Search Results (119)

Indonesia’s tropical peatlands hold vast carbon stores but face degradation from anthropogenic pressures such as agriculture, logging, and mining. The main objective of this study is to identify the determinants of community willingness to pay (WTP) for agrosilvofishery and to estimate its economic value to support sustainable peatland management. This study surveyed 617 residents of Perigi Village, Indonesia. A structured questionnaire was used to assess smallholder farmers’ WTP for agrosilvofishery models. Using a double-bounded dichotomous choice contingent valuation method and econometric estimation, the findings indicate that higher bid prices reduce WTP; respondents preferred low and medium bids. Overall, most respondents expressed a willingness to participate financially in agrosilvofishery practices. Significant factors influencing WTP include birthplace, income, regular income, expenditure, previous agrosilvofishery experience, experience with droughts or fires, expected profit, and environmental risk perception. Flood variables had no effect, while drought and fire significantly increased WTP. The findings highlight the importance of effective communication strategies and policy design to address perceived barriers and promote the benefits of agrosilvofishery. Full article

Over the past decades, global air traffic has increased continuously, with passenger kilometers roughly doubling every fifteen to twenty years, and this trend is estimated to continue, with some adjustments due to COVID-19 impact. In response to the resulting environmental challenges, the European initiatives Flightpath 2050 and Clean Sky serve as central drivers of technological development aimed at achieving ambitious sustainability goals. Flightpath 2050 targets, relative to a reference engine from the year 2000, include a 75% reduction in CO₂ emissions per passenger kilometer, a 90% reduction in NO_x emissions, and a 65% reduction in noise emissions. These objectives highlight the urgent need for emission reduction strategies across all manufacturing domains, including turbine component production. This study evaluates the environmental impacts of the preturning and roughing operations employed in turbine disk production. The analysis focuses on these specific processes rather than the entire product, as the approach of process-level Life Cycle Assessments (LCA) are more universally applicable across different products, and their systematic combination can ultimately form a comprehensive product-level LCA. Operational data, such as energy usage, cooling lubricants, and compressed air, were gathered and processed from the equipment involved in manufacturing. The collected data were analyzed and modeled in Spheras life cycle assessment software LCA for Experts (version 10.9.0.20) to quantify the environmental effects of each process. The findings of the current research emphasize notable patterns of resource utilization and their respective environmental impacts. Furthermore, the Industrial Digital Twin Association (IDTA) Product Carbon Footprint (PCF) template was utilized to present the findings in a standardized manner, enabling effective data transfer between stakeholders. The results demonstrate the critical need to leverage machine data for sustainability analysis, providing inputs for industry practice enhancement and progress toward better environmental performance. Full article

Amid global sustainability transitions, campus energy systems confront growing pressure to balance operational efficiency, resilience to extreme weather events, and sustainable development education. This study proposes an artificial intelligence-driven framework for smart campus microgrids that synergistically advances environmental sustainability and disaster resilience, while deepening students’ understanding of sustainable development. The framework integrates an enhanced multi-scale gated temporal attention network (MS-GTAN+) to realize end-to-end meteorological hazard-state recognition for adaptive dispatch mode selection. Compared with Transformer and Informer baselines, MS-GTAN+ reduces prediction RMSE by approximately 48.5% for wind speed and 46.0% for precipitation while maintaining a single-sample inference time of only 1.82 ms. For daily operations, a multi-intelligence co-optimization algorithm dynamically balances economic efficiency with carbon reduction objectives. During disaster scenarios, an improved PageRank algorithm incorporating functional necessity and temporal sensitivity enables precise identification of critical loads and adaptive power redistribution, achieving an average critical-load assurance rate of approximately 75%, nearly doubling the performance of the traditional topology-based method. Furthermore, the framework bridges the divide between theoretical knowledge and educational practice via an educational digital twin platform. Simulation results demonstrate that the framework substantially improves carbon footprint reduction, resilience to power disruptions, and student sustainability competency development. By unifying technical innovation with pedagogical advancement, this study offers a holistic model for educational institutions seeking to advance sustainability transitions while preparing the next generation of sustainability leaders. Full article

In the initial phase of large-scale disasters, delayed external relief supplies make scientific local emergency supply allocation crucial—not only for reducing casualties, but also for advancing sustainable disaster response, a key link in enhancing post-disaster resilience. Existing research mostly focuses on cross-regional material allocation while overlooking local challenges like low resource efficiency and unbalanced supply–demand dynamics. To tackle these limitations in the existing research, this study develops a multi-objective collaborative local emergency supply allocation model centered on sustainability. It uses an improved TOPSIS method to quantify the urgency of needs in disaster-stricken areas, prioritizing material distribution to vulnerable regions in line with the principle of “no vulnerable area left neglected in relief efforts”. The study also integrates the entropy weight method and analytic hierarchy process (AHP) to ensure rational indicator weighting, and designs a double-layer encoded genetic algorithm to obtain optimal allocation schemes that balance efficiency, fairness, and sustainability. Validated using the 2013 Ya’an Earthquake case study, the model outperforms traditional local allocation approaches: it boosts resource utilization efficiency by reducing material shortage rates, accelerates post-disaster recovery by shortening response times, and improves allocation fairness. Findings provide empirical support for the establishment of “local–external” collaborative rescue systems and sustainable disaster risk reduction frameworks. Empirical calculations using case-specific data and real-world estimates verify the model’s practical applicability: it meets the requirements for fair and rapid allocation needs, aligns with the goals of sustainable disaster management, and lowers the carbon footprint of relief operations by lessening reliance on long-distance external materials. Full article

The electricity–hydrogen–electricity conversion chain offers an effective solution for integrating clean energy into the grid while addressing multiple grid control requirements. Moreover, multiregional, interconnected, and integrated energy systems (IESs) can significantly increase overall energy utilization efficiency and operational flexibility through spatiotemporal coordination among diverse energy sources. However, few researchers have considered these two aspects in a unified framework. To address this gap, a low-carbon economic dispatch model and control strategy for a multiregional hydrogen-blended IES are proposed in this work. The model is constructed based on a system architecture that incorporates electricity–hydrogen–electricity conversion links while accounting for source–load uncertainties and peak shaving requirements. We solve the resulting distributed nonconvex nonlinear optimization problem using the alternating direction method of multipliers (ADMM). Furthermore, we analyze how uncertainty factors and peak shaving needs affect the maximum allowable hydrogen blending ratio in the gas grid, as well as the corresponding dynamic blending strategy. Our findings demonstrate that the proposed multiregional hydrogen-blended integrated energy system, with dynamic hydrogen blending control, significantly enhances the capacity for clean energy integration and reduces carbon emissions by approximately 12.3%. The peak-shaving demand is addressed through a coordinated mechanism involving electrolyzers (ELs), gas turbines (GTs), and hydrogen fuel cells (HFCs). This coordinated mechanism enables hydrogen fuel cells to double their output during peak hours, while electrolyzers increase their power consumption by approximately 730 MW during off-peak hours. The proposed dispatch model employs conditional risk measures to quantify the impacts of uncertainty and uses economic coefficients to balance various cost components. This approach enables effective coordination among economic objectives, risk management, and system performance (including peak shaving capability), thereby improving the practical applicability of the model. Full article

Carbon sequestration by vegetation and soil conservation are vital components in balancing greenhouse gas emissions and enhancing terrestrial ecosystem carbon sinks. They also represent an efficient pathway towards achieving carbon neutrality objectives and addressing numerous environmental challenges arising from global warming. Soil and water conservation, as crucial elements of ecological civilisation development, constitute a key link in realising carbon neutrality. This study systematically quantifies and forecasts the spatiotemporal characteristics of carbon sink capacity in soil and water conservation within the study area of Puding County, a typical karst region in Guizhou Province, China. Following a research approach of “mechanism elucidation–model construction–categorised estimation”, we established a carbon sink calculation system based on the dual mechanisms of vertical biomass carbon fixation via vegetative measures and horizontal soil organic carbon (SOC) retention using engineering measures. This system combines forestry, grassland, and engineering, with the aim of quantifying regional carbon sinks. Machine learning regression algorithms such as Random Forest, ExtraTrees, CatBoost, and XGBoost are used for backtracking estimation and optimisation modelling of soil and water conservation as carbon sinks from 2010 to 2022. The results show that the total carbon sink capacity of soil and water conservation in Puding County in 2017 was 34.53 × 10⁴ t, while the contribution of engineering measures was 22.37 × 10⁴ t. The spatial distribution shows a pattern of “higher in the north and lower in the south”. There are concentration hotspots in the central and western regions. Model comparison demonstrates that the Random Forest and extreme gradient boosting regression models are the best models for plantations/grasslands and engineering measures, respectively. The LSTM model was applied to predict carbon sink variables over the next ten years (2025–2034), showing that the overall situation is relatively stable, with only slight local fluctuations. This study solves the problem of the lack of quantitative data on soil and water conservation as carbon sinks in karst areas and provides a scientific basis for regional ecological governance and carbon sink management. Our findings demonstrate the practical significance of promoting the realisation of the “double carbon” goal. Full article

Background/Objectives: An increasing number of older individuals require general anaesthesia for major non-cardiac surgery, with 20% displaying postoperative complications. Regional cerebral oxygen saturation (rSO₂) correlates with the gold standard of mixed venous oxygen saturation, indicating global perfusion. We hypothesised that rSO₂-based anaesthesia reduces organ dysfunction and morbidity after major non-cardiac surgery. Methods: In Singapore and Toronto, we conducted a prospective, double-blind, randomised controlled trial in elderly patients undergoing major non-cardiac surgery, after obtaining research ethics board permission and informed consent. This RCT followed the CONSORT guidelines. Patients received bilateral cerebral oximetry sensors, and the control group received standard care. In the intervention group, an algorithm restored rSO₂ if it dropped 10% below baseline for >15 s by adjusting cerebral perfusion pressure, inspired oxygen concentration, end-tidal carbon dioxide, depth of anaesthesia, haemoglobin, and cardiac index. Postoperative complications and outcomes were noted. Categorical data were analysed using Chi-square or Fisher’s exact tests and continuous data using a t-test or a Mann–Whitney U test. The study was powered for 394 patients, but due to the COVID-19 pandemic and funding constraints, this study was terminated at 101 patients. Results: Of 101 patients, 49 were randomised to the control and 52 to the intervention group. A total of 31 (63%) patients in the control group and 30 (58%) in the interventional exhibited bilateral cerebral desaturation. Time of cumulative cerebral desaturation was longer in the control group (23 ± 48 min vs. 9 ± 15 min, respectively, p = 0.01). A total of 142 algorithm-based treatments were employed, restoring rSO₂ in 29 (86%) patients. Both groups displayed equal postoperative outcomes. Conclusions: In major non-cardiac surgery, cerebral desaturation is prevalent in over 85% of patients. Although algorithm-guided therapy restored rSO₂ in the majority of patients, it did not result in reduced postoperative morbidity. Full article

Plant roots influence soil infiltration by altering its properties like porosity and bulk density, which are essential for ecohydrological cycles. Moso bamboo (Phyllostachys edulis), using its well-developed underground root system, invades neighbor forest communities, thereby influencing root characteristics and soil properties. Although Moso bamboo invasion may alter soil hydrology, its specific impact on soil infiltration capacity and water flow connectivity remains unclear. This work took a fir forest (Cunninghamia lanceolata), mixed fir and bamboo forest, and a bamboo forest which represent three different degrees of invasion: uninvaded, partially invaded, and completely invaded, respectively, as study objects, using double-ring dyeing infiltration method to measure soil infiltration capacity and calculating water flow connectivity index for the root zone. To assess the effects of soil properties and root characteristics on soil infiltration capacity and water flow connectivity, we employed random forest and structural equation modeling. The analysis revealed that Moso bamboo invasion significantly enhanced soil infiltration capacity. Specifically, in partially invaded forests, the initial infiltration rate, stable infiltration rate, and average infiltration rate increased by 31.5%, 26.1%, and 28.5%, respectively. In completely invaded forests, the corresponding increases were 6.6%, 35.6%, and 28.5%. Also, Moso bamboo invasion increased water flow connectivity of root zone, compared to the uninvaded forest, the water flow connectivity index increased by 29.4% in the completely invaded forest and by 15.6% in the partially invaded forest. The marked increase in fine root biomass density (RBD1), fine root length density (RLD1), soil organic carbon (SOC), and non-capillary pores (NCP) and the decrease in soil bulk density (SBD) followed by Moso bamboo invasion effectively improved water flow connectivity and soil infiltration capacity. The analysis identified that RBD1, RLD1, NCP, and SBD as the key drivers of soil infiltration capacity, whereas the water flow connectivity index was controlled mainly by SOC, NCP, RLD1, and RBD1. These findings help clarify the mechanistic pathways of Moso bamboo’s effects on soil infiltration. Full article

Background/Objectives: An elevated level of lipoprotein(a) [Lp(a)] is a genetically determined risk factor for cardiovascular disease. The atherogenic properties of Lp(a) include attribution to its role as a carrier of oxidized phospholipids (OxPL). Despite genetic control, Lp(a) levels increase with dietary saturated fat (SFA) reduction. However, little is known about the impact of dietary factors on Lp(a) risk properties. Methods: We assessed total Lp(a)-OxPL concentration, Lp(a)-OxPL subspecies abundance, and Lp(a) lipidomics in response to SFA reduction in two multicenter, randomized, controlled, crossover feeding trials, DELTA (Dietary Effects on Lipoproteins and Thrombogenic Activity) 1 (96 healthy individuals) and 2 (79 metabolically challenged individuals). In both trials, significant increases in Lp(a) levels were reported previously. Results: While no between-diet differences in the concentrations of total Lp(a)-OxPL and four major OxPL subspecies (ALDOPC, POVPC, PAzPC, and PGPC) were observed in DELTA 1, ALDOPC decreased significantly in DELTA 2 when SFA was replaced with carbohydrates (p = 0.014). Of 440 individual lipid species annotated in an untargeted analysis of the Lp(a) lipidome, 87 lipids differed significantly (p < 0.05 adjusted for multiplicity) between diets, with triacylglycerol species showing the most pronounced changes in both trials. For all intervention diets, triacylglycerol species with a higher average number of carbon atoms and double bonds increased the most in abundance with SFA reduction. Conclusions: In parallel with an increase in plasma Lp(a) levels, significant changes in Lp(a) lipid composition occurred. The findings demonstrate the dynamic nature of intraindividual Lp(a) lipid composition in response to diet interventions. Full article

Green and low-carbon development constitutes an essential pathway toward high-quality socioeconomic transformation, with improving urban green total factor energy efficiency (GTFEE) critical to achieving this objective. Based on the sample data of Chinese cities from 2013 to 2022, this study systematically investigated the impact and mechanism of critical peak pricing on urban GTFEE by using the double machine learning method, effectively supplementing the existing literature. This study finds that this policy significantly enhances urban GTFEE. Mechanism analysis indicates that critical peak pricing generates a dual effect by increasing the price difference between peak and off-peak hours and enhancing energy efficiency through two important channels: market expansion and technology-driven innovation. Heterogeneity analysis indicates that the critical peak pricing policy has a more significant promotion effect on non-resource-based, strong government administrative power, as well as central and eastern regions. These findings advance the power marketization reform framework and provide new theoretical support for promoting low-carbon energy transformation. Full article

High-performance concrete (HPC) is an essential construction material used for modern buildings and infrastructure assets, recognized for its exceptional strength, durability, and performance under harsh situations. Nonetheless, the HPC production process frequently correlates with elevated carbon emissions, principally attributable to the high quantity of cement utilized, which significantly influences its carbon footprint. In this study, data-driven modeling and optimization strategies are employed to minimize the carbon footprint of high-performance concretes while keeping their performance properties. Starting from an experimental dataset, artificial neural networks (ANNs), ensemble techniques (ETs), and Gaussian process regression (GPR) are employed to yield predictive models for compressive strength of HPC mixes. The model’s input variables are the various components of HPC: cement, water, superplasticizer, fly ash, blast furnace slag, and coarse and fine aggregates. Models are trained using a dataset of 356 records. Results proved that the GPR-based model exhibits excellent accuracy with a determination coefficient of 0.90. The prediction model is used in a double objective optimization task formulated to identify mix configurations that allow for high mechanical performance aligned with a reduced carbon emission. The multi-objective optimization task is undertaken using genetic algorithms (GAs). Promising results are obtained when the machine learning prediction model is associated with GA optimization to identify strong yet sustainable mix configurations. Full article

Efficient water decolorization techniques are vital for ensuring fresh water for future generations. Azo dyes are used heavily in the textile industry and are a challenge to remove from industrial wastewater. This research expands on recent innovative work where anionic azo dyes themselves were used to functionalize track-etched porous polycarbonate filtration membranes with decolorized water obtained as a byproduct. The objective of this research is to determine whether the observed dye rejection is dependent on the magnitude of the intrinsic charge of the dye molecule or on its structure, using two selectively chosen anionic azo dye series during functionalization. The first group is a negative two intrinsic charge series with six dyes, each differing in structure, and the second group is a five-dye series that increases from −1 to −6 in intrinsic charge. Rejection measurements as a function of both time and concentration during functionalization are made using ultraviolet-visible light spectroscopy. For 100 µM aqueous dyes, comparing pre- and post-functionalization, a systematically increasing trend in the ability to functionalize porous polycarbonate based on the number of double 6-carbon ring structures in the dyes is illustrated and found to be independent of intrinsic charge. Full article

Background/Objectives: This study examined the effects of 2 weeks of betaine versus placebo supplementation (3 g/d) on 60 km cycling performance, gut permeability, and shifts in plasma metabolites. Methods: Participants included 21 male and female non-elite cyclists. A randomized, placebo-controlled, double-blind, crossover design was used with two 2-week supplementation periods and a 2-week washout period. Supplementation periods were followed by a 60 km cycling time trial. Six blood samples were collected before and after supplementation (overnight fasted state), and at 0 h, 1.5 h, 3 h, and 24 h post-exercise. Five-hour urine samples were collected pre-supplementation and post-60 km cycling after ingesting a sugar solution containing lactulose 5 g, ¹³C mannitol 100 mg, and ¹²C mannitol 1.9 g in 450 mL water. Other outcome measures included plasma intestinal fatty acid binding protein-1 (I-FABP), muscle damage biomarkers (serum creatine kinase, myoglobin), serum cortisol, complete blood cell counts, and shifts in plasma metabolites using untargeted metabolomics. Results: The time to complete the 60 km cycling bout differed significantly between the betaine and placebo trials (mean ± SE, 112.8 ± 2.3, 114.2 ± 2.6 min, respectively, (−1.41 ± 0.7 min) (effect size = 0.475, p = 0.042). No trial differences were found for I-FABP (interaction effect, p = 0.076), L:¹³CM (p = 0.559), the neutrophil/lymphocyte ratio (p = 0.171), serum cortisol (p = 0.982), serum myoglobin (p = 0.942), or serum creatine kinase (p = 0.694). Untargeted metabolomics showed that 214 metabolites exhibited significant trial treatment effects and 130 significant trial x time interaction effects. Betaine versus placebo supplementation was linked to significant increases in plasma betaine, dimethylglycine (DMG), sarcosine, methionine, S-adenosylhomocysteine (SAH), alpha-ketoglutaramate, and 5′methylthioadensone (MTA), and decreases in plasma carnitine and numerous acylcarnitines. Conclusions: Betaine supplementation modestly improved 60 km cycling performance but had no effect on gut permeability. The metabolomics data supported a strong influence of 2-week intake of betaine on the one-carbon metabolism pathway during the 24 h recovery period. Full article

Under the background of “double carbon” and sustainable development, aimed at the problem of resource capacity planning in the integrated energy system (IES), at improving the economy of system planning operation and renewable energy (RE) consumption, and at reducing carbon emissions, this paper proposes a multi-objective bi-level sustainability planning method for IES considering the bilateral response of supply and demand and hydrogen utilization. Firstly, the multi-energy flow in the IES is analyzed, constructing the system energy flow framework, studying the support ability of hydrogen utilization and the bilateral response of supply and demand to system energy conservation, emission reduction and sustainable development. Secondly, a multi-objective bi-level planning model for IES is constructed with the purpose of optimizing economy, RE consumption, and carbon emission. The non-dominated sorting genetic algorithm II (NSGA-II) and commercial solver Gurobi are used to solve the model and, through the simulation, verify the model’s effectiveness. Finally, the planning results show that after introducing the hydrogen fuel cells, hydrogen storage tank, and bilateral response, the total costs and carbon emissions decreased by 29.17% and 77.12%, while the RE consumption rate increased by 16.75%. After introducing the multi-objective planning method considering the system economy, RE consumption, and carbon emissions, the system total cost increased by 0.34%, the consumption rate of RE increased by 0.6%, and the carbon emissions decreased by 43.61t, which effectively provides reference for resource planning and sustainable development of IES. Full article

LPG fuel supply systems are increasingly important for improving energy efficiency and reducing carbon emissions in the shipping industry. The primary objective of this research is to investigate the heat transfer phenomena to enhance the thermal performance of double-pipe heat exchangers (DPHEs) in LPG fuel supply systems. This study investigates the heat transfer performance of a glycol–steam double-pipe heat exchanger (DPHE) within an LPG fuel supply system under varying operating conditions. A computational model and methodology were developed and validated by comparing the numerical results with experimental data obtained from commissioning tests. Additionally, the effects of turbulence models and parametric variations were evaluated by analyzing the glycol–water mixing ratio and flow direction—both of which are critical operational parameters for DPHE systems. Numerical validation against the commissioning data showed a deviation of ±2% under parallel-flow conditions, confirming the reliability of the proposed model. With respect to the glycol–water mixing ratio and flow configuration, thermal conductance (UA) decreased by approximately 11% in parallel flow and 13% in counter flow for every 20% increase in glycol concentration. Furthermore, parallel flow exhibited approximately 0.6% higher outlet temperatures than counter flow, indicating superior heat transfer efficiency under parallel-flow conditions. Finally, the heat transfer behavior of the DPHE was further examined by considering the effects of geometric characteristics, pipe material, and fluid properties. This study offers significant contributions to the engineering design of double-pipe heat exchanger systems for LPG fuel supply applications. Full article