Search Results (719)

The integration of renewable energy into power grids is imperative for reducing carbon emissions and mitigating reliance on depleting fossil fuels. In this paper, we develop symmetric and asymmetric evolutionary game-theoretic models to analyze how user–grid cooperation in peak shaving can be enhanced by incorporating whole-process democracy (deliberative governance) into decision-making. Our framework captures excess returns, cooperation-driven profits, energy pricing, participation costs, and benefit-sharing coefficients to identify equilibrium conditions under varied subsidy, cost, and market scenarios. Furthermore, this study integrates the theory, path, and mechanism of deliberative procedures under the perspective of whole-process democracy, exploring how inclusive and participatory decision-making processes can enhance cooperation in renewable energy systems. We simulate seven scenarios that systematically adjust subsidy rates, cost–benefit structures, dynamic pricing, and renewable-versus-conventional competitiveness, revealing that robust cooperation emerges only under well-aligned incentives, equitable profit sharing, and targeted financial policies. These scenarios systematically vary these key parameters to assess the robustness of cooperative equilibria under diverse economic and policy conditions. Our findings indicate that policy efficacy hinges on deliberative stakeholder engagement, fair profit allocation, and adaptive subsidy mechanisms. These results furnish actionable guidelines for regulators and grid operators to foster sustainable, low-carbon energy systems and inform future research on demand response and multi-source integration. Full article

Background: Addressing undernutrition and malnutrition requires a multi-pronged approach targeting different populations with appropriate interventions. Knowledge and perception (K&P) of Individuals and communities about nutrition to human health relationship/continuum is a prerequisite for addressing malnutrition in rural and mountain communities. Assessing K&P is essential for developing strategic interventions to up-scaling K&P of communities and achieving nutrition security. Homestead gardens are a proven intervention for achieving nutrition security for all family members of gardeners. Methods: This paper includes homestead gardeners from the Himalayan Mountain Region (HMR) as respondents. We developed a scale to assess the K&P of respondents, based on ratings from 20 judges. A total of 134 issues/items have been retained in the scale from macronutrients, micronutrients, minerals, and vitamins. A framework has also been developed and adopted for the study. A knowledge and perception index (KPI) has been developed based on the respondents’ responses. We have reviewed and analysed the national policy interventions for augmenting the K&P of the study community to achieve nutrition security. Results: The nutrition K&P of respondents are inadequate and far from the desirable level. Policy review and analysis indicate that the creation of K&P in the community to contribute to self and family nutrition security was previously highly neglected. Conclusions: The policy process of national, state, and county/district-level development sectors in developing countries under the HMR may take the initiative to ensure self-nutrition security by creating K&P of the community on nutrition issues. The designed scale is prudent requires testing and validation for measuring farmers’ K&P on nutrition, which may be adopted in future studies and policymaking not only nationally but also from an international perspective. Full article

The use of the Soil and Water Assessment Tool (SWAT) model driven by China Meteorological Assimilation Driving Datasets (CMADS) for runoff simulation research is of great significance for regional flood prevention and control. Therefore, from the perspective of production-living-ecological space, this article combined multi-source data such as DEM, soil texture and land use type, in order to construct scenarios of territorial spatial change (TSC) across distinct periods. Based on the CMADS-L40 data and the SWAT model, it simulated the runoff dynamics in the Xitiaoxi River Basin, and analyzed the hydrological response characteristics under different TSCs. The results showed that The SWAT model, driven by CMADS-L40 data, demonstrated robust performance in monthly runoff simulation. The coefficient of determination (R²), Nash–Sutcliffe efficiency coefficient (NSE), and the absolute value of percentage bias (|PBIAS|) during the calibration and validation period all met the accuracy requirements of the model, which validated the applicability of CMADS-L40 data and the SWAT model for runoff simulation at the watershed scale. Changes in territorial spatial patterns are closely correlated with runoff variation. Changes in agricultural production space and forest ecological space show statistically significant negative correlation with runoff change, while industrial production space change exhibits a significant positive correlation with runoff change. The expansion of production space, particularly industrial production space, leads to increased runoff, whereas the enlargement of agricultural production space and forest ecological space can reduce runoff. This article contributes to highlighting the role of land use policy in hydrological regulation, providing a scientific basis for optimizing territorial spatial planning to mitigate flood risks and protect water resources. Full article

The contamination of drinking water sources with selenium (Se) oxyanions, including selenite (Se(IV)) and selenate (Se(VI)), contains serious health hazards with an oral intake exceeding 400 µg/day and therefore requires urgent attention. Various natural and anthropogenic sources are responsible for high Se concentrations in aquatic environments. In addition, the chemical behavior and speciation of selenium can vary noticeably depending on the origin of the source water. The Se(VI) oxyanion is more soluble and therefore more abundant in surface water. Se levels in contaminated waters often exceed 50 µg/L and may reach several hundred µg/L, well above drinking water limits set by the World Health Organization (40 µg/L) and Germany (10 µg/L), as well as typical industrial discharge limits (5–10 µg/L). Overall, Se is difficult to remove using conventionally available physical, chemical, and biological treatment technologies. The recent literature has therefore highlighted promising advancements in Se removal using emerging technologies. These include advanced physical separation methods such as membrane-based treatment systems and engineered nanomaterials for selective Se decontamination. Additionally, other integrated approaches incorporating photocatalysis coupled adsorption processes, and bio-electrochemical systems have also demonstrated high efficiency in redox transformation and capturing of Se from contaminated water bodies. These innovative strategies may offer enhanced selectivity, removal, and recovery potential for Se-containing species. Here, a current review outlines the sources, distribution, and chemical behavior of Se in natural waters, along with its toxicity and associated health risks. It also provides a broad and multi-perspective assessment of conventional as well as emerging physical, chemical, and biological approaches for Se removal and/or recovery with further prospects for integrated and sustainable strategies. Full article

Ensuring sustainable food systems is an urgent global priority as populations grow and environmental pressures mount. Technological innovations such as genetic engineering (GE) and nanotechnology (nano) have been promoted as promising pathways for achieving greater sustainability in agriculture and food production. Yet, the sustainability of these technologies is not defined by technical performance alone; it hinges on how they are perceived by key stakeholders and how well they align with broader societal values. This study addresses the critical question of how expert stakeholders evaluate the sustainability of GE and nano-based food and agriculture (agrifood) products. Using a multi-method online platform, we engaged 42 experts across academia, government, industry, and NGOs in the United States to assess six real-world case studies—three using GE and three using nano—across ten different dimensions of sustainability. We show that nano-based products were consistently rated more favorably than their GE counterparts in terms of environmental, economic, and social sustainability, as well as across ethical and societal dimensions. Like prior studies, our results reveal that stakeholders see meaningful distinctions between nanotechnology and biotechnology, likely due to underlying value-based concerns about animal welfare, perceived naturalness, or corporate control of agrifood systems. The fruit coating and flu vaccine—both nano-enabled—received the most positive ratings, while GE mustard greens and salmon were the most polarizing. These results underscore the importance of incorporating stakeholder perspectives in technology assessment and innovation governance. These results also suggest that responsible innovation efforts in agrifood systems should prioritize communication, addressing meaningful societal needs, and the contextual understanding of societal values to build trust and legitimacy. Full article

Underwater acoustic (UWA) communication supports many critical applications but still faces several physical-layer signal processing challenges. In response, recent advances in machine learning (ML) and deep learning (DL) offer promising solutions to improve signal detection, modulation adaptability, and classification accuracy. These developments highlight the need for a systematic evaluation to compare various ML/DL models and assess their performance across diverse underwater conditions. However, most existing reviews on ML/DL-based UWA communication focus on isolated approaches rather than integrated system-level perspectives, which limits cross-domain insights and reduces their relevance to practical underwater deployments. Consequently, this systematic literature review (SLR) synthesizes 43 studies (2020–2025) on ML and DL approaches for UWA communication, covering channel estimation, adaptive modulation, and modulation recognition across both single- and multi-carrier systems. The findings reveal that models such as convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and generative adversarial networks (GANs) enhance channel estimation performance, achieving error reductions and bit error rate (BER) gains ranging from ${10}^{-3}$ to ${10}^{-6}$. Adaptive modulation techniques incorporating support vector machines (SVMs), CNNs, and reinforcement learning (RL) attain classification accuracies exceeding 98% and throughput improvements of up to 25%. For modulation recognition, architectures like sequence CNNs, residual networks, and hybrid convolutional–recurrent models achieve up to 99.38% accuracy with latency below 10 ms. These performance metrics underscore the viability of ML/DL-based solutions in optimizing physical-layer tasks for real-world UWA deployments. Finally, the SLR identifies key challenges in UWA communication, including high complexity, limited data, fragmented performance metrics, deployment realities, energy constraints and poor scalability. It also outlines future directions like lightweight models, physics-informed learning, advanced RL strategies, intelligent resource allocation, and robust feature fusion to build reliable and intelligent underwater systems. Full article

As industrial companies transition from the Industry 4.0 stage to the more human-centric and resilient Industry 5.0 paradigm, there is a growing need for structured assessment tools to prioritize modern technologies. This paper presents an integrated multi-criteria decision analysis (MCDA) approach to support the strategic assessment of technologies from three complementary perspectives: economic, organizational, and technological. The proposed model encompasses six key transformation areas and 22 technologies representing both the Industry 4.0 and 5.0 paradigms. A hybrid approach combining the DEMATEL (Decision-Making Trial and Evaluation Laboratory) and PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluation) methods is used to identify cause–effect relationships between the transformation areas and to construct technology rankings in each of the assessed perspectives. The results indicate that technologies such as the Internet of Things (IoT), cybersecurity, and supporting IT systems play a central role in the transition process. Among the Industry 5.0 technologies, hyper-personalized manufacturing, smart grids and new materials stand out. Moreover, the economic perspective emerges as the dominant assessment dimension for most technologies. The proposed analytical framework offers both theoretical input and practical decision-making support for companies planning their transformation towards Industry 5.0, enabling a stronger alignment between implemented technologies and long-term strategic goals. Full article

Under the continuous disturbance of ecosystems driven by urbanization, landscape fragmentation and the disruption of ecological processes and functions are key challenges in optimizing ecological networks (EN). This study aims to examine the spatiotemporal evolution of topological patterns, ecological processes, and ecosystem services (ES) in Wuhan from the “pattern–process–function” perspective. To overcome the lag in research concerning the coupling of ecological processes, functions, and spatial patterns, we explore the long-term dynamic evolution of ecosystem structure, process, and function by integrating multi-source data, including remote sensing, enabling comprehensive spatiotemporal analysis from 2000 to 2020. Addressing limitations in current EN optimization approaches, we integrate morphological spatial pattern analysis (MSPA), use circuit theory to identify EN components, and conduct spatial optimization accurately. We further assess the effectiveness of two scenario types: “pattern–function” and “pattern–process”. The results reveal a distinct “increase-then-decrease” trend in EN structural attributes: from 2000 to 2020, source areas declined from 39 (900 km²) to 37 (725 km²), while corridor numbers fluctuated before stabilizing at 89. Ecological processes and functions exhibited phased fluctuations. Among water-related indicators, water conservation (as a core function), and modified normalized difference water index (MNDWI, as a key process) predominantly drive positive correlations under the “pattern–function” and “pattern–process” scenarios, respectively. The “pattern–function” scenario strengthens core area connectivity (24% and 4% slower degradation under targeted/random attacks, respectively), enhancing resistance to general disturbances, whereas the “pattern–process” scenario increases redundancy in edge transition zones (21% slower degradation under targeted attacks), improving resilience to targeted disruptions. This complementary design results in a gradient EN structure characterized by core stability and peripheral resilience. This study pioneers an EN optimization framework that systematically integrates identification, assessment, optimization, and validation into a closed-loop workflow. Notably, it establishes a quantifiable, multi-objective decision basis for EN optimization, offering transferable guidance for green infrastructure planning and ecological restoration from a pattern–process–function perspective. Full article

Accurate body condition score (BCS) monitoring in dairy cows is essential for optimizing health, productivity, and welfare. Traditional manual scoring methods are labor-intensive and subjective, driving interest in automated imaging-based systems. This study evaluated the effectiveness of 2D imaging and deep learning for BCS classification using three camera perspectives—front, back, and top-down—to identify the most reliable viewpoint. The research involved 56 Norwegian Red milking cows at the Center for Livestock Experiments (SHF) of Norges Miljo-og Biovitenskaplige Universitet (NMBU) in Norway. Images were classified into BCS categories of 2.5, 3.0, and 3.5 using a YOLOv8 model. The back view achieved the highest classification precision (mAP@0.5 = 0.439), confirming that key morphological features for BCS assessment are best captured from this angle. Challenges included misclassification due to overlapping features, especially in Class 2.5 and background data. The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. Integration with precision farming tools enables continuous monitoring and early detection of health issues. This research highlights the potential of 2D imaging as a cost-effective alternative to 3D systems, particularly for small and medium-sized farms, supporting more effective herd management and improved animal welfare. Full article

This study conducts a comparative evaluation of Turkiye’s carbon emission profile from both sectoral and global perspectives. Utilizing 2022 data from 76 countries, it applies two widely recognized multi-criteria decision-making (MCDM) methods: MEREC, for determining objective weights of criteria, and PROMETHEE II, for ranking countries based on these criteria. All data used in the analysis were obtained from the World Bank, a globally recognized and credible statistical source. The study evaluates seven criteria, including carbon emissions from the energy, transport, industry, and residential sectors, along with GDP-related indicators. The results indicate that Turkiye’s carbon emissions, particularly from industry, transport, and energy, are substantially higher than the global average. Moreover, countries with higher levels of industrialization generally rank lower in environmental performance, highlighting a direct relationship between industrial activity and increased carbon emissions. According to PROMETHEE II rankings, Turkiye falls into the lower-middle tier among the assessed countries. In light of these findings, the study suggests that Turkiye should implement targeted, sector-specific policy measures to reduce emissions. The research aims to provide policymakers with a structured, data-driven framework that aligns with the country’s broader sustainable development goals. MEREC was selected for its ability to produce unbiased criterion weights, while PROMETHEE II was chosen for its capacity to deliver clear and meaningful comparative rankings, making both methods highly suitable for evaluating environmental performance. This study also offers a broader analysis of how selected countries compare in terms of their carbon emissions. As carbon emissions remain one of the most pressing environmental challenges in the context of global warming and climate change, ranking countries based on emission levels serves both to support scientific inquiry and to increase international awareness. By relying on recent 2022 data, the study offers a timely snapshot of the global carbon emission landscape. Alongside its contribution to public awareness, the findings are expected to support policymakers in developing effective environmental strategies. Ultimately, this research contributes to the academic literature and lays a foundation for more sustainable environmental policy development. Full article

The building sector holds a significant position in the global energy consumption share, and its environmental impact continues to intensify, making the construction industry a key player in sustainable development. The application of life cycle assessment on buildings (LCA-B) is widely employed to evaluate building energy and environment performance, and thus is of great significance for ensuring the sustainability of the project. This work aims to provide a systematic overview of LCA-B development based on reviewed literature. A three-stage mixed research method is adopted in this study: Firstly, an overall analysis framework is constructed, and 327 papers related to building life cycle assessment published between 2009 and 2025 are screened out by using the bibliometric method; Then, through scientometrics analysis, the journal regions, sources, scholars, and keyword evolution are revealed and analyzed using VOSviewer tool, and the hotspots in the field of LCA-B (e.g., integration of building information modeling (BIM) in LCA-B, multi-dimensional framework of environment–society–culture) are preliminarily explored based on the selected highly cited papers. The research finds that: (1) the performance of low energy buildings is better than that of net zero energy buildings from the perspective of LCA; (2) software compatibility and data exchange are the main obstacles in the integration of BIM-LCA; (3) a multi-dimensional LCA framework covering the social or cultural aspects is expected for a comprehensive assessment of building performance. This study provides a systematic analysis and elaboration of review articles related to LCA-B and thereby provides researchers with in-depth insight into this field. Full article

Developmental theories posit that social perspective-taking, the social-cognitive process of adopting another person’s viewpoint to understand the person’s thoughts and feelings, is important for youths’ successful functioning in close relationships, yet this idea has received little empirical attention. Guided by a social-emotional adjustment trade-offs framework, the current study tested the proposal that adolescents’ (N = 300, M age = 14.76) social perspective-taking would be linked with positive aspects of friendship in terms of friendship quality but also maladaptive aspects of friendship, namely co-rumination (i.e., excessive problem discussion between friends). This study used a multi-method design including surveys, laboratory tasks, and observations and extended past work by considering multiple dimensions of social perspective-taking including ability, tendency, and accuracy. Results provided support for friendship adjustment trade-offs of social perspective-taking. Full article

Designing sustainable farming systems in mountainous regions is particularly challenging because of complex economic, social, and environmental factors. Production models prioritizing sustainability and environmental protection require integrated assessment methodologies that can address multiple criteria and incorporate diverse stakeholders’ perspectives while ensuring accuracy and applicability. Life cycle assessment (LCA) and multi-actor multi-criteria analysis (MAMCA) are two complementary approaches that support “eco-design” strategies aimed at identifying the most sustainable options, including on-farm transformation processes. This study presents an integrated application of LCA and MAMCA to four supply chains: rye bread, barley beer, cow cheese, and goat cheese. The results show that cereal-based systems have lower environmental impacts than livestock systems do, although beer’s required packaging significantly increases its footprint. The rye bread chain emerged as the most sustainable and widely preferred option, except under high-climatic risk scenarios. In contrast, livestock-based systems were generally less favorable because of greater impacts and risks but gained preference when production security became a priority. Both approaches underline the need for a deep understanding of production performance. Future assessments in mountain contexts should integrate logistical aspects and cooperative models to enhance the resilience and sustainability of short food supply chains. Full article

Rapid urban expansion has led to an increasing number of people relocating to Urban–Rural Fringe Areas (URFAs) in China, with related development placing pressure on ecosystems in these locations. Community parks (CPs) are a key category of urban public park (UPPs) in Chinese planning and play a vital role in improving residents’ quality of life and enhancing regional environment, whilst also promoting sustainable urban development. Consequently, CPs are considered by many to be integral components of “communities” in Chinese cities. Drawing on documentary analysis and field research, this paper explores the socio-economic and ecological values associated with CP investments in URFAs in China. It assesses governmental policies and expert perspectives concerning CPs’ development in URFAs and analyses the factors influencing their planning and delivery. The research highlights how policy and stakeholders’ viewpoints impact the development of sustainable green space in URFAs. To enhance the construction of multi-functional CPs in URFAs, we propose a series of characteristics that need to be considered in future developments, including stakeholder engagement, resident needs, and park design. These insights offer an evidence-based reference for decision-makers, aiming to better meet the requirements of residents and support the development of urban sustainability. Full article

Multi-view classification (MVC) typically focuses on categorizing objects into distinct classes by employing multiple perspectives of the same objects. However, in numerous real-world applications, such as industrial inspection and quality control, there is an increasing need to distinguish particular objects from a pool of similar ones while simultaneously disregarding unknown objects. In these scenarios, relying on a single image may not provide sufficient information to effectively identify the scrutinized object, as different perspectives may reveal distinct characteristics that are essential for accurate classification. Most existing approaches operate within closed-set environments and are focused on generalization, which makes them less effective in distinguishing individual objects from others. This limitations are particularly problematic in industrial quality assessment, where distinguishing between specific objects and discarding unknowns is crucial. To address this challenge, we introduce a View-Compatible Feature Fusion (VCFF) method that utilizes images from predetermined positions as an accurate solution for multi-view classification of specific objects. Unlike other approaches, VCFF explicitly integrates pose information during the fusion process. It does not merely use pose as auxiliary data but employs it to align and selectively fuse features from different views. This mathematically explicit fusion of rotations, based on relative poses, allows VCFF to effectively combine multi-view information, enhancing classification accuracy. Through experimental evaluations, we demonstrate that the proposed VCFF method outperforms state-of-the-art MVC algorithms, especially in open-set scenarios, where the set of possible objects is not fully known in advance. Remarkably, VCFF achieves an average precision of 1.0 using only 8 cameras, whereas existing methods require 20 cameras to reach a maximum of 0.95. In terms of AUC-ROC under the constraint of fewer than 3$\sigma$ false positives—a critical metric in industrial inspection—current state-of-the-art methods achieve up to 0.72, while VCFF attains a perfect score of 1.0 with just eight cameras. Furthermore, our approach delivers highly accurate rotation estimation, maintaining an error margin slightly above 2° when sampling at 4° intervals. Full article