Search Results (10,527)

This study examines how user-generated content (UGC) is incorporated and negotiated within platformized public service media (PSM) journalism, using the German content network funk as a case study. Based on a qualitative content analysis of selected formats and their social media posts, the study shows that participatory affordances offered by social media platforms (SMPs) are present but rarely foregrounded as central elements of storytelling. Instead, UGC is typically used as illustrative material or selectively embedded within editorial narratives. The analysis investigates how UGC is solicited, incorporated, and visually integrated into editorial storytelling across different formats. The findings identify three recurring patterns of UGC integration that illustrate how audience participation is negotiated within everyday editorial production: (1) illustrative UGC integration, (2) community-oriented UGC integration, and (3) minimalist UGC integration. Overall, the study highlights how platformized PSM journalism integrates UGC in ways that remain strongly editorially moderated rather than fully participatory, demonstrating how participation is enabled, constrained, and strategically applied within platform infrastructures. Full article

Environmental, Social, and Governance (ESG) text classification is important for applications in sustainable finance. However, it remains a challenging task due to domain terminology and regulatory constraints. While transformer-based models achieve strong predictive performance, they often lead to high energy costs and provide limited interpretability. To address these limitations, we introduce ESG-Graph, a lightweight and interpretable graph-based framework for modeling ESG disclosures. In our approach, each sentence is represented as a token-level dependency graph augmented with virtual nodes initialized from a European Sustainability Reporting Standards (ESRS)-based taxonomy, enabling the addition of new ESG concepts without retraining. A multi-layer Graph Attention Network is used instead of transformer encoders, allowing grammatical structure and domain semantics to be modeled jointly. Experiments on three ESG benchmark datasets show that ESG-Graph achieves performance comparable to efficient transformer baselines while consuming up to 60× less energy and using 10× fewer parameters. Additional attribution and ablation studies suggest the method’s policy alignment, interpretability, and robustness. Full article

Entrepreneurship is recognised globally as the vehicle for economic development and poverty eradication, yet in developing economies, it is not receiving the support it deserves. Based on the institutional framework, this study explores its role in fostering the development of an entrepreneurial mindset in Masvingo Province, Zimbabwe. Being grounded in the interpretivist research philosophy and following an inductive qualitative research design, the study adopted a case study strategy. Data were collected through in- depth interviews with 12 participants, purposively selected from industry leaders and entrepreneurs. Thematic analysis was used to inductively generate contextual insights from the interaction between the regulatory, socio-economic, and cultural pillars of the institutional framework and individual capabilities. The findings show that entrepreneurship development in Masvingo Province, Zimbabwe, is influenced to a greater extent by the institutional framework, which is characterised by economic volatility, infrastructure gaps, and evolving regulatory demands. The formal institutional framework was noted to confer legitimacy while, at the same time, imposing obligations on institutions; informal institutional frameworks rooted in communal values, social capital, and professional bodies helped fill gaps in the formal framework. The study also demonstrates that entrepreneurial mindset development is an integrated output of continuous learning, strategic networking, and individual capability. In reinforcing the normative dimensions of institutional theory, it was noted that entrepreneurs do not only have profit-maximisation goals but also long-term sustainability and survival targets. The study contributes to scarce empirical research on the nexus between institutional framework and entrepreneurship development in emerging economies. The findings reinforce the need for an integrated approach that streamlines the regulatory process, strengthens infrastructure, supports capacity building, and recognises the role of the informal institutional network in enhancing entrepreneurship development. Even though the qualitative, cross-sectional design limits the generalizability of the study’s findings, the study offers insights into fostering entrepreneurship development in emerging markets. Full article

This study examines how the allocation of export expansion across sectors affects economy-wide outcomes in Bangladesh. Using a Social Accounting Matrix (SAM) framework, we combine linkage analysis with simulation to evaluate how sectoral export growth propagates through the production network. The results show that the impact of export diversification depends critically on sectoral allocation rather than export intensity alone. While aggregate differences between scenarios are modest, reallocating export growth toward sectors with stronger intersectoral linkages generates larger economy-wide gains in GDP and labor income. In particular, sectors with low initial export shares but high network connectivity—such as agriculture, hunting, forestry, and fishing; retail trade; other community, social and personal services; and inland transport—produce stronger multiplier effects than most export-intensive sectors. These findings highlight a key distinction between export intensity and network centrality, demonstrating that sectors with limited direct export participation can play a central role in transmitting economic gains. The results provide a network-based perspective on export diversification and offer policy-relevant insights for designing strategies that promote more inclusive and efficient economic growth. Full article

Background/Objectives: Although clinically distinct, bipolar disorder (BP), schizophrenia (SZ), major depressive disorder (MDD), and social anxiety disorder (SAD) share fundamental biology. We mapped these transdiagnostic systemic mechanisms. Methods: Weighted Gene Co-Expression Network Analysis (WGCNA) of peripheral blood RNA-Seq datasets evaluated module preservation, hub gene disruption, and microRNA (miRNA) networks. Results: Seven modules showed robust cross-disease preservation. Overall, 56 of 105 candidate hub genes exhibited altered expression, with 22 passing the false discovery rate (FDR) correction. Hubs like IL1B, TLR2, and MMP9 dominated networks linked to altered inflammatory signaling and structural remodeling. Downregulated ribosomal hubs characterized systemic metabolic stress. Discussion: These signatures capture extensive systemic dysregulation. Inflammation and metabolic shifts correlate strongly with pathways regulating chronic neuroinflammation, epigenetic control, and dendritic pruning. Computational models suggest these cascades evade miRNA controls, potentially compromising structural neural plasticity. Conclusions: This shared transcriptomic architecture challenges rigid diagnostic boundaries. Identifying systemic immune dysregulation and translational alterations as core pathogenic denominators provides a rationale for transdiagnostic therapies targeting upstream systemic networks to mitigate neural vulnerabilities. Full article

Chaotic itinerancy—irregular switching among metastable collective states—provides a dynamical substrate for flexible social coordination, yet its mechanistic origin in multi-agent systems remains unclear. We present a multi-agent Active Inference model in which chaotic itinerancy emerges from Expected Free Energy minimisation without outcome-level social priors. Agents select actions to minimise Expected Free Energy while updating preferences through a precision-gated learning mechanism modulated by interpersonal trust. Hill-function nonlinearity in state transitions creates bistable “affordance landscapes” that gate behavioural mode switching. Simulations with small number of agents on an Erdos–Rényi trust network reveal spontaneous alternation among multiple metastable behavioural clusters, heavy-tailed dwell-time distributions, and sign-changing finite-time Lyapunov exponents—three hallmarks of chaotic itinerancy. Crucially, replacing Hill-function dynamics with linear transitions reduces the chaotic-itinerancy detection rate from 80% to 20%, demonstrating that nonlinear affordance structure is necessary for generating metastable switching. We further show that agents with simplified internal models of the world sustain richer itinerant dynamics as a group than “perfect-foresight” agents, suggesting that bounded rationality may be functionally advantageous for maintaining behavioural flexibility. These results establish active inference as a principled framework for modelling chaotic itinerancy in social systems and offer a computational account of trust-mediated collective transitions observed in theatre workshops and group dynamics. Full article

The skin–brain axis constitutes a complex, bidirectional network integrating cutaneous sensory, immune, and neuroendocrine systems with central neural circuits involved in emotion regulation, stress responsivity, and social cognition. Advances in psychodermatology and cosmetic science have progressively extended this framework to the emerging field of neurocosmetics, which explores how topical formulations, sensorial properties, and cutaneous neuromodulators may influence psychological well-being, affective states, and perceived stress. The aim of this narrative review is to synthesize current evidence on the biological foundations of the skin–brain axis and to critically examine the implications of these mechanisms for neurocosmetic interventions from a psychological and psychiatric perspective. It describes the biological substrates underlying skin–brain communication, including the cutaneous hypothalamic–pituitary–adrenal axis, neuropeptides, neurotrophins, transient receptor potential channels, and endocannabinoid signaling, and examines how these pathways are targeted by neurocosmetic interventions. Particular attention is devoted to neuroactive compounds, such as peptides, cannabinoids, botanicals, and aromatherapeutic molecules, as well as to sensorial strategies involving texture, temperature, and olfactory cues, which may modulate mood, anxiety, and self-perception through peripheral mechanisms. From a psychological and psychiatric perspective, the review discusses the intersection between stress-related skin conditions, body image disturbances, and emotional dysregulation, highlighting how cosmetic practices may influence subjective well-being beyond purely aesthetic outcomes. Methodological limitations of the existing literature, including the heterogeneity of study designs and outcome measures, as well as ethical considerations related to mood- and stress-related claims in cosmetic products, are critically examined. Finally, future research directions are outlined, and a translational framework is proposed to integrate dermatology, neuroscience, and mental health within next-generation cosmetic science. Full article

Hydrogen refueling stations (HRSs) are a critical enabling infrastructure for fuel cell electric vehicles (FCEVs), yet their deployment in dense metropolitan areas often faces a dual challenge: limited travel-time accessibility for users and low public acceptance driven by perceived safety risks. This study develops an integrated, city-scale framework to quantify HRS accessibility and resident acceptance and to identify expansion priorities for Seoul, South Korea. We combine (i) an online perception survey of 1000 adult residents (October 2024) capturing environmental awareness, perceived safety, siting preferences, and willingness-to-travel distance; (ii) spatial demand data on FCEV registrations by administrative dong (n = 2443 vehicles, 2022); and (iii) network-based travel-time analysis using the Seoul road network and the current HRS supply (n = 10, 2024). Accessibility is evaluated under three travel-time thresholds (10, 15, and 20 min), with service-area delineation and demand-weighted underserved-area diagnosis. Candidate expansion sites are generated and screened using operational and regulatory constraints (e.g., site area and proximity to protected facilities), followed by a p-median location-allocation optimization to select five additional sites that minimize demand-weighted travel impedance. Results indicate that, under the 20 min threshold (7.7 km at an average operating speed of 23.1 km/h), 50 of 425 dongs (11.8%) and 244 of 2443 FCEVs (10.0%) are outside the baseline service coverage. After adding five sites (total n = 15), underserved dongs decrease to 5 (1.2%) and underserved FCEVs to 26 (1.1%) for the 20 min threshold, with consistent improvements across shorter thresholds. Survey responses further reveal that only 12.5% of respondents perceive HRSs as safe, while 46.5% report a maximum willingness-to-travel distance of up to 5 km, underscoring the need for both accessibility enhancement and risk-aware communication. The proposed workflow offers a transparent, reproducible approach to support equitable and risk-informed HRS planning by jointly considering network accessibility, demand distribution, and social acceptance, thereby contributing to sustainable urban mobility, low-carbon transport transition, and socially acceptable hydrogen infrastructure deployment. Beyond local accessibility improvement, the study is framed in the broader context of sustainability, as equitable and socially acceptable hydrogen refueling infrastructure can support low-carbon urban transport transitions and more resilient metropolitan energy-mobility systems. Full article

Graph neural networks (GNNs) have achieved great success in graph classification, with graph pooling methods being widely adopted for related tasks. Existing approaches typically rely on node ranking or clustering to coarsen graphs, but often fail to effectively leverage global structural information, leading to loss of critical substructures and limited interpretability—key limitations in molecular analysis and social network mining. To address these issues, we propose SparsePool, a graph pooling method that integrates node features and structural patterns through atomic decomposition. By dynamically decomposing graphs into interpretable atomic units via Boolean matrix factorization, SparsePool preserves semantically meaningful substructures while providing transparent evidence of retained patterns. We further introduce an Atomic Pooling Neural Network (APNN) for graph representation learning. Extensive experiments on relevant benchmarks including biochemical and social network datasets demonstrate that SparsePool outperforms state-of-the-art pooling methods, achieving an average classification accuracy improvement of 1.03% over baseline models while reducing structural information loss. We also discuss its compatibility with emerging quantum computing paradigms, such as quantum-accelerated sparse decomposition, as a promising direction for scaling graph processing in industrial contexts. Full article

Short-form video content has gained importance as a popular form of digital media due to the rising popularity of social media platforms and the decreasing attention spans of consumers. However, a major obstacle to popularity detection in short-form content is the heterogeneous nature of the data, encompassing textual, visual, and metadata components. To tackle this challenge, we propose FUSEPOP, a robust multi-modal architecture. The proposed framework utilizes ResNet-50 for visual feature extraction and XLM-RoBERTa for encoding multilingual textual information. FUSEPOP employs a mutual information-based modality weighting mechanism with logarithmic smoothing and a 0.7 weight ceiling to balance contributions from each input stream. Furthermore, FUSEPOP implements a robust stacked generalization strategy trained via stratified 5-fold cross-validation. This approach utilizes a logistic regression meta-learner to dynamically synthesize predictions from random forest, XGBoost, and a neural network-based classifier. Experimental results show that this architecture significantly outperforms benchmark models, achieving an accuracy of 0.980 and an average F1-score of 0.964 on the feature configuration selected for this study, and remains competitive on a literature-aligned alternative configuration. These findings confirm that the proposed model successfully detects popularity on short-form social media content. Full article

To enhance the performance of the Social Network Search (SNS) algorithm in solving complex numerical optimization problems, this paper proposes a Multi-strategy Enhanced Social Network Search (MESNS) algorithm. The original SNS simulates human social behaviors through four decision-making emotions—imitation, conversation, disputation, and innovation—to perform population-based search. However, its uniform emotion selection mechanism and purely random interaction strategy may reduce convergence efficiency and weaken exploitation capability, particularly in the later stages of optimization. To overcome these limitations, MESNS incorporates three improvement strategies. First, an adaptive decision-making emotion selection mechanism is developed to dynamically adjust the probabilities of exploration and exploitation behaviors according to the iteration progress, thereby promoting a more symmetric and coordinated search transition over time. Second, an elite-guided communication strategy is introduced to enhance information propagation by integrating high-quality individuals into the interaction process, which improves convergence while maintaining population diversity. Third, a dynamic interaction radius adjustment mechanism is designed to adaptively regulate the search step size, achieving a better balance and dynamic symmetry between global exploration and local refinement. Extensive experiments are conducted on the IEEE CEC2014, CEC2017, and CEC2022 benchmark suites under multiple dimensional settings. The results demonstrate that MESNS achieves superior optimization accuracy, faster convergence speed, and improved solution stability compared with several state-of-the-art metaheuristic algorithms. Furthermore, the proposed algorithm is successfully applied to the three-dimensional wireless sensor network deployment optimization problem, where it produces a more uniformly distributed and spatially balanced sensor layout, reduces coverage holes and redundant overlaps, and thus exhibits desirable symmetry in deployment structure and sensing coverage. These findings indicate that MESNS is an effective and competitive optimization framework for complex global optimization tasks with both theoretical significance and practical value from the perspective of symmetry. Full article

Background: Communication, language, and speech disorders are highly prevalent in children with cerebral palsy (CP) and substantially impact social, educational, and community participation. However, few studies have systematically characterized communicative and linguistic profiles using standardized assessments. This paper outlines the work of the ‘Italian CP & Language Network’ over the last two years, focusing on identifying research priorities, developing specialized assessment protocols, and proposing a shared classification system for speech and language disorders in children with CP. Methods: A survey was sent to 11 specialized centers to investigate clinical practices and assessment tools. Based on the results and an extensive literature review, the group developed three age- and complexity-based diagnostic protocols and a shared classification system. Results: The survey highlighted high variability in test selection, especially for speech and pragmatic assessment, and a significant need for ad hoc tools for augmentative and alternative communication (AAC). Three standardized protocols were defined: (1) early language (<48 months), (2) school-age language and pragmatics (4–12 years), and (3) minimally verbal children (6–12 years). A multi-level classification system for language and speech disorders was proposed to improve diagnostic consistency. Conclusions: Standardizing assessment is a critical step toward early identification of communicative vulnerabilities to guide tailored interventions and promote participation and quality of life across developmental stages. The group provides a framework for prospective multicenter data collection to correlate linguistic and speech phenotypes with neuroradiological features and motor outcomes. Full article

When people interact, their actions reflect mood, attitude, and intention. Stern termed the affective qualities conveyed by actions, such as gentleness or rudeness, Vitality Forms (VFs). Previous research shows that children with autism spectrum disorder (ASD) differ from neurotypical (NT) peers in both perceiving and expressing these fundamental aspects of communication. It remains unclear whether these differences arise from structural or connectivity alterations in brain regions involved in VF processing. This study investigated structural and microstructural brain differences between children with ASD and NT peers, focusing on the VF-related network, which includes the dorso-central insula (DCI), premotor cortex (PM), middle cingulate cortex (MCC), and dorsolateral prefrontal cortex (DLPFC). Structural MRI data were collected from 60 right-handed boys aged 6–10 years (30 ASD, 30 NT), with diffusion MRI data available for a subset (20 ASD, 20 NT). A multimodal approach combined voxel-based morphometry (VBM), tract-based spatial statistics (TBSS), and probabilistic tractography. VBM revealed increased grey-matter volume in the PM, DLPFC, and MCC in the ASD group, with no differences in the DCI. TBSS showed white-matter microstructural alterations in premotor-related pathways. Probabilistic tractography further indicated atypical organization of tracts connecting the PM with the DLPFC, MCC, and DCI in children with ASD. Overall, the findings suggest atypical development of the premotor cortex and its associated white-matter connections in ASD, supporting theoretical accounts that link this network to altered processing of affective action dynamics during social interaction. Full article

Understanding how sentiment propagates in signed networks is crucial for uncovering mechanisms behind opinion polarization, trust formation, and information cocoons in digital communities. This paper investigates the generation of signed edges, representing positive or negative sentiments, in online social networks. We propose an analytical framework that models the dynamic growth of sentiment as a diffusion process. By introducing a walker on an infinite one-dimensional lattice, we derive a time-fractional diffusion equation that captures subdiffusive, normal diffusive, and superdiffusive behaviors. The model is empirically validated using two large-scale temporal signed networks: RedditHyperlinks and Bitcoin OTC. Our findings reveal that sentiment diffusion exhibits distinct regimes depending on the stage of network evolution, providing a foundation for further theoretical analysis and applications in signed social networks. Full article