Search Results (411)

Hybrid-electric propulsion and alternative energy carriers are being considered to mitigate the climate impact of short-range regional aviation. Within this framework, the HERA (Hybrid Electric Regional Architecture) project investigates advanced propulsion architectures for a next-generation 72 passenger regional platform. This work presents a cradle-to-grave Life Cycle Assessment of two HERA reference configurations and compares them with a conventional 70 passenger turboprop representative of current service aircraft. The analysis focuses on lithium–sulphur batteries, proton exchange membrane fuel cells, liquid hydrogen storage tanks, and electric motors. The assessment is implemented through a parametric LCA tool supported by a detailed Life Cycle Inventory based on Ecoinvent v3.8 and evaluated using ReCiPe 2016 midpoint indicators. The system boundary includes raw material extraction, manufacturing and assembly, operation under defined mission profiles, maintenance with component replacement, and End-of-Life (EoL) treatment. Results show that the operational phase remains the main driver of climate change impacts, exceeding 95% of total CO₂ equivalent emissions across configurations. The battery-based hybrid reduces fuel consumption but increases manufacturing and maintenance burdens. The fuel cell configuration shows a more balanced life cycle profile, with platinum identified as a critical hotspot. Full article

This study aims to show a detailed life cycle assessment (LCA) approach of battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs), with an emphasis on determining the electrical carbon intensity at which these vehicles reach life-cycle greenhouse gas (GHG) parity. The analysis was conducted in openLCA v2.0.3 using the Ecoinvent v3.9.1 database under a European use-phase context, with a functional unit of 150,000 km. BEVs were evaluated for two representative lithium-ion battery chemistries (NMC622 and LFP) under three electricity carbon intensity scenarios (50, 400, and 850 g CO₂/kWh), while ICEVs were modeled for both gasoline and diesel pathways. Results show that BEV life-cycle GHG emissions vary between 91 and 221 g CO₂-eq/km across different combinations of electricity mix, battery chemistry, and end-of-life conditions. When isolating electricity carbon intensity as the primary variable under a fixed BEV configuration, emissions increase approximately linearly with grid emission factor. Under average European electricity conditions (400 g CO₂/kWh), BEVs exhibit lower life-cycle GHG emissions than gasoline ICEVs, whereas under coal-intensive electricity conditions (850 g CO₂/kWh) this advantage may be reduced or reversed. The break-even electricity carbon intensity is derived by linear interpolation under a fixed BEV configuration (NMC622, 60 kWh, constant lifetime and EoL conditions), yielding a threshold of approximately 600 g CO₂/kWh. The results further indicate that this threshold is influenced by battery chemistry, production-related emissions, recycling efficiency, and assumed vehicle lifetime. These findings highlight the importance of simultaneous progress in electricity decarbonization and end-of-life recycling to secure the environmental benefits of vehicle electrification, and they provide a threshold-oriented framework for policy-relevant interpretation of comparative vehicle LCA results. Full article

Sustainability assessment of textile materials has traditionally relied on origin-based classifications and indicator-driven life cycle assessment (LCA), often treating sustainability as an inherent or material-intrinsic property. However, materials sharing similar biological origins or “bio-based” labels frequently exhibit substantially different sustainability outcomes when processing pathways, composite structures, and end-of-life (EoL) compatibility are taken into account. To address this limitation, this study develops a qualitative, multidimensional analytical framework that conceptualizes textile material sustainability as a pathway-dependent and system-mediated outcome rather than an inherent material attribute. The framework integrates four interrelated dimensions—renewability, process sustainability, EoL options, and material source—derived from a structured review of academic, policy, and technical literature. To demonstrate the analytical scope and internal logic of the framework, a selected set of 65 innovative textile materials was systematically analyzed using a three-tier qualitative coding scheme (favorable, conditional, and unfavorable) under conservative data validation criteria. The analysis shows that sustainability performance is primarily shaped by pathway configurations—particularly processing intensity, binder chemistry, and EoL compatibility—rather than material origin alone and that similar bio-based materials can exhibit fundamentally different sustainability profiles depending on these factors. By reframing sustainability from a material-centered perspective to a pathway-oriented and system-based perspective, the proposed framework provides a structured basis for integrating material innovation, process design, and end-of-life planning in sustainability-oriented textile research and development and establishes a conceptual foundation for future empirical and quantitative extensions. Full article

Isothermal dispatch models for battery energy storage systems (BESSs) systematically underestimate degradation costs because dispatch-induced Joule heating elevates cell temperature and accelerates ageing through Arrhenius-type kinetics. This paper proposes three integrated contributions. First, a thermal–electrochemical coupling loop embeds a first-order lumped thermal model within the dispatch simulation: cell temperature is updated from I²R heat generation and Newton cooling at each time step, and the resulting temperature trajectory feeds into the Arrhenius stress factors of a semi-empirical degradation model combining $\sqrt{\Delta t}$-based calendar ageing with Rainflow-based cycle ageing, enabling the optimiser to discover thermally self-regulating strategies. This coupling is critical because, as the results demonstrate, ignoring it leads to systematic underestimation of degradation costs by up to 13%. Second, the resulting four-objective problem (negative profit, thermally coupled degradation cost, SOC deviation, and CVaR imbalance penalty) is solved by a hypervolume-contribution-enhanced NSGA-III (HVC-NSGA-III), which augments reference-point selection with an archive pruned by removing the solution of the smallest individual hypervolume contribution, concentrating Pareto resolution in the knee region. Third, an SOH-adaptive knee-point selection assigns the degradation weight as a monotone function of ageing degree $(1-\mathrm{SOH})/(1-{\mathrm{SOH}}_{\mathrm{EOL}})$, automatically tightening dispatch conservatism as remaining useful life diminishes. Simulations on ENTSO-E data over 96 h show the following: (i) thermal coupling shifts the Pareto front by 8–15% in the degradation dimension with temperature excursions up to 7 K; (ii) HVC-NSGA-III improves hypervolume by 8.7% over standard NSGA-III; (iii) SOH-adaptive selection reduces capacity loss by 27.4% at only 9.1% revenue cost; and (iv) ablation confirms Rainflow (24.8%) and thermal coupling (13.1%) as the two largest contributors. Full article

As population aging accelerates, the demand for high-quality end-of-life (EOL) care continues to rise. However, a substantial proportion of patients with terminal cancer still experience death in acute-care hospitals without adequate palliative care. Consultative palliative care (CPC) represents a feasible model for delivering palliative care without requiring dedicated inpatient units, yet evidence evaluating its clinical impact remains limited. In this study, we developed a structured hospital-based CPC model tailored to the Korean healthcare system, the Korea Holistic Optimized Palliative care for End-of-life (K-HOPE) model, and prospectively evaluated its clinical impact. K-HOPE was delivered by an interdisciplinary CPC team in a tertiary hospital. Unmet needs were assessed using the Integrated Palliative care Outcome Scale (IPOS), and longitudinal changes were analyzed using mixed-effects models for repeated measures. Among patients who died during hospitalization, quality of death was evaluated using the Good Death Scale (GDS). A total of 84 patients with terminal cancer received K-HOPE. The total IPOS score significantly decreased over time (β = −10.4, 95% CI −12.8 to −8.0; p < 0.001), indicating reduced overall burden and unmet needs. Significant improvements were observed in psychological distress (p = 0.010) and communication and information needs (p < 0.001), whereas changes in physical symptoms and practical concerns were not statistically significant. Among 22 patients who died during hospitalization, 59.1% achieved a good quality of death (GDS ≥ 12). Longer duration of CPC involvement was significantly associated with higher quality of death and remained an independent predictor in multivariable analysis. These findings suggest that the K-HOPE CPC model improves communication and overall EOL care experiences among hospitalized patients with terminal cancer, indicating that meaningful improvements in EOL care can occur even during short periods of CPC involvement. Structured CPC integrated into routine oncology practice represents a feasible strategy for improving EOL care in tertiary hospitals, and a standardized CPC framework may enhance the consistency and reproducibility of care delivery within the Korean healthcare system. Full article

This Special Issue focuses on innovative approaches and methodologies for valorizing waste, including mineral waste and end-of-life (EoL) products, through the recycling and recovery of critical and strategic metals, including, but not limited to, rare earth elements, cobalt, copper, nickel, lithium, silver, aluminum, titanium, and silicon [...] Full article

The capacitance of the DC link is an important variable for the prediction of remaining useful life and failures in frequency inverters. The direct measurement of the DC-link capacitance in inverters operating under load is technically challenging and generally impractical. Recently, a great focus has been given to data-based soft sensors for estimating this variable. These methods, however, are evaluated based only on the estimate errors, and do not take into account the metrological aspects of these estimators. This paper proposes an uncertainty analysis method based on Monte Carlo simulations and bootstrapping that can be applied to all recently published methods for end-of-life (EOL) estimation based on data-driven regression and neural networks. A state-of-the-art model of EOL monitoring based on capacitance estimation was evaluated using the proposed framework, and an experimental study with a frequency converter drive for a brushless DC motor was performed, considering multiple output frequencies, loads and DC-link capacitance conditions. The output distributions are not symmetrical and show that the variable with the most significant impact in the propagated uncertainty is the DC link voltage. The results show confidence interval widths ranging from 12 μF to 61 μF, with wider confidence intervals obtained at higher power setpoints. Full article

To address the non-stationary fluctuations caused by capacity regeneration and measurement noise during lithium-ion battery aging, this paper proposes a decomposition-guided heterogeneous prognostic framework for capacity forecasting and remaining useful life (RUL) inference. First, the raw capacity sequence is decomposed by CEEMDAN to separate the long-term degradation trend from short-term regeneration-related disturbances across different time scales. Next, a temporal convolutional network (TCN) is employed to model the trend component, while Gaussian process regression (GPR) is used to characterize local fluctuation behavior and provide predictive uncertainty. Finally, Dempster–Shafer (D-S) evidence theory is introduced to fuse multi-source prognostic outputs, yielding a more robust capacity trajectory for end-of-life (EOL) threshold localization and RUL estimation. Experiments are conducted on the lithium-ion battery dataset released by NASA Ames. Across the four tested battery cells, the proposed method achieves RMSE values of 0.0257–0.0445 Ah and EOL cycle deviations of 1.17–5.53 cycles, while yielding a more balanced trade-off than representative baselines between point-wise prediction accuracy and threshold-crossing stability. Moreover, under direct multi-step forecasting, the prediction error increases with the forecasting horizon, which is consistent with the expected characteristics of long-horizon capacity extrapolation. Overall, this work provides an implementable and interpretable prognostic framework for battery health assessment in the presence of capacity regeneration phenomena. Full article

Lithium-ion batteries are widely used across diverse applications due to their high energy density, long cycle life, and fast charging capabilities. As battery-powered systems become increasingly critical, accurate estimation of the Remaining Useful Life (RUL) is essential for ensuring reliability, safety, and effective maintenance planning. This work investigates Evidential Deep Learning (EDL) for data-driven RUL estimation and introduces a novel risk-aware loss function designed to enhance both predictive accuracy and uncertainty quantification in the End-of-Life (EoL) region, where precise and trustworthy predictions are most needed. Using a publicly available dataset of lithium iron phosphate (LFP) cells, we benchmark the proposed approach against a baseline Conv–LSTM model, Monte Carlo (MC) Dropout, and Deep Ensembles. The results show that integrating the risk-aware loss into the EDL framework substantially improves the calibration of predictive uncertainty while achieving state-of-the-art accuracy near EoL. Unlike MC Dropout and Deep Ensembles, which exhibit increasing or unstable uncertainty as degradation accelerates, the proposed EDL model demonstrates a consistent reduction in uncertainty and significantly higher reliability in late-stage predictions. The findings indicate that the risk-aware evidential framework offers a reliable and computationally efficient solution for battery RUL estimation, enabling more informed decision-making in both safety-critical and consumer-oriented applications. Full article

Rare Earth Elements (REEs) represent strategic and critical raw materials for the energy transition and must therefore be integrated into efficient and functional recycling processes. Their adoption in electric motors is rapidly expanding, raising significant challenges for end-of-life (EoL) management, starting from the collection phase. In this context, this work proposes the integration of an image-based classification framework within the Waste Electrical and Electronic Equipment (WEEE) recycling pipeline to selectively identify electric motors containing permanent magnets (PMs) and direct them toward dedicated recycling processes for rare earth recovery. The proposed methodology relies on a Discriminative Transfer Learning (DTL) approach based on a ResNeXt convolutional neural network (CNN), adapted to a proprietary and heterogeneous dataset of electric motors acquired in an industrial recycling facility. The objective is twofold: first, to identify motors containing PMs; second, to classify motors into construction categories according to their likelihood of incorporating PMs. Experimental results show promising performance in terms of PM-containing motor detection capability, establishing a robust foundation for the automated recovery of REEs at an industrial scale. Furthermore, the model’s generalization capabilities can be further enhanced through the expansion of collaborative datasets and the integration of advanced scanning technologies. Full article

The growing interest in bio-based and biodegradable polymer composites reinforced with agricultural waste reflects global efforts to reduce dependence on fossil resources and improve the sustainability of materials. However, biocomposites are not necessarily more sustainable, and their environmental performance requires careful life cycle assessment (LCA). This review critically analyses recent LCA studies of biodegradable biocomposites reinforced with agricultural waste, focusing on methodological choices, data quality, results and limitations. A systematic literature review was conducted using the Scopus database, focusing on studies from the last five years. Selected studies were examined using a structure consistent with ISO 14040, with defined data extraction categories and key questions. The analysis shows that although biocomposites often demonstrate advantages in terms of climate change and fossil resource depletion compared to traditional materials, the results vary significantly depending on the definition of the functional unit, geographical context, processing pathways, and data assumptions. Limitations include reliance on laboratory data, uncertainties, incomplete system boundaries, inconsistent allocation methods, and limited end-of-life (EoL) modelling. Overall, the review highlights the need for improved data quality, performance-based functional units, geographically representative inventories, and more standardised LCA practices to ensure meaningful comparisons and support the sustainable development of biocomposites. Full article

Textile fabrics intended for use in protective clothing, workwear, and uniforms are subjected to repeated high-temperature industrial washing and drying processes. It is evident that due to the rigorous nature of the prescribed preservation conditions, textiles that are currently utilised for this purpose do not contain elastomeric yarns: a consequence of their suboptimal thermal stability. However, elastomers enable garments to better fit the wearer’s figure and enhance safety and comfort during occupational activities. Currently, no investigations of EOL (elastolefin) yarn elastic durability under commercial maintenance conditions have been conducted. The publication evaluates the elastic properties and pilling resistance of fabrics with EOL-core weft yarns before and after repeated industrial washing under conditions that are typical of rental use. Additionally, an analysis using SEM, FTIR spectroscopy, thermal and thermogravimetric techniques of core-yarns and the core itself was performed. The tested fabrics retained a high elasticity index, even after 100 industrial washing cycles, as confirmed by instrumental analysis. In conclusion, fabrics with EOL-core yarns can be used for garments that are subjected to intensive maintenance in industrial washing conditions without losing their elastic properties. Full article

Introduction: End-of-life (EOL) HIV cure-related research offers a unique opportunity to advance scientific discovery while honoring the values, dignity, and legacy of people with HIV. However, participation remains demographically skewed, mirroring long-standing inequities in who is informed, invited, and supported to take part. Synthesizing eight years of experience, published literature reviews, and community engagement from the University of California San Diego’s Last Gift program, we propose strategies to embed justice, equity, diversity, inclusion, and accessibility (JEDIA) throughout the design and implementation of EOL HIV cure-related studies. Discussion: Using intersectionality as a structural analytic framework, we examine how interlocking systems and social determinants shape access, consent, and participant experience, and we translate ethics into action across three themes and eight domains. As examples, we facilitate equitable access by implementing solutions that address gaps limiting awareness and feasibility of participation. We establish ongoing consent through multi-session consent processes with teach-back methods, clear healthcare proxy pathways, and explicit separation of research activities from clinical care. We center lived experiences by partnering with people with HIV and community groups, customizing participation, and honoring cultural and spiritual needs. We enable real-time course correction by using a dashboard that monitors enrollment patterns and representation. Conclusions: An intersectionality-informed, participant-centered approach is both feasible and essential to ensure HIV cure-related research advances with fairness, trust, and global relevance. Programs such as the Last Gift show that scientific rigor, integrity, and participant dignity can coexist, establishing a model for equitable HIV cure discovery. Full article

The extensive integration of lithium-ion batteries (LIBs) into modern technologies—including portable electronics, electric vehicles (EVs), and battery energy storage systems (BESSs)—has created a critical dependency on the supply of raw materials. The ongoing shift toward clean mobility is expected to further intensify this demand. This trend coincides with a projected increase in battery waste: over the next decade, millions of tons of EV and BESS batteries will reach their end-of-life (EOL), alongside the generation of considerable manufacturing scrap. Recycling is essential for recovering critical materials and reducing dependency on primary mining, thereby benefiting the circular economy and environmental sustainability. However, EOL-LIBs are more prone to thermal runaway due to defects and aging-induced degradation, which can lead to fire and explosion incidents, as well as associated environmental and health hazards. Such incidents have been increasingly reported in recent years during transportation, storage, handling, and illegal disposal, resulting in potential loss of life, property damage, and ecological degradation. To ensure the safe design and operation of the battery recycling industry, this work provides an updated overview of the health, safety and environment (HSE) hazards posed by EOL-LIBs and the safety measures required to mitigate these hazards. First, this work outlines the structures, components, and aging mechanisms of LIBs. Second, it summarizes the state-of-the-art recycling pathways and relevant process risks, such as deactivation, dismantling, and mechanical and thermal pretreatments. Third, it reviews recent safety incidents initiated by thermal runaway of EOL-LIBs and recycling intermediates like black mass, with an emphasis on storage and handling. Fourth, recommendations for future work regarding the safe storage and processing of EOL batteries are provided. Finally, conclusions and perspectives on future research directions are presented. Continued research and development in this field are essential to improve recycling methods, optimize processes, and ensure the safe and sustainable management and legislation of EOL lithium-ion batteries. Full article

Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy with poor prognosis, frequent relapse, and treatment-related toxicity. The discovery of novel anti-leukemic agents with improved selectivity remains an urgent clinical need. In this study, rhizomes of Angiopteris evecta, a medicinal plant used in Thai traditional medicine, were collected from twelve locations in Thailand and extracted using solvents of increasing polarity. Among thirty-six crude fractional extracts, the ethyl acetate crude fractional extract from source No. 003 (AE EtOAc No. 003) exhibited the strongest cytotoxic activity against KG-1a and EoL-1 leukemic cell lines, with low toxicity toward normal peripheral blood mononuclear cells. Bioactivity-guided fractionation yielded the ternary mixture, a furanone-rich mixture dominated by 5-(1-hydroxyethyl)-dihydro-2-furanone. The ternary mixture inhibited leukemic cell proliferation by inducing apoptosis, causing cell cycle arrest, and downregulating WT1 expression in EoL-1 cells. Network pharmacology and molecular docking analyses implicated AKT1, MAPK signaling, apoptosis-related pathways, and WT1 as key molecular targets. In addition, AE EtOAc No. 003 and the ternary mixture suppressed TNF-α and IL-6 production in LPS-stimulated macrophages. Collectively, A. evecta-derived furanone compounds represent promising lead candidates for anti-leukemic drug development. Full article