Search Results (4,910)

Decarbonising the maritime sector demands a transition away from conventional fossil fuel combustion toward zero-carbon alternatives, yet the technical and operational implications of integrating ammonia-based power systems into existing vessel architectures remain insufficiently characterised. This study presents a dynamic simulation framework for the component sizing and performance evaluation of ammonia-based marine power systems, applied to a case study vessel across six power system configurations: a conventional MGO diesel generator baseline, an ammonia dual-fuel generator benchmark, and four hybrid configurations integrating solid oxide fuel cells at different power coverage scopes. The methodology combines an operationally based component sizing model with a time-domain dynamic simulation that captures load-dependent SOFC performance, stack degradation, transient battery buffering, heat recovery interactions, and energy management under realistic voyage conditions, a combination not previously applied to ammonia-SOFC marine power system assessment. Results demonstrate that dynamic simulation is essential for reliable sizing of transient-sensitive components, yielding battery capacities of 1500 kWh and 2900 kWh for auxiliary-only and auxiliary-plus-manoeuvring SOFC coverage scopes respectively. The ADFG–SOFC-B configuration achieves the strongest performance across all indicators: a 26.7% reduction in total annual energy consumption, a net electrical efficiency of 50.7%, and a well-to-wake GHG emission reduction of 85.6% relative to the diesel baseline. All ammonia dual-fuel configurations maintain IMO Net-Zero Framework compliance through 2039 or beyond, with SOFC-integrated configurations avoiding Tier 2 penalties through 2045. These findings establish that a full transition to green ammonia as the primary fuel, rather than SOFC integration alone, is the prerequisite for achieving both deep decarbonisation and long-term regulatory viability in maritime power systems. Full article

This study analyzed the spatial distribution and seasonal variation characteristics of Volatile Organic Compounds (VOCs) at four sites (GS, GJ, BHS, and JN) representing different emission environments in Seoul and identified potential pollution sources using principal component analysis (PCA). The results showed that VOC concentrations were relatively high at the GS site, which is influenced by both industrial and traffic emissions, and at the JN site, characterized by heavy urban traffic, whereas the BHS site, representing a background area, exhibited the lowest concentrations, indicating clear spatial heterogeneity. Alkanes accounted for the largest proportion of VOCs at all sites, and low-molecular-weight alkanes as well as combustion-related compounds showed elevated concentrations during winter. In contrast, aromatic compounds exhibited site-specific seasonal patterns, with relatively higher concentrations observed during summer or autumn at some locations. The diurnal variation patterns displayed a bimodal distribution with concentration peaks during morning and evening rush hours, indicating the direct influence of traffic emissions. Furthermore, the T/B ratio and PCA results suggested that vehicle emissions and combustion sources were the dominant contributing factors (PC1) to ambient VOCs in Seoul, while non-road emission sources such as solvent use and industrial activities, characterized mainly by aromatic compounds, also contributed significantly (PC2). The findings of this study can serve as fundamental data for future quantitative source apportionment studies and the development of risk-based air quality management strategies for VOCs in Seoul. Full article

This study, through a hybrid approach to post-occupancy evaluation (POE) of four types of high-energy-efficiency housing in rural Scotland, explores the manifestation, formation mechanism, and mitigation pathways of energy risks in high-energy-efficiency housing from environmental and socioeconomic dimensions. The findings reveal a “high-efficiency paradox”: better fabric performance and lower heating demand do not guarantee reduced carbon emissions, fuel poverty alleviation, or energy resilience. Actual energy risks are formed by the combined effects of multiple factors, including building size, energy infrastructure, resident characteristics, energy prices, and policy, exhibiting a clear systemic coupling characteristic. The study further verifies that, in the context of rural Scotland, relying solely on indicators such as EPC may lead to misjudgements of housing sustainability. Heating demand, total energy consumption, carbon emissions, and energy expenditure exhibit a partially decoupled relationship. Thus, rural housing sustainability should shift from a technically efficient approach to a comprehensive strategy integrating design, infrastructure, affordability, and social equity. The study proposes context-specific mitigation pathways including multi-source energy systems, place-sensitive policies, socio-economic support, and a multi-criteria assessment framework, providing empirical references for rural housing energy transition and energy risk governance. Full article

This study addresses the multi-objective optimization of characterizing a flat glass processing plant. To assess the operational conditions required for a flat glass processing small and medium-sized enterprise (SME) to become a prosumer compatible with renewable energy community (REC) participation. This work is motivated by the presence of more than 300 SMEs in Italy, like this, where RECs represent one of the few viable strategies for achieving the European Union’s 2050 decarbonization targets. The research is carried out in two scenarios; Scenario-I includes Stage-i and Stage-ii with the mutual goal of forecasting and optimizing. Forecasting is used in Stage-i to optimize the factory load, and in Stage-ii to shift and curtail energy loads based on the forecast, considering the Italian national energy price and the regional price bands (“fasce orarie”) F1, F2, and F3. Forecasting and the indicators of environmental and social performance are the means to ensure the best energy utilization and management, as they prove that the reduction in CO₂ emissions and benefits on the community level can be both obtainable. Subsequently, the techno-economic analysis and evaluation of prosumer-readiness conditions are carried out through the optimization of industrial energy demand: three optimization objectives are assessed in this study (i) energy cost, (ii) carbon emission, and (iii) load curtailment. Four algorithms are put into effect to solve the tri-objective optimization: multi-objective particle swarm optimization (MOPSO), multi-objective ant nesting algorithm (MOANA), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimization (MOGWO). The algorithms are validated in Stage-ii to find the desired optimum in the cost of energy, reduce peak formation, and carbon emissions. To achieve this goal, a stochastic approach based on Monte Carlo simulations and VIKOR is used to optimally select the results. The findings show that the NSGA-II, MOPSO, and MOANA are more effective in solving the problem, while the MOGWO algorithm more quickly finds the optimal solution. Based on the defined objectives, a new configuration for the energy community is introduced, together with a community well-being index and an evaluation of the resulting benefits for the factory. In Scenario-II, the PV plants’ installation on the factory is sized, and the excess energy shared with the grid is evaluated. The Scenario-II results show that 497.184 MWh (33.9%) of energy is shared with the grid. Both results suggest how optimized industrial demand profiles improve SME participation in future RECs. Full article

Feed intake and diet quality are key factors influencing enteric methane (CH₄) emissions in ruminants. Low-quality C₄ grasses typically limit intake and are associated with high CH₄ yield. Nitrogen supplementation may improve rumen function and reduce CH₄ emissions per unit of feed intake, although responses under low-quality forage conditions remain insufficiently characterized. The goal of the study was to evaluate the effects of nitrogen supplementation (urea- or nitrate-containing supplements) on the utilization of low-quality weeping lovegrass hay (Eragrostis curvula) and CH₄ yield in beef steers. Twenty-four Aberdeen Angus steers (326 ± 27 kg body weight) were assigned to three treatments: (1) weeping lovegrass hay alone; (2) weeping lovegrass hay + sunflower expeller + urea; and (3) weeping lovegrass hay + sunflower expeller + potassium nitrate (KNO₃). The proportion of non-protein nitrogen (NPN; urea and KNO₃) included in the supplements was set according to the maximum tolerated threshold. Methane emissions were measured using the SF₆ tracer technique. Compared with the hay-only treatment, supplemented animals increased dry matter intake (DMI) by 35% and 38% in the urea and nitrate treatments, respectively (p < 0.01). Total CH₄ emissions (g/d) were not affected by treatment (p = 0.16). However, CH₄ yield (g CH₄/kg DMI) decreased by 27% and 38% in the urea and nitrate treatments, respectively (p < 0.01). The methane conversion factor (Ym) was also reduced in supplemented animals. Under the conditions of this study, supplementation of low-quality weeping lovegrass hay with nitrogen-containing supplements increased feed intake and reduced CH₄ yield without affecting total CH₄ emissions. These findings highlight the importance of considering CH₄ emission intensity, in addition to absolute emissions, when evaluating mitigation opportunities in forage-based beef production systems. Full article

This study addresses the technical, environmental, economic, and systemic role of multi-apartment residential buildings as hydrogen consumption nodes within urban energy systems. A representative five-story building comprising 30 apartments and 2400–2800 m² of heated floor area, located in a cold European climate, was modelled with an annual heat demand of approximately 185,000 kWh. Four heating configurations were assessed: a conventional natural gas/biomethane boiler (baseline), a hydrogen boiler, a hydrogen-fuel-cell combined heat and power (CHP) system, and a hybrid heat-pump–hydrogen solution. Dynamic simulations indicate that all hydrogen-based systems can fully satisfy space heating and domestic hot water demand without modifications to the internal hydronic distribution network. The fuel cell CHP achieved an overall efficiency of 93%. It generated approximately 54,000 kWh/year of on-site electricity, while the hybrid configuration reached a seasonal efficiency of 108% and the highest primary energy reduction (46%). Operational CO₂ emissions decreased from 37,800 kg/year (gas baseline) to 1900 kg/year (green hydrogen boiler), 1200 kg/year (fuel cell CHP), and 900 kg/year (hybrid system), corresponding to reductions of up to 98%. Peak-load analysis demonstrated improved operational stability in CHP and hybrid systems, characterised by reduced cycling frequency and enhanced thermal resilience through hydrogen storage integration. Capital expenditure (CAPEX) ranged from 41,000 EUR (gas baseline) to 101,000 EUR (fuel cell CHP), reflecting additional storage, safety, and control requirements. Over a 20-year lifecycle (5% discount rate), the hybrid system achieved the lowest levelized cost of heat (0.076 EUR/kWh), followed by fuel cell CHP (0.081 EUR/kWh), compared to 0.087 EUR/kWh for gas. Payback periods ranged between 9 and 13 years, depending on configuration and hydrogen pricing assumptions. Sensitivity analysis identified a break-even hydrogen price of approximately 0.085 EUR/kWh, while carbon pricing above 100 EUR/t CO₂ significantly improves economic competitiveness. District-scale aggregation modelling suggests that hydrogen-equipped multi-apartment buildings can reduce grid electricity imports by 30–40% through on-site generation and seasonal storage. The findings confirm that multi-apartment buildings offer structural and economic advantages for early hydrogen deployment compared to dispersed housing typologies. By combining high demand density, centralised infrastructure, and compatibility with sector-coupling strategies, such buildings can function as distributed energy hubs within decarbonized urban systems. Full article

Against the backdrop of the deep advancement of the carbon peak and carbon neutrality goals and the superposition of the transportation power strategy, leveraging the spatial restructuring of highway networks to optimize the low-carbon layout of county-level industries has become a crucial lever for balancing economic quality improvement with carbon intensity control. This study selects panel data from 129 counties in Yunnan Province spanning 2015–2024, constructing a comprehensive highway network development index from four dimensions: highway density, road network connectivity, weighted hierarchical structure, and county accessibility. Using a two-way fixed effects benchmark model, a stepwise mediation effect testing framework, and a regional heterogeneity identification strategy, the paper systematically examines the marginal effects, transmission pathways, and spatially differentiated characteristics of highway network development on county-level industrial carbon emission intensity. Key findings are as follows: Enhanced highway network development significantly suppresses the increase in county-level industrial carbon emission intensity, and a well-developed road network can provide long-term empowerment for the low-carbon transformation of county-level industries. Mechanism analysis confirms that highway network development reduces emissions through two core pathways: first, a direct emission reduction effect achieved by optimizing the county-wide freight organization system, reducing inefficient transport energy consumption, and improving overall transport efficiency; second, an indirect low-carbon enabling effect realized by breaking down administrative barriers in county markets, lowering cross-regional business transaction costs, deepening industrial division of labor and collaboration, and forcing resource allocation improvements. Heterogeneity analysis reveals that the low-carbon dividends of highway network development exhibit significant gradient differentiation: the emission reduction enabling effect is strongest in counties within the Central Yunnan urban agglomeration, followed by cultural tourism counties in western Yunnan and border counties in southern Yunnan, with the weakest marginal enabling effect observed in traditional agricultural counties in northeastern Yunnan. Full article

Fatigue health monitoring of engineering structures requires continuous degradation assessment, yet ground-truth health labels are unavailable during run-to-failure tests. Existing self-supervised approaches rely on monotonic degradation assumptions that are violated by the structured non-monotonic behaviour of acoustic emission signals during fatigue. A self-supervised framework called Cross-Modal Degradation Rivalry (CMDR) is proposed, which introduces the Modal Rivalry Index (MRI) as a directional measure of cross-modal predictability between heterogeneous sensor modalities. CMDR comprises a label-free representation-learning stage trained via the Cross-Modal Prediction Asymmetry (CMPA) pretext task, followed by a lightweight supervised stage that maps MRI features to scalar health indicators (HIs) using normalised lifecycle labels. The MRI is conceptually related, under the stated assumptions only loosely met in practice, to the Transfer Entropy difference between sensor latent channels. Experiments on a structural fatigue dataset with seven specimens under two loading conditions demonstrate that CMDR achieves competitive trendability and prognosability, as well as the lowest remaining useful life (RUL) error in three of four scenarios. RUL evaluations are additionally repeated under a fully online estimator that uses only training specimens. A strictly inductive ablation that re-pre-trains the self-supervised stage within each leave-one-specimen-out fold confirms a bounded transductive-vs-inductive gap, and CMDR remains the best against three further self-supervised baselines on the within-condition and mixed-condition scenarios. Ablation studies confirm the necessity of directional asymmetry, bottleneck architecture, and momentum-updated target encoders. Full article

Buildings account for 40% of global energy consumption and one-third of greenhouse gas emissions. Renewable energy systems (RESs), such as solar photovoltaic (PV) and geothermal heat pumps, are critical technological solutions for decarbonization. Despite the growing literature, existing reviews lack a comprehensive synthesis integrating machine learning (ML), Internet of Things (IoT), and Building Information Modeling (BIM). Following the PRISMA protocol, this paper presents a systematic review of 41 studies published between 2012 and 2025. The review evaluates four primary domains: RES performance, building energy prediction, HVAC optimization, and occupancy-aware management. Quantitative findings reveal that solar PV-integrated buildings achieve electricity cost reductions of 35–64%, while ML-enhanced energy prediction models attain accuracies up to R² = 0.989. Critical research gaps are identified, including the scarcity of real-time sensor integration and geographically inclusive multi-climate datasets. Ultimately, this review contributes a structured synthesis of effective technologies, a comparative analysis of methodological approaches (ML, simulation, hybrid), and actionable future directions. It provides practical guidance for researchers and policymakers toward achieving net-zero energy buildings. This study serves as a definitive reference for the development of sustainable, low-energy built environments. Full article

The development of low-emission and reliable propulsion systems is essential for extending the operational capability of unmanned aerial vehicles (UAVs). Although aviation decarbonization is widely recognized as an important objective, it must be considered within the broader context of limited renewable-energy availability. Recent system-level analyses of transportation decarbonization have shown that the allocation of renewable electricity and sustainable fuels should prioritize sectors where direct electrification is most efficient, while hard-to-electrify sectors require alternative pathways. Aviation is one of the most difficult transport sectors to electrify because of strict energy-density requirements, especially for long-endurance airborne platforms. Therefore, sustainable liquid fuels and hybrid propulsion systems should not be considered universal replacements for electrification, but rather complementary solutions for applications where batteries alone cannot provide the required endurance, payload capacity or operational flexibility. In this context, the present study focuses on alcohol–kerosene blends for hybrid UAV power systems, where liquid-fuel energy density and partial emission reduction remain relevant engineering requirements. This work provides one of the first systematic experimental evaluations of ethanol–, butanol– and octanol–kerosene blends in a micro-turboprop engine operating as part of a hybrid UAV power-generation architecture. Unlike previous studies focused mainly on micro-turbojet thrust response, the present work evaluates the coupled influence of alcohol chain length and blending ratio on exhaust gas temperature, gaseous emissions, electrical output and operational stability under multi-load conditions representative of UAV operation. Jet-A and nine alcohol–kerosene blends containing 10%, 20% and 30% ethanol, butanol or octanol by volume were tested over four operating regimes, from idle to 2500 W electrical load. The results show that ethanol blends provided the strongest CO reduction, with E30 reducing CO by 24.9% relative to Jet-A under R3, while E10 offered the most balanced behavior across the full operating range. Higher ethanol fractions improved CO suppression but introduced NOx and low-load stability penalties. Octanol blends, particularly O20, exhibited the most kerosene-like and stable response, supporting reliable power delivery with reduced operational variability. Butanol blends showed intermediate behavior without providing a dominant advantage. A multi-criteria evaluation combining emissions, EGT behavior, relative performance, operational stability and cost identified E10 as the best overall compromise for hybrid UAV use. The study demonstrates that alcohol chain length produces nonlinear system-level effects in hybrid micro-turboprop architectures and provides an experimental basis for fuel selection in low-emission UAV power systems. Full article

Rural electrification in Sub-Saharan Africa faces a trilemma: cutting carbon emissions, making it economically viable, and achieving fair access to energy for all. This paper develops a multi-objective framework that optimises carbon revenue, net present value (NPV), total energy supply, cooking fuel (firewood and LPG), health costs, and benefit to society. The model uses continuous decision variables: daily energy allocation among four sources (solar, generator, firewood, LPG) to three population groups (men, women, children). The case study is a rural community of 7000 people in Nigeria (Tier 1 energy consumers). Six policy scenarios are considered: baseline, high carbon price, low carbon price, microfinance, government subsidy and community cooperative. This study compared algorithms and identified a hybrid Non-dominated Sorting Genetic Algorithm and Particle Swarm Optimisation II as the most suitable algorithm for solving the formulated optimisation problem. It was found that NPV and unit cost of energy would increase to $175,500 and 26.4 ¢/kWh, respectively, by increasing the price of carbon from $8/ton to $12/ton. Firewood generates health savings and carbon revenue in the range of $4100–$12,270/year. Prices below $8/ton do not induce optimal reconfigurations in the system. The best energy supply (2825 kWh/day) and the lowest unsatisfied demand occur in the government subsidy scenario with the greatest disparity index, displaying an equity-efficiency trade-off. The framework shows that sustainable access to energy can be unlocked using strategic integration of carbon finance, valuation of health benefits and equity constraints. Full article

Background and Clinical Significance: Many diagnostic radiopharmaceuticals are excreted in the urine. This can pose a diagnostic challenge when urine-containing structures are in atypical locations, particularly in review of planar imaging without anatomical details from cross-sectional imaging. This case highlights a challenging ^99mTc-methylene diphosphonate (^99mTc-MDP) bone scan in a patient with an inguinal hernia containing a portion of the urinary bladder. Subsequently, we review diagnostic challenges on conventional and molecular imaging following surgical repair of the inguinal hernia. Case Presentation: A 79-year-old man with prostate cancer underwent initial staging prior to prostatectomy with ^99mTc-MDP bone scintigraphy. Anterior and posterior images showed focal uptake overlying the pubic symphysis. Lateral views showed that the activity was extraosseous. Follow-up CT urography showed a bladder hernia as the cause of the abnormality on bone scan. Prostatectomy and inguinal hernia repair were performed as a combination case. Four years postoperatively, follow-up ⁶⁸Ga-PSMA-11 positron emission tomography/computed tomography (PET/CT) showed no recurrence. The CT component of the exam showed an intermediate-density focus at the right inguinal hernia repair site, corresponding to a plugoma related to a polypropylene mesh plug, and a hyperattenuating Gore-Tex mesh repair of the left inguinal hernia. Conclusions: This case highlights the importance of lateral projections in resolving scintigraphic pitfalls and recognizing mesh-related imaging appearances to prevent misinterpretation. Full article

Global demand for sustainability drives interest in bioenergy from sustainable feedstock. Agro-industrial waste such as brewer’s spent grains (BSG) is an important by-product of brewing. This study provides a comprehensive review of the current technologies of BSG for energy recovery and BSG-based materials for energy storage applications. The latest scientific progress, not only from conventional processes on anaerobic digestion, combustion, gasification, pyrolysis, torrefaction, and hydrothermal liquefaction but also from several integrated technologies, pretreatment methods, and additives/catalysts regarding the improvement of energy efficiency and process sustainability, was reviewed. In addition, the co-feedstock practices (co-combustion, anaerobic co-digestion, hydrothermal co-liquefaction, anaerobic co-fermentation) and co-production were examined. AD of BSG yields about 302 NL CH₄/kg COD, generating roughly 0.39 kWh of electricity/kg BSG and 1.71 MJ of thermal energy/kg BSG. Ultrasonic pretreatment enhances methane production up to four times (107 L CH₄/kg TVS) and reduces CO₂ emissions by 0.083 t CO₂eq/t BSG. Anaerobic co-digestion of BSG with other brewery waste increased the yield up to 88 mL CH₄/g TVS, generated approx. 0.348 kWh/kg TVS electricity, and reduced emissions by 0.114 kg CO₂eq/kg TVS. Bioethanol yields can reach 72%, while biohydrogen generation was up to 5154 mL H₂/g glucose. BSG pyrolysis provides up to 71.8% bio-oil, and its calorific value is 18–25 MJ/kg. BSG-derived activated biocarbon has a notable surface area (1792 m²/g) for lithium–sulfur batteries. The assessment showed that BSG’s transformation into bioenergy and energy storage materials aligns with waste reduction and sustainable development goals. However, future research on combined alternative wastes, integrated technologies, green nanotechnology, and artificial intelligence technology could lead to optimal performance and facilitate their industrial application. Full article

Background/Objectives: Differentiating recurrent disease from postsurgical changes on ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) remains challenging in malignant pleural mesothelioma (MPM). This study aimed to characterize the temporal patterns of postsurgical FDG uptake and evaluate the diagnostic performance of FDG PET/CT for detecting recurrent disease after radical surgery. Methods: We retrospectively analyzed 91 postsurgical PET/CT scans from 45 patients with MPM who underwent extrapleural pneumonectomy (EPP; n = 29) or pleurectomy/decortication (P/D; n = 16). Scans were stratified into four postoperative time intervals: <6 months, 6 to <12 months, 12 to <24 months, and ≥24 months. FDG uptake in the postsurgical bed and local recurrent lesions was quantified using maximum standardized uptake value ratios normalized to the mediastinal blood pool and liver. Recurrence was confirmed by histopathology or follow-up imaging. Results: Postsurgical FDG uptake showed a time-dependent decline, with significantly lower uptake beyond 24 months postoperatively (p < 0.05). EPP patients demonstrated significantly higher postsurgical FDG uptake than P/D patients (p < 0.01). FDG PET/CT identified occult recurrence in 23.4% of CT-negative scans. Local recurrent lesions showed significantly higher FDG uptake than postsurgical changes across all postoperative intervals (p < 0.001). Conclusions: Postsurgical FDG uptake in MPM demonstrates a time-dependent decline, and surgical extent is an important determinant of background metabolic activity. Despite this variable background, FDG PET/CT demonstrated high diagnostic accuracy for detecting recurrent disease, including CT-occult recurrences. Incorporating surgical type and postoperative interval into PET/CT interpretation may improve diagnostic accuracy in postoperative MPM surveillance. Full article

Waste-to-energy (WtE) systems are frequently proposed as complementary waste-management strategies; however, their climate performance depends on the interaction between thermodynamic efficiency, material circularity, and electricity-system characteristics. Existing life-cycle assessments generally provide static comparisons between landfill and WtE but rarely identify the operating conditions under which WtE remains environmentally competitive. To address this gap, a parametric life cycle–energy framework was developed by integrating attributional LCA with an analytical energy model capable of evaluating critical efficiency thresholds under varying recovery rates and electricity-grid conditions. Four representative thermoplastics (PET, HDPE, PP, and LDPE) were evaluated using ReCiPe 2016 Midpoint (H) in SimaPro under Mexican electricity conditions ($E{F}_{\mathrm{grid}}=0.444$ kg CO₂eq/kWh). Results indicate that total life-cycle climate impacts are dominated by upstream polymer production, whereas end-of-life management contributes only marginally to overall GWP. Critical-efficiency analysis revealed strong sensitivity to both recovery rate and electricity-grid carbon intensity. For PET, the minimum efficiency required for WtE to outperform landfill increased from 13.1% to 73.5% across the evaluated scenarios, whereas HDPE remained competitive at efficiencies below 1.3%. Monte Carlo simulations (10,000 realizations) further demonstrated that avoided emissions decline systematically with increasing recovery rates, with LDPE exhibiting the highest mean avoided emissions (1735 kg CO₂eq) and PET the lowest (811 kg CO₂eq). These results demonstrate that WtE climate performance is governed primarily by residual waste availability and electricity-system evolution rather than thermodynamic efficiency alone. Consequently, WtE should be interpreted as a transitional residual-waste management strategy whose long-term climate relevance decreases as material circularity and electricity-grid decarbonization advance. Full article