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Regional poverty is one of the most serious challenges facing the world today. Poverty, antipoverty, and poverty alleviation are the focus of the attention of scholars and the public. This paper takes China's counties as the research unit, selects the influencing factors of poverty from natural and socio-economic factors, establishes an evaluation index system, simulates the natural poverty index and socio-economic poverty eradication index of each county, and clarifies the distribution characteristics of spatial poverty using GIS spatial analysis and BP artificial neural network. The results indicate that natural factors are the main cause of poverty in Chinese counties, with 710 counties having a high natural poverty index, accounting for nearly 30% of the total number of counties in the country. The national county-level natural poverty index shows a clear strip distribution pattern along latitude and longitude, with a strip distribution from north to south and from west to east; socio-economic factors have played a certain role in poverty alleviation, with as many as 1521 counties with low socio-economic poverty alleviation indices, accounting for approximately 64% of the total number of counties in the country. The spatial distribution of the county-level socio-economic poverty alleviation index is relatively fragmented. Through spatial scanning statistics, a total of 44 county poverty pressure index risk clusters reached a statistical significance level, involving 243 counties and districts. In poverty reduction practice, the internal counties and districts of contiguous poverty-stricken areas should strengthen cooperation and exchange. In the process of poverty alleviation and development, targeted poverty alleviation and economic development should be carried out based on the poverty-dominant type and self-development ability of the county, in order to improve efficiency. Regions that are relatively prosperous and have taken the lead in poverty reduction should play a leading and exemplary role in strengthening the radiation power of regional central cities. The prominent feature of this study is the comprehensive utilization of multisource data and the use of new spatial analysis methods (flexible spatial scanning method is widely used in the field of infectious disease prevention and control research). By constructing a multidimensional poverty measurement system that includes natural and social factors, it distinguishes the differences between the factors that cause poverty and the factors that eliminate poverty in regional poverty. At the same time, the flexible spatial scanning detection method was used to detect the differentiation mechanism of poverty spatial patterns. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms underlying ADHD remain inadequately understood, and current approaches do not well link neural networks and attention networks within brain networks. Our objective is to investigate the neural mechanisms related to attention and explore neuroimaging biological tags that can be generalized within the attention networks. In this paper, we utilized resting-state functional magnetic resonance imaging data to examine the differential functional connectivity network between ADHD and typically developing individuals. We employed a graph convolutional neural network model to identify individuals with ADHD. After classification, we visualized brain regions with significant contributions to the classification results. Our results suggest that the frontal, temporal, parietal, and cerebellar regions are likely the primary areas of dysfunction in individuals with ADHD. We also explored the relationship between regions of interest and attention networks, as well as the connection between crucial nodes and the distribution of positively and negatively correlated connections. This analysis allowed us to pinpoint the most discriminative brain regions, including the right orbitofrontal gyrus, the left rectus gyrus and bilateral insula, the right inferior temporal gyrus and bilateral transverse temporal gyrus in the temporal region, and the lingual gyrus of the occipital lobe, multiple regions of the basal ganglia and the upper cerebellum. These regions are primarily involved in the attention executive control network and the attention orientation network. Dysfunction in the functional connectivity of these regions may contribute to the underlying causes of ADHD. [ABSTRACT FROM AUTHOR]

The low-bit quantization can effectively reduce the deep neural network storage as well as the computation costs. Existing quantization methods have yielded unsatisfactory results when being applied to lightweight networks. Additionally, following network quantization, the differences in data types between the operators can cause issues when deploying networks on Field Programmable Gate Arrays (FPGAs). Moreover, some operators cannot be accelerated heterogeneously on FPGAs, resulting in frequent switching between the Advanced RISC Machine (ARM) and FPGA environments for computation tasks. To address these problems, this paper proposes a custom network quantization approach. Firstly, an improved PArameterized Clipping Activation (PACT) method is employed during the quantization aware training to restrict the value range of neural network parameters and reduce the loss of precision arising from quantization. Secondly, the Consecutive Execution Of Convolution Operators (CEOCO) strategy is utilized to mitigate the resource consumption caused by the frequent environment switching. The proposed approach is validated on Xilinx Zynq Ultrascale+MPSoC 3EG and Virtex UltraScale+XCVU13P platforms. The MobileNetv1, MobileNetv3, PPLCNet, and PPLCNetv2 networks were utilized as testbeds for the validation. Moreover, experimental results are on the miniImageNet, CIFAR-10, and OxFord 102 Flowers public datasets. In comparison to the original model, the proposed optimization methods result in an average decrease of 1.2% in accuracy. Compared to conventional quantization method, the accuracy remains almost unchanged, while the frames per second (FPS) on FPGAs improves by an average of 2.1 times. [ABSTRACT FROM AUTHOR]

The nonlinear sine-Gordon equation is a prevalent feature in numerous scientific and engineering problems. In this paper, we propose a machine learning-based approach, physics-informed neural networks (PINNs), to investigate and explore the solution of the generalized non-linear sine-Gordon equation, encompassing Dirichlet and Neumann boundary conditions. To incorporate physical information for the sine-Gordon equation, a multiobjective loss function has been defined consisting of the residual of governing partial differential equation (PDE), initial conditions, and various boundary conditions. Using multiple densely connected independent artificial neural networks (ANNs), called feedforward deep neural networks designed to handle partial differential equations, PINNs have been trained through automatic differentiation to minimize a loss function that incorporates the given PDE that governs the physical laws of phenomena. To illustrate the effectiveness, validity, and practical implications of our proposed approach, two computational examples from the nonlinear sine-Gordon are presented. We have developed a PINN algorithm and implemented it using Python software. Various experiments were conducted to determine an optimal neural architecture. The network training was employed by using the current state-of-the-art optimization methods in machine learning known as Adam and L-BFGS-B minimization techniques. Additionally, the solutions from the proposed method are compared with the established analytical solutions found in the literature. The findings show that the proposed method is a computational machine learning approach that is accurate and efficient for solving nonlinear sine-Gordon equations with a variety of boundary conditions as well as any complex nonlinear physical problems across multiple disciplines. [ABSTRACT FROM AUTHOR]

Bridge expansion joints (BEJs) in service are susceptible to damage from various factors such as fatigue, impact, and environmental conditions. While visual inspection is the most common approach for inspecting BEJs, it is subjective and labor-intensive. In this paper, we propose a novel methodology for detecting the fault status of BEJs, inspired by voiceprint recognition (VPR) based on audio signals. We establish an Artificial Neural Network to filter nonevent segments from low signal-to-noise ratio signals, achieving an AuC value of 0.981. We design and improve ConFormer VPR models with a multifeature aggregation strategy and cascade them to realize fault detection of BEJs. For three successive tasks in classifying environment sound types, vehicle impact types, and faults, the ConFormer VPR models achieve AuC values of 0.975, 0.925, and 0.886, respectively, demonstrating the feasibility of our methods for unmanned inspection of BEJs. In future research, the introduction of multiple types of damage and the implementation of benchmarking tests are planned to further enhance the capabilities of the system. [ABSTRACT FROM AUTHOR]

In this paper, a compact and simple structure of an elliptic microstrip lowpass filter (LPF) is designed for harmonic suppression in microwave quadrature hybrid coupler (QHC) applications. A radial resonator and a rectangular resonator are used to produce an elliptic LPF. The proposed LPF is used on the outer sides of the branch line coupler, which has improved the coupler harmonic suppression. Furthermore, artificial neural networks (ANNs) are incorporated to improve the LPF design process. The LPF best structure is obtained using the proposed ANN model. The proposed LPF has a compact size, which only occupies 16.4 mm × 7.3 mm equals to 0.164 λg × 0.073 λg, has a cut frequency of 2.2 GHz, and shows a sharp transmission band with a roll-off rate of 158.3 dB/GHz. Finally, the deigned QHC operates correctly at 1 GHz, which shows high harmonic suppression ability. The proposed QHC provides wide suppression band from 2.25 GHz up to more than 14 GHz, which can effectively suppress 3rd, to 14th harmonics. The proposed coupler features desirable parameters of S11, S21, S31, and S41, with magnitude of −21 dB, −3.4 dB, −3.3 dB, and −22.5 dB, at the operating frequency. The proposed approach mitigates the complexity of the circuit fabrication, compared with the previous methods while achieved desirable performances for the proposed QHC. [ABSTRACT FROM AUTHOR]

Deep learning has recently received extensive attention in the field of rolling-bearing fault diagnosis owing to its powerful feature expression capability. With the help of deep learning, we can fully extract the deep features hidden in the data, significantly improving the accuracy and efficiency of fault diagnosis. Despite this progress, deep learning still faces two outstanding problems. (1) Each layer uses the same convolution kernel to extract features, making it difficult to adaptively select convolution kernels based on the features of the input image, which limits the network's adaptability to different input features and leads to weak feature extraction. (2) Large number of parameters and long training time. To solve the above problems, this paper proposes an integrated deep neural network that combines an improved selective kernel network (SKNet) with an enhanced Inception-ResNet-v2, named SIR-CNN. First, based on the SKNet, a new three-branch SKNet was designed. Second, the new SKNet is embedded into a depthwise separable convolution network such that the model can adaptively select convolution kernels of different sizes during training. Furthermore, the convolution structure in the Inception-ResNet-v2 network was replaced by the improved depthwise separable convolution network to achieve effective feature extraction. Finally, the time-frequency maps of the raw vibration signals are obtained through short-time Fourier transform (STFT) and then sent to the proposed SIR-CNN network for experiments. The experimental results show that the proposed SIR-CNN achieves superior performance compared to other methods. [ABSTRACT FROM AUTHOR]

Accurate fault detection in high-voltage direct current (HVDC) transmission lines plays a pivotal role in enhancing operational efficiency, reducing costs, and ensuring grid reliability. This research aims to develop a cost-effective and high-performance fault detection solution for HVDC systems. The primary objective is to accurately identify and localize faults within the power system. In pursuit of this goal, the paper presents a comparative analysis of current and voltage characteristics between the rectifier and inverter sides of the HVDC transmission system and their associated alternating current (AC) counterparts under various fault conditions. Voltage and current features are extracted and optimized using a metaheuristic approach, specifically Harris Hawk's optimization method. Leveraging machine learning (ML) and artificial neural networks (ANN), this technique demonstrates its effectiveness in generating a fault locator with exceptional accuracy. With a substantial volume of data employed for learning and training, the Harris Hawks optimization method exhibits faster convergence compared to other metaheuristic methods examined in this study. The research findings are applied to simulate diverse fault types and unknown fault locations at multiple system points. Evaluating the fault detection system's effectiveness, quantified through metrics such as specificity, accuracy, F1 score, and sensitivity, yields remarkable results, with percentages of 99.01%, 98.69%, 98.64%, and 98.67%, respectively. This research underscores the critical role of accurate fault detection in HVDC systems, offering valuable insights into optimizing grid performance and reliability. [ABSTRACT FROM AUTHOR]

Compressive strength of concrete is an important parameter in the design of concrete structures and the prediction of their durability. Therefore, it is of great significance to predict the compressive strength of concrete. In this study, a fully connected neural network model is developed using the PyTorch framework to predict the compressive strength of concrete and compared with six other machine learning models. These models are multiple linear regression, K-nearest neighbor regression, support vector machine, decision tree, random forest, light gradient boosting machine, and artificial neural network. The model is trained using 4,253 data with seven input parameters, including cement (C), fly ash (F), mineral powder (K), fine aggregate (FA), coarse aggregate (CA), water reducer admixture (WRA), and water (W). Three thousand six hundred twenty-one data in the datasets are used to train the prediction model after data cleaning, and 632 data are used to validate the model. The results show that the fully connected neural network model based on PyTorch frame can predict the compressive strength of concrete with higher accuracy. Therefore, it is a reliable and useful method to optimize the artificial network model. So, it has important application value in practice. The strength of concrete can be predicted in advance, making the project more efficient and reducing costs. Besides, by adjusting the mix ratio, combining the strength prediction results in different environments and industries to ensure the quality of construction. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) was revealed as a highly efficient approach to enhance the prediction's accuracy, demonstrating the reliability of the simulation through uncertainty quantification analysis. However, in the numerical simulations, most of the tasks are highly nonlinear relationships between the parameters that directly affect the efficiency of the DA such as the large search space dimension, resulting in significant computational costs. This limitation can be overcome by implementing machine learning (ML) predictions that can efficiently expand the DA search space. Therefore, this study is aimed at enhancing the performance of DA by integrating ML and DA (IMD), proposing a new method to overcome the stand-alone DA limitations. The approach involved implementing a stand-alone DA using the multidimensional analysis of reactor safety (MARS) code to enhance the prediction of reflood tests. The output datasets obtained from the stand-alone DA were then used to train the deep neural networks (DNNs), and the accuracy of the DNN's prediction was thoroughly evaluated with varying dataset sizes. This DNN subsequently can predict the enormous unobserved samples, enabling the identification and investigation of the prospective candidates for the subsequent DA process. The results demonstrated that the stand-alone DA achieved an accuracy enhancement of up to 41.3% in reflood test predictions, while the IMD yielded even more significant improvements, with a performance enhancement of 47.0%. These findings reveal that the IMD approach outperformed the stand-alone DA approach, particularly in the case of high flooding rate tests. In this context, the proposed integrating system can effectively overcome the high computational cost and enhance the performance of stand-alone DA. [ABSTRACT FROM AUTHOR]

The present study investigates the performance of photovoltaic thermal (PVT) systems that employ silver, aluminum oxide, copper, and titanium dioxide nanoparticles with distilled water as a solvent. The volume portions of the nanoparticles considered are 2% and 5% by weight. The study employs an energy balance equation to encompass circular geometries for fluid flow channels and a flow velocity ranging from 1 × 10 − 4 to 3 × 10 − 4 m/s. A numerical model has been established to investigate the performance of the photovoltaic thermal system and obtained the highest performance in Cu/water nanofluid for a uniform mass flow rate of 0.0670 kg/s and volume portion of 5% compared to other nanofluids, and the average electrical, thermal, and overall performance achieved is 15.8%, 30.2%, and 45.3%, respectively. Moreover, an artificial neural network (ANN) was developed to predict the electrical and thermal efficiency of the PVT system, and the mean absolute percentage error (MAPE) between array error of the thermal and electrical efficiency of the system is 4.98% and 2.61%, respectively. This value shows the strong validation of the numerical and ANN simulation values. [ABSTRACT FROM AUTHOR]

The study is aimed at determining how the quality of Antarctic krill (Euphausia superba) sauce (AkS) changed over time, including changes in color, moisture content, acid value (AV), peroxide value (POV), thiobarbituric acid reactive substance (TBARS), aerobic plate count, and sensory score. Quality variations and shelf life of AkS were estimated using kinetic model and back propagation (BP) neural network model. The results showed that sensory score, moisture content, and a ∗ values of AkS declined as storage temperature increased at 4, 25, and 37°C. In addition, the L ∗ values, b ∗ values, AV, POV, and TBARS of AkS increased as storage duration increased, indicating that high storage temperature of the samples accelerated quality degradation. The primary reason for AkS degradation was the oxidation of proteins and lipids. The POV, TBARS, and total sensory evaluation rating exhibited a highly significant correlation, and therefore, POV and TBARS were selected as the indicators for the two models. The BP neural network outperformed the kinetic model in predicting quality changes over the whole storage period, with relative errors of less than 10%. In terms of shelf-life prediction, the BP neural network's relative errors were 11.76% and 13.39% in POV and TBARS, respectively. POV and TBARS had experimental shelf lengths of 119 and 142 d, respectively. Compared with the kinetic model, the BP neural network model predicted the quality changes and shelf life of AkS with greater accuracy and stability. The findings offer fundamental insights and innovative concepts for the production of high-value Antarctic krill products, as well as the exploitation of Antarctic krill resources. [ABSTRACT FROM AUTHOR]

Noise is an undesirable and disturbing effect that degrades the quality of an image. The importance of noise reduction in images and its wide-ranging applications are essential. Most popular image noise filters rely on static parameters that are often challenging to fine-tune. Dynamically adapting these static parameters for image noise filters is a critical area of research. In this study, a combination model between the features of complex networks and artificial neural networks is proposed to automatically find the noise reduction parameter of the block-matching and 3D filtering method. Experimental results on the black and white MRI image set have shown that the model correctly predicted the parameters of the BM3D filter and removed the noise in the images of those MRI images. The model gave high denoising results with PSNR of 51.94 and SSIM of 0.998. [ABSTRACT FROM AUTHOR]

We present a deep learning-based method that corrects motion artifacts and thus accelerates data acquisition and reconstruction of magnetic resonance images. The novel model, the Motion Artifact Correction by Swin Network (MACS-Net), uses a Swin transformer layer as the fundamental block and the Unet architecture as the neural network backbone. We employ a hierarchical transformer with shifted windows to extract multiscale contextual features during encoding. A new dual upsampling technique is employed to enhance the spatial resolutions of feature maps in the Swin transformer-based decoder layer. A raw magnetic resonance imaging dataset is used for network training and testing; the data contain various motion artifacts with ground truth images of the same subjects. The results were compared to six state-of-the-art MRI image motion correction methods using two types of motions. When motions were brief (within 5 s), the method reduced the average normalized root mean square error (NRMSE) from 45.25% to 17.51%, increased the mean structural similarity index measure (SSIM) from 79.43% to 91.72%, and increased the peak signal-to-noise ratio (PSNR) from 18.24 to 26.57 dB. Similarly, when motions were extended from 5 to 10 s, our approach decreased the average NRMSE from 60.30% to 21.04%, improved the mean SSIM from 33.86% to 90.33%, and increased the PSNR from 15.64 to 24.99 dB. The anatomical structures of the corrected images and the motion-free brain data were similar. [ABSTRACT FROM AUTHOR]

In order to predict the freshness of grass carp, a novel data preprocessing method was proposed for electronic nose (E-nose) signals. The signal sequences from six sensors were selected and subsequently normalized. The direct signal sequence merging (DSSM) and reversed signal sequence merging (RSSM) modes were used for signal sequence merging. Subsequently, the genetic algorithm (GA) was used to evaluate the contribution of diverse sensors, and the merged data sequence was compressed using wavelet transform (WT). Using approximation coefficient and detail coefficient based on different scales and different signal sequence merging modes, principal component analysis (PCA) discriminated successfully storage time of chilled fish fillet. The PCA plots clearly demonstrated that all extracted feature data fully retain the signal characters. The partial least squares (PLS) and artificial neural network (ANN) models were used to establish prediction models for the freshness of grass carp during storage. The DSSM-ANN- A 5 and DSSM-PLS- D 4 models were chosen as the TVB-N content prediction models, while the DSSM-ANN- D 5 and RSSM-PLS- A 0 models were selected as the K value prediction models. The R 2 values of these models are higher than 0.9, and they have a good coefficient of determination. The results of this study suggest that it using E-nose signals to predict TVB-N content and K value is an effective method for assessing the freshness of grass carp during storage. [ABSTRACT FROM AUTHOR]

Studies from around the world show that engines using biofuel, LPG, and CNG emit fewer pollutants than those using conventional fuels. Experimental research has focused on a rapid compression and expansion machine (RCEM) that resembles a compression ignition (CI) engine. It uses dual direct injection fuel, diesel and propane (DP), with propane injection timing varying from 0 to 40 before top dead center (BTDC) and diesel injection timing remaining at 10 BTDC. The compression ratio was changed at points 17 and 19 by adjusting the RCEM connecting rod. A converge simulation program was used to run the simulation model, which was used to examine how the fire and inflow inside the chamber developed. The ANN method was used to predict pressure, temperature, power, TKE, and ITE data output based on propane energy fraction, compression ratio, and SOI of propane as input data parameters. It was noticed that the ANN prediction on experimental data has a higher accuracy compared to the simulation prediction. The R and MSE values were used to identify the accuracy of the prediction on output parameter data. ANN generalization capability is comparatively high when trained with large enough databases. The highest accuracy of prediction was produced on TKE, which had an MSE of 0.003715 and R value of 0.99981 from 287900 sample data. This shows that the ANN model is quite accurate in forecasting output experimental data. [ABSTRACT FROM AUTHOR]

The cloudification of telecommunication network functions with 5G is a novelty that offers higher performance than that of previous generations. However, these virtual network functions (VNFs) are exposed to internet threats when hosted in the cloud, resulting in new security challenges. Another fact is that many VNFs vendors with different security policies will be implied in 5G deployment, creating a heterogeneous 5G network. The authorities also require data privacy enhancement in 5G deployment and there is the fact that mobile operators need to inspect data for malicious traffic detection. In this situation, how can network traffic inspections be conducted effectively without infringing on data privacy? This study addresses this gap by proposing a novel state-of-the-art hybrid deep neural network that combines a convolutional neural network (CNN) stacked to bidirectional long short-term memory (BiLSTM) and unidirectional long short-term memory (LSTM) for the deep inspection of network flow for malicious traffic detection. The approach utilizes federated learning (FL) to facilitate multiple VNFs vendors to collaboratively train the proposed model without sharing VNFs' raw data, which can mitigate the risk of data privacy violation. The proposed framework incorporates transport layer security (TLS) encryption to prevent data tempering or man-in-the-middle attacks between VNFs. The framework was validated through simulation using open-access benchmark datasets (InSDN and CICIDS2017). They achieved 99.99% and 99.58% accuracy and 0.048% and 0.617% false-positive rates for the InSDN and CICIDS2017 datasets, respectively, for FL. This study demonstrates the potential of hybrid deep learning-based FL for heterogeneous 5G network VNFs security monitoring. [ABSTRACT FROM AUTHOR]

This research introduces a unified power quality conditioner (UPQC) that integrates solar photovoltaic (PV) system and battery energy systems (SBES) to address power quality (PQ) issues. The reference signals for voltage source converters of UPQC are produced by the Levenberg–Marquardt back propagation (LMBP) trained artificial neural network control (ANNC). This method removes the necessity for conventional dq0, abc complex shifting. Moreover, the optimal choice of parameters for the adaptive neuro-fuzzy inference system (ANFIS) was achieved through the integration of the enhanced harmony search algorithm (EHSA) and the predator-prey-based firefly algorithm (PPFA) in the form of the hybrid metaheuristic algorithm (PPF-EHSA). In addition, the algorithm is employed to optimize the selection of resistance and inductance values for the filters in UPQC. The primary objective of the ANNC with predator-prey-based firefly algorithm and enhanced harmony search algorithm (PPF-EHSA) is to enhance the stability of the DC-link capacitor voltage (DLCV) with reduced settling time amid changes in load, solar irradiation (G), and temperature (T). Moreover, the algorithm seeks to achieve a reduction in total harmonic distortion (THD) and enhance power factor (PF). The method also focuses on mitigating fluctuations such as swell, harmonics, and sag and also unbalances at the grid voltage. The proposed approach is examined through four distinct cases involving various permutations of loads and sun irradiation (G). However, in order to demonstrate the performance of the suggested approach, a comparison is conducted with the ant colony and genetic algorithms, i.e., (ACA) (GA), as well as the standard methods of synchronous reference frame (SRF) and instantaneous active and reactive power theory (p-q). The results clearly demonstrate that the proposed method exhibits a reduced mean square error (MSE) of 0.02107 and a lower total harmonic distortion (THD) of 2.06% compared to alternative methods. [ABSTRACT FROM AUTHOR]

The study involved subjecting sohshang (Elaeagnus latifolia) juice (SJ) to thermosonications (TS), a process integrating ultrasound and heat, with a range of independent variables. Specifically, three explored distinct amplitudes (30%, 40%, and 50%) alongside three temperature settings (30°C, 40°C, and 50°C) and four treatment durations (15, 30, 45, and 60 minutes) were used in the experiment. A variety of quality parameters were analyzed such as antioxidant activity (AOA), ascorbic acid (AA), total flavonoid content (TFC), total phenolic content (TPC), yeast and mold count (YMC), and total viable count (TVC). Thermosonicated sohshang juices (TSSJ) successfully achieved highest content of AA (69.15 ± 0.99 mg/100 ml), AOA (72.93 ± 1.62 %), TPC (122.03 ± 4.23 mg GAE/ml), and TFC (116.14 ± 3.29 mg QE)/ml) under ideal circumstances. Also, minimal TVC and YMC in these juices have been observed. The best results for AA and TFC were observed at 40°C with 40% and 50% amplitude over processing times of 45 and 60 min. To optimize the extraction processes with various objectives, artificial neural network (ANN) was established with an original experimental planning methodology. Overall, the investigation demonstrated that TS is an effective method to enhance the nutritional and microbiological qualities of sohshang fruit juice. The use of ANN in the optimization process is particularly valuable in achieving desirable outcomes. As the food and pharmaceutical industries seek natural and bioactive substances, TSSJ holds great potential for various applications. [ABSTRACT FROM AUTHOR]

In rehabilitation, the correct performance of the exercises the specialist prescribes wrist movement is crucial. However, this may have the disadvantage of the patient's subjectivity. Moreover, recent studies show that feedback through electrostimulation devices is beneficial during the process that leads to neuromotor rehabilitation. Besides, the electromyographic (EMG) signals give information about the actual degree of rehabilitation. This work examines whether temporal features can be used to classify wrist movements using back-propagation artificial neural networks and superficial EMG (sEMG) signals. The data for the evaluation were based on the information acquired from sEMG signals of two forearm muscles: the flexor carpi ulnaris (FCU) and the brachioradialis (B). These sEMG signals were analyzed to find the most critical parameters for classifying the wrist's movement and to configure a multilayer perceptron (MLP) capable of classifying such movements. [ABSTRACT FROM AUTHOR]

This study proposes an uncertainty quantification method based on deep neural networks and Catmull–Clark subdivision surfaces for vibroacoustic problems. The deep neural networks are utilized as a surrogate model to efficiently generate samples for stochastic analysis. The training data are obtained from numerical simulation by coupling the isogeometric finite element method and the isogeometric boundary element method. In the simulation, the geometric models are constructed with Catmull–Clark subdivision surfaces, and meantime, the physical fields are discretized with the same spline functions as used in geometric modelling. Multiple deep neural networks are trained to predict the sound pressure response for various parameters with different numbers and dimensions in vibroacoustic problems. Numerical examples are provided to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]