Search Results (1,402)

The global horizontal irradiance (GHI) is the most important metric for evaluating solar resources. The accurate prediction of GHI is of great significance for effectively assessing solar energy resources and selecting photovoltaic power stations. Considering the time series nature of the GHI and monitoring sites dispersed over different latitudes, longitudes, and altitudes, this study proposes a model combining deep neural networks and deep convolutional neural networks for the multi-step prediction of GHI. The model utilizes parallel temporal convolutional networks and gate recurrent unit attention for the prediction, and the final prediction result is obtained by multilayer perceptron. The results show that, compared to the second-ranked algorithm, the proposed model improves the evaluation metrics of mean absolute error, mean absolute percentage error, and root mean square error by 24.4%, 33.33%, and 24.3%, respectively. Full article

Background/Objectives: The recurrence of glioblastoma is an inevitable event in this disease’s course. In this study, we sought to identify the metabolomic signature in patients with recurrent glioblastomas undergoing surgery and radiation therapy. Methods: Blood samples collected prospectively from six patients with recurrent IDH-wildtype glioblastoma who underwent one surgery at diagnosis and a second surgery at relapse were analyzed using untargeted gas chromatography–time-of-flight mass spectrometry to measure metabolite abundance. The data analysis techniques included univariate analysis, correlation analysis, and a sample t-test. For predictive modeling, machine learning (ML) algorithms such as multinomial logistic regression, gradient boosting, and random forest were applied to predict the classification of samples in the correct treatment phase. Results: Comparing samples after the first surgery and after the relapse surgeries to the pre-operative samples showed a significant decrease in sorbitol and mannitol; there was a significant increase in urea, oxoproline, glucose, and alanine. After chemoradiation, two metabolites, erythritol and 6-deoxyglucitol, showed a decrease, with a cut-off of three and a significant reduction for 6-deoxyglucitol, while 2,4-difluorotoluene and 9-myristoleate showed an increase post radiation, with a fold-change cut-off of three. The gradient-boosting ML model achieved a high performance for the prediction of tumor conditions in patients with glioblastoma who had undergone relapse surgery. Conclusions: We developed an ML predictor for tumor phase based on the plasma metabolomic profile. Our study suggests the potential of combining metabolomics with ML as a new tool to stratify the risk of tumor progression in patients with glioblastoma. Full article

Membrane proteins constitute essential biomolecules attached to or integrated into cellular and organelle membranes, playing diverse roles in cellular processes. Their precise localization is crucial for understanding their functions. Existing protein subcellular localization predictors are predominantly trained on globular proteins; their performance diminishes for membrane proteins, explicitly via deep learning models. To address this challenge, the proposed study segregates membrane proteins into three distinct locations, including the plasma membrane, internal membrane, and membrane of the organelle, using deep learning algorithms including recurrent neural networks (RNN) and Long Short-Term Memory (LSTM). A redundancy-curtailed dataset of 3000 proteins from the MemLoci approach is selected for the investigation, along with incorporating pseudo amino acid composition (PseAAC). PseAAC is an exemplary technique for extracting protein information hidden in the amino acid sequences. After extensive testing, the results show that the accuracy for LSTM and RNN is 83.4% and 80.5%, respectively. The results show that the LSTM model outperforms the RNN and is most commonly employed in proteomics. Full article

The complexities inherent in drug development are multi-faceted and often hamper accuracy, speed and efficiency, thereby limiting success. This review explores how recent developments in machine learning (ML) are significantly impacting target-based drug discovery, particularly in small-molecule approaches. The Simplified Molecular Input Line Entry System (SMILES), which translates a chemical compound’s three-dimensional structure into a string of symbols, is now widely used in drug design, mining, and repurposing. Utilizing ML and natural language processing techniques, SMILES has revolutionized lead identification, high-throughput screening and virtual screening. ML models enhance the accuracy of predicting binding affinity and selectivity, reducing the need for extensive experimental screening. Additionally, deep learning, with its strengths in analyzing spatial and sequential data through convolutional neural networks (CNNs) and recurrent neural networks (RNNs), shows promise for virtual screening, target identification, and de novo drug design. Fragment-based approaches also benefit from ML algorithms and techniques like generative adversarial networks (GANs), which predict fragment properties and binding affinities, aiding in hit selection and design optimization. Structure-based drug design, which relies on high-resolution protein structures, leverages ML models for accurate predictions of binding interactions. While challenges such as interpretability and data quality remain, ML’s transformative impact accelerates target-based drug discovery, increasing efficiency and innovation. Its potential to deliver new and improved treatments for various diseases is significant. Full article

With the evolution of cellular networks and wireless-local-area-network-based communication technologies, services for smart device users have appeared. With the popularity of 4G and 5G, smart device users can now consume larger bandwidths than before. Consequently, the demand for various services, such as streaming, online games, and video conferences, has increased. For improved quality of experience (QoE), streaming services utilize adaptive bitrate (ABR) algorithms to handle network bandwidth variations. ABR algorithms use network bandwidth history for future network bandwidth prediction, allowing them to perform efficiently when network bandwidth fluctuations are minor. However, in environments with frequent network bandwidth changes, such as wireless networks, the QoE of video streaming often degrades because of inaccurate predictions of future network bandwidth. To address this issue, we utilize the gated recurrent unit, a time series prediction model, to predict the network bandwidth accurately. We then propose a buffer-based ABR streaming technique that selects optimized video-quality settings on the basis of the predicted bandwidth. The proposed algorithm was evaluated on a dataset provided by Zeondo by categorizing instances of user mobility into walking, bus, and train scenarios. The proposed algorithm improved the QoE by approximately 11% compared with the existing buffer-based ABR algorithm in various environments. Full article

Deep learning models, such as recurrent neural network (RNN) models, are suitable for modeling and forecasting non-stationary time series but are not interpretable. A prediction model with interpretability and high accuracy can improve decision makers’ trust in the model and provide a basis for decision making. This paper proposes a double decomposition strategy based on wavelet decomposition (WD) and empirical mode decomposition (EMD). We construct a prediction model of high-order fuzzy cognitive maps (HFCM), called the WE-HFCM model, which considers interpretability and strong reasoning ability. Specifically, we use the WD and EDM algorithms to decompose the time sequence signal and realize the depth extraction of the signal’s high-frequency, low-frequency, time-domain, and frequency domain features. Then, the ridge regression algorithm is used to learn the HFCM weight vector to achieve modeling prediction. Finally, we apply the proposed WE-HFCM model to stationary and non-stationary datasets in simulation experiments. We compare the predicted results with the autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models.For stationary time series, the prediction accuracy of the WE-HFCM model is about 45% higher than that of the ARIMA, about 35% higher than that of the SARIMA model, and about 16% higher than that of the LSTM model. For non-stationary time series, the prediction accuracy of the WE-HFCM model is 69% higher than that of the ARIMA and SARIMA models. Full article

This study explores Human Activity Recognition (HAR) using smartphone sensors to address the challenges posed by position-dependent datasets. We propose a position-independent system that leverages data from accelerometers, gyroscopes, linear accelerometers, and gravity sensors collected from smartphones placed either on the chest or in the left/right leg pocket. The performance of traditional machine learning algorithms (Decision Trees (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Classifier (SVC), and XGBoost) is compared against deep learning models (Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer models) under two sensor configurations. Our findings highlight that the Temporal Convolutional Network (TCN) model consistently outperforms other models, particularly in the four-sensor non-overlapping configuration, achieving the highest accuracy of 97.70%. Deep learning models such as LSTM, GRU, and Transformer also demonstrate strong performance, showcasing their effectiveness in capturing temporal dependencies in HAR tasks. Traditional machine learning models, including RF and XGBoost, provide reasonable performance but do not match the accuracy of deep learning models. Additionally, incorporating data from linear accelerometers and gravity sensors led to slight improvements over using accelerometer and gyroscope data alone. This research enhances the recognition of passenger behaviors for intelligent transportation systems, contributing to more efficient congestion management and emergency response strategies. Full article

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing. Full article

To address the performance and computational complexity issues in speech enhancement for hearing aids, a speech enhancement algorithm based on a microphone array and a lightweight two-stage convolutional recurrent network (CRN) is proposed. The algorithm consists of two main modules: a beamforming module and a post-filtering module. The beamforming module utilizes directional features and a complex time-frequency long short-term memory (CFT-LSTM) network to extract local representations and perform spatial filtering. The post-filtering module uses analogous encoding and two symmetric decoding structures, with stacked CFT-LSTM blocks in between. It further reduces residual noise and improves filtering performance by passing spatial information through an inter-channel masking module. Experimental results show that this algorithm outperforms existing methods on the generated hearing aid dataset and the CHIME-3 dataset, with fewer parameters and lower model complexity, making it suitable for hearing aid scenarios with limited computational resources. Full article

Accurate prediction of lithium-ion batteries’ (LIBs) state-of-health (SOH) is crucial for the safety and maintenance of LIB-powered systems. This study addresses the variability in degradation trajectories by applying gated recurrent unit (GRU) networks alongside principal component analysis (PCA), Granger causality, and K-means clustering to analyze the relationships between operating conditions—such as temperature and load profiles—and battery performance degradation. This paper uses a publicly accessible dataset derived by aging three prismatic LIB cells under a realistic forklift operation profile. First, we identify the features that are relevant to driving variance, then we employ the winning algorithm of K-means clustering for the classification of operational states. Granger causality later investigates the inter-group relationships. Our GRU-BPP model achieves an RMSE value of 0.167 and an MAE of 0.129 for the reference performance testing (RPT) dataset and an RMSE of 0.032 with an MAE of 0.025 for the aging dataset, thus outperformed benchmark methods such as GRU, LME, and XGBoost. These results further enhance the predictiveness and robustness of this approach and yield a holistic solution to the conventional challenges in battery management and their remaining useful life (RUL) predictions. Full article

Gravity data, comprising a key foundational dataset, are crucial for various research, including land subsidence monitoring, geological exploration, and navigational positioning. However, the collection of gravity data in specific regions is difficult because of environmental, technical, and economic constraints, resulting in a non-uniform distribution of the observational data. Traditionally, interpolation methods such as Kriging have been widely used to deal with data gaps; however, their predictive accuracy in regions with sparse data still needs improvement. In recent years, the rapid development of artificial intelligence has opened up a new opportunity for data prediction. In this study, utilizing the EGM2008 satellite gravity model, we conducted a comprehensive analysis of three machine learning algorithms—random forest, support vector machine, and recurrent neural network—and compared their performances against the traditional Kriging interpolation method. The results indicate that machine learning methods exhibit a marked advantage in gravity data prediction, significantly enhancing the predictive accuracy. Full article

With the increasing global demand for renewable energy and heightened environmental awareness, electric vehicles (EVs) are rapidly becoming a popular clean and efficient mode of transportation. However, the widespread adoption of EVs has presented several challenges, such as the lagging development of charging infrastructure, the impact on the power grid, and the dynamic changes in user charging behavior. To address these issues, this paper first proposes a vehicle-to-grid (V2G) optimization framework that responds to regional dynamic pricing. It also considers power balancing in charging and discharging stations when a large number of EVs are involved in scheduling, with the aim of maximizing the benefits for EV owners. Next, by leveraging the interaction between environmental states and the dynamic behavior of EVs, we design an optimization algorithm that combines the recurrent proximal policy optimization (RPPO) algorithm and long short-term memory (LSTM) networks. This approach enhances system convergence and improves grid stability while maximizing benefits for EV owners. Finally, a simulation platform is used to validate the practical application of the RPPO algorithm in optimizing V2G and grid-to-vehicle (G2V) charging strategies, providing significant theoretical foundations and technical support for the development of smart grids and sustainable transportation systems. Full article

Background/Objectives: Ventral hernia repair (VHR) is a common surgical intervention linked to specific surgical site complications. In such occurrences, the related morbidity is often substantial. Although known risk factors have long been recognized, their systematic inclusion in risk stratification systems lacks universal validation. This study evaluates the effectiveness and correspondence of three risk assessment tools—CeDAR, VHWG, and the modified VHWG—in predicting postoperative wound complications in VHR patients. Methods: We analyzed data from 203 patients who underwent VHR for incisional midline or lateral wall hernia across two surgical departments between 2019 and 2023. Each patient was scored using CeDAR, VHWG, and the modified VHWG systems. Outcomes were assessed based on surgical site occurrences (SSOs) such as seroma formation, wound infections, and recurrences. Results: The incidence of SSOs was 8.9%, with two recorded deaths (0.89%). CeDAR scores showed a statistically significant relationship with SSOs but failed to accurately predict complication rates across subgroups. The VHWG grading system effectively predicted higher complication rates for grades III and IV compared to grades I and II, though its modified version did not show significant predictive improvements. Secondary outcomes indicated a higher SSO rate in patients requiring posterior component separation (TAR) and those with larger hernia defects, though the differences were not statistically significant. Major preoperative risk factors, including smoking, diabetes, and obesity, did not show significant correlations with SSO rates in this study. Conclusions: Current risk estimation tools inadequately predict SSOs in VHR. Enhancing prediction accuracy will require incorporating both patient-specific and surgical factors, potentially through advanced algorithms and large-scale studies. Full article

With the development of IoT technology, central cloud servers and edge-computing servers together form a cloud–edge communication network to meet the increasing demand for computing tasks. The data transmitted in this network is of high value, so the ability to quickly and accurately predict the traffic load of each link becomes critical to ensuring the security and stable operation of the network. In order to effectively counter the potential threat of flood attacks on network stability, we combine the Bi-directional Gated Recurrent Unit (BiGRU) model with the Dung Beetle Optimizer (DBO) algorithm to design a DBO-BiGRU short-term traffic load prediction model. Experimental validation on a public dataset shows that the proposed model has better prediction accuracy and fit than the mainstream models of RNN, LSTM, and TCN. Full article

As the decarbonization strategies of automated container terminals (ACTs) continue to advance, electrically powered Battery-Automated Guided Vehicles (B-AGVs) are being widely adopted in ACTs. The U-shaped ACT, as a novel layout, faces higher AGV energy consumption due to its deep yard characteristics. A key issue is how to adopt charging strategies suited to varying conditions to reduce the operational capacity loss caused by charging. This paper proposes a simulation-based optimization method for AGV charging strategies in U-shaped ACTs based on an improved Proximal Policy Optimization (PPO) algorithm. Firstly, Gated Recurrent Unit (GRU) structures are incorporated into the PPO to capture temporal correlations in state information. To effectively limit policy update magnitudes in the PPO, we improve the clipping function. Secondly, a simulation model is established by mimicking the operational process of the U-shaped ACTs. Lastly, iterative training of the proposed method is conducted based on the simulation model. The experimental results indicate that the proposed method converges faster than standard PPO and Deep Q-network (DQN). When comparing the proposed method-based charging threshold with a fixed charging threshold strategy across six different scenarios with varying charging rates, the proposed charging strategy demonstrates better adaptability to terminal condition variations in two-thirds of the scenarios. Full article