Search Results (42,762)

Background: Climate change increasingly threatens global health, with adolescents among the most vulnerable. Hospitals are major emitters of greenhouse gases, making carbon reduction in healthcare a pressing challenge. Nurses play central roles in implementing sustainability, while adolescents can contribute to long-term resilience. Methods: A scoping review of peer-reviewed articles (1990–2023) and World Bank datasets was conducted. Comparative analysis focused on Norway and South Korea, with the United States and Australia reviewed narratively. Inclusion criteria targeted studies on hospital-based carbon reduction and youth/nurse engagement; unrelated studies were excluded. Results: Three domains emerged: (1) governance approaches—Norway applied top-down integrated monitoring, while Korea showed fragmented progress, especially in private hospitals; (2) roles of adolescents and nurses—nurses led quality improvement in energy efficiency and waste reduction, while adolescents contributed through school–hospital partnerships and youth initiatives; and (3) barriers and enablers—key barriers included limited youth decision-making and lack of councils, while enablers included certification frameworks and WHO guidelines. Conclusions: Nurses and adolescents are complementary partners in sustainable healthcare. Establishing hospital green teams, integrating climate literacy into curricula, and fostering government–healthcare–education partnerships can reduce emissions and strengthen climate-resilient health systems. Full article

Due to their unique physicochemical properties, carbon black nanoparticles represent a promising alternative for solving analytical problems. However, diagnostic reagents based on carbon black nanoparticles have not yet found widespread practical application. This review examines the development and application of carbon black nanoparticle conjugates with recognition molecules as diagnostic reagents in test systems that enable non-instrumental interpretation of results. The review critically evaluates the methods for synthesis and characterization of carbon black-based diagnostic reagents. Furthermore, the review summarizes and discusses existing studies comparing the effectiveness of carbon black nanoparticle-based bioconjugates with traditional colorimetric labels. The scientific articles included in the review were carefully analyzed for the presence of an assessment of the reproducibility of methods for obtaining diagnostic reagents based on carbon black nanoparticles and their long-term storage. The main challenges and future prospects of using carbon black nanoparticles in immunoassays are discussed. Full article

Background: Foot morphology plays a central role in musculoskeletal development during childhood. Variations in the medial longitudinal arch may influence walking mechanics, and excess body weight can further affect plantar structure and gait. Objective: This study examined the relationship between foot type, body mass index (BMI), and gait function in school-aged children, with particular focus on gait symmetry as a sensitive marker. Methods: Ninety-eight children aged 8–16 years were evaluated. Foot type was classified using a pressure platform, and gait was assessed with a wearable sensor. Outcomes included gait symmetry, walking speed, cadence, Timed Up and Go (TUG), and Six-Minute Walk Distance (6MWD). Results: Mixed bilateral foot patterns were observed in 46 of the 98 participants (47%). Significant associations were found between foot type, BMI, and gait symmetry (p < 0.01), while other mobility measures (speed, cadence, TUG, 6MWD) remained stable across groups. Children with normal bilateral feet showed the best gait symmetry, whereas mixed patterns had the lowest. Conclusions: Gait symmetry is a sensitive indicator of functional imbalance in schoolchildren and is strongly influenced by both foot morphology and body weight. Incorporating plantar assessment and BMI monitoring into routine pediatric evaluations may help clinicians identify children at risk for long-term musculoskeletal problems at an early stage. Full article

Low Earth orbit (LEO) satellites, characterized by low altitudes, high velocities, and strong ground signal reception, have become an essential and dynamic component of modern global navigation satellite systems (GNSS). However, orbit decay induced by atmospheric drag poses persistent challenges to maintaining stable trajectories. Frequent orbit maneuvers, though necessary to sustain nominal orbits, introduce significant difficulties for precise orbit determination (POD) and navigation augmentation, especially under complex operational conditions. Unlike most existing methods that rely on Two-Line Element (TLE) data—often affected by noise and limited accuracy—this study directly utilizes onboard GNSS observations in combination with real-time precise ephemerides. A novel time-series indicator is proposed, defined as the geometric root-mean-square (RMS) distance between reduced-dynamic and kinematic orbit solutions, which is highly responsive to orbit disturbances. To further enhance robustness, a sliding window-based adaptive thresholding mechanism is developed to dynamically adjust detection thresholds, maintaining sensitivity to maneuvers while suppressing false alarms. The proposed method was validated using eight representative maneuver events from the GRACE-FO satellites (May 2018–June 2022), successfully detecting seven of them. One extremely short-duration maneuver was missed due to the limited number of usable GNSS observations after quality-control filtering. To examine altitude-related applicability, two Sentinel-3A maneuvers were also analyzed, both successfully detected, confirming the method’s effectiveness at higher LEO altitudes. Since the thrust magnitudes and durations of the Sentinel-3A maneuvers are not publicly available, these cases primarily serve to verify applicability rather than to quantify sensitivity. Experimental results show that for GRACE-FO maneuvers, the proposed method achieves near-real-time responsiveness under long-duration, high-thrust conditions, with an average detection delay below 90 s. For Sentinel-3A, detections occurred approximately 7 s earlier than the reported maneuver epochs, a discrepancy attributed to the 30 s observation sampling interval rather than methodological bias. Comparative analysis with representative existing methods, presented in the discussion section, further demonstrates the advantages of the proposed approach in terms of sensitivity, timeliness, and adaptability. Overall, this study presents a practical, efficient, and scalable solution for real-time maneuver detection in LEO satellite missions, contributing to improved GNSS augmentation, space situational awareness, and autonomous orbit control. Full article

The rise in atmospheric CO₂ intensified the urgency for carbon capture and storage (CCS), yet uncertainties remain in predicting evolution of reservoir properties under CO₂ injection. This study investigates how CO₂–brine–rock interactions alter porosity and permeability in carbonate and sandstone reservoirs. We quantify pore-scale changes and effects of CO₂-saturated brine on rock. In calcite-rich carbonates, CO₂-induced acidification enhances permeability through selective dissolution. Dolomite-rich samples and sandstones exhibit suppressed permeability response due to slower dissolution and pore clogging. μCT and SEM reveal that although bulk porosity changes are small, local changes—especially formation of micropores and mineral occlusions—substantially influence permeability. Geochemical modeling confirms three-stage evolution: early dissolution, intermediate buffering with onset of precipitation, and long-term mineral trapping with near-steady porosity. The results indicate that early injectivity gains may be temporary and that proactive monitoring and management are required to safeguard long-term storage integrity. The findings provide actionable insight for sustainable CCS design, risk assessment, and reservoir stewardship. Full article

In the current economic context, organizations providing automotive repair services face significant challenges in ensuring service quality, operational efficiency, and long-term sustainability. This paper examines the importance of implementing process monitoring systems through the integration of European quality frameworks and Japanese operational principles such as Kaizen, Lean Manufacturing, and Poka-Yoke, to improve the quality of services and increase performance within automotive repair organizations. The research is grounded in Sustainable Development Goals (SDG 9—Industry, Innovation and Infrastructure, and SDG 12—Responsible Consumption and Production), demonstrating how structured quality practices contribute to reducing waste, optimizing processes, and delivering responsible services. The main objectives of the study are to identify the elements that influence the performance of service-specific processes, to improve the quality management practices related to these processes, to eliminate non-conformities, and to enhance profitability and competitive differentiation through service quality assurance. A mixed-methods research design was applied, including direct participatory observation, performance monitoring, and correlational statistical analysis over a six-month period in two Romanian automotive service centers. Key performance indicators (KPIs) such as technician efficiency, rework rate, and order throughput time were collected and analyzed before and after the implementation of selected tools. Findings demonstrate measurable improvements: rework rates decreased from 7.8% to 2.6%, technician efficiency improved from 89% to 105%, and average service completion time was reduced by 1.6 days. Correlation analysis confirmed strong relationships between visual management adoption and rework reduction (r = −0.75), as well as between Lean implementation and technician efficiency (r = +0.89). The study’s novelty lies in its integration of cross-cultural quality management practices into a replicable and sustainable operational model for post-sale service environments. The results validate that implementing monitoring systems, combined with Kaizen, Lean, and Poka-Yoke, supported by visual management and active employee engagement, can lead to superior service quality management, increased customer satisfaction, and long-term organizational success in the automotive repair industry. Full article

Background/Objectives: The aging population faces a multitude of health challenges, particularly when it comes to maintaining proper nutrition. Age-related physiological changes, such as decreased metabolism, diminished taste perception, and difficulty in chewing, can lead to insufficient nutrient intake, ultimately resulting in malnutrition. It is crucial to address these issues to promote not only physical health but also overall well-being. In this modern era, artificial intelligence (AI) technologies, including robots and machine learning algorithms, are being increasingly harnessed to encourage healthy eating habits among older adults. This is critical to support healthy aging and mitigate diet-related chronic diseases. However, little or no synthesis has established their effectiveness in delivering personalized, scalable, and adaptive interventions for older adults. This systematic review considers the state-of-the-art application of AI-based interventions aimed at improving dietary behaviors and nutritional outcomes in older adults. Methods: Following the PRISMA 2020 guidelines and a registered PROSPERO protocol (ID: CRD420241045268), we systematically analyzed 30 studies we collected from five databases, published between 2015 and 2025 based on different AI techniques, including machine learning, natural language processing, and recommender systems. We synthesized data collected from these studies to examine the intervention types, outcomes, and methodological approaches. Results: Findings from our review highlight the potential of AI-based interventions to promote engagement among older adults and improve adherence to healthy eating guidelines. Additionally, we found some challenges related to ethical concerns such as privacy and transparency, and limited evidence of their long-term effectiveness. Conclusions: AI-based interventions offer significant promise in promoting healthy eating among older adults through personalized, adaptive, and scalable interventions. Yet, current evidence is constrained by some methodological limitations and ethical concerns, which calls for future research to design inclusive, evidence-based AI interventions that address the unique physiological, psychological, and social needs of older adults. Full article

Early warning of structural damage from induced seismic events requires rapid and reliable ground motion forecasting. This study presents a novel real-time framework that couples a deep learning approach with structural fragility assessment to generate immediate damage alerts following the onset of seismic shaking. Long Short-Term Memory (LSTM) neural networks are employed to predict full S-wave accelerograms from initial P-wave inputs, trained and tested on accelerometric records from induced seismicity scenarios. The predicted S-wave motion is then used as input for a suite of fragility curves in real time to estimate the probability of structural damage for masonry buildings typical in rural areas of geothermal platforms. The proposed method captures both the temporal evolution of shaking and the structural response potential, offering critical seconds of lead time for automated decision-making systems. Results demonstrate high predictive accuracy of the LSTM model and effective early classification of structural risk. This integrated system provides a practical tool for early warning or rapid response in regions experiencing anthropogenic seismicity, such as those affected by geothermal operations. Full article

Hyperbolic discounting, a key determinant of intertemporal behavior, captures individuals’ preferences for smaller immediate rewards over larger delayed ones. This study examined how hyperbolic discounting influences investment loss tolerance using a large-scale dataset of Japanese investors. Loss tolerance is defined as the extent of financial loss that an investor is willing to endure before changing their investment strategy. Although hyperbolic discounting shapes intertemporal investment decisions, its role in explaining loss tolerance remains largely unknown. Using a large dataset from the “Survey on Life and Money” comprising 107,294 observations and employing ordered probit regression, we found a significant negative relationship between hyperbolic discounting and investment loss tolerance: investors exhibiting stronger hyperbolic discounting are more likely to exit positions prematurely during market downturns, despite potential long-term recovery. The estimated marginal effect (−0.070 ***) underscores the economic significance of the association between hyperbolic discounting and loss tolerance. These results provide evidence that time-inconsistent preferences not only shape intertemporal choices but also reduce resilience to financial losses. The findings carry important implications for investors, highlighting the value of commitment mechanisms and education programs to counteract short-termism, and for policymakers seeking to design behavioral interventions that promote stable, long-term participation in financial markets. Full article

Tennis serves heavily impact match outcomes, yet analysis by coaches is limited by human vision. The design of an automated tennis serve analysis system could facilitate enhanced performance analysis. As serve location and serve success are directly correlated, predicting the outcome of a serve could provide vital information for performance analysis. This article proposes a tennis serve analysis system powered by Machine Learning, which classifies the outcome of serves as “in”, “out” or “net”, and predicts the coordinate outcome of successful serves. Additionally, this work details the collection of three-dimensional spatio-temporal data on tennis serves, using marker-based optoelectronic motion capture. The classification uses a Stacked Bidirectional Long Short-Term Memory architecture, whilst a 3D Convolutional Neural Network architecture is harnessed for serve coordinate prediction. The proposed method achieves 89% accuracy for tennis serve classification, outperforming the current state-of-the-art whilst performing finer-grain classification. The results achieve an accuracy of 63% in predicting the serve coordinates, with a mean absolute error of 0.59 and a root mean squared error of 0.68, exceeding the current state-of-the-art with a new method. The system contributes towards the long-term goal of designing a non-invasive tennis serve analysis system that functions in training and match conditions. Full article

Precise wind power forecasting has several benefits, such as optimized peak regulation in power systems, enhanced safety analysis, and improved energy efficiency. Considering the substantial influence of meteorological data, such as wind speed and temperature, on wind power generation, and to minimize the impact of fluctuations and complexity of wind power data on the forecast results, this paper proposes a combined wind power forecasting method. This approach is based on the long short-term memory network (LSTM) model, using the maximal information coefficient (MIC) method to select numerical weather prediction (NWP) and combining the efficiency of complete EEMD with the adaptive noise (CEEMDAN) method for nonlinear signal decomposition. Results indicate that the accuracy of the forecast results is supported by NWP. Moreover, wind power data are decomposed by the CEEMDAN algorithm and converted into relatively regular sub-sequences with small fluctuations. The MIC algorithm effectively reduces the redundant information in NWP data, and the LSTM algorithm addresses the uncertainty of wind power data. Finally, the wind power of multiple wind farms is forecasted. Comparison of the forecast results of different methods revealed that the NWP-CEEMDAN-LSTM method proposed in this paper, which considers feature extraction using MIC, effectively tracks power fluctuations and improves forecast performance, thereby reducing the forecast error of wind power. Full article

Background: Immunotherapy has emerged as a breakthrough for the treatment of advanced non-small-cell lung cancer (NSCLC), significantly improving patients’ progression-free survival (PFS) and overall survival (OS). However, the medical burden of response assessment has worsened for long-term maintenance therapy. It remains unclear whether a specific response assessment interval could provide both survival benefits and cost savings. Methods: We retrospectively included patients with advanced NSCLC who underwent immunotherapy and achieved PFS > 12 months. We utilized propensity score matching (PSM) to reduce the selection bias. The survival outcomes were evaluated using the log-rank test and Cox proportional hazard models, while the economic impact was assessed through the performance of a cost minimization analysis (CMA). A medical expenditure extrapolation model was developed based on epidemiological statistics and data from clinical trials. Results: After PSM, a total of 376 patients were included. The survival difference was not significant [hazard ratio (HR) = 0.78, 95% confidence intervals (CIs) = 0.53–1.14; p = 0.200] between the 2-month response assessment group (n = 188) and the 3-month response assessment group (n = 188). Patients receiving immunotherapy alone and those with a positive PD-L1 expression experienced a significant survival benefit. Our extrapolation model projects that, annually, there will be approximately 7026 new long-term responders to immunotherapy in the United States. Adopting a 3-month assessment strategy could reduce annual healthcare expenditure by nearly USD 6 million. Conclusions: This study presented the first statistical evidence supporting a refined response assessment strategy for long-term responders to immunotherapy with advanced NSCLC. These findings support the adoption of a less frequent, yet equally effective, monitoring approach to make tumor surveillance more precise and cost-effective. Full article

To improve the accuracy of deformation prediction in concrete buildings and large-scale infrastructures such as dams, this study proposes an Attention-BiLSTM model integrated with a parameter-optimized Variational Mode Decomposition (VMD). Specifically, the Pelican Optimization Algorithm (POA) is employed to optimize VMD parameters, enhancing signal decomposition efficiency for structural deformation time series. The optimized VMD is then coupled with a BiLSTM network embedded with an attention mechanism, forming a hybrid prediction framework that captures both temporal dependencies and key feature weights in monitoring data. Using three sets of engineering-measured deformation datasets, the proposed model is validated through comparative analyses with conventional single models (e.g., standalone BiLSTM and VMD-BiLSTM without attention). Results demonstrate that the developed model achieves superior accuracy and stability, significantly outperforming all comparative methods, with the highest R² reaching 0.996, while reducing MAE and RMSE by over 60% and 30%, respectively. Quantitative evaluation indicators (e.g., RMSE, MAE, and R²) confirm that the approach effectively captures both short-term fluctuations and long-term trends of structural deformation. These findings verify its reliability and applicability for intelligent safety monitoring of concrete buildings and infrastructures. Full article

Reliable DC cable insulation is crucial for photovoltaic (PV) systems and high-voltage DC (HVDC) networks. However, conventional materials such as cross-linked polyethylene (XLPE) and polyvinyl chloride (PVC) face challenges under prolonged DC stress—notably space charge buildup, dielectric losses, and thermal aging. Cross-linked polyolefin (XLPO) has emerged as a halogen-free, thermally stable alternative, but its comparative DC performance remains underreported. Methods: We evaluated the insulations of virgin XLPO, XLPE, and PVC PV cables under ±1 kV DC using time-domain indices (IR, DAR, PI, Loss Index), supported by MATLAB and FTIR. Multi-layer cable geometries were modeled in MATLAB to simulate radial electric field distribution, and Fourier-transform infrared (FTIR) spectroscopy was employed to reveal polymer chemistry and functional groups. Results: XLPO exhibited an IR on the order of 10⁸–10⁹ Ω, and XLPE (IR ~ 10⁸ Ω) and PVC (IR ~ 10⁷ Ω, LI ≥ 1) at 60 s, with favorable polarization indices under both polarities. Notably, they showed high insulation resistance and low-to-moderate loss indices (≈1.3–1.5) under both polarities, indicating controlled relaxation with limited conduction contribution. XLPE showed good initial insulation resistance but revealed polarity-dependent relaxation and higher loss (especially under positive bias) due to trap-forming cross-linking byproducts. PVC had the lowest resistance (GΩ-range) and near-unit DAR/PI, dominated by leakage conduction and dielectric losses. Simulations confirmed a uniform electric field in XLPO insulation with no polarity asymmetry, while FTIR spectra linked XLPO’s low polarity and PVC’s chlorine content to their electrical behavior. Conclusions: XLPO outperforms XLPE and PVC in resisting DC leakage, charge trapping, and thermal stress, underscoring its suitability for long-term PV and HVDC applications. This study provides a comprehensive structure–property understanding to guide the selection of advanced, polarity-resilient cable insulation materials. Full article

In 2024, the global average temperature reached 1.55 °C above the pre-industrial level for the first time. However, we could still keep the long-term global average temperature below 2 °C if all possible measures are taken to mitigate greenhouse gases. It is widely accepted that methane (CH₄) mitigation can slow global warming in the near term. Among all approaches toward this goal, the utilization of aerobic methanotrophs, which are natural catalysts for the conversion of CH₄, emerges as a promising solution. Previously, we identified a candidate for CH₄ mitigation, Methylotuvimicrobium buryatense 5GB1C, which exhibits a greater growth rate and CH₄ consumption rate than other known methanotrophs at 500 ppm CH₄. In this study, we address aspects of the practical applications of this methanotroph for CH₄ mitigation. We first examined temperature and medium conditions to optimize M. buryatense 5GB1C growth at 500 ppm CH₄. The results show that M. buryatense 5GB1C has a broad optimal temperature range for growth at 500 ppm, from 15 °C to 30 °C, and that its growth rate is consistently improved by 20–30% in 10-fold-diluted medium. Next, to demonstrate the feasibility of CH₄ removal at low concentrations by this methanotroph, we applied it in a laboratory-scale packed-bed column reactor for the treatment of 500 ppm CH₄ and tested different packing materials. The column reactor experiments revealed a maximum elimination capacity of 2.1 g CH₄ m⁻³ h⁻¹ with 2 mm cellulose beads as the packing material. These results demonstrate that with further technological innovation, this methanotroph has the potential for real-world methane mitigation. Full article