Search Results (660)

Efficient management of soil–water resources is critical for global food security under intensifying climatic and demographic pressures. This review provides a comprehensive synthesis of artificial intelligence (AI) and distributed deep learning methodologies applied to soil–water interactions in precision agriculture. The physical and hydraulic foundations of soil–water systems—including water retention, unsaturated flow governed by the Richards equation, and soil degradation processes—are examined and situated within a unified framework of AI-based modeling and decision support. Classical machine learning (ML) algorithms (Random Forests, Support Vector Machines, gradient boosting) and deep learning architectures (convolutional neural networks, long short-term memory networks, transformers) are evaluated with respect to their capacity to predict soil moisture dynamics, estimate hydraulic properties, support smart irrigation scheduling, and generate digital soil maps at field-to-regional scales. Distributed training paradigms, federated learning for privacy-preserving multi-farm analytics, and edge AI deployment on low-power IoT hardware are assessed as enabling infrastructures for scalable agricultural intelligence. This review further addresses explainability, uncertainty quantification, and ethical dimensions inherent to AI-driven agricultural systems. Key challenges—including training data scarcity in data-poor regions, model interpretability, integration with physics-based hydrological models, and real-time deployment constraints—are critically discussed. Prospective research directions encompass physics-informed neural networks, foundation models for earth observation, autonomous digital twins of soil–water systems, and federated learning architectures aligned with data sovereignty frameworks. The synthesis underscores AI’s transformative potential for sustainable agricultural water management while delineating the technical and sociotechnical barriers that must be resolved to realize this potential at a global scale. Full article

The efficiency of airport resource allocation is improved through the establishment of a scientific multi-granularity configuration scheme for flight slot coordination parameters. In this study, a collaborative configuration method for hourly and 15 min coordination parameters is proposed, with Beijing Capital International Airport serving as a case study. Short-term traffic clusters are frequently omitted by traditional hourly parameters, thereby leading to sudden delay surges. First, local delays were extracted from March 2024 Automatic Dependent Surveillance-Broadcast (ADS-B) trajectory data. Subsequently, a delay prediction model was constructed through the integration of a non-stationary queuing model and a gradient boosting regression tree. Second, simulated timetables were generated via a Monte Carlo method under various parameter combinations. With a constant daily flight volume utilized as the experimental baseline, a mapping relationship was established between parameter combinations and expected local delays. Finally, feasible delay regions were delineated and interpretable configuration rules were extracted via a decision tree to maximize schedule flexibility. It was indicated by the results that at an hourly parameter of 70 flights, the target delay is maintained below 8 min by tightening the 15 min parameter to 19 flights. The findings suggest that average load is controlled by hourly parameters, while traffic clustering in high-load scenarios is effectively suppressed by 15 min parameters. A quantitative reference is provided by this method for the configuration of multi-granularity time parameters at hub airports. Full article

Experiencing the forest landscape in its natural state is one of the factors that positively affect people, especially younger generations exposed to stress. The study assessed the impact of listening to nature sounds and observing forest landscapes on the mood and well-being of young adults exposed to a real forest environment. The experiment consisted of two sessions, allowing us to compare the regenerative effects of observing the forest with full engagement of the senses of sight and hearing, and by listening exclusively to the sounds of nature (birdsong, rustling leaves). The relaxation benefits were compared using psychological tests, including the Positive and Negative Affect Schedule (PANAS), Profile of Mood States (POMS), Restorative Outcome Scale (ROS), and Subjective Vitality Scale (SVS), administered before and after each exposure. The study involved 31 volunteers from Warsaw, the Polish capital (17 women and 14 men, with an average age of 25). A significant improvement in mood (as measured by the POMS) was observed, particularly through a reduction in Anger and Confusion. Both sessions (with and without a blindfold) significantly reduced negative affect (PANAS Negative) and increased restorative outcomes (ROS). However, no significant differences were found between full immersion (sight and hearing) and auditory-only exposure, suggesting that the acoustic layer of the forest environment plays a dominant role in the short-term psychological regeneration of young adults. In summary, these results suggest that both forms of exposure to nature have a relaxing effect on humans. However, full immersion, which involves being in the forest and viewing it, combined with listening to the sounds of nature, provides by far the most benefits for improving the well-being and mood of forest visitors. Full article

Massive Internet of Things (IoT) and sensor-network services in 5G/6G systems increasingly rely on network slicing to support large-scale sensing, monitoring, and mission-critical applications. In such sliced infrastructures, Proportional Fair (PF) allocation assigns resources according to slice-reported demands. This reliance on trusted demand reporting makes coexisting slices, including mMTC-based IoT sensor slices, vulnerable to resource exhaustion attacks, where a malicious slice inflates its demand to monopolize shared resources and induce Service Level Agreement (SLA) violations. Existing unsupervised defenses mainly focus on anomaly detection, while the translation of detection results into resource-level mitigation remains insufficiently addressed. To bridge this gap, this paper proposes AutoGuard-Hybrid, an unsupervised detection-to-mitigation framework that combines complementary anomaly detectors with allocation-aware mitigation policies to preserve slice-level service availability. Unlike prior detection-only approaches, AutoGuard-Hybrid converts unsupervised anomaly evidence into allocation-aware demand purification before PF scheduling. Its key design is a closed-loop integration of Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) as spatial and temporal front-end detectors with Adaptive Clipping and a Safety Cap, which translate anomaly scores into demand purification actions. Experiments show that AutoGuard-Hybrid remains comparable to Isolation Forest under Continuous attacks and improves the mean system-wide SLA violation rate by 27.6% under Adaptive Probing attacks. Stage activation analysis further shows that LSTM-AE activations increase from 9.3 under Continuous attacks to 29.4 under Adaptive Probing attacks. Ablation results show that Adaptive Clipping alone reduces the system-wide SLA violation rate by 75.0%, while the full mitigation pipeline achieves an 84.6% total reduction. AutoGuard-Hybrid operates within the 1 ms Transmission Time Interval (TTI) constraint and provides a practical defense framework for next-generation network slicing-enabled IoT and sensor-network services. Full article

Accurate multi-horizon Significant Wave Height (SWH) forecasting is vital for offshore safety and efficiency. Beyond scheduling maintenance windows, reliable lead-time predictions provide critical early warnings to protect personnel and high-value assets from hazardous high-wave conditions. However, the non-stationary and multi-scale nature of sea states poses challenges for consistent long-term accuracy. To address this challenge, we propose a robust three-stage framework for decomposition, feature selection, and multi-horizon forecasting. Specifically, Optimal Variational Mode Decomposition (OVMD) is adopted to construct multiscale and multi-view representations of nonlinear SWH sequences, while a Triangulated Maximally Filtered Graph (TMFG) constructs a sparse dependency network to select informative and non-redundant predictors from decomposed components and environmental variables. A hybrid prediction model then combines a Temporal Convolutional Network (TCN) for local multi-scale patterns with a Bidirectional Gated Recurrent Unit (BiGRU) for long-range dependencies. Experiments on real-world buoy observations show that the proposed approach improves accuracy and robustness over commonly used statistical and deep-learning baselines across short-, medium-, and long-term horizons. Ablation studies confirm that integrating modal decomposition with sparse feature selection enhances model robustness, offering reliable decision support for offshore window planning and high-wave condition monitoring. Full article

Medium- and long-term dispatching of renewable energy bases is an important method for ensuring large-scale transmission and consumption. However, most existing medium- and long-term dispatching methods ignore the uncertainties of wind and photovoltaic power output, resulting in excessive maintenance-window margins and insufficient regulation reserves. However, relevant studies that consider such uncertainties are mostly limited to short-term scheduling and are therefore inadequate for medium- and long-term dispatching needs. To this end, a two-stage robust optimal dispatch method for renewable energy bases that considers the impacts of wind and photovoltaic output uncertainties and unit maintenance is proposed. Firstly, the first stage decision variables consist of the on/off and maintenance statuses of thermal power units. Next, the output of each power source is taken as the conventional decision variables in the second stage, while the curtailed wind/photovoltaic power and load shedding are taken as the unconventional decision variables when the balance cannot be achieved by adjusting the power source output under the given wind and solar power output scenarios. In the end, a polyhedron set based on an uncertainty budget was adopted to describe the fluctuations in wind and photovoltaic output, and the minimum scheduling cost in the worst scenarios was solved using the column and constraint algorithm. A renewable energy base in Northwest China was selected as a case to validate the proposed model’s effectiveness. The results show that the proposed model significantly reduces the operating cost in actual operation compared to deterministic optimization and pre-maintenance robust optimization. Full article

Emotion recognition plays a crucial role in human–computer interaction, health monitoring, and affective computing by analysing physiological signals. Despite recent advancements, current research still faces challenges, including the lack of effective fusion strategies for diverse physiological modalities, difficulties in handling high-dimensional feature representations, and limited use of efficient temporal modelling techniques to capture complex emotional patterns. This study proposes a deep learning-based approach that fuses multiple physiological modalities, including Electroencephalography (EEG), Electrooculography (EOG), Electromyography (EMG), Galvanic Skin Response (GSR), Respiratory Rate (RR), Skin Temperature (SKT), and Photoplethysmography (PPG), to improve emotion recognition. Arousal and valence ratings were binarized into two classes (low/high) using a threshold of 4.5, formulating a binary classification problem. In addition to utilising Bidirectional Long Short-Term Memory (Bi-LSTM), the study employs Temporal Convolutional Networks (TCN), a widely used approach for time-series analysis, to efficiently capture temporal dependencies. The proposed model optimises feature selection through channel-wise strategies, incorporates advanced learning rate scheduling, and reduces computational overhead. Furthermore, window-wise, block-wise, and trial-wise evaluation protocols were investigated to assess the impact of temporal information leakage on emotion recognition performance. Using the DEAP dataset for validation, the proposed TCN-based approach achieved classification accuracies of 88.42% for valence and 86.35% for arousal under an overlapping block-wise evaluation protocol, demonstrating improved performance in binary emotion recognition and highlighting the importance of leakage-aware model assessment. Full article

Electric vehicle (EV) charging networks face major operational challenges, including demand uncertainty, peak-load congestion, and anomalous charging behavior, particularly in multi-station environments. This study proposes an anomaly-aware Q-learning framework for real-time scheduling in multi-station EV charging systems by integrating short-term load forecasting, anomaly detection, and intelligent scheduling within a unified operational pipeline. The framework combines Prophet, XGBoost, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models for short-term demand forecasting, while Convolutional Neural Networks (CNN), Autoencoders, and Isolation Forests are employed for anomaly detection. Forecasting and anomaly information are incorporated into a Q-learning scheduler to support adaptive charger allocation and congestion management. Evaluation using a four-year, real-world dataset comprising more than 2000 EV charging sessions demonstrates improved scheduling performance, achieving reductions in peak load and waiting time while improving energy delivery consistency. The framework further demonstrates low scheduling latency, supporting suitability for real-time deployment in OCPP-compliant smart charging infrastructures. Full article

This study presents a forecast-driven Advanced Forecasting Model (AFM) and Virtual Power Plant (VPP) framework for a hybrid renewable energy system comprising utility-scale solar PV, wind generation, and a Battery Energy Storage System. Long Short-Term Memory neural networks provide real-time short-term forecasts to dynamically schedule power flows based on battery state-of-charge, grid import limits, and system constraints. Solar irradiance forecasting achieved MAE = 10.674 W/m², RMSE = 16.348 W/m², and MAPE = 14.18%, while wind speed forecasting achieved MAE = 0.880 m/s, RMSE = 1.115 m/s, and MAPE = 22.01%. Two dispatch scenarios were evaluated over a 72 h window: a reactive baseline and the proposed AFM/VPP strategy. The AFM reduced total grid imports by 57.48% (1466.34 MWh to 623.47 MWh), increased renewable utilization, and minimized curtailment. Financial analysis indicates an accelerated break-even (Year 6 vs. Year 9), a higher net present value, and cumulative 20-year profits exceeding R26.01 billion despite marginally higher capital expenditure. Emissions analysis shows annual CO₂ reductions from 123,680 t to 61,841 t, yielding 1.236 million tons of avoided emissions over 20 years. These results confirm that forecast-driven dispatch enhances operational efficiency, economic performance, and environmental sustainability, establishing a scalable approach for VPP operation in renewable-rich energy systems. Full article

Background: Thanks to its capacity to increase wound healing, NPWD (Negative-Pressure Wound Dressing) showed promising results in breast surgery. The authors developed the NDoCaSco system for select patients that may benefit the most from NPWD after breast oncologic surgery, aiming to improve outcomes in patients at risk for wound dehiscence and breast reconstruction failure. Methods: Patients scheduled for breast oncologic surgery were enrolled between 2022 and 2023. Surgical wound dressing was selected prior to assessing the risk for post-operative complications with the NDoCaSco. Low-risk patients (NDoCaSco score: 15–21) received traditional compressive dressing, while moderate- (NDoCaSco: 8–14) and high-risk (NDoCaSco: 0–7) patients received short-term or long-term NPWD, respectively. Results: Healing time and outcomes were compared to a retrospective control group that underwent the same surgeries between 2019 and 2021 and received traditional compressive wound dressing in all cases. The study population included 739 patients with an average age of 62.3 years (range, 29–95) and a mean BMI of 25.2 kg/m² (range, 16–46). Breast-conserving surgery was performed in 437 cases, and 302 received mastectomy with implant-based reconstruction. A total of 152 patients scored medium (140 cases) or low (12 cases) NDoCaSco and received NPWD. Post-operative complications’ incidence, healing time, and drain removal time were lower in the study group, while scar quality was consistently improved with NPWD when comparing the two middle-risk groups. Conclusions: NDoCaSco helped in identifying patients who benefit the most from NPWD, achieving faster healing and reduction in outpatient visits and hospital admissions, leading to a lower expenditure of resources. Full article

Aluminum alloy A356 (Al-7Si-0.3Mg) is widely employed in automotive structural components due to its favorable strength-to-weight ratio, yet its mechanical performance is highly sensitive to T6 heat-treatment processes. Conventional heat-treatment schedules are typically based on uniform, empirically derived parameters and fail to consider variations in component size, geometry, or thermal mass. Consequently, applying a single schedule across all component sizes often leads to inconsistent microstructural development, energy inefficiency, and elevated scrap rates. Smaller components tend to be over-processed, while larger components may be under-processed, both resulting in suboptimal mechanical properties and increased production costs. To overcome these limitations, this study presents a scalable heat-treatment optimization framework that integrates physics-based thermal simulations with machine learning techniques. The framework combines a transient thermal simulator with Long Short-Term Memory (LSTM) networks to predict sample temperature evolution, Random Forest regressors to estimate mechanical properties such as yield strength, hardness, and modulus of toughness, and Bayesian optimization to generate size-dependent, property-compliant heat-treatment schedules. Unlike traditional methods, this approach dynamically adjusts furnace parameters to individual component characteristics, optimizing both processing time and energy consumption while minimizing scrap. Application of the framework to components ranging from 0.5 to 10 kg demonstrates internally consistent simulation-based predictions of temperature profiles, phase-fraction evolution, and mechanical-property trends within the assumed modelling framework. Optimized schedules achieved 15–25% reductions in cycle time while maintaining properties within T6 specifications. These findings underscore the potential of AI-assisted heat-treatment optimization to enhance energy efficiency, reduce material waste, and improve the consistency of mechanical performance in automotive casting operations. Full article

Accurate short-term electricity load forecasting is crucial for day-ahead scheduling and secure operation of power systems. However, electricity load series exhibit significant non-stationarity, with complex coupling between low-frequency trends and high-frequency fluctuations, making it difficult for conventional forecasting models to simultaneously characterize the overall trend and stochastic disturbances. To address this issue, this paper proposes a short-term load forecasting model based on STL decomposition and CNN-BiLSTM optimized by deep reinforcement learning. First, the original load series is decomposed into trend, seasonal, and residual components using the STL algorithm. Second, a dual-channel parallel forecasting architecture is constructed: the linear channel uses a linear regression model to predict the trend and seasonal components, thereby characterizing the low-frequency variations in the load; the nonlinear channel uses a CNN-BiLSTM framework optimized by deep reinforcement learning to predict the high-frequency residual component, and this process is formulated as a Markov decision process. Specifically, the attention-based CNN-BiLSTM serves as the policy network, and its forecasting strategy is dynamically optimized under the guidance of a reward function to enhance the modeling capability for high-frequency stochastic fluctuations. Finally, the load forecasting results for the next 24 h are obtained through dual-channel result reconstruction. Experimental results based on the ERCOT system-level load data show that the proposed model achieves superior forecasting performance, with a root mean square error of 976.4 MW and a mean absolute percentage error of 1.81%. Further multi-season testing, meteorological perturbation analysis, fair comparison under the same STL preprocessing, and ablation experiments demonstrate that the proposed model maintains good forecasting performance under different seasonal scenarios, meteorological input errors, and fair experimental settings, thereby validating its effectiveness for short-term load forecasting. Full article

Accurate short-term forecasting of Limnospira platensis biomass is essential for optimizing experimental scheduling and cultivation strategies, yet small datasets and strong temporal autocorrelation pose significant challenges for model reliability. In this study, we developed a leakage-safe, data-driven framework for direct multi-step forecasting of biomass concentration based on experimental time-series data from nine independent cultivation trials conducted under heterogeneous nutritional and environmental conditions. Gradient Boosting consistently outperformed a persistence baseline across all forecasting horizons (R² ≈ 0.915 at h = 1, 0.935 at h = 2, 0.814 at h = 3), demonstrating strong predictive capability under Leave-One-Experiment-Out cross-validation, which ensures generalization to unseen experiments. Residual analysis and prediction intervals confirmed robust uncertainty quantification and revealed condition-dependent variability in predictive performance. Overall, the results show that rigorously validated machine learning models can reliably forecast biomass trajectories beyond naïve baselines, even under limited and heterogeneous datasets. This approach provides a scalable and reproducible methodological framework for predictive modeling in algal biotechnology; however, because the training data were collected at flask scale, direct transfer to larger photobioreactor or outdoor systems should be considered a future validation step rather than an immediate deployment outcome. Full article

This paper addresses the operational challenges introduced by the growing share of intermittent renewable energy sources in islanded microgrids. Traditional unit commitment (UC) methods struggle to manage the continuous variations in demand and renewable generation effectively because dispatch setpoints remain fixed between scheduling intervals. To overcome these limitations, a dynamic voltage and frequency controller (DVFC) is proposed. The DVFC uses adaptive critic control and approximate dynamic programming to update mid-level control actions based on measured microgrid states, technical constraints, and look-ahead utility functions. The proposed method is applied to short-term UC, ensuring frequency and voltage regulation while maintaining microgrid stability. Simulation results on the modified CIGRE test system demonstrate that the DVFC reduces frequency deviations by up to 40–50% and voltage deviations by 60–65% compared to conventional UC. In addition, the method lowers operating costs by up to 6% and extends the effective battery lifecycle by nearly twofold by reducing stress and cycling. These results confirm that the DVFC significantly outperforms conventional UC algorithms in both technical performance and economic efficiency. Full article

Precision battery-health monitoring and rapid degradation detection are essential for improving the security, durability, and efficacy of electric vehicles (EVs). By incorporating short-term State-of-Health (SoH) forecasting, mid-term deterioration alarms, and degradation acceleration risk modeling into a temporally consistent machine learning architecture, this research suggests a hierarchical predictive-maintenance framework. The rolling-origin cross-validation approach is implemented to maintain the chronological order of the data and prevent any potential information leaks. The predictive core employs an ensemble learning approach that integrates Random Forest, Extremely Randomized Trees, and Histogram-Based Gradient Boosting. Validation-driven model blending and training only feature selection are implemented to improve generalizability. The one-hour SoH forecasting model for short-horizon monitoring exhibits exceptional accuracy in an assessment of health prediction, with an R² of 0.9254, an RMSE of 0.0033, and a MAPE of 0.32%. Early detection of anomalies and the provision of a seven-day degradation warning may be achieved by a proactive maintenance scheduling model with an area under the curve (AUC) of 0.7838 and a recall of 0.8205. In addition, the degradation acceleration risk module could identify rapid health decline with a robustness of 0.8796 and a precision–recall AUC of 0.7101 when operating under significant stress. Reliability in critical domains is demonstrated through validation using scenarios that simulate severe temperature and stress conditions. Achieving intelligent predictive maintenance of electric vehicle battery packs is now feasible due to the proposed multi-layer ensemble structure. Full article