Search Results (938)

With the rapid advancement of portable wearable electronics, flexible supercapacitors have ushered in new development opportunities. In recent years, MXene and its composites have demonstrated potential as advanced supercapacitor electrode materials due to their outstanding theoretical capacitance, specific surface area, conductivity, hydrophilicity, and mechanical flexibility. This review traces the development of MXene and summarizes common synthesis strategies, with a focus on the effects of different preparation methods on its structure and properties. Departing from previously reported work, this review draws from the practical requirements of flexible supercapacitors to conduct an in-depth analysis of the key factors influencing the charge storage, rate capability, cycling life, and mechanical flexibility of the devices. It summarizes common design strategies for MXene composites currently used to enhance device performance. Additionally, this study analyzes key challenges facing MXene-based electrode materials, including issues such as self-stacking of layers, insufficient oxidation stability, limited energy density, and structural degradation under complex deformation conditions. Mitigation strategies are summarized, including optimizing synthesis methods and constructing composite systems integrating carbon materials, conducting polymers, and transition metal compounds. Finally, future research directions for MXene in flexible energy storage are explored, emphasizing the need to achieve a balance between performance and manufacturability through synergistic regulation at structural design, interfacial engineering, and device levels. This review aims to provide theoretical guidance for the development of practical MXene-based wearable energy storage devices. Full article

This study presents a numerical investigation of biogas–natural gas co-firing for greenhouse heating, integrating lumped-parameter energy balance, multi-objective optimization, and life cycle assessment (LCA) for a Syrian coast case study (48 dairy cows, 100 m² greenhouse). Five blends (0–100% biogas) were evaluated using a zero-dimensional model implemented in MATLAB R2024a (The MathWorks, Inc., Natick, MA, USA) and verified with Python (version 3.11, Python Software Foundation, Beaverton, OR, USA). The 70% biogas–30% natural gas blend exhibited the most favorable trade-off among conditionally feasible scenarios (requiring external biogas sourcing) with a model-predicted system thermal efficiency of 84.5% (LHV basis) and a model-estimated thermal NOx reduction of 75–85%, which represents a mathematical extrapolation beyond the experimentally validated range of 0–50% biogas and excludes prompt NOx (5–20% of total) and should be interpreted as an indicative trend requiring experimental confirmation. For self-sufficient operation using only on-site biogas production (24 m³ day⁻¹), the maximum achievable blend is 32% biogas, offering a 13.8% cost reduction and a 13.5% GWP reduction. Pure biogas achieves a 41.5% GWP reduction and 48.5% lower daily operating costs under the assumption of expanded on-site production capacity but requires 3.3 times the current production volume. Multi-objective optimization reveals stakeholder-specific optima ranging from 50% to 91% biogas, with a robust compromise region of 65–75%. All predictions for NOx emissions above 50% biogas are mathematical extrapolations requiring experimental validation. For farms without access to external biogas markets, the 32% blend (self-sufficient optimum) is the currently implementable solution, offering a 13.8% cost reduction. For farms with access to regional biogas markets, the 70% blend represents the conditional techno-economic optimum, achieving a 15.3% cost reduction but requiring 29.12 m³ day⁻¹ of external biogas procurement. Full article

LIFE REVIVE aims to restore ecological status and ecosystem services in the Lima and Vouga river basins (NW Iberian Peninsula), where hydromorphological alteration and hydropower-driven flow regulation are major causes of water bodies failing to reach Good Ecological Status under the EU WFD. The project targets key pressures such as longitudinal fragmentation by weirs and dams, artificial flow regimes, degradation of spawning substrates, and the spread of invasive aquatic plants, which strongly affect fish communities, including sea lamprey, salmonids, and other diadromous species. Technically, the project combines barrier removal or eco-adaptation, nature-like fish passes, and spawning-habitat renaturalisation with optimized environmental flow regimes (EFR) downstream of important hydropower systems, explicitly accounting for present and future hydroclimatic scenarios. Multi-scale ecohydrological modelling (species distribution models, habitat suitability models, GLM/GAM approaches) will quantify fish–flow–habitat relationships and support the definition of operational EFR guidelines that balance ecological requirements with hydropower and agricultural constraints through joint work with the main Portuguese hydropower operator, EDP. Impact evaluation is structured around a rigorous BACI monitoring design in intervention and control tributaries, using standard WFD biological indices for fish and aquatic/riparian vegetation, hydromorphological indices (HQA, HMS, RHS), and project-specific Key Performance Indicators for water quality, biodiversity, and habitat. Expected outcomes include the restoration of at least 51 km of rivers towards free-flowing conditions, reduced hydromorphological pressure in more than 20 km of heavily modified river stretches, and measurable increases in the distribution and abundance of fish species and native vegetation. A strong communication and capacity-building programme underpins public engagement, while a decision matrix for barrier prioritization, technical workshops, and pilot replications in additional basins (e.g., Alva, Mouro, Deva, and Tea in Galicia) are designed to maximize transferability, policy uptake, and long-term sustainability of the solutions beyond the project lifetime. Full article

Work-related stress is an important concern in veterinary medicine because it may affect veterinarians’ work, personal life, and well-being. This study described self-reported work-related stressors and their perceived effects on professional and personal life among veterinarians from selected European countries. Between July 2021 and February 2022, an online questionnaire was completed by 724 veterinarians from Hungary, Finland, Sweden, Germany, Denmark, Estonia, and Norway. Participants were recruited through convenience sampling via online channels and professional veterinary networks. The questionnaire assessed 16 stressors, including fatigue, emotional exhaustion, burnout-related symptoms, fear of making mistakes, client expectations, and negative online comments. Mean Likert-scale scores were analyzed using one-way ANOVA and Pearson’s χ² tests. Fatigue and emotional exhaustion were among the most burdensome internal stressors, with the highest mean score in the Hungarian sample (mean: 4.15 ± 1.05) and the lowest in the Finnish sample (mean: 3.68 ± 1.06; ANOVA: p < 0.0001). Euthanasia-related stress was rated lower in Finland (mean: 1.68 ± 0.83) and Sweden (mean: 1.88 ± 0.95) than in Germany (mean: 2.41 ± 1.17) and Hungary (mean: 2.64 ± 1.27; ANOVA: p < 0.0001). In Hungary, younger and female veterinarians reported greater sensitivity to several stressors. The findings are descriptive and exploratory rather than representative cross-country comparisons. Full article

Lymphoid neoplasms such as multiple myeloma (MM), indolent non-Hodgkin lymphoma, and chronic lymphocytic leukemia are increasingly managed as chronic, relapsing conditions characterized by prolonged surveillance, repeated treatment transitions, and cumulative self-management demands. These trajectories expose patients and caregivers to persistent illness uncertainty, fluctuating fear of progression, symptom and comorbidity burden, communication challenges, and treatment-related workload. This theory-informed framework development paper uses an overview of selected psycho-oncological, hematological, nursing, theoretical, and patient-reported outcome literature to propose the LUMINA framework: Longitudinal illness trajectory, Uncertainty fields, Multidimensional symptom and comorbidity load, Information and interaction context, Navigation work and self-management load, and Adaptive outcomes and alignment. LUMINA is intended as a hypothesis-generating conceptual structure to organize clinically relevant domains, clarify potential relationships among uncertainty, symptom burden, communication, navigation work, and adaptive outcomes, and guide future assessment, validation, and intervention research in chronic lymphoid neoplasms. The framework builds on prior theories of illness uncertainty, treatment burden, workload–capacity balance, fear of recurrence/progression, and lymphoma-specific qualitative work on uncertainty management and psychosocial adaptation. Potential research applications include structured assessment, shared decision-making research, and domain-matched supportive-care concepts; however, these applications remain theoretical and require empirical testing. Future studies should evaluate feasibility, acceptability, construct validity, domain overlap, predictive validity beyond quality of life, and the clinical utility of LUMINA-informed research profiles. Until such validation is available, LUMINA should be interpreted as a conceptual model rather than a validated clinical tool or care pathway. Full article

Phycobiliproteins (PBPs) are a family of pigment-proteins renowned for their exceptional light-harvesting, fluorescent, and antioxidant properties. Among cyanobacteria, Spirulina stands out as one of the richest natural sources of PBPs, particularly phycocyanin (PC) and allophycocyanin (APC), yet the large-scale production of analytical-grade PBPs remains hampered by an inherently complex downstream process that relies on multiple purification steps, compromising both yield and scalability. This work presents a streamlined strategy to obtain analytical-grade PC, combining ultrasound-assisted extraction (UAE) with an aqueous ionic liquid (IL) solution and a single hydrophobic interaction chromatography (HIC) step, integrated within a biorefinery framework. The proposed approach yielded analytical-grade PC with a recovery of up to 50.44% and enhanced APC purity up to 10.57-fold. Furthermore, the IL was successfully reused in both extraction and purification steps without compromising yield or purity. The environmental performance of the proposed process was assessed through a cradle-to-gate life cycle assessment (LCA), with system boundaries encompassing the following biorefinery stages: cultivation, harvesting and drying, PC extraction and purification, post-processing, and spent biomass valorization via anaerobic digestion. The LCA identified the main environmental hotspots and guided the proposal of targeted process improvements—particularly HIC salt substitution and increased IL recovery—which reduced environmental impacts by 65.9–89.8% across most categories. The proposed strategy was further benchmarked against two model scenarios for analytical-grade PC production, one conventional and one innovative, revealing its relative advantages and limitations. Overall, this work demonstrates a viable pathway for producing high-purity PC that balances process efficiency with environmental sustainability, supporting the development of greener microalgae-based bioprocesses. Full article

Background/Objectives: Immune checkpoint inhibitors (ICIs) have changed the treatment landscape for several advanced malignancies, but their use is accompanied by immune-related adverse events, including liver injury. Some cases resemble autoimmune hepatitis (AIH), although many are more accurately described as AIH-like immune-mediated hepatitis rather than classical AIH. This distinction matters, as diagnosis is often based on exclusion and management must balance hepatic recovery against interruption of potentially life-prolonging cancer therapy. This systematic review summarised the clinical phenotype, diagnostic assessment, treatment strategies, treatment response, ICI discontinuation, and rechallenge outcomes in patients with ICI-associated AIH-like liver injury. Methods: A systematic PubMed search was performed for English-language human studies reporting autoimmune hepatitis, AIH-like liver injury, or immune-mediated hepatitis following exposure to ICIs. Eligible studies included case reports, case series, retrospective cohorts, prospective cohorts, and pharmacovigilance-type studies with extractable clinical, treatment, or outcome data. Reviews, guidelines, non-original articles, animal studies, non-English publications, and reports without usable liver injury data were excluded. The review followed PRISMA principles. Risk of bias was assessed using Joanna Briggs Institute tools and summarised with ROBVIS. Given the heterogeneity of study design, diagnostic criteria, treatment definitions, and outcome reporting, formal meta-analysis was not appropriate; results were therefore synthesised descriptively. Results: Twenty-two studies were included, comprising 195 patients with ICI-associated AIH-like or immune-mediated hepatitis. Of these, 140 patients received active treatment, and 133/140 achieved clinical or biochemical recovery with varying therapies. Corticosteroids were the most frequently used first-line therapy, with recovery reported in 102/105 patients treated with corticosteroids alone. Mycophenolate mofetil was the main second-line agent for steroid-refractory disease, with response reported in 9/10 treated patients. Other therapies, including tacrolimus, azathioprine, ursodeoxycholic acid, bezafibrate, tocilizumab, basiliximab, infliximab, budesonide, and double plasma molecular adsorption system with or without plasma exchange, were described only in small numbers or isolated cases. Spontaneous recovery without pharmacological treatment was reported in 19 patients. ICI interruption or discontinuation occurred in 141 patients, and rechallenge was reported in 55 patients after recovery, with no recurrent hepatic toxicity documented in the extracted dataset. Conclusions: ICI-associated AIH-like liver injury is an important immune-related toxicity, but the available literature remains fragmented and methodologically heterogeneous. Most reported patients recovered, particularly with corticosteroids, and MMF appears to be the most consistently used escalation therapy in steroid-refractory cases. However, the strength of evidence is limited by uncontrolled designs, variable terminology, inconsistent diagnostic work-up, and non-standardised outcome definitions. Future studies should separate classical AIH from AIH-like immune-mediated hepatitis, use uniform criteria for severity and response, and report treatment denominators clearly, especially for rechallenge and steroid-refractory disease. Full article

In a time of pandemic interruptions, work arrangements and flexible work environments are becoming more and more crucial in service firms. While this issue is central to the ethics and effectiveness of human–AI interaction, it has received limited focused attention in both research and practice. As businesses increasingly deploy AI to enhance productivity and efficiency, concerns are emerging about its potential impact on employee well-being resulting specifically in work–life conflict. This study investigates how AI implementation can simultaneously drive performance and contribute to burnout, drawing on an empirical framework. Using a quantitative research design, data will be collected from employees at a university in Kuwait actively integrating AI technologies into their workflows. Guided by the IMPACT model and grounded in the Conservation of Resources (COR) theory and the Social Cognitive Theory (SCT), this study explores how organizational investment in AI influences employees’ experiences of work–life conflict. The findings will highlight AI’s dual role as a productivity enhancer and a potential stressor within a Kuwaiti institution. The study underscores the importance of balanced digital strategies—aligning technological advancement with leadership empathy, robust support systems, and employee well-being initiatives. By contextualizing global research within Kuwait’s evolving digital landscape, this study contributes region-specific insights and practical recommendations for fostering human-centered, sustainable AI integration. Ultimately, it aims to guide organizations in designing AI policies that enhance productivity without compromising employee health, advancing the responsible and ethical management of AI in the workplace. Full article

Background/Objectives Occupational burnout is a significant health concern among healthcare professionals, particularly among nurses working in anesthesiology and intensive care units who are exposed to high psychological workload. Previous studies indicate a relationship between burnout and deteriorating mental health; however, the role of individual burnout dimensions in shaping overall mental health remains insufficiently explored. Methods: This cross-sectional study included 842 nurses working in anesthesiology and intensive care units. Occupational burnout was assessed using the Oldenburg Burnout Inventory (OLBI), while mental health was evaluated using the General Health Questionnaire (GHQ-28). Sociodemographic variables and subjective assessment of work–life balance were also included. Statistical analyses comprised non-parametric tests, Spearman’s rank correlation, and multiple regression models. Results: The strongest correlations were observed between the exhaustion dimension and the overall GHQ-28 score (rS = 0.539; p < 0.001). Additionally, poorer work–life balance was associated with increased symptoms of mental health disorders (rS = 0.42; p < 0.001). Regression analysis showed that gender, exhaustion, and work–life balance were significant predictors of mental health (p < 0.001). Conclusions: The results indicate a significant relationship between occupational burnout and mental health in nurses working in anesthesiology and intensive care units. In particular, exhaustion and work–life balance were identified as important predictors of mental health outcomes. Full article

Sepsis, a life-threatening condition causing significant global mortality, requires rapid diagnosis and intervention. Although recent advances in machine learning have supported clinical decision-making, existing sepsis classification approaches exhibit several limitations, including inadequate temporal modeling of disease progression, lack of systematic hyperparameter optimization, fragmented interpretability approaches that do not fully address multi-stakeholder clinical needs, and challenges in achieving balanced sensitivity–specificity trade-offs. These limitations restrict effective extraction of knowledge from complex temporal clinical data and hinder actionable decision-making. To address these challenges, this work proposes BayeStack, a temporal knowledge-extraction framework that integrates Bayesian optimization-driven ensemble learning with hierarchical interpretability to optimize sepsis classification. This framework captures the progression of sepsis through multi-window temporal aggregation, performs optimal classification by applying AUROC-maximizing hyperparameter space exploration, and enables comprehensive clinical knowledge extraction by applying a three-level interpretability framework that includes global feature importance, population-level partial dependence analysis, and patient-specific contribution-level analysis. Evaluation results indicated that BayeStack achieved an AUROC of 0.99 with balanced sensitivity and specificity of 0.97, substantially outperforming all baseline methods ($p<0.001$). Ablation studies validated that temporal aggregation and data balancing contributed to performance improvements. A strong Spearman correlation ($\rho =0.856$) validated the feature ranking convergence and effectiveness of the ensemble strategy. The interpretability framework provides insights into complementary model behavior and extracts evidence-based clinical thresholds for priority-based treatment monitoring, thereby enabling robust clinical decision support. This first phase systematic integration framework of traditional machine learning models establishes baseline performance and explainability standards for subsequent deep learning advancements. Full article

The use of prosthetic devices can significantly improve the quality of life of individuals with limb amputations. However, existing prosthetic hands face multiple engineering and manufacturing challenges, making them economically inaccessible to a large portion of the population. This study focuses on the design and analysis of a cost-effective prosthetic hand capable of performing five fundamental grasp types: tripod, cylindrical, spherical, lateral, and pinch. The development process began with a biomechanical analysis of the human hand, followed by the derivation of a kinematic model. To ensure anthropomorphic fidelity, finger trajectories were synthesized using a computer vision-based algorithm that captured natural human motion. These trajectories were then mapped to the prosthetic control system. Experimental validation was conducted through rigorous goniometric analysis of the prototype’s execution. The results demonstrated the system’s effectiveness in replicating functional grasps, with a Root Mean Square Error (RMSE) within acceptable thresholds for assistive tasks. While the prototype achieved high motion correspondence, higher deviations were observed in distal joints due to mechanical transmission resistance and spring-return torque requirements. This work provides a scalable framework for tendon-driven prostheses, balancing advanced trajectory synthesis with a robust and accessible mechanical architecture. Full article

Background: Burnout is common among academicians and is linked to both mental and physical health problems. However, its connection to musculoskeletal disorders (MSDs) is not well understood. This study aimed to investigate how burnout, job stress, and work–life balance are related to multisite musculoskeletal pain in academicians. Methods: In this cross-sectional study, 99 academicians at Yalova University completed validated instruments assessing MSDs, burnout, work-related stress, and work–life balance. The study employed a voluntary convenience sampling method and included academicians who agreed to participate. Data were analysed using correlation and ordinal regression analysis. Results: The one-year prevalence of MSDs was 88.9% and most commonly involved the neck, lower back, and upper back. While overall burnout scores were not significantly related to the number of painful regions, the number of painful regions was positively correlated with job stress and computer usage and negatively with work–life balance and personal accomplishment (p < 0.05). Ordinal regression analysis showed that job stress, computer usage, and impaired work–life balance were associated with greater multisite pain burden (p < 0.01). Conclusions: Although overall burnout was not associated with multisite musculoskeletal pain, psychosocial factors such as job stress and work–life balance were significantly related to multisite pain. These findings suggest that occupational stress may be linked to physical health outcomes before the full manifestation of burnout develops. Addressing job stress and work–life balance may help reduce MSDs among academicians. Full article

Cardiac arrhythmias are a leading cause of cardiovascular mortality worldwide, necessitating accurate automated detection systems for continuous monitoring and clinical decision support. This study addresses the critical challenge of severe class imbalance in ECG beat classification, where normal beats comprise 82.8% of samples while life-threatening ventricular arrhythmias represent only 6.5%. We propose a lightweight one-dimensional convolutional neural network (1D-CNN) trained with a two-pronged class-balancing strategy: random oversampling of minority classes to 35% of the majority class size, combined with class-weighted cross-entropy loss. Recent work has achieved accuracies approaching 99–100% on the MIT-BIH database through increasingly complex architectures, including transfer learning, attention mechanisms, and multi-channel fusion. However, these approaches often require millions of parameters, limiting deployability on resource-constrained wearables. Despite the recent trend toward complexity, our simple four-block CNN with only 398,469 parameters achieves 99.18% overall test accuracy and a 96.38% macro-averaged F1-score on the MIT-BIH Arrhythmia Database—competitive with state-of-the-art methods while using 90–96% fewer parameters. Critically, the model attains 98.32% recall on ventricular beats, demonstrating high sensitivity for detecting life-threatening arrhythmias. Ablation studies confirm that both oversampling and weighted loss are essential: removing either component causes catastrophic performance degradation. Our results challenge the assumption that architectural complexity is necessary for ECG classification and demonstrate that proper class imbalance handling enables simple models to achieve state-of-the-art performances with superior computational efficiency suitable for deployment in wearable cardiac monitoring devices. Full article

In the contemporary digital economy, digital human resource management is reshaping organizational practices and enhancing both operational efficiency and the employee experience. As Generation Z (those born between 1995 and 2009) becomes the core demographic in the workforce, their pronounced emphasis on work–life balance introduces novel managerial challenges. Drawing on conservation of resources (COR) theory, this study develops and tests a moderated mediation model examining how digital human resource management (HRM) influences sense of relaxation among Generation Z employees. Analyzing survey data from 364 Generation Z employees, we first develop and validate a measurement scale for employee relaxation, identifying four distinct dimensions: work disengagement, work adaptation, emotional regulation, and physical load. The findings reveal that digital HRM significantly enhances employee relaxation, with work autonomy serving as a partial mediator in this relationship. Furthermore, digital self-efficacy positively moderates both the direct effect of digital HRM on work autonomy and the indirect effect on employee relaxation through work autonomy. These findings offer theoretical insights into how digital HRM links to employee well-being and provide practical guidance for organizations managing a Generation Z workforce. Full article

Background/Objectives: Work–family conflict is a relevant psychosocial factor in nursing and may influence turnover intention. This study analyzed the association between work–family conflict and turnover intention among nursing professionals in Portugal. Methods: A cross-sectional study was conducted between April and July 2025 with 1097 nurses from a local health unit in northern Portugal. Data were collected using a sociodemographic and professional questionnaire, the Work–Family Conflict (WFC) and Family–Work Conflict (FWC) Scale, and the Turnover Intention Scale. Nonparametric tests, Spearman’s correlation, and linear regression models were used. Results: Participants were aged 23–66 years, with a median age of 42 years; most were women (84.0%) and held a bachelor’s degree (85.0%). Mean scores were 24.5 (SD = 7.7) for WFC, 13.2 (SD = 6.6) for FWC, and 34.8 (SD = 15.7) for turnover intention. Both conflict dimensions were positively correlated with turnover intention, with a stronger association for WFC (ρ = 0.347; p < 0.001) than for FWC (ρ = 0.186; p < 0.001). In the fully adjusted regression model, WFC remained positively associated with turnover intention (β = 0.383; SE = 0.062; t = 6.149; p < 0.001), whereas FWC was no longer significant. Adding job satisfaction, perceived work-related stress, and nurse manager support increased explanatory capacity. Conclusions: Work–family conflict, especially work-to-family interference, was associated with nurses’ turnover intention. These findings highlight the need for organizational strategies that reduce work demands, support work–life balance, and address broader occupational and psychosocial conditions. Full article