Search Results (10,739)

The Q-Learning Relational Game (QLRG) framework provides a theoretically rigorous method for identifying minimal winning coalitions in online social networks (OSNs) under the restrictive assumption of global agent symmetry or uniform matroid structure. Real-world OSNs, however, exhibit significant asymmetry. This paper introduces the Cluster-Based Q-Learning Relational Game (C-QLRG), a practical extension that relaxes the global symmetry requirement by leveraging community structure. We partition the agent set into communities with bounded internal variation and represent the state solely by community membership counts of the seed set. Because the closure operator already captures all eventual influence spread, the problem reduces to a sequential seed selection task where the agent decides, at each step, from which community to add the next seed. We prove that the optimal Q-function of a suitably regularized reach-efficiency objective is Lipschitz continuous and derive a performance bound for the learned policy. The full algorithm is presented, and its complexity is analyzed. Empirical evaluations on a synthetic asymmetric network and Zachary’s Karate Club demonstrate that C-QLRG is highly sensitive to reward parameters, where default settings lead to premature stopping, but parameter tuning combined with a corrected minimality verification recovers high-efficiency coalitions by removing non-contributing agents. With tuned parameters, C-QLRG produces a near-winning coalition of size 11 and 99% reach on the synthetic network, surpassing the greedy baseline’s efficiency (size 12) despite a one-node coverage gap, while identifying the optimal winning coalition of size 1 on the Karate Club dataset, matching all baselines. The framework thus offers a principled trade-off between model fidelity and scalability, with the reward design choice being critical for practical deployment. Full article

Artificial Neural Networks (ANNs) are a key component of construction research as Construction 4.0 and data-based problem-solving continue to shape the construction industry. In this paper, a Scopus-based bibliometric analysis of ANNs in construction research was conducted from 2015 to 2025. From an initial set of 9149 publications, 3800 English-language publications were identified and analysed using publication, source, country, citation, and keyword mapping techniques in VOSviewer (version 1.6.20). The publications showed a significant increase after 2018, peaking in 2024. China, India, and the US were key players in ANNs in construction research, and key publications focused on optimisation, concrete property prediction, machine learning, and deep learning. Key publications in ANNs in construction came from Construction and Building Materials, IEEE Transactions on Geoscience and Remote Sensing, and Energy. ANNs in construction research are moving towards hybrid, digitally integrated, and data-based applications, although gaps persist in sustainability, social equity, climate resilience, and underrepresented regions. Full article

This study investigates how AI-assisted plagiarism changes dishonest academic behavior in a socially interactive learning environment under different educational conditions. To this end, this study develops a scenario-based simulation to examine how AI-assisted plagiarism influences dishonest academic behavior in socially interactive learning environments. The model represents students as autonomous agents embedded in local peer networks who adapt their weekly behavior under academic pressure, institutional intervention, and available cheating options. Two behavioral scenarios are considered: a conventional plagiarism environment, in which agents choose between honest submission and direct copying, and an AI-augmented environment, in which AI-assisted plagiarism is introduced as an additional dishonest strategy. Intervention is modeled through environmental and institutional conditions, specifically detection probability and sanction severity, rather than through direct internal reward manipulation. Q-learning is used as a simplified adaptive mechanism for repeated agent choice. Experimental results show that the possibility of producing and assessing a simulation to see the availability of AI-assisted plagiarism substantially changes the behavioral composition of misconduct by increasing total dishonest behavior and shifting a large share of it toward the AI-assisted category. In the simulation, active intervention reduces dishonest behavior overall but does not eliminate AI-assisted plagiarism as the dominant dishonest strategy in the AI-augmented environment. These observations in the simulation suggest that academic misconduct in the AI era should be understood not only as a problem of deterrence but also as a problem of behavioral adaptation under changing technological and institutional conditions. To support the realism assessment of the simulation design, the study also conducts a structured qualitative plausibility review using multiple large language models under a shared prompt. Across these reviews, the model is judged to be acceptable as a first-stage stylized baseline, while important limitations are identified in agent heterogeneity, social influence depth, and the use of Q-learning as a simplified adaptive heuristic to reproduce the behaviors of actors in there. Full article

Money laundering remains a critical challenge for financial systems because of the complex, hidden, and interlinked nature of illicit financial transaction networks. Understanding how these networks respond to targeted disruption is essential for exposing structural vulnerabilities and refining existing anti-money laundering (AML) prevention and intervention strategies. This study involves a social network analysis (SNA)-based resilience framework to evaluate the robustness of financial transaction networks through targeted node removal. In this approach, a network is represented as a directed graph, where nodes correspond to accounts and edges represent transactions. Centrality measures (i.e., degree, closeness, betweenness and pagerank), which capture local influence, global reach, and control over information flow, are applied to identify the most influential nodes. Network resilience is assessed by analyzing the variation in the size of the Largest Connected Component (LCC) under progressive node removal. An adaptive LCC-based resilience strategy is used, starting with large batches of nodes and gradually moving to smaller ones until the LCC drops below 50% of its original size, allowing for a more detailed analysis near the fragmentation threshold. The findings reveal that Betweenness centrality is the most effective metric in disrupting network connectivity under targeted attack scenarios, both outflow- and inflow-based analyses. Specifically, targeting only the top 2% of nodes by Betweenness centrality collapses the network’s core, reducing the Largest Connected Component (LCC) to 60% of its original size. In contrast, random attack strategy exhibit limited impact on overall network resilience compared to targeted approaches. Our findings provide actionable AML insights, showing that resilience-driven targeting of structurally critical accounts can effectively fragment money laundering networks and support more focused interdiction strategies. Full article

The complexity of authentic assessment within Deep Learning frameworks often hinders teacher adoption. This study analyses the diffusion process of such an innovation at SMP Taruna Bakti Bandung using an Explanatory Sequential Mixed Methods design. Through Social Network Analysis (SNA) of the entire teacher population and in-depth interviews, this study maps communication patterns and the roles of key actors. SNA results reveal a network structure with moderate density and subject-based clustering patterns. Qualitative findings confirm that adoption success relies heavily on opinion leaders acting as “pedagogical translators” to simplify the technical complexities of assessment. Through collaborative strategies, innovation barriers are reduced by enhancing aspects of trialability and observability. The study concludes that the adoption of authentic assessment requires synergy between formal institutional support and technical validation fostered within interpersonal trust networks, rather than relying solely on managerial instruction. Full article

The rapid proliferation of image editing tools poses unprecedented challenges to information sustainability and social trust, as malicious digital forgeries can easily contaminate public discourse, news reporting, and legal forensics. Advanced image editing techniques make image tampering increasingly difficult for the naked eye to recognize, which requires highly accurate methods for detecting and localizing image tampering. In this paper, an end-to-end network model named AEM-Net is proposed. AEM-Net combines RGB and SRM features to enhance the model’s sensitivity to image details and potentially tampered regions through multi-scale feature extraction and fusion. AEM-Net consists of the HRNet-based Multiscale Feature Extraction Module and the Context-Aggregated Pyramid Localization Module (CAPLM). The multi-scale feature extraction module utilizes the Attentional Perceptual Feature Fusion Module to adaptively focus on the anomalous regions. In contrast, the CAPLM utilizes the Expanded Convolutional Feedback Enhancement Module to effectively exploit contextual feature information for achieving pixel-level localization of tampered regions. Experimental results on public benchmark datasets demonstrate that AEM-Net achieves superior performance compared with existing state-of-the-art methods. In particular, AEM-Net achieves an AUC/F1 score of 95.36%/67.19% on CasiaV1, 93.25%/79.75% on Coverage, and 87.36%/66.24% on NIST16, while requiring only 0.09 s to process a single image, demonstrating both high localization accuracy and computational efficiency. Full article

The city of Bologna (Italy) boasts an outstanding hydraulic heritage linked to the development of the silk industry, embodied in an extensive and valuable canal network. These public works, such as the Canale di Reno and the Canale Navile, were fundamental to the urban and economic shaping of the city from the Middle Ages onwards; however, many were concealed or dismantled from the 19th century. This article analyses recent heritage engagement and dissemination strategies regarding Bologna’s historic canals and proposes new tools to overcome their spatial fragmentation and enhance their interpretation as a continuous network. The methodology combines analysis, fieldwork or valorisation of the hydraulic system, proposing two complementary promotion actions: the design of a mobile application and the development of a straightforward urban intervention consisting of linear pavement marking of the underground canals layout. The proposed operational hypotheses suggest that integrating digital resources with on-site signage brings invisible heritage to light, improves the spatial understanding of the hydraulic system, and fosters both community and tourist engagement. The study concludes that these strategies reinforce the territorial understanding and social awareness of civil engineering heritage, offering a transferable approach for the outreach of hydraulic networks. Full article

Background: Antimicrobial resistance (AMR) poses an escalating threat to global health, agriculture, and the environment, demanding urgent multisectoral action under the One Health framework. Despite global awareness efforts, understanding of AMR among the general population remains insufficient, particularly in low- and middle-income countries such as Brazil. This study aimed to evaluate the knowledge, attitudes, and perceptions (KAP) of the Brazilian population regarding AMR. Methods: An online questionnaire was distributed through social media platforms between April and August 2025, resulting in 945 valid responses after data cleaning. Quasi-Poisson models were applied to identify demographic predictors of KAP scores while logistic regression models were used to assess the association between KAP scores and antibiotic use-related practices. Results: Education level was the strongest predictor of higher KAP scores, whereas age and gender showed inconsistent influence. Only 40.3% of respondents correctly identified antibiotics among commonly used medicines, and 25.9% reported proper disposal of antibiotic packaging. More than half (54.2%) were willing to pay more for antibiotic-free products, although only 26.7% had ever noticed such labeling. Network analysis of open-ended responses indicated that concerns about potential health risks and AMR awareness were the primary motivators for purchasing antibiotic-free products. Conclusions: These findings reveal significant gaps in public understanding of antibiotic use and resistance in Brazil, emphasizing the urgent need for targeted educational initiatives, improved public communication, and behavioral interventions to support antimicrobial stewardship and sustainable antibiotic use. Full article

Digital transformation has become an essential component of sustainable rural development, yet substantial inequalities persist in how communities access, adopt, and benefit from digital technologies. Understanding these disparities requires not only information about technological resources but also knowledge of the relational structures through which information, support, and opportunities circulate. This article presents a reproducible synthetic socio-digital network dataset designed to support the analysis of digital gaps in community-based tourism (CBT) environments. Rather than containing original respondent-level observations, the repository was computationally reconstructed from aggregate statistics derived from field studies conducted in three rural communities in the province of Guayas, Ecuador: Bucay (5 de Septiembre), Manglares Churute, and Ruta de los Chirijos. All node-level records, survey variables, and support relationships included in the repository were synthetically generated to preserve aggregate community characteristics while protecting participant confidentiality and preventing individual re-identification. The repository contains synthetic actor metadata, reconstructed socio-digital variables, directed support networks, graph representations in interoperable formats, and precomputed Social Network Analysis (SNA) indicators. The dataset includes 90 synthetic actors, more than one thousand generated support interactions distributed across multiple socio-digital dimensions, machine-readable metadata, and reusable scripts for preprocessing, validation, graph construction, and metric computation. The represented dimensions include financial assistance, training support, information exchange, technological support, social media promotion, institutional collaboration, trust, and emotional closeness. To facilitate reuse, all resources are distributed in standardized formats compatible with NetworkX, Gephi, Neo4j, and graph-learning frameworks. The repository follows FAIR principles and includes documentation intended to support transparency, reproducibility, and methodological benchmarking. Potential applications include social network analysis, graph mining, graph neural networks, digital inequality research, computational social science, community resilience studies, and educational activities. By providing an openly documented synthetic dataset and reproducible computational workflow, the repository contributes to the study of socio-digital systems, privacy-preserving data sharing, and community-level digital transformation processes. Full article

This contribution examines the role of artificial intelligence technologies in the co-construction of social reality, with specific attention to AI-generated data as emergent agents of knowledge production. Building on perspectives from science and technology studies and recent debates on algomorphic sociology, the contribution conceptualizes generative AI systems not as research instruments, but as active participants in epistemic processes. The analysis argues that AI-generated data exhibit a performative character: they do not simply represent social phenomena but actively contribute to their stabilization, classification, and circulation. This performativity fosters a shift from researcher-centered interpretation toward hybrid configurations in which meaning emerges through human–machine assemblages. Through a theoretical synthesis of recent methodological and epistemological reflections, the contribution highlights a transition from anthropocentric models of knowledge production to post-anthropocentric, relational frameworks in which agency, cognition, and sense-making are distributed across sociotechnical networks. The contribution concludes by outlining the implications of this shift for the future of digital social research and also for reflexivity, methodological design, and the ethics of social research, advocating a critical and adaptive stance toward AI as a co-producer of knowledge rather than a subordinate analytical tool. Full article

To address frequent street-level flooding, inadequate targeted management, and unbalanced cost-effectiveness in the old urban area, this study takes Yong’an Subdistrict in Quanshan District, Xuzhou, as a typical case, regards the street-level as its fundamental analytical unit and constructs a systematic “simulation–assessment–strategy” framework, focusing on evaluating and enhancing flood resilience in old urban districts. First, numerical simulation quantifies water depth under extreme rainfall to identify the flood risk spatial distribution. Second, a flood resilience assessment system is established based on the “exposure–vulnerability–adaptive capacity” framework, using the TOPSIS method to measure and grade street resilience. Finally, differentiated flood management strategies are proposed by integrating assessment results with regional characteristics. This study shows that high-risk flooding zones are clustered, with resilience results significantly correlated with the flood risk distribution. Low-resilience areas highly overlap with high-risk zones, mainly due to deficiencies in engineering, ecological, and social resilience. Accordingly, differentiated strategies—”pipe network upgrades + permeable paving”, “retention facilities + smart drainage”, and “micro-topography modifications”—are applied to old residential areas, core commercial districts, and new development peripheries. This approach balances management costs and effectiveness, providing theoretical and practical support for precise street-level flood management and spatial optimization in old urban districts. Full article

Introduction: The colossal European catfish (Silurus glanis) is the largest invasive freshwater fish on the Iberian Peninsula, reaching up to 2.8 metres and 130 kg in weight. Its large size makes it a highly valued target for recreational anglers, leading to repeated illegal introductions across several Iberian watersheds. Despite its appeal to anglers, this species is recognised as a high-impact invasive predator with substantial ecological consequences for European freshwater ecosystems. Recently, large catfish aggregations have been reported by anglers and environmentalists in several areas of Portugal and Spain. These impressive aggregations are frequently documented on videos and posted on social media networks (Facebook, WhatsApp groups, etc) or shared directly with our team members. Objective: Such records provide a valuable source of information for identifying the habitats and seasonal periods associated with aggregation behaviours and may therefore support more efficient management and population control actions. Methodology: We compiled information on European catfish aggregation events in Southern Iberia, namely date and location. The catfish aggregations were mapped, and their general habitat characteristics were described. Results: We recorded 10 catfish aggregation events, most of which occurred between May and June. These were generally located in transitional areas between lentic and lotic habitats, especially in narrower river sections. Possible explanations include hydromorphological constraints, seasonal environmental conditions, and species-specific behavioural responses, although these mechanisms require further investigation. Conclusions: Within the LIFE PREDATOR project, which focuses on the management of European catfish in the Tagus watershed, knowledge of aggregation locations is important to direct population control efforts aimed at reducing the abundance of this invasive fish. Moreover, the identification of common habitat characteristics may help predict other potential aggregation sites and improve the planning of future management actions. Full article

Within the conceptual framework of the Unified Flexibility & Mindfulness (UFM) model, the current study applied a contextual behavioral science lens to understanding the challenges and dynamics of classroom teaching in the United States. In particular, the study sought to highlight the specific flexibility processes linked to lower teacher burnout and to greater use of adaptive instructional and behavior management strategies—deepening the conceptualization and operationalization of teachers’ Social and Emotional Competence (SEC). Toward that end, a sample of 308 K-12 teachers (79% female, 85% white, M_age = 42 years old) with an average of 13 years of teaching experience completed a relational task (RT) indirectly assessing relational thinking about students along with teacher-report measures of: (1) their own use of 14 forms of mindful flexibility (and distracted, reactive inflexibility) in the classroom, (2) their conscious perceptions of student engagement and disaffection with learning, (3) their use of adaptive instructional and behavior management strategies, and (4) a measure of work-related and student-related burnout. Exploratory network analyses largely supported the Unified Flexibility and Mindfulness model shaping teachers’ functioning in the classroom. The results further highlighted unique links from categorical thinking on the RT (i.e., viewing all positive or negative adjectives as essentially the same in students) to greater burnout and unique links from more nuanced thinking on the RT (i.e., the ability to see negative and positive traits coexisting in students) to greater perceptions of both student engagement and disaffection. Teachers’ engagement of committed action and self-as-context (maintaining a broader perspective in the face of disruptive behavior) along with perceptions of greater student engagement emerged as some of the most robust predictors of using adaptive classroom strategies. In contrast, teachers’ engagement in fusion and inaction (along with perceptions of greater student disaffection and lower student engagement) emerged as the most robust predictors of teacher burnout. Implications are discussed. Full article

Background: Culturally and Racially Marginalised (CaRM) communities in Australia encounter subtle and covert forms of prejudice, commonly referred to as “new racism”. Within healthcare settings, these experiences can shape trust, engagement, and patterns of help-seeking. Mental health nurses are often the first point of contact in care delivery, and their ability to recognise, respond to, and mitigate the impacts of new racism is critical for fostering therapeutic relationships and supporting equitable access. Understanding how CaRM communities perceive the conditions that influence their mental health service use is fundamental for informing more equitable and culturally responsive care. Objective: This study explored the viewpoints of CaRM community members regarding the factors they consider important for addressing new racism in healthcare systems and supporting engagement with mental health services. Design: Q methodology was used to identify statistically derived viewpoints that reflect shared viewpoints about the conditions perceived as critical for addressing the impacts of new racism on mental health service use. Setting: Participants were recruited from culturally and linguistically diverse communities across Australia through community settings, social media, and professional networks. Participants: Thirty-five individuals from CaRM backgrounds completed the Q-sort. Methods: This Q methodology consisted of five steps: (1) set up of the Q-sorting instrument, (2) selection of participants, (3) data collection, (4) factor analysis, and (5) factor interpretation. Results: Three distinct viewpoints were identified: (1) raising awareness of mental health issues within CaRM communities (community-focused), (2) providing visible anti-racism and culturally safe services (service-focused), and (3) recognising and formally addressing new racism within healthcare systems (policy-focused). Conclusions: This study offers the first empirically derived, community-informed set of viewpoints on addressing new racism in Australian mental healthcare. While exploratory, the findings highlight multi-level considerations that are potentially relevant to mental health nursing practice, and may be useful to inform future research, policy development, and service redesign aimed at strengthening cultural responsiveness and equity in mental health systems. Full article

In recent years, China’s urban underground rail transit has developed rapidly, and the development of underground space has become increasingly complex, exposing the system to multiple operational risks such as structural instability, excessive deformation, equipment failures and emergencies. Existing studies often evaluate individual hazards or isolated stakeholder risks, while insufficient attention has been paid to how sudden events interact and propagate as disaster chains. To address this gap, this study develops a disaster-chain network framework for operational risk management in underground rail transit. Twenty sudden disaster risk events are first identified through literature review, expert consultation, system investigation, and HAZOP (Hazard and Operability) analysis. A database of 595 historical events is then used to construct co-occurrence and adjacency matrices. And the Jaccard index is used only to quantify association strength, while temporal order, HAZOP-based causal screening, and expert verification are introduced to distinguish plausible triggering relationships from simple correlations. Network indicators, including degree, betweenness, modified clustering coefficient, path length, connectivity, and edge vulnerability, are applied to identify critical nodes and propagation paths. The results indicate that functional failure of civil structures, fire, and crowd stampede are the dominant risk nodes. The proposed framework provides a transparent and replicable basis for prioritizing monitoring, emergency response, and link-cutting mitigation measures. The findings are intended as system-specific decision support rather than universal risk rankings and should be updated when new local operational data become available. Full article