Search Results (1,237)

The integration of Internet of Things (IoT) systems with social networking concepts has opened new business and social opportunities, particularly by allowing smart objects to autonomously establish social relationships with each other and exchange information. However, these relations must be properly quantified and integrated with trust in order to proliferate the provisioning of IoT composite services. Therefore, this proposed work focuses on quantitatively computing social strength and trust among smart objects in IoT for the purpose of aiding efficient service composition with reasonable accuracy. In particular, we propose a trust-augmented social strength (TASS) management protocol that can cope with the heterogeneity of IoT and demonstrate high scalability and resiliency against various malicious attacks. Afterward, we show how the TASS measurements can be applied to service planning in IoT service composition. Based on the experimental results, we conclude that the proposed protocol is, in fact, capable of exhibiting the above-mentioned characteristics in real-world settings. Full article

Background: International migration is a growing global phenomenon involving diverse groups, such as labor migrants, international marriage migrants, refugees, and international students. International migrants face unique mental health challenges influenced by adversities such as social isolation and limited access to mental health services. This study employs bibliometric methods to systematically analyze the global body of literature on international migrants’ mental health. Methods: The literature on the mental health of international migrants published until October 2024 was searched using the Web of Science database. The search terms included (‘International migrants’ OR ‘migrant workers’ OR ‘international students’ OR ‘refugees’ OR ‘asylum seekers’ OR ‘smuggled migrants’) AND ‘mental health’. VOSviewer was used to conduct bibliometric analysis, focusing on co-authorship patterns, keyword co-occurrence, and citation networks. Results: Over the past four decades, research on the mental health of international migrants has grown substantially, with major migration destinations such as the United States, Europe, and Australia playing prominent roles in this field. ‘Post-traumatic stress disorder (PTSD)’ was the most frequent keyword in publications, with strong links to ‘trauma’ and ‘depression’. In recent years, with the impact of global socioenvironmental changes and emergencies such as the COVID-19 pandemic, the research focus has gradually shifted towards social support, service accessibility, and cultural adaptation. Conclusions: International migration is a far-reaching global phenomenon, and addressing the mental health of migrant populations is essential for advancing public health, social cohesion, and sustainable development. This study provides the first bibliometric overview of research in this domain, mapping its thematic evolution and collaborative structure. The findings offer valuable insights into the field’s development and may support future interdisciplinary collaboration and the formulation of culturally informed, evidence-based approaches in migrant mental health. Full article

This study analyses the conditions under which co-opetition improves the supply of healthcare services in Benin. Using non-centralised administrative data from a sample of public and private health centres, we apply network theory and negative binomial regression to assess the extent to which competition affects collaboration between public and private healthcare providers. We found that competition reduces the degree of collaboration between private and public health providers. However, the COVID-19 pandemic significantly mitigated this effect, highlighting the potential for competition within the healthcare system without compromising social welfare. Notwithstanding that, we show that these benefits are not sustained over time. These findings have policy implications for the sustainability of health system financing in Africa, particularly by promoting sustainable financial mechanisms for the private sector and more inclusive governance structures. Full article

Public service innovation research often focuses on the private or general public sectors, leaving the distinct challenges government entities face unexplored. An empirical study was carried out to bridge this gap using survey results from the United Arab Emirates (UAE) government entities. This study built on that research by further analyzing the relationships among these challenges through a social network approach, visualizing and analyzing the connections between them by utilizing betweenness centrality and eigenvector centrality as key metrics. Based on this analysis, the challenges were classified into different categories; 8 out of 22 challenges were identified as critical due to their high values in both metrics. Addressing these critical challenges is expected to create a cascading impact, helping to resolve many others. Targeted strategies are proposed, and leveraging open innovation is highlighted as an effective and versatile solution to address and mitigate these challenges. This study is one of the few to adopt a social network analysis perspective to visualize and analyze the relationships among challenges, enabling the identification of critical ones. This research offers novel and valuable insights that could assist decision-makers in UAE government entities and countries with similar contexts with actionable strategies to advance public service innovation. Full article

In higher education, the employability and entrepreneurship services play an essential role in the labor market insertion of graduates. The management of public relations promotes institutional projection, the creation of networks with companies, and the dissemination of initiatives to strengthen professional skills. This research analyzes how the communication strategies of Andalusian public universities improve employability and encourage entrepreneurship. A methodological triangulation is used: literature review, analysis of social networks with Fanpage Karma, and study of corporate websites. The results show a focus on counseling, training, workshops, job fairs, and networking events, strengthening the brand of universities and their commitment to the professional development of students. Full article

Intelligent transport systems are revolutionising all aspects of modern life, increasing the efficiency of commerce, modern living, and international travel. Intelligent transport systems are systems of systems comprised of cyber, physical, and social nodes. They represent unique opportunities but also have potential threats to system operation and correctness. The emergent behaviour in Complex Cyber–Physical–Social Systems (C-CPSSs), caused by events such as cyber-attacks and network outages, have the potential to have devastating effects to critical services across society. It is therefore imperative that the risk of cascading failure is minimised through the fortifying of these systems of systems to achieve resilient mission assurance. This work designs and implements a programmatic model to validate the value of cascading failure simulation and analysis, which is then tested against a C-CPSS intelligent transport system scenario. Results from the model and its implementations highlight the value in identifying both critical nodes and percolation of consequences during a cyber failure, in addition to the importance of including social nodes in models for accurate simulation results. Understanding the relationships between cyber, physical, and social nodes is key to understanding systems’ failures that occur because of or that involve cyber systems, in order to achieve cyber and system resilience. Full article

People with severe mental disorders (SMD) face long-term functional impairments requiring integrated, community-based, recovery-oriented care. Italy provides two main housing models for people with SMD: private accommodation (PA) and supported accommodation (SA). This exploratory study investigated differences in recovery outcomes across these settings using the Mental Health Recovery Star (MHRS). A six-month longitudinal study was conducted within the South Verona Community Mental Health Service. Nineteen trained mental health professionals assessed 25 people with SMD (14 in PA, 11 in SA) at baseline (BL) and follow-up (FU) using standardized tools for recovery (MHRS), functioning, psychopathology, functional autonomy, and needs. Group comparisons and within-group changes were analyzed using paired and independent t-tests. At BL, people with SMD in PA showed better functioning (p = 0.040) and fewer needs than those in SA (p = 0.008). Recovery goals differed, with people with SMD in PA focusing on health and networks, while people with SMD in SA emphasized functioning. At FU, people with SMD in PA improved across all MHRS domains (p < 0.001), with significant reductions in symptom severity and unmet needs. People with SMD in SA showed targeted improvements in functioning, autonomy, and MHRS social networks (p < 0.001), with increases in met needs but non-significant changes in unmet needs. When comparing PA and SA at FU, the differences were relatively modest. Recovery is achievable in both housing settings, although outcomes differ. People with SMD in PA experienced broader improvements, while people with SMD in SA progressed in their prioritized areas, likely reflecting more complex initial needs. These findings underscore the value of aligning recovery-oriented care with the specific needs and contexts of different residential settings. Further research is needed to confirm and expand these results. Full article

This study investigates how demographic characteristics, severity of intimate partner violence (IPV), social support, gender equality awareness, and demographic factors (e.g., living with children at home) influence help-seeking behaviors among 2527 IPV-experienced women in Beijing, Shanghai, Guangzhou, and Shenzhen. Drawing on the help-seeking model and conservation of resource theory, the study distinguishes between formal (e.g., police, legal aid) and informal (e.g., family, friends) help-seeking. Logistic regression results reveal that greater violence severity, stronger perceived social support, and higher gender equality awareness significantly increase both formal and informal help-seeking. Notably, living with children is associated with a higher likelihood of seeking formal help, possibly due to increased concerns for children’s safety and the desire to change the abusive environment. While help-seeking behavior is on the rise overall, particularly among women with strong support networks and progressive gender attitudes, structural and cultural barriers remain. The findings underscore the need to improve public education, reduce stigma, and enhance accessibility of support systems. This study contributes to the growing literature on IPV in China and provides evidence for refining policy interventions and service delivery. Full article

Personality is a fundamental psychological trait that exerts a long-term influence on human behavior patterns and social interactions. Automatic personality recognition (APR) has exhibited increasing importance across various domains, including Human–Robot Interaction (HRI), personalized services, and psychological assessments. In this study, we propose a multimodal personality recognition model that classifies the Big Five personality traits by extracting features from three heterogeneous sources: audio processed using Wav2Vec2, video represented as Skeleton Landmark time series, and text encoded through Bidirectional Encoder Representations from Transformers (BERT) and Doc2Vec embeddings. Each modality is handled through an independent Self-Attention block that highlights salient temporal information, and these representations are then summarized and integrated using a late fusion approach to effectively reflect both the inter-modal complementarity and cross-modal interactions. Compared to traditional recurrent neural network (RNN)-based multimodal models and unimodal classifiers, the proposed model achieves an improvement of up to 12 percent in the F1-score. It also maintains a high prediction accuracy and robustness under limited input conditions. Furthermore, a visualization based on t-distributed Stochastic Neighbor Embedding (t-SNE) demonstrates clear distributional separation across the personality classes, enhancing the interpretability of the model and providing insights into the structural characteristics of its latent representations. To support real-time deployment, a lightweight thread-based processing architecture is implemented, ensuring computational efficiency. By leveraging deep learning-based feature extraction and the Self-Attention mechanism, we present a novel personality recognition framework that balances performance with interpretability. The proposed approach establishes a strong foundation for practical applications in HRI, counseling, education, and other interactive systems that require personalized adaptation. Full article

In social networks like community Question-Answering (cQA) services, members interact with each other by asking and answering each other’s questions. This way they find counsel and solutions to very specific real-life situations. Thus, it is safe to say that community fellows log into this kind of social network with the goal of satisfying information needs that cannot be readily resolved via traditional web searches. And in order to expedite this process, these platforms also allow registered, and many times unregistered, internauts to browse their archives. As a means of encouraging fruitful interactions, these websites need to be efficient when displaying contextualized/personalized material and when connecting unresolved questions to people willing to help. Here, demographic factors (i.e., gender) together with frontier deep neural networks have proved to be instrumental in adequately overcoming these challenges. In fact, current approaches have demonstrated that it is perfectly plausible to achieve high gender classification rates by inspecting profile images or textual interactions. This work advances this body of knowledge by leveraging lexicalized dependency paths to control the level of abstraction across texts. Our qualitative results suggest that cost-efficient approaches exploit distilled frontier deep architectures (i.e., DistillRoBERTa) and coarse-grained semantic information embodied in the first three levels of the respective dependency tree. Our outcomes also indicate that relative/prepositional clauses conveying geographical locations, relationships, and finance yield a marginal contribution when they show up deep in dependency trees. Full article

This paper presents an integrated Smart City platform that combines digital twin technology, advanced machine learning, and a private blockchain network to enhance data-driven decision making and operational efficiency in both public enterprises and small and medium-sized enterprises (SMEs). The proposed cloud-based business intelligence model automates Extract, Transform, Load (ETL) processes, enables real-time analytics, and secures data integrity and transparency through blockchain-enabled audit trails. By implementing the proposed solution, Smart City and public service providers can significantly improve operational efficiency, including a 15% reduction in costs and a 12% decrease in fuel consumption for waste management, as well as increased citizen engagement and transparency in Smart City governance. The digital twin component facilitated scenario simulations and proactive resource management, while the participatory governance module empowered citizens through transparent, immutable records of proposals and voting. This study also discusses technical, organizational, and regulatory challenges, such as data integration, scalability, and privacy compliance. The results indicate that the proposed approach offers a scalable and sustainable model for Smart City transformation, fostering citizen trust, regulatory compliance, and measurable environmental and social benefits. Full article

This study uses national data to contribute to ongoing discussions regarding social media’s role in influencing investors in the digital economy. Grounded in social network theory, social media engagement was examined for its influence on FinTech usage, specifically cryptocurrency investments, mobile trading applications, and financial podcasts. Results showed a significant relationship between social media use for investment decisions and the embrace of FinTech. Individuals who actively engage with social media for this purpose had higher odds of investing in cryptocurrency and a higher likelihood of using both mobile trading applications and financial podcasts. However, these results were not consistent across all platforms amongst social media users. Our findings show that social media platforms enable peer influence and recommendations through networks that shape financial decisions and behaviors. FinTech firms can strategically harness social ties and the inherent information flows within social networks to broaden their reach and impact in the financial services landscape. Full article

With the advent of Autonomous Vehicles (AV) technology, extensive research around the design of on-demand mobility systems powered by such vehicles is performed. An important part of these studies consists in the evaluation of the economic impact of such systems for involved stakeholders. In this work, a cost–benefit analysis (CBA) is applied to the introduction of AV services in Paris-Saclay, an intercommunity, south of Paris, simulated through MATSim, an agent-based model capable of capturing complex travel behaviors and dynamic traffic interactions. AVs would be implemented as a feeder service, first- and last-mile service to public transit, allowing intermodal trips for travelers. The system is designed to target the challenges of public transport accessibility in periurban areas and high private car use, which the AV feeder service is designed to mitigate. To our knowledge, this study is one of the first CBA analyses of an intermodal AV system relying on an agent-based simulation. The introduction of AV in a periurban environment would generate more pressure on the road network (0.8% to 1.7% increase in VKT for all modes, and significant congestion around train stations) but would improve traveler utilities. The utility gains from the new AV users benefiting from a more comfortable mode offsets the longer travel times from private car users. A Stop-Based routing service generates less congestion than a Door-to-Door routing service, but the access/egress time counterbalances this gain. Finally, in a periurban environment where on-demand AV feeder service would be added to reduce the access and egress cost of public transit, the social impact would be nuanced for travelers (over 99% of gains captured by the 10% of most benefiting agents), but externality would increase. This would benefit some travelers but would also involve additional congestion. In that case, a Stop-Based routing on a constrained network (e.g., existing bus network) significantly improves economic viability and reduces infrastructure costs and would be less impacting than a Door-to-Door service. Full article

As a catalyst for sustainable urbanization, urban vibrancy drives human interactions, economic agglomeration, and resilient development through its spatial manifestation of diverse activities. While previous studies have emphasized the connection between built environment features—especially street network centrality—and urban vibrancy, the broader mechanisms through which the full spectrum of street configuration dimensions shape vibrancy patterns remain insufficiently examined. To address this gap, this study applies a GeoXAI approach that synergizes random forest modeling and GeoShapley interpretation to reveal the influence of street configuration on urban vibrancy. Leveraging multi-source geospatial data from Xiamen Island, China, we operationalize urban vibrancy through a composite index derived from three-dimensional proxies: life service review density, social media check-in intensity, and mobile device user concentration. Street configuration is quantified through a tripartite measurement system encompassing network centrality, detour ratio, and shape index. Our findings indicate that (1) street network centrality and shape index, as well as their interactions with location, emerge as the dominant influencing factors; (2) The relationships between street configuration and urban vibrancy are predominantly nonlinear, exhibiting clear threshold effects; (3) The impact of street configuration is spatially heterogeneous, as evidenced by geographically varying coefficients. The findings can enlighten urban planning and design by providing a basis for the development of nuanced criteria and context-sensitive interventions to foster vibrant urban environments. Full article

As sustainability becomes increasingly central to organizational strategy, social economy organizations (SEOs) are rethinking their business models. This study employs stakeholder analysis using the value mapping (VM) tool developed by Short, Rana, Bocken, and Evans for the development of the VOLTO JÁ project. The objective of the VOLTO JÁ project is to operationalize a senior exchange programme between SEOs. The VM approach extends beyond conventional customer value propositions to prioritize sustainability for all stakeholders and identify key drivers of sustainable business model (SBM) innovation. The multi-stakeholder methodology comprises the following elements: (1) sequential focus groups aimed at enhancing sustainable business thinking; (2) semi-structured interviews; and (3) workshop to facilitate qualitative analysis and co-create the VM. The findings are then categorized into four value dimensions: (1) value captured—improved participant well-being, enhanced reputational capital, mitigation of social asymmetries, and affordable service experiences; (2) value lost—underused community assets; (3) value destroyed—institutional and systemic barriers to innovation; and (4) new value opportunities—knowledge sharing, service diversification, and open innovation to foster collaborative networks. The study demonstrates that the application of VM in SEOs supports SBM development by generating strategic insights, enhancing resource efficiency, and fostering the delivery of socially impactful services. Full article