Search Results (3,743)

Demand Side Management (DSM) plays a crucial role in modern energy systems, enabling more efficient use of energy resources and contributing to the sustainability of the power grid. This study examines DSM strategies within a multi-environment context encompassing residential, commercial, and industrial sectors, with a focus on diverse appliance types that exhibit distinct operational characteristics and user preferences. Initially, a single-objective optimization approach using Genetic Algorithms (GAs) is employed to minimize the total energy cost under a real Time-of-Use (ToU) pricing scheme. This heuristic method allows for the effective scheduling of appliance operations while factoring in their unique characteristics such as power consumption, usage duration, and user-defined operational flexibility. This study extends the optimization problem to a multi-objective framework that incorporates the minimization of CO₂ emissions under a real annual energy mix while also accounting for user discomfort. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is utilized for this purpose, providing a Pareto-optimal set of solutions that balances these competing objectives. The inclusion of multiple objectives ensures a comprehensive assessment of DSM strategies, aiming to reduce environmental impact and enhance user satisfaction. Additionally, this study monitors the Peak-to-Average Ratio (PAR) to evaluate the impact of DSM strategies on load balancing and grid stability. It also analyzes the impact of considering different periods of the year with the associated ToU hourly schedule and CO₂ emissions hourly profile. A key innovation of this research is the integration of detailed, category-specific metrics that enable the disaggregation of costs, emissions, and user discomfort across residential, commercial, and industrial appliances. This granularity enables stakeholders to implement tailored strategies that align with specific operational goals and regulatory compliance. Also, the emphasis on a user discomfort indicator allows us to explore the flexibility available in such DSM mechanisms. The results demonstrate the effectiveness of the proposed multi-objective optimization approach in achieving significant cost savings that may reach 20% for industrial applications, while the order of magnitude of the trade-offs involved in terms of emissions reduction, improvement in discomfort, and PAR reduction is quantified for different frameworks. The outcomes not only underscore the efficacy of applying advanced optimization frameworks to real-world problems but also point to pathways for future research in smart energy management. This comprehensive analysis highlights the potential of advanced DSM techniques to enhance the sustainability and resilience of energy systems while also offering valuable policy implications. Full article

In the context of achieving low-carbon goals, building low-carbon energy systems is a crucial development direction and implementation pathway. Renewable energy is favored because of its clean characteristics, but the access may have an impact on the power grid. Microgrid technology provides an effective solution to this problem. Uncertainty exists in single microgrids, so multiple microgrids are introduced to improve system stability and robustness. Electric carbon trading and profit redistribution among multiple microgrids have been challenges. To promote energy commensurability among microgrids, expand the types of energy interactions, and improve the utilization rate of renewable energy, this paper proposes a cooperative operation optimization model of multi-microgrids based on the green certificate and carbon trading mechanism to promote local energy consumption and a low carbon economy. First, this paper introduces a carbon capture system (CCS) and power-to-gas (P2G) device in the microgrid and constructs a cogeneration operation model coupled with a power-to-gas carbon capture system. On this basis, a low-carbon operation model for multi-energy microgrids is proposed by combining the local carbon trading market, the stepped carbon trading mechanism, and the green certificate trading mechanism. Secondly, this paper establishes a cooperative game model for multiple microgrid electricity carbon trading based on the Nash negotiation theory after constructing the single microgrid model. Finally, the ADMM method and the asymmetric energy mapping contribution function are used for the solution. The case study uses a typical 24 h period as an example for the calculation. Case study analysis shows that, compared with the independent operation mode of microgrids, the total benefits of the entire system increased by 38,296.1 yuan and carbon emissions were reduced by 30,535 kg through the coordinated operation of electricity–carbon coupling. The arithmetic example verifies that the method proposed in this paper can effectively improve the economic benefits of each microgrid and reduce carbon emissions. Full article

In engineering design, optimization is crucial for achieving sustainable goals. This involves creating environmentally responsible structures. Optimizing the design is the first step in reducing the environmental impact of construction. Topology optimization (TO) is one way to do this. TO is the process of designing the material layout in the design domain according to selected criteria. The criteria can be explicitly defined to promote sustainability. As a result, a new structure topology is proposed to make the structure both lightweight and durable, with the aim of improving its functionality and reducing its environmental impact. In optimal engineering design, it is particularly important to take into account the structure’s special operating conditions, e.g., loads subject to random changes. Predicting topologies under such conditions is important since random load changes can significantly affect the resulting topologies. In this paper, an easy to implement numerical method for this kind of problem is proposed. The basic idea is to transform a random loads case into the deterministic problem of multiple loads. A heuristic method of Cellular Automata is proposed as a numerical optimization tool. The examples of topology optimization have been performed to illustrate the concept, confirming the efficiency, versatility, and ease of its implementation. Full article

Enhancing the nutritional content of crops is crucial for safeguarding human health and mitigating global hunger. A viable method for attaining this goal is the planned implementation of various agronomic practices, including tillage and nutrient provision. A field experiment was executed at the Hungarian University of Agriculture and Life Sciences in Gödöllő in the 2023 and 2024 growing seasons. The study aimed to assess the effects of foliar nutrient supply and soil tillage methods on the grain nutritional composition and mineral content of winter barley. Employing a split-plot design with three replications, the experiment included four nutrient treatments (control, bio-cereal, bio-algae, and MgSMnZn blend) and two soil tillage types (i.e., plowing and cultivator). The results indicated that while protein content was not influenced by the main effects of nutrients and tillage, the levels of β-glucan, starch, crude ash, and moisture content were significantly (p < 0.05) affected by the nutrient treatments and by growing year, treated as a random factor. Notably, bio-algae and bio-cereal nutrients, combined with cultivator tillage, enhanced β-glucan content. All applied nutrient treatments increased the level of starch compared to the control. With regard to grain mineral content, the iron and zinc content responded to the nutrient supply, tillage, and growing year. However, applying a multiple-nutrient composition-based treatment did not increase iron and zinc levels, suggesting that individual applications may be more effective for increasing the content of these minerals in grains. Cultivator tillage improved iron and zinc levels. Moreover, manganese (Mn) and copper (Cu) were predominantly affected by nutrient availability and by growing seasons as a random factor. Therefore, to improve grain quality, this study emphasizes the significance of proper nutrient and tillage methods by focusing on the intricate relationships between agronomic techniques and environmental factors that shape barley’s nutritional profile. Full article

Promoting sustainable development requires a clear understanding of how short-term fluctuations in anthropogenic emissions affect urban environmental quality. This is especially relevant for cities experiencing rapid industrial changes or emergency policy interventions. Among key environmental concerns, variations in ambient pollutants like ozone (O₃) are closely tied to both public health and long-term sustainability goals. However, traditional chemical transport models often face challenges in accurately estimating emission changes and providing timely assessments. In contrast, statistical approaches such as the difference-in-differences (DID) model utilize observational data to improve evaluation accuracy and efficiency. This study leverages the synthetic difference-in-differences (SDID) approach, which integrates the strengths of both DID and the synthetic control method (SCM), to provide a more reliable and accurate analysis of the impacts of interventions on city-level air quality. Using Shanghai’s 2022 lockdown as a case study, we compare the deweathered ozone (O₃) concentration in Shanghai to a counterfactual constructed from a weighted average of cities in the Yangtze River Delta (YRD) that did not undergo lockdown. The quasi-natural experiment reveals an average increase of 4.4 μg/m³ (95% CI: 0.24–8.56) in Shanghai’s maximum daily 8 h O₃ concentration attributable to the lockdown. The SDID method reduces reliance on the parallel trends assumption and improves the estimate stability through unit- and time-specific weights. Multiple robustness checks confirm the reliability of these findings, underscoring the efficacy of the SDID approach in quantitatively evaluating the causal impact of emission perturbations on air quality. This study provides credible causal evidence of the environmental impact of short-term policy interventions, highlighting the utility of SDID in informing adaptive air quality management. The findings support the development of timely, evidence-based strategies for sustainable urban governance and environmental policy design. Full article

Refining the land use structure can boost land utilization efficiency and curtail regional carbon emissions. Nevertheless, prior research has predominantly concentrated on static linear planning analysis. It has failed to account for how future dynamic alterations in driving factors (such as GDP and population) affect simulation outcomes and how the land use spatial configuration impacts the attainment of the carbon-neutrality goal. In this research, 1 km spatial resolution LULC products were employed to meticulously simulate multiple land use scenarios across China at the national level from 2030 to 2060. This was performed by taking into account the dynamic changes in driving factors. Subsequently, an analysis was carried out on the low-carbon land use spatial structure required to reach the carbon-neutrality target. The findings are as follows: (1) When employing the PLUS (Patch—based Land Use Simulation) model to conduct simulations of various land use scenarios in China by taking into account the dynamic alterations in driving factors, a high degree of precision was attained across diverse scenarios. The sustainable development scenario demonstrated the best performance, with kappa, OA, and FoM values of 0.9101, 93.15%, and 0.3895, respectively. This implies that the simulation approach based on dynamic factors is highly suitable for national-scale applications. (2) The simulation accuracy of the PLUS and GeoSOS-FLUS (Systems for Geographical Modeling and Optimization, Simulation of Future Land Utilization) models was validated for six scenarios by extrapolating the trends of influencing factors. Moreover, a set of scenarios was added to each model as a control group without extrapolation. The present research demonstrated that projecting the trends of factors having an impact notably improved the simulation precision of both the PLUS and GeoSOS-FLUS models. When contrasted with the GeoSOS-FLUS model, the PLUS model attained superior simulation accuracy across all six scenarios. The highest precision indicators were observed in the sustainable development scenario, with kappa, OA, and FoM values reaching 0.9101, 93.15%, and 0.3895, respectively. The precise simulation method of the PLUS model, which considers the dynamic changes in influencing factors, is highly applicable at the national scale. (3) Under the sustainable development scenario, it is anticipated that China’s land use carbon emissions will reach their peak in 2030 and achieve the carbon-neutrality target by 2060. Net carbon emissions are expected to decline by 14.36% compared to the 2020 levels. From the perspective of dynamic changes in influencing factors, the PLUS model was used to accurately simulate China’s future land use. Based on these simulations, multi-scenario predictions of future carbon emissions were made, and the results uncover the spatiotemporal evolution characteristics of China’s carbon emissions. This study aims to offer a solid scientific basis for policy-making related to China’s low-carbon economy and high-quality development. It also intends to present Chinese solutions and key paths for achieving carbon peak and carbon neutrality. Full article

Pancreatic neuroendocrine neoplasms (PNENs) are a diverse group of rare tumor subtypes, representing less than 2% of all pancreatic tumors. Often detected late in the clinical course, they are associated with high rates of morbidity and mortality. Hereditary syndromes such as multiple endocrine neoplasia type-1 and von Hippel–Lindau are associated with the development of PNENs, although only a small portion of total tumors have a genetic basis. This review aims to explore the recent advances in laboratory diagnostics, imaging modalities, medical management, and surgical approaches to hormone-producing PNENs (including some common, less common, and some rare subtypes), with the goal of assisting physicians in the integration of evidence-based information into their practice. Full article

Refractory full-thickness macular holes (rFTMHs) present a significant challenge in vitreoretinal surgery, with reported incidence rates of 4.2–11.2% following standard vitrectomy with internal limiting membrane (ILM) peeling and gas tamponade. Risk factors include large hole size (>400 µm), chronicity (>6 months), high myopia, incomplete ILM peeling, and post-operative noncompliance. Multiple surgical techniques exist, though comparative evidence remains limited. Current options include the inverted ILM flap technique, autologous ILM transplantation (free flap or plug), lens capsular flap transplantation (autologous or allogenic), preserved human amniotic membrane transplantation, macular subretinal fluid injection, macular fibrin plug with autologous platelet concentrates, and autologous retinal transplantation. Closure rates range from 57.1% to 100%, with selection depending on hole size, residual ILM, patient posturing ability, etc. For non-posturing patients, fibrin plugs are preferred. Residual ILM cases may benefit from extended peeling or flap techniques, while large holes often require scaffold-based (lens capsule, amniotic membrane) or fibrin plug approaches. Pseudophakic patients should avoid posterior capsular flaps due to lower success rates. Despite promising outcomes, the lack of randomized trials necessitates further research to establish evidence-based guidelines. Personalized surgical planning, considering anatomical and functional goals, remains crucial in optimizing visual recovery in rFTMHs. Full article

To achieve the goals of productivity and sustainability across diverse livestock systems, reproductive factors play a pivotal role. Historically, reproductive research has primarily focused on females, as they are responsible for maintaining pregnancy and delivering offspring following oocyte fertilization. However, since the early 2000s, the biological significance of sperm RNAs has been increasingly recognized in various livestock species. These RNAs contribute both genetically and epigenetically at the time of fertilization and during early embryonic development. Multiple types of sperm RNA have been identified in bovine, porcine, ovine, buffalo, and caprine spermatozoa. Notably, transcriptomic profiling has shown potential to differentiate between high- and low-fertility males, even when conventional semen quality values appear normal in both groups. This opens the possibility for more accurate identification of highly fertile sires. Nevertheless, a definitive marker or set of markers has yet to be established, likely due to the transcriptome’s sensitivity to environmental conditions and to the variability in evaluation methodologies. Therefore, global scientific efforts should aim to establish standardized, robust protocols, as sperm RNA represents a promising avenue for enhancing the sustainability of animal production systems. Full article

Hospitals rank among the most energy-intensive public building typologies and offer substantial potential for carbon mitigation. However, their construction phase has received limited scholarly attention within China’s ‘dual carbon’ agenda. To address this research gap, this study develops and empirically validates an integrated Technology Acceptance Model and Technology-Organization-Environment framework tailored for hospital construction projects. The study not only identifies 12 critical adoption factors but also offers recommendations and discusses the relevance to multiple Sustainable Development Goals. This research provides both theoretical and practical insights for promoting sustainable hospital construction practices. Full article

As sustainable cultural heritage tourism increasingly demonstrates its unique value and appeal, effectively stimulating tourists’ emotional experiences and value co-creation behaviors has become a focal issue. This study investigates how multiple tourist motivations (self-enhancement, escapism, and social interaction) shape value co-creation through emotional mediators—namely aesthetic, nostalgic, and flow experiences. Data were collected from 470 valid responses from visitors to the UNESCO-listed Suzhou Classical Gardens in China and analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that these emotional experiences significantly drive value co-creation behavior: self-enhancement motivation enhances all three experiences, escapism mainly promotes nostalgic and flow experiences, and social interaction primarily affects aesthetic experience. These findings clarify the psychological mechanisms through which tourists’ motivations and emotional experiences influence value co-creation behavior in cultural heritage tourism. This research advances our understanding of the motivation–experience–behavior framework and emphasizes that enhancing emotional engagement is key to fostering sustainable cultural heritage tourism practices. The study provides practical implications for designing experiences and strategies that balance visitor satisfaction with the long-term vitality of cultural heritage sites and local communities, thereby contributing to broader sustainable development goals. Full article

With the proposal of China’s “dual carbon” goal, the carbon market has become a vital tool for controlling carbon emissions. This study constructs a system dynamics model encompassing carbon trading, the economy, energy, population, and the environment, and conducts simulation analysis against the backdrop of China’s national carbon market’s implementation. The results indicate that the implementation of China’s national carbon market significantly promotes carbon emissions reduction, albeit at the cost of some economic development in the short term. However, the suppressive effect of the carbon market on carbon emissions is stronger than its negative impact on economic growth. The effects of carbon reduction strengthen with increases in carbon price, quota auction, CCER price, penalty severity, and the quota reduction rate and weaken with a higher CCER offset ratio. A moderate reduction in the tightening quota reduction rate is more conducive to achieving coordinated development across the multiple objectives of carbon reduction, economic development, and energy structure. Under the constraints of multiple objectives involving carbon reduction, economic development, and energy structure, the reasonable range for carbon prices is between CNY 77.9 and CNY 118.9 per ton, with the maximum quota auction of 23.4%. Additionally, the reasonable range for the quota reduction rates is between 0.84% and 2.18%, with the penalty severity set at 7. Full article

In arid and semi-arid climates, where water scarcity is a persistent challenge, accurately estimating reference evapotranspiration (ET) becomes essential for sustainable water management and agricultural planning. The objectives of this study are to compare hourly ET among P–M ASCE, P–M FAO, and P–M KSA mathematical models. In addition to the accuracy assessment of daily ET derived from hourly timestep calculations for the P–M ASCE, P–M FAO, and P–M KSA. To achieve these goals, a total of 525,600-min data points from the Riyadh region, KSA, were used to compute the reference ET at multiple temporal resolutions: hourly, daily, hourly averaged over 24 h, and daily as the sum of 24 h values, across all selected Penman–Monteith (P–M) models. For hourly investigation, the comparison between reference ET computed as average hourly values and as daily/24 h values revealed statistically and practically significant differences. The Wilcoxon test confirmed a statistically significant difference (p < 0.0001) with R² of 94.75% for ASCE, 94.87% for KSA at h_plt = 50 cm, 92.41% for FAO, and 92.44% for KSA at h_plt = 12 cm. For daily investigation, comparing the sum of 24 h ET computations to daily ET measurements revealed an underestimation of daily ET values. The Wilcoxon test confirmed a statistically significant difference (p < 0.0001), with R² exceeding 90% for all studied reference ET models. This comprehensive approach enabled a rigorous evaluation of reference ET dynamics under hyper-arid climatic conditions, which are characteristic of central Saudi Arabia. The findings contribute to the growing body of literature emphasizing the importance of high-frequency meteorological data for improving ET estimation accuracy in arid and semi-arid regions. Full article

Background: Aortic stenosis (AS) has long been considered a degenerative disease and is typically diagnosed in older men at an advanced stage. However, accumulating evidence has highlighted the similarities between AS and atherosclerosis, particularly regarding shared risk factors and overlapping pathophysiological mechanisms. This connection has led to a paradigm shift, suggesting that AS may be preventable in its early stages. Methods: This narrative review synthesizes the existing literature exploring the parallels between AS and atherosclerosis, focusing on common risk factors, pathogenic pathways, and evolving therapeutic strategies. Clinical trials and translational studies were examined to assess the effectiveness of atherosclerosis-based treatments for AS. Results: Multiple studies have confirmed the shared inflammatory, lipid-mediated, and calcific mechanisms of AS and atherosclerosis. Despite these similarities, therapeutic strategies effective in atherosclerosis, such as statin therapy, have not consistently shown benefits in AS. New medical approaches aim to delay aortic valve replacement and reduce the associated morbidity. The partially overlapping pathogenesis continues to guide future research. Conclusions: While AS and atherosclerosis share several pathogenic features, their clinical courses and treatment responses diverge. Understanding the limits and potential of their overlap may inform future preventive and therapeutic strategies. Earlier detection and targeted intervention in AS remain key goals, drawing on insights from cardiovascular disease management. Full article

The integration of vision and language processing into a cohesive system has already shown promise with the application of large language models (LLMs) in medical image analysis. Their capabilities encompass the generation of medical reports, disease classification, visual question answering, and segmentation, providing yet another approach to interpreting multimodal data. This survey aims to compile all known applications of LLMs in the medical image analysis field, spotlighting their promises alongside critical challenges and future avenues. We introduce the concept of X-stage tuning which serves as a framework for LLMs fine-tuning across multiple stages: zero stage, one stage, and multi-stage, wherein each stage corresponds to task complexity and available data. The survey describes issues like sparsity of data, hallucination in outputs, privacy issues, and the requirement for dynamic knowledge updating. Alongside these, we cover prospective features including integration of LLMs with decision support systems, multimodal learning, and federated learning for privacy-preserving model training. The goal of this work is to provide structured guidance to the targeted audience, demystifying the prospects of LLMs in medical image analysis. Full article