Search Results (54,701)

Introduction: Accurately meeting the energy requirements of patients in intensive care units (ICUs) is crucial to prevent catabolism, muscle loss, and complications. We assessed their energy needs in this study using indirect calorimetry (IC) and predictive formulas, comparing the results with delivered energy intake and evaluating agreement. Materials and Methods: A total of 38 mechanically ventilated patients in seven ICUs at Kayseri City Hospital were included; eligible patients were ≥18 years old and mechanically ventilated for at least 24 h. Disease severity and nutritional risk were evaluated using validated indices (prognostic nutritional index (PNI) and Modified Nutrition Risk in the Critically Ill (mNUTRIC)), and basal energy expenditure (BEE) was measured by IC and calculated using the Harris–Benedict (HB) and ESPEN formulas. IC measurements lasted 15 min under resting conditions in conscious patients and, according to acute phase criteria, in unconscious patients in a quiet, temperature-controlled environment. Nutrition was provided enterally or parenterally based on patient condition and disease severity. Agreement between IC and predictive formulas was assessed using Bland–Altman analysis, a statistical method that evaluates agreement between two measurement techniques. Results: Estimated energy requirements differed significantly from delivered energy intake (p < 0.001). IC-derived values were significantly lower than those estimated by the HB equation and ESPEN recommendations (p < 0.001), suggesting that predictive equations may overestimate energy requirements in this population. By contrast, delivered energy intake was lower than IC-measured values, with a mean difference of approximately 503 kcal, indicating a potential risk of underfeeding in clinical practice. Weak correlations were observed between methods (IC vs. HB: r = 0.35, p = 0.003; IC vs. ESPEN: r = −0.21, p = 0.02), indicating limited agreement between predictive equations and IC measurements, and Passing–Bablok regression analysis further supported this lack of agreement between methods. Conclusions: The energy intake delivered to patients was lower than the calculated values. Indirect calorimetry is important for accurately monitoring and determining energy requirements based on delivered energy intake, and further research in this area is needed. These findings highlight the importance of individualized monitoring of energy expenditure in critically ill patients and suggest that reliance solely on predictive equations may lead to clinically relevant discrepancies in energy delivery. Full article

This study investigates the thermal and vibration-attenuation performance of a novel 7-inch FPV drone frame manufactured from cast AZ31 magnesium alloy (MG), compared to 6061-T6 aluminum (AL) and carbon fiber (CF) composite structures under an extreme payload of 2 kg. Using quantitative spectral analysis of Blackbox flight logs, the research demonstrates that the MG frame provides superior system-level vibration damping, particularly under high-stress conditions. Under a 2 kg payload, the MG frame exhibited a 49% reduction in vibration power compared to the AL frame. Spectral data identified primary resonance peaks for the MG frame at 147 Hz (0 kg) and 204 Hz (2 kg), whereas the AL frame showed significantly higher frequency peaks at 179.5 Hz (0 kg) and 239.4 Hz (2 kg). Comparative modal hammer tests further validated these findings, with the magnesium design exhibiting lower impulse energy (0.22 mW/Hz) and faster decay than aluminum (0.24 mW/Hz). Thermal imaging analysis showed better motor cooling for the metallic frames; average motor temperatures on the magnesium frame (51.8 °C) and AL frame (50.3 °C) were significantly lower than on the CF structure (77.5 °C). The findings establish that AZ31 magnesium alloy offers an excellent synergy of lightweight stiffness and damping capacity, making it a viable alternative for heavy-duty FPV platforms requiring high signal integrity. Full article

The escalating global challenge of waste management, combined with the urgent need to reduce greenhouse gas emissions, has intensified interest in waste-to-energy (WtE) technologies as integrated solutions for sustainable energy recovery. This review critically examines advanced WtE technologies through three interconnected dimensions: the strength of the evidence base supporting performance and environmental claims, the challenges associated with scalability and system integration, and the implications of these technologies for net-zero energy transitions. The analysis covers thermochemical, biochemical, and hybrid conversion pathways, including pyrolysis, gasification, hydrothermal liquefaction, and anaerobic digestion, with particular emphasis on identifying inconsistencies in the literature and clarifying key uncertainties. A persistent gap between laboratory-scale performance and commercial-scale operation is identified and characterised across conversion pathways. Its principal drivers of feedstock heterogeneity, heat transfer limitations, and operational complexity are examined. Environmental assessments are shown to be highly sensitive to system boundary definitions and carbon accounting methodologies, with lifecycle results varying substantially depending on energy substitution assumptions and biogenic carbon treatment. The integration of WtE within circular economy frameworks demonstrates that energy recovery is most effective when positioned as a complement to material recycling rather than a substitute. The roles of combined heat and power configurations, district heating, carbon capture and storage, and emerging reactor technologies in advancing net-zero contributions are assessed. Significant data gaps are identified in long-term operational performance, modelling transparency, and reporting standardisation. The review concludes that WtE technologies represent valuable components of integrated waste and energy management systems, but their long-term contribution to decarbonisation requires careful system design, sound operational strategies, and harmonised performance evaluation frameworks. Full article

Bacterial persistence, a non-heritable high-antibiotic-tolerance phenotype, is a key driver of recurrent clinical infections and antibiotic treatment failure. The pheromone-responsive pCF10 plasmid in Enterococcus faecalis (E. faecalis) mediates antibiotic resistance gene dissemination, but its role in bacterial persister formation remains unclear. This study systematically investigated the regulatory role of pheromone cCF10 in the persister phenotype of pCF10-carrying E. faecalis and its underlying molecular mechanisms. We confirmed that cCF10 enhanced persistence against levofloxacin in OG1RF (pCF10), with the persister frequency increasing from 0.291% to 16.466% upon treatment. Transcriptomic analysis revealed that cCF10 activated the (p)ppGpp-mediated stringent response and downregulated the expression of genes associated with energy-intensive pathways, including those involved in DNA repair, protein folding, and respiration. Concurrently, cCF10 enhanced the expression of genes related to biofilm formation and cell lysis resistance and downregulated components of its own sensing and uptake systems. These findings demonstrate that cCF10 induces transcriptional reprogramming associated with increased persister formation in E. faecalis carrying the pCF10 plasmid and identify potential targets within the stringent response and associated metabolic pathways for the development of anti-persister strategies. Full article

Enhancing urban energy efficiency is central to low-carbon transition and broader urban sustainability. However, whether innovation-oriented urban policy can generate such gains, through which channels it operates, and whether its effects extend beyond pilot cities remain insufficiently understood. Focusing on China’s Innovative City Pilot (ICP) program, this study uses panel data for 274 Chinese cities from 2006 to 2022 and treats the staggered implementation of the program as a quasi-natural experiment. A multi-period difference-in-differences model is employed to examine the impact of the ICP program on urban energy efficiency. The results show that the ICP program significantly improves urban energy efficiency, and this conclusion remains robust across a series of robustness checks. Mechanism analysis further suggests that the policy effect operates through lower per capita carbon emissions and stronger green technological innovation. Heterogeneity analysis shows that the effect is more pronounced in larger cities, economically more developed cities, and cities with stronger pre-existing innovation capacity. Spatial analysis indicates that the program generates not only significant local benefits but also positive spillover effects on neighboring cities. Overall, these findings suggest that innovation-oriented urban policies can promote energy-efficient, low-carbon, and more sustainable urban development, while highlighting the importance of regional coordination and local innovation capacity in shaping policy effectiveness. Full article

Indigenous pigs constitute crucial genetic reservoirs. Adipose tissue is central to pig growth and metabolism, yet its molecular ontogeny remains poorly characterized in indigenous breeds such as the Ningxiang pig. We employed mass spectrometry to profile backfat proteomes across six postnatal stages (60–360 days). Proteomes clearly separated early (60–120 days) from late stages (300–360 days). Older pigs showed enrichment in processes linked to energy metabolism, translation, immune function, and mitochondrial activity. We identified 43 lipid metabolism proteins exhibiting significant age-dependent abundance. Weighted co-abundance network analysis revealed four protein modules significantly correlated with lean meat percentage, fat percentage, and carcass weight. Intramodular analysis identified four hub proteins—ALDH18A1, FABP4, FBP1, and HADHB—as putative candidates associated with lipid transport, gluconeogenesis, and fatty acid oxidation. This study links temporal proteomic profiles with key carcass traits, providing a data resource and a network-based framework for future research. Full article

The development of high-performance electrode materials with controlled morphology remains a key challenge for advancing supercapacitor technologies. In this study, polyethylene glycol (PEG)-assisted hydrothermal synthesis was employed to engineer NiCo₂S₄ nanostructures with controlled morphology for enhanced supercapacitor performance. The influence of PEG concentration on nucleation behavior, structural evolution, and electrochemical characteristics was systematically investigated. The optimized NiCo₂S₄ electrode synthesized with 0.2% PEG (NiCoS-P2) exhibited a hierarchical flower-like nanosheet architecture with reduced agglomeration and improved electrochemically accessible surface area. As a result, the electrode delivered a high areal capacitance of 13.689 F/cm² (specific capacitance of 6845 F/g) at 5 mA/cm², along with excellent rate capability and superior cycling stability, retaining 84.16% capacitance after 12,000 cycles. Electrochemical analysis revealed that the charge storage process is predominantly diffusion-controlled with enhanced ion transport kinetics. Furthermore, an asymmetric supercapacitor device assembled using NiCoS-P2 as the positive electrode and activated carbon as the negative electrode demonstrated a wide operating voltage of 1.5 V, delivering an areal capacitance of 0.409 F/cm² (specific capacitance of 204.5 F/g), an energy density of 0.128 mWh/cm², and a power density of 2.99 mW/cm². The device also exhibited excellent long-term stability with 85.3% capacitance retention after 7000 cycles. This work highlights the effectiveness of polymer-assisted structural engineering in optimizing transition metal sulfide electrodes for advanced energy storage applications.: Full article

Hydrogen refueling stations (HRSs) are a critical enabling infrastructure for fuel cell electric vehicles (FCEVs), yet their deployment in dense metropolitan areas often faces a dual challenge: limited travel-time accessibility for users and low public acceptance driven by perceived safety risks. This study develops an integrated, city-scale framework to quantify HRS accessibility and resident acceptance and to identify expansion priorities for Seoul, South Korea. We combine (i) an online perception survey of 1000 adult residents (October 2024) capturing environmental awareness, perceived safety, siting preferences, and willingness-to-travel distance; (ii) spatial demand data on FCEV registrations by administrative dong (n = 2443 vehicles, 2022); and (iii) network-based travel-time analysis using the Seoul road network and the current HRS supply (n = 10, 2024). Accessibility is evaluated under three travel-time thresholds (10, 15, and 20 min), with service-area delineation and demand-weighted underserved-area diagnosis. Candidate expansion sites are generated and screened using operational and regulatory constraints (e.g., site area and proximity to protected facilities), followed by a p-median location-allocation optimization to select five additional sites that minimize demand-weighted travel impedance. Results indicate that, under the 20 min threshold (7.7 km at an average operating speed of 23.1 km/h), 50 of 425 dongs (11.8%) and 244 of 2443 FCEVs (10.0%) are outside the baseline service coverage. After adding five sites (total n = 15), underserved dongs decrease to 5 (1.2%) and underserved FCEVs to 26 (1.1%) for the 20 min threshold, with consistent improvements across shorter thresholds. Survey responses further reveal that only 12.5% of respondents perceive HRSs as safe, while 46.5% report a maximum willingness-to-travel distance of up to 5 km, underscoring the need for both accessibility enhancement and risk-aware communication. The proposed workflow offers a transparent, reproducible approach to support equitable and risk-informed HRS planning by jointly considering network accessibility, demand distribution, and social acceptance, thereby contributing to sustainable urban mobility, low-carbon transport transition, and socially acceptable hydrogen infrastructure deployment. Beyond local accessibility improvement, the study is framed in the broader context of sustainability, as equitable and socially acceptable hydrogen refueling infrastructure can support low-carbon urban transport transitions and more resilient metropolitan energy-mobility systems. Full article

For offshore renewable energy planning and intelligent power management, access to long-term, high-resolution, and physically consistent meteorological and power generation records is essential. Such data supports a wide range of tasks, including resource assessment, hybrid system capacity sizing, grid operation planning, and data-driven forecasting model development. This article presents the construction of a 10-year continuous hourly dataset for 16 deep-sea grid sites in the Beibu Gulf, China, spanning from January 2016 to December 2025. The raw meteorological variables, including 10 m wind speed, wind direction, solar irradiance, and 2 m air temperature, were retrieved from the NASA POWER satellite database and subsequently cleaned using a 24 h periodic substitution algorithm designed to preserve the physical integrity of daily weather cycles. The dataset is organized into two sub-datasets, the Historical Weather Dataset and the Normalized Power Yield Dataset, with the latter providing normalized wind and solar power outputs on a 1.0 per-unit (p.u.) basis derived from a wind turbine power curve model and a PV thermodynamic model. All 32 CSV files are freely accessible online with UTF-8 encoding. The utility of the dataset is illustrated through two representative application cases including offshore site selection with hybrid capacity sizing and physics-informed deep learning forecasting, demonstrating its suitability for both engineering analysis and machine learning model development. Full article

Aiming at the difficult problem of floor heave control in deep coal mine roadways, this paper took the 1224 transportation roadway of Shuguang Coal Mine in Shanxi as the engineering background and carried out the first underground industrial test of floor-slotting pressure relief technology by using special slotting equipment. The aim is to reveal the energy transfer law of the floor rock mass during slotting pressure relief and clarify its inherent connection with stress redistribution and floor heave deformation control. The research adopts a combination of theoretical analysis, numerical simulation, and field tests to systematically explore the energy accumulation characteristics of the floor and the induced mechanism of floor heave. Results show that the maximum energy accumulated in the floor after roadway excavation reaches 6.0 × 10⁵ J, which is the fundamental cause of floor heave. After optimizing the slotting parameters (depth 2.5 m, width 0.2 m), numerical simulation indicates that the surrounding rock stress concentration zone migrates to the deep part, the energy peak shifts down by 2.5 m, the floor plastic zone expands, and the range of the high-energy zone shrinks. Field test results show that the floor heave amount decreases from 30 cm to 20 cm, with a reduction rate of 33%. This study reveals the synergistic mechanism of “energy transfer–stress regulation–deformation control”, verifies the effectiveness and feasibility of the slotting pressure relief technology in the floor heave control of deep, high-stress roadways, and provides a guarantee for the safe and efficient advancement of the working face. Full article

The transition from traditional synchronous generators to intermittent renewable sources, combined with increasingly variable and difficult-to-control energy demand, is creating a growing need for large-scale reserves and energy storage. At the same time, reduced system inertia and evolving electricity market regimes are emerging as important challenges that may affect grid stability, reliability, and economic performance. Advanced storage technologies, particularly those with fast ramping and high-response capabilities, offer a potential means of providing near-instantaneous support in response to unexpected system disturbances or market signals, thereby helping to mitigate inertia-related risks. This paper investigates four technologies: pumped hydroelectric storage, battery energy storage systems, synchronous condensers, and special protection schemes, with a focus on their capability to deliver rapid responses to large-scale disturbances. The analysis is conducted using a deliberately simplified power system model to provide qualitative insights into system behavior and control interactions. The results indicate that automation-enabled responses to system imbalances, including support from synchronous condensers and the rapid activation of additional generation, can enhance system performance under disturbance conditions within the considered framework. These findings demonstrate the feasibility and potential value of such approaches; however, further validation using higher-fidelity models and system-specific data is required to quantify their operational and economic impacts. Full article

With the advancement of digital hydropower stations, the requirements of real-time, high-precision industrial soft measurement of key power equipment operating status are attracting more and more attention. However, it is difficult to transfer energy to the monitoring sensor in strong electromagnetic environments. In this paper, a high-efficiency, high-power-density magnetic field energy harvester is proposed for monitoring sensors in hydropower stations, which captures the energy from the magnetic flux leakage of a hydroelectric generating set. Efficient magnetic energy capture is achieved by modeling material properties and optimizing the receiver’s magnetic core parameters via a Genetic Algorithm. The theoretical analysis of charging characteristics is given, and a Maximum Power Point Tracking (MPPT) control circuit is proposed, realizing high-efficiency energy conversion. Finally, an experimental planet is built. Under 70–130 Gs power-frequency magnetic fields, the system delivers 2.8–5.1 V open-circuit voltage, 66 mW maximum load power, and 6.5 mW/cm³ power density. Full article

The dynamic development of the food delivery sector and the resulting increase in last-mile distribution operations generate the need to simultaneously improve the efficiency of delivery processes and reduce the environmental impacts of urban logistics. In this context, shortening delivery time contributes not only to higher service quality and competitiveness but also to lower energy consumption and carbon dioxide emissions, which are key elements of sustainable urban mobility and logistics. Therefore, the aim of this study is to develop a delivery time optimization algorithm for the food delivery sector using selected machine learning methods, supporting the implementation of sustainable development principles in the operations of transport enterprises. This study presents an integrated approach to modelling delivery time in food distribution as a tool for building the competitive advantage of logistics enterprises under the conditions of implementing sustainable development principles. The study combines a literature review on sustainable last-mile logistics and data-driven optimization with an empirical analysis using traditional methods such as multiple regression and selected machine learning methods: decision trees, the Gradient Boosting Machine (GBM) method, and the XGBoost algorithm. The operational data include parameters related to delivery execution, such as supplier characteristics, vehicle type, order execution date, weather conditions and traffic situation. The developed mathematical models enable high-accuracy prediction of delivery time and the identification of the most important factors affecting both timeliness and potential energy consumption in the delivery process. The comparative assessment of the applied methods makes it possible to indicate the algorithms that provide the best forecast quality and practical usefulness in logistics decision-making. The proposed delivery time optimization algorithm supports data-driven decision-making that leads to shorter delivery times and lower energy intensity and thus to a reduction in the carbon footprint of last-mile operations, simultaneously strengthening the competitiveness and environmental responsibility of logistics enterprises. The results contribute to the development of sustainable urban logistics by linking predictive modelling with the economic, environmental and operational dimensions of efficiency in last-mile transport processes. Overall, this study offers an original, high-quality contribution to sustainable last-mile food delivery by integrating large-scale operational data with advanced machine learning models to deliver practically relevant, highly accurate delivery time predictions for logistics enterprises. Full article

The synergistic and mutually reinforcing relationship between the development of the new energy industry and comprehensive land consolidation is crucial for integrating ecologically fragile areas into the national “dual carbon” goals and supporting regional high-quality development. Based on a systematic literature review, field investigations in typical regions, and multi-case comparative analysis, this paper analyzes the challenges and opportunities for the new energy industry in ecologically fragile areas as well as the mutually reinforcing mechanisms between new energy industry development and land consolidation. On this basis, it explores pathways for comprehensive land consolidation in coordination with new energy development. Building on local practices, it further identifies five typical models. The results show the following: (1) The development of the new energy industry in ecologically fragile areas faces multiple challenges, including a fragile ecological environment, inadequate infrastructure, a mismatch between resource supply and demand, and land use conflicts. Against the backdrop of the energy transition, breakthroughs in key technologies, and the guidance of territorial spatial planning, the value of wind and solar resources in these areas are becoming increasingly prominent, offering broad prospects for the new energy industry. (2) The development of the new energy industry and comprehensive land consolidation in ecologically fragile areas are mutually reinforcing. Factors such as resource endowment, ecological constraints, new quality productive forces, and investment and financing mechanisms interact and integrate with each other, resulting in diversified synergistic pathways. (3) Based on the priorities of new energy industry development and the primary objectives of consolidation, five models are identified: Ecological Restoration-led Model, Resource Development-led Model, Industrial Collaboration-led Model, Technological Innovation-led Model and Integrated Development Model. Each model has distinct priorities and applicable scenarios. This study will provide a reference for new energy development and sustainable development in ecologically fragile areas, including desertified and Gobi desert areas, coal mining subsidence areas, and areas rich in wind, solar, and hydropower resources. Full article

Accurate, high-resolution emission inventories are essential for air quality modeling and policy evaluation, yet national-scale inventories for Thailand remain limited in spatial and temporal detail. This study develops a comprehensive national emission inventory for Thailand in 2019 (EI–TH 2019), covering 12 major air pollutants and greenhouse gases across key sectors, including energy, transport, industry, agriculture, waste, and residential activities. The inventory is constructed using country-specific activity data from official statistics and sectoral surveys, combined with GAINS-consistent emission factors and control assumptions. Emissions are resolved at 1 × 1 km spatial resolution and monthly temporal resolution to capture Thailand-specific emission dynamics. The results show that emissions across major pollutants are dominated by a limited number of source groups, with biomass burning and residential solid-fuel use driving particulate matter, transport dominating NO_x and CO emissions, large-scale combustion and industry controlling SO₂ emissions, and agriculture contributing the majority of NH₃ emissions. Strong seasonal variability is observed in PM_2.5, CO, and NH₃, primarily driven by dry-season biomass burning, whereas NO_x and SO₂ exhibit relatively stable temporal patterns. The reliability of EI–TH 2019 is supported by a multi-dimensional evaluation framework. Temporal consistency is demonstrated through strong agreement between modeled PM_2.5 emissions and ground-based observations, as well as between NO_x emissions and satellite-derived TROPOMI NO₂ (r = 0.93; ρ = 0.96). Biomass burning timing is further validated using satellite fire activity (VIIRS), showing consistent seasonal patterns. Comparisons with global inventories (EDGAR v8.1, HTAP v3.2, and GFED5.1) reveal systematic differences in sectoral contributions, temporal profiles, and emission magnitudes, particularly for biomass burning, reflecting the importance of country-specific data and assumptions. Overall, EI–TH 2019 provides a robust, high-resolution, and policy-relevant emission dataset that improves the representation of emission processes in Thailand. The results highlight key priority sectors—biomass burning, transport, industry, and agriculture—for targeted emission-reduction strategies and support applications in chemical transport modeling, exposure assessment, and integrated air-quality and climate-policy analysis. Full article