Search Results (2,369)

Objective: This study aims to explore the role of angiogenesis-related genes in chronic lung diseases (ILD and COPD) using bioinformatics methods, with the goal of identifying novel therapeutic targets to slow disease progression and prevent its deterioration into fibrosis or pulmonary artery hypertension. Methods: The research methods encompassed differential analysis, WGCNA (Weighted Gene Co-expression Network Analysis), and multiple machine learning approaches to screen for key genes. Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to assess related biological functions and pathways. Additionally, immune cell infiltration was analyzed to evaluate the immune status of the disease and the correlation between genes and immunity. Results: COPD and ILD are closely associated with pathways related to angiogenesis, immune responses, and others, with differential genes in both groups linked to inflammation-related signaling pathways. The study established a chronic lung disease-related gene set comprising 171 genes and further screened out 21 genes related to angiogenesis. Ultimately, four key genes—COL10A1, EDN1, MMP1, and RRAS—were identified through machine learning methods. These four genes are closely related to angiogenesis and immune processes, and clustering analysis based on them can reflect different disease states and variations in immune cell infiltration. Conclusions: COL10A1, EDN1, MMP1, and RRAS represent potential therapeutic targets for slowing the progression of chronic lung diseases and preventing their deterioration. Furthermore, monocytes exhibited consistent infiltration patterns across disease and control groups, as well as among different subgroups, suggesting their potential significant role in the development of chronic lung diseases. Full article

In this study, we propose a novel method for generating untargeted adversarial examples using a Generative Adversarial Network (GAN) in an unrestricted black-box environment. The proposed approach produces adversarial examples that are classified into random classes distinct from their original labels, while maintaining high visual similarity to the original samples from a human perspective. This is achieved by leveraging the capabilities of StyleGAN to manipulate the latent space representation of images, enabling precise control over visual distortions. To evaluate the efficacy of the proposed method, we conducted experiments using the CelebA-HQ dataset and TensorFlow as the machine learning framework, with ResNet18 serving as the target classifier. The experimental results demonstrate the effectiveness of the method, achieving a 100% attack success rate in a black-box environment after 3000 iterations. Moreover, the adversarial examples generated by our approach exhibit a distortion value of 0.069 based on the Learned Perceptual Image Patch Similarity (LPIPS) metric, highlighting the balance between attack success and perceptual similarity. These findings underscore the potential of GAN-based approaches in crafting robust adversarial examples while preserving visual fidelity. Full article

This study presents a comprehensive investigation into the application of reservoir simulation and machine learning techniques to improve the understanding and prediction of reservoir behavior, focusing on the Sarir C-Main field. The research uses various data sources to develop robust reservoir static and dynamic models, including seismic cubes, well logs, base maps, check shot data, and production history. The methodology involves data quality control, log interpretation, seismic interpretation, horizon and surface interpretation, fault interpretation, gridding, domain conversion, property and petrophysical modeling, well completion, fluid model definition, and rock physics functions. History matching and prediction are performed using simulation cases, and machine learning techniques such as data gathering, cleaning, dynamic time warping (DTW), long short-term memory (LSTM), and transfer learning are applied. The results obtained through the Petrel simulation demonstrate the effectiveness of depletion strategy, history matching, and completion in capturing reservoir behavior. Furthermore, the machine learning techniques, specifically DTW and LSTM, exhibit promising results in predicting oil production. The study concludes that machine learning approaches, such as the LSTM model, offer distinct advantages. They require significantly less time and can yield reliable predictions. By leveraging the power of transfer learning, accurate predictions can be achieved efficiently when limited data are available, offering a more streamlined and practical alternative to traditional reservoir simulation methods. Full article

The integration of photovoltaic (PV) power generation systems has significantly increased the complexity of voltage distribution in power grids, making it challenging for conventional Load Ratio Control Transformers (LRTs) to manage voltage fluctuations caused by weather-dependent PV output variations. Power Conditioning Systems (PCSs) interconnected with PV installations are increasingly considered for voltage control to address these challenges. This study proposes a Machine Learning (ML)-based control method for sub-transmission grids, integrating long-term LRT tap-changing with short-term reactive power control of PCSs. The approach estimates the voltage at each grid node using a Deep Neural Network (DNN) that processes measurable substation data. Based on these estimated voltages, the method determines optimal LRT tap positions and PCS reactive power outputs using Deep Reinforcement Learning (DRL). This enables real-time voltage monitoring and control using only substation measurements, even in grids without extensive sensor installations, ensuring all node voltages remain within specified limits. To improve the model’s transparency, Shapley Additive Explanation (SHAP), an Explainable AI (XAI) technique, is applied to the DRL model. SHAP enhances interpretability and confirms the effectiveness of the proposed method. Numerical simulations further validate its performance, demonstrating its potential for effective voltage management in modern power grids. Full article

Rotor imbalance in quadrotor UAVs poses a critical challenge, compromising flight stability, increasing maintenance demands, and reducing overall operational efficiency. Traditional vibration analysis methods, such as Fast Fourier Transform (FFT) and wavelet analysis, often struggle with non-stationary signals and real-time data processing, limiting their effectiveness under dynamic UAV operating conditions. To address these challenges, this study develops a machine learning-based vibration analysis system using a Multi-Layer Perceptron (MLP) neural network for real-time rotor imbalance detection. The system integrates Micro-Electro-Mechanical Systems (MEMS) sensors for vibration data acquisition, preprocessing techniques for noise reduction and feature extraction, and an optimized MLP architecture tailored to high-dimensional vibration data. Experimental validation was conducted under controlled flight scenarios, collecting a comprehensive dataset of 800 samples representing both balanced and imbalanced rotor conditions. The optimized MLP model, featuring five hidden layers, achieved a Root Mean Squared Error (RMSE) of 0.1414 and a correlation coefficient (R²) of 0.9224 on the test dataset, demonstrating high accuracy and reliability. This study highlights the potential of MLP-based diagnostics to enhance UAV reliability, safety, and operational efficiency, providing a scalable and effective solution for rotor imbalance detection in dynamic environments. The findings offer significant implications for improving UAV performance in addition to minimizing downtime in various industrial and commercial applications. Full article

The refinement of acquired well logs has traditionally relied on predefined rock physics models, albeit with their inherent limitations and assumptions. As an alternative, effective yet less explicit machine learning (ML) techniques have emerged. The integration of these two methodologies presents a promising new avenue. In our study, we used four ML algorithms: Random Forests (RF), Gradient Boosting Decision Trees (GBDT), Multilayer Perceptrons (MLP), and Linear Regression (LR), to predict porosity and clay volume fraction from well logs. Throughout the entire workflow, from feature engineering to outcome interpretation, our predictions are guided by rock physics principles, particularly the Gardner relations and the Larionov relations. Remarkably, while the predictions themselves are satisfactory, SHapley Additive exPlanations (SHAP) analysis uncovers consistent patterns across the four algorithms, irrespective of their distinct underlying structures. By juxtaposing the SHAP explanations with rock physics concepts, we discover that all four algorithms align closely with rock physics principles, adhering to its cause–effect relationships. Nonetheless, even after intentionally excluding crucial controlling input features that would inherently compromise prediction accuracy, all four ML algorithms and the SHAP analysis continue to operate, albeit in a manner that seems irrational and starkly contradicts the fundamental principles of rock physics. This integration strategy facilitates a transition from solely mathematical explanations to a more philosophical interpretation of ML-based predictions, effectively dismantling the traditional black box nature of these ML models. Full article

Salurnis marginella causes agricultural and forest damage in various Asian environments. However, considering the environmental adaptability of pests and the active international trade, it may invade other regions in the future. As the damage to local communities caused by pests becomes difficult to control after invasion, it is essential to establish measures to minimize losses through pre-emptive monitoring and identification of high-risk areas, which can be achieved through model-based predictions. The aim of this study was to evaluate the potential distribution of S. marginella by developing multiple species distribution modeling (SDM) algorithms. Specifically, we developed the CLIMEX model and three machine learning-based models (MaxEnt, random forest, and multi-layer perceptron), integrated them to conservatively assess pest occurrence under current and future climates, and overlaid the host distribution with climatically suitable areas of S. marginella to identify high-risk areas vulnerable to the spread and invasion of the pest. The developed model, demonstrating a true skill statistic >0.8, predicted the potential continuous distribution of the species across the southeastern United States, South America, and Central Africa. This distribution currently covers approximately 9.53% of the global land area; however, the model predicted this distribution would decrease to 6.85%. Possible areas of spread were identified in Asia and the southwestern United States, considering the host distribution. This study provides data for the proactive monitoring of pests by identifying areas where S. marginella can spread. Full article

Background/Objectives: In tennis, timing plays a crucial factor as it influences the technique and effectiveness of strokes and, therefore, matches results. However, traditional technical evaluation methods rely on subjective observations or video motion-tracking technology, mainly focusing on spatial components. This study evaluated the reliability of an acoustic detection system in analyzing key temporal elements of the game, such as the rally rhythm and timing of strokes. Methods: Based on a machine learning algorithm, the proposed acoustic detection system classifies the sound of the ball’s impact on the racket and the ground to measure the time between them and give immediate feedback to the player. We performed trials with expert and amateur players in controlled settings. Results: The ML algorithm showed a detection accuracy higher than 95%, while the average accuracy of the whole system that was applied on-court was 85%. Moreover, this system has proven effective in evaluating the technical skills of a group of players on the court and highlighting their areas for improvement, showing significant potential for practical applications in player training and performance analysis. Conclusions: Quantitatively assessing timing offers a new perspective for coaches and players to improve performance and technique, providing objective data to set training regimens and optimize game strategies. Full article

The COVID-19 pandemic brought telepresence systems into the spotlight, yet manually controlling remote robots often proves ineffective for handling complex manipulation tasks. To tackle this issue, we present a machine learning-based assistive manipulation approach. This method identifies target objects and computes an inverse kinematic solution for grasping them. The system integrates the generated solution with the user’s arm movements across varying inverse kinematic (IK) fusion levels. Given the importance of maintaining a sense of body ownership over the remote robot, we examine how haptic feedback and assistive functions influence ownership perception and task performance. Our findings indicate that incorporating assistance and haptic feedback significantly enhances the control of the robotic arm in telepresence environments, leading to improved precision and shorter task completion times. This research underscores the advantages of assistive manipulation techniques and haptic feedback in advancing telepresence technology. Full article

Background: Brain–computer interface (BCI) technology opens up new avenues for human–machine interaction and rehabilitation by connecting the brain to machines. Electroencephalography (EEG)-based motor imagery (MI) classification is a key component of BCI technology, which is capable of translating neural activity in the brain into commands for controlling external devices. Despite the great potential of BCI technology, the challenges of extracting and decoding brain signals limit its wide application. Methods: To address this challenge, this study proposes a novel hybrid deep learning model, CLTNet, which focuses on solving the feature extraction problem to improve the classification of MI-EEG signals. In the preliminary feature extraction stage, CLTNet uses a convolutional neural network (CNN) to extract time series, channel, and spatial features of EEG signals to obtain important local information. In the deep feature extraction stage, the model combines the long short-term memory (LSTM) network and the Transformer module to capture time-series data and global dependencies in the EEG. The LSTM explains the dynamics of the brain activity, while the Transformer’s self-attention mechanism reveals the global features of the time series. Ultimately, the CLTNet model classifies motor imagery EEG signals through a fully connected layer. Results: The model achieved an average accuracy of 83.02% and a Kappa value of 0.77 on the BCI IV 2a dataset, and 87.11% and a Kappa value of 0.74 on the BCI IV 2b dataset, both of which outperformed the traditional methods. Conclusions: The innovation of the CLTNet model is that it integrates multiple network architectures, which offers a more comprehensive understanding of the characteristics of the EEG signals during motor imagery, providing a more comprehensive perspective and establishing a new benchmark for future research in this area. Full article

Accurate flow stress prediction is vital for optimizing the manufacturing of lightweight materials under high-temperature conditions. In this study, a boron nitride (BN)-reinforced AZ80 magnesium composite was subjected to hot compression tests at temperatures of 300–400 °C and strain rates ranging from 0.01 to 10 s⁻¹. A data-driven Support Vector Regression (SVR) model was developed to predict flow stress based on temperature, strain rate, and strain. Trained on experimental data, the SVR model demonstrated high predictive accuracy, as evidenced by a low mean squared error (MSE), a coefficient of determination (R²) close to unity, and a minimal average absolute relative error (AARE). Sensitivity analysis revealed that strain rate and temperature exerted the greatest influence on flow stress. By integrating machine learning with experimental observations, this framework enables efficient optimization of thermal deformation, supporting data-driven decision-making in forming processes. The results underscore the potential of combining advanced computational models with real-time experimental data to enhance manufacturing efficiency and improve process control in next-generation lightweight alloys. Full article

This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a vision-driven follow-and-track control strategy is proposed. Subsequently, an algorithm for recognizing human motion intentions based on machine learning is proposed for human-robot collaboration tasks. A muscle–machine interface is constructed using a bi-directional long short-term memory (BiLSTM) network, which decodes multichannel surface electromyography (sEMG) signals into flexion and extension angles of the hip and knee joints in the sagittal plane. The hyperparameters of the BiLSTM network are optimized using the quantum-behaved particle swarm optimization (QPSO) algorithm, resulting in a QPSO-BiLSTM hybrid model that enables continuous real-time estimation of human motion intentions. Further, to address the uncertain nonlinear dynamics of the wearer-exoskeleton robot system, a dual radial basis function neural network adaptive sliding mode Controller (DRBFNNASMC) is designed to generate control torques, thereby enabling the precise tracking of motion trajectories generated by the muscle–machine interface. Experimental results indicate that the follow-up-assisted frame can accurately track human motion trajectories. The QPSO-BiLSTM network outperforms traditional BiLSTM and PSO-BiLSTM networks in predicting continuous lower limb motion, while the DRBFNNASMC controller demonstrates superior gait tracking performance compared to the fuzzy compensated adaptive sliding mode control (FCASMC) algorithm and the traditional proportional–integral–derivative (PID) control algorithm. Full article

This article describes the design, construction, and programming of a microcontroller-based system, which uses hand gestures with machine learning algorithms to control an unmanned aerial vehicle (UAV). A neural network is used as a model, and an IMU sensor detects the gestures. The developed gesture recognition system, besides the IMU sensor, is composed of a Raspberry Pi Pico and radio communication module. The benefits and drawbacks of deploying machine learning models on microcontrollers, as opposed to units superior in terms of clocking are also discussed. Full article

Background/Objectives: This study investigates the potential of Pulse Transit Time (PTT) derived from Impedance Plethysmography (IPG), Photoplethysmography (PPG), and Electrocardiography (ECG) for non-invasive and cuffless blood pressure monitoring. IPG measures blood volume changes through electrical conductivity, while PPG detects variations in microvascular blood flow, providing essential insights for wearable health monitoring devices. Methods: Data were collected from 100 healthy participants under resting and post-exercise conditions using a custom IPG system synchronized with ECG, PPG, and blood pressure readings to create controlled blood pressure variations. Machine learning models, including Random Forest, Logistic Regression, Support Vector Classifier, and K-Neighbors, were applied to predict blood pressure categories based on PTT and cardiovascular features. Results: Among the various machine learning models evaluated, Random Forest demonstrated effective performance, achieving an overall accuracy of 90%. The model also exhibited robustness, effectively handling the challenge of unbalanced classes, with a 95% confidence interval (CI) for accuracy ranging from 80% to 95%. This indicates its reliability across different data splits despite the class imbalance. Notably, PTT derived from PPG emerged as a critical predictive feature, further enhancing the model’s ability to accurately classify blood pressure categories and solidifying its utility in non-invasive cardiovascular monitoring. Conclusions: The findings affirm the efficacy of using PTT measurements from PPG, IPG, and ECG as reliable predictors for non-invasive blood pressure monitoring. This study substantiates the integration of these techniques into wearable devices, offering a significant advancement for continuous, cuffless, and non-invasive blood pressure assessment. Full article

Accurate prediction of the height of water-conducting fissure zone (HWCFZ) is an important issue in coal water control and a prerequisite for ensuring the safe production of coal mines. At present, the prediction model of HWCFZ has some issues such as poor prediction accuracy. Based on the widely collected measured data of the HWCFZ in different coal mines in northern Shaanxi Province, China, the HWCFZ in shallow-buried coal seams is categorized into two types, i.e., typical shallow-buried coal seams and near-shallow-buried seams, according to the different depths of burial and base-loading ratios. On the basis of summarizing the research results of the previous researchers, three factors, namely, mining thickness, coal seam depth, and working length, were selected, and the data of the height of the water-conducting fissure zone in the study area were analyzed by using a multivariate nonlinear regression method. Subsequently, each group of the data was randomly divided into training data and validation data with a ratio of 70:30. Then, the training data were used to build a neural network model (BP), random forest model (RF), a hybrid integration of particle swarm optimization and the support vector machine model (PSO-SVR), and a hybrid integration of genetic algorithm optimization and the support vector machine model (GA-SVR). Finally, the test samples were used to test the model accuracy and evaluate the generalization ability. Accordingly, the optimal prediction model for the typical shallow-buried area and near-shallow-buried area of Jurassic coal seams in northern Shaanxi was established. The results show that the HWCFZ for the typical shallow-buried coal seam is suitable to be determined by the multivariate nonlinear regression method, with an accuracy of 0.64; the HWCFZ for near-shallow-buried coal seams is suitable to be predicted by the two-factor PSO-SVR computational model of mining thickness and the burial depth, with a prediction accuracy of 0.84; and machine learning methods are more suitable for near-shallow-buried areas, dealing with small-scale data and discrete data. Full article