Search Results (10,631)

Active mobility in peri-urban areas is influenced by sprawl, limited public transportation, and reliance on private vehicles. This study redefines active mobility in peri-urban and low-density contexts from a territorial perspective and reframes micro-hubs as socially oriented, network-integrated elements rather than scaled-down urban hubs. This study adopts a qualitative, theory-driven methodology combining a multidisciplinary review of the active mobility concept with thematic analysis to identify mobility hub characteristics, followed by analytical synthesis, the classification of mobility hub types, and a set of social indicators for analyzing their performance. These methods are used to develop a framework for understanding micro-hubs as socio-spatial components of active mobility networks. Results indicate that a network of minor roads and micro-hubs can support shifts toward active mobility when aligned with daily mobility patterns and supported by multi-level governance. This study conceptualizes micro-hubs as socio-spatial nodes embedded within dispersed mobility networks and, drawing on the Veneto region as an analytical context, employs an existing typological classification—network, welfare, and civic—to interpret their roles in shaping a context-sensitive framework for active mobility in peri-urban and low-density areas. Micro-hubs become socially integrated spaces that may contribute to strategies for reducing car dependency while providing transferable policy-oriented actions for similar peri-urban and low-density areas. Full article

Real-world information diffusion frequently spans multiple heterogeneous platforms and relational layers, making multilayer influence maximization (MLIM) a critical and challenging problem. Existing methods for multilayer networks often rely on local structural signals for surrogate evaluation, failing to accurately characterize multi-hop diffusion and inter-layer coupling effects. In discrete combinatorial search, meta-heuristic random exploration often disrupts the structural inheritance and reuse of effective node configurations, compromising search stability and quality. To address these challenges, this paper proposes a Probabilistic-Based Multilayer Slime Mold Algorithm (PB-MSMA). It employs the slime mold algorithm as its search framework to perform discrete combinatorial optimization within a controlled candidate space. It utilizes the Preference-based Expected Diffusion Value (P-EDV) as a surrogate fitness metric during the search phase. This design reduces the need for repeated Monte Carlo simulations for iterative candidate evaluation while improving the characterization of inter-layer and higher-order diffusion effects. Furthermore, a probabilistic pipeline mechanism is introduced to encode recurring effective node configurations from historical searches as statistical priors, guiding the search process to enhance structural inheritance and stability. After the seed sets are obtained, the final influence spread of all compared methods is evaluated using 10,000 Monte Carlo simulations under the MLIC model. Experiments on six real-world multilayer network datasets and nine seed budgets show that PB-MSMA achieves a dataset-level improvement range of 3.68–14.50% over representative baselines, including CELF, DPSOMIM, Degree, DIRCI, and PRGC, with an average improvement of 10.32%. These results indicate that PB-MSMA provides an efficient seed-selection strategy for multilayer diffusion scenarios where repeated simulation-based evaluation is costly. Full article

This study examined rural pre-service teachers’ preferences for online learning platforms, Telegram, WhatsApp, and Moodle discussion forums in the Open Distance e-Learning environment. This group of students experiences digital illiteracy, limited access to assistive technologies, and network challenges, which may prevent them from optimally utilising formal learning platforms such as Moodle. They can, however, use Telegram and WhatsApp, as they regularly engage informally on these platforms. Against this backdrop, this study explored rural pre-service teachers’ experiences with Moodle and these social media platforms in an Open-Distance e-Learning space. This study employed a descriptive, qualitative case study with semi-structured interviews, guided by Siemens’ Connectivism theory. Fifteen student teachers from the College of Education in an ODeL institution were purposively sampled to provide in-depth insights into their lived experiences of platform use. The findings revealed that, although each platform served a unique instructional function, their perceived professionalism, safety, and interactivity differed substantially. Social media platforms such as Telegram and WhatsApp were lauded for their immediacy, accessibility, and low bandwidth usage, chiefly among rural pre-service teachers from economically disadvantaged communities. However, participants perceived these platforms as unprofessional, disruptive, and unsafe. Conversely, Moodle’s discussion forum was viewed as a credible, structured space that fostered academic discipline through the presence and guidance of lecturers. These contrasting perceptions highlight tensions between accessibility and academic regulation within ODeL environments. Although prior studies support incorporating social media platforms into LMSs, this research extends this discourse by emphasising the need to balance accessibility, interaction, and academic integrity within resource-constrained contexts. The study concludes that social media platforms and discussion forums can complement each other in ODeL, encouraging student interaction and inclusion, while discussion forums ensure educational rigour, safety, and institutional integrity. Full article

Graph-based fraud detection in multi-relational social networks must capture heterogeneous relation semantics and diverse fraud patterns while preserving geometric consistency and remaining scalable. Existing methods often either force all relations into a shared Euclidean or single-curvature space, or fuse relation-wise embeddings after mapping them to tangent coordinates, which weakens curvature-dependent metric information. We propose Relation-Specific Curvature Fields on Product Manifolds (RSCF-PM), a geometry-consistent framework that learns relation-specific curvature and represents each node as a tuple on a Riemannian product manifold. Each relation is encoded in its own hyperbolic space, and cross-relation fusion is performed directly through the product metric rather than Euclidean concatenation. On top of this representation, we introduce a multi-prototype classifier to model multiple fraud modes within each class. To support large-scale training, we adopt tangent-space aggregation as an efficient approximation to the Fréchet mean. Experiments on four public fraud detection benchmarks, including the 5.78M-node T-Social network, show that RSCF-PM achieves the best results on T-Social, FDCompCN, and YelpChi, while remaining highly competitive on Amazon, with up to 4.96% AUC improvement over strong baselines. Ablation and efficiency studies further confirm the complementary value of each component and the practical scalability of the framework. Full article

Despite the growing participation of women in higher education worldwide, they continue to face persistent challenges in their career advancement, including limited promotion opportunities, underrepresentation in leadership positions, lower research productivity, and unequal access to institutional resources. These challenges are shaped by a range of structural, institutional, and socio-cultural constraints within academia. Understanding these influencing factors is essential for promoting gender equity within universities. This study investigates the factors influencing the career advancement-related challenges experienced by women academics in Jordanian higher education institutions, focusing on career experience, family responsibilities, and organisational support. Grounded in Gendered Organisations Theory, Work-Family Conflict Theory, and Social Support Theory, the study develops and empirically tests an integrated conceptual model. Data were collected through a questionnaire survey of women academics across Jordanian universities. The quantitative data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), while qualitative responses on strategies for overcoming challenges were examined using directed qualitative content analysis. The findings indicate that family responsibilities represent the most influential driver of perceived challenges, highlighting the continuing tension between professional and domestic roles. Career experience is found to reduce perceived challenges, suggesting that accumulated professional capital and institutional familiarity enhance women’s ability to navigate academic environments. Organisational and social support not only directly reduce perceived challenges but also buffer the impact of family responsibilities. Multi-group analysis further reveals differences in the strength of these relationships between teaching-research academics and those occupying leadership roles. The qualitative results identify key strategies for addressing these challenges, including mentoring systems, flexible institutional policies, professional networking, and leadership development initiatives. By integrating structural modelling with qualitative insights, this study advances understanding of the complex dynamics shaping women’s academic careers and provides evidence-based recommendations for fostering more inclusive and supportive higher education environments. Full article

Somali refugee mothers navigating parenting in the United States face compounding challenges that extend well beyond the initial resettlement period. This study employed a multi-method qualitative design, including utilizing a focus group and follow-up key informant interviews with Somali refugee mothers. Thematic framework analysis identified three overarching domains of challenges and resilience. First, a pervasive deficit of functional literacy, defined as the practical capacity to navigate American institutional systems, emerged as the primary stressor, superseding material poverty as a barrier to daily functioning. Second, significant intergenerational tensions were documented, including role reversal between mothers and children, erosion of parental authority, and breakdown of the traditional expectations that adult children provide financial and social support to aging parents. Third, single motherhood amplified all other stressors, producing progressive role strain and mental health decline in the absence of extended family support. Despite these challenges, participants demonstrated substantial resilience through informal mutual aid networks, religious practice, and deliberate cultural and linguistic preservation. Findings have direct implications for the design of culturally responsive resettlement programming, family counseling services, and mental health interventions for Somali refugee populations. Full article

Objectives: The rise in the frequency of mental health concerns (depression and suicide) expressed on social media calls for reliable, explainable, and efficient computational methods for mental health surveillance. In this paper, we propose an interpretable framework for text-based detection of post- and community-level mental health risk on social media. Methods: The framework combines (i) Secretary Bird Optimization (SBO) for feature selection of informative linguistic and psychological features, (ii) a BERT (Bidirectional Encoder Representations from Transformers)—CNN (Convolutional Neural Network) model for post-level reasoning, and (iii) a Graph Neural Network (GraphSAGE) for community-level reasoning. The graph is estimated based on semantic similarity between posts and author relations, instead of social interactions (e.g., mentions, replies) between authors. We use SHAP and LIME for model interpretability, uncertainty, and calibration analysis to evaluate the trustworthiness of predictions. Results: The model delivers 93.1% accuracy, 0.91 F1-score, and 0.944 ROC-AUC on the eRisk and CLPsych datasets using a strict user-disjoint validation strategy. SBO lowers the number of features by about 38%, leading to better generalization. The graph-based model enables improved learning of post and user representations by capturing relational dependencies. Conclusions: Our approach offers an explainable and robust means of detecting mental health risk from text. Graph-based representations of semantic and authorship interactions enable community-level analyses, while interpretability and uncertainty estimation facilitate possible human-in-the-loop decision-making. This research does not explicitly consider a human-in-the-loop experiment. Full article

With the deep integration of network information technology and social platforms, the quantified data sharing of consumers’ green behaviors is reshaping the participation logic of individual and group green consumption. Using a pilot experiment and two scenario-based experiments, this study investigates how self-quantification influences consumers’ green behavioral autonomy and sustained willingness under different contextual conditions from a community network perspective. The results indicate that, in promoting goal-oriented green consumption, self-quantification significantly reduces consumers’ green behavioral autonomy by enhancing group identity but does not influence their sustained participation willingness. However, consumers under egoistic goal appeals demonstrate higher behavioral autonomy and sustained participation willingness compared to those under altruistic goal appeals. In defensive goal-oriented green consumption, self-quantification effectively enhances consumers’ green behavioral autonomy by weakening group identity and positively promotes their sustained participation willingness. Nevertheless, consumers under egoistic goal appeals outperform those under altruistic goal appeals in both behavioral autonomy and sustained willingness. This study makes three key contributions: it extends the application boundaries of self-quantification theory, reveals the differential effect mechanisms of self-quantification in community environments, and provides new theoretical perspectives and practical guidance for the sustainable development of green consumption. Full article

This study examines whether stricter enforcement of mandatory social insurance contributions affects corporate income tax behavior in China. In the Chinese institutional context, mandatory social insurance refers to payroll-based employer and employee contributions to five statutory programs: basic pension insurance, basic medical insurance, work-injury insurance, unemployment insurance, and maternity insurance. These programs are directly related to social sustainability because they finance old-age income security, medical protection, workplace injury compensation, unemployment support, maternity protection, and labor-market stability. Using China’s 2018 social insurance collection reform as a quasi-natural experiment, we analyze A-share listed companies from 2014 to 2024 through a difference-in-differences design based on differential exposure between private firms and state-owned enterprises. To assess the reliability of the identification strategy, we employ firm and year fixed effects, event-study analysis, placebo tests, alternative measures of tax avoidance, and propensity score matching difference-in-differences robustness checks. The findings show a tax-fee seesaw effect: private firms subject to extensive regulatory scrutiny respond to more rigorous enforcement of social insurance contributions by increasing corporate income tax avoidance. Analysis of the mechanisms shows that the Whited-Wu index of financial constraints partially explains this phenomenon. The effect is more pronounced in firms with higher labor costs and greater administrative expense intensity, indicating that the increased response is driven by labor cost exposure and organizational discretion. By contrast, the effect is weaker among firms audited by the Big Four accounting networks—Deloitte, PricewaterhouseCoopers, Ernst & Young, and KPMG—indicating that high-quality external audits constrain aggressive tax planning. Regionally, the effect is most pronounced in eastern China, where markets, labor costs, and tax-planning services are more developed. The findings contribute to the sustainable development literature by demonstrating that reforms designed to strengthen social insurance sustainability can unintentionally weaken tax compliance if payroll contributions, tax administration, and corporate financial pressures are not coordinated. The study highlights the importance of integrated fiscal governance for achieving socially sustainable and fiscally balanced development. Full article

The transition toward a circular economy (CE) in the plastic recycling sector requires integrated management frameworks that align technical performance with organizational governance. This study proposes an exploratory diagnostic framework for formalized recycling SMEs, integrating Latent Dirichlet Allocation (LDA) and Random Forest (RF) algorithms. Given the specialized nature of the sector, a purposive sample of 16 ‘pioneer’ SMEs in Bogotá was analyzed. Data were standardized through a 5-point ordinal scale, and the Spearman rank correlation analysis ($\rho \ge 0.85)$ revealed high internal consistency and structural synchronization. This high correlation reflects the operational homogeneity of the analyzed vanguard rather than a universal statistical claim. The findings suggest that, for these leading firms, circularity is driven by social impact, collaborative networks, and systemic process reengineering. Consequently, the proposed framework is presented as an exploratory diagnostic tool tailored to the specific structural characteristics of formalized recycling SMEs, providing a methodological basis for understanding circularity within this specialized niche. Full article

The integration of digital technologies is increasingly recognized as a critical enabler of lean practices in prefabricated construction projects. However, a systematic understanding of the underlying factors that drive this lean–digital transformation remains limited. To address the gap, this study identified 18 factors through an in-depth review of 30 papers and a follow-up questionnaire survey. The factors are divided into five dimensions, i.e., organizational, social, technological, economic and environmental, according to an extended framework of the Socio-Technical Systems (STS) and Technology–Organization–Environment (TOE). These 18 factors were then analyzed via a back propagation (BP) neural network model. The empirical data were collected from 148 practitioners across 11 regions in China where PC industrialization, digital technology adoption, and lean-related practices are relatively mature. These regions were selected because digitalization-driven lean practices are more observable in such contexts, allowing the BP model to capture the comprehensive contribution of key factors more effectively. The findings reveal that the effective implementation of the smart lean practices via digitalization is primarily driven by a systematic process, where greater attention should be directed toward simulation-based process optimization, robust information management, integrated design and construction, lean management systems, and the workers’ digital skills. Although the empirical evidence is derived from relatively mature PC and digital construction markets in China, the identified factors provide reference insights for broader PC projects including less mature regions to make effective measures to improve lean implementation. This study contributes to the existing knowledge body of lean in PC by extending the theories of STS and TOE to advance the understanding of digital drivers. Additionally, the results serve as a reference for stakeholders by informing strategic priorities such as resource allocation for workforce development, advancing the realization of smart lean prefabricated construction. Full article

In hot, semi-arid zones, cities are experiencing longer and more intense warm spells. Although the literature offers strategies to mitigate this threat, studies verifying their feasibility are limited. In this study, we aim to ascertain the feasibility of reducing land surface temperature (LST) through greening. We combine LST analysis with a feasibility assessment of cooling measures and consider physical and ownership dimensions alongside environmental and social factors, with Ouagadougou (Burkina Faso) serving as a case study. The average LST during the hottest period (April–May) was calculated from ECOSTRESS and Landsat remotely sensed data, and multiple regression models were used to analyse the relationship between LST and land cover/land use across the city’s districts and sectors. Our assessment incorporates greening scenarios, SWOT analyses, and equity assessments, and our results indicate that barren land is the primary determinant of diurnal LST. Planting 0.45 million trees could reduce LST by up to 2.4 °C in peripheral sectors if large roads, utilities, and vacant lands are targeted. This may reduce disparities in tree cover between sectors but could widen the gap between districts. Recommendations include a more hierarchical street network, enhancing utility provision, and reducing barren land in the peripheral sectors. Full article

Africans are widely recognised for their deeply rooted communal orientation. This ethos is intricately embedded in cultural practices such as burial rites, matrimonial customs, ritual observances, and broader conceptions of kinship. Within many African societies, the notion of family transcends the boundaries of the living, encompassing ancestors often conceptualised as the “living-dead” as well as extended familial networks. Despite the historical introduction and sustained influence of missionary and colonial religions, particularly Christianity, African Traditional Religion (ATR) continues to shape the beliefs and practices of many South Africans. Although Christianity remains a dominant religious tradition in South Africa, the persistence of ATR generates both points of convergence and sites of tension within the lived religious experiences of adherents. Against this backdrop, the present study critically examines the intersections and complexities between ATR and Christianity in South Africa, with particular emphasis on the African philosophy of community. Employing a qualitative research design informed by social cognitive theory and utilising a self-selection sampling strategy, data were collected through interviews with young adults (aged 25–40) affiliated with three mainline churches in Mamelodi, Pretoria, South Africa. The findings indicate that, while notable convergences exist between ATR and Christianity, significant complexities persist, particularly when interpreted through the lens of African communal philosophy. Full article

The recycling and reuse of discarded express delivery cartons can yield environmental, economic, and social benefits. A key factor influencing the volume of express packaging collected is the uncertainty in the total amount of such packaging within the service range of each collection point. Additional uncertainties include the costs associated with the construction of recycling stations, operational expenses, transportation costs, additional recycling fees, and government subsidies. To address the issue of express packaging recycling, a fuzzy integer programming model for the reverse logistics network of express packaging is constructed. The model aims to minimise the total network cost and maximise the total recycling rate while enabling decisions regarding the location of recycling facilities and the flow between facilities. Then, a memetic algorithm based on dynamic local search is designed. Several alternative solution approaches were considered to evaluate the proposed algorithm, including the precision optimization method (CPLEX) and a hybrid priority-based genetic algorithm. The results confirm the feasibility of the memetic algorithm. Finally, the applicability of this fuzzy programming model is analysed and validated by changing the confidence level. The case study results reveal quantifiable trade-offs: as the confidence level (α) increases from 0.75 to 0.90 under a fixed recycling rate threshold (ε = 80%), the total network cost rises approximately linearly, while the required number of recycling stations increases, with their average facility level upgrading accordingly. Variations in confidence levels and the degree of total recycling rate achievement can significantly influence the increase in target values. Moreover, the magnitude of this influence exhibits irregularity, indicating that changes in confidence levels entail a certain degree of risk. Full article

To better meet the actual demand for local renewable energy consumption and accelerate the achievement of the “dual carbon” goals, this paper proposes a robust optimization model for a multi-microgrid integrated energy system that incorporates hydrogen storage characteristics and an energy-sharing station. First, a framework consisting of external energy networks, energy-sharing stations, and multi-microgrid systems is developed, and a specific system model is defined. Second, a multi-time-scale hydrogen energy storage model is designed to enhance renewable energy utilization and increase the seasonal supportive effect of electricity. Third, a typical scenario selection method is developed to capture short-term fluctuations, seasonal trends, and structural characteristics. This method combines the synchronous backward reduction method, the Quantity-Contour method, and the modified Ward method. Next, considering the uncertainty of renewable energy, a multi-scenario confidence gap decision model is constructed with the system operation cost as the optimization objective. Finally, a case study is conducted to demonstrate the effectiveness of the proposed model. The results show that the proposed approach can reduce the total annual operating cost of the system by 82.64% while increasing renewable energy utilization. This study provides a reference for the efficient use of renewable energy and cross-seasonal energy interaction in multi-microgrid integrated energy systems, thereby promoting low-carbon and sustainable social development. Full article