs and policymakers and formulates propositions for future testing on sustainable innovation in the tourism sector. Full article

Automated medical report generation (MRG) faces a critical hurdle in seamlessly integrating detailed visual evidence with accurate clinical diagnoses. Current approaches often rely on static knowledge transfer, overlooking the complex interdependencies among pathological findings and their nuanced alignment with visual evidence, often yielding reports that are linguistically sound but clinically misaligned. To address these limitations, we propose a novel graph-driven medical report generation framework with adaptive knowledge distillation. Our architecture leverages a dual-phase optimization process. First, visual–semantic enhancement proceeds through the explicit correlation of image features with a structured knowledge network and their concurrent enrichment via cross-modal semantic fusion, ensuring that generated descriptions are grounded in anatomical and pathological context. Second, a knowledge distillation mechanism iteratively refines both global narrative flow and local descriptive precision, enhancing the consistency between images and text. Comprehensive experiments on the MIMIC-CXR and IU X-Ray datasets demonstrate the effectiveness of our approach, which achieves state-of-the-art performance in clinical efficacy metrics across both datasets. Full article

This paper introduces KG-FLoc, a knowledge graph-enhanced framework for secondary circuit fault localization in intelligent substations. The proposed KG-FLoc innovatively formalizes secondary components (e.g., circuit breakers, disconnectors) as graph nodes and their multi-dimensional relationships (e.g., electrical connections, control logic) as edges, constructing the first comprehensive knowledge graph (KG) to structurally and operationally model secondary circuits. By reframing fault localization as a knowledge graph link prediction task, KG-FLoc identifies missing or abnormal connections (edges) as fault indicators. To address dynamic topologies and sparse fault samples, KG-FLoc integrates two core innovations: (1) a relation-aware gated unit (RGU) that dynamically regulates information flow through adaptive gating mechanisms, and (2) a hierarchical graph isomorphism network (GIN) architecture for multi-scale feature extraction. Evaluated on real-world datasets from 110 kV/220 kV substations, KG-FLoc achieves 97.2% accuracy in single-fault scenarios and 93.9% accuracy in triple-fault scenarios, surpassing SVM, RF, MLP, and standard GNN baselines by 12.4–31.6%. Beyond enhancing substation reliability, KG-FLoc establishes a knowledge-aware paradigm for fault diagnosis in industrial systems, enabling precise reasoning over complex interdependencies. Full article

Background: Ongoing challenges such as geopolitical conflicts, trade disruptions, economic sanctions, and political instability have underscored the urgent need for large manufacturing enterprises to improve resilience and reduce dependence on global supply chains. Integrating regional and local Small- and Medium-Sized Enterprises (SMEs) has been proposed as a strategic approach to enhance supply chain localization, yet barriers such as limited visibility, qualification hurdles, and integration difficulties persist. Methods: This study proposes a comprehensive knowledge graph driven framework for representing and discovering SMEs, implemented as a proof-of-concept in the U.S. BioPharma sector. The framework constructs a curated knowledge graph in Neo4j, converts it to Resource Description Framework (RDF) format, and aligns it with the Schema.org vocabulary to enable semantic interoperability and enhance the discoverability of SMEs. Results: The developed knowledge graph, consisting of 488 nodes and 11,520 edges, enabled accurate multi-hop SME discovery with query response times under 10 milliseconds. RDF serialization produced 16,086 triples, validated across platforms to confirm interoperability and semantic consistency. Conclusions: The proposed framework provides a scalable, adaptable, and generalizable solution for SME discovery and supply chain localization, offering a practical pathway to strengthen resilience in diverse manufacturing industries. Full article

Rice (Oryza sativa L.) is a salt-sensitive crop, where even moderate soil salinity (electrical conductivity ≥ 3.5 dS/m) can cause significant yield reduction. During the seedling stage, the underdeveloped root system has limited capacity for salt uptake and translocation, making root system architecture (RSA) a crucial trait for enhancing salinity tolerance. In this study, we used 165 individuals from the 3K Rice Genome Project to comprehensively measure multidimensional root morphological traits at the early seedling stage under salt stress, thereby overcoming the limitations of conventional methods that mainly rely on root length and biomass. We identified 78 quantitative trait nucleotides (QTNs) associated with eight root morphological traits through genome-wide association studies (GWAS) of 3VmrMLM. Among these, 12 QTNs co-localized within genomic regions of previously cloned salt tolerance-related genes. Additionally, six salt-tolerant lines were selected based on significantly increased root volume (RV) and surface area (SA), suggesting that their adaptive mechanism under salinity involves optimized spatial root distribution rather than radial thickening. Our findings show that high-resolution root scanning-based phenotyping provides a reliable platform for screening and breeding salt-tolerant rice varieties, offering valuable indicators for assessing seedling-stage salt tolerance. Full article