Search Results (71)

With the rapid development of recycling and secondary utilization of end-of-life battery materials, it is crucial to clarify the impact of full-lifecycle degradation on the thermal safety limits of lithium-ion batteries. This study focuses on a 16 Ah NCM613|graphite pouch battery. First, it analyzes the evolution of capacity decay, thickness expansion, and internal resistance during cycling at room temperature (25 °C) and high temperature (45 °C). Furthermore, an adiabatic accelerated calorimeter (ARC) is employed to investigate the influence of different states of health (SOH) levels (95% and 85%) on the battery’s thermal runaway characteristics. The findings indicate that, macroscopically, batteries in all states follow similar voltage–temperature failure pathways, with mass loss rates confined to a narrow range of approximately 16%, emphasizing the low catastrophic potential of mid-nickel chemistry. However, the microscopic kinetic mechanisms exhibit significant anisotropy: although thickness and internal resistance display no apparent abrupt increase during the late stage of room temperature aging, the capacity exhibits a highly nonlinear plunge behavior. The severe internal lithium plating side reaction triggered by this phenomenon causes the self-heating onset temperature to drop rapidly from 130.0 °C in the fresh state to 79.7 °C. Concurrently, the activation energy of the exothermic side reaction, fitted using a simplified Arrhenius equation, exhibits a non-monotonic variation with aging progress. In the early stages of aging at 95% SOH, due to high temperatures promoting more significant growth of the interfacial film or moderate film formation at room temperature enhancing interfacial thermal stability, the activation energies for both aged batteries increase, and the energy barrier at high temperatures is slightly higher than at room temperature; however, during the deep aging stage at 85% SOH, due to the degradation of active material components and the emergence of lithium plating characteristics, the energy barrier significantly decreases, with high-temperature-aged batteries exhibiting a greater reduction, highlighting the cumulative negative impact of prolonged high-temperature exposure on thermal safety. The research provides a core scientific basis for establishing a battery safety early warning and dynamic health management system covering the entire lifecycle. Full article

As extreme heat events increase in frequency, intensity, and duration due to climate change, forecasting these events has become vital for early warning systems, public health preparedness, and climate adaptation strategies, especially in parts of the world that are already subject to extreme heat, such as tropical regions. In recent years, machine learning (ML) has increasingly been applied to environmental and meteorological data to improve the prediction of heatwaves and extreme heat events. This scoping review examines global peer-reviewed literature on the application of ML techniques for extreme heat prediction using environmental variables. This includes heatwave prediction, environmental and meteorological predictors used in these models, and the geographical distribution of existing research. A total of 23 peer-reviewed studies meeting the inclusion criteria were included in the review, following the PRISMA-ScR guidelines. The findings indicate that artificial neural networks and random forest models were most frequently reported as high performing within individual studies. However, direct comparisons across studies are limited by heterogeneity in prediction targets, validation strategies, lead times, heatwave definitions, and performance metrics. Temperature-related variables, especially maximum temperature, were consistently identified as the most influential predictors across studies. Furthermore, the evidence base was heavily concentrated in Europe, Asia, and North America, with comparatively limited representation from low- and middle-income countries respective to population, despite these regions often experiencing disproportionate impacts of climate change and extreme heat exposure. By synthesising current evidence on ML-based heatwave prediction, associated environmental predictors, and geographical research trends, this review provides insights to support the development of more robust, context-aware, and globally representative heatwave forecasting frameworks. Full article

Extreme temperatures are an emerging public health concern due to their significant impact on humans, yet the evidence remains limited in tropical countries. This study examined the non-linear relationship between ambient temperature and non-accidental and cause-specific mortality in two northern parts of Peninsular Malaysia, from 2011 to 2019. Daily mortality and meteorological data were analyzed using a quasi-Poisson Generalized Linear Model with a Distributed Lag-Non-Linear model to estimate the relationship between temperature and mortality. A U-shaped and J-shaped relationship was observed for the cumulative effects of 21-day lag periods for Kedah and Penang, respectively. The minimum mortality temperature (MMT) at 27.4 °C in Kedah and 28.2 °C in Penang was observed. Extremely high temperatures were associated with an increased non-accidental mortality, with a 16% increase at cumulative lag days 0–3 in Kedah and a 21% increase at cumulative lag days 0–7 in Penang. Vulnerable groups included individuals with respiratory diseases, the elderly, both genders and those residing in both urban and rural areas. These findings highlight the acute impact of heat on mortality in Malaysia and underscore the need for targeted public health interventions. Strengthening heat-health warning systems, improving healthcare preparedness, and prioritizing vulnerable populations are essential to mitigate the health impacts of rising temperatures in tropical regions. Full article

Chillers serve as the core high-energy-consuming equipment in heating, ventilation, and air conditioning (HVAC) systems. During long-term continuous operation, they tend to suffer gradual subtle degradation, with a performance deviation less than 5%. Conventional fault diagnosis methods rely on manual threshold judgment or labeled fault data, which fail to realize accurate early warning signals. In addition, existing algorithms lack multi-dimensional baseline comparisons to verify their practical engineering performance. To address these limitations, this paper proposes an unsupervised health assessment method combining an LSTM autoencoder and Euclidean distance (LSTM-AE-ED). A multi-gradient fault time-series dataset is generated via a MATLAB R2022b/Simscape mechanism model verified by both summer field measurements and refrigeration pressure-enthalpy cycles, which resolves the practical engineering challenges of scarce on-site fault samples and potential equipment damage caused by actual fault tests. The proposed model is trained solely on healthy time-series data. It extracts dynamic coupling characteristics of chillers through LSTM, constructs a dimensionless health index based on Euclidean distance in feature space, and introduces the standard deviation of health index to improve evaluation stability. Baseline comparisons with vanilla AE and single-layer LSTM are carried out. Experimental results demonstrate that the proposed method achieves an identification accuracy of 96.3% and exhibits high sensitivity to mild degradation of four typical faults, adapting to dynamic multi-working-condition scenarios. This approach requires no additional acquisition devices for derived parameters such as power consumption and COP; online assessment can be realized merely with standard temperature, pressure, and flow sensors equipped on chillers. With lightweight inference performance, it is suitable for edge monitoring terminals of chillers in data centers, providing a low-cost and practical quantitative technical scheme for predictive maintenance and hierarchical early warning signals of refrigeration equipment. Full article

Arboviral diseases are emerging as important public health threats in Saudi Arabia, driven by rapid urbanization, climate variability, the expansion of Aedes aegypti populations, international travel, and large-scale religious mass gatherings. Dengue virus remains the most established arboviral infection in the Kingdom, particularly in the southwestern regions such as Jazan and the western urban centers of Makkah and Jeddah, where ecological and climatic conditions are conducive to sustained vector survival and transmission. This review synthesizes current evidence on the epidemiology, vector ecology, climatic determinants, diagnostics, and prevention strategies of arboviral diseases in Saudi Arabia. Particular attention is paid to the impacts of rising temperatures, changes in rainfall patterns, urban heat island effects, population mobility, and cross-border movement on vector expansion and disease emergence. The review also identifies gaps in surveillance, diagnostics, insecticide resistance monitoring, and integrated vector management programs. Emerging preparedness strategies include climate-informed early warning systems, Geographic Information System-based risk mapping, multiplex molecular diagnostics, genomic surveillance, and community-based vector control. The review emphasizes the importance of implementing a One Health approach that combines data on humans, the environment, entomology, and climate. Currently, sustained endemic transmission of chikungunya and Zika viruses has not been conclusively demonstrated in Saudi Arabia, but increased environmental suitability and connectivity with other areas highlight the need for proactive surveillance and preparedness. Full article

Heatwaves, intensified by climate change and urbanization, pose increasing threats to human health, with occupational populations facing disproportionate risks due to prolonged exposure and high metabolic demands. Existing evidence remains fragmented, particularly regarding the integration of acute and chronic health effects in workplace settings. This narrative review synthesizes current knowledge on occupational heat exposure, highlighting emerging risks such as cumulative physiological strain, heat-related chronic diseases, and mental health impacts. We identify key occupational-specific pathways that amplify vulnerability beyond that of the general population. Despite growing awareness, substantial gaps persist in the implementation of effective adaptation strategies, especially in low- and middle-income countries, where regulatory, economic, and structural barriers limit intervention uptake. To address these challenges, we emphasize the need for adaptive work–rest scheduling, dynamic early warning systems, and cross-sectoral collaboration to enhance occupational heat resilience under a changing climate. Full article

In polar environments, the thermoviscous behavior and heat dissipation characteristics of deck hydraulic systems are severely affected, resulting in response delays and increased failure risk during high-load operations such as anchor retrieval. To address the limited availability of polar field test samples and the multi-scale nature of oil-temperature responses—featuring short-term abrupt variations and slow-varying hysteresis—this study proposes a Multi-Scale Attention Transformer (MSA-Transformer). Through parallel multi-scale attention branches, the model collaboratively captures both transient and gradual dynamics, thereby improving prediction robustness under polar extreme cold conditions. Based on anchor-retrieval test data collected in Genhe, China’s Cold Pole, at −30 °C, −35 °C, and −40 °C, a dataset containing 18 load cycles was constructed. Experimental results based on 5-fold stratified cross-validation results show that the MSA-Transformer achieves the best performance across evaluation metrics, attaining an average coefficient of determination (R²) of 0.9119 along with the lowest error rates (MAE, RMSE, MSE) on the test set, thereby outperforming LSTM, CNN-LSTM, and the standard Transformer. This work provides an effective tool for state prediction, maintenance optimization, and anomaly early warning in polar deck hydraulic systems, supporting the intelligent health management of hydraulic equipment. Full article

Taiwan’s rapid demographic shift toward a super-aged society has heightened demand for long-term care, yet limited staffing creates safety risks from fires; heating, ventilation, and air conditioning failures; and health incidents. To address this, we propose an IoT-based intelligent environmental monitoring and early-warning system designed for care facilities. The three-layer architecture integrates sensors for temperature, humidity, light, air quality, and noise; employs ESP-NOW and wireless fidelity mesh for reliable networking; and supports user interfaces with real-time anomaly alerts. Using PCA and Isolation Forest for efficient anomaly detection, the modular, node-based design enhances safety, reduces manpower burden, and enables scalable smart services. Full article

In view of the highly coupled, time-varying, and susceptible to differences in aircraft configuration of the Boeing 787 Electric Environmental Control System (E-ECS), a simplified mechanism model based on effectiveness-number of transfer units is proposed. Firstly, considering the influence of differences in aircraft configuration, part number, and optional components, a heat conduction correction coefficient is introduced to adjust the calculation process of heat exchange efficiency. Secondly, the steady-state characteristic equation of the electric compressor/turbine is established by utilizing the principle of isentropic work. Then, the outlet temperature value of the water removal component is calculated by using secondary heat recovery technology. Finally, to solve the problem of easily getting stuck in local optima during high-dimensional parameter identification, an adaptive hybrid optimization algorithm combining Dung Beetle Optimization (DBO) with mutation operator and Particle Swarm Optimization (PSO) is proposed. The experimental results show that the proposed mechanism model can achieve dynamic representation of the outlet temperature of each component of E-ECS under different aircraft stages. The DBO-PSO algorithm has a fast convergence speed and a low probability of falling into local optima. The temperature values calculated by the model have high computational accuracy, which can provide reliable data support for component level E-ECS health monitoring and early fault warning. Full article

Extreme heat disrupts labour, infrastructure, and health systems, yet most response frameworks intervene after clinical impact is confirmed. This review analyzes documented cases across sectors and regions to determine where heat effects are first detected and why intervention timing varies. The analysis used institutional reports, epidemiological summaries and occupational data to map how early functional signals appear across systems. A conceptual matrix is proposed to permit action to be authorized at the earliest sign of functional stress, using mortality, productivity, service instability, vulnerability, and adaptive capacity as operational inputs rather than retrospective outcomes. The analysis suggests that heat becomes observable first through reduced work capacity or infrastructure strain, not through hospital data, and that systems with predefined activation criteria engage earlier and with less irreversible loss. The matrix provides a transferable basis for integrating occupational, infrastructural, and clinical information into a unified heat response mechanism. This approach supports a transition from post-impact validation to forward-based decision logic, particularly in settings where vulnerable workers remain outside formal surveillance. Full article

The year 2024 has been recorded as the warmest year on record, with global temperatures temporarily exceeding the 1.5 °C threshold owing to rising anthropogenic greenhouse gas emissions. This has intensified global attention on heatwaves, which are a major public health threat linked to increased morbidity and mortality rates. This study conducted a bibliometric analysis of 901 Web of Science-indexed journal articles (2004–2024) using the term “heat wave health.” The findings revealed a significant increase in global temperatures, with an increasing frequency, intensity, and duration of extreme heat events. Heatwaves have been linked to higher rates of injuries, mental health disorders, and mortality, particularly in urban areas, due to ozone pollution, atmospheric contaminants, and the urban heat island effect, leading to increased emergency hospitalisation. Rural populations, especially outdoor labourers, face occupational heat stress and a higher risk of fatality. Adaptation measures, including early warning systems, heat indices, air conditioning, white and green roofs, and urban cooling strategies, offer some mitigation but are inadequate in the long term. Significant knowledge gaps persist regarding regional vulnerabilities, adaptation effectiveness, and socio-economic disparities, underscoring the urgent need for interdisciplinary research to inform heat-resilient public health policies and climate adaptation strategies. This study highlights the urgent need for further interdisciplinary research and targeted policy interventions to enhance heatwave resilience, particularly in under-researched and highly vulnerable regions of the world. Full article

Extreme events have become an integral aspect of the unusually intensified climate change characterizing this century. This study examines extreme heat waves and tropical nights—phenomena historically uncommon in the mid-latitude Southeastern Baltic Sea region. Extreme heat and heat waves are defined as any period during which the daily maximum air temperature exceeds 30 °C, and a tropical night is one in which the daily minimum air temperature does not fall below 20 °C. Both in situ observations and model output from the Copernicus Climate Change Service were employed in the 1982–2024 analysis. The results reveal that the frequency of extreme heat waves is increasing. Since 2018, the southeastern Baltic Sea coast has experienced at least one extreme heat wave and one tropical night each year. The observed rise in mean air and sea-surface temperatures has driven an uptick in tropical night occurrence. Forecasts of tropical-night formation could be substantially improved by integrating sea-surface temperature assessments for the southeastern Baltic coast. Moreover, timely adaptation to evolving weather conditions—through enhanced forecasting techniques and the incorporation of high-resolution reanalysis datasets—is essential for optimizing early-warning systems capable of safeguarding human health and lives. Climate change increases the frequency and intensity of heat waves, posing significant challenges to public health, the economy, the environment, and infrastructure. Therefore, advancing the understanding of extreme heat events through the use of cutting-edge technologies, remote sensing, and Copernicus reanalysis data represents a key sustainability task. Such approaches enable more accurate assessments and forecasts of extremes, thereby supporting a safer, healthier, and more resilient future. Full article

The Eastern Mediterranean is a rapidly warming climate change hotspot where heat and air pollution increasingly interact to affect human health. This study quantifies the mortality burden attributed to the synergistic effects of thermal stress and air pollution in Thessaloniki, Greece. Daily mortality data (2001–2019) were analyzed together with pollutant concentrations (PM10, NO₂, O₃) and the modified Physiologically Equivalent Temperature (mPET) using a hierarchical Generalized Additive Model with Distributed Lag Non-Linear terms to capture combined, lagged, and age-specific responses. A refined, count-independent definition of the Attributable Fraction (AF) was introduced to improve stability in small strata. The results show that heat and pollution act synergistically, explaining on average 20–30% of daily mortality during severe co-occurrence events. Seniors were most affected during hot, polluted summers (AF ≈ 27%), while adults showed higher burdens during cold, polluted winters (AF ≈ 30%). Intra-urban analyses revealed stronger simultaneous effects in the western, more industrial districts, reflecting combined environmental and socioeconomic vulnerability. The findings demonstrate that temperature extremes amplify pollution-related mortality and underline the need to integrate air quality and bioclimatic indicators into early warning and adaptation systems in Eastern Mediterranean cities. Full article

The rising frequency of heat-related illnesses (HRIs) under climate change presents urgent public health challenges, particularly in urban environments. This study develops an explainable machine learning (ML) model to predict HRI risk using metrological data from seven major South Korean metropolitan cities between May and September 2021–2024. We applied eXtreme Gradient Boosting (XGBoost) to model relationships between daily meteorological variables, including maximum and mean daily temperatures, humidity, solar radiation, wind speed, and precipitation, and HRI occurrence. Model performance was validated using 2025 data and demonstrated strong predictive accuracy, with area under the curve (AUC) values 0.895. To enhance interpretability, Shapley Additive exPlanations (SHAP) analysis identified mean daily temperature, solar radiation, and minimum temperature as the strongest contributors to HRI risk. Time-series comparisons of predicted and actual HRI occurrences further validated the model’s effectiveness in real-world settings. These findings underscore the potential of eXplainable Artificial Intelligence (XAI) for localized health-risk forecasting and support a data-driven basis for developing early warning systems for climate-sensitive diseases to guide proactive public health planning amid escalating urban heat risks. Full article

Background: Almaty, located in a mountain–valley basin, frequently experiences stagnant conditions that trap pollutants and cause sharp diurnal contrasts in air quality. Current forecasting systems either offer detailed physical realism at high computational cost or yield statistically accurate but physically inconsistent results. Urban air quality in mountain–valley cities is strongly shaped by thermal inversions and weak nocturnal ventilation that trap pollutants close to the surface. We present a hybrid physics–machine-learning framework that combines a Navier–Stokes surface-layer model with data-driven post-processing to produce short-term forecasts of wind, temperature, and particulate matter while preserving physical consistency. The approach captures diurnal ventilation patterns and the well-known negative linkage between near-surface wind and particulate loadings during wintertime inversions. Compared with purely statistical baselines, the hybrid system improves short-range forecast skill and maintains interpretability through physically grounded diagnostics. Beyond Almaty, the workflow is transferable to other mountain–valley environments and is directly actionable for early warning, traffic and heating-related emission management, and health-risk communication. By uniting physically meaningful fields with lightweight Machine Learning correction, the method offers a practical bridge between computational fluid dynamics and operational decision support for cities facing recurrent stagnation episodes. Aim: Develop and verify a method for the diagnostics and short-term forecasting of surface circulation and particle concentrations in Almaty (2024), ensuring physical consistency of fields, increased forecast accuracy on 6–24 h horizons, and interpretability of risk factors. Compared to purely statistical baselines (R² ≈ 0.55 for PM forecasts), our hybrid framework achieved a 16% gain in explained variance and reduced RMSE by 25%. This improvement was most evident during winter inversion episodes. Methods: This study introduces a hybrid modeling framework that integrates the Navier–Stokes equations with machine-learning algorithms to diagnose and forecast surface air circulation and particulate matter concentrations. The approach ensures both physical consistency and improved predictive accuracy for short-term horizons (6–24 h). The Navier–Stokes equations in the Boussinesq approximation, the energy equation, and K-closure particulate matter transport were used. The numerical solution is based on the projection method (convection—TVD/QUICK, pressure—Poisson equation). The ML module is gradient boosting and decision trees for meteorological parameters, lags, and diagnostic quantities. The 2024 data are cleaned, normalized, and visualized. Results: The hybrid model reproduces the diurnal cycle of ventilation and concentrations, especially during winter inversions. For 6 h: wind RMSE ≈ 1.2 m/s (R² ≈ 0.71), temperature RMSE ≈ 1.8 °C (R² ≈ 0.78), and particles RMSE ≈ 0.012 mg/m³ (R² ≈ 0.64). Errors are higher for 24 h. A negative relationship between wind and concentration was established: +1 m/s reduces the median by 10–15% during winter nights. Conclusions: The approach can be generalized to other mountain–valley cities beyond Almaty. Combining the physical model and ML correction improves short-term predictive ability and maintains physical consistency. The method is applicable for air quality risk assessment and decision support; further clarification of emissions and consideration of urban canyon geometry are required. The results support early-warning systems, health risk communication, and urban planning. Full article