Search Results (2,597)

In rural and mountainous regions, power supply reliability remains a persistent challenge due to structural vulnerabilities, data incompleteness, and limited automation. In this study, a data-driven methodology is leveraged, wherein a validated machine learning framework comprising Random Forest (RF), Lasso Regression, and Recursive Feature Elimination (RFE) is applied to analyze outage data. The machine learning models, validated on a held-out test set, demonstrated modest but positive predictive performance, confirming a quantifiable, non-random relationship between grid structure and restoration time. This validation provides a credible foundation for the subsequent feature importance analysis. Through a transparent, consensus-based analysis of these models, the most robust influencing factors were identified. The results reveal that key structural indicators related to network redundancy (e.g., Inter-Bus Loop Rate) and electrical stress (e.g., Peak Daily Load Current, Load Factor) are the most significant predictors of prolonged outages. Furthermore, statistical analyses confirm that increasing structural redundancy and regulating line loads can effectively reduce outage duration. These findings offer practical, data-driven guidance for prioritizing investments in rural grid planning and reinforcement. This study contributes to the broader application of machine learning in energy systems, particularly showcasing a robust methodology for identifying key drivers under data and resource constraints. Full article

Background: The World Health Organization (WHO) defines health promotion as the process of enabling individuals to gain control over and improve their health. This shifts the focus from lifestyle choices to broader social determinants of health, requiring involvement from healthcare, authorities, industry, civil society, and the media. Civil society engagement in health initiatives offers benefits such as empowerment, service delivery, flexibility, policy participation, and credibility. However, identifying specific health promotion actions for civil society organizations (CSOs) is challenging. The lack of assessment tools for CSOs hinders evaluation and improvement. Objective: To develop a tool, ‘PromoACTIVA-SC’, to assess CSOs’ health promotion practice by identifying essential actions that constitute the health promotion process. Methods: ‘PromoACTIVA-SC’ was developed through documentary analysis and validated by experts. CSOs’ members participated in cognitive interviews for comprehensibility, and the tool was pilot tested for administration and Likert scale evaluation. Results: The final questionnaire, consisting of 8 phases and 40 items, demonstrated good content validity. Its use can help to map CSOs’ practices and identify areas needing improvement. CSOs can use it for self-assessment and in collaborative health promotion and disease prevention efforts. Conclusions: ‘PromoACTIVA-SC’ is the first tool designed to assess civil society’s role in the health promotion process. Its future use will reveal the extent to which civil society organizations actively participate in health promotion. It can also be used to promote CSOs’ involvement in health policy and administration, enhancing public health outcomes through collaborative, cross-sectoral efforts. Full article

Sustainability has become a key focus in the hospitality industry, with travelers increasingly seeking accommodations with strong environmental commitments. As part of this trend, many hotels are adopting green marketing strategies to improve their brand image and appeal to eco-conscious consumers. However, the challenge lies in ensuring that these strategies are perceived as genuine rather than as “greenwashing,” which undermines their effectiveness and harms the brand’s credibility. This study examines the impact of green marketing strategies on the brand image of 5-star hotels in Lima, Peru. A survey of 206 hotel clients reveals that the implementation of green marketing positively influences the perceived benefits, corporate image, trust, and loyalty associated with these establishments. The results highlight that younger generations, particularly Millennials and Generation Z, are more likely to value sustainability initiatives, making them an important target for hotels seeking to enhance their brand image through eco-friendly practices. The findings suggest that effective communication of sustainable practices and transparency are essential to avoid greenwashing and build customer loyalty. This research contributes to the limited knowledge on green marketing in the Peruvian hotel sector and provides insights for both hotel managers and researchers on the importance of integrating genuine sustainability efforts into their marketing strategies. Full article

Digital interventions are widely promoted as levers of institutional change, yet their effects often prove fragile. We examine why some interventions persist while others fade. Using crisp-set Qualitative Comparative Analysis (csQCA) on 13 large-scale cases from India and abroad, we identify the configurations of conditions under which digital systems become self-sustaining. We conceptualise persistence as a shift in the Nash equilibrium: when incentives realign, the new behaviour maintains itself without continuing external push. The analysis shows that software openness is neither necessary nor sufficient for durable change. Instead, six non-technological conditions—regulatory enablement, a credible revenue model, substantial scale, a clearly targeted systemic barrier, presence of enabling prerequisites, and sufficient time—are each necessary and, in combination, sufficient for an equilibrium shift; no single condition is enough on its own. Successful cases (e.g., Aadhaar, UPI, Chalo, Swiggy) meet these conditions in combination, whereas others (e.g., ONDC, DIKSHA, ICDS-CAS) illustrate how missing elements limit institutional embedding. The paper contributes a theory-informed diagnostic that links game-theoretic stability to configurational evaluation and provides practical “if–then” decision rules for appraisal. We argue that policy and investment decisions should prioritise incentive-compatible ecosystems over software attributes, and judge success by whether interventions reconfigure the rules of the game rather than by short-term uptake. This perspective clarifies when digital systems can contribute to sustainable, inclusive institutional transformation. Full article

Background/Objectives: The relationship between dairy consumption and cardiovascular or bone health outcomes remains controversial, with inconsistent findings across existing meta-analyses. In this study, we aimed to systematically evaluate and synthesize the evidence from published meta-analyses on dairy consumption and cardiovascular and bone health outcomes in adults, and to conduct updated meta-analyses incorporating recently published prospective cohort studies. Methods: We performed an umbrella review following PRISMA guidelines, searching published and grey literature up to April 2024. Meta-analyses evaluating dairy intake and its impact on cardiovascular and bone health outcomes were included. Updated meta-analyses were conducted for cardiovascular outcomes, while bone health outcomes were synthesized qualitatively. Methodological quality was assessed using the Joanna Briggs Institute checklist. Random-effects models were applied, and heterogeneity, small-study effects, excess significance, and prediction intervals were evaluated. Results: We included 33 meta-analyses (26 on cardiovascular, 7 on bone health outcomes). Updated meta-analyses showed that total dairy (RR: 0.96), milk (RR: 0.97), and yogurt (RR: 0.92) were significantly associated with reduced CVD risk. Total dairy and low-fat dairy were inversely linked to hypertension (RRs: 0.89, 0.87), and milk and low-fat dairy were associated with reduced stroke risk. Small-study effects were absent for most associations. Credibility was rated as “weak” for most associations, with total dairy and stroke, and total dairy and hypertension showing "suggestive" evidence. For bone health, dairy—especially milk—was linked to higher bone mineral density (BMD). Evidence on osteoporosis risk was mixed, and while total dairy and milk showed inconsistent associations with fractures, cheese and yogurt showed more consistent protective effects. Limited evidence suggested milk may reduce bone resorption markers. Conclusions: This review suggests that dairy consumption, particularly milk and yogurt, is modestly associated with reduced cardiovascular risk, while dairy intake appears to benefit BMD and fracture prevention. However, further research is needed to confirm these associations. Full article

This study examines the intersection of artificial intelligence-driven personal branding strategies and female entrepreneurship within Indonesia’s unique hijabi startup ecosystem. Through a mixed-methods approach combining sentiment analysis of 2847 social media posts, in-depth interviews with 35 hijabi entrepreneurs, and machine learning analysis of branding patterns, this research reveals how AI technologies can be leveraged to create culturally sensitive personal branding frameworks for Muslim female entrepreneurs. The findings demonstrate that successful hijabi entrepreneurs employ distinct AI-enhanced communication strategies that balance religious identity, professional credibility, and market positioning. The study introduces the “Halal Personal Branding Framework,” a novel theoretical model that integrates Islamic values with contemporary digital marketing practices. Results indicate that AI-driven personal branding increases startup funding success rates by 34% and market reach by 58% among hijabi entrepreneurs when culturally appropriate algorithms are employed. This research contributes to entrepreneurship communication theory while providing practical guidelines for developing inclusive AI systems that respect religious and cultural diversity in the digital economy. Full article

Introduction: The organization of mental health care is undergoing a transformation from an institutionalized model to a community-centered model. Due to the critical specialist workforce shortage, insufficient funding, and the large number of children in crisis, its implementation presents a challenge. The aim of this study is to analyze the current situation regarding access to system-based care under contracts with the National Health Fund in various provinces in Poland. Materials and Methods: Based on an analysis of data, resources available to patients were assessed—specifically, information was obtained from the National Health Fund website entitled “NFZ Treatment Waiting Times.” From this, the waiting times for appointments in child and adolescent mental health care facilities, the availability of mental health care facilities under contracts with the National Health Fund in Poland, legal acts, and data from the Central Statistical Office were extracted. Then, an analysis of the current accessibility to child and adolescent mental health services was conducted. The inclusion criteria for data sources were as follows: accessibility—the data had to be openly available to researchers without restrictions; credibility—the data had to be verified by individual health care facilities; usefulness—the data had to accurately reflect the actual availability of services and the needs within the child and adolescent psychiatric care system. Results: There are significant differences and deviations from the average number of facilities and waiting times when comparing the 16 provinces. Notably, some of the analyzed facilities are already operating within the framework of Child and Adolescent Mental Health Centers, where the mean waiting period for inpatient care is 105 days, the mean waiting period for day-care units is 61 days, and the mean waiting period for outpatient clinics is 257 days. The number of facilities is increasing under the reform, with new level I reference centers being opened, which ensures prevention and early support is provided by a pedagogue, psychologist, and non-medical staff, providing enhanced accessibility to care without the need for a visit to a child and adolescent psychiatrist, of whom there are only 579 for the entire child population in Poland. This metric primarily refers to first-time appointments in public institutions, with notable disparities between urban and rural areas. Conclusions: The development of the reform offers hope for quicker access to mental health support for children and adolescents. With the consistent implementation of the reform and further support from non-governmental organizations, there is a high chance of building an effective community-based model with a short waiting time for help and reducing ineffective hospitalizations, among other things, in terms of costs. Full article

This study investigated how specific sustainability topics disclosed in standalone sustainability reports influence firm value and whether third-party assurance moderates this relationship. Drawing on signaling, agency, stakeholder, and legitimacy theories, we applied latent Dirichlet allocation (LDA) to extract latent topics from U.S. corporate sustainability reports. We analyzed their impact on Tobin’s Q using panel regressions and supplement our findings with discrete Bayesian networks (DBNs) and Shapley additive explanations (SHAP) to capture non-linear patterns. We identified six core topics: environmental impact, sustainable consumption, daily necessities, socio-economic impact, healthcare, and operations. The results revealed that topics of healthcare and daily necessities have immediate and sustained positive effects on firm value, while environmental and socio-economic impact topics demonstrate lagged effects, primarily two years after disclosure. The presence of assurance, however, produces mixed outcomes: it enhances credibility in some cases, but reduces firm value in others, especially when applied to environmental and socio-economic disclosures. This suggests a dual signaling effect of assurance, potentially increasing investor scrutiny when gaps in performance are highlighted. Our findings underscore the importance of topic selection, consistency in reporting, and strategic application of assurance in ESG communications to maintain stakeholder trust and market value. Full article

With the widespread application of 3D models derived from oblique photography, the need for copyright protection and trusted transactions has risen significantly. Traditional transactions often depend on third parties, making it difficult to balance copyright protection with transaction credibility and to safeguard the rights and interests of both parties. To address these challenges, this paper proposes a novel trusted-transaction scheme that integrates smart contracts with zero-watermarking technology. Firstly, the skewness of the oblique-photography 3D model data is employed to construct a zero-watermark identifier, which is stored in the InterPlanetary File System (IPFS) alongside encrypted data for trading. Secondly, smart contracts are designed and deployed. Lightweight information, such as IPFS data addresses, is uploaded to the blockchain by invoking these contracts, and transactions are conducted accordingly. Finally, the blockchain system automatically records the transaction process and results on-chain, providing verifiable transaction evidence. The experimental results show that the proposed zero-watermarking algorithm resists common attacks. The trusted-transaction framework not only ensures the traceability and trustworthiness of the entire transaction process but also safeguards the rights of both parties. This approach effectively protects copyright while ensuring the reliability of the transactions. Full article

Background: This network meta-analysis aimed to assess the relative efficacy of macronutrient dietary groups with varying carbohydrate, fat, and protein ratios on weight control and cardiovascular risk factors improvement in adults. Methods: We searched PubMed, the Cochrane Central Register of Controlled Trials (CENTRAL), Embase, Web of Science Core Collection, and ClinicalTrials.gov from inception to 30 November 2024, as well as reference lists of related systematic reviews. Eligible randomized controlled trials (RCTs) were included. Literature screening, data extraction, and risk of bias assessment were conducted independently by two reviewers. The changes in body weight, blood glucose, systolic blood pressure, diastolic blood pressure, high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol, triglycerides, and total cholesterol were the study outcomes. Utilizing a Bayesian framework, a series of random-effects network meta-analyses were conducted to estimate mean difference (MD) with 95% credible interval (CrI) and determine the relative effectiveness of the macronutrient dietary groups. The quality of evidence for each pair of dietary groups was assessed based on the online tool called confidence in network meta-analysis (CINeMA). Results: This study initially identified 14,988 studies and ultimately included 66 eligible RCTs involving 4301 participants in the analysis. The very low carbohydrate–low protein (VLCLP, MD −4.10 kg, 95% CrI −6.70 to −1.54), the moderate carbohydrate–high protein (MCHP, MD −1.51 kg, 95% CrI −2.90 to −0.20), the very low carbohydrate–high protein (VLCHP, MD −1.35 kg, 95% CrI −2.52 to −0.26) dietary groups might lead to weight loss compared with the moderate fat–low protein (MFLP) dietary group. Among the dietary groups relative to the MFLP dietary group, the moderate carbohydrate–low protein (MCLP, MD 0.09 mmol/L, 95% CrI 0.02 to 0.16) and VLCHP (MD 0.16 mmol/L, 95% CrI 0.08 to 0.24) dietary groups were less effective in lowering HDL cholesterol, and the VLCHP (MD 0.50 mmol/L, 95% CrI 0.26 to 0.75) dietary group was less effective in lowering LDL cholesterol. In terms of triglyceride reduction, the MCLP (MD −0.33 mmol/L, 95% CrI −0.44 to −0.22), VLCHP (MD −0.31 mmol/L, 95% CrI −0.42 to −0.18), VLCLP (MD −0.14 mmol/L, 95% CrI −0.25 to −0.02), and moderate fat–high protein (MFHP, MD −0.13 mmol/L, 95% CrI −0.21 to −0.06) dietary groups were more efficacious than the MFLP dietary group, while any pair of dietary group interventions showed minimal to no difference in the effects on blood glucose, blood pressure, and total cholesterol. Conclusions: High or moderate certainty evidence reveals that the VLCLP dietary group is the most appropriate for weight loss, while the MCLP dietary group is best for reducing triglycerides. For control of blood glucose, blood pressure, and cholesterol levels, there is little to no difference between macronutrient dietary groups. Additionally, future studies in normal-weight populations are needed to verify the applicability of our findings. Full article

This research examines the cognitive and psychological mechanisms underlying young adults’ reactions to ESG-labeled online advertisements, specifically resistance to persuasion and purchase intention. Based on dual-process theories of persuasion and digital literacy theory, we develop and test a structural equation model (SEM) of perceived greenwashing, online advertising literacy, source credibility, persuasion knowledge, and advertising skepticism as predictors of behavioral intention. Data were gathered from 690 Greek consumers between the ages of 18–35 years through an online survey. All the direct effects hypothesized were statistically significant, while advertising skepticism was the strongest direct predictor of purchase intention. Mediation tests indicated that persuasion knowledge and skepticism partially mediated perceptions of greenwashing, literacy, and credibility effects, in favor of a complementary dual-route process of ESG message evaluation. Multi-group comparisons revealed significant moderation effects across gender, age, education, ESG familiarity, influencer trust, and ad-avoidance behavior. Most strikingly, women evidenced stronger resistance effects via persuasion knowledge, whereas younger users and those with lower familiarity with ESG topics were more susceptible to skepticism and greenwashing. Education supported the processing of source credibility and digital literacy cues, underlining the contribution of informational capital to persuasion resilience. The results provide theoretical contributions to digital persuasion and resistance with practical implications for marketers, educators, and policymakers seeking to develop ethical ESG communication. Future research is invited to broaden cross-cultural understanding, investigate emotional mediators, and incorporate experimental approaches to foster consumer skepticism and trust knowledge in digital sustainability messages. Full article

Quasi-experimental methods are a cornerstone of applied social science, answering causal questions to inform policy and practice. Although open science principles have influenced experimental research norms across the social sciences, related practices are rarely implemented in quasi-experimental scholarship. In this perspective article, we describe open science research practices and discuss practical strategies for quasi-experimental researchers to implement or adapt these practices. We also emphasize the shared responsibility of external stakeholders, including data providers, journals, and funders, to create the circumstances and incentives for open science practices to proliferate. While individual quasi-experimental studies may be incompatible with some or most practices, we argue that all quasi-experimental work can benefit from an open science mentality and that shifting research norms toward open science principles will ultimately enhance the transparency, accessibility, replicability, unbiasedness, and credibility of quasi-experimental social science research. Full article

In the context of Industry 4.0 and intelligent manufacturing, the scale and complexity of complex equipment systems are continuously increasing, making effective high-precision modeling, simulation, and prediction in the engineering field significant challenges. Digital twin technology, by establishing real-time connections between virtual models and physical systems, provides strong support for the real-time monitoring, optimization, and prediction of complex systems. However, traditional digital twin models face significant limitations when synchronizing with high-dimensional nonlinear and non-stationary dynamical systems due to the latter’s dynamic characteristics. To address this issue, we propose a multi-layer deep Koopman operator-based (MDK) credibility-based self-evolution algorithm for equipment digital twins. By constructing multiple time-scale embedding layers and combining deep neural networks for observability function learning, the algorithm effectively captures the dynamic features of complex nonlinear systems at different time scales, enabling their global dynamic modeling and precise analysis. Furthermore, to enhance the model’s adaptability, a trustworthiness-based evolution-triggering mechanism and an adaptive model fine-tuning algorithm are designed. When the digital twin model’s trustworthiness assessment indicates a decline in prediction accuracy, the evolution mechanism is automatically triggered to optimize and update the model with the fine-tuning algorithm to maintain its stability and robustness during dynamic evolution. The experimental results demonstrate that the proposed method achieves significant improvements in prediction accuracy within unmanned aerial vehicle (UAV) systems, showcasing its broad application potential in intelligent manufacturing and complex equipment systems. Full article

Meta-analytic deconvolution seeks to recover the distribution of true effects from noisy site-specific estimates. While Efron’s log-spline prior provides an elegant empirical Bayes solution with excellent point estimation properties, its plug-in nature yields severely anti-conservative uncertainty quantification for individual site effects—a critical limitation for what Efron terms “finite-Bayes inference.” We develop a fully Bayesian extension that preserves the computational advantages of the log-spline framework while properly propagating hyperparameter uncertainty into site-level posteriors. Our approach embeds the log-spline prior within a hierarchical model with adaptive regularization, enabling exact finite-sample inference without asymptotic approximations. Through simulation studies calibrated to realistic meta-analytic scenarios, we demonstrate that our method achieves near-nominal coverage (88–91%) for 90% credible intervals while matching empirical Bayes point estimation accuracy. We provide a complete Stan implementation handling heteroscedastic observations—a critical feature absent from existing software. The method enables principled uncertainty quantification for individual effects at modest computational cost, making it particularly valuable for applications requiring accurate site-specific inference, such as multisite trials and institutional performance assessment. Full article

This study introduces an inverted version of the three-parameter Hjorth lifespan model, characterized by one scale parameter and two shape parameters, referred to as the inverted Hjorth (IH) distribution. This asymmetric distribution can fit various positively skewed datasets more accurately than several existing models in the literature, as it can accommodate data exhibiting an inverted (upside-down) bathtub-shaped hazard rate. We derive key properties of the model, including quantiles, moments, reliability measures, stress–strength reliability, and order statistics. Point estimation of the IH model parameters is performed using maximum likelihood and Bayesian approaches. Moreover, for interval estimation, two types of asymptotic confidence intervals and two types of Bayesian credible intervals are obtained using the same estimation methodologies. As an extension to a complete sampling plan, Type-II censoring is employed to examine the impact of data incompleteness on IH parameter estimation. Monte Carlo simulation results indicate that Bayesian point and credible estimates outperform those obtained via classical estimation methods across several precision metrics, including mean squared error, average absolute bias, average interval length, and coverage probability. To further assess its performance, two real datasets are analyzed: one from the environmental domain (minimum monthly water flows of the Piracicaba River) and another from the pharmacological domain (plasma indomethacin concentrations). The superiority and flexibility of the inverted Hjorth model are evaluated and compared with several competing models. The results confirm that the IH distribution provides a better fit than several existing lifetime models—such as the inverted Gompertz, inverted log-logistic, inverted Lomax, and inverted Nadarajah–Haghighi distributions—making it a valuable tool for reliability and survival data analysis. Full article