Search Results (1,093)

With the popularization of social media, information related to public health events has seen explosive growth online, making it essential to accurately identify informative tweets with decision-making and management value for public health emergency response and risk monitoring. However, existing methods often suffer performance degradation during cross-event transfer due to differences in data distribution, and research specifically targeting public health events remains limited. To address this, we propose the Label Confusion Domain Adversarial Network (LCDAN), which innovatively integrates label confusion with domain adaptation to enhance the detection of informative tweets across different public health events. First, LCDAN employs an adversarial domain adaptation model to learn cross-domain feature representation. Second, it dynamically evaluates the importance of different source domain samples to the target domain through label confusion to optimize the migration effect. Experiments were conducted on datasets related to COVID-19, Ebola disease, and Middle East Respiratory Syndrome public health events. The results demonstrate that LCDAN significantly outperforms existing methods across all tasks. This research provides an effective tool for information detection during public health emergencies, with substantial theoretical and practical implications. Full article

Art therapy serves as a crucial intervention modality for children with autism spectrum disorder (ASD), demonstrating unique value in emotional expression, sensory integration, and social communication. However, current practice presents critical challenges, including the disconnect between design expertise and clinical needs, unclear mechanisms of environmental factors’ impact on therapeutic outcomes, and insufficient evidence-based support for technology integration. Purpose: This study aimed to construct an evidence-based theoretical framework for art therapy environment design for children with autism, clarifying the relationship between environmental design elements and therapeutic effectiveness. Methodology: Based on the Web of Science database, this study employed a dual-track approach comprising bibliometric analysis and micro-qualitative content analysis to systematically examine the knowledge structure and developmental trends. Research hotspots were identified through keyword co-occurrence network analysis using CiteSpace, while 24 representative design cases were analyzed to gain insights into design concepts, emerging technologies, and implementation principles. Key Findings: Through keyword network visualization analysis, this study identified ten primary research clusters that were systematically categorized into four core design elements: sensory feedback design, behavioral guidance design, emotional resonance design, and therapeutic support design. A responsive therapeutic environment conceptual framework was proposed, encompassing four interconnected components based on the ABC model from positive psychology: emotional, sensory, environmental, and behavioral dimensions. Evidence-based design principles were established emphasizing child-centeredness, the promotion of multisensory expression, the achievement of dynamic feedback, and appropriate technology integration. Research Contribution: This research establishes theoretical connections between environmental design elements and art therapy effectiveness, providing a systematic design guidance framework for interdisciplinary teams, including environmental designers, clinical practitioners, technology developers, and healthcare administrators. The framework positions technology as a therapeutic mediator rather than a driver, ensuring technological integration supports rather than interferes with children’s natural creative impulses. This contributes to creating more effective environmental spaces for art therapy activities for children with autism while aligning with SDG3 goals for promoting mental health and reducing inequalities in therapeutic access. Full article

Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous multi-source information, capturing global semantic relationships, and adapting to dynamic network evolution. This paper proposes a novel unsupervised multimodal community detection algorithm (UMM) based on fractal iteration. The core idea is to design a dual-channel encoder that comprehensively considers node semantic features and network topological structures. Initially, node representation vectors are derived from structural information (using feature vectors when available, or singular value decomposition to obtain feature vectors for nodes without attributes). Subsequently, a parameter-free graph convolutional encoder (PFGC) is developed based on fractal iteration principles to extract high-order semantic representations from structural encodings without requiring any training process. Furthermore, a semantic–structural dual-channel encoder (DC-SSE) is designed, which integrates semantic encodings—reduced in dimensionality via UMAP—with structural features extracted by PFGC to obtain the final node embeddings. These embeddings are then clustered using the K-means algorithm to achieve community partitioning. Experimental results demonstrate that the UMM outperforms existing methods on multiple real-world network datasets. Full article

News recommendation systems must simultaneously address multiple challenges, including dynamic user interest modeling, nonlinear popularity patterns, and diversity recommendation in cold-start scenarios. We present a Popularity-Aware Dynamic Multi-Perspective Fusion Network (PAD-MPFN) that innovatively integrates three key components: adaptive subspace projection for multi-source interest fusion, logarithmic time-decay factors for popularity bias mitigation, and dynamic gating mechanisms for personalized recommendation weighting. The framework uniquely combines sequential behavior analysis, social graph propagation, and temporal popularity modeling through a unified architecture. Experimental results on the MIND dataset, an open-source version of MSN News, demonstrate that PAD-MPFN outperforms existing methods in terms of recommendation performance and cold-start scenarios while effectively alleviating information overload. This study offers a new solution for dynamic interest modeling and diverse recommendation. Full article

Amid ongoing societal development, tourists’ travel behavior patterns have been undergoing substantial transformations, and understanding their evolution has emerged as a key area of scholarly interest. Taking Beijing as a case study, this research aims to uncover the spatiotemporal evolution patterns of tourist flows and their underlying driving mechanisms. Based on digital footprint relational data, a dual-perspective analytical framework—“tourist perception–tourist flow network”—is constructed. By integrating the center-of-gravity model, social network analysis, and regression models, the study systematically examines the dynamic spatial structure of tourist flows in Beijing from 2012 to 2024. The findings reveal that in the post-pandemic period, Beijing tourists place greater emphasis on the cultural connotation and experiential aspects of destinations. The gravitational center of tourist flows remains relatively stable, with core historical and cultural blocks retaining strong appeal, though a slight shift has occurred due to policy influences and emerging attractions. The evolution of the spatial network structure reveals that tourism flows have become more dispersed, while the influence of core scenic spots continues to intensify. Government policy orientation, tourism information retrieval, and the agglomeration of tourism resources significantly promote the structure of tourist flows, whereas the general level of tourism resources exerts no notable influence. These findings offer theoretical insights and practical guidance for the sustainable development and regional coordination of tourism in Beijing, and provide a valuable reference for the spatial restructuring of urban tourism in the post-COVID-19 era. Full article

Nomadic heritage villages constitute significant material cultural heritage. Under China’s cultural revitalization and rural development strategies, these villages face spatial degradation driven by tourism and urbanization. Current research predominantly employs isolated analytical approaches—space syntax often overlooks social dynamics while social network analysis (SNA) overlooks physical interfaces—hindering the development of holistic solutions for socio-spatial resilience. This study proposes a multi-scale integrated assessment framework combining social network analysis (SNA) and space syntax to systematically evaluate public space structures in traditional nomadic villages of Xinjiang. The framework provides scientific evidence for optimizing public space design in these villages, facilitating harmonious coexistence between spatial functionality and cultural values. Focusing on three heritage villages—representing compact, linear, and dispersed morphologies—the research employs a hierarchical “village-street-node” analytical model to dissect spatial configurations and their socio-functional dynamics. Key findings include the following: Compact villages exhibit high central clustering but excessive concentration, necessitating strategies to enhance network resilience and peripheral connectivity. Linear villages demonstrate weak systemic linkages, requiring “segment-connection point supplementation” interventions to mitigate structural elongation. Dispersed villages maintain moderate network density but face challenges in visual integration and centrality, demanding targeted activation of key intersections to improve regional cohesion. By merging SNA’s social attributes with space syntax’s geometric precision, this framework bridges a methodological gap, offering comprehensive spatial optimization solutions. Practical recommendations include culturally embedded placemaking, adaptive reuse of transitional spaces, and thematic zoning to balance heritage conservation with tourism needs. Analyzing Xinjiang’s unique spatial–social interactions provides innovative insights for sustainable heritage village planning and replicable solutions for comparable global cases. Full article

Individual willingness to pay (WTP) for green energy plays a vital role in mitigating climate change. Based on social cognitive theory (SCT), which emphasizes the dynamic interaction among individual cognition, behavior and the environment, this study develops a theoretical model to identify factors influencing green energy WTP. The study is based on 585 valid questionnaire responses from urban areas in China and uses Structural Equation Modeling (SEM) to reveal the linear causal path. Meanwhile, fuzzy-set Qualitative Comparative Analysis (fsQCA) is utilized to identify the combined paths of multiple conditions leading to a high WTP, making up for the limitations of SEM in explaining complex mechanisms. The SEM analysis shows that social trust, social networks, and social norms have a significant positive impact on individual green energy WTP. And this influence is further transmitted through the mediating role of environmental self-efficacy and expectations of environmental outcomes. The FsQCA results identified three combined paths of social capital and environmental cognitive conditions, including the Network–Norm path, the Network–efficacy path and the Network–Outcome path, all of which can achieve a high level of green energy WTP. Among them, the social networks are a core condition in every path and a key element for enhancing the high green energy WTP. This study promotes the expansion of SCT, from emphasizing the linear role of individual cognition to focusing on the configuration interaction between social structure and psychological cognition, provides empirical evidence for formulating differentiated social intervention strategies and environmental education policies, and contributes to sustainable development and the green energy transition. Full article

This paper presents a meta-analysis of social research on water, offering a novel methodological contribution to the study of emerging interdisciplinary research fields. We propose and implement a mixed methods framework that integrates quantitative network analysis with qualitative research, aiming to enhance both to give access to new emerging empirical fields and enhance the analytical depth of empirical social research. Drawing on a dataset of publications from the Web of Science over four distinct time intervals, we identify thematic clusters through keyword co-occurrence networks that reveal the evolving structure and internal dynamics of the field. Our findings show a clear trend toward increasing interdisciplinarity, responsiveness to global events, and contemporary challenges such as the emergence of COVID-19 and the continued centrality of topics related to water management and evaluation. By uncovering latent structures, our approach not only maps the field’s development but also lays the foundation for targeted qualitative analysis of articles representative of identified clusters. This methodological design contributes to the broader discourse on mixed methods research in the social sciences by demonstrating how computational tools can enhance the transparency and reliability of qualitative inquiry without sacrificing its interpretive richness. Furthermore, this study opens new avenues for critically reflecting on the epistemic culture of social water research, particularly in relation to its proximity to applied science and governance-oriented perspectives. The proposed method holds potential relevance for both academic researchers and decision makers in the water sector, offering a means to systematically access dispersed knowledge and identify underrepresented subfields. Overall, the study showcases the potential of mixed methods designs for navigating and structuring complex interdisciplinary research landscapes. Full article

This study examines the complex process of Islam’s localization in Sudan, focusing on how hospitality, Sufi dhikr, and Mawlid celebrations integrate with Islamic practices. Drawing on three years of qualitative fieldwork, it demonstrates how Sudan’s geography, ethnic diversity, and historical heritage enable the blending of core religious principles with local customs. Sufi brotherhoods—particularly Qādiriyya, Tījāniyya, Shādhiliyya, and Khatmiyya—play a pivotal role in local culture by incorporating traditional musical, choreographic, and narrative art forms into their rituals, resulting in highly dynamic worship and social interaction. In Sudan, hospitality emerges as a near-sovereign social norm, reflecting the Islamic ethics of charity and mutual assistance while remaining deeply intertwined with local traditions. Islam’s adaptability toward local customs is further illustrated by the vibrant drumming, chanting, and dancing that enhance large-scale Mawlid al-Nabi celebrations, uniting Muslims under a religious identity that goes beyond dogmatic definitions. Beyond their spiritual meanings, these Sufi practices and networks also serve as tools for social cohesion, often functioning as support systems in regions with minimal state presence. They help prevent disputes and foster unity, demonstrating the positive impact of a flexible Islam—one that draws on both scripture and local traditions—on peacebuilding in Sudan. While highlighting the country’s social realities, this study offers insights into how Islam can function as a transformative force within society. Full article

Developing agricultural new productive forces (ANPF) according to local conditions is a key strategy for agricultural modernization. Using panel data from 16 prefecture-level cities in Anhui Province from 2010 to 2022, this study constructed indicator systems for ANPF and the construction of a strong agricultural province (CSAP). The entropy-weight TOPSIS method was used to calculate the levels of ANPF and the SAP index. This study employed a modified gravity model and social network analysis (SNA) to investigate the spatial correlation and evolutionary characteristics of these networks. Geographical detectors were also used to identify the driving factors behind agricultural transformation. The findings indicate that both ANPF and CSAP showed an upward trend during the study period, with significant regional heterogeneity, with Central Anhui being the most prominent. This study revealed spatial spillover effects and strong network correlations between ANPF and CSAP, with the spatial network structure exhibiting characteristics of multi-core, multi-association, and multidimensional connections. The entities within the network are tightly connected, with no “isolated island” phenomenon, and Hefei, as the central hub, showed the highest number of connections. Laborer quality, tangible means of production, and new-quality industries emerged as the core driving forces, working in synergy to propel CSAP. This study contributes new insights into the spatial network dynamics of agricultural development and offers actionable recommendations for policymakers to enhance agricultural modernization globally. Full article

Governance challenges, success factors, and stakeholder dynamics are central to the implementation of Positive Energy District (PED) Labs, which aim to develop energy-positive and sustainable urban areas. In this paper, a qualitative analysis combining expert surveys, participatory workshops with practitioners from the COST Action PED-EU-NET network, and comparative case studies across Europe identifies key barriers, drivers, and stakeholder roles throughout the implementation process. Findings reveal that fragmented regulations, social inertia, and limited financial mechanisms are the main barriers to PED Lab development, while climate change mitigation goals, strong local networks, and supportive policy frameworks are critical drivers. The analysis maps stakeholder engagement across six development phases, showing how leadership shifts between governments, industry, planners, and local communities. PED Labs require intangible assets such as inclusive governance frameworks, education, and trust-building in the early phases, while tangible infrastructures become more relevant in later stages. The conclusions emphasize that robust, inclusive governance is not merely supportive but a key driver of PED Lab success. Adaptive planning, participatory decision-making, and digital coordination tools are essential for overcoming systemic barriers. Scaling PED Labs effectively requires regulatory harmonization and the integration of social and technological innovation to accelerate the transition toward energy-positive, climate-resilient cities. Full article

Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. This paper presents SE-TFF, a multi-scale tourism-flow forecasting framework that couples a Squeeze-and-Excitation (SE) network with reinforcement-driven optimization to adaptively re-weight environmental, economic, and social features. A benchmark dataset of 17.8 million records from 64 countries and 743 cities (2016–2024) is compiled from the Open Travel Data repository in github (OPTD) for training and validation. SE-TFF introduces (i) a multi-channel SE module for fine-grained feature selection under heterogeneous conditions, (ii) a Top-K attention filter to preserve salient context in highly sparse matrices, and (iii) a Double-DQN layer that dynamically balances prediction objectives. Experimental results show SE-TFF attains 56.5% MAE and 65.6% RMSE reductions over the best baseline (ARIMAX) at 20% sparsity, with 0.92 × 10³ average MAE across multi-task outputs. SHAP analysis ranks climate anomalies, tourism revenue, and employment as dominant predictors. These gains demonstrate SE-TFF’s ability to deliver real-time, interpretable forecasts for data-limited destinations. Future work will incorporate real-time social media signals and larger multimodal datasets to enhance generalizability. Full article

Primary-level social risk prevention and control is a complex, systemic endeavor that requires close cooperation among various local government departments. Within this context, addressing bureaucratic segmentation and strengthening interdepartmental collaboration are critical issues in primary-level social risk governance. This study uses social network analysis and the exponential random graph model to examine the collaborative network structure and driving mechanisms among government departments engaged in risk prevention, with J City as a network study. The findings reveal that (1) while a collaborative governance framework exists, the network has low overall density, strong localized clustering, and a clear core-periphery structure, indicating the need for improved coordination and more refined collaborative mechanisms; (2) the formation of the risk prevention network is influenced by both endogenous structural factors and exogenous actor attributes. Endogenously, reciprocity and transitivity play significant roles in tie formation; exogenously, departments with similar resource mobilization capacities are more likely to collaborate, while those with strong communication, digital technology, and resource mobilization capabilities are more likely to initiate collaborations, and those with high communication capacity are more likely to accept collaborative offers. This study offers insights into the dynamics and formation mechanisms of departmental collaborative networks in primary-level social risk governance. Full article

Traditional collision risk warning methods primarily focus on vehicle-to-vehicle collisions, neglecting conflicts between vehicles and vulnerable road users (VRUs) such as pedestrians, while the difficulty in predicting pedestrian trajectories further limits the accuracy of collision warnings. To address this problem, this study proposes a vehicle-to-everything-based (V2X) multi-fusion vehicle–pedestrian collision warning method, aiming to enhance the traffic safety protection for VRUs. First, Unmanned Aerial Vehicle aerial imagery combined with the YOLOv7 and DeepSort algorithms is utilized to achieve target detection and tracking at unsignalized intersections, thereby constructing a vehicle–pedestrian interaction trajectory dataset. Subsequently, key foundational modules for collision warning are developed, including the vehicle trajectory module, the pedestrian trajectory module, and the risk detection module. The vehicle trajectory module is based on a kinematic model, while the pedestrian trajectory module adopts an Attention-based Social GAN (AS-GAN) model that integrates a generative adversarial network with a soft attention mechanism, enhancing prediction accuracy through a dual-discriminator strategy involving adversarial loss and displacement loss. The risk detection module applies an elliptical buffer zone algorithm to perform dynamic spatial collision determination. Finally, a collision warning framework based on the Monte Carlo (MC) method is developed. Multiple sampled pedestrian trajectories are generated by applying Gaussian perturbations to the predicted mean trajectory and combined with vehicle trajectories and collision determination results to identify potential collision targets. Furthermore, the driver perception–braking time (TTM) is incorporated to estimate the joint collision probability and assist in warning decision-making. Simulation results show that the proposed warning method achieves an accuracy of 94.5% at unsignalized intersections, outperforming traditional Time-to-Collision (TTC) and braking distance models, and effectively reducing missed and false warnings, thereby improving pedestrian traffic safety at unsignalized intersections. Full article

The Buades Gallery (1973–2003) was not merely a commercial space in Madrid. In the history of art in Spain, it served as a professional and political node for Spanish conceptualism, an art form which, due to its idiosyncrasies, required its own channels of distribution. This article seeks to examine the trajectory of Mercedes Buades in alignment with this movement, re-evaluating her role from a feminist perspective and highlighting the importance of certain agents who have traditionally been invisibilised. To this end, a theoretical approach is adopted, following the sociology of art and the social history of art, paying particular attention to the contributions of Enrico Castelnuovo, Pierre Bourdieu and Núria Peist. These frameworks enable an analysis of the role of the gallerist as a structuring agent within the artistic field, capable of generating symbolic capital and establishing dynamics of production, circulation and consumption in the context of post-Franco Spain, a country that lacked a consolidated museum infrastructure at the time. Even so, Mercedes Buades established a model of gallery practice that, beyond its commercial dimension, contributed decisively to the symbolic configuration of contemporary art in Spain and formed part of a network of artistic visibility that promoted experimental art. Full article