Search Results (3,755)

The advanced determination of the type (foreshock–aftershock–swarm) of an ongoing seismic cluster is quite challenging; only retrospective solutions have thus far been proposed. In the period of January–March 2025, a seismic cluster, recorded between Santorini volcano and Amorgos Island, South Aegean Sea, caused considerable social concern. A rapid increase in both the seismicity rate and the earthquake magnitudes was noted until the mainshock of M_L = 5.3 on 10 February; afterwards, activity gradually diminished. Fault-plane solutions indicated SW-NE normal faulting. The epicenters moved with a mean velocity of ~0.72 km/day from SW to NE up to the mainshock area at a distance of ~25 km. Crucial questions publicly emerged during the cluster. Was it a foreshock–aftershock activity or a swarm of possibly volcanic origin? We performed real-time discrimination of the cluster type based on a daily re-evaluation of the space–time–magnitude changes and their significance relative to background seismicity using earthquake statistics and the topological metric betweenness centrality. Our findings were periodically documented during the ongoing cluster starting from the fourth cluster day (2 February 2025), at which point we determined that it was a foreshock and not a case of seismic swarm. The third day after the M_L = 5.3 mainshock, a typical aftershock decay was detected. The observed foreshock properties favored a cascade mechanism, likely facilitated by non-volcanic material softening and the likely subdiffusion processes in a dense fault network. This mechanism was possibly combined with an aseismic nucleation process if transient geodetic deformation was present. No significant aftershock expansion towards the NE was noted, possibly due to the presence of a geometrical fault barrier east of the Anydros Ridge. The 2025 activity offered an excellent opportunity to investigate deciphering the type of ongoing seismicity cluster for real-time discrimination between foreshocks, aftershocks, and swarms. Full article

Floods pose a growing threat globally, causing tragic loss of life, billions in economic damage annually, and disproportionately affecting socio-economically vulnerable populations. This paper aims to improve flood-risk communication for community leaders by exploring the application of artificial intelligence. We categorize U.S. flood information sources, review communication modalities and channels, synthesize the literature on community leaders’ roles in risk communication, and analyze existing technological tools. Our analysis reveals three key challenges: the fragmentation of flood information, information overload that impedes decision-making, and the absence of a unified communication platform to address these issues. We find that AI techniques can organize data and significantly enhance communication effectiveness, particularly when delivered through infographics and social media channels. Based on these findings, we propose FLAI (Flood Language AI), an AI-driven flood communication platform that unifies fragmented flood data sources. FLAI employs knowledge graphs to structure fragmented data sources and utilizes a retrieval-augmented generation (RAG) framework to enable large language models (LLMs) to produce contextualized narratives, including infographics, maps, and cost–benefit analyses. Beyond flood management, FLAI’s framework demonstrates how AI can transform public service data management and institutional AI readiness. By centralizing and organizing information, FLAI can significantly reduce the cognitive burden on community leaders, helping them communicate timely, actionable insights to save lives and build flood resilience. Full article

Nigerian poverty research is often fragmented and focuses on samples with minimal actionable strategies. This study aims to identify essential poverty alleviation and climate change strategies by synthesizing existing research, extracting the most critical poverty alleviation and climate change factors, and assessing strategies to combat poverty and climate change in Nigeria. We obtained, utilizing the centrality measures of social network analysis and the visualization tools of bibliometric analysis, the research hotspots extracted from 119 articles from the SCOPUS database for the period 1994–2023, compared outcomes with other countries, and analyzed their implications for eradicating poverty in Nigeria. We find that low agricultural productivity and food insecurity are some of the essential poverty-engendering factors in Nigeria, which are being intensified by climate change irregularities. Also, researchers demonstrate weak collaboration and synergy, as only 0.02% of researchers collaborated. Our findings highlight the need to direct poverty alleviation efforts to the key areas identified in this study and increase cooperation between poverty alleviation and climate researchers. Full article

Understanding the social drivers of pro-environmental behavior in urban forests and green spaces is critical for addressing sustainability challenges. Subjective norms serve as a key pathway through which social expectations influence individuals’ behavioral intentions. Despite mixed findings in the literature regarding the impact of subjective norms on individuals’ intentions, there is a research gap about the determinants of this construct. This study was conducted to explore how social expectations shape perceived subjective norms among visitors of urban forests. A theoretical model was developed with subjective norms at its center, incorporating their predictors including social identity, media influence, interpersonal influence, and institutional trust, personal norms as a mediator, and behavioral intention as the outcome variable. Using structural equation modeling, data was collected and analyzed from a sample of visitors of urban forests in Tehran, Iran. The results revealed that subjective norms play a central mediating role in linking external social factors to behavioral intention. Social identity emerged as the strongest predictor of subjective norms, followed by media and interpersonal influence, while institutional trust had no significant effect. Subjective norms significantly influenced both personal norms and intentions, and personal norms also directly predicted intention. The model explained 50.9% of the variance in subjective norms and 39.0% in behavioral intention, highlighting its relatively high explanatory power. These findings underscore the importance of social context and internalized norms in shaping sustainable behavior. Policy and managerial implications suggest that strategies should prioritize community-based identity reinforcement, media engagement, and peer influence over top-down institutional messaging. This study contributes to environmental psychology and the behavior change literature by offering an integrated, empirically validated model. It also provides practical guidance for designing interventions that target both social and moral dimensions of environmental action. Full article

This paper investigates how governance frameworks shape sustainable tourism outcomes in protected areas by comparing Tara National Park (Serbia) and Triglav National Park (Slovenia). Both parks, established in 1981 and classified under IUCN Category II, exhibit rich biodiversity and mountainous terrain but differ markedly in governance structures, institutional integration, and local community engagement. Using a qualitative, indicator-based methodology, this research evaluates ecological, economic, and social dimensions of sustainability. The findings reveal that Triglav NP demonstrates higher levels of participatory governance, tourism integration, and educational outreach, while Tara NP maintains stricter ecological protection with less inclusive management. Triglav’s zoning model, community council, and economic alignment with regional development policies contribute to stronger sustainability outcomes. Conversely, Tara NP’s centralized governance and infrastructural gaps constrain its potential despite its significant conservation value. This study highlights the importance of adaptive, inclusive governance in achieving the Sustainable Development Goals (SDGs) within protected areas. It concludes that hybrid approaches, combining legal rigor with participatory flexibility, can foster resilience and sustainability in ecologically sensitive regions. Full article

The introduction of mobile banking is essential in today’s financial sector, where technological innovation plays a critical role. To remain competitive in the current market, businesses must analyze client attitudes and perspectives, as these influence long-term demand and overall profitability. While previous studies have explored general adoption behaviors, limited research has examined how individual factors such as social influence, lifestyle compatibility, financial technology self-efficacy, and perceived usage cost affect mobile banking adoption among specific generational cohorts. This study addresses that gap by offering insights into these variables, contributing to the growing literature on mobile banking adoption, and presenting actionable recommendations for financial institutions targeting younger market segments. Using a structured questionnaire survey, data were collected from both users and non-users of mobile banking among the Gen Z population in the United States. The regression model significantly predicts mobile banking adoption, with an intercept of $-0.548$ ($p < 0.001$). Among the independent variables, perceived cost of usage has the strongest positive effect on adoption ($B=0.857$, $\beta =0.722$, $p < 0.001$), suggesting that adoption increases when mobile banking is perceived as more affordable. Social influence also has a significant positive impact ($B=0.642$, $\beta =0.643$, $p < 0.001$), indicating that peer influence is a central driver of adoption decisions. However, self-efficacy shows a significant negative relationship ($B=-0.343$, $\beta =-0.339$, $p < 0.001$), and lifestyle compatibility was found to be statistically insignificant ($p=0.615$). These findings suggest that reducing perceived costs, through lower fees, data bundling, or clearer communication about affordability, can directly enhance adoption among Gen Z consumers. Furthermore, leveraging peer influence via referral rewards, Partnerships with influencers, and in-app social features can increase user adoption. Since digital self-efficacy presents a barrier for some, banks should prioritize simplifying user interfaces and offering guided assistance, such as tutorials or chat-based support. Future research may employ longitudinal designs or analyze real-life transaction data for a more objective understanding of behavior. Additional variables like trust, perceived risk, and regulatory policies, not included in this study, should be integrated into future models to offer a more comprehensive analysis. Full article

Background: Schizophrenia affects patients’ organizational thinking, as well as their ability to identify problems. The main objective of this study was to explore healthcare consultants’ application of AI mind maps to educate patients with schizophrenia regarding their perceptions of family function, social support, quality of life, and loneliness, and to help these patients think more organizationally and understand problems more effectively. Methods: The study used a survey research design and purposive sampling method to recruit 66 participants with schizophrenia who attended the psychiatric outpatient clinic of a hospital in central Taiwan. They needed to be literate, able to respond to the topic, and over 18 years old (inclusive), and they attended individual and group health education using AI mind maps over a 3-month period during regular outpatient clinic visits. Results: The study results show that patients’ family function directly affects their quality of life (p < 0.05) and loneliness (p < 0.05), satisfaction with social support affects quality of life and loneliness directly (p < 0.05), and satisfaction with social support is a mediating factor between family function and quality of life (p < 0.05), as well as a mediating factor between family function and loneliness (p < 0.05). Conclusions: Therefore, this study confirms the need to provide holistic, integrated mental health social care support for patients with schizophrenia, showing that healthcare consultants can apply AI mind maps to empower patients with schizophrenia to think more effectively about how to mobilize their social supports. Full article

With the accelerating globalization and rapid development of science and technology, scientific collaboration has become a key driver of knowledge production, yet its patterns vary significantly across disciplines. This study aims to explore the disciplinary differences in scholars’ scientific collaboration patterns and their underlying mechanisms. Data were collected from the China National Knowledge Infrastructure (CNKI) database, covering papers from four disciplines: mathematics, mechanical engineering, philosophy, and sociology. Using social network analysis, we examined core network metrics (degree centrality, neighbor connectivity, clustering coefficient) in collaboration networks, analyzed collaboration patterns across scholars of different academic ages, and compared the academic age distribution of collaborators and network characteristics across career stages. Key findings include the following. (1) Mechanical engineering exhibits the highest and most stable clustering coefficient (mean 0.62) across all academic ages, reflecting tight team collaboration, with degree centrality increasing fastest with academic age (3.2 times higher for senior vs. beginner scholars), driven by its reliance on experimental resources and skill division. (2) Philosophy shows high degree centrality in early career stages (mean 0.38 for beginners) but a sharp decline in clustering coefficient in senior stages (from 0.42 to 0.17), indicating broad early collaboration but loose later ties due to individualized knowledge production. (3) Mathematics scholars prefer collaborating with high-centrality peers (higher neighbor connectivity, mean 0.51), while sociology shows more inclusive collaboration with dispersed partner centrality. Full article

In the digital era, understanding the intention to use generative AI is critical, as it enhances productivity, transforms workflows, and enables humans to focus on higher-value tasks. Drawing upon the unified theory of acceptance and use of technology (UTAUT) and the technology threat avoidance theory (TTAT), this research integrates perceived AI literacy into the AI acceptance–avoidance framework as a central variable. This study gathered 583 valid survey responses from China and validated its model using a dual-phase, combined method that integrates structural equation modeling and artificial neural networks. Research findings indicate that the model explains 51.6% of the variance in generative AI usage intention. Except for social influence, all variables within the extended framework significantly impact the usage intention, with perceived AI literacy being the strongest predictor (β = 0.33, p < 0.001). Additionally, perceived AI literacy mitigates the adverse effect of perceived threats on the intention to use AI. Practical implications suggest that enterprises adopt a tiered strategy, as follows: maximize perceived benefits by integrating AI skills into reward systems and providing task-automation training; minimize perceived costs through dedicated technical support and transparent risk mitigation plans; and cultivate AI literacy via progressive learning paths, advancing from data analysis to innovation. Full article

Urban blue-green infrastructure (UBGI) is a comprehensive solution that balances environmental, social, and economic development objectives and has emerged as a critical approach for fostering urban resilience and sustainable development. This paper conducts a systematic bibliometric analysis of 975 academic articles published between 2000 and 2023 in the Web of Science Core Collection, focusing specifically on the ecosystem services associated with UBGI. Employing CiteSpace visualization technology, this study elucidates the major research trends, thematic clusters, and international collaboration patterns shaping this field. The research delves into the diverse range of ecosystem services provided by blue-green infrastructure and analyzes their contributions to urban well-being. Findings indicate that regulatory services—particularly climate regulation, biodiversity enhancement, and water resource management—have become central research foci within the contexts of urban green infrastructure (UGI), urban blue infrastructure (UBI), and UBGI. Co-citation and keyword analyses reveal that nature-based solutions, hybrid green–gray infrastructure, and the application of urban resilience frameworks are gaining increasing scholarly attention. By summarizing the evolutionary trajectory and priority directions of UBGI research, this study provides significant insights for future interdisciplinary research aimed at enhancing the supply of urban environmental ecosystem services. Full article

Background/Objectives: Prioritizing diet- or exercise-related self-efficacy and social support with their interactions may improve the effectiveness of interventions aimed at increasing daily fruit/vegetable intake and exercise, thereby reducing the risk of metabolic disorders in abdominally obese women. This study aimed to identify the profiles of diet- or exercise-related self-efficacy and social support among women with abdominal obesity, examine profiles related to insufficient fruit/vegetable intake and exercise, and explore associating factors of these profiles. Methods: A cross-sectional investigation in central south mainland China collected sociodemographic, anthropometric, and health-related variables, diet-related self-efficacy (Diet-SE) and social support (Diet-SS), exercise-related self-efficacy (Exercise-SE) and social support (Exercise-SS), and daily fruit/vegetable intake and exercise. We used latent profile analysis to identify distinct profiles, and binary logistic regression to examine the profiles’ behaviors and associating factors. Results: A total of 327 abdominally obese women were categorized into four profiles of Diet-SE and Diet-SS, and five profiles of Exercise-SE and Exercise-SS. Women in the Diet Dual-Low Group were associated with insufficient daily fruits/vegetables intake. Women in the Exercise Dual-Low Group or Exercise-SS Medium–Low Group were more likely to engage in insufficient daily exercise. Conclusions: Our findings align with previous evidence that women with low diet- or exercise-related self-efficacy and social support are at increased risk for insufficient daily fruit/vegetable intake or exercise. Additionally, medium Exercise-SS is associated with insufficient exercise behaviors, suggesting that interventions targeting healthy exercise should be initiated earlier among women with medium Exercise-SS, rather than waiting for it to decline to low level. Full article

The measurement of national well-being has become central to both academic and policy debates, particularly within the framework of sustainable development. In this context, this study investigates the relationship between macroeconomic conditions and subjective well-being in Portugal. Using annual data from 2004 to 2022, we explore the effects of GDP per capita, unemployment, and inflation on the Global Well-Being Index (GWBI). Employing ordinary least squares (OLS) regression, the results indicate a significant positive relationship between GDP per capita and subjective well-being, while inflation is negatively associated. Contrary to expectations, the unemployment rate showed a positive and significant association with the GWBI. This counterintuitive result may reflect institutional buffering effects, such as social safety nets, strong family structures, or lagged responses in perceptions of well-being. Similar patterns were observed in other southern European countries with strong informal social support systems. These findings contribute to a deeper understanding of how economic indicators relate to perceived well-being, particularly in the context of a southern European country. The study offers relevant insights for public policy, including the alignment of macroeconomic management with the Sustainable Development Goals (SDGs), especially SDG 3 (Good Health and Well-being) and SDG 8 (Decent Work and Economic Growth). Full article

We investigated the spatial structure and colony itinerancy of Audouin’s gull (Ichthyaetus audouinii) adult breeders across multiple breeding sites in the central Mediterranean Sea during 25 years of fieldwork. Using cluster analysis of marked individuals from different years and sites, we identified five spatial breeding units of increasing hierarchical scale—Breeding Sites, Colonies, Districts, Regions and Marine Sectors—which reflect biologically meaningful boundaries beyond simple geographic proximity. To determine the most appropriate scale for monitoring local populations, we applied multievent capture–recapture models and examined variation in survival and site fidelity across these units. Audouin’s gulls frequently change their location at the Breeding Site and Colony levels from one year to another, without apparent survival costs. In contrast, dispersal beyond Districts boundaries was found to be rare and associated with reduced survival rates, indicating that breeding Districts represent the most relevant biological unit for identifying local populations. The survival disadvantage observed in individuals leaving their District likely reflects increased extrinsic mortality in unfamiliar environments and the selective dispersal of lower-quality individuals. Within breeding Districts, birds may benefit from local knowledge and social information, supporting demographic stability and higher fitness. Our findings highlight the value of adopting a District-based framework for long-term monitoring and conservation of this endangered species. At this scale, demographic trends such as population growth or decline emerge more clearly than when assessed at the level of singular colonies. This approach can enhance our understanding of population dynamics in other mobile species and support more effective conservation strategies aligned with natural population structure. 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

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