Search Results (778)

The commercialization of lithium metal batteries, a key technology for high-density energy storage, is hindered by issues with coulombic efficiency, which dictates battery stability and life. In this paper, we propose a machine learning framework to forecast liquid electrolyte efficiency, where two experimental data sources were combined to create a curated dataset of 283 records. In addition, to assess several ensemble learning algorithms, thirteen chemical descriptors were used, as well as interpretability analysis and Bayesian optimization to guarantee physicochemical consistency. We found that the optimized CatBoost model achieved a coefficient of determination (R2) of 0.61 on the test set and a mean squared error (MSE) of 0.0924, representing a significant improvement in predictive accuracy compared to previous standards. Furthermore, these results demonstrate that regulating oxygen levels in solvent environments is a key component of high-density energy storage. These results can serve as a virtual screening tool in order to discover high-performance electrolytes with the minimum experimental costs. Full article

Dengue Virus (DENV) circulates as four antigenically distinct serotypes whose dominance fluctuates over time in many endemic regions, a phenomenon known as serotype shift that is frequently associated with large outbreaks and increased disease severity. This review, through a synthesis of epidemiological, virological, immunological, entomological, and environmental evidence, observes that serotype shift likely arises from the interaction of multiple determinants rather than solely from viral evolution, with population immunity playing a central role. The accumulation of serotype-specific herd immunity, together with short-lived cross-protection and Antibody-Dependent Enhancement (ADE), reshapes population susceptibility and creates ecological space for heterologous serotypes with higher transmission potential. The synthesis of global dengue studies indicates that these immune dynamics interact with viral genetic diversity, vector competence, climate variability, and human factors such as demography, socioeconomic status, population density and mobility to drive cyclical and sometimes abrupt changes in serotype dominance. Notably, the review indicates that serotype changes often precede or coincide with more clinical severity and patterns of outbreaks, with direct implications for the process of forecasting outbreaks, vaccine performance, and preparedness to respond with appropriate health measures. On the whole, this review confirms the opinion that the change of dengue serotype occurrence becomes a consequence of interconnected biological and ecological processes involved in the transmission of dengue serotype shifts in hyperendemic areas. Full article

Reliable, high-quality rainfall data are vital for soil and water management, crop forecasting, and risk assessment. These applications are essential for food security, climate resilience, biodiversity monitoring, and rural livelihoods. Rainfall monitoring in Thailand is challenging due to the limited density of official stations and the inconsistent quality of data from multiple sources, compounded by calibration issues. This study introduces a comprehensive quality control (QC) approach tailored for the Thai context, presenting a systematic pipeline that clarifies the hierarchy and sequence of operations. The method uses rainfall data from 3075 stations of the Thai Meteorological Department (TMD) and the Thaiwater network. It includes basic QC for data completeness and advanced QC using a quality (Q) index to assess station reliability, diving the stations into five groups: poor (<50), moderate (50–80), acceptable (80–85), good (85–90), and excellent (>90). The results indicate that Thaiwater consistently achieved moderate to excellent Q index values, exceeding 70% annually, with values surpassing 90% in 2023. In contrast, the TMD maintained excellent quality, with values above 90% for all years. Out of over one million daily entries, 87% were verified as correct, though the Thaiwater data for 2024 showed only 70% accuracy. The QC procedures significantly improved data reliability, reducing the root mean square error for GSMaP and IMERG by 1.7% and 1.5%, respectively, and lowering the false alarm rate by approximately 0.001–0.002 without compromising heavy rainfall detection. A systematic QC framework is essential for ensuring high-quality datasets in rainfall applications. Full article

Financial instability is traditionally measured using indicators such as volatility levels, financial stress indices, or forecast errors, limiting the ability to capture the state-conditional and distributional properties of market dynamics. In this study, financial instability is reformulated as deviations from the conditional return distribution under the prevailing macro-financial state. To operationalize this formulation, a latent macro-financial state is estimated using a Dynamic Factor Model and integrated with KOSPI returns through an AI-based conditional density modeling framework consisting of a Conditional Time Variational Autoencoder combined with a state-conditional spline-flow density. Financial instability is then measured as the negative log-likelihood of the observed return under the estimated conditional density. The resulting index aligns with established benchmarks such as the CBOE Volatility Index and the South Korea Financial Instability Index, while capturing state-dependent distributional abnormalities that are not fully reflected in conventional volatility-based measures. It exhibits heightened sensitivity to periods of acute financial stress and identifies state-dependent anomalies that remain largely undetected by existing indicators. The proposed framework establishes a probabilistic and distribution-aware interpretation of financial instability, providing an interpretable foundation for sustainable financial risk management and long-term financial resilience beyond traditional volatility-based approaches. Full article

This paper presents a full-scale case study on real-time corrosion monitoring in an underground reinforced-concrete potable water tank built in 1968. The study aims to demonstrate how continuous electrochemical monitoring can support durability assessment and predictive maintenance in ageing water-retaining infrastructure, where direct inspection is often limited and exposure conditions are spatially variable. Fourteen monitoring points were installed in beams, columns and domes subjected to different exposure conditions. Corrosion potential, concrete resistivity, corrosion current density and temperature were recorded every 3 h and used to assess the corrosion state of the reinforcement. The monitored durability indicators were reinforcement section loss, estimated from corrosion current density using Faraday’s law, and corrosion-induced crack-width evolution, used as a serviceability-related indicator for maintenance planning. The results show that beams remained predominantly passive, with corrosion current densities below 0.1 µA/cm² and incremental sectional losses below approximately 2 µm during the monitoring period. Columns showed the highest vulnerability, particularly at lower elevations subjected to prolonged immersion, with estimated incremental section losses reaching approximately 4–6 µm and a clear correlation between submerged time and corrosion progression. Domes exhibited intermediate behaviour, with occasional activation events associated with environmental fluctuations. A multivariable model combining resistivity and temperature was used to interpret corrosion kinetics, while Faraday-based section-loss estimates were coupled with empirical crack-width models to forecast serviceability indicators up to 2045. These forecasts are presented as scenario-based maintenance-support indicators rather than deterministic predictions of future damage, since corrosion propagation and crack development may evolve nonlinearly under changing exposure conditions. The proposed approach demonstrates how continuous corrosion monitoring can be linked to durability limit-state assessment, enabling risk-informed and performance-based maintenance of critical water infrastructure. Full article

We address this question by jointly modeling the distributional dynamics of the U.S. and Canadian term premia. Our approach combines a flexible marginal specification—the Skewed Generalized Error Distribution—with a flexible bivariate copula (BB7) to capture evolving cross-market dependence. We illustrate the usefulness of this framework by examining December 2024, a period marked by a sharp rise in the U.S. term premium, and track how the forecasted joint distributions evolved throughout this episode. We document a striking change in conditional tail dependence between U.S. and Canadian term premia over this period. While term premia serve as a motivating application, our framework is applicable to a broad class of asset prices and macro-financial variables. Full article

The risk of wildfires is increasing due to high temperatures and dry weather conditions caused by climate change. Outbreaks and spread of wildfires are usually conditioned by weather, topography, and fuel characteristics. In the Republic of Korea (hereafter, the ROK), most wildfires are caused by anthropogenic factors rather than natural ones. However, the current forest fire forecasting system being operated in the ROK does not account for anthropogenic factors. To analyze the impact of human and physical factors on wildfire occurrence, a binary logistic regression model was constructed using data from the Gangwon and Gyeongbuk provinces from January 2022 to August 2025. The dependent variable was defined as the occurrence of a wildfire, while the independent variables comprised meteorological, seasonal, stand, and anthropogenic factors. To address multicollinearity, variables with high correlation coefficients were excluded from the independent variables, which were selected by three estimating approaches, including logistic regression and two machine learning techniques (namely, Random Forest and XGBoost). With machine learning, the variables with high feature importance were identified. The explanatory power of the logistic regression analysis with independent variables selected by the machine learning models was about 1.3 times higher than that of the model using variables adjusted solely for multicollinearity. The results of logistic regression analysis revealed that weather and coniferous forests are the most important factors fostering wildfires, while the mean stand age was the most significant factor in hindering wildfires. Among the anthropogenic factors, forest road density acted as a suppressor of wildfire spread rather than a promoter of occurrence. Conversely, trail density tends to increase the risk of wildfire occurrence. Among forest management activities, plantation forests may increase the risk of forest fires, although this remains uncertain. These findings suggest that preventing wildfires requires a paradigm shift in forest resource management policies, including extending forest rotation ages and converting coniferous forests to broadleaf forests. Meanwhile, it also indicates the need to restrict the expansion of hiking trails and improve regulations regarding hiker access and behavior to prevent wildfires. Full article

This study provides a rigorous analysis of the phase stability, mechanical behavior, and surface integrity of a non-equiatomic (FeCoNiCr)₈₅Ga₁₅ high-entropy alloy (HEA). By transitioning from the conventional equiatomic design to a gallium-doped 3d-transition metal matrix, we explore the interplay between lattice distortion and phase separation. Synthesized via vacuum arc melting, the as-cast alloy exhibits a non-homogeneous dendritic morphology consisting of a Cr-Fe-Co rich face-centered cubic (FCC) matrix and Ni-Ga rich body-centered cubic (BCC) interdendritic regions. While global thermodynamic criteria (δ = 3.65, ΔH_mix = −9.28 kJ/mol, and Ω = 2.23) favor single-phase solid solution stability, the Valence Electron Concentration (VEC = 7.46) precisely forecasts this dual-phase structure. Following low-temperature annealing at 250 °C for 24 h, high lattice strain energy drives a significant morphological transformation where the continuous interdendritic network resolves into discrete, phase-separated B2/BCC “islands”. Mechanical and tribological characterizations reveal that this low-temperature thermal activation triggers precipitate hardening; the macro-hardness increases from 146 ± 11 HB to 153 ± 7.5 HB and the micro-hardness rises from 186 ± 4 HV_0.5 to 206 ± 17.5 HV_0.5, yielding enhanced resistance to oxidation-delamination wear. However, electrochemical evaluation in a 3.5 wt.% NaCl solution highlights a fundamental trade-off: the formation of localized galvanic micro-cells between the phase-separated islands and the matrix causes the corrosion current density (i_corr) to increase from ≈10⁻⁹ A/cm² in the as-cast state to ≈10⁻⁶ A/cm² post-heat treatment, accompanied by a heightened susceptibility to localized pitting. These findings elucidate the primary role of electronic structure and minor p-block additions in regulating the lifecycle performance of transition metal HEAs under extreme conditions. Full article

Accurate precipitation forecasting on the Qinghai-Tibet Plateau (QTP) remains a significant challenge due to complex terrain and nonlinear atmospheric dynamics. This study evaluates an XGBoost-SHAP framework for 24 h precipitation forecasting at Maqu Station, leveraging multi-source observations from 2020 to 2022. Vertical profile analyses via microwave radiometer (MWR) indicate that moisture is predominantly confined to altitudes below 4 km (AGL), with Integrated Water Vapor (IWV) and Liquid Water Path (LWP) typically varying between 0–15 mm and 0–2.5 mm, respectively. The optimized XGBoost model achieves an annual R² of 0.872 and a Root Mean Square Error (RMSE) of 1.609 mm, showing improved statistical consistency compared with standard Random Forest baselines. While the framework maintains robust performance for winter stratiform precipitation (RMSE = 0.32 mm), predictive variance increases during summer convective periods (RMSE = 3.26 mm). SHAP diagnostic analysis identifies Dew Point Temperature (DPT) as a consistent year-round predictor. Feature sensitivity analysis further reveals shifting seasonal driving mechanisms: spring precipitation appears sensitive to mid-tropospheric geopotential height, whereas summer forecasts are more strongly modulated by 500 hPa specific humidity and lower-level water vapor density. Overall, the XGBoost-SHAP framework serves as a transparent and physically plausible diagnostic tool for examining seasonal moisture–dynamic coupling. While these site-specific results are encouraging, they represent a localized empirical baseline; further cross-site validation is required to assess regional generalizability. Full article

Reliable short-term wind power forecasting is crucial for smart grid stability. However, high-dimensional noise and stochastic fluctuations in wind sequences often degrade the accuracy of traditional forecasting models. Moreover, wind power time series typically exhibit asymmetric rising and decaying patterns, which further complicate accurate modeling. To address these challenges, this study proposes a hybrid intelligent system that integrates three components: data preprocessing, heterogeneous ensemble learning, and probabilistic interval forecasting. First, we build a multi-stage preprocessing workflow. Adaptive DBSCAN and Local Outlier Factor (LOF) remove spatial and density anomalies. Then multivariate variational mode decomposition (MVMD) synchronously separates multi-scale oscillatory patterns while preserving cross-channel correlations and frequency-domain symmetry across input variables. SHAP analysis quantifies feature importance, ensuring interpretability. The selected features are fed into a heterogeneous ensemble model consisting of Transformer, BPNN, ELM, XGBoost, and QRLSTM, which collectively capture multi-scale temporal dependencies and diverse data patterns. The ensemble weights are dynamically optimized by a modified multi-objective dragonfly algorithm (MMODA) that balances forecast accuracy and stability. Based on this ensemble, we apply MMODA to tune kernel density estimation for generating high-quality forecast intervals, maximizing coverage while minimizing interval width. Experiments on two wind farms in Shandong show that our MMODA-optimized ensemble reduces mean absolute percentage error by about 44.7% compared to single models, and ablations confirm that MVMD preprocessing adds a further 10.7% reduction. The proposed system provides an interpretable and reliable decision-support tool for sustainable grid operations. Full article

Infrared radiation spectra produced by vibration–rotation transitions in multicomponent gases within the vacuum plume of attitude and orbital control engines constitute crucial radiation sources for optical target identification and space maneuver recognition, and rapid prediction of these signatures is essential for real-time forecasting. This study introduces an axisymmetric vacuum plume flow field model based on a simplified point-source approach that accommodates multicomponent combustion gases. Using the Maxwellian velocity distribution and a velocity–position angle algorithm, normalized number density, velocity, and temperature distributions are derived. A plume–freestream interaction model founded on noncentral fully elastic collision theory is incorporated, and overall plume properties are obtained via density-weighted averaging. Neglecting non-equilibrium radiation effects, the high-temperature gas absorption coefficient is calculated using a statistical narrowband model and radiative transfer is solved via the line-of-sight method. The model is validated against direct simulation Monte Carlo results for single-gas and MBB bipropellant plumes and confirmed using infrared spectral data in the 2.0–4.5 μm band. The proposed framework achieves 10²–10³-fold higher computational efficiency than conventional DSMC approaches. Freestream effects on plume diffusion and momentum exchange diminish with increasing altitude, as does the freestream velocity’s enhancement of radiation intensity, whereas greater plume expansion at higher altitudes increases overall radiation intensity. Full article

Quantifying the distribution and spatiotemporal variation of water vapor and liquid water is of great significance for understanding the atmospheric thermodynamic processes during extreme meteorological events. The water vapor and liquid water data obtained from ground-based measurements by three MP-3000A microwave radiometers (MWRs) over the middle reaches of the Yangtze River Basin were analyzed. Firstly, a comparison between MWRs and radiosonde was conducted, and the co-located observation results indicated that MWRs used in this study feature high detection accuracy and favorable consistency. The integrated water vapor (IWV) measured by one of MWRs (Serial No. 3115) was with the best performance for IWV observation, and the bias and RMSE were 0.22 cm and 0.18 cm. In addition, the detection biases of integrated liquid water (ILW) between three MWRs in pre-precipitation were smaller than those in non-precipitation. All three instruments captured the diurnal variation characteristics of vapor density (VD) and liquid water content (LWC) profiles. The variation in ILW and IWV in different stations showed that ILW maintained low values before precipitation and increased sharply during the pre-precipitation stage, indicating strong indicative significance for rainfall occurrence. The ILW increment was more remarkable in Wuhan station, where mostly covered with urban and water body underlying surfaces. However, the magnitude of IWV variation before precipitation was smaller than that of ILW, especially in Jingzhou station. Under non-precipitation condition, VD and LWC vertical profiles at the three stations were relatively stable. Before precipitation, they exhibited substantial increases with obvious spatial discrepancies: sharp growth in Wuhan, moderate enhancement in Xianning, and slight increment in Jingzhou. Overall, atmospheric water vapor and liquid water increase significantly before precipitation, and their distribution spatiotemporal differences are closely related to local underlying surfaces and precipitation characteristics, which can provide meaningful references for short-term precipitation forecasting. Full article

This paper presents a cross-scale model for predicting the tribological behavior of textured coatings made of high-entropy alloys. The research methodology includes molecular dynamic modeling, a modified fractal surface model, and the Green’s method with fast Fourier transform. The main results demonstrate the existence of an optimal range of parameters: a fractal dimension of 2.45–2.55 and a texturing density of 15–20%, which reduces the coefficient of friction to 40% compared with untextured surfaces. The practical significance of the work lies in the creation of a theoretical basis for the integrated design and forecasting of the tribological properties of high-entropy coatings. Full article

This study estimates daily nitrogen dioxide (NO₂) concentrations at ground level across the Marmara Region of Türkiye at 0.01° resolution. The framework integrates Sentinel-5P (S5P) TROPOspheric Monitoring Instrument (TROPOMI) and GEOS Composition Forecast (GEOS-CF) tropospheric NO₂ vertical column density (VCD) data with meteorological, topographic, land-use, socioeconomic, and temporal features through four tree-based ensemble algorithms trained on 74 ground station observations. Under a temporal split (2019–2022 training, 2023 validation, 2024 testing), S5P-Categorical Boosting (CatBoost) achieved the best performance (Pearson correlation coefficient (R) = 0.706, R² = 0.498, root mean square error (RMSE) = 14.31 µg/m³). Random splitting inflated R by +0.168 due to temporal autocorrelation, while leave-one-station-out and leave-one-province-out cross-validation reduced R to ~0.50 by removing spatial dependence, together revealing the combined effect of temporal and spatial autocorrelation. SHapley Additive exPlanations (SHAP) analysis identified TROPOMI NO₂ VCD, population density, road length, and nighttime light as dominant predictors; population density was the top predictor in the GEOS-CF model, followed by VCD. Concentration maps for 2024 showed that 95.9% of the region’s 26.74 million inhabitants were exposed above the WHO annual air quality guideline of 10 µg/m³, with a population-weighted mean of 21.08 µg/m³. Full article

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article