Optimization of Ionic Liquid Based Simultaneous Ultrasonic- and Microwave-Assisted Extraction of Rutin and Quercetin from Leaves of Velvetleaf ( Abutilon theophrasti

An ionic liquids based simultaneous ultrasonic and microwave assisted extraction (ILs-UMAE) method has been proposed for the extraction of rutin (RU), quercetin (QU), from velvetleaf leaves. The influential parameters of the ILs-UMAE were optimized by the single factor and the central composite design (CCD) experiments. A 2.00 M 1-butyl-3-methylimidazolium bromide ([C4mim]Br) was used as the experimental ionic liquid, extraction temperature 60°C, extraction time 12 min, liquid-solid ratio 32 mL/g, microwave power of 534 W, and a fixed ultrasonic power of 50 W. Compared to conventional heating reflux extraction (HRE), the RU and QU extraction yields obtained by ILs-UMAE were, respectively, 5.49 mg/g and 0.27 mg/g, which increased, respectively, 2.01-fold and 2.34-fold with the recoveries that were in the range of 97.62–102.36% for RU and 97.33–102.21% for QU with RSDs lower than 3.2% under the optimized UMAE conditions. In addition, the shorter extraction time was used in ILs-UMAE, compared with HRE. Therefore, ILs-UMAE was a rapid and an efficient method for the extraction of RU and QU from the leaves of velvetleaf

Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs

The path planning of unmanned aerial vehicles (UAVs) in the threat and countermeasure region is a constrained nonlinear optimization problem with many static and dynamic constraints. The fruit fly optimization algorithm (FOA) is widely used to handle this kind of nonlinear optimization problem. In this paper, the multiple swarm fruit fly optimization algorithm (MSFOA) is proposed to overcome the drawback of the original FOA in terms of slow global convergence speed and local optimum, and then is applied to solve the coordinated path planning problem for multi-UAVs. In the proposed MSFOA, the whole fruit fly swarm is divided into several sub-swarms with multi-tasks in order to expand the searching space to improve the searching ability, while the offspring competition strategy is introduced to improve the utilization degree of each calculation result and realize the exchange of information among various fruit fly sub-swarms. To avoid the collision among multi-UAVs, the collision detection method is also proposed. Simulation results show that the proposed MSFOA is superior to the original FOA in terms of convergence and accuracy

Sensorless control of a PMSM based on an RBF neural network-optimized ADRC and SGHCKF-STF algorithm

For the problem of the rotor position estimation and control accuracy of permanent magnet synchronous motor (PMSM), this paper proposes a PMSM sensorless based on radial basis function (RBF) neural network optimized Automatic disturbance rejection control (RBF-ADRC) and strong tracking filter (STF) improved square root generalized fifth-order cubature Kalman filter (SGHCKF-STF). The Automatic disturbance rejection control (ADRC) has strong robustness, but there are many parameters and difficult to adjust. Now we use RBF neural network to adjust the parameters in ADRC online so as to improve the robustness and anti-disturbance ability. In order to improve the estimation accuracy of rotor position and speed, the orthogonal triangle (QR) decomposition and STF are introduced on the basis of the generalized fifth-order cubature Kalman filter (GHCKF) to design the SGHCKF-STF algorithm that not only ensure the non-positive nature of the covariance matrix but also improve the ability to cope with sudden changes in state during the filtering process. Experimental results show that the combination of RBF-ADRC and SGHCKF-STF improve the sensorless control effect of the PMSM to some extent

Few-Shot Hyperspectral Image Classification Based on Convolutional Residuals and SAM Siamese Networks

With the development of few-shot learning, significant progress has been achieved in hyperspectral image classification using related networks, leading to improved classification outcomes. However, practical few-shot hyperspectral image classification encounters challenges such as network overfitting and insufficient feature extraction during the model training process. To address these issues, we propose a model called CRSSNet (Convolutional Residuals and SAM Siamese Networks) for few-shot hyperspectral image classification. In this model, we deepen the network depth and employ the convolutional residual technique to enhance the feature extraction capabilities and alleviate the problem of network gradient degradation. Additionally, we introduce the Spatial Attention Mechanism (SAM) to effectively leverage spatial information features in hyperspectral images. Lastly, metric learning is employed by comparing the distance between two output feature vectors to determine the label category. Experimental results demonstrate that our method achieves superior classification performance compared to other methods

Motion intention recognition of the affected hand based on the sEMG and improved DenseNet network

The key to sEMG (surface electromyography)-based control of robotic hands is the utilization of sEMG signals from the affected hand of amputees to infer their motion intentions. With the advancements in deep learning, researchers have successfully developed viable solutions for CNN (Convolutional Neural Network)-based gesture recognition. However, most studies have primarily concentrated on utilizing sEMG data from the hands of healthy subjects, often relying on high-dimensional feature vectors obtained from a substantial number of electrodes. This approach has yielded high-performing sEMG recognition systems but has failed to consider the considerable inconvenience that the abundance of electrodes poses to the daily lives and work of patients. In this paper, we focused on transradial amputees and used sEMG data from the Ninapro DB3 database as our dataset. Firstly, we introduce a STFT (Short-Time Fourier Transform)-based time-frequency feature fusion map for sEMG. This map includes both time-frequency features and the time-frequency localization of sEMG signals. Secondly, we propose an Improved DenseNet (Dense Convolutional Network) model for recognizing motion intentions in the affected hand of amputees based on their sEMG signals. Finally, addressing the issue of optimizing the number of electrodes carried by amputees, we introduce the PCMIRR (Pearson Correlation and Motion Intention Recognition Rate) algorithm. This algorithm optimizes the number of channels by considering the Pearson correlation between the sEMG channels of amputees and the recognition rate of motion intentions in the affected hand based on single-channel sEMG data. The experimental results reveal that the recognition accuracy, recall, and F1 score achieved by the Improved DenseNet model were 93.82%, 93.61%, and 93.65%, respectively. When the number of electrodes was optimized to 8, the recognition accuracy reached 94.50%. In summary, this paper ultimately attained precise recognition of motion intentions in amputees' affected hands while utilizing the minimum number of sEMG channels. This method offers a novel approach to sEMG-based control of bionic robotic hands

Dielectric Manipulated Charge Dynamics in Contact Electrification

Surface charge density has been demonstrated to be significantly impacted by the dielectric properties of tribomaterials. However, the ambiguous physical mechanism of dielectric manipulated charge behavior still restricts the construction of high-performance tribomaterials. Here, using the atomic force microscopy and Kelvin probe force microscopy, an in situ method was conducted to investigate the contact electrification and charge dynamics on a typical tribomaterial (i.e., BaTiO3/PVDF-TrFE nanocomposite) at nanoscale. Combined with the characterization of triboelectric device at macroscale, it is found that the number of transferred electrons increases with contact force/area and tends to reach saturation under increased friction cycles. The incorporated high permittivity BaTiO3 nanoparticles enhance the capacitance and electron trapping capability of the nanocomposites, efficiently inhibiting the lateral diffusion of electrons and improving the output performance of the triboelectric devices. Exponential decay of the surface potential is observed over monitoring time for all dielectric samples. At high BaTiO3 loadings, more electrons can drift into the bulk and combine with the induced charges on the back electrode, forming a large leakage current and accordingly accelerating the electron dissipation. Hence, the charge trapping/storing and dissipating, as well as the charge attracting properties, should be comprehensively considered in the design of high-performance tribomaterials

Establishment of an LC-MS/MS Method for the Determination of 45 Pesticide Residues in Fruits and Vegetables from Fujian, China

Pesticide residues in food have become an important factor seriously threatening human health. Therefore, this study was conducted to determine the pesticide residues in fruits and vegetables commonly found in Fujian, China, with the aim of constructing a simple and rapid method for pesticide residue monitoring. We collected 5607 samples from local markets and analyzed them for the presence of 45 pesticide residues. A fast, easy, inexpensive, effective, robust, and safe (QuEChERS) multi-residue extraction method followed by liquid chromatography equipped with triple-quadrupole mass spectrometry (LC-MS/MS) was successfully established. This 12-min-long analytical method detects and quantifies pesticide residues with acceptable validation performance parameters in terms of sensitivity, selectivity, linearity, the limit of quantification, accuracy, and precision. The linear range of the calibration curves ranged from 5 to 200 mg/L, the limits of detection for all pesticides ranged from 0.02 to 1.90 μg/kg, and the limits of quantification for the pesticides were 10 μg/kg. The recovery rates for the three levels of fortification ranged from 72.0% to 118.0%, with precision values (expressed as RSD%) less than 20% for all of the investigated analytes. The results showed that 726 (12.95%) samples were contaminated with pesticide residues, 94 (1.68%) samples exceeded the maximum residue limit (MRL) of the national standard (GB 2763-2021, China), 632 (11.23%) samples were contaminated with residues below the MRL, and 4881 (87.05%) samples were pesticide residue-free. In addition, the highest number of multiple pesticide residues was observed in bananas and peppers, which were contaminated with acetamiprid, imidacloprid, pyraclostrobin, and thiacloprid

The modern scientific mystery of traditional Chinese medicine processing——take some common traditional Chinese medicine as examples

The processing of traditional Chinese medicine (TCM) is a unique traditional pharmaceutical technology in China, which is the most important feature that distinguishes Chinese medicine from natural medicine and plant medicine. Since the record in Huangdi Neijing (Inner Canon of the Yellow Emperor), till now, the processing of TCM has experienced more than 2000 years of inheritance, innovation, and development, which is a combination of TCM theory and clinical practice, and plays an extremely important position in the field of TCM. In recent years, as a clinical prescription of TCM, Chinese herbal pieces have played a significant role in the prevention and control of the COVID-19 and exhibited their unique value, and therefore they have become the highlight of China's clinical treatment protocol and provided Chinese experience and wisdom for the international community in the prevention and control of the COVID-19 epidemic. This paper outlines the research progress in the processing of representative TCM in recent years, reviews the mechanism of the related effects of TCM materials after processing, such as changing the drug efficacy and reducing the toxicity, puts forward the integration and application of a variety of new technologies and methods, so as to reveal the modern scientific mystery of the processing technology of TCM