Search Results (2,541)

Nitrogen (N) and Phosphorus (P) are essential macronutrients whose unsustainable extraction and use pose growing environmental and geopolitical challenges. In the European Union, tightening regulatory frameworks, including the Urban Waste Water Treatment Directive (EU 2024/3019) and the Farm to Fork Strategy, have positioned nutrient recovery as a fundamental pillar of the circular economy. Despite the availability of mature technologies, comprehensive techno-economic assessments applied comparatively across multiple industrial sectors remain scarce. This study addresses that gap by evaluating the economic feasibility of five nutrient recovery systems across European waste valorization case studies: ammonia stripping from digestate (Spain), sewage sludge composting (Latvia/Lithuania), whey valorization via ultrafiltration and reverse osmosis (Hungary), algae-based dairy wastewater treatment (Slovakia), and pyrolysis of sewage sludge (Denmark). A structured data collection methodology was applied to assess capital expenditures (CAPEX), operational expenditures (OPEX), mass and energy flows, and nutrient recovery yields. Results demonstrate that all five systems show technical operability and economically relevant cost structures, with unit treatment costs ranging from €0.005/kg to €1.60/kg of waste treated, supporting their further development and scale-up as viable nutrient recovery pathways. N recovery was prioritized in most configurations, while P was predominantly co-recovered in solid residues. The findings provide a cross-sectoral comparative framework to support decision-making in the transition towards sustainable nutrient management and circular economy models. Full article

With the strategic advancement of energy structure transformation and the implementation of carbon peaking and carbon neutrality goals, the Integrated Energy System (IES) has become a core research direction owing to its superior performance in multi-energy complementation, operational efficiency, and low-carbon emission characteristics. Nevertheless, existing studies reveal that the optimal operation of IES still faces significant challenges, including the high complexity of multi-energy coupling, supply–demand imbalance caused by renewable energy penetration, and insufficient exploitation of demand-side flexibility. As a core measure of demand-side management, demand response (DR) provides an effective approach to motivate users to adjust power load via price incentives or direct load control. DR can effectively smooth load profiles, improve resource utilization, and boost the consumption level of renewable energy. To meet the operational demands of modern IES, this paper establishes a security-constrained economic dispatch model embedded with multi-level demand response mechanisms. The proposed framework is divided into four key modules: First, a price-based demand response strategy is developed to dynamically guide users in regulating multi-energy consumption behaviors. Second, electric vehicles (EVs) are considered flexible demand-side resources with unique response characteristics. An aggregated EV charging–discharging model is established to suppress power fluctuations and support high proportions of renewable energy integration. Third, to precisely calculate the overall operating cost of IES, a combined economic evaluation index integrating time-of-use tariff and Levelized Cost of Electricity is adopted. It maintains a balance between amortized long-term generation investment and short-term operational expenditure, and coordinates the economic benefits and operational reliability of the whole system. Finally, numerical simulations are performed on a coupled test system comprising an IEEE 33-bus distribution network and a 20-node natural gas network. Simulation results verify that the proposed co-optimization model can effectively reduce total system operating costs and greatly improve the local assumption of fluctuating renewable energy. Full article

Sustainable rural development requires households to move beyond defensive medical spending and emergency borrowing toward more productive, forward-looking resource allocation. This study uses panel data from the China Household Finance Survey (CHFS), covering the 2017, 2019, and 2021 waves plus a newly released 2023 green-channel wave. We examine whether improvements in safe drinking water, clean cooking energy, and sanitation are associated with lower rural household economic vulnerability. We employ a staggered difference-in-differences design with household and year fixed effects, complemented by event–study tests, mediation analysis, and robustness checks. Environmental infrastructure improvements are significantly associated with lower child hospitalization and out-of-pocket medical expenditure, reduced reliance on high-cost informal credit, and higher income-generating asset shares. Mechanism analysis supports a “health–credit–income” channel, in which environmental improvements reduce preventable health shocks, ease emergency borrowing, and relax liquidity constraints on productive asset allocation. Threshold results further show that these financial-resilience benefits are strongest among households with the lowest baseline resource endowments. The study focuses on rural China, yet the identified health–credit–income mechanism offers a broader, scalable framework. Environmental infrastructure first reduces preventable disease burden, then eases emergency informal borrowing, and finally frees liquidity for income-generating assets. This sequence helps explain how environmental investment can create the financial preconditions for sustainable consumption and investment across developing economies. These findings offer micro-level evidence for integrating environmental infrastructure, rural financial resilience, and ESG social-value assessment. Full article

Background/Objectives: Total or partial pancreatectomy is associated with significant metabolic stress and high risk of postoperative malnutrition. Accurate estimation of resting energy expenditure (REE) is essential, as predictive equations may not reflect true energy needs. Methods: A prospective study among patients undergoing total or partial pancreatectomy for pancreatic tumors was conducted. REE was measured by indirect calorimetry (mREE) and compared with the Harris–Benedict and Schofield equations and the weight-based approaches (25 and 30 kcal/kg). Agreement was assessed using linear regression and Bland–Altman analysis; accuracy indices included ±10%, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Results: In 26 patients (mean age, 66.7 ± 8.7 years; 53.8% male) undergoing pancreatic resection (17 pancreaticoduodenectomies, 8 distal pancreatectomies, 1 total pancreatectomy), 60% were at preoperative malnutrition risk. The median measured REE was 1484 kcal/day, rising to 1706 kcal/day after activity adjustment (×1.15) within 14 postoperative days. At 3–6 months postoperatively, patients demonstrated significant declines in nutritional status with a median body weight reduction of −7.3% and a decrease in BMI of −2 kg/m². The 30 kcal/kg method showed the lowest accuracy (MAPE 23.2%, RMSE 416 kcal/day) and overestimated energy needs. Harris–Benedict underestimated mREE in 61.5% of cases, while the 25 kcal/kg approach showed more balanced performance. Conclusions: Postoperative energy expenditure in patients undergoing pancreatic resection appeared elevated relative to predictive equations. Predictive equations lack reliability, favoring indirect calorimetry for precision. Sustained weight loss underscores the need for prolonged nutritional surveillance. Full article

Energy industrial zones can act as a transformative model for industrial decarbonization by integrating renewable energy infrastructure directly with industrial production. By combining energy industrial zones with the energy community framework and peer-to-peer (P2P) energy trading, this study proposes a pathway toward 100% renewable energy sources. The model was tested using a techno-economic assessment applied to the Bravar-Jasenice case study in Croatia featuring 12 MW of solar PV, 10 MW of wind power, and a 9.3 MW biogas cogeneration plant. This integrated approach can achieve 80–90% energy self-sufficiency and reduce electricity expenditures for participating enterprises by approximately 15%. Furthermore, the system facilitates an annual reduction of roughly 20,000 tonnes of CO₂ emissions, thus directly supporting European Green Deal objectives. The study also highlights the potential for industrial symbiosis, including green hydrogen production, data centre integration, and waste heat recovery. Ultimately, the proposed framework provides a robust strategy for enhancing industrial competitiveness and ensuring energy security through localized, sustainable energy management. Full article

This study conducts a comprehensive investigation into prospective policy alternatives within Pakistan’s power sector using the PAK-TIMES model, targeting the critical challenges of energy scarcity and environmental degradation. Focused on the period from 2022 to 2050, the research evaluates the impact of various policies on energy consumption, supplies, carbon emissions, and expenditures in alignment with Pakistan’s Nationally Determined Contributions (NDC) directed at combatting climate change. The study explores three distinct scenarios: a business-as-usual (BAU) scenario, along with five policy (5% Eff, 10% Eff, 15% REN, 30% REN, 50% REN) scenarios categorized into energy efficiency and renewable integration. The first scenario concentrates on the deployment of energy-efficient devices, while the second scenario delves into diverse levels of renewable energy integration. Key results reveal that energy demand is projected to surge substantially under the BAU scenario, increasing significantly from 3459 PJ in 2022 to 7912 PJ by 2050. In contrast, scenarios prioritizing energy efficiency can potentially curb the total energy supply by 2.3%, while renewable energy integration can expand up to 1.3% compared to business-as-usual by 2050. These alternative scenarios also exhibit the potential to slash greenhouse gas (GHG) emissions from the power sector by up to 15%. Notably, the PAK-TIMES model emerges as a valuable decision support tool for the Pakistani government to facilitate the execution of energy efficiency and renewable energy policies aimed at fulfilling its NDCs, while also contributing to the fulfillment of Sustainable Development Goals (SDGs) 7 (affordable and clean energy) and 13 (climate action). The study underscores the pivotal role of policy interventions in simultaneously mitigating energy challenges and combatting climate change for sustainable development. Full article

Apolipoprotein A-IV (ApoA-IV) is a glycoprotein secreted by the small intestine to regulate lipid metabolism and satiety. Its role in insulin-independent glucose homeostasis remains largely unknown. In this study, we demonstrate that intestinal ApoA-IV overexpression significantly attenuates diet-induced obesity and hyperglycemia following severe β-cell loss. Over a 20-week high-fat diet challenge, ApoA-IV transgenic (ApoA-IV-Tg) mice maintained significantly lower adiposity than wild-type controls, driven by elevated energy expenditure and fatty acid oxidation rather than reduced caloric intake. Beyond weight maintenance, ApoA-IV maintained excellent systemic glycemic control and enhanced peripheral insulin sensitivity. Most notably, ApoA-IV significantly attenuated hyperglycemia following streptozotocin (STZ)-induced β-cell ablation, maintaining glucose stability despite severe insulin deficiency. Mechanistically, this protection results from a blunted glucagon response and the subsequent suppression of the hepatic pCREB-G6Pase gluconeogenic signaling pathway. In vitro evidence confirms that ApoA-IV directly inhibits pancreatic α-cell glucagon secretion through an LDL receptor-related protein 1 (LRP1)-dependent pathway, reinforced by the precise co-localization of LRP1 and glucagon in pancreatic islets. Furthermore, ApoA-IV-Tg mice were protected from the STZ-induced corticosterone surge and systemic lipolysis. Collectively, these findings establish the ApoA-IV–LRP1 signaling axis as a potent metabolic switch, providing a promising insulin-independent strategy for managing obesity and diabetes. Full article

The increasing penetration of variable renewable energy sources (RESs) poses significant challenges to power system flexibility and reliability, particularly in systems with high solar generation. At the same time, existing Concentrating Solar Power (CSP) plants in Europe face declining economic viability due to high capital costs and the expiration of incentivized tariff schemes. This study proposes and evaluates a novel approach to repurpose CSP plants as flexible energy assets through the integration of cold thermal energy storage (CTES) within a Thermally Integrated Power-to-Heat-to-Power Energy Storage (TI-PTES) framework. The proposed system combines an ice/water-based cold storage with a CO₂-based refrigeration cycle to enhance the efficiency of the CSP steam cycle by reducing condenser temperatures, while also enabling temporal shifting of electricity consumption. A techno-economic optimization model based on PyPSA is developed to determine the optimal sizing and operation of the storage and refrigeration system under realistic load and electricity price conditions representative of the Spanish market. Results show that the integration of cold storage significantly alters system operation, shifting the chiller from a continuous demand-following mode to an intermittent, high-intensity regime. This leads to a reduction in annual operating expenditures by approximately 32% and an increase in annual profit and net present value (NPV), despite higher capital investment. While hourly net revenue becomes more volatile, with negative values during charging periods, cumulative annual performance improves due to effective temporal optimization. However, the absence of strong electricity price arbitrage and negative price signals limits the revenue potential of the storage system, which primarily acts as a cost-reduction mechanism. The findings demonstrate that cold thermal storage can successfully reposition CSP plants as flexible, value-generating assets in modern electricity systems. The proposed concept offers a promising pathway for extending the operational lifetime of existing CSP infrastructure while supporting higher integration of renewable energy sources. Full article

Sustainable agricultural development requires regional agricultural systems to balance output growth, resource efficiency, ecological protection, and long-term resilience. In mountainous and ecologically sensitive regions, identifying the development constraints and statistically derived marginal contribution thresholds of agriculture is essential for promoting green transformation and sustainable land use. Taking Yunnan Province, China, as a representative plateau mountainous agricultural region, this study uses provincial annual data from 1990 to 2023 to quantitatively identify the key drivers and threshold characteristics of agricultural development under multidimensional constraints. A multidimensional indicator system was constructed covering fiscal and investment support, agricultural production inputs, rural infrastructure, and labor and population conditions. Ridge regression was employed to address multicollinearity among explanatory variables, Bootstrap approximate inference was used to improve the robustness of coefficient estimation, and the SHAP interpretation framework was introduced to rank key driving factors and identify marginal contribution thresholds. By integrating ridge regression, Bootstrap approximate inference, SHAP-based contribution ranking, and threshold identification, the proposed framework advances prior agricultural sustainability studies by linking coefficient-based factor analysis with interpretable marginal contribution thresholds under conditions of high multicollinearity and multidimensional resource constraints. The results show that agricultural development in Yunnan is characterized by multidimensional resource and infrastructure constraints. Rural electricity consumption, total reservoir storage capacity, fixed asset investment in agriculture, forestry, animal husbandry and fisheries, local public fiscal budget expenditure, and agricultural population generally act as positive supporting factors. Rural electricity consumption is the most stable and core driver across the aggregate and three sectoral models. In contrast, pesticide and fertilizer inputs show significant negative associations in most models, suggesting that future agricultural development in Yunnan is unlikely to be sustainably supported by continued expansion of high-intensity chemical inputs. Sectoral heterogeneity is also evident: agriculture and animal husbandry are more dependent on energy, water resources, and mechanization, whereas forestry shows a more distinct operational structure. The SHAP dependence analysis identifies several statistically derived marginal contribution thresholds, including rural electricity consumption of approximately 6.055 billion kWh, total reservoir storage capacity of approximately 10.395 billion m³, total agricultural machinery power of approximately 19.8324 million kW, pesticide use of approximately 37,500 tons, and fertilizer application of approximately 1.5238 million tons. These values should be interpreted as empirical transition points in the modeled marginal contributions rather than definitive biophysical ecological limits. They indicate that the sustainability-related constraint structure of agricultural development in Yunnan is not a single output ceiling but a composite interval shaped by infrastructure support capacity, factor allocation conditions, and the declining marginal contribution of high-intensity chemical inputs. The findings provide directional quantitative evidence for sustainable agricultural governance, agricultural green transformation, and differentiated policy discussion in mountainous agricultural regions and offer reference implications for advancing SDG 2 and SDG 15 through the coordination of food-related production, resource use efficiency, and ecosystem conservation. The identified thresholds should be interpreted as model-derived marginal contribution transition points rather than operational policy cutoffs or directly enforceable ecological standards. Full article

Background/Objectives: Human nutritional requirements are closely linked to energy expenditure, which depends on daily and occupational activities. Studies on groups performing tasks requiring increased physical effort may support determining their energy and nutritional needs. The study population consisted of soldiers performing a wide range of tasks domestically and during missions abroad, whose physical effort may be comparable to that of athletes in demanding sports disciplines. The aims of this study were (1) to assess the energy expenditure of soldiers performing training tasks in two different special forces units, as a basis for evaluating the physical demands of their work; (2) to evaluate the daily energy expenditure value as a basis for planning the energy and nutritional value of the daily food ration, as well as to develop a proposal for a nutritional standard dedicated to collective feeding in special forces units. Methods: The study included soldiers from the Special Unit “GROM” and from the Special Branch of the Military Police (MP). Energy expenditure was measured using heart rate monitoring and analysis of heart rate variability. Results: The average daily energy expenditure related to field training was 4175 ± 723.7 kcal for GROM soldiers and 5014.8 ± 666.3 kcal for soldiers of MP. Conclusions: To ensure safe and adequate nutrition for special forces soldiers, the energy value of the daily food ration—after applying a 5–10% safety margin—should be no less than 4400 kcal. Considering the significant increase in energy expenditure during intense training, the average value of this increase was determined to be approximately 500 kcal. It was proposed to increase the energy value of the daily food ration by 500 kcal. Based on the findings, nutritional requirements were determined as a proposal for a basic nutritional standard for soldiers of Polish special units. Full article

This study develops a cooperative multi-agent deep reinforcement learning (MARL) framework for simulation-based cost-overrun mitigation in smart grid construction projects under dynamic engineering uncertainty. Modern smart grid construction involves digital substations, renewable-energy-connected facilities, flexible transmission assets, intelligent monitoring systems, and geographically distributed contractors; therefore, cost escalation is driven by sequential interactions among procurement, schedule execution, equipment deployment, supervision, weather, logistics, and price volatility. The proposed framework models procurement management, construction scheduling, equipment allocation, and supervision-control units as decentralized agents embedded in a calibrated construction simulation environment. The environment is parameterized from 42 smart grid construction projects in Henan Province, China and generates disturbance scenarios involving weather efficiency loss, transportation delay, market-price volatility, labor shortage, and supply-chain interruption. A hybrid DQN–PPO mechanism represents mixed decision structures: value-based DQN modules handle discrete managerial choices such as task acceleration, supplier switching, and procurement timing, whereas PPO modules adjust continuous resource-allocation and recovery-intensity decisions. A hierarchical reward function combines local departmental objectives with project-level penalties for cost overrun, schedule delay, idle resources, recovery expenditure, safety risk, and environmental impact. The experimental protocol uses 30 paired random seeds, nonparametric bootstrap confidence intervals, Holm-adjusted Wilcoxon signed-rank tests, and comparison with deterministic optimization, rolling-horizon MPC, stochastic/robust optimization, single-agent DRL, MAPPO, MADDPG/MATD3, QMIX, and HAPPO baselines. The proposed framework achieves a mean cost-overrun rate of 6.83% and a mean schedule deviation of 16.82 days, reducing cost overrun by 18.7% and schedule deviation by 21.4% relative to rule-based construction management under the reported disturbance settings. The calibrated simulation evidence establishes a statistically evaluated decision-support framework for coordinated construction cost control and provides an artifact-level reproducibility pathway through configuration files, random-seed lists, anonymized synthetic benchmarks, and aggregated logs. Full article

New Zealand working farm dogs can struggle to maintain their body weight and condition during periods of high workload, despite substantial energy intake, highlighting the need to optimise energy balance. One potential strategy for reducing overall energy demands is to reduce energy expenditure for thermoregulation, particularly during colder conditions, which may improve both recovery and energy utilisation. This study investigated whether wearing coats influenced the sleep behaviour, activity, and apparent nutrient digestibility of outdoor-kennelled dogs. Eight adult working-breed dogs (n = 8) were studied using a randomised cross-over design, in which dogs wore a coat or no coat during overnight periods (15:00–09:00 h). Behaviour was monitored using triaxial accelerometers and classified using a validated machine learning model, while apparent nutrient digestibility was determined from pooled faecal samples. Dogs spent more time sleeping when wearing a coat compared to no coat (48.1% vs. 40.0%, p = 0.008), with the effect being most evident during cooler evening hours. Time spent resting and being active was reduced when coats were worn (p < 0.05), while overall activity did not differ (p = 0.856). No differences were observed in apparent digestibility of energy or nutrients (p > 0.05), although protein digestibility tended to be higher when coats were worn (p = 0.079). These findings suggest that coats can improve sleep behaviour, likely through improved thermal comfort. While this study was conducted in research dogs under relatively mild temperature conditions, the results indicate the potential welfare and functional benefits for working farm dogs. Full article

Medium-chain triglycerides (MCTs) are functional lipids with unique physicochemical properties and metabolic advantages. Recently, their regulatory roles in various chronic diseases have attracted considerable attention. This review systematically summarizes recent research progress and the proposed mechanisms of MCTs and their metabolites in metabolic diseases, neurological disorders, gut health, and muscle function. In the metabolic field, MCTs offer potential nutritional strategies for managing obesity, type 2 diabetes mellitus (T2DM), and various metabolic liver diseases. These effects are primarily mediated by improving insulin sensitivity, regulating lipid metabolism, and modulating energy expenditure. In neurological diseases, MCTs demonstrate potential for preventing and treating Alzheimer’s disease (AD), Parkinson’s disease (PD), and epilepsy through multiple pathways, including ketogenic energy supply, anti-inflammatory and antioxidant effects, and mitochondrial protection. Regarding gut health, MCTs and their derivatives may benefit digestive health by modulating gut microbiota and enhancing barrier function. For muscle health, MCTs help optimize energy metabolism and protein homeostasis, showing promise for countering sarcopenia and improving exercise performance. In conclusion, the prospects for MCTs are broad. Future research should focus on promoting their scientific application in precision nutrition and disease therapy, and more rigorous clinical trials are needed to confirm their efficacy and safety. Full article

Metformin and aerobic exercise are routinely co-prescribed in the management of metabolic syndrome, yet evidence regarding their interaction on cardiorespiratory fitness and substrate oxidation adaptations remains inconsistent. This study aimed to investigate the effects of combined metformin and aerobic training on peak oxygen uptake (VO₂peak), maximal fat oxidation (MFO), submaximal substrate utilization, and perceived exertion in metformin-naïve adults with metabolic syndrome. In this randomized, placebo-controlled trial, 24 metformin-naïve adults with metabolic syndrome were allocated to receive either metformin (1000 mg/day; MET-EX) or a matched placebo (PLA-EX) combined with supervised aerobic training (5 sessions/week, 60% VO₂peak, 500 kcal/session) for five weeks; 22 participants (n = 11 per group) completed the protocol. VO₂peak, MFO, fat and carbohydrate oxidation, energy expenditure, and rating of perceived exertion (Borg 6–20) were assessed before and after the intervention. The absolute VO₂peak gain was modestly attenuated in MET-EX relative to PLA-EX (group × time interaction p = 0.042; +0.11 vs. +0.26 L·min⁻¹), whereas the interaction for relative VO₂peak did not reach significance (p = 0.088). In contrast, MFO increased substantially more in MET-EX than in PLA-EX (+0.13 vs. +0.04 g·min⁻¹; p = 0.001), accompanied by greater fat oxidation, energy expenditure, and perceived exertion during moderate-to-high submaximal exercise intensities. Moreover, VO₂peak improvement was negatively correlated with age exclusively in MET-EX (r = −0.87, p < 0.001). These findings suggest that metformin induces a dissociated adaptation profile during aerobic training in metabolic syndrome, characterized by enhanced lipid oxidation alongside attenuated cardiorespiratory adaptations and greater perceived effort, particularly in older individuals. Full article

This research focuses on developing a deep learning-based framework to forecast maintenance expenditures within the residential sector. To maintain building value, resident safety, and energy efficiency, consistent facility maintenance is indispensable. This necessity is especially heightened given the recent increase in the construction of supertall and high-performance buildings. However, estimating repair costs for residential facilities is challenging due to the diverse building types, ownership structures, and occupancy patterns compared to other property uses. Therefore, this research proposes a deep-learning model to establish a highly reliable and scientific method for estimating repair costs using empirical data gathered from actual residential facilities. Among the deep learning algorithms, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRUs) were adopted to develop models and optimize them through a fixed split. The framework and results of this paper facilitate the prediction of maintenance costs for residential facilities, which can contribute to budget planning, long-term facility management, preventive maintenance, resource management, and advanced decision-making. Moreover, it will contribute to the advancement of facility management of residential facilities. Full article