Search Results (9,801)

The sustainable collaborative operation of multi-park integrated energy systems (MPIESs) with shared energy storage (SES) provides a significant pathway for low-carbon transition, renewable energy utilization, and energy efficiency improvement, thereby supporting regional energy sustainability. However, realizing this potential faces challenges, including source-load uncertainty, conflicts of interest among multiple entities, and the need for privacy-preserving distributed coordination. To address these issues, this paper proposes a distributed robust energy management strategy for MPIESs with SES, which is decomposed into two sub-problems. In the first sub-problem, a robust optimization model incorporating the SES leasing mechanism is established to handle the uncertainties of photovoltaic (PV) generation and loads. In the second sub-problem, a cooperative game model based on Nash bargaining theory is constructed to fairly allocate the cooperative surplus among participating parks. The alternating direction method of multipliers (ADMM) is employed to solve the overall model in a distributed manner, and enabling collaborative scheduling with limited information exchange. Case studies indicate that the proposed strategy reduces the total system operating cost by 17.57% compared to the independent operation mode. The benefit allocation mechanism achieves Pareto improvement and effectively mitigates the uneven distribution of cooperative surplus among parks. Furthermore, the distributed algorithm converges within 13 iterations in the test case, demonstrating good computational tractability. Consequently, the results verify the effectiveness of the proposed framework in balancing economy, fairness, and robustness, thereby promoting the low-carbon and sustainable operation of regional integrated energy systems. Full article

The contemporary media landscape has sustained a substantial transformation with the rise of AI-driven algorithmic platforms that enable activist organizations to produce and disseminate their own forms of political communication and campaigns. This study examines the YouTube channel of Code Pink, a prominent U.S.-based anti-war and social justice organization, to explore how activist media practices intersect with contemporary forms of journalism. Over a one-month period, video transcripts from the organization’s YouTube channel were analyzed using NVivo 15, employing a hybrid qualitative approach that combined inductive and deductive coding. Deductive codes were informed by sustained observation of the channel over one year (short and long videos on YouTube, TikTok, and X), supplemented by engagement with relevant news coverage, while inductive coding followed grounded theory principles, allowing themes to emerge directly from the transcripts. Large Language Models (LLMs) were employed as exploratory analytic tools to support AI-assisted qualitative analysis, complementing manual coding processes. The analysis focuses on how Code Pink frames political events and U.S. foreign policy through confrontational interviews, protest documentation, and the dissemination of commentary to online audiences. Findings suggest that the organization’s video content operates simultaneously as political activism, protest performance, and quasi-journalistic reporting. Activists frequently adopt journalistic techniques—including interviewing political figures, providing on-the-ground commentary, and framing narratives around public accountability—while also advancing explicit ideological positions that challenge dominant media narratives. The study highlights how platform-based activist media blurs the boundaries between journalism, advocacy, and political performance, contributing to the construction of alternative public narratives in the digital age. Full article

The rapid proliferation of generative artificial intelligence (GenAI) tools has created an urgent need for instruments to evaluate their educational value as teachers, faculty, administrators, and instructional designers consider adopting them. While rubrics exist to assess mobile applications and virtual reality tools, no comparable instrument has been developed specifically for large language models (LLMs) and AI media generators. The authors reviewed existing evaluation rubrics for edtech and GenAI tools, with edtech meaning digital tools that support ethical teaching to improve student learning and GenAI referring to neural networks that simulate human interactions by contextualizing relevant content based on learning needs. Grounded in Waks’ framework, the resulting Edu-GenAI Rubric comprises multiple dimensions organized into five domains: the Instrumental, Technical, Hedonic, Use, and Beneficial values. Dimensions include accuracy, productivity, personalization, citation, user interface, user experience, sharing, storage, and ethical dimensions encompassing data privacy, data transparency, guardrails, fair use, and algorithmic discrimination. The Edu-GenAI Rubric offers decision-makers with a preliminary, theory-informed instrument for evaluating GenAI tools in educational contexts that can be applied to institutional adoption decisions, developer benchmarking, and future research. Full article

This study proposes an innovative instructional approach to promote scientific literacy by integrating the Nature of Science and the Nature of Scientific Inquiry with experimental practice and the history of physics. The aim is to foster a deep understanding of how scientific knowledge is constructed and to promote informed trust in science. Using an explicit and reflective methodology, the intervention combines experimental activities with historical reflection. The core of the learning sequence is the experimental reconstruction of Galileo’s studies on falling bodies, based on the historical manuscript folio 116v, an original document that provides the empirical evidence for the law of falling bodies, illustrating the transition from raw experimental data to mathematical formalization. Through this activity, students engage with key epistemic aspects of scientific practice, including the management of uncertainty—distinguished into statistical/aleatory and structural/epistemic forms—the probabilistic nature of scientific knowledge, the predictive power of models and theories, and the underdetermination of scientific theories. Additional themes addressed include the role of thought experiments, the importance of communicating results for scrutiny and validation, the function of models as mediators between theory and phenomena, and the process of de-idealization. The study also challenges the persistent myth of a single, linear “scientific method,” highlighting instead the theory-laden character of scientific inquiry and the central role of the scientific community. This dimension is explored through the historical comparison between Galileo and Mersenne, which illustrates elements of the scientific ethos and the role of peer review as a mechanism for the correction and refinement of knowledge. The results obtained with pre-service teachers, with whom this instructional sequence was implemented, indicate that this contextualized approach facilitates the overcoming of a view of science as a set of absolute truths. Instead, it promotes a more mature understanding of science as a dynamic, provisional, and self-correcting human enterprise, while equipping future citizens with the critical tools necessary to navigate the challenges of the twenty-first century. Full article

Existential, spiritual, and religious themes often evoke strong emotions in therapy, yet little is known about how clients’ emotion regulation relates to these aspects. Spiritual psychotherapy for inpatient residential and intensive treatment (SPIRIT) integrates meaning in life within a cognitive-behavioral treatment (CBT) framework in acute and intensive mental health care and provides an appropriate context for examining this. This qualitative study explores: (1) clients’ beliefs about expressing, managing, or suppressing emotions related to meaning in life, spirituality, or religion (MSR); (2) how emotion regulation strategies (e.g., reappraisal, acceptance, and distress tolerance) are influenced by addressing MSR in therapy; and (3) whether engaging with MSR activates emotion regulation mechanisms for clients’ experienced distress. We analyzed 118 client evaluation forms and 19 semi-structured client interviews using a thematic approach informed by emotion regulation theory. SPIRIT-CBT made implicit beliefs about (MSR-related) emotion regulation explicit, and group interactions sometimes led to changes. Clients showed various regulation strategies, for example: MSR-based reappraisal, connectedness, reflection, and positive refocusing. However, emotional tension and suppression were also reported. Particularly from the interviews, it emerged that the therapy facilitated regulation mechanisms, including narrative processing, perspective shifting, sense-making, and social belonging. Focusing on MSR and existential themes addresses an important gap in mental health care and may contribute to supporting clients’ emotional recovery and overall well-being. Full article

In fault diagnosis, multi-source information fusion (MSIF) is usually more reliable than single-source information, and Dempster–Shafer (D-S) evidence theory provides a universal and popular decision-level fusion framework for MSIF. However, existing evidence fusion methods still have two limitations: (1) the overweighting effect of high information volume on unreliable evidence is ignored, and (2) the fusion accuracy cannot be further improved, as only one-time evidence correction is considered. To overcome these limitations, an evidence fusion method based on optimal coordination and iterative correction is proposed for fault diagnosis. Firstly, the credibility and information volume of each piece of evidence are quantified by the Jousselme distance and Deng entropy, respectively. Then, using game theory combination weighting (GTCW), credibility and information volume are optimally coordinated to correct all pieces of evidence, which are then initially fused with Dempster’s rule. Ultimately, taking the initial fusion result as the reference, the credibility is iteratively recalculated to correct and fuse all pieces of evidence until the fusion result converges. The optimal coordination suppresses the overweighting effect caused by high information volume, and the iterative correction breaks the limitation of one-time fusion. Experimental results demonstrate that the proposed method outperforms existing methods and can significantly improve the fusion results in fault diagnosis. Full article

Background/Objectives: Diet is implicated in the high burden of mental health on society, and research examining associations between these two fields is growing. However, qualitative explorations are lacking, especially within culturally diverse settings. This study aims to explore the beliefs on the mechanisms of the relationship between diet, food systems, and mental health, and the lived experience of such, through a case study of one Caribbean Small Island Developing State, to inform culturally grounded public health strategies that integrate nutritional and psychological well-being. Methods: Fifteen interviews with food system stakeholders and five focus groups with the general public were conducted. Transcripts were analyzed using a grounded theory approach with a critical realist epistemological stance. Results: Four major categories centered on beliefs of mechanistic effects of diet on mental health, as well as broader perspectives of the relationship between food systems, food experiences, and mental health. Participants believed that (1) unhealthy diets of processed and chemically treated foods contribute to poor mental health and that (2) food insecurity is a key threat to mental health, but they also believed that (3) consumption of locally produced foods and (4) residing in agricultural communities can be beneficial to mental health. Conclusions: Participants recognize that diet influences mental health through physiological, social, and structural pathways, but this connection is threatened by rising dependence on imported, processed foods. Along with complementary quantitative research, the findings highlight the potential of expanding nutritional health literacy and clinical guidance and strengthening local food systems and traditional diets for mental well-being among Caribbean Small Island Developing States. Full article

Preventive health behaviors play a critical role in reducing disease risks and improving public health outcomes, particularly among vulnerable populations such as women in rural communities. However, limited research has explored the determinants of intentions to adopt preventive health behaviors in developing contexts among women in rural communities. This study applies and extends the Theory of Planned Behavior (TPB) to examine these determinants in Algeria. A cross-sectional study was conducted using convenience sampling among 205 women in rural communities aged 20–60 years across five Algerian cities. Data were collected through a self-administered questionnaire and analyzed using hierarchical multiple regression. The results indicate that attitude, subjective norms, and perceived behavioral control have significant positive effects on behavioral intention. The inclusion of health literacy significantly enhances the model’s explanatory power, with higher literacy associated with stronger intentions. In contrast, perceived healthcare discrimination does not have a statistically significant effect. The extended model explains 57.5% of the variance in behavioral intention. These findings underscore the importance of psychosocial and informational factors in shaping preventive health intentions and support the extension of TPB in this context. They also provide practical implications for policymakers and healthcare practitioners to design targeted social marketing interventions aimed at improving preventive health behaviors and reducing health disparities among women in rural communities. Full article

China’s rural public open spaces (POS) are largely governed as common-pool resources through self-organized collective arrangements, often regarded as a viable pathway to sustainable commons management. Yet, in practice, these systems remain prone to overuse and under-maintenance, reflecting collective action failures associated with the tragedy of the commons. The governance of rural POS therefore constitutes a complex social–ecological problem shaped by the interplay of institutional rules, biophysical conditions, and user–stakeholder interactions. Taking Taigu District in Shanxi Province—characterized by heterogeneous social–ecological contexts and collective action dilemmas—as the empirical case, this study develops a meso-level baseline model to identify the key conditions (design principles) for sustainable rural POS governance. Adopting an expert-based epistemological approach, 24 specialists in rural governance (scholars, planners, and local administrators) were engaged. Grounded in commons and collective action theories within the Social–Ecological Systems (SES) framework and informed by Transaction Cost Economics (TCE), the study operationalizes a Delphic Hierarchy Process (DHP), combining three rounds of Delphi to establish consensus on governance conditions with the Analytic Hierarchy Process (AHP) to derive their relative weights. The model specifies 14 governance conditions across four interrelated dimensions: ecological (e.g., clearly defined resource boundaries and congruence between resource characteristics and user needs), institutional (e.g., simple and enforceable rules, accessible conflict-resolution mechanisms, accountable monitoring, and calibrated external support), social (e.g., social capital, leadership capacity, clearly defined user boundaries, and group interdependence), and interactional (e.g., resource dependence, equity in benefit distribution, and supply–demand alignment). It further clarifies their relative importance and systemic interdependencies. By operationalizing commons design principles within a meso-level analytical framework, the study advances their empirical application in rural planning and offers five targeted managerial implications to strengthen institutional robustness and the long-term sustainability of self-governed rural POS. Full article

To address issues existing in current multi-media pollution assessment, such as data mismatch, parameter conflicts, and inadequate characterization of nonlinear effects, this study developed a multi-factor synergistic assessment methodological system encompassing “data preprocessing-parameter calibration-damage quantification-model coupling”. A three-stage parameter calibration system of “inheritance-linkage-sensitivity screening” was established to achieve cross-media parameter synergy; an Environmental Damage Entropy (EDE) model was constructed based on information entropy to quantify the nonlinear coupled damage of multiple factors; and the optimal governance threshold was determined by combining the coupling theory of marginal damage and governance cost. Taking a multi-media pollution incident (atmosphere-soil-surface water-groundwater) caused by a chemical plant explosion as a case study, pollution chain identification, damage quantification, ecological risk cascading effect analysis, and health risk assessment were conducted. The results show that this method can accurately identify key pollution pathways. Based on the calculation of Environmental Damage Entropy (EDE = 0.604) and the synergy coefficient (δ = 1.32), the comprehensive damage value was quantified as 8.21 million yuan. Additionally, the threshold exceedance characteristics of various media were identified, reflecting the cumulative and lagging nature of ecological risk cascading effects. The method proposed in this study can accurately identify key pollution pathways and quantify comprehensive damage as well as ecological risks, providing scientific support for the allocation of multi-media pollution governance responsibilities and precise prevention and control. Full article

This paper considers the incentive-based demand response (IDR) economic dispatch problem (EDP) in smart grid while preserving the privacy of sensitive information, where the sensitive information is the consumers’ electricity consumption. The incentive-based demand response (IDR) optimization objective function as an EDP is established. A novel state decomposition-based privacy-preserving distributed consensus algorithm (SDecPP-DC) is designed to address this EDP, where the state decomposition mechanism is proposed to preserve the privacy of sensitive information. The feedback gains in the SDecPP-DC algorithm for the mismatch variables are non-coordinated and constant. The convergence of the proposed SDecPP-DC algorithm is theoretically proved by using multi-parameter perturbation theory. It is shown that the SDecPP-DC algorithm can deal with the directed network topology with a row-stochastic matrix, and the convergence point is the optimal solution of EDP. Finally, the correctness and effectiveness of SDecPP-DC are confirmed by the experiments. Full article

Background: The Double Empathy Theory proposes that communication difficulties between autistic and non-autistic individuals arise from mutual misunderstandings rather than individual deficits. This study examines how autistic–autistic, non-autistic–non-autistic, and mixed-neurotype adult pairs coordinate conversations. We aimed to determine how neurotype matches or mismatches affect the types and durations of turn-taking transitions, backchannels, temporal alignment, and task performance. Methods: Thirty-two autistic and thirty-six non-autistic English-speaking adults were paired into autistic–autistic, non-autistic–non-autistic, or mixed-neurotype dyads. Each pair interacted virtually in a tangram task, alternating roles as describer and selector. A turn-taking coding scheme identified utterance segmentation and conversational events. Results: Turn-exchanges with a gap (perceived silence) were the most frequent transition type across all pairs. Matched autistic pairs produced significantly more gapless transitions than the other dyads. Mixed-neurotype dyads showed significantly longer gap durations between turns than both autistic–autistic and non-autistic–non-autistic dyads. Non-autistic–non-autistic pairs exhibited the highest proportion of backchanneling, while autistic–autistic pairs exhibited the highest proportion of simultaneous talk. Only in non-autistic–non-autistic pairs overlap frequency was associated with reduced rapport. Conclusions: Findings demonstrate distinct patterns in turn-taking dynamics across neurotype pairings, supporting the Double Empathy Theory highlighting the need for neurodiversity-informed rather than deficit-based approaches. Full article

Urbanization increases pluvial flood risk by expanding impermeable surfaces, which is a trend likely to intensify with climate change. Permeable pavement (PePav) made from industrial byproducts, in accordance with circular economy principles, may improve soil permeability. Public acceptance remains a critical barrier to its implementation. Existing measures of willingness to accept (WtA) new technologies are inconsistent, limiting interdisciplinary collaboration. Therefore, a concise WtA scale was adapted from the Bogardus Social Distance Scale to assess acceptance of PePav at varying levels of proximity in residential contexts, from public flood-prone roads to private yards. The scale was evaluated across three studies: Study 1 (N = 195) and Study 2 (N = 187) utilized mixed student samples, while Study 3 (N = 625) involved a non-student sample. The 5-item solution, identified through factor analysis in Study 1, consistently demonstrated a unidimensional and cumulative structure and satisfactory reliability, even after the proposed PePav ingredient modification in subsequent studies. The scale correlated with recycling experience and professional background, indicating convergent validity, but not with flooding or informal construction experience, across all samples. Study 3 provided evidence of external validity by incorporating empirically well-established Theory of Planned Behavior (TPB) constructs and showing that WtA predicted PePav use beyond TPB variables and demographics. The scale also showed measurement invariance across sample type (student vs. general population) and different levels of construction experience. The constructed WtA scale is suitable for efficiently assessing professional and public acceptance of circular building materials and may have broad cross-disciplinary relevance. This enables timely, targeted interventions and informed policy decisions to advance sustainable technologies in the built environment, with substantial implications for education, professional policy, and sustainable engineering. Nevertheless, further validation is required. Full article

This study examines the impact of Sustainable Livelihood Capital (SLC) on women’s participation in production activities in Vietnam’s mangrove areas, using a gender-focused approach to advance gender-sensitive livelihood theory. Employing binary Logit regression and cross-tabulation analysis, model robustness was confirmed via the Bayesian Information Criterion (BIC). Findings identify financial and human capital as core factors influencing participation, with access to credit emerging as the factor exhibiting the strongest correlation. Livelihood stability and practical vocational training support women’s long-term productive engagement. The study highlights the livelihood paradox and resource lock-in effects: over-reliance on mangrove income reduces participation, limiting diversification, while over-exploitation correlates positively with participation as a survival strategy, exerting short-term environmental pressure. Conversely, owning traditional assets like fishing boats negatively affects participation, showing traditional factors hinder economic restructuring. The findings of heterogeneity analysis emphasize the necessity of policy intervention. For example, women under 56 are primarily financially driven; those over 56 exhibit lower participation and face severe human capital bottlenecks, especially education. Larger households face significant financial barriers despite having abundant human capital. Married women face dual constraints from financial and traditional physical capital, while single/divorced women hold advantages in education and opportunities. Furthermore, In areas far from fishing ports, financial and human capital are core drivers. This research provides quantitative evidence on the complex, heterogeneous effects of SLC on women’s productive engagement, offering a scientific foundation for multi-dimensional, targeted policy measures to foster sustainable livelihood diversification. Full article

The growing integration of immersive technologies into education is opening new possibilities for teaching and learning, while also raising concerns about the reliability and potential distortion of knowledge in artificial intelligence-mediated environments. Understanding how users perceive and accept artificial intelligence-generated content in immersive learning systems is therefore essential. This study explores the factors that influence user acceptance of artificial intelligence-driven virtual reality educational applications and explains it through a multidimensional framework that extends the Technology Acceptance Model, the Theory of Reasoned Action, and the Theory of Planned Behavior—a new ED-SCALE model. We innovated the previous models by adding an ergonomic dimension, often overlooked in virtual reality-based education. To test the model, we developed an artificial intelligence-driven virtual reality educational escape room designed to simulate adaptive and interactive learning experiences. Data were collected from 213 participants using a questionnaire measuring subjective norms, perceived behavioral control, attitudes toward artificial intelligence-mediated instruction, perceived informational efficacy, and ergonomic quality. The findings show that ergonomic quality, intuitive interfaces, physical comfort, and social influence play an important role in shaping user trust and long-term adoption intentions. The results suggest that the success of artificial intelligence-driven immersive learning systems depends not only on technological performance but also on user experience and social context, confirming our first hypothesis regarding new variables that are conditional for virtual technology acceptance. Full article