Search Results (255)

Background and Objective: This study aimed to examine the possibility of using surface electromyography (sEMG) to aid in assessing the correctness of overhead deep squat performance. Electromyography signals were recorded for 20 athletes from the lower (rectus femoris (RF), vastus medialis (VM), biceps femoris (BF), and gluteus (GM)) and upper (deltoid (D), latissimus dorsi (L)) muscles. The sEMG signals were categorized into three groups based on physiotherapists’ evaluations of deep squat correctness. Methods: The raw sEMG signals were filtering at 10–250 Hz, and then the mean frequency, median frequency, and kurtosis were calculated. Next, the maximum excitation of the muscles expressed in percentage of maximum voluntary contraction (%MVC) and co-activation index (CAI) were estimated. To determine the muscle excitation level, the pulse interference filter and variance analysis of the sEMG signal derivative were applied. Next, analysis of variance (ANOVA) tests, that is, nonparametric Kruskal–Wallis and post hoc tests, were performed. Results: The parameter that most clearly differentiated the groups considered turned out to be %MVC. The statistically significant difference with a large effect size in the excitation of RF & GM (p = 0.0011) and VM & GM (p = 0.0002) in group 3, where the correctness of deep squat execution was the highest and ranged from 85% to 92%, was pointed out. With the decrease in the correctness of deep squat performance, an additional statistically significant difference appeared in the excitation of RF & BF and VM & BF for both groups 2 and 1, which was not present in group 3. However, in group 2, with the correctness of the deep squat execution at 62–77%, the statistically significant differences in muscle excitation found in group 3 were preserved, in contrast to group 1, with the lowest 23–54% correctness of the deep squat execution, where the statistical significance of these differences was not confirmed. Conclusions: The results indicate that sEMG can differentiate muscle activity and provide additional information for physiotherapists when assessing the correctness of deep squat performance. The proposed analysis can be used to evaluate the correctness of physical exercises when physiotherapist access is limited. Full article

Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) and phosphorus (P). Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” We systematically collected a total of 9000 leaf samples from Korla fragrant pear orchards and acquired spectral data using a benchtop near-infrared spectrometer. After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. Multiplicative scatter correction (MSC) and first derivative (FD) are synergistically employed for preprocessing to eliminate scattering interference and enhance the resolution of characteristic peaks. Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. Moreover, it elucidates the physiological mechanism by which potassium influences spectral response through the regulation of water metabolism. Full article

Intrinsically disordered regions (IDRs), defined as protein segments lacking stable tertiary structures, are ubiquitously present in the human proteome and enriched with disease-associated mutations. IDRs harbor molecular recognition features (MoRFs) and post-translational modification sites (e.g., phosphorylation), enabling dynamic intermolecular interactions through conformational plasticity. Furthermore, IDRs drive liquid–liquid phase separation (LLPS) of biomacromolecules via multivalent interactions such as electrostatic attraction and pi–pi interactions, generating biomolecular condensates that are essential throughout the cellular lifecycle. These condensates separate intracellular space, forming a physical barrier to avoid interference between other molecules, thereby improving reaction specificity and efficiency. As a dynamically equilibrated process, LLPS formation and maintenance are regulated by multiple factors, endowing the condensates with rapid responsiveness to environmental cues and functional versatility in modulating diverse signaling cascades. Consequently, disruption of LLPS homeostasis can derail its associated biological processes, ultimately contributing to disease pathogenesis. Moreover, precisely because liquid–liquid phase separation (LLPS) is co-regulated by multiple factors, it may provide novel insights into the pathogenic mechanisms of disorders such as autism spectrum disorder (ASD), which result from the cumulative effects of multiple etiological factors. Full article

This study explores the application of near-infrared (NIR) spectroscopy combined with machine learning for the non-destructive detection of aflatoxin in peanuts contaminated by Aspergillus flavus (A. flavus). The key innovation lies in the development of an optimized spectral processing pipeline that effectively overcomes moisture interference while maintaining high sensitivity to low aflatoxin concentrations. NIR spectra were collected from peanut samples at different incubation times within the spectral range of 950 to 1650 nm. Spectral data were preprocessed, and Competitive Adaptive Reweighted Sampling (CARS) selected ten characteristic bands. Correlation analysis was performed to examine the relationships between physicochemical properties, characteristic bands, and aflatoxin content. Three machine learning models—Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF)—were used to predict aflatoxin levels. The SNV-SVM model demonstrated superior performance, achieving calibration metrics (R²_C = 0.9945, RMSE_C = 9.92, RPD_C = 14.59) and prediction metrics (R²_P = 0.9528, RMSE_P = 19.58, RPD_P = 7.01), along with leave-one-out cross-validation (LOOCV) results (R² = 0.9834, RMSE = 11.20). The results demonstrate that NIR spectroscopy combined with machine learning offers a rapid, non-destructive approach for aflatoxin detection in peanuts, with significant implications for food safety and agricultural quality control. Full article

Accurate dynamic estimation is of vital importance for the real-time monitoring of the operating status of power systems. To address issues such as non-Gaussian noise and outlier interference, a cubature Kalman filter state estimation method based on robust functions (RF-CKF) is proposed. Firstly, based on the exponential absolute value, an estimator is established, which is represented by the exponential absolute value and quadratic functions. Secondly, the regression form of batch processing mode is established, and the estimator based on the exponential absolute value is integrated into the cubature Kalman filter framework. Finally, an example of a standard IEEE 39-bus system is used to verify the effectiveness of the proposed method. Compared with the unscented Kalman filter, cubature Kalman filter and H-infinity CKF, the proposed method has better estimation accuracy and stronger robustness in an anomaly environment. Full article

The increasing demand for high-capacity, energy-efficient wireless networks poses significant challenges in maintaining spectral efficiency, minimizing interference, and ensuring sustainability. Traditional direct-link communication suffers from signal degradation due to path loss, multipath fading, and interference, limiting overall performance. To mitigate these challenges, this paper proposes a unified RIS framework that integrates passive and active Reconfigurable Intelligent Surfaces (RISs) for enhanced communication and ambient RF energy harvesting. Our methodology optimizes RIS-assisted beamforming using successive convex approximation (SCA) and adaptive phase shift tuning, maximizing desired signal reception while reducing interference. Passive RIS efficiently reflects signals without external power, whereas active RIS employs amplification-assisted reflection for superior performance. Evaluations using realistic urban macrocell and mmWave channel models reveal that, compared to direct links, passive RIS boosts SNR from 3.0 dB to 7.1 dB, and throughput from 2.6 Gbps to 4.6 Gbps, while active RIS further enhances the SNR to 10.0 dB and throughput to 6.8 Gbps. Energy efficiency increases from 0.44 to 0.67 (passive) and 0.82 (active), with latency reduced from 80 ms to 35 ms. These performance metrics validate the proposed approach and highlight its potential applications in urban 5G networks, IoT systems, high-mobility scenarios, and other next-generation wireless environments. Full article

Underwater wireless communications face significant challenges due to high attenuation, turbulence, and water turbidity. Traditional methods like acoustic and radio frequency (RF) communication suffer from low data rates (<100 kbps), high latency (>1 s), and limited transmission distances (<10 km).Visible Light Communication (VLC) emerges as a promising alternative, offering high-speed data transmission (up to 5 Gbps), low latency (<1 ms), and immunity to electromagnetic interference. This paper provides an in-depth review of underwater VLC, covering fundamental principles, environmental factors (scattering, absorption), and dynamic water properties. We analyze modulation techniques, including adaptive and hybrid schemes (QAM-OFDM achieving 4.92 Gbps over 1.5 m), and demonstrate their superiority over conventional methods. Practical applications—underwater exploration, autonomous vehicle control, and environmental monitoring—are discussed alongside security challenges. Key findings highlight UVLC’s ability to overcome traditional limitations, with experimental results showing 500 Mbps over 150 m using PAM4 modulation. Future research directions include integrating quantum communication and Reconfigurable Intelligent Surfaces (RISs) to further enhance performance, with simulations projecting 40% improved spectral efficiency in turbulent conditions. Full article

Monitoring algal growth rates and estimating microalgae concentration in photobioreactor systems are critical for optimizing production efficiency. Traditional methods—such as microscopy, fluorescence, flow cytometry, spectroscopy, and macroscopic approaches—while accurate, are often costly, time-consuming, labor-intensive, and susceptible to contamination or production interference. To overcome these limitations, this study proposes an automated, real-time, and cost-effective solution by integrating machine learning with image-based analysis. We evaluated the performance of Decision Trees (DTS), Random Forests (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (k-NN) algorithms using RGB color histograms extracted from images of Scenedesmus dimorphus cultures. Ground truth data were obtained via manual cell enumeration under a microscope and dry biomass measurements. Among the models tested, DTS achieved the highest accuracy for cell count prediction (R² = 0.77), while RF demonstrated superior performance for dry biomass estimation (R² = 0.66). Compared to conventional methods, the proposed ML-based approach offers a low-cost, non-invasive, and scalable alternative that significantly reduces manual effort and response time. These findings highlight the potential of machine learning–driven imaging systems for continuous, real-time monitoring in industrial-scale microalgae cultivation. Full article

Accurate remote sensing identification of river ice not only provides scientific evidence for climate change but also offers early warning information for disasters such as ice jams. Currently, many researchers have used remote sensing index-based methods to identify river ice in alpine regions. However, in high-altitude areas, these index-based methods face limitations in recognizing river ice and distinguishing ice-snow mixtures. With the rapid advancement of machine learning techniques, some scholars have begun to use machine learning methods to extract river ice in northern latitudes. However, there is still a lack of systematic studies on the ability of machine learning to enhance river ice identification in high-altitude, complex terrains. The study evaluates the performance of machine learning methods and the RDRI index method across six aspects: river type, altitude, river width, ice periods, satellite data, and snow cover interference. The results show that machine learning, particularly the RF method, demonstrates superior generalization ability and higher recognition accuracy for river ice in the complex high-altitude terrain of the Tibetan Plateau by leveraging a variety of input data, including spectral and topographical information. The RF model performs best under all types of test conditions, with an average Kappa coefficient of 0.9088, outperforming other machine learning methods and significantly outperforming the traditional exponential method, demonstrating stronger recognition capabilities. Machine learning methods are adaptable to different types of river ice, showing particularly improved recognition of river ice in braided river systems. RF and SVM exhibit more accurate river ice recognition across different altitudinal gradients, with RF and SVM significantly improving the identification accuracy of river ice (0–90 m) on the plateau. RF and SVM methods offer more precise boundary recognition when identifying river ice across different ice periods. Additionally, RF demonstrates better generalization in the transfer of multisource satellite data. RF’s performance is outstanding under different snow cover conditions, overcoming the limitations of traditional methods in identifying river ice under thick snow. Machine learning methods, which are well suited for large sample learning and have strong generalization capabilities, show significant potential for application in river ice identification within high-altitude, complex terrains. Full article

In this paper, a method of estimating the probability of susceptibility of a component on a circuit board to electromagnetic interference (EMI) is presented. The integrated circuit electromagnetic compatibility (IC EMC) standard IEC 62132-4 enables the assessment of the susceptibility of an IC by determining the forward power incident on each pin required to induce a malfunction. Although we focus on IC susceptibility, the method might be applied to other components and sub-circuits where the same information is known. Building upon a previously established numerical model capable of estimating the average coupled forward power at the end of a trace of a lossless PCB trace for a known load in a reverberant environment, this paper updates the model by incorporating PCB losses and utilizes the updated model to estimate the distribution of coupled forward power at the package pin over a number of boundary conditions in a reverberant field. Thus, the probability of failure can be predicted from the known component susceptibility level, the length, transmission line parameters, and the loading of the track to which it is attached. To validate this numerical model, the paper includes measurements obtained with a custom-designed RF IC detector, created for the purpose of measuring RF power coupled into the package pin via test PCB tracks. Full article

The compatibility and safety of contrast media injectors (CMIs) at ultra-high magnetic field strengths remains a critical challenge. This study aimed to investigate a custom-designed CMI powered by a ceramic motor in a newly developed 5T MRI environment, comparing it with a commercial CMI commonly used in a clinic. Three key performance aspects of the CMI were assessed in the 5T environment: translational attraction force, injection flow rates, and total injected volume. Potential imaging artifacts were checked. The custom-designed CMI demonstrated robust performance in the 5T environment, maintaining injection accuracy across all test locations and ensuring translational attraction forces remained within safe thresholds, even in the most challenging positions. Importantly, the custom-designed CMI exhibited no significant radiofrequency (RF) interference, and no imaging artifacts were observed across routine clinical sequences. In contrast, the commercial 3T CMI showed RF interference in several sensitive tests, such as the gradient echo (GRE) sequence with a 0° flip angle and frequency-based detection methods, underscoring the need for field-specific CMI designs tailored to ultra-high field environments. Further tests were performed in monkey livers and a human brain in vivo. The custom-designed CMI proved to be safe, accurate, and fully compatible with the 5T environment. Full article

As sensor technology integrates into modern life, diverse sensing devices have become essential for collecting critical data that enables human–machine interfaces such as autonomous vehicles and healthcare monitoring systems. However, the growing number of sensor devices places significant demands on network capacity, which is constrained by the limitations of radio frequency (RF) technology. RF-based communication faces challenges such as bandwidth congestion and interference in densely populated areas. To overcome these challenges, a combination of RF with free-space optical (FSO) communication is presented. FSO is a laser-based wireless solution that offers high data rates and secure communication, similar to fiber optics but without the need for physical cables. However, FSO is highly susceptible to atmospheric turbulence and conditions such as fog and smoke, which can degrade performance. By combining the strengths of both RF and FSO, a hybrid FSO/RF system can enhance network reliability, ensuring seamless communication in dynamic urban environments. This review examines hybrid FSO/RF systems, covering both theoretical models and real-world applications. Three categories of hybrid systems, namely hard switching, soft switching, and relay-based mechanisms, are proposed, with graphical models provided to improve understanding. In addition, multi-platform applications, including autonomous, unmanned aerial vehicles (UAVs), high-altitude platforms (HAPs), and satellites, are presented. Finally, the paper identifies key challenges and outlines future research directions for hybrid communication networks. Full article

This paper addresses the critical issue of unwanted interference in airborne GNSS receivers, crucial for navigational safety. Previous studies often simplified the problem, but this work offers a comprehensive approach, considering factors like Earth’s reflective properties, 3D calculations, and distinct radiation patterns. It introduces Spatial Interference Distribution Expression Heat-map and Operation Efficacy Plot graphs to visualize interference distribution along flight paths. The results highlight the significance of physical configuration and distance from interference sources on receiver performance. The algorithm developed can assess interference effects on GNSS receivers and aid in selecting optimal flight paths for minimal interference. This research enhances understanding and management of unintentional interference in airborne navigation systems. Full article

Indoor low-power positioning systems have received much attention, and visible light positioning (VLP) shows great potential for its high accuracy and low power consumption. However, VLP also exhibits some limitations like small coverage area and the requirement of line of sight. Moreover, most VLP applications require the receiver to be within the coverage of at least three LEDs simultaneously, which seriously confines the availability of VLP when LEDs are sparsely deployed. Conversely, radio frequency (RF)-based positioning systems provide large coverage area, but suffer from low positioning accuracy due to multipath interference. In this work, we harnessed the complementary strengths of multiple technologies to develop a hybrid RF-VLP indoor positioning system for improving localization accuracy under sparse anchors. The RF-network-assisted visible light positioning enables each receiver to determine its position autonomously, using signals from a single LED anchor and neighboring receivers, and without needing RF anchors. To validate the effectiveness of the proposed method, we utilize commercial off-the-shelf LED and ESP32 to build up a prototype system. Comprehensive experiments are performed to evaluate the performance of the positioning system, and the results show that the proposed system achieves an overall root mean square error (RMSE) of 26.1 cm, representing a 28.5% improvement in positioning accuracy compared to traditional RF-based positioning methods, which makes it highly feasible for deployment. Full article

Global Navigation Satellite System (GNSS) reference receivers are an essential part of ground stations that make the operation of Satellite-Based Augmentation Systems (SBAS) possible. Recently, there has been increasing concern about spoofing and interference events, which may seriously hinder the operation of GNSS receivers in liability- and safety-critical applications and, in particular, SBAS ground stations. In this context, the goal of this paper is two-fold. On the one hand, a set of spoofing and interference detection techniques should be presented specifically tailored to operate with the outputs provided by a NovAtel G-III SBAS reference receiver. On the other hand, assessing these techniques with various tests conducted using a Safran Skydel GSG-8 GNSS RF simulator in order to validate their implementation and effectiveness is necessary. This work concludes with an analysis of the obtained results, providing insightful recommendations and guidelines. Full article