Search Results (1,194)

The ongoing evolution of SARS-CoV-2 has given rise to variants with enhanced transmissibility and pathogenicity, many of which harbor mutations in the receptor-binding domain (RBD) of the viral spike protein. These mutations often confer increased viral fitness and immune evasion by modulating interactions with the human ACE2 receptor (hACE2) and escaping neutralizing antibodies. Accurate prediction of the functional consequences of such mutations—particularly their effects on receptor binding and antibody escape—is critical for assessing the public health threat posed by emerging variants. In this study, we apply a Mutation-dependent Biomacromolecular Quantitative Structure–Activity Relationship (MB-QSAR) framework to quantitatively model the biochemical phenotypes of RBD variants. Trained on comprehensive deep mutational scanning (DMS) datasets, our models exhibit strong predictive performance, achieving correlation coefficients (r²) exceeding 0.8 for hACE2 binding affinity and 0.7 for antibody neutralization escape. Importantly, the MB-QSAR approach generalizes well to multi-mutant variants and currently circulating lineages. Structural analysis based on model-derived interaction profiles offers mechanistic insights into key RBD–ACE2 and RBD–antibody interfaces, helping the rational design of broadly protective vaccines and therapeutics. This work establishes MB-QSAR as a rapid, accurate, and interpretable tool for the prediction of protein–protein interaction and forecasting viral adaptation, thereby facilitating early risk assessment of novel SARS-CoV-2 variants. Full article

Charging electric vehicles (EVs) and integrating renewable energy sources (RESs) are becoming key aspects of residential energy systems. However, the variability of RES generation, combined with uncontrolled EV charging, poses challenges for reliability, power quality, and supply-demand balancing within communities. The challenges only grow when considering other electrified building loads as well. Accurate forecasting of power demand and renewable generation is essential for efficient and sustainable grid operation, optimal use of RESs, and effective energy trading within communities. Deep learning (DL), including supervised, unsupervised, and reinforcement learning (RL), has emerged as a promising solution for predicting consumer demand, renewable generation, and managing energy flows in residential environments. This paper provides a comprehensive review of the development and application of these methods for forecasting and energy management in residential communities. Evaluation metrics across studies indicate that supervised learning can achieve highly accurate forecasting results, especially when integrated with unsupervised K-means clustering and data decomposition. These methods help uncover patterns and relationships within the data while reducing noise, thereby enhancing prediction accuracy. RL shows significant potential in control applications, particularly for charging strategies. Similarly to how V2G-simulators model individual EV usage and simulate large fleets to generate grid-scale predictions, RL can be applied to various aspects of EV fleet management, including vehicle dispatching, smart scheduling, and charging coordination. Traditional methods are also used across different applications and help utilities with planning. However, these methods have limitations and may not always be completely accurate. Our review suggests that integrating hybrid supervised-unsupervised learning methods with RL can significantly improve the sustainability and resilience of energy systems. This approach can improve demand and generation forecasting while enabling smart charging coordination and scheduling for scalable EV fleets integrated with building electrification measures. Furthermore, the review introduces a unifying conceptual framework that links forecasting, optimization, and policy coupling through hierarchical deep learning layers, enabling scalable coordination of EV charging, renewable generation, and building energy management. Despite methodological advances, real-world deployment of hybrid and deep learning frameworks remains constrained by data-privacy restrictions, interoperability issues, and computational demands, highlighting the need for explainable, privacy-preserving, and standardized modeling approaches. To be effective in practice, these methods require robust data acquisition, optimized forecasting and control models, and integrated consideration of transport, building, and grid domains. Furthermore, deployment must account for data privacy regulations, cybersecurity safeguards, model interpretability, and economic feasibility to ensure resilient, scalable, and socially acceptable solutions. Full article

Typhoons pose severe threats to coastal regions through destructive winds and extreme rainfall, with rainfall-induced flooding often causing more casualties and economic damage than wind damage alone. Accurate precipitation forecasting is therefore paramount for effective disaster risk management. This study proposes a trajectory-based framework for predicting cumulative rainfall from typhoon events, based on the premise that cyclones with similar tracks yield comparable precipitation due to topographic interactions. An extensive dataset of typhoons over East Asia (1979–2022) is analyzed, and two new similarity metrics—the Kernel Density Similarity Index (KDSI) and the Comprehensive Index (CI)—are introduced to quantify track resemblance. Their predictive skill is benchmarked against existing indices, including fuzzy C-means, convex hull area, and triangle mesh methods. Optimal performance is achieved using an ensemble of 13 analogous cyclones, which minimizes root-mean-square error (RMSE). Validation across a large sample demonstrates that the proposed model overcomes limitations of earlier approaches, providing a robust and efficient tool for forecasting typhoon-induced rainfall. Full article

Background: In a fast-changing sector, apparel distribution centers (DCs) are under increasing pressure to meet labor intensive operational requirements, short delivery windows, and variable demand in the rapidly changing apparel industry. Traditional labor forecasting methods often fail in dynamic environments, leading to inefficiencies, inadequate staffing, and reduced responsiveness. Methods: This comprehensive review discusses AI-enhanced labor forecasting tools that support flexible workforce planning in apparel DCs using a PRISMA methodology. To provide proactive, data-driven scheduling recommendations, the model combines machine learning algorithms with workforce metrics and real-time operational data. Results: Key performance indicators such as throughput per work hour, skill alignment among employees, and schedule adherence were used to assess performance. Apparel distribution centers can significantly benefit from real-time, adaptive decision-making made possible by AI technologies in contrast to traditional models that depend on static forecasts and human scheduling. These include LSTM for time-series prediction, XGBoost for performance-based staffing, and reinforcement learning for flexible task assignments. Conclusions: The paper demonstrates the potential of AI in workforce planning and provides useful guidance for the digitization of labor management in the clothing distribution industry for dynamic and responsive supply chains. Full article

Integrated energy load forecasting plays a crucial role in optimizing the operation and economic dispatch of integrated energy systems. Its forecasting accuracy is not only time-dependent but also influenced by the coupling characteristics among energy sources. Solely relying on time-scale training methods cannot adequately capture the strong correlations among multiple energy sources. To address challenges in extracting coupled load forecasting features, obtaining periodic characteristics, and setting model network structures, this paper proposes an Integrated Energy Short-Term Adaptive Load Forecasting Method Based on Coupled Feature Extraction (AP-CFE). This approach integrates high-dimensional coupling features and periodic temporal features effectively using ensemble algorithms. To prevent overfitting or underfitting issues, an Adaptive learning algorithm (AP) is introduced. The load demonstrates highly stochastic behavior in response to external factors, resulting in rapid, volatile fluctuations in grid demand. The strategy of employing sparse self-attention to approximate the residual terms effectively mitigates this issue. Simulation results using comprehensive energy load data from Australia demonstrate that the proposed model outperforms existing models, achieving better capture of energy coupling characteristics with average absolute percentage errors reduced by 20.75%, 28.48%, and 21.64% for electricity, heat, and gas loads, respectively. Full article

Accurately identifying baseflow-dominant (BFD) periods in streamflow records is crucial for evaluating low-flow conditions, groundwater interactions, and other water resource management issues. While baseflow separation methods are widespread, the definition and identification of BFD flows are relatively new areas. Here, we define BFD periods as flow conditions that occur with minimal contribution from quickflow, including periods dominated by bank flow, groundwater interaction, or residual flow routing through the system. We develop a comprehensive, expert-labeled dataset of BFD periods from 182 USGS stream gages across diverse hydrological settings in the continental United States as ground truth. Using this dataset, we evaluate various automated BFD identification methods, including three new approaches, a machine learning classifier, a gradient-based method, and a statistical method, as well as two established techniques: the BN77 and Strict Baseflow methods. Our results demonstrate that the machine learning model (RF-BFD) outperforms all other approaches, achieving an F1 score of 0.92 and 92% accuracy. This study characterizes challenges in identifying BDF periods and establishes benchmarks for improving BFD identification in large-scale hydrological studies. The findings offer a pathway toward more robust and scalable BFD identification techniques, enhancing low-flow forecasting and groundwater-surface water interaction assessments. Full article

This paper proposes a convolutional Long Short-Term Memory (ConvLSTM) network integrated with multi-channel features dedicated to ionospheric total electron content (TEC) forecasting. To improve generalization, solar, and geomagnetic activity indices are added as auxiliary channel inputs. The model is built upon an Encoder–Decoder (ED) architecture enhanced with residual connections and convolutional channel projection, which collectively improve the synergy among its core components. Based on this framework, we developed ED-ConvLSTM-Res, a multi-channel feature-based global ionospheric TEC prediction model. Comprehensive accuracy evaluation and comparative tests were carried out using datasets from the solar minimum year of 2019 and the current solar maximum year of 2024. The results indicate that the proposed model consistently achieves strong predictive performance compared with other models, along with a significantly enhanced feature representation capability. Specifically, the Root Mean Square Errors (RMSE) of the ED-ConvLSTM-Res model’s predictions in 2019 and 2024 are 1.28 TECU and 5.28 TECU, respectively, while the corresponding Mean Absolute Errors (MAE) are 0.87 and 3.87, and the coefficients of determination (R²) are 0.95 and 0.94. In the current high solar activity year 2024, the proposed model achieves error reductions of 13.6% in MAE and 11.6% in RMSE compared with the Center for Orbit Determination in Europe (CODE)’s one-day-ahead forecast product, c1pg. These results confirm that the proposed model not only outperforms the ConvLSTM model without additional indices and c1pg but also exhibits strong generalization capability, maintaining stable performance with low errors under both high and low solar activity conditions. Full article

This paper presents a comparative empirical analysis of three modeling approaches applied to the forecasting of Morocco’s Real Effective Exchange Rate (REER): the ARIMA model, the Random Forest algorithm, and the Dynamic Factor Model (DFM). Utilizing a comprehensive macroeconomic quarterly dataset spanning from 1999Q4 to 2021Q3, the study assesses the out-of-sample predictive performance of these models over a structurally dynamic period, including the transition to a more flexible exchange rate regime in 2018 and the global shock induced by the COVID-19 pandemic. The findings reveal that the Random Forest model significantly outperforms both ARIMA and DFM in terms of accuracy and adaptability to structural breaks. Variable importance analysis highlights the dominant role of real economic fundamentals, particularly industrial value added, inflation, and exports in explaining REER movements. In contrast, the ARIMA model underreacts to exogenous shocks due to its univariate structure, while the DFM suffers from a loss of predictive power likely caused by excessive dimensionality reduction. Full article

Underground gas storage (UGS) facilities are fundamental for national energy security and global decarbonization efforts. However, solid phase production in carbonate reservoirs, such as Qianmi Bridge, poses a significant operational challenge by compromising wellbore integrity and formation permeability. To address this, this study develops a novel, comprehensive methodology for predicting and mitigating solid phase production risk in carbonate UGS under dynamic operating conditions, specifically focusing on the Qianmi Bridge gas storage. This approach systematically integrates qualitative susceptibility assessments (using acoustic time difference, B index, and S index) with quantitative models for critical and ultimate pressure difference forecasting. Crucially, the methodology rigorously accounts for dynamic process parameters, including rock strength degradation due to acidizing, in situ stress variations, and fluid flow dynamics throughout the reservoir’s operational life cycle, a critical aspect often overlooked in conventional models designed for sandstone reservoirs. Analysis reveals that the safe operating pressure window dramatically narrows as formation pressure declines and rock strength is weakened, especially under high-intensity, multi-cycle alternating loads. Specifically, acidizing treatments can reduce the critical pressure difference by over 50% (e.g., from 40.49 MPa to 19.63 MPa), and under depleted conditions (0.6 P₀, 0.8 UCS), the reservoir’s ability to resist solid phase production approaches zero, highlighting an extremely high risk. These findings provide an essential theoretical and technical basis for formulating robust operational control strategies, enabling data-driven decision-making to enhance the long-term safety, efficiency, and overall process integrity of carbonate gas storage operations. Full article

The accurate forecasting of surface air temperature (T2M) is crucial for climate analysis, agricultural planning, and energy management. This study proposes a novel forecasting framework grounded in structured temporal decomposition. Using the Kolmogorov–Zurbenko (KZ) filter, all predictor variables are decomposed into three physically interpretable components: long-term, seasonal, and short-term variations, forming an expanded multi-scale feature space. A central innovation of this framework lies in training a single unified model on the decomposed feature set to predict the original target variable, thereby enabling the direct learning of scale-specific driver–response relationships. We present the first comprehensive benchmarking of this architecture, demonstrating that it consistently enhances the performance of both regularized linear models (Ridge and Lasso) and tree-based ensemble methods (Random Forest and XGBoost). Under rigorous walk-forward validation, the framework substantially outperforms conventional, non-decomposed approaches—for example, XGBoost improves the coefficient of determination ($R^2$) from 0.80 to 0.91. Furthermore, temporal decomposition enhances interpretability by enabling Ridge and Lasso models to achieve performance levels comparable to complex ensembles. Despite these promising results, we acknowledge several limitations: the analysis is restricted to a single geographic location and time span, and short-term components remain challenging to predict due to their stochastic nature and the weaker relevance of predictors. Additionally, the framework’s effectiveness may depend on the optimal selection of KZ parameters and the availability of sufficiently long historical datasets for stable walk-forward validation. Future research could extend this approach to multiple geographic regions, longer time series, adaptive KZ tuning, and specialized short-term modeling strategies. Overall, the proposed framework demonstrates that temporal decomposition of predictors offers a powerful inductive bias, establishing a robust and interpretable paradigm for surface air temperature forecasting. Full article

Continuous monitoring of electrical parameters is essential for understanding energy consumption, assessing power quality, and analyzing load behavior. This paper presents a dataset comprising measurements of three-phase voltages and currents, active and reactive power (per phase and total), power factor, and system frequency. The data was collected between April and December 2024 in the low-voltage system of a university laboratory, using high-accuracy power analyzers installed at the point of common coupling. Measurements were recorded every 10 min, generating 79 files with 432 records each, for a total of approximately 34,128 entries. To ensure data quality, the values were validated, erroneous entries removed, and consistency verified using power triangle relationships. The curated dataset is provided in tabular (CSV) format, with each record including a timestamp, three-phase voltages, three-phase currents, active and reactive power (per phase and total), power factor (per phase and global), and system frequency. This dataset offers a comprehensive characterization of electrical behavior in a university laboratory over a nine-month period. It is openly available for reuse and can support research in power system analysis, renewable energy integration, demand forecasting, energy efficiency, and the development of machine learning models for smart energy applications. Full article

Background: The Fontan procedure is a palliative surgery for patients with single-ventricle congenital heart disease (CHD), but it is associated with postoperative and long-term mortality and morbidity. Accurate, individualized risk stratification remains a challenge with traditional models. This study aimed to develop and validate a deep learning (DL) model to predict postoperative mortality after the Fontan procedure and to identify key predictive factors. Methods: We retrospectively analysed data from 230 patients who underwent the Fontan procedure between 2010 and 2024. A Deep Neural Network (DNN) model was developed using comprehensive preoperative, intraoperative, and postoperative clinical, biochemical, and hemodynamic variables. The dataset was split using five-fold cross-validation, with 80% for training and 20% for testing in each fold. The Synthetic Minority Over-sampling Technique (SMOTE) was used to fix class imbalance. Model performance was evaluated using five-fold stratified cross-validation. We assessed accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). SHapley Additive exPlanations (SHAP) analysis was employed to enhance model interpretability and identify the importance of features. A user-friendly clinical application interface was developed using Streamlit. This study was reported in accordance with the TRIPOD + AI reporting guidelines. Results: The DNN model demonstrated superior performance in predicting postoperative mortality, achieving an overall accuracy of 91.5% (95% CI: 87.2–94.8%), precision of 83.3% (95% CI: 76.5–89.1%), recall (sensitivity) of 90.9% (95% CI: 85.2–95.1%), specificity of 92.5% (95% CI: 88.3–95.7%), F1-score of 87.0% (95% CI: 82.1–91.3%), and an AUC-ROC of 0.94 (95% CI: 0.88–0.99). SHAP analysis identified key predictors of mortality, such as pulmonary artery pressure, ventricular end-diastolic pressure, preoperative BNP levels, and severity of AV valve regurgitation. The Streamlit application offered a user-friendly interface for personalized risk evaluation. Conclusions: A deep learning model that incorporates detailed clinical data can precisely forecast postoperative mortality in patients undergoing Fontan surgeries. This AI-based method, combined with interpretability techniques, provides a valuable tool for personalized risk assessment. It has the potential to improve preoperative counseling, optimize perioperative care, and enhance patient outcomes. However, additional external validation is needed to verify its broader applicability and clinical usefulness. Full article

This paper addresses the cold-start problem and rare event prediction challenges in Olympic medal forecasting by proposing a predictive framework that integrates multi-granularity transfer learning with extreme value theory. The framework comprises two main components, a Multi-Granularity Transfer Learning Core (MG-TLC) and a Rare Event Analysis Module (RE-AM), which address multi-level prediction for data-scarce countries and first medal prediction tasks. The MG-TLC incorporates two key components: Dynamic Feature Space Reconstruction (DFSR) and the Hierarchical Adaptive Transfer Strategy (HATS). The RE-AM combines a Bayesian hierarchical extreme value model (BHEV) with piecewise survival analysis (PSA). Experiments based on comprehensive, licensed Olympic data from 1896–2024, where the framework was trained on data up to 2016, validated on the 2020 Games, and tested by forecasting the 2024 Games, demonstrate that the proposed framework significantly outperforms existing methods, reducing MAE by 25.7% for data-scarce countries and achieving an AUC of 0.833 for first medal prediction, 14.3% higher than baseline methods. This research establishes a foundation for predicting the 2028 Los Angeles Olympics and provides new approaches for cold-start and rare event prediction, with potential applicability to similar challenges in other data-scarce domains such as economics or public health. From a symmetry viewpoint, our framework is designed to preserve task-relevant invariances—permutation invariance in set-based country aggregation and scale robustness to macro-covariate units—via distributional alignment between data-rich and data-scarce domains and Olympic-cycle indexing. We treat departures from these symmetries (e.g., host advantage or event-program changes) as structured asymmetries and capture them with a rare event module that combines extreme value and survival modeling. Full article

The global transition toward carbon neutrality has accelerated the adoption of electric vehicles (EVs), prompting the need for smarter infrastructure planning in urban environments. This study presents a novel framework that integrates machine learning–based EV adoption forecasting with multi-objective optimization (MOO) using the NSGA-II algorithm. The forecasting component leverages neural networks to predict the percentage of EV sales relative to total vehicle sales, which is then used to derive infrastructure demand, energy consumption, and traffic congestion. These derived forecasts inform the optimization model, which balances conflicting objectives—namely infrastructure costs, energy usage, and traffic congestion—to support data-driven decision-making for smart city planners. A comprehensive dataset covering EV metrics from 2011 to 2024 is used to validate the framework. Experimental results demonstrate strong predictive performance for EV adoption, while downstream derivations highlight expected patterns in infrastructure cost and energy usage, and greater variability in traffic congestion. The NSGA-II algorithm successfully identifies Pareto-optimal trade-offs, offering urban planners flexible strategies to align infrastructure development with sustainability goals. This research underscores the benefits of integrating adoption forecasting with optimization in dynamic, real-world planning contexts. These results can significantly inform future smart city planning and optimization of EV infrastructure deployment in rapidly urbanizing regions. Full article

This paper proposes a Light Gradient Boosting Machine (LightGBM) model for medium-term photovoltaic (PV) power forecasting by integrating meteorological features with historical generation data. This approach addresses prediction biases that often arise when relying solely on a single meteorological data source. Historical power output and meteorological variables (irradiance, temperature, humidity, etc.) were collected from a PV station and preprocessed through data cleaning, standardization, and temporal alignment to construct a multivariate prediction framework. A comprehensive feature set was then built, including meteorological, temporal, interaction, and lag features. Feature importance analysis and Recursive Feature Elimination (RFE) were employed for input optimization, while feature-layer concatenation was applied for data fusion. Finally, the LightGBM (Version 2.3.1) framework, combined with Bayesian optimization and time-series cross-validation, was used to enhance generalization and predictive robustness. Experimental results confirm that the model achieved an MAE of 37.49, RMSE of 64.67, and R² of 0.89. The model effectively captured high-dimensional nonlinear relationships, thereby improving the accuracy of medium-term photovoltaic forecasts and providing reliable decision support for power system scheduling and renewable energy integration. Full article