Search Results (37)

In today’s densely populated and technology-driven smart cities, natural and human-made disasters increasingly threaten the resilience of communication infrastructures, creating critical challenges for maintaining reliable connectivity. The failure of conventional networks during crises significantly hampers emergency response, coordination, and information dissemination. To address these challenges, this paper presents Weighted Average Algorithm-based Clustering and Routing (WAA-CR), a novel, secure, and adaptive UAV-based framework for disaster response and recovery. WAA-CR integrates three key components: shelters or Ground Control Stations (GCSs) as communication anchors and support hubs, survivable clustering and routing using a WAA-based metaheuristic optimizer, and secure and trustworthy drone communication enabled by a lightweight trust evaluation mechanism, and authentication model. The framework formulates a multi-objective optimization model that simultaneously minimizes the number of active UAVs and routing cost, while maximizing trust, communication reliability, and coverage. Cluster head (CH) election and routing decisions are guided by a composite fitness function that considers residual energy, link stability, mobility, and dynamic trust scores. Additionally, an adaptive maintenance mechanism enables dynamic reconfiguration to handle CH failures, trust degradation, or mobility-driven topology changes. Extensive simulations conducted in MATLAB R2020ademonstrate that WAA-CR significantly outperforms existing baseline FANET protocols in terms of energy efficiency, cluster stability, trust accuracy, and end-to-end delivery performance. These results validate the proposed framework’s effectiveness in building resilient, scalable, and secure UAV-based communication networks for post-disaster environments. Full article

Influence maximization is a crucial research domain in social network analysis, playing a vital role in optimizing information dissemination and managing online public opinion. Traditional IM models focus on network topology, often overlooking user heterogeneity and server-driven propagation dynamics, which often leads to limited model adaptability. To overcome these shortcomings, this study proposes the “Social–Interest Hybrid Influence Maximization” (SIHIM) problem, which explicitly models the joint influence of social topology and user interest in server-mediated propagation, aiming to enhance the effectiveness of information propagation by integrating users’ social relationships and interest preferences. To model this problem, we develop a Server-Based Independent Cascading (SB-IC) model that captures the dynamics of influence propagation. Based on this model, we further propose a novel hybrid centrality algorithm named Pascal Centrality (PaC), which integrates both topological and interest-based attributes to efficiently identify key seed nodes while minimizing influence overlap. Experimental evaluations on ten real-world social network datasets demonstrate that PaC improves influence spread by 5.22% under the standard IC model and by 7.04% under the SB-IC model, outperforming nine state-of-the-art algorithms. These findings underscore the effectiveness and adaptability of the proposed algorithm in complex scenarios. Full article

The proliferation of disinformation on social media platforms poses a significant challenge to the reliability of online information ecosystems and the protection of public discourse. This study investigates the role of emotional sequences in detecting intentionally misleading messages disseminated on social networks. To this end, we apply a methodological pipeline that combines semantic segmentation, automatic emotion recognition, and sequential pattern mining. Emotional sequences are extracted at the subsentence level, preserving each message’s temporal order of emotional cues. Comparative analyses reveal that disinformation messages exhibit a higher prevalence of negative emotions, particularly fear, anger, and sadness, interspersed with neutral segments. Moreover, false messages frequently employ complex emotional progressions—alternating between high-intensity negative emotions and emotionally neutral passages—designed to capture attention and maximize engagement. In contrast, messages from reliable sources tend to follow simpler, more linear emotional trajectories, with a greater prevalence of positive emotions such as joy. Our dataset encompasses multiple categories of disinformation, enabling a fine-grained analysis of how emotional sequencing varies across different types of misleading content. Furthermore, we validate our approach by comparing it against a publicly available disinformation dataset, demonstrating the generalizability of our findings. The results highlight the importance of analyzing temporal emotional patterns to distinguish disinformation from verified content, reinforcing the value of integrating emotional sequences into machine learning pipelines to enhance disinformation detection. This work contributes to the growing body of research emphasizing the relationship between emotional manipulation and the virality of misleading content online. Full article

In the context of urban extreme weather events, the efficacy of the “emergency language” employed by governments and public institutions on social media in effectively reaching and guiding the public in a timely manner necessitates a quantifiable evaluation framework. An indicator system was constructed on the basis of Hovland’s persuasion theory. This system comprised five input characteristics (word count/structural clarity, first/second-person perspective, emotional appeal, evidence and framing, and media format) along with three output indicators (reposts, comments, and likes). A data envelopment analysis (DEA) model that is oriented towards output was employed, with disseminators being categorized into four distinct decision-making units: central mainstream media, other government media, local government media, and other media. It is imperative to note that the outputs were subjected to a process of normalization through the implementation of a scale factor. The data were sourced from the Weibo platform within the specified time window, which was from 10:00 on 24 July 2025, to 12:00 on 19 August 2025, with a sample size of 744. The findings revealed substantial disparities in technical efficiency across different disseminator types. A subset of local government media demonstrated a technical efficiency ≈ 1.00 yet low scale efficiency. Posts exhibiting clear structures, actionable points, and accompanying images or videos achieved higher cross-efficiency scores. It is therefore evident that the proposed DEA model provides a benchmark for maximizing dissemination effectiveness under given information characteristics. It is recommended that posting frequencies be maintained at consistent intervals during periods of heightened activity, that a template structure be adopted in accordance with the “fact–action–assistance channel” model, and that the proportion of rich media content be augmented. Full article

Currently, exploring digital immersive experiences is a new trend in the innovation and development of cultural tourism. This study addresses the growing demand for digital immersion in cultural tourism by examining the integration of spatial narrative and digitally gamified architectural scenarios. This study synthesizes an optimized framework for narrative design in digitally gamified architectural scenarios, integrating spatial narrative theory and feedback-informed design. The proposed model comprises four key components: (1) developing spatial narrative design methods for such scenarios; (2) constructing a spatial language system for spatial narratives using linguistic principles to organize narrative expression; (3) building a preliminary digitally gamified scenario based on the “Wuhu Jiaoji Temple Renovation Project” after architectural and environmental enhancements; and (4) optimization through thermal feedback experiments—collecting visitor trajectory heatmaps, eye-tracking heatmaps, and oculometric data. The results show that the optimized design, validated in the original game Dreams of Jiaoji, effectively enhanced spatial narrative execution by refining both on-site and in-game architectural scenarios. Post-optimization visitor feedback confirmed the validity of the proposed optimization strategies and principles, providing theoretical and practical references for innovative digital cultural tourism models and architectural design advancements. In the context of site-specific architectural conservation, this approach achieves two key objectives: the generalized interpretation of architectural cultural resources and their visual representation through gamified interactions. This paradigm not only enhances public engagement through enabling a multidimensional understanding of historical building cultures but also accelerates the protective reuse of heritage sites, allowing heritage value to be maximized through contemporary reinterpretation. The interdisciplinary methodology promotes sustainable development in the digital transformation of cultural tourism, fostering user-centered experiences and contributing to rural revitalization. Ultimately, this study highlights the potential use of digitally gamified architectural scenarios as transformative tools for heritage preservation, cultural dissemination, and rural community revitalization. Full article

With the rapid evolution of social computing, online sentiments have become valuable information for analyzing the latent structure of social networks. Sentiment leaders in social networks are those who bring in new information, ideas, and innovations, disseminate them to the masses, and thus influence the opinions and sentiment of others. Identifying sentiment leaders can help businesses predict marketing campaigns, adjust marketing strategies, maintain their partnerships, and improve their products’ reputations. However, capturing the complex sentiment dynamics from multi-hop interactions and trust/distrust relationships, as well as identifying leaders within sentiment-aligned communities while maximizing sentiment spread efficiently through both direct and indirect paths, is a significant challenge to be addressed. This paper pioneers a challenging and important problem of sentiment leader identification in social networks. To this end, we propose an original solution framework called “SentiRank” and develop the associated algorithms to identify sentiment leaders. SentiRank contains three key technical steps: (1) constructing a sentiment graph from a social network; (2) detecting sentiment communities; (3) ranking the nodes on the selected sentiment communities to identify sentiment leaders. Extensive experimental results based on the real-world datasets demonstrate that the proposed framework and algorithms outperform the existing algorithms in terms of both one-step sentiment coverage and all-path sentiment coverage. Furthermore, the proposed algorithm performs around 6.5 times better than the random approach in terms of sentiment coverage maximization. Full article

Efficient clustering algorithms are critical for enabling the timely dissemination of emergency messages across maximum coverage areas in vehicular networks. While existing clustering approaches demonstrate stability and scalability, there has been a limited amount of work focused on leveraging roadside units (RSUs) for cluster head selection. This research proposes a novel framework that utilizes RSUs to facilitate cluster head election, mitigating the cluster head selection process, clustering overhead, and broadcast storm problem. The proposed scheme mandates selecting an optimal number of cluster heads to maximize information coverage and prevent traffic congestion, thereby enhancing the quality of service through improved cluster head duration, reduced cluster formation time, expanded coverage area, and decreased overhead. The framework comprises three key components: (I) an acknowledgment-based system for legitimate vehicle entry into the RSU for cluster head selection; (II) an authoritative node behavior mechanism for choosing cluster heads from received notifications; and (III) the role of bridge nodes in maximizing the coverage of the established network. The comparative analysis evaluates the clustering framework’s performance under uniform and non-uniform vehicle speed scenarios for time-barrier-based emergency message dissemination in vehicular ad hoc networks. The results demonstrate that the proposed model’s effectiveness for uniform highway speed scenarios is 100% whereas for non-uniform scenarios 99.55% information coverage is obtained. Furthermore, the clustering process accelerates by over 50%, decreasing overhead and reducing cluster head election time using RSUs. The proposed approach outperforms existing methods for the number of cluster heads, cluster head election time, total cluster formation time, and maximum information coverage across varying vehicle densities. Full article

Metropolitan areas increasingly confront complex challenges related to food security, social inequality, environmental degradation, and resource scarcity, exacerbated by rapid urbanization, climate change, and the reliance on extended, fragile supply chains. Urban and peri-urban agriculture (UPA) is recognized as a promising approach to mitigate these issues. For example, it enhances food security and nutrition by strengthening local food supply systems, improves livelihoods by providing employment and income for local residents, and promotes environmental sustainability through the creation of greening spaces and reduction of food miles. However, the full potential of UPA remains constrained by various technological, economic, and social barriers, such as limited growing spaces, lack of land tenure security, low economic efficiency, and insufficient public awareness and acceptance. Given that the technological innovations are critical in overcoming these barriers and maximizing the positive impacts of UPA, this review provides a state-of-the-art overview of advanced technologies and tools applicable to UPA, aiming to inform how these innovations can be better enabled to enhance UPA’s contributions to sustainable urban food systems. The review begins by defining UPA, categorizing its various forms, and exploring its multifunctional roles within urban contexts. It then presents a thorough analysis of a range of UPA technologies that serve specific purposes, including productivity and product quality improvement, space utilization optimization, resource recycling, and land use management. Furthermore, the review evaluates the current challenges faced by these technologies throughout the stages of research and development (R&D), dissemination and extension, and application and commercialization, employing an analytical framework adapted from Technology Life Cycle theories. In conclusion, the review emphasizes the crucial roles that UPA and relevant technological innovations play in transforming food systems and urban environments. It proposes four key recommendations: (1) enhancing funding mechanisms and fostering interdisciplinary collaboration for UPA R&D, (2) strengthening UPA technology dissemination systems, (3) promoting economic feasibility and market integration within UPA business models, and (4) establishing supportive environments among all stakeholders in the innovation process. These targeted strategies are essential for scaling UPA technologies, thereby strengthening food security, environmental sustainability, and socio-economic resilience in metropolitan areas. Full article

In recent years, social networks have become increasingly popular as platforms for personal expression, commercial transactions, and government management. The way information propagates on these networks influences the quality and expenses of social network activities, garnering substantial interest. This study addresses the enhancement of information spread in large-scale social networks constrained by resources, by framing the issue as a unique weighted k-vertex cover problem. To tackle this complex NP-hard optimization problem, a rapid local search algorithm named FastIM is introduced. A fast constructive heuristic is initially used to quickly find a starting solution, while a sampling selection method is incorporated to minimize complexity during the local search. When the algorithm stalls in local optima, a random walk operator reorients the search towards unexplored regions. Comparative tests highlight the proposed method’s robustness, scalability, and efficacy in maximizing information distribution across social networks. Moreover, strategy validation trials confirm that each element of the framework enhances its overall performance. Full article

The widespread dissemination of fake news on social media has necessitated the development of more sophisticated detection methods to maintain information integrity. This research systematically investigates the effectiveness of different word embedding techniques—TF-IDF, Word2Vec, and FastText—when applied to a variety of machine learning (ML) and deep learning (DL) models for fake news detection. Leveraging the TruthSeeker dataset, which includes a diverse set of labeled news articles and social media posts spanning over a decade, we evaluated the performance of classifiers such as Support Vector Machines (SVMs), Multilayer Perceptrons (MLPs), and Convolutional Neural Networks (CNNs). Our analysis demonstrates that SVMs using TF-IDF embeddings and CNNs employing TF-IDF embeddings achieve the highest overall performance in terms of accuracy, precision, recall, and F1 score. These results suggest that TF-IDF, with its capacity to highlight discriminative features in text, enhances the performance of models like SVMs, which are adept at handling sparse data representations. Additionally, CNNs benefit from TF-IDF by effectively capturing localized features and patterns within the textual data. In contrast, while Word2Vec and FastText embeddings capture semantic and syntactic nuances, they introduce complexities that may not always benefit traditional ML models like MLPs or SVMs, which could explain their relatively lower performance in some cases. This study emphasizes the importance of selecting appropriate embedding techniques based on the model architecture to maximize fake news detection performance. Future research should consider integrating contextual embeddings and exploring hybrid model architectures to further enhance detection capabilities. These findings contribute to the ongoing development of advanced computational tools for combating misinformation. Full article

This study utilized Facebook and Instagram as communication channels for disseminating evidence-based food and nutrition information to low-income adults. From February 2021 to October 2022, 442 identical posts were shared across both platforms for audience reach and engagement. Posts were categorized in two ways: hedonic and three levels of utilitarian (informative, convenience, utility), based on widely applied social media uses and effects theory (Uses and Gratifications Perspective); and food/nutrition topics (dietary guidance, mealtime behaviors, recipes, food resource management, health behaviors, and community building). From predominantly image-based posts (82.6%), reach and engagement for Instagram (136,621 versus 6096, respectively) outperformed Facebook (83,275 versus 1276, respectively). Analysis of covariance of rank-order reach and engagement metrics (likes, replies, shares) showed Facebook engagement was consistent across hedonic and utilitarian categories while Instagram showed highest reach and engagement for utilitarian posts, especially those emphasizing food affordability. Facebook and Instagram differed in which food/nutrition topics achieved maximal reach and engagement. Fifteen posts were randomly selected for qualitative analysis to identify features reflecting engagement levels. Low-engagement posts featured low-color-contrast or less-appealing images, especially on Instagram. This study offers insights for practitioners and researchers aiming to use social media to promote healthy food and nutrition. Full article

This paper investigates the drivers and dynamics of clustering and inter-project collaboration within the framework of the Horizon Europe and Horizon 2020 projects. Leveraging a survey-based approach, we examine key themes surrounding the perception of clustering, the willingness to share information under legal confidentiality, and motivations for engaging with partners from different projects. The survey instrument, implemented via Microsoft Forms, was distributed among the consortia of eight EU projects participating in the SOLID4B cluster. Notably, the questionnaire was meticulously crafted based on an in-depth analysis of the SOLID4B case and comprehensive discussions with project coordinators and communication and dissemination managers from all participating projects. These discussions aimed to establish a clear roadmap for the cluster, ensuring the questionnaire’s relevance and usefulness for all participants. Data analysis was conducted within the same platform, facilitating efficient data processing and visualization. Our findings reveal that a significant majority of respondents (48 out of 55) perceive clustering as a valuable asset, indicative of a positive shift in perspectives. Challenges related to confidentiality were addressed through nuanced insights, with respondents demonstrating a willingness to share routine best practices, significant breakthroughs, and deliverables within a legally protected framework. Furthermore, a robust majority (40 out of 55) expressed a keen interest in collaborative endeavors, underscoring a collective drive to extend activities beyond individual project boundaries. The study highlights the importance of clustering with other projects in maximizing the impact of the Horizon program, extending stakeholder networks, and sharing knowledge and achievements in research and innovation. These insights contribute to a deeper understanding of the motivations and challenges surrounding clustering and collaboration within the Horizon Europe and Horizon 2020 projects. Ultimately, the findings pave the way for informed strategies aimed at fostering a dynamic and interconnected research community. Full article

This study investigates the impact of hospitalist system awareness, motivation, and behavior on value creation within the healthcare context of Taiwan. As population aging and the prevalence of chronic diseases continue to rise, accompanied by increased medical resource consumption, the Taiwan Ministry of Health and Welfare introduced the hospitalist system. Despite its implementation, the number of participating hospitals remains low. Using a questionnaire survey conducted from October 2021 to March 2022, data were collected from medical teams involved in the hospitalist system. A total of 324 valid questionnaires were analyzed. The results reveal that hospitalist awareness positively influences participation motivation (β = 0.846, p < 0.001), which subsequently impacts participation behavior positively (β = 0.888, p < 0.001). Moreover, participation behavior significantly contributes to value creation (β = 0.869, p < 0.001), along with the direct effect of awareness (β = 0.782, p < 0.001) on value creation. In conclusion, the successful promotion and implementation of the hospitalist system rely heavily on the support and active participation of medical staff. Effective interactions and comprehensive information dissemination are essential for maximizing healthcare value creation. Full article

More than 13.5 billion COVID-19 vaccine doses were delivered between 2021 and 2023 through a mix of delivery platforms, with mass vaccination campaigns being the main approach. In 2022, with the continued circulation of SARS-CoV2 and the need for periodic boosters being most likely, countries were required to plan for more sustainable approaches to provide COVID-19 vaccinations. In this context of uncertainty, a global tool for integrating COVID-19 vaccines into immunization programs and as part of broader health systems was published jointly by the WHO and UNICEF to respond to country needs. This paper summarizes the approach to, and lessons learned during, the development of a global guidance document and describes some examples of its early use in low- and middle-income countries (LMICs). The guidance leveraged existing health system frameworks, proposed four steps for planning and implementing the COVID-19 vaccination integration journey, and identified investment areas. The development process maximized robust global stakeholder and country engagement, and the timeframe was aligned with donor funding windows to support countries with the integration of COVID-19 vaccination. The rapid dissemination of the guidance document allowed countries to ascertain their readiness for integrating COVID-19 vaccination and inform the development of national plans and funding applications. While progress has been made in specific areas (e.g., optimizing cold chain and logistics leveraging COVID-19 vaccination), in the context of decreasing demand for COVID-19 vaccines, reaching adult COVID-19 vaccine high-priority-use groups and engaging and coordinating with other health programs (beyond immunization) remain challenges, particularly in LMICs. We share the learning that despite the uncertainties of a pandemic, guidance documents can be developed and used within a short timeframe. Working in partnership with stakeholders within and beyond immunization towards a common objective is powerful and can allow progress to be made in terms of integrating health services and better preparing for future pandemics. Full article

Unexpected traffic incidents cause safety concerns and intense traffic congestion on crowded urban road networks. Vehicular ad-hoc network (VANET)-aided Intelligent Transport Systems (ITS) aim to mitigate these risks through timely dissemination of alert messages. However, conventional Radio frequency (RF) mobile ad-hoc routing protocols are ill-suited for dynamic VANET environments due to high mutual interference, packet collisions, high end-to-end delay from frequent route discoveries, and periodic beaconing requirements. Fortunately, the quickly emerging Visible Light Communications (VLC) provide complementary short-range connectivity with high bandwidth and low interference. This paper proposes an efficient adaptive routing protocol for emergency messages in dense VANET scenarios leveraging a hybrid RF/VLC system. When an incident or congestion happens, the source vehicle disseminates the information to the incoming vehicles as quickly as possible using a combination of VLC and RF communication networks. Multi-hop relays extend the connectivity if the direct links are blocked. The coverage area is partitioned into zones based on road segments, intersections, and traffic flows. The Road Side Units (RSU)s are intelligently assigned to zones and they analyze the historical traffic data to characterize each zone and decide a response strategy. We also propose a congestion detection scheme that utilizes traffic simulations to forecast the clearance times under different response strategies. The highest-scoring strategy is selected based on the predicted impacts on travel time, emissions, and driver stress levels. The proposed algorithm adaptively uses the selected strategy to proactively alleviate the predicted congestion through optimized routing and control. Overall, the protocol maximizes safety and efficiency during emergencies by leveraging the hybrid RF/VLC, incorporating real-time congestion forecasting and dynamic rerouting into the response strategies. Full article