Search Results (339)

Measles remains one of the most contagious infectious diseases globally and continues to pose substantial public health risks despite decades of effective vaccination. This narrative review examines both classical and contemporary computational approaches used for measles monitoring, prediction, and control, with particular attention given to the emerging role of artificial intelligence (AI). We synthesized findings from 46 studies; 31 focused directly on measles and 15 on methodologically relevant studies from related infectious diseases (COVID-19, influenza, malaria), selected through searches of PubMed, Scopus, Web of Science, IEEE Xplore, and preprint servers, conducted between June and December 2025. Traditional compartmental models (SIR, SEIR, MSEIR), statistical tools (ARIMA, SARIMA), and seroepidemiological analysis provide transparent, well-characterized frameworks for estimating transmission dynamics and simulating intervention scenarios. Spatial modeling, network analysis, and Monte Carlo simulations have added geographic granularity to outbreak characterization. More recently, AI and machine learning (ML) methods, including supervised algorithms (Random Forest, XGBoost, SVM), deep learning architectures (CNN, LSTM), and hybrid mechanistic ML models, have shown improved predictive performance by integrating multiple data sources: epidemiological records, demographic profiles, mobility patterns, and behavioral indicators. AI-based approaches appear most valuable for high-dimensional risk prediction and image-based diagnostic tasks, while classical models retain clear advantages for policy-oriented scenario analysis. However, no AI-based or hybrid model identified in this review has been adopted into routine national measles surveillance or used for vaccination policy decisions at scale. Important challenges remain: data quality varies across settings, model generalizability cannot be assumed, and computational infrastructure disparities limit deployment in high-burden regions. Explainable AI, federated learning, workforce training for model interpretation, and integration of vaccination registries with mobility and genomic surveillance data represent concrete future directions for strengthening computational support for measles elimination. Full article

Reliable short-term prediction of bus travel time on signalized urban arterials is essential for improving service reliability and may provide a useful forecasting basis for prediction-informed transit signal priority (TSP) and arterial coordination applications. However, bus operations on urban arterials are highly variable due to stop dwell times, signal delays, and interactions with mixed traffic, leading to nonlinear and nonstationary travel time patterns with strong spatiotemporal dependence. This study proposes a hybrid KNN–ConvLSTM framework for short-term arterial bus travel time prediction using real-world field data. A K-nearest neighbors (KNNs) module is first employed to retrieve historical operation sequences that are most similar to the current corridor state, thereby reducing interference from mismatched traffic regimes and improving robustness. Smart-card (IC card) transaction data are incorporated as demand-related features to represent passenger activity and its impact on dwell time and travel time variability. The selected sequences are then organized into a corridor-ordered spatiotemporal representation and further refined by lightweight temporal enhancement operations, including relevance gating, multi-scale aggregation, adaptive feature fusion, and residual enhancement, before being fed into the convolutional long short-term memory (ConvLSTM) predictor. The proposed approach is evaluated using weekday service-hour data extracted from 30 days of real-world bus operation records collected from a typical urban arterial corridor in Changchun, China, and is compared with several benchmark models, including ARIMA, KNN, LSTM, CNN, ConvLSTM, Transformer, and DCRNN. The results indicate that the proposed KNN–ConvLSTM framework achieves an MAE of 40.1 s, an RMSE of 55.8 s, a SMAPE of 10.7%, and an R² of 0.878, outperforming all benchmark models. Specifically, compared with the Transformer baseline, the proposed framework reduces MAE by 1.5%, RMSE by 5.1%, and SMAPE by 7.0%, while increasing R² by 0.014. Compared with the DCRNN baseline, it reduces MAE by 10.7%, RMSE by 1.9%, and SMAPE by 2.7%, while increasing R² by 0.008. These findings demonstrate that similarity-aware retrieval combined with spatiotemporal deep learning can substantially enhance short-term bus travel time prediction on signalized urban arterials. More accurate short-term forecasts may support prediction-informed transit signal priority and arterial coordination by providing more reliable downstream arrival-time estimates. However, the generalizability of the reported results is still constrained by the relatively short 30-day observation period and the single-corridor case setting, and the operational and environmental effects of downstream applications remain to be validated through dedicated closed-loop control evaluation in future work. Full article

Hydrogen production is gaining increasing importance as a key component of the transition toward carbon-neutral energy systems. In this study, the prediction of hydrogen generation in membraneless alkaline water electrolyzers (MAWEs) is investigated using deep-learning-based time-series modeling. A single-input modeling framework is adopted, where only the system current is used as the input variable. Experimental current signals obtained from long-duration tests conducted at electrolyte concentrations between 5 and 35 g KOH (7200 s per experiment) are employed as the model inputs, while mass-based hydrogen production (in grams) is used as the output variable. Two recurrent neural network architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), are implemented, and their predictive performance is comparatively evaluated using RMSE, MAE, and R² metrics. In addition to deep learning models, classical approaches including Linear Regression, ARIMA, and Naïve Forecast are also considered for comparison. The results show that both models are capable of accurately reproducing the hydrogen-production dynamics across the entire concentration range. In particular, the prediction accuracy improves notably at medium and high electrolyte concentrations, where the coefficient of determination (R²) approaches 0.98. The residual distributions remain narrow and symmetric around zero, indicating the absence of systematic estimation bias. The results also show that classical models can achieve comparable performance under stable operating conditions, while deep learning models provide advantages in capturing nonlinear and dynamic behavior. While LSTM and GRU exhibit comparable accuracy, each architecture provides complementary advantages under different operating conditions. These findings indicate that deep-learning-based time-series modeling constitutes a lightweight and reliable framework for prediction and control applications in MAWE systems. Overall, this study demonstrates the applicability of data-driven models for the dynamic characterization of membraneless water electrolysis. Full article

In highly digitalized and networked modern trading systems, large volumes of heterogeneous data are continuously generated from multiple sources during market operations. However, due to the complexity of data structures, significant differences in temporal scales, and constraints imposed by data privacy protection, traditional single-source modeling approaches are unable to fully exploit multisource information. To address this issue, a federated multimodal prediction framework for complex market systems, termed Federated Market-Sensor Transformer (FMST), is proposed. In this framework, data originating from different information sources are uniformly modeled as multimodal time series. A multimodal market-sensor representation module is constructed to perform unified feature encoding, and a cross-modal Transformer fusion architecture is employed to characterize dynamic interaction relationships among different information sources. Meanwhile, a federated collaborative learning mechanism is introduced during the training phase, enabling multiple data nodes to perform collaborative model optimization without sharing raw data. In this manner, data privacy can be preserved while improving the cross-region generalization capability of the model. Systematic experimental evaluation is conducted on the constructed multimodal market-sensor dataset. The experimental results demonstrate that the proposed method consistently outperforms traditional statistical models and deep learning approaches across multiple evaluation metrics. In the main prediction experiment, FMST achieves a root mean square error (RMSE) of $0.1136$, a mean absolute error (MAE) of $0.0832$, and a coefficient of determination $R^2$ of $0.8517$, while the direction prediction accuracy reaches $74.56\%$, clearly outperforming baseline models including ARIMA, LSTM, Temporal CNN, Transformer, and FedAvg-LSTM. In the cross-region generalization experiment, FMST maintains strong performance, achieving an RMSE of $0.1242$, an MAE of $0.0908$, an $R^2$ value of $0.8261$, and a direction prediction accuracy of $72.48\%$. The ablation study further indicates that the three core components—multimodal market-sensor representation, cross-modal Transformer fusion, and federated collaborative learning—each make important contributions to the overall model performance. These experimental findings demonstrate that the proposed method can effectively integrate multisource market information and significantly enhance the prediction capability for complex market dynamics, providing a new technical pathway for the application of artificial intelligence-driven multimodal sensing systems in economic data analysis. Full article

Accurate forecasting of fatal construction accidents is critical for proactive safety management; however, accident time series exhibit strong non-stationarity, nonlinear dynamics, and multi-scale temporal patterns that challenge conventional models. This study proposes a hybrid STL–BiGRU framework that integrates Seasonal–Trend decomposition using Loess (STL) with a Bidirectional Gated Recurrent Unit (BiGRU) network to deliver robust and interpretable forecasts tailored to construction safety needs. STL first decomposes the original monthly accident series (January 2012–December 2024, OSHA) into trend, seasonal, and residual components, reducing structural complexity and mitigating non-stationarity. Independent BiGRU models are then trained on each component to capture bidirectional temporal dependencies, and final forecasts are reconstructed through component aggregation. Comparative experiments against Gated Recurrent Units (GRUs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), Support Vector Regression (SVR), Autoregressive Integrated Moving Average (ARIMA), and their STL-enhanced variants demonstrate that the proposed STL–BiGRU model achieves superior performance across both short-term and medium-term horizons. The model achieves the lowest error levels, with a short-term Root Mean Squared Error (RMSE) of 6.8522 and a medium-term RMSE of 7.0568, and shows consistent improvements in Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Results indicate that multi-scale decomposition combined with bidirectional deep learning provides a practical, forward-looking tool. It helps regulators and contractors anticipate high-risk periods, optimize resource allocation, and reduce fatal accidents through targeted preventive measures. Full article

Government bond yields and volatility exhibit nonlinearity, complexity, and noise, making accurate forecasting challenging for conventional econometric or deep learning models alone. This study develops a multiscale nonlinear forecasting framework that combines variational mode decomposition (VMD) with a long short-term memory (LSTM) model to forecast China’s government bond yields and volatility. By decomposing the time series into trend, periodic, and disturbance components, the hybrid model effectively captures both linear and nonlinear patterns while mitigating overfitting. In the empirical analysis, five loss functions—MSE, RMSE, MAE, MAPE, SMAPE—and the DM test are used as evaluation criteria to compare the predictive performance of ARIMA, SVM, LSTM, VMD-SVM, and VMD-LSTM models. Using the yields and volatility of 3-year government bonds as the benchmark case and 1-year government bonds for robustness tests, the results indicate that the VMD-LSTM model achieves superior predictive accuracy, demonstrating its effectiveness and robustness. The proposed hybrid model offers a novel framework for government bond yield forecasting, providing valuable insights for monetary policy and financial risk monitoring. Full article

Accurate prediction of the air quality index (AQI) is crucial for understanding urban pollution dynamics and protecting public health. This study proposes a dual-branch fusion framework (CL-XGB-Season) to address seasonal heterogeneity in AQI prediction by integrating temporal dynamic features and static patterns. The CNN-LSTM branch captures short-term temporal fluctuations, while a seasonally split XGBoost branch fits long-term static patterns via independent submodels for spring, summer, autumn, and winter. SHAP-based interpretability analysis revealed the dominant drivers across different seasons: the “temperature × O₃” interaction feature plays a key role in summer, characterizing the ozone formation mechanism dominated by photochemical reactions under conditions of high temperature and strong solar radiation; whereas the PM_2.5/PM₁₀ ratio is crucial in winter (where pollution is primarily driven by pollutant accumulation). The dual-branch fusion framework was validated using hourly resolution data from Chongqing for the 2020–2025 period. Results indicate that the framework achieved a prediction accuracy of 0.197 root mean square error (nRMSE) and 0.9611 coefficient of determination (R²) on the test set, outperforming eight ablation variants and five baseline models (ARIMA, Transformer, etc.) in comparative experiments. Ablation studies confirm the necessity of dual branches and seasonal modeling, with the full model reducing nRMSE by 19–63% versus single-model variants. This framework maintains stable seasonal performance and provides actionable insights for targeted air quality management. Full article

This paper presents a hybrid econometric and machine-learning framework for forecasting GDP that bridges long-run structure with short-run regime dynamics. Using annual World Bank data spanning 1960 to 2024, the framework combines three complementary components: an ARIMA baseline that captures persistence, a three-state Hidden Markov Model (HMM) that provides probabilistic regime identification, and an LSTM-based extension that learns nonlinear patterns associated with regime transitions. Detailed out-of-sample forecasting evidence is reported for five representative countries (the United States, China, Germany, India, and Greece), chosen to illustrate performance across different volatility profiles and economic environments. Across these case studies, the integrated HMM–LSTM approach often delivers lower forecast errors than the benchmark alternatives, although the magnitude of the gains is not uniform across countries. Beyond point forecasting performance, the regime layer yields an interpretable probabilistic representation of business cycle conditions that can support real-time monitoring and early-warning assessment. By combining transparency with adaptability, the proposed framework contributes to the forecasting literature and provides a practical decision-support tool under heightened macroeconomic uncertainty. Full article

Ozone monitoring in laboratory environments is essential for ensuring personnel safety and maintaining stable experimental conditions, particularly in enclosed facilities where ozone may accumulate during high-energy radiation operations. This study investigates the short-term prediction of ozone concentration using data obtained from a sensor-based ozone monitoring system deployed at the National Synchrotron Radiation Research Center (NSRRC). Ozone concentration measurements were collected using a UV absorption-based ozone analyzer and analyzed as a time-series dataset under controlled experimental conditions. Three forecasting models—Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and linear regression—were evaluated for short-term ozone concentration prediction. Experimental results indicate that the ARIMA model provides superior predictive performance for the small-sample dataset used in this study. In the Right direction, ARIMA achieved R² values of 89.5%, 86.3%, and 81.1% at distances of 5 cm, 10 cm, and 15 cm, respectively, while also demonstrating stable performance in the Up direction. The results highlight the effectiveness of classical time-series models for sensor data analysis in environments with limited sensing data. The proposed framework demonstrates the potential of integrating sensing devices with predictive data analytics to support real-time environmental monitoring and safety management in laboratory facilities. Full article

The crude oil market has obvious financial entropy, and there are characteristics such as continuous uncertainty, multi-scale fluctuations and nonlinear state transitions. These characteristics bring challenges to the traditional prediction method. In this context, in order to improve the accuracy of energy financial market prediction, this study proposes an artificial intelligence-driven hybrid prediction framework, ALA-VMD-CASA. This framework is divided into three stages. First, with the goal of minimizing envelope entropy, ALA is introduced to adaptively optimize the hyperparameters of VMD, so as to generate informative sub-modes with reduced entropy. Next, the parallel prediction of each sub-mode is carried out by using the score attention mechanism based on the CNN autoencoder, and its linear time complexity can capture volatility clustering and sudden price fluctuations. Finally, the final price prediction is generated through the aggregation component. The empirical experiment of Brent crude oil spot prices from 2010 to 2025 shows that the ALA-VMD-CASA framework is superior to benchmark models such as ARIMA, RW, RWWD, LSTM, GRU, Transformer and Informer. Compared with the best standalone model, the proposed framework reduces the mean square error by more than 63% and obtains a perfect win rate in expanding-window evaluations. These results prove that the proposed framework is effective and robust for modeling financial entropy and improving energy price forecasting. Full article

Accurate prediction of wind speed at sea is crucial for the site selection of wind farms, the layout of wind turbines, and the estimation of power generation. To improve the accuracy of short-term predictions under limited data conditions, this study proposes a backpropagation (BP) neural network prediction model optimized by the particle swarm optimization algorithm (PSO). This model is trained using hourly wind speed data from meteorological stations along the northeastern coast of China from 2020 to 2022, and two modeling strategies, namely the unified training model over multiple years and the seasonal model, are constructed for comparison. The validation using the measured data from January to July 2023 indicates that the unified model with a root mean square error of 1.235 and an average absolute error of 0.924 demonstrates superior generalization performance, outperforming the seasonal models (such as the spring model with RMSE = 1.243 and the summer model with RMSE = 1.324). Benchmark comparisons against LSTM, ARIMA, and persistence models further confirmed the superiority of the proposed approach. To address the stochastic nature of wind speed and support grid operation, we extended the deterministic forecasts to probabilistic prediction intervals using Monte Carlo Dropout, achieving a prediction interval coverage probability of 81.2% with a mean width of 1.38 m/s. The results indicate that while seasonal modeling offers insights into intra-annual wind variations, it does not exceed the accuracy of the globally trained multi-year model under limited data conditions. In conclusion, the proposed BP-PSO hybrid model provides a robust and low-cost solution for offshore wind speed forecasting, with the probabilistic forecasting framework offering actionable uncertainty information for grid integration. The multi-year training framework demonstrates stronger practical utility, and the findings support the application of hybrid optimization algorithms in real-world wind resource assessment. Full article

Short-term forecasting of solar radiation is essential for the efficient operation of solar energy systems. This study presents a neural network-based approach for short-term solar radiation forecasting using a hybrid framework that integrates temporal characteristics with weather-based features. The proposed model combines a Gated Recurrent Unit (GRU) to capture short-term temporal dynamics, a Transformer Encoder, and a Multilayer Perceptron (MLP) to integrate these representations for final prediction. Key meteorological variables, including temperature, humidity, and wind speed, are incorporated along with engineered time-related features such as lagged values, rolling statistics, and cyclical time-of-day encodings. The results demonstrate that the hybrid model effectively integrates sequential learning and feature interaction, leading to improved forecasting accuracy. The proposed approach achieves a test Mean Absolute Error (MAE) of 0.056, Root Mean Square Error (RMSE) of 0.086, and coefficient of determination (R²) of 0.92, outperforming benchmark models such as AutoRegressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), GRU, and Extreme Gradient Boosting (XGBoost). The model maintains stable performance across cross-validation folds, multiple forecasting horizons, and varying weather conditions. These findings indicate that the proposed framework provides a reliable and practical solution for accurate short-term solar radiation forecasting, supporting real-time solar energy management and renewable energy system optimization. Full article

The Longmen Shan Fault Zone is marked by intricate geological structures and frequent seismic activity, which gives rise to persistent seismic hazards. To tackle the challenge of capturing the multi-temporal characteristics of earthquake frequency, this study combines machine learning with time series analysis to conduct earthquake frequency prediction research. Based on the 1970–2023 seismic dataset from the China Earthquake Networks Center, the seismic records were structured into four temporal scales: daily, weekly, monthly and quarterly. The minimum completeness magnitude (Mc) was determined as M3.0 by applying the G–R relationship. After conducting white noise tests and data normalization, ACF and PACF were utilized to select the optimal time-step parameters for the LSTM model. Considering the inherent characteristics of the seismic data, the 99th percentile of the frequency series was set as the threshold, and an auxiliary parameter was introduced to label high-frequency earthquake days for the construction of the LSTM model. Upon the completion of LSTM model fitting, heteroscedasticity tests were performed on the residuals between the predicted and observed values. Confirming the presence of significant heteroscedasticity, the GARCH model was incorporated to process these residuals, thus establishing a complete LSTM-GARCH coupled model. The results reveal that seismic activity in this region is normally low-frequency with occasional high-frequency occurrences. The proposed model achieves R² above 0.80 across all four temporal scales, accompanied by superior performance in all error metrics. This study validates that the LSTM-GARCH model can effectively extract the multi-scale patterns of earthquake frequency, with the best performance observed at the daily scale. Ablation experiments further demonstrate that this coupled model outperforms both the ARIMA and single LSTM models, providing reliable technical support for short-to-long-term earthquake prediction and regional disaster risk assessment. Full article

This study compares classical and deep learning models (ARIMA, Random Forest, RNN, LSTM, CNN, and Transformer) for forecasting one-day-ahead log returns $r_{t+1}=ln(P_{t+1}/P_t)$ using daily data for six U.S.-listed equities (NVDA, TSLA, SMCI, GOOGL, PYPL, SNAP) from 2014 to 2024. Predictors include lagged price/return information, lagged macroeconomic variables (CPI, policy rate, GDP) to reflect information availability, and technical indicators (SMA, RSI, MACD) computed using rolling windows ending at day t to avoid look-ahead bias. Performance is evaluated in a walk-forward out-of-sample design, with hyperparameters selected using time-series validation within each training window. Empirically, results are asset-dependent: ARIMA and Random Forest remain strong baselines; deep learning models show asset-dependent performance, with LSTM occasionally competitive in some settings, and the Transformer competitive but not uniformly dominant. For context, this study also reports a rule-based SMA(10/50) crossover benchmark evaluated net of transaction costs. Overall, the findings suggest that predictive signals in daily equity returns, when present, are modest and must be assessed under strict leakage controls and realistic evaluation protocols. Full article

Background: Amid rising trade, urbanization, and carbon emissions in MENA countries, sustainable logistics faces major constraints. This study develops an enhanced Green Logistics Performance Index (GLPI) using min-max normalization and Principal Component Analysis (PCA) to integrate the World Bank’s Logistics Performance Index (LPI) and Yale’s Environmental Performance Index (EPI). The study uses fixed-effects panel regression on data from 20 MENA countries (2018–2024), identifies key drivers, and applies ARIMA and LSTM models for 2030 projections. The prior ratio-based GLPI suffered from scale sensitivity and volatility; this refined version provides improved stability and predictive utility for Green Supply Chain Management (GSCM). Methods: Panel data from 20 MENA countries (2018–2024) were analyzed. The enhanced GLPI normalizes and weights LPI and EPI scores via PCA. Fixed-effects regression identifies drivers, while ARIMA and LSTM enable scenario-based forecasting (baseline, optimistic, and pessimistic). Results: Renewable energy share positively influences GLPI, while trade openness has a negative effect. Projections indicate the regional GLPI will reach about 0.65 by 2030, with Saudi Arabia potentially achieving 25% higher under optimistic conditions. Conclusions: The refined GLPI advances GSCM theory by operationalizing triple bottom line trade-offs through a robust, predictive metric. It bridges descriptive limitations in prior literature, enabling forward-looking insights into sustainable logistics in emerging economies, with potential applicability beyond MENA. Full article