Search Results (8,178)

Smart microgrid is promising in providing a more affordable, efficient, and sustainable energy solution with increasing energy production from distributed renewable sources and diverse household electricity usage with large amounts of connected smart devices. Accurate prediction of the household electricity load and renewable energy production plays a significant role in achieving optimized efficiency of the microgrid. Meanwhile, the privacy and security of data sharing over the smart grid are crucial. This paper proposes a blockchain-enabled microgrid Internet of Things (MIoT) with accurate predictions of renewable energy production and household electricity load. The blockchain framework can guarantee the privacy and security of data sharing over the microgrid. An improved model by stacking long short-term memory (LSTM) and gated recurrent units (GRUs) is proposed for energy generation and electricity load predictions using historical data in the microgrid and the weather forecasting data. The sparrow search algorithm optimized by Levy flights (LevySSA) is used to optimize the hyperparameters of the stacked LSTM-GRU method. The experimental results verify the accuracy and robustness of the proposed method in the prediction of electricity load and renewable energy production for effective smart microgrid operation. For PV forecasting, the proposed LevySSA-LSTM-GRU achieves nRMSE = 0.0535, nMAE = 0.0455, and R² = 0.9898, outperforming the strongest baseline. For load forecasting, averaged over four test intervals, it yields nRMSE = 0.1034, nMAE = 0.0836, with R² = 0.9340, demonstrating consistent superiority compared with conventional baseline models. Overall, the proposed framework enables secure data sharing and high-accuracy forecasting, offering strong potential to support real-time energy management and operational optimization in smart microgrids. Full article

Lost Circulation (LC) is one of the most common and high-risk complex situations encountered during drilling operations, posing a serious threat to the safe extraction and economic viability of oil and gas resources. Traditional wellbore leakage detection methods based on human experience often suffer from delays and uncertainties, making it difficult to meet real-time warning requirements under complex geological conditions. This paper proposes an LC warning method that combines a physical model with a combination of neural networks (Crested Porcupine Optimizer (CPO)–Long Short-Term Memory (LSTM)–Random Forest (RF)). The physical model utilises changes in mud pit volume, inlet–outlet flow rate differences, and riser pressure to construct interpretable event labels, thereby enhancing the physical plausibility of prediction results. The deep learning component employs LSTM networks to extract temporal features and RF for non-linear discrimination and introduces the CPO algorithm for feature selection and hyperparameter optimisation, thereby enhancing the model’s stability and generalisation capability. Validation using actual field data from the western Bohai Bay oilfield demonstrates that the proposed method outperforms traditional models in accuracy, precision, recall, and F1-score. It also offers a significant improvement in early warning time, detecting potential leakage about 17 min before traditional methods. These results highlight the effectiveness of the approach in managing risks during drilling operations. Full article

Wind energy is a core pillar of global green and sustainable energy transition. However, existing wind power prediction models face three key challenges: traditional long short-term memory (LSTM) models struggle to capture long-term temporal dependencies efficiently and have high training latency, while Transformer-based models exhibit excessive computational complexity and are prone to overfitting for short-term fluctuating data; meanwhile, few models integrate seasonal trend modeling with multi-scale temporal feature extraction, leading to large prediction errors in seasonal transitions. To address these issues, this paper proposes a hybrid prediction framework combining a novel T-LSTM recurrent unit with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The T-LSTM unit fuses a simplified Transformer module and an improved LSTM structure. Thus, the design can synergistically capture both short-term fluctuations and long-term dependencies in wind power data. Complementarily, SARIMA is integrated via weighted fusion to model seasonal trends, addressing the neglect of seasonal characteristics in existing deep learning models. A diverse set of benchmark methods for wind power prediction are selected for comparison, including LSTM, convolutional neural network-gated recurrent unit (CNN-GRU), ns_Transformer, Autoformer, Reformer and least squares support vector machine (LSSVM), with experiments conducted across various prediction horizons. The results show that the proposed T-LSTM model outperformed most benchmark methods in key evaluation metrics across multiple prediction horizons and exhibited no statistically significant difference from Autoformer only in the 90 min horizon, validating its superiority in handling complex wind power time series. Full article

This paper presents a hybrid deep learning framework for real-time sign language recognition (SLR) tailored to Internet of Things (IoT)-enabled environments, enhancing accessibility for Deaf communities. The proposed system integrates a Long Short-Term Memory (LSTM) network for static gesture recognition and a 3D Convolutional Neural Network (3D CNN) for dynamic gesture recognition. Implemented on a Raspberry Pi device using MediaPipe for landmark extraction, the system supports low-latency, on-device inference suitable for resource-constrained edge computing. Experimental results demonstrate that the LSTM model achieves its highest stability and performance for static signs at 1000 training epochs, yielding an average F1-score of 0.938 and an accuracy of 86.67%. In contrast, at 2000 epochs, the model exhibits a catastrophic performance collapse (F1-score of 0.088) due to overfitting and weight instability, highlighting the necessity of careful training regulation. Despite this, the overall system achieves consistently high classification performance under controlled conditions. In contrast, the 3D CNN component maintains robust and consistent performance across all evaluated training phases (500–2000 epochs), achieving up to 99.6% accuracy on dynamic signs. When deployed on a Raspberry Pi platform, the system achieves real-time performance with a frame rate of 12–15 FPS and an average inference latency of approximately 65 ms per frame. The hybrid architecture effectively balances recognition accuracy with computational efficiency by routing static gestures to the LSTM and dynamic gestures to the 3D CNN. This work presents a detailed epoch-wise comparative analysis of model stability and computational feasibility, contributing a practical and scalable IoT-enabled solution for inclusive, real-time sign-to-text communication in intelligent environments. Full article

The performance degradation of electronic power components during long-term operation can compromise system reliability and safety. Therefore, accurately predicting their remaining useful life (RUL) is critical for the reliability of safety-critical systems that utilize these components. This paper proposes a hybrid model integrating bidirectional long short-term memory networks (BiLSTM) and Gaussian process regression (GPR) for RUL prediction of electronic power components. The BiLSTM module provides high-precision point predictions, while the GPR module leverages the sequence features and trend information extracted by BiLSTM to deliver reliable interval predictions and high-confidence probabilistic outputs. The model’s predictive accuracy was validated using NASA’s publicly available lithium-ion battery dataset. Experimental results demonstrate that, compared to existing models, the proposed model achieves at least a 9.6% improvement in point prediction performance and a 63% improvement in interval prediction performance, fully validating the reliability and accuracy of the BiLSTM-GPR approach. The model was further applied to predict the RUL of DC-DC power modules. The predicted Continuous Ranked Probability Score (CRPS) reached a maximum of 0.050405, while the Probability Integral Transform (PIT) results exhibited a uniform distribution within the (0,1) range, further demonstrating the model’s high reliability and predictive confidence. Full article

Non-line-of-sight (NLOS) propagation remains a major obstacle to high-accuracy ultra-wideband (UWB) indoor positioning. To address this issue, this study investigates solutions from two complementary perspectives: NLOS identification and error mitigation. First, an NLOS signal classification model is proposed based on multidimensional statistics of the channel impulse response (CIR). The model incorporates an attention mechanism and an improved snake optimization (ISO) algorithm, achieving significantly enhanced classification accuracy and robustness. For error mitigation, a UKF–BiLSTM dual-directional mutual calibration framework is proposed to dynamically compensate for NLOS errors. The framework embeds the constant turn rate and velocity (CTRV) motion model within an unscented Kalman filter (UKF) to enhance trajectory modeling. It establishes a bidirectional correction loop with a bidirectional long short-term memory (BiLSTM) network. Through the synergy of physical constraints and data-driven learning, the framework adaptively suppresses NLOS errors. Experimental results show that the proposed framework achieves state-of-the-art–comparable performance with improved model efficiency in complex indoor UWB positioning scenarios. Full article

Accurate forecasting of agricultural commodity prices is essential for managing market volatility, improving supply chain coordination, and supporting food security-related decision-making. Recent advances in deep learning have demonstrated strong potential for capturing nonlinear and temporal dependencies in commodity price dynamics. In this study, we propose a hybrid long short-term memory–multi-head attention (LSTM–MHA) framework for agricultural commodity price forecasting using global futures market data. The model is trained and evaluated on multivariate global commodity futures prices, reflecting internationally traded benchmark markets rather than region-specific domestic prices. While the empirical analysis is based on global data, the study is motivated by the relevance of international price movements for import-dependent regions, particularly West Africa, where global price transmission plays a critical role in domestic market dynamics. The experimental results demonstrate that the proposed model effectively captures short-term temporal dependencies and provides interpretable attention-based insights into lag relevance. An ablation study further highlights the trade-offs between forecasting accuracy and interpretability across different model configurations. The hybrid architecture combines the time-based pattern identification and weighting capabilities of multi-head attention with the sequential learning capabilities of LSTM. Mean absolute error (MAE), root mean squared error (RMSE), and mean squared error (MSE) were used to evaluate the model’s performance. With an MSE of 0.0124, an RMSE of 0.1114, and an MAE of 0.1097, the model outperformed conventional models like ARIMA and standalone LSTM by three to four times in error reduction. The findings suggest that attention-enhanced deep learning models can serve as valuable analytical tools for understanding global price dynamics and informing policy analysis and risk management in West African agricultural markets. Full article

Vehicle-to-grid (V2G) services are gaining attention as a strategy to integrate electric vehicles (EVs) into sustainable energy systems. Although technological aspects have been widely studied, methodologies for identifying optimal V2G hubs and forecasting the energy available for grid transfer remain limited. This study introduces a data-driven approach to (i) identify the optimal V2G region based on the aggregated parking duration using floating car data (FCD; collected from GPS-enabled vehicles); (ii) estimate the surplus battery capacity of electric vehicles in that region; and (iii) forecast the energy transferable to the grid. The methodology applies spatial k-means clustering to define candidate zones, computes aggregated parking durations, and selects the optimal hub. The surplus energy is estimated considering the daily mobility needs of users, 20% reserve, and transfer rates. For forecasting, autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are implemented and compared. The proposed methodology has been applied to a real case study, using 58 days of FCD observations. The empirical findings of this study show the goodness of the proposed methodology, and the opportunity offered V2G technology to support the sustainable use of energy. The ARIMA model demonstrated a superior forecasting performance with an RMSE of 52.424, MAE of 36.05, and MAPE of 12.98%, outperforming LSTM (RMSE of 99.09, MAE of 80.351, and MAPE of 53.20%) under the current data conditions. The results of this study suggest that for supporting grid needs of a medium-sized city, V2G plays a key role, and at the current status of the EV penetration, the use of FCD and predictive approaches is paramount for making an informed decision. Full article

This study proposes a video-based approach for quantitatively evaluating upper-limb joint abnormalities in individuals with brain lesions during resistance exercises. While the Fugl–Meyer Assessment (FMA) is a reliable clinical tool, its use is limited by the need for expert involvement and repeated assessments. To address this issue, skeletal joint data were extracted from RGB exercise videos using OpenPose, and joint abnormalities were identified by learning normal movement patterns from non-disabled participants. A total of 26 non-disabled individuals and 12 individuals with brain lesions performed chest press, shoulder press, and arm curl exercises. Joint movement patterns were analyzed using correlation analysis and a long short-term memory (LSTM) autoencoder. Only joints relevant to each exercise were evaluated, and joint-level results were integrated to compute arm-level abnormality rates. The correlation-based abnormality rate showed a significant negative correlation with FMA scores (r = −0.7789, p = 2.83 × 10⁻³), while the LSTM autoencoder-based abnormality rate exhibited a stronger correlation(r = −0.8454, p = 5.33 × 10⁻⁴). In addition, affected-side classification accuracy reached 78.0% and 83.3% for correlation analysis and the LSTM autoencoder, respectively. These results indicate that the proposed method is consistent with clinical assessments and can serve as a non-invasive, cost-effective tool for video-based rehabilitation evaluation. Full article

Infrasound, physically defined as sound at frequencies below 20 Hertz, can travel long distances with minimal attenuation and permeate biological tissues due to its marked particle displacement and deep penetration. Generated by both natural phenomena and human-made systems, infrasound has drawn increasing scientific and public attention regarding its potential physiological and psychological effects. Experimental studies demonstrate that infrasound can modulate mechanosensitive structures at the cellular level, particularly pressure-sensitive ion channels such as PIEZO1 and TRPV4, leading to intracellular calcium influx, oxidative stress, altered intercellular communication, and in some settings, apoptosis. These responses vary according to sound pressure levels, frequencies, exposure duration, and tissue type. In the cardiovascular system, higher sound pressures have been associated with mitochondrial injury and fibrosis, whereas low sound pressures may exert context-dependent protective effects. In animal models, prolonged or intense exposure to infrasound has been shown to induce neuroinflammatory responses and memory impairment. Short-term studies in humans at moderate intensities have reported minimal physiological changes, with psychological and contextual factors influencing symptom perception. Occupational environments such as factories and agricultural settings may contain elevated levels of infrasound, underscoring the importance of systematic measurements and exposure assessments. At the same time, controlled infrasound stimulation has shown potential as an adjunct modality in bone repair and tissue regeneration, highlighting its dual capacity as both a biological stressor and a possible therapeutic tool. Overall, existing data indicate that infrasound may be harmful at chronic exposure depending on intensity and frequency, yet beneficial when precisely regulated. Future research should standardize exposure metrics, refine measurement technologies, and clarify dose–response relationships to better define the health risks and therapeutic applications of infrasound. Full article

Flood events driven by climate variability and change pose significant risks for socio-economic activities in the Awash Basin, necessitating advanced forecasting tools. This study benchmarks five deep learning (DL) architectures, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), and a Hybrid CNN–LSTM, for daily discharge forecasting for the Hombole catchment in the Upper Awash Basin (UAB) using 40 years of hydrometeorological observations (1981–2020). Rainfall, lagged discharge, and seasonal indicators were used as predictors. Model performance was evaluated against two baseline approaches, a conceptual HBV rainfall–runoff model as well as a climatology, using standard and hydrological metrics. Of the two baselines (climatology and HBV), the climatology showed limited skill with large bias and negative NSE, whereas the HBV model achieved moderate skill (NSE = 0.64 and KGE = 0.82). In contrast, all DL models substantially improved predictive performance, achieving test NSE values above 0.83 and low overall bias. Among them, the Hybrid CNN–LSTM provided the most balanced performance, combining local temporal feature extraction with long-term memory and yielding stable efficiency (NSE ≈ 0.84, KGE ≈ 0.90, and PBIAS ≈ −2%) across flow regimes. The LSTM and GRU models performed comparably, offering strong temporal learning and robust daily predictions, while BiLSTM improved flood timing through bidirectional sequence modeling. The CNN captured short-term variability effectively but showed weaker representation of extreme peaks. Analysis of peak-flow metrics revealed systematic underestimation of extreme discharge magnitudes across all models. However, a post-processing flow-regime classification based on discharge quantiles demonstrated high extreme-event detection skill, with deep learning models exceeding 89% accuracy in identifying extreme-flow occurrences on the test set. These findings indicate that, while magnitude errors remain for rare floods, DL models reliably discriminate flood regimes relevant for early warning. Overall, the results show that deep learning models provide clear improvements over climatology and conceptual baselines for daily streamflow forecasting in the UAB, while highlighting remaining challenges in peak-flow magnitude prediction. The study indicates promising results for the integration of deep learning methods into flood early-warning workflows; however, these results could be further improved by adopting a probabilistic forecasting framework that accounts for model uncertainty. Full article

The Damietta–Port Said coast, Nile Delta, has experienced extreme morphological change over the past four decades due to sediment reduction due to Aswan High Dam and continued anthropogenic pressures. Using multi-temporal Landsat (1985–2025) and high-resolution RapidEye and PlanetScope imagery with 50 m-spaced transects, the study documents major shoreline shifts: the Damietta sand spit retreated by >1 km at its proximal apex while its distal tip advanced by ≈3.1 km southeastward under persistent longshore drift. Sectoral analyses reveal typical structure-induced patterns of updrift accretion (+180 to +210 m) and downdrift erosion (−50 to −330 m). To improve predictive capability beyond linear DSAS extrapolation, Nonlinear Autoregressive Exogenous (NARX) and Bidirectional Long Short-Term Memory (BiLSTM) neural networks were applied to forecast the 2050 shoreline. BiLSTM demonstrated superior stability, capturing nonlinear sediment transport patterns where NARX produced unstable over-predictions. Furthermore, coupled wave–flow modeling validates a sustainable management strategy employing successive short groins (45–50 m length, 150 m spacing). Simulations indicate that this configuration reduces longshore current velocities by 40–60% and suppresses rip-current eddies, offering a sediment-compatible alternative to conventional breakwaters and seawalls. This integrated remote sensing, hydrodynamic, and AI-based framework provides a robust scientific basis for adaptive, sediment-compatible shoreline management, supporting the long-term resilience of one of Egypt’s most vulnerable deltaic coasts under accelerating climatic and anthropogenic pressures. Full article

The immune status of dogs is shaped by continuous exposure to antigenic and various environmental stimuli, which together influence the development, regulation, and effectiveness of immune responses. Stress-related immune alterations may not be evident at the systemic level but can emerge at cellular and molecular scales. Therefore, this study aimed to comprehensively characterize the hematological and immunological profiles of dogs in different environments. We evaluated lymphocyte responses under basal conditions and following CD3/CD28-mediated in vitro activation, with subsequent long-term culture. Gene expression analyses targeted markers of early T cell activation, cytotoxic effector function, cytokine signaling, and inhibitory immune regulation. The memory phenotype of T lymphocytes was evaluated after blood collection and prolonged in vitro culture. In addition, hematological and biochemical profiles were assessed, including basic parameters, cortisol, and C-reactive protein. Our results revealed that client-owned dogs exhibited lower baseline expression of activation markers, especially in comparison with the short-term stay group, indicating an early immune activation state upon entry to the shelter environment. Furthermore, T lymphocytes from short- and long-term shelter dogs exhibited marked differences in the distribution of naïve and effector-memory subsets as well as different expansion capacity. These alterations persisted during prolonged in vitro culture, indicating that stress duration and environmental antigen exposure differentially shape immune responsiveness. In summary, chronic stress modulates canine immune status in a time-dependent manner, highlighting the importance of integrated cellular and molecular approaches in assessing the impact of environmental stressors on dogs’ health and welfare. Full article

DNA sequence classification is a fundamental problem in bioinformatics, playing an indispensable role in gene annotation and disease prediction. Whereas most deep learning models, such as CNNs, BiLSTM networks, and GRUs, have been found individually optimal, each of these methods excels in modeling a specific aspect of sequence data: local motifs, long-range dependencies, and efficient temporal modeling of the sequences. Here, we present and evaluate an ensemble model that integrates CNN, BiLSTM, and GRU architectures via a majority voting combination scheme so that their complementary strengths can be harnessed. We trained and evaluated each standalone and the integrated model on a DNA dataset comprising 4380 sequences falling under five functional categories. The ensemble model achieved a classification accuracy of 90.6% with precision, recall, and F1 score equal to 0.91, thereby outperforming the state-of-the-art techniques by large margins. Although previous studies have tried analyzing each Deep Learning method individually for DNA classification tasks, none have attempted a systematic combination of CNN, BiLSTM, and GRU based on their ability to extract features simultaneously. The current research aims at presenting a novel method that combines these architectures based on a Majority Voting strategy and proves how their combination is better at extracting local patterns and long dependency information when compared individually. In particular, the proposed ensemble model smoothed the high recall of BiLSTM with the high precision of CNN, leading to more robust and reliable classification. The experiments involved a publicly available DNA sequence data set of 4380 sequences distributed over 5 classes. Our results emphasized the prospect of hybrid ensemble deep learning as a strong approach for complex genomic data analysis, opening ways toward more accurate and interpretable bioinformatics research. Full article

High-accuracy short-term electric load forecasting is essential for ensuring the security of power systems and enhancing energy efficiency. Power load sequences are characterized by strong randomness, non-stationarity, and nonlinearity over time. To improve the precision and efficiency of short-term load forecasting in microgrids, a hybrid predictive model combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and a multi-strategy enhanced Whale Optimization Algorithm (WOA) with Long Short-Term Memory (LSTM) neural networks has been proposed. Initially, this study employs CEEMD to decompose the short-term electric load time series. Subsequently, a multi-strategy enhanced WOA with chaotic initialization and reverse learning is introduced to enhance the search capability of model parameters and avoid entrapment in local optima. Finally, considering the distinct characteristics of each component, the multi-strategy improved WOA is utilized to optimize the LSTM model, establishing individual predictive models for each component, and the predictions are then aggregated. The proposed method’s forecasting accuracy has been validated through multiple case studies using the UC San Diego microgrid data, demonstrating its reliability and providing a solid foundation for microgrid system planning and stable operation. Full article