Search Results (729)

Full use of the upper limb is necessary to carry out most tasks of daily life. Upper limb deficiencies, whether through complete or incomplete paralysis, inevitably lead to a loss of autonomy. Assistive orthoses are a potential method for restoring some autonomy. Pronation and supination, the turning of the wrist relative to the elbow, receives less focus than other joint movements in the arm. First, the utility of this degree-of-freedom in the arm is less obvious. Second, when compared to flexion and extension of the elbow, wrist prono-supination has no clear center of rotation due to the combined movement of the ulna and the radius bones as they cross and uncross in the forearm. This paper presents initial work in the design of a mechanism for a portable assistive orthosis that is expected to include powered prono-supination. The component proposed in this work is based on a spherical mechanism architecture. The capacity of these mechanisms to have a hollow center and to produce paths that follow arcs on spheres makes them worth consideration in this application. An optimization was carried out to perform path generation of a single spherical four-bar with the intent of replicating it three times to create the device proposed in this work. The mechanical design was modeled and a conceptual prototype was constructed to perform preliminary operational evaluations. Full article

The transition toward renewable-powered greenhouse agriculture offers opportunities for reducing operational costs and environmental impacts, yet challenges remain in managing fluctuating energy loads and optimizing agricultural inputs. While second-life lithium-ion batteries provide a cost-effective energy storage option, their thermal and electrical characteristics under real-world greenhouse conditions are poorly documented. Similarly, although plasma-activated water (PAW) shows potential to reduce chemical fertilizer usage, its integration with renewable-powered systems requires further investigation. This study develops an adaptive monitoring and modeling framework to estimate the thermal resistances (R_u, R_c) and internal resistance (R_int) of second-life lithium-ion batteries using operational data from greenhouse applications, alongside a field trial assessing PAW effects on beefsteak tomato cultivation. The adaptive control algorithm accurately estimated surface temperature (T_s) and core temperature (T_c), achieving a root mean square error (RMSE) of 0.31 °C, a mean absolute error (MAE) of 0.25 °C, and a percentage error of 0.31%. Thermal resistance values stabilized at R_u ≈ 3.00 °C/W (surface to ambient) and R_c ≈ 2.00 °C/W (core to surface), indicating stable thermal regulation under load variations. Internal resistance (R_int) maintained a baseline of ~1.0–1.2 Ω, with peaks up to 12 Ω during load transitions, confirming the importance of continuous monitoring for performance and degradation prevention in second-life applications. The PAW treatment reduced chemical nitrogen fertilizer use by 31.2% without decreasing total nitrogen availability (69.5 mg/L). The NO₃⁻-N concentration in PAW reached 134 mg/L, with an initial pH of 3.04 neutralized before application, ensuring no adverse effects on germination or growth. Leaf nutrient analysis showed lower nitrogen (1.83% vs. 2.28%) and potassium (1.66% vs. 2.17%) compared to the control, but higher magnesium content (0.59% vs. 0.37%), meeting Japanese adequacy standards. The total yield was 7.8 kg/m², with fruit quality comparable between the PAW and control groups. The integration of adaptive battery monitoring with PAW irrigation demonstrates a practical pathway toward energy efficient and sustainable greenhouse operations. Full article

As we close the second edition of the Life Special Issue “Cardiovascular Diseases: From Basic Research to Clinical Application”, we would like to reflect on the progress made in understanding the molecular and physiological mechanisms underlying cardiovascular diseases and their implications in clinical interventions [...] Full article

Remaining useful life (RUL) prediction is a core technology in prognostics and health management (PHM), crucial for ensuring the safe and efficient operation of modern industrial systems. Although deep learning methods have shown potential in RUL prediction, they often face two major challenges: an insufficient generalization ability when distribution gaps exist between training data and real-world application scenarios, and the difficulty of comprehensively capturing complex equipment degradation processes with single-modal data. A key challenge in current research is how to effectively fuse multimodal data and leverage transfer learning to address RUL prediction in small-sample and cross-condition scenarios. This paper proposes an innovative deep multimodal fine-tuning regression (DMFR) framework to address these issues. First, the DMFR framework utilizes a Convolutional Neural Network (CNN) and a Transformer Network to extract distinct modal features, thereby achieving a more comprehensive understanding of data degradation patterns. Second, a fusion layer is employed to seamlessly integrate these multimodal features, extracting fused information to identify latent features, which are subsequently utilized in the predictor. Third, a two-stage training algorithm combining supervised pre-training and fine-tuning is proposed to accomplish transfer alignment from the source domain to the target domain. This paper utilized the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbine engine dataset publicly released by NASA to conduct comparative transfer experiments on various RUL prediction methods. The experimental results demonstrate significant performance improvements across all tasks. Full article

As the demand for second-life lithium-ion battery applications continues to grow, efficient cell equalization has become essential to mitigate parameter inconsistencies and extend system longevity. Owing to their diverse origins and varying aging paths, second-life batteries exhibit significant parameter dispersion, which poses distinct challenges. In light of these issues, this paper presents a comprehensive review of passive, active, and dynamic equalization technologies. It analyzes the circuit topologies and control strategies associated with each method, with a particular focus on their applicability to second-life battery systems. Furthermore, emerging trends toward intelligent, modular, and adaptive equalization are discussed. Full article

The growing need for efficient battery reuse and recycling requires rapid, reliable methods to assess the state of health (SoH) of lithium-ion cells. Conventional SoH estimation based on full charge–discharge cycling is slow, energy-intensive, and unsuitable for dismantled cells with unknown histories. This work presents an automated diagnostic approach using Electrochemical Impedance Spectroscopy (EIS) combined with Electrical Equivalent Circuit (EEC) modeling for fast, non-invasive SoH estimation. A correlation between fitted EIS parameters and cell degradation stages was established through controlled aging tests on NMC-based lithium-ion cells. The methodology was implemented in custom software (BaterurgIA) integrated into a robotic testing bench, enabling automatic EIS acquisition, data fitting, and SoH determination. The system achieves SoH estimation with 5–10% accuracy for cells in intermediate and advanced degradation stages, while additional parameters improve sensitivity during early aging. Compared to conventional cycling methods, the proposed approach reduces diagnostic time from hours to minutes, minimizes energy consumption, and offers predictive insights into internal degradation mechanisms. This enables fast and reliable cell grading for reuse, reconditioning, or recycling, supporting the development of scalable solutions for battery second-life applications and circular economy initiatives. Full article

The problem of end-of-life (EoL) fibre-reinforced polymer (FRP) wind turbine blades (WTBs) poses a growing challenge due to the absence of an integrated circular value chain currently available on the market. A key barrier is the information gap between the EoL condition of WTB components and their second-life application requirements. This study addresses this question by focusing on the spar cap, which is an internal structural component with high repurposing potential. A framework has been developed to determine the as-received mechanical properties of spar caps from different EoL WTB models, targeting repurpose in the construction sector. The experimental programme encompasses fibre architecture assessment, calcination processes and mechanical tests in both longitudinal and transverse directions of three different WTB models. Results suggest that the spar caps appear to retain their strength and stiffness, with no evidence of degradation from previous service life. However, notable variation in properties is observed. To account for this, a prediction tool is proposed to estimate the as-received mechanical properties based on practically accessible parameters, thereby supporting decision-making. The results of this study contribute to enabling the repurposing of EoL spar cap beams from the wind energy sector for applications in the construction sector. Full article

This study presents an integrated machine learning framework to evaluate the aging states of lithium-ion batteries and to classify them according to their second-life application potential. The methodology combines two key components: a set of regression models to estimate critical health indicators, such as capacity and internal resistance, and a classification stage to group batteries based on these parameters. The proposed models were trained and validated using the NASA Battery Aging Datasets. Through an in-depth analysis of environmental conditions, the study identifies their influence on aging metrics, reinforcing the relevance of the input features selected. Furthermore, a clustering-based approach was employed to validate the classification performance and to reveal the link between a battery’s operation and its aging in the Euclidean space. The results show accurate predictions without signs of overfitting or underfitting, and the classification framework proved robust across the evaluated cases. This suggests that the proposed method can serve as a scalable and adaptable tool to guide battery repurposing strategies. Overall, the findings contribute to bridging the gap between battery diagnostics and real-world energy storage applications, offering practical insights to optimize second-life deployment. Full article

The current research on smart construction sites is mainly from the perspective of the whole life cycle of the project, and often focuses on the identification of factors at the macro level. It lacks in-depth quantitative analysis of the complex interdependence between influencing factors, and it is difficult to accurately identify key driving factors and weight distribution. This paper takes engineering project management as the perspective, constructs a smart site construction model. The advantages of DEMATEL method and ANP method are innovatively combined to construct the DEMATEL-ANP evaluation model, which overcomes the limitations of single method in weight determination and relationship analysis, and provides a more detailed and scientific analysis framework for the evaluation of smart site construction, and, taking the Urumqi region as an example, its smart construction is evaluated and analyzed. The results of the study show that the correlation between the indicators affecting the construction of smart construction sites is strong, in which the comprehensive influence of personnel safety management, construction quality management, and construction safety management play a greater role, with the comprehensive weights of 0.0917, 0.0817 and 0.0767, respectively; the total score of smart construction site construction of Urumqi region is 63.959, which is in the primary construction stage. Among them, the construction and application of meteorological monitoring are the best, scoring 70.26; the construction and application of most indicators, such as personnel safety management, cost comparison decision-making, dust monitoring, and noise monitoring, are the second best; the construction progress control and wastewater monitoring construction are poor, scoring 55.21 and 57.741. The results of this study can provide direct value to key audiences such as construction enterprise managers, government regulators, and smart site solution providers. This paper considers regions with unique climate types to provide a reference for the construction of intelligent construction sites in the same type of regions. Full article

This study presents a comprehensive study integrating machine learning, life cycle assessment (LCA) and heuristic optimization to achieve a low-carbon medical waste (MW)-to fuel process. A detailed process simulation coupled with cradle to gate LCA is employed to generate a dataset covering diverse process operation conditions, embodied carbon of supplying H₂ and the associated carbon emission factor of MW treatment (CEF). Four machine learning techniques, including support vector machine, artificial neural network, Gaussian process regression, and XGBoost, are trained, each achieving test R² close to 0.90 and RMSE of ~0.26. These models are integrated with heuristic algorithms to optimize operating parameters under various green hydrogen mixes (20–80%). Our results show that machine learning models outperform the detailed process model (DPM), achieving a minimum CEF of ~1.3 to ~1.1 kg CO₂-eq/kg MW with higher computational stabilities. Importantly, the optimization times dropped from hours (DPM) to seconds (machine learning models) and the combination of Gaussian process regression and particle swarm optimization is highlighted, with an optimization time under one second. The optimized process holds promise in carbon reduction compared to traditional MW disposal methods. These findings show machine learning can achieve high predictive accuracy while dramatically enhancing optimization speed and stability, providing a scalable framework for extensive scenario analysis during waste-to-energy process design and further real-time optimization application. Full article

Background/Objectives: The extension of euthanasia and physician-assisted suicide to individuals with mental disorders presents a profound ethical, clinical, and legal challenge. While increasingly accepted in some jurisdictions, their application in psychiatric contexts—particularly in cases of depression—raises concerns about diagnostic precision, therapeutic adequacy, and the validity of informed consent. This study examines two controversial Belgian cases to explore the complexities of euthanasia for psychological suffering. Methods: A qualitative case analysis was conducted through a qualitative analysis of publicly available media sources. The cases were examined through clinical, psychoanalytic, and medico-legal lenses to assess diagnostic clarity, treatment history, and ethical considerations. No access to official medical records was available. Case Presentation: The first case involved a young woman whose depressive symptoms were reportedly linked to trauma from a terrorist attack. The second concerned a middle-aged woman convicted of infanticide and later diagnosed with Major Depression. Discussion: In both cases, euthanasia was granted on the grounds of “irreversible psychological suffering.” However, the absence of detailed clinical documentation, potential unresolved trauma, and lack of psychodynamic assessment raised doubts about the robustness of the evaluations and the validity of informed consent. Conclusions: These findings highlight the need for a more rigorous, multidisciplinary, and ethically grounded approach to psychiatric euthanasia. This study underscores the importance of precise diagnostic criteria, comprehensive treatment histories, and deeper exploration of unconscious and existential motivations. Safeguarding clinical integrity and ethical standards is essential in end-of-life decisions involving mental illness. Full article

Introduction. The Italian Committee for Tailored BIOlogic Therapy (ITABIO), in a first report, has reviewed the literature to identify the best strategy for the choice of second-line biologic therapy in patients with rheumatoid arthritis (RA), spondyloarthritis (SpA), and psoriatic arthritis (PsA). To verify the application of ITABIO recommendations in real life and how the recommendations perform in maintaining the health status of patients affected by inflammatory arthritis (RA, SpA, PsA), a database has been developed by Pharmaceutical Governance to evaluate the appropriateness of prescriptions. Methods. We have analyzed retrospectively 616 patients, 288 (46.7%) affected by RA, 117 (19%) affected by SpA, and 211 (34.3%) affected by PsA. Age, sex, diagnosis, current treatment, previous treatments with csDMARDs, b-DMARDs, ts-DMARDs, presence of risk factors for cardiovascular (CV) events, liver disease, infections, extra-articular manifestations such as interstitial lung disease (ILD) for RA, enthesitis, dactylitis, uveitis, inflammatory bowel disease for SpA and PsA, neoplasms, diabetes, presence or absence of rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPA) for RA were evaluated. Results. The percentage of treatments with anti-TNF biosimilars was 65.1, 52.4, and 24.3% in SpA (76 patients(pt)), PsA (110 pt), and RA (69 pt), respectively. The percentage of monotherapy was 68% (418 pt) in the three diseases. For RA, 34.2% of patients were difficult to treat (D2T) (98 pt), 54.8% (157 pt) were in monotherapy (tocilizumab-sarilumab-upadacitinib-filgotinib). Abatacept was the most prescribed treatment in RF and ACPA-positive patients and in those with ILD. The anti-IL-17A secukinumab was prescribed in 12% of SpA, of which 71% had enthesitis and dactylitis (14 pt). Ixekizumab was prescribed in 10.4% of PsA patients over 65 years with previous CV events, enthesitis, and dactylitis (21 pt). Apremilast was present in 71% of PsA with previous cancer. Conclusions. The cross-sectional analysis of prescriptions in patients with RA, SpA, and PsA demonstrates how the ITABIO recommendations can guide towards the correct appropriateness of prescription. RA and especially D2T-RA remains the disease with the greatest therapeutic failures, with the highest percentage of monotherapy (anti-IL-6 and Jak-i) and of discontinuation of MTX. Full article

Nowadays, the circular economy is driving the construction industry towards greater sustainability for both environmental and financial purposes. One prominent area of research with significant contributions to circular economy is the reuse of steel from decommissioned structures in new construction projects. This approach is deemed far more efficient than ordinary steel recycling, due to the fact that it contributes towards reducing both the cost of the new project and the associated carbon emissions. Along these lines, the feasibility of utilizing steel wind turbine towers (WTTs) as part of a new structure is investigated herein, considering that wind turbines are decommissioned after a nominal life of approximately 25 years due to fatigue limitations. General principles of structural steel reuse are first presented in a systematic manner, followed by two case studies. Realistic data about the geometry and cross-sections of previous generation models of WTTs were obtained from the Greek Center for Renewable Energy Sources and Savings (CRES), including drawings and photographic material from their demonstrative wind farm in the area of Keratea. A specific wind turbine was selected that is about to exceed its life expectancy and will soon be decommissioned. Two alternative applications for the reuse of the tower were proposed and analyzed, with emphasis on the structural aspects. One deals with the use of parts of the tower as a small-span pedestrian bridge, while the second addresses the transformation of a tower section into a water storage tank. Several decision factors have contributed to the selection of these two reuse scenarios, including, amongst others, the geometric compatibility of the decommissioned wind turbine tower with the proposed applications, engineering intuition about the tower having adequate strength for its new role, the potential to minimize fatigue loads in the reused state, the minimization of cutting and joining processes as much as possible to restrain further CO₂ emissions, reduction in waste material, the societal contribution of the potential reuse applications, etc. The two examples are briefly presented, aiming to demonstrate the concept and feasibility at the preliminary design level, highlighting the potential of decommissioned WTTs to find proper use for their future life. Full article

The shift from Internal Combustion Engine Vehicles (ICEVs) to Battery Electric Vehicles (BEVs) has accelerated global efforts to decarbonize transportation. However, battery degradation, high costs, and limited lifespan remain critical barriers. This review synthesizes recent innovations to extend Li-ion battery life in BEVs by exploring advances in degradation modeling, adaptive Battery Management Systems (BMSs), electronic component simulations, and real-world usage profiling. The authors have systematically analyzed over 80 recent studies using a PRISMA-guided review protocol. A novel comparative framework highlights gaps in current literature, particularly regarding real-world driving impacts, ripple current effects, and second-life battery applications. This review article critically compares model-driven, data-driven, and hybrid model approaches, emphasizing trade-offs in interpretability, accuracy, and deployment feasibility. Finally, the review links battery life extension to broader sustainability metrics, including circular economy models and predictive maintenance algorithms. This review offers actionable insights for researchers, engineers, and policymakers aiming to design longer-lasting and more sustainable electric mobility systems. Full article

Background: The long-term clinical and laboratory results of a 33-year follow-up of a Greek family with abetalipoproteinemia (ABL) are described. Case Report: The patients (two brothers and their sister, aged 57, 49, and 62 years, respectively) are still alive, being under close surveillance. In two of the three patients, diarrhea appeared in early infancy, while in the third, it appeared during adolescence. CNS symptomatology worsened after the second decade of life. At the same time, night blindness appeared in the advanced stages of the disease, resulting in almost complete loss of vision in one of the male patients and severe impairment in the other. The diagnosis was based on the clinical picture, ophthalmological findings, serum lipid estimations, and presence of peripheral acanthocytosis. All patients exhibited typical serum lipidemic profile, ophthalmological findings, and acanthocytes in the peripheral blood. During the follow-up period, strict dietary modifications were applied, including the substitution of fat with medium-chain triglycerides (MCT oil). After 33 years since the initial diagnosis, all patients are alive without any sign of liver dysfunction despite continuous use of MCT oil. However, symptoms from the central nervous system and vision impairment worsened. Conclusion: The course of these patients suggests that the application of a modified diet, including MCT oil, along with close surveillance, could prolong the survival of patients without significant side effects from the liver. Full article