Search Results (10,507)

Health and spa tourism is a rapidly growing sector that merges traditional healing with modern innovations to meet increasingly diverse client needs. Understanding professionals’ perspectives is crucial for developing sustainable strategies that enhance service quality, organizational performance, and long-term business viability. Drawing on qualitative narrative analysis and thematic network analysis, this study explores the key factors that spa tourism professionals in Băile Felix—the largest spa resort in Romania—associate with business success, competitive differentiation, and sustainable development. Data were collected through semi-structured interviews with 41 entrepreneurs and managers who provided detailed narratives on their strategic goals and market positioning. Rather than measuring customer psychological constructs directly, this study captures professionals’ expert attributions regarding service quality, staff professionalism, infrastructure investment, and economic objectives, and interprets these as managerial perceptions grounded in operational experience. Five research propositions guided the interpretive analysis: (P1) professionals narratively associate service quality and treatment diversity with perceived business performance and guest retention signals; (P2) staff professionalism and attitude are attributed as the primary drivers of competitive differentiation; (P3) infrastructure investment and innovation are framed as prerequisites for sustaining market positioning; (P4) the identified themes form a structurally interconnected network with key bridging nodes; and (P5) professional narratives reveal tensions between short-term economic objectives and longer-term commitments to service quality and sustainability. Thematic network analysis identified four central constructs—service quality and treatment diversity, staff professionalism and attitude, innovation and infrastructure investment, and economic and development objectives—and mapped 16 interconnected sub-themes, with modularity analysis (Q = 0.42) confirming a moderately cohesive structure. Sustainable innovation was operationalized across environmental efficiency, social value, and economic resilience dimensions, and found to be embedded systemically across multiple thematic clusters rather than treated as an isolated practice. The originality of this study lies in integrating narrative and thematic network analysis to reveal how these constructs co-evolve within a sustainability-oriented system, offering a novel methodological lens for spa tourism research in post-transitional Central and Eastern European contexts. Findings provide actionable insights for spa managers, policymakers, and investors seeking to balance modernization with tradition in resource-constrained destinations. Full article

This article presents findings from a pilot that tested a novel “client network audit” approach, designed to enhance supervision by mapping social networks through structured input from frontline practitioners. Adapting the group audit methodology for collecting network information that is used extensively in gang violence interventions, this project measured cognitive network data from two probation officers and a community-based partner to examine areas of consensus and divergence in perceptions of influential relationships in a client’s life. A focus group conducted with participants after the study revealed several themes, including the utility of identifying hidden risks and opportunities for intervention and enhancing multi-agency coordination. This exploratory study finds that, while there are key areas of overlap in these perceptions, there are substantial gaps, indicating that individuals possess unique information about social relationships that is unknown to other respondents. As jurisdictions seek innovative strategies to improve interventions for youth entrenched in high-harm networks, this model offers a potentially promising pathway. Full article

Pediatric pain remains a highly prevalent and under-addressed health problem worldwide, largely due to educational gaps, limited humanization of care, and insufficient integration of digital and pedagogical innovations in higher education, and the purpose of this study is to describe and implement an international, higher education–driven model to improve training in humanized pediatric pain management. This multicenter mixed-methods study involves 15 universities from Europe, Africa, and Latin America and includes the development and cross-cultural validation of the HUPEDCARE-Q questionnaire to identify knowledge gaps, the design of an open-access, multilingual digital learning platform (PEDCARE) that integrates learning management and social networking functions, and the implementation of capacity-building workshops based on a training-the-trainers model for students, educators, health professionals, and families. The expected outcomes of the project include the establishment of a standardized instrument for assessing educational needs, the creation of a scalable digital educational environment, and the feasibility of international academic collaboration to strengthen competencies in pediatric pain care. The study suggests that higher education, combined with digital transformation and culturally sensitive approaches, may support the humanization of pediatric pain management and address educational and health inequities, although further research is needed to confirm these potential impacts. Full article

Children are some of the most vulnerable members of society who must be protected at all costs. Zimbabwe has a long history of disjointed formal and indigenous social protection systems, which have resulted in the exclusion of many children, leading to high levels of child abuse, neglect, exploitation and violence. In policy and practice, there is a strong bias towards the ineffective statist formal system, yet the indigenous social protection system is the mainstay for the protection of most children. The study aimed to explore how asset-based community development can be used as a strategy to integrate the fragmented formal and indigenous social protection systems for sustainable child protection. An explanatory sequential mixed-methods research design was employed, collecting both quantitative and qualitative data from 76 participants. The study findings indicate that asset-based community development by positioning the indigenous social protection system at the centre of the social protection framework provides a blueprint for a community-led and integrated social protection system, which can translate into effective child protection. This system, which utilises a wider network of community and external resources, can counteract the limits of fragmented social protection and sustainably promote child protection among impoverished households in Zimbabwe and similar contexts. The recommendation is that asset-based community development should be promoted as a strategy towards integrated social protection and sustainable child protection. Full article

Pedestrian trajectory prediction is a fundamental task in autonomous driving and mobile robotics, where accurate forecasting requires modeling of both social interactions and scene-related constraints. However, existing methods typically rely on a fixed interaction modeling strategy, which may be insufficient under heterogeneous crowd densities. To address this limitation, we propose DAGH-Net, a density-adaptive gated hybrid network for pedestrian trajectory prediction. Built upon an SR-LSTM (State Refinement for LSTM) backbone, the proposed framework integrates two complementary reasoning pathways: a data-driven social interaction branch and a hybrid knowledge graph branch that encodes structured relational priors among pedestrians, obstacles, and walkable regions. A local-density-conditioned gating mechanism is further introduced to adaptively fuse these features according to the surrounding crowd condition of each pedestrian. This design helps suppress redundant interaction cues in sparse settings while strengthening socially compliant and scene-consistent reasoning in dense or conflict-prone environments. Experimental results on the ETH (Eidgenössische Technische Hochschule Zürich) and UCY (University of Cyprus) benchmarks, evaluated using Mean Average Displacement (MAD) and Final Average Displacement (FAD), show that DAGH-Net improves the average MAD and FAD by 1.6% and 4.2%, respectively, compared with SR-LSTM. Ablation studies further support the complementary contributions of the hybrid knowledge graph and the density-adaptive gating mechanism. We also discuss the limitations of the current density formulation and benchmark scale, which suggest several directions for future improvement. Full article

Existing Environmental, Social, and Governance (ESG) monitoring approaches rely primarily on static reports and dashboard-based interfaces, limiting real-time analysis and interactive exploration of sustainability data in complex built environments. In addition, current digital twin systems often lack integration with IoT-based sensing or depend on cloud-based rendering infrastructures, increasing deployment complexity and restricting accessibility. This study proposes a lightweight Web3D-based digital twin framework for real-time ESG monitoring in smart buildings. The system integrates an independently developed IoT sensor network with a browser-native 3D visualization platform, enabling real-time monitoring of ESG indicators—including electricity consumption—without requiring proprietary software or dedicated rendering hardware. ESG indicators are derived using a rule-based classification aligned with the WELL Building Standard v1. The framework was validated through a 12-month real-world deployment involving 60 IoT sensors. Results demonstrate stable performance, achieving 66 FPS rendering, 78 ms system latency, and 98% sensor data consistency based on cross-sensor agreement. The system also enabled timely detection of environmental anomalies, leading to measurable improvements in air quality and lighting conditions. Unlike prior digital twin systems, the proposed framework delivers a fully browser-native, lightweight architecture that integrates real-time IoT sensing, adaptive Web3D visualization, and structured ESG monitoring within a single deployable system. This approach provides a practical solution with potential for broader deployment in real-time sustainability monitoring for smart buildings. Full article

Trust decay in social media is a serious threat to user experience and platform ecology. To solve this problem, this paper focuses on triadic closure in the infrastructure of social networks and explores its mechanism in trust decay prevention. Based on the systematic comparison of the ER random graph, the BA scale-free network, a forest fire model, and complete graph approaches, two core metrics, the trust decay risk index and trust resilience index, are proposed in this paper. Combined with structural indices such as the clustering coefficient, the average path length, and the triangular closure number and its growth rate, the quantitative relationship between network structure evolution and trust decay risk is established. It is found that the forest fire model exhibits optimal trust resilience in structure due to its power-law growth characteristics of high clustering, short path length and triangular closure; the dynamic mechanism of trust decay under different network growth modes is significantly different. The validity of the theoretical framework is further supported by the verification of Sina Weibo attention relationship network data. The analysis framework of network growth evolution based on fusion triangle closure and the risk and resilience indicators defined in this paper provides a computable theoretical tool for understanding and predicting trust evolution in social media from the perspective of network structure. Full article

Background/Objectives: Single-parent families represent a growing and particularly vulnerable family structure within community and primary health care contexts. These families often experience cumulative burdens related to caregiving overload, socioeconomic constraints, social isolation, and fragmented support networks, which directly affect health and well-being. This integrative review aimed to synthesize and critically analyse direct and conceptually transferable evidence relevant to Family Health Nursing interventions supporting single-parent families in community and primary health care contexts, identify existing knowledge gaps, and inform the development of a relational empowerment model. Methods: An integrative literature review was conducted following PRISMA 2020 guidelines. A comprehensive search was performed across three electronic databases (PubMed, CINAHL, and Scopus) covering publications from 2020 to 2025. Inclusion criteria comprised peer-reviewed empirical studies and reviews addressing nursing or health interventions relevant to single-parent families in community or primary health care contexts. Data were extracted and synthesized thematically, with attention to theoretical frameworks, intervention characteristics, and reported outcomes. Results: Twenty-nine studies met the inclusion criteria. The synthesis revealed four main thematic domains: (1) caregiving burden and psychosocial vulnerability, (2) access to and coordination of community-based resources, (3) nurse–family relational processes, and (4) empowerment-oriented nursing interventions. Theoretical underpinnings frequently included family systems perspectives, the Calgary Family Assessment and Intervention Models, and empowerment-oriented frameworks. Conclusions: Nursing interventions for single-parent families in community health settings should prioritise relational empowerment approaches that acknowledge family diversity, contextual vulnerability, and dynamic caregiving demands. The proposed relational empowerment model offers a practice-informed framework to guide Family Health Nursing interventions, education, and policy development, supporting more responsive and equitable care for single-parent families. Full article

The widespread dissemination of fake news across different domains exerts a negative impact on social order. Current fake news detection models face two major challenges. First, the issue of domain shift constrains the generalization capability of models in cross-domain scenarios. Second, general neural networks struggle to extract features between distant words in text, resulting in poor quality of original features and adversely affecting the final detection outcomes. In response to the aforementioned issues, this paper proposes a multi-domain fake news detection framework based on multi-view hybrid attention enhancement. Firstly, superior original feature extraction is achieved through Recurrent Convolutional Neural Networks (RCNN) and Bidirectional Long Short-Term Memory (BiLSTM). Secondly, a hybrid attention mechanism is established between features and domains across multiple views—including news semantics, sentiment, and style—thereby forming domain-specific memory. This enables the model to achieve more precise classification of news within specific, subdivided domains. Finally, experiments conducted on the public dataset Weibo21 demonstrate that the proposed method attains F1 scores of 93.26% and 85.31% on Chinese and English datasets. Full article

This study investigates how a network’s structural and relational capabilities condition the influence of social CRM capabilities on innovation novelty, highlighting a deeper network paradox. Drawing on survey evidence from social enterprises, the analyses indicate that social CRM capabilities meaningfully contribute to the generation of novel innovations. Yet the two network capabilities move in opposite directions: structural capability amplifies the innovative gains derived from social CRM, whereas relational capability tends to dilute them. These divergent effects reflect the simultaneous pull of structural-hole and network-closure mechanisms within the same organizational setting. The results suggest that organizations aiming to translate social CRM investments into innovation may benefit more from structurally expansive network positions than from tightly embedded relational ties. Future work could employ longitudinal and cross-institutional designs to strengthen causal insight and broaden the study’s applicability. Full article

Influence Maximization (IM) is a fundamental problem in social network analysis that aims to identify a set of k seed nodes that maximizes influence spread under a given propagation model. Despite its importance in applications such as viral marketing and epidemic control, the IM problem is NP-hard, making exact solutions computationally infeasible for large-scale networks. Existing approximation methods typically rely either on static centrality heuristics, which often ignore global network structure, or on metaheuristic algorithms, which may suffer from slow convergence due to random initialization. This paper proposes a novel approach, termed Advanced Centrality-Driven Discrete Particle Swarm Optimization (DPSO), which integrates a weighted hybrid centrality score combining Degree, PageRank, and Betweenness centrality to guide the stochastic search process. In addition, a systematic grid search methodology is employed to determine the optimal weight configuration of the hybrid score. Experiments conducted on three real-world datasets (Twitter, ego-Facebook, and ca-HepTh) demonstrate that the optimal seeding strategy is strongly dependent on network topology. The results show that dense social networks favor popularity-based metrics such as Degree and PageRank, whereas sparse collaboration networks benefit significantly from bridge-oriented metrics such as Betweenness centrality. Overall, the proposed method achieves consistent improvements in influence spread across different network types, with the largest gains (up to 70%) observed in sparse network settings. Full article

Event detection is crucial for disaster response, public safety, and trend analysis, enabling real-time identification of critical events. Social media platforms provide a vast content source, offering timely and diverse event coverage compared to traditional news reports. However, challenges arise due to the informal and noisy nature of the text, along with the limited availability of ground truth data for training models. This study introduces SOCIAL (Social Media Event Classification using Integrated Artificial Learning and Natural Language Processing), a mathematically grounded framework for real-time social media event detection. SOCIAL integrates a formal representation of social media text with a customized CNN–LSTM architecture, combining convolutional operations for local feature extraction with sequential modeling to capture temporal dependencies, thereby enhancing classification accuracy. Generative AI is employed to create synthetic event-related samples, addressing data scarcity and ensuring a balanced dataset, while the design incorporates quantitative principles to guide embedding selection and model optimization. This study systematically evaluates six experimental configurations with TF-IDF and Word2Vec embeddings. The TF-IDF-based CNN–LSTM model achieved top performance with 98.59% accuracy, 98.13% precision, 99.06% recall, and 0.9719 MCC. Additionally, the $F_{0.5}$, $F_1$, and $F_2$ scores were 98.31%, 98.59%, and 98.87%, respectively, confirming the model’s strong predictive capabilities. TF-IDF integration enhanced event-specific term recognition, reducing misclassifications and improving reliability. These results demonstrate that SOCIAL is not only a fast, accurate, and scalable tool for crisis event detection, but also a formally principled framework for modeling and analyzing social media signals. Full article

Self-harm represents a significant mental health concern during adolescence and is associated with various psychological risk factors. The present exploratory probe examines the mediating role of perceived social support in the relationship between self-esteem and self-harm among adolescents. The sample consisted of 155 adolescents aged 13 to 18 years (M = 16.35, SD = 1.73). Self-esteem was measured using the Rosenberg Self-Esteem Scale (RSES), self-harm was assessed using a modified version of the Self-Harm Inventory (SHI), and perceived social support was measured using the Multidimensional Scale of Perceived Social Support (MSPSS). Data were analyzed using correlation analysis, linear regression, and mediation. More than half of the participants (53.5%) reported repeated engagement in self-harming behavior. Self-esteem was significantly negatively associated with self-harm (ρ = −0.508, p < 0.001) and explained approximately 22% of the variance in self-harm. Mediation analysis indicated that perceived social support partially mediated the relationship between self-esteem and self-harm. Lower self-esteem was associated with lower perceived social support, which in turn predicted higher levels of self-harm. The indirect effect was significant (B = −0.31, 95% BootCI (−0.63, −0.09)). These findings highlight the protective role of perceived social support and suggest that strengthening adolescents’ self-esteem and social support networks may contribute to the prevention of self-harm. Full article

Understanding the sustainable management of the complex adaptive tourism systems requires an integrated research approach that combines environmental processes with stakeholder behaviors. Agent-based modelling (ABM) has emerged as a pivotal tool for decoding the resilience, adaptability, and sustainability of tourism systems. However, the current application landscape, methodological limitations, and future research directions of ABM remain insufficiently synthesized, thereby constraining its full potential in advancing sustainable tourism management. This study examines 137 publications on the application of ABM in tourism research between 1989 and 2025, aiming to clarify the application characteristics and evolutionary trajectories. The results show the following: (1) ABM applications in tourism have become increasingly comprehensive and refined, evolving from simplistic simulations based on simplex agents and static spatial representations toward integrated models incorporating heterogeneous agents, fine-grained spatial environments, and multiple contextual factors. (2) Behavioral modeling has progressed from basic human–space interactions to complex, co-evolutionary dynamics among human, social, and ecological systems. (3) ABM applications exhibit context specificity: climate-sensitive scenarios emphasize resource dynamics and adaptation strategies; disaster-prone contexts focus on multi-agent responses and emergency management; conservation-oriented systems support sustainable policy development; and management-centric scenarios prioritize technological innovation and macro-level regulation. Future research should prioritize refining agent interactions through dynamic social network integration, incorporating cross-scale and long-distance system linkages, and strengthening the connection between theoretical modeling and real-world applications. This study would provide a comprehensive knowledge base for advancing the innovative application of ABM in sustainable tourism research and contribute to strengthening resilience, adaptive governance, and long-term sustainability within complex tourism systems. Full article

Social Network Analysis (SNA) has been widely applied to risk identification research. However, two key constraints, namely rating bias and the subjectivity of threshold selection, undermine the reliability and reproducibility of analytical results. To address this di-lemma, this study aims to construct a standardized and robust analytical framework for SNA-based risk identification. The core research objectives are as follows: elucidate the differential impact mechanism of threshold variation on the macro-topological structure and micro-level node ranking of risk networks, examine the cross-threshold robustness of core risk node rankings, and delimit the effective threshold range for stable risk identification. Accordingly, to fulfill the above objectives, this study proposes a multi-threshold robustness inspection method based on individual rating patterns, and conducts systematic empirical analysis with industrial projects in the post-support period of reservoir resettlement as research cases. The results indicate that threshold variation exerts marked systematic effects on the macro-topological structure of risk networks, whereas the relative rankings of core risk nodes remain robust. The effective threshold range for risk identification in such projects is α ∈ [0.1,0.3]. This study provides a repeatable quality control framework for SNA-based risk identification, with favorable cross-domain transferability. Full article