Search Results (503)

To address the limitations of low detection efficiency and poor spatial resolution of traditional cable insulation diagnosis methods, a novel cable insulation diagnosis method based on impedance spectroscopy has been proposed. An impedance spectroscopy analysis model of the frequency response of high-voltage single-core cables under different aging conditions has been established. The initial classification of insulation condition is achieved based on the impedance phase deviation between the test cable and the reference cable. Under localized aging conditions, the impedance phase spectroscopy is more than twice as sensitive to dielectric changes as the amplitude spectroscopy. Leveraging this advantage, a multi-parameter diagnostic framework is developed that integrates key spectral features such as the first phase angle zero-crossing frequency, initial phase, and resonance peak amplitude. The proposed method enables quantitative estimation of aging severity, spatial extent, and location. This technique offers a non-invasive, high-resolution solution for advanced cable health diagnostics and provides a foundation for practical deployment of power system asset management. Full article

In high-density and land-scarce urban environments such as Taipei—a typical example of compact development in East Asia—informal green spaces (IGSs)—defined as unmanaged or unplanned vegetated urban areas such as vacant lots, street verges, and railway margins—play a growing role in urban environmental and social dynamics. This study explores residents’ perceptions of IGSs and examines how these spaces contribute to urban sustainability and land governance. Using a mixed-methods approach that combines the literature review, field observations, and a structured public opinion survey in Taipei’s Wenshan District, the study identifies key perceived benefits and drawbacks of IGSs. Findings show that residents highly value IGSs for enhancing urban greenery, offering recreational opportunities, and promoting physical and mental health. However, concerns persist regarding safety, sanitation, and maintenance—particularly fears of waste accumulation, mosquito breeding, and risks to children. The results highlight the dual nature of IGSs as both vital ecological assets and potential sources of urban disorder. These insights underscore the need for inclusive, community-based governance models that can transform IGSs into legitimate components of green infrastructure. The study contributes to emerging discussions on adaptive urban land governance by proposing that informal spaces be strategically integrated into urban planning frameworks to enhance environmental equity, resilience, and citizen well-being. Full article

This paper analyzes the factors affecting the willingness to pay of urban households concerned with efficient waste management in Bangladesh. The multistage random sampling approach selected 1400 families from seven major cities in Bangladesh. This study addresses the socioeconomic and environmental factors that influence urban households’ willingness to pay for improved waste management services in Bangladesh. This study uniquely contributes to the literature by providing a large-scale empirical analysis of 1470 households using a logit model, revealing income, education, and environmental awareness as key predictors of WTP. Detailed survey data from respondents were then analyzed using a logit model based on the contingent valuation method. Indeed, the logit model showed that six variables (education, monthly income, value of the asset, knowledge of environment, and climate change) had a statistically significant effect on the WTP of the households. The results show that 63% of respondents were willing to pay BDT 250 or more per month. The most influential factors driving this willingness to pay were income (OR = 1.35), education level (OR = 1.45), and environmental awareness (OR = 3.56). These variables all contribute positively towards WTP. The idea is that families have some socioeconomic characteristics, regardless of which they are ready to pay for a higher level of waste collection. It is recommended that government interference be affected through various approaches, as listed below: support for public–private sector undertaking and disposal, an extensive cleaning campaign, decentralized management, cutting waste transport costs, and privatization of some waste management systems. These could be used to develop solutions to better waste management systems and improve public health. Full article

The integration of renewable energy sources, battery energy storage, and electric vehicles into industrial systems unlocks new opportunities for reducing emissions and improving sustainability. However, the coordination and management of these new technologies also pose new challenges due to complex interactions. This paper proposes a methodology for designing a holistic energy management system, based on advanced digital twins and optimization techniques, to minimize the cost of supplying industry loads and electric vehicles using local renewable energy sources, second-life battery energy storage systems, and grid power. The digital twins represent and forecast the principal energy assets, providing variables necessary for optimizers, such as photovoltaic generation, the state of charge and state of health of electric vehicles and stationary batteries, and industry power demand. Furthermore, a two-layer optimization framework based on mixed-integer linear programming is proposed. The optimization aims to minimize the cost of purchased energy from the grid, local second-life battery operation, and electric vehicle fleet charging. The paper details the mathematical fundamentals behind digital twins and optimizers. Finally, a real-world case study is used to demonstrate the operation of the proposed approach within the context of the waste collection and management industry. The study confirms the effectiveness of digital twins for forecasting and performance analysis in complex energy systems. Furthermore, the optimization strategies reduce the operational costs by 1.3%, compared to the actual industry procedure, resulting in daily savings of EUR 24.2 through the efficient scheduling of electric vehicle fleet charging. Full article

The Morehouse School of Medicine Prevention Research Center (MSM-PRC) conducted a Community Health Needs and Assets Assessment (CHNAA) survey using a Community-Based Participatory Research (CBPR) approach. In this article, we will demonstrate the application of CBPR in informing research agenda and implementation strategies. We will discuss the practical considerations and potential benefits of engaging the community in data collection, interpretation, and utilization to address community health challenges. Emphasizing collaboration, co-learning, and respect, and guided by the CBPR principles, CHNAA ensured that community voices led to the identification and integration of the research priorities. Overseen by the Community Coalition Board (CCB) and its Data Monitoring and Evaluation (DME) Committee, the survey featured closed- and open-ended questions addressing social determinants of health. Out of 1000 targeted participants, 754 provided valid responses, with a 75% response rate. Most respondents were female and represented a racially diverse group. Descriptive statistics and thematic analysis revealed that key health concerns were diabetes, COVID-19, mental health, and high blood pressure. Barriers to care included lack of food access, affordable housing, and limited mental health services. The findings led to five public health initiatives launched between 2023 and 2024 demonstrating the CBPR model’s effectiveness in aligning community needs with actionable solutions. Full article

Understanding the complex and multidimensional nature of poverty is essential for designing effective and targeted policy interventions in rural Ethiopia. This study examined the determinants of multidimensional poverty in Bilate River Basin in South Ethiopia, employing cross-sectional household survey data collected in 2024. A total of 359 households were selected using a multistage sampling technique, ensuring representation across agro-ecological and socio-economic zones. The analysis applied the Generalized Ordered Logit (GOLOGIT) model to categorize households into four mutually exclusive poverty statuses: non-poor, vulnerable, poor, and extremely poor. The results reveal that age, dependency ratio, education level, livestock and ox ownership, access to information and credit, health status, and grazing land access significantly influence poverty status. Higher dependency ratios and poor health substantially increase the likelihood of extreme poverty, while livestock ownership and access to grazing land reduce it. Notably, credit use and access to information typically considered poverty reducing were associated with increased extreme poverty risks, likely due to poor financial literacy and exposure to misinformation. These findings underscored the multidimensional and dynamic nature of poverty, driven by both structural and behavioral factors. Policy implications point to the importance of integrated interventions that promote education, health, financial literacy, and access to productive assets to ensure sustainable poverty reduction and improved rural livelihoods in Ethiopia. Full article

Information availability through the web has been both a challenge and an asset for healthcare support, as evidence-based information coexists with unsupported claims. With the emergence of artificial intelligence (AI), this situation may be enhanced or improved. The aim of the present study was to compare the quality assessment of online dietary weight loss information conducted by an AI assistant (ChatGPT 4.5) to that of health professionals. Thus, 177 webpages publishing dietary advice on weight loss were retrieved from the web and assessed by ChatGPT-4.5 and by dietitians through (1) a validated instrument (DISCERN) and (2) a self-made scale based on official guidelines for weight management. Also, webpages were assessed by a ChatGPT custom scoring system. Analysis revealed no significant differences in quantitative quality scores between human raters, ChatGPT-4.5, and the AI-derived system (p = 0.528). On the contrary, statistically significant differences were found between the three content accuracy scores (p < 0.001), with scores assigned by ChatGPT-4.5 being higher than those assigned by humans (all p < 0.001). Our findings suggest that ChatGPT-4.5 could complement human experts in evaluating online weight loss information, when using a validated instrument like DISCERN. However, more relevant research is needed before forming any suggestions. Full article

Adequate and diverse diets are essential for children’s physical and cognitive development, yet food insecurity and malnutrition continue to threaten this fundamental right, which remains a pressing concern in many resource-poor settings. This study investigated food and nutrition security in Early Childhood Development (ECD) centres in Makhanda, South Africa, through a community-based participatory research approach. Using a mixed-methods approach combining questionnaire interviews, focus group discussions, direct observations, and community asset mapping across eight ECD centres enrolling 307 children aged 0–5 years, the study engaged ECD facilitators and analysed dietary practices across these centres. Results indicated that financial constraints severely affect the quality and diversity of food provided at the centres, thus undermining the ability to provide nutritionally adequate meals. The average amount spent on food per child per month at the centres was R90 ± R25 (South African Rand). Although three meals were generally offered daily, cost-driven dietary substitutions with cheaper, less diverse alternatives, often at the expense of nutritional value, were common. Despite guidance from Department of Health dieticians, financial limitations contributed to suboptimal feeding practices, with diets dominated by grains and starchy foods, with limited access to and rare consumption of protein-rich foods, dairy, and vitamin A-rich fruits and vegetables. ECD facilitators noted insufficient parental contributions and low engagement in supporting centre operations and child nutrition provision, indicating a gap in awareness and limited nutrition knowledge regarding optimal infant and young child feeding (IYCF) practices. The findings emphasise the need for sustainable, multi-level and community-led interventions, including food gardening, creating ECD centre food banks, parental nutrition education programmes, and enhanced financial literacy among ECD facilitators. Strengthening local food systems and establishing collaborative partnerships with communities and policymakers are essential to improve the nutritional environment in ECD settings. Similarly, enhanced government support mechanisms and policy-level reforms are critical to ensure that children in resource-poor areas receive adequate nutrition. Future research should focus on scalable, locally anchored models for sustainable child nutrition interventions that are contextually grounded, community-driven, and should strengthen the resilience of ECD centres in South Africa. Full article

A persistent challenge in digital asset management is the lack of standardized models for integrating health assessment—such as the Asset Health Index (AHI)—into Digital Twins, limiting their extended implementation beyond individual projects. Asset managers in the energy sector face challenges of digitalization such as digital environment selection, employed digital modules (absence of an architecture guide) and their interconnection, sources of data, and how to automate the assessment and provide the results in a friendly decision support system. Thus, for energy systems, the integration of Asset Assessment in virtual replicas by Digital Twins is a complete way of asset management by enabling real-time monitoring, predictive maintenance, and lifecycle optimization. Another challenge in this context is how to compound in a structured assessment of asset condition, where the Asset Health Index (AHI) plays a critical role by consolidating heterogeneous data into a single, actionable indicator easy to interpret as a level of risk. This paper tries to serve as a guide against these digital and structured assessments to integrate AHI methodologies into Digital Twins for energy converters. First, the proposed AHI methodology is introduced, and after a structured data model specifically designed, orientated to a basic and economic cloud implementation architecture. This model has been developed fulfilling standardized practices of asset digitalization as the Reference Architecture Model for Industry 4.0 (RAMI 4.0), organizing asset-related information into interoperable domains including physical hierarchy, operational monitoring, reliability assessment, and risk-based decision-making. A Unified Modeling Language (UML) class diagram formalizes the data model for cloud Digital Twin implementation, which is deployed on Microsoft Azure Architecture using native Internet of Things (IoT) and analytics services to enable automated and real-time AHI calculation. This design and development has been realized from a scalable point of view and for future integration of Machine-Learning improvements. The proposed approach is validated through a case study involving three high-capacity converters in distinct operating environments, showing the model’s effective assistance in anticipating failures, optimizing maintenance strategies, and improving asset resilience. In the case study, AHI-based monitoring reduced unplanned failures by 43% and improved maintenance planning accuracy by over 30%. Full article

In recent decades, the intensification of extreme events, such as floods, earthquakes, and hydrogeological instability, together with the spread of pollutants harmful to health, has highlighted the vulnerability of territories and the need to direct urban policies towards sustainable strategies. The built assets and the real estate sector play a key role in this context; indeed, being among the first ones to be exposed to the effects of climate change, they serve as a crucial tool for the implementation of governance strategies that are more focused on environmental issues. However, the insufficient allocation of public resources to interventions to secure the territory has made it essential to involve private capital interested in combining the legitimate needs of performance with the “ethicality” of the investment. In light of the outlined framework, real estate managers are called upon to take into consideration the environmental risks associated with real estate investments and accurately represent them to investors, especially in the fundraising phase. The tools currently used for the analysis of such risks are based on their perception measured by the “risk premium” criterion, reconstructed on the basis of previous trends and the analyst’s expertise. The poor ability to justify the nature of the risk premium and the uncertainty about future scenario evolutions make this approach increasingly less valid. The present work, starting from the aspects of randomness of the risk premium criterion, aims at its evolution through the inclusion of environmental risk components (seismic, hydrogeological, and pollution). Full article

The transition toward sustainability in the construction industry requires integrated tools that align with circular economy principles. This study introduces the Interactive Stakeholder Journey Map in Construction (ISJMC), an innovative visual and systemic tool that supports waste management and circularity throughout the life cycle of construction assets. Although the sector is economically significant, it remains one of the main contributors to environmental degradation due to high resource consumption and low waste recovery rates. Developed according to EN 15643-3:2012, a European standard that provides a framework for assessing the social sustainability of construction works, focusing on aspects such as accessibility, health, and comfort and grounded in the Design Thinking methodology, ISJMC enables mapping stakeholder interactions, touchpoints, and responsibilities across all life cycle stages, including initiative, design, procurement, construction, use, and end of life. A systematic literature review and collaborative workshops guided the tool’s development and validation. The application in a real case involving a medium-sized Brazilian construction company helped identify significant pain points and opportunities for implementing circular practices. The results demonstrate that ISJMC (i) facilitates a systemic and visual understanding of material and information flows, (ii) promotes transparent mapping of resource value to support better decision-making, and (iii) encourages the identification of circularity opportunities while fostering collaboration among stakeholders. The tool revealed critical challenges related to waste generation and management. It supported co-creating sustainable strategies, including improved material selection, lean construction practices, and stronger supplier engagement. By translating complex standards into accessible visual formats, ISJMC contributes to the academic field, supports practical applications, and offers a foundation for expanding circular approaches in construction projects. Full article

Convolutional Neural Networks (CNNs) have been demonstrated to be one of the most powerful methods for image recognition, being applied in many fields, including civil and structural health monitoring in infrastructure asset management. Current State-of-the-Art CNN models are now accessible as open-source and available on several Artificial Intelligence (AI) platforms, with TensorFlow being widely used. Besides CNN models, Vision Transformers (ViTs) have recently emerged as a competitive alternative. Several demonstrations have indicated that ViT models, in many instances, outperform the current CNNs by almost four times in terms of computational efficiency and accuracy. This paper presents an investigation into defect detection for civil and structural components using CNN and ViT models available on TensorFlow. An empirical study was conducted using a database of cracks. The severity of crack is categorized into binary states: “with crack” and “without crack”. The results confirm that the accuracies of both CNN and ViT models exceed 95% after 100 epochs of training, with no significant difference observed between them for binary classification. Notably, the cost of this AI-based approach with images taken by lightweight and low-cost drones is considerably lower compared to high-speed inspection cars, while still delivering an expected level of predictive accuracy. Full article

Background/Objectives: Prior research has established the significant role of financial support in shaping older adults’ physical health but often overlooks the heterogeneous effects of distinct financial support types and their underlying mechanisms. This study addresses these gaps by investigating how property-based support and children’s financial support differentially influence the health of older people, aiming to inform targeted interventions for healthy aging. Methods: Based on 2020 microdata from the China Health and Retirement Longitudinal Study (CHARLS), the analysis was conducted using the ordered logistic regression model and the mediation effect model. Results: Property ownership demonstrated a significant positive association with older adults’ physical health (β = 0.21, p < 0.01), while children’s financial support showed an adverse effect (β = −0.14, p < 0.05). These relationships were mediated by two key pathways: enhanced social participation (accounting for 32%) and increased engagement in sports activities (accounting for 28%). Conclusions: The study underscores the need to differentiate between financial support sources when designing aging policies. Recommendations include incentivizing asset accumulation among older adults, promoting delayed retirement for capable individuals, and fostering community-based initiatives to boost social and physical activity participation. These findings advocate for integrated policy frameworks that combine financial empowerment with social engagement opportunities to address aging challenges in China. Full article

The Shapley value is a fundamental concept in data science, providing a principled framework for fair resource allocation, feature importance quantification, and improved interpretability of complex models. Its fundamental theory is based on four axiomatic proper ties, which underpin its widespread application. To address the inherent computational challenges of exact calculation, we discuss model-agnostic approximation techniques, such as Random Order Value, Least Squares Value, and Multilinear Extension Sampling, as well as specialized fast algorithms for linear, tree-based, and deep learning models. Recent extensions, such as Distributional Shapley and Weighted Shapley, have broadened the applications to data valuation, reinforcement learning, feature interaction analysis, and multi-party cooperation. Practical effectiveness has been demonstrated in health care, finance, industry, and the digital economy, with promising future directions for incorporating these techniques into emerging fields, such as data asset pricing and trading. Full article

Background/Objectives: Evidence indicates a high prevalence of stunting and overweight among Iranian children. This study explores their socioeconomic patterning and the mediating role of nutrition adequacy. Methods: The data were derived from the most recent 2017 Demography and Health Survey and the Multiple Indicator Cluster Survey, which were conducted in Iran. Children aged between 2 and 5 years were selected for the study through a two-stage random sampling process (n = 11,147). The probability of stunting and overweight was modeled using logistic regression. Parental education, occupation, and living conditions (areas, rooms, and assets of the household) were explanatory variables, with the diet diversity score (DDS) as a mediator. Analyses were adjusted for age and sex. Results: Children had over 1.7 times higher odds of stunting with a primary-educated father [95% CI: 1.13–2.62] and twice the odds with an illiterate mother [95% CI: 1.30–3.30]. The risk of stunting was almost 1.5 higher in children living in smaller houses [95% CI: 1.12–2.04]. Finally, a significant association was observed between low asset ownership and stunting [OR = 2.01; 95% CI: 1.23–3.27]. The results showed no significant relationship between socioeconomic factors and children’s overweight, indicating that overweight was less socially patterned. Higher DDS was associated with lower stunting and higher overweight prevalence but did not mediate the effects of socioeconomic status. Conclusions: Stunting disproportionately affects children from households with a lower socioeconomic background in Iran. Parental education, area, and assets were key factors, highlighting the need for targeted nutrition education programs. Full article