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Dissociation as well as Suicidality within Eating Disorders: The particular Mediating Aim of Entire body Impression Trouble, and the Moderating Function regarding Anxiety and depression.
Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning models, and their multilayer nonlinear mapping capability can improve the accuracy of intelligent fault diagnosis. However, problems such as gradient disappearance occur as the number of network layers increases. Moreover, directly taking the raw vibration signals of rolling bearings as the network input results in incomplete feature extraction. In order to efficiently represent the state characteristics of vibration signals in image form and improve the feature learning capability of the network, this paper proposes fault diagnosis model MTF-ResNet based on a Markov transition field and deep residual network. First, the data of raw vibration signals are augmented by using a sliding window. Then, vibration signal samples are converted into two-dimensional images by MTF, which retains the time dependence and frequency structure of time-series signals, and a deep residual neural network is established to perform feature extraction, and identify the severity and location of the bearing faults through image classification. Lastly, experiments were conducted on a bearing dataset to verify the effectiveness and superiority of the MTF-ResNet model. Features learned by the model are visualized by t-SNE, and experimental results indicate that MTF-ResNet showed better average accuracy compared with several widely used diagnostic methods.Three-dimensional object detection in the point cloud can provide more accurate object data for autonomous driving. In this paper, we propose a method named MA-MFFC that uses an attention mechanism and a multi-scale feature fusion network with ConvNeXt module to improve the accuracy of object detection. The multi-attention (MA) module contains point-channel attention and voxel attention, which are used in voxelization and 3D backbone. By considering the point-wise and channel-wise, the attention mechanism enhances the information of key points in voxels, suppresses background point clouds in voxelization, and improves the robustness of the network. The voxel attention module is used in the 3D backbone to obtain more robust and discriminative voxel features. The MFFC module contains the multi-scale feature fusion network and the ConvNeXt module; the multi-scale feature fusion network can extract rich feature information and improve the detection accuracy, and the convolutional layer is replaced with the ConvNeXt module to enhance the feature extraction capability of the network. The experimental results show that the average accuracy is 64.60% for pedestrians and 80.92% for cyclists on the KITTI dataset, which is 1.33% and 2.1% higher, respectively, compared with the baseline network, enabling more accurate detection and localization of more difficult objects.Flexible sensor arrays are widely used for wearable physiological signal recording applications. A high density sensor array requires the signal readout to be compatible with multiple channels. This paper presents a highly-integrated remote health monitoring system integrating a flexible pressure sensor array with a multi-channel wireless readout chip. The custom-designed chip features 64 voltage readout channels, a power management unit, and a wireless transceiver. The whole chip fabricated in a 65 nm complementary metal-oxide-semiconductor (CMOS) process occupies 3.7 × 3.7 mm2, and the core blocks consume 2.3 mW from a 1 V supply in the wireless recording mode. The proposed multi-channel system is validated by measuring the ballistocardiogram (BCG) and pulse wave, which paves the way for future portable remote human physiological signals monitoring devices.Distributed Energy Resources (DERs) are growing in importance Power Systems. Battery Electrical Storage Systems (BESS) represent fundamental tools in order to balance the unpredictable power production of some Renewable Energy Sources (RES). Nevertheless, BESS are usually remotely controlled by SCADA systems, so they are prone to cyberattacks. This paper analyzes the vulnerabilities of BESS and proposes an anomaly detection algorithm that, by observing the physical behavior of the system, aims to promptly detect dangerous working conditions by exploiting the capabilities of a particular neural network architecture called the autoencoder. The results show the performance of the proposed approach with respect to the traditional One Class Support Vector Machine algorithm.Inertial-measurement-unit (IMU)-based human activity recognition (HAR) studies have improved their performance owing to the latest classification model. In this study, the conformer, which is a state-of-the-art (SOTA) model in the field of speech recognition, is introduced in HAR to improve the performance of the transformer-based HAR model. The transformer model has a multi-head self-attention structure that can extract temporal dependency well, similar to the recurrent neural network (RNN) series while having higher computational efficiency than the RNN series. However, recent HAR studies have shown good performance by combining an RNN-series and convolutional neural network (CNN) model. Therefore, the performance of the transformer-based HAR study can be improved by adding a CNN layer that extracts local features well. The model that improved these points is the conformer-based-model model. To evaluate the proposed model, WISDM, UCI-HAR, and PAMAP2 datasets were used. A synthetic minority oversampling technique was used for the data augmentation algorithm to improve the dataset. From the experiment, the conformer-based HAR model showed better performance than baseline models the transformer-based-model and the 1D-CNN HAR models. Moreover, the performance of the proposed algorithm was superior to that of algorithms proposed in recent similar studies which do not use RNN-series.In this study, we implemented a remote sensing-based approach for monitoring abandoned agricultural land in the Yarmouk River Basin (YRB) in Southern Syria and Northern Jordan during the Syrian crisis. A time series analysis for the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) was conducted using 1650 multi-temporal images from Landsat-5 and Landsat-8 between 1986 and 2021. Selleckchem Bulevirtide We analyzed the agricultural phenological profiles and investigated the impact of the Syrian crisis on agricultural activities in YRB. The analysis was performed using JavaScript commands in Google Earth Engine. The results confirmed the impact of the Syrian crisis on agricultural land use. The phenological characteristics of NDVI and NDMI during the crisis (2013-2021) were compared to the phenological profiles for the period before the crisis (1986-2010). The NDVI and NDMI profiles had smooth, bell-shaped, and single beak NDVI and NDMI values during the period of crisis in comparison to those irregular phenological profiles for the period before the crisis or during the de-escalation/reconciliation period in the study area. The maximum average NDVI and NDMI values was found in March during the crisis, indicating the progress of natural vegetation and fallow land, while they fluctuated between March and April before the crisis or during the de-escalation/reconciliation period, indicating regular agricultural and cultivation practices.Bolt-supporting technology has been widely used in mine roadway support, and its own working conditions have important reference value for roadway safety support. In order to realize the continuous and reliable monitoring of the bolt rod's working condition, this paper analyzes the existing problems of the existing fiber Bragg grating force-measuring bolt (FBG-FMB), and proposes a fiber grating strain desensitization sensing theory. Based on this theory, a desensitized FBG-FMB is developed with the spring as the elastic sensitive element. A mechanical analysis and drawing test show that the strain of the force-measuring bolt is greater than 60 times the micro-strain of the fiber grating, which verifies the feasibility of the structure design of the FBG-FMB. Finally, through the field application in the coal mine roadway, the working conditions of the bolt body at the two measuring points of the roadway are obtained to verify the reliability of the force-measuring bolt. In addition, the desensitized FBG-FMB can be widely used in the supporting fields of underground engineering such as slopes, tunnels, and foundation pits.This paper presents an innovative application of a 6-DOF robot in the field of rehabilitation training. This robot operates in a parallel fashion for lower limb movement, which adopts a new structure that can help patients to carry out a variety of rehabilitation exercises. Traditional parallel robots, such as the Stewart robot, have the characteristics of strong bearing capacity. However, it is difficult to achieve high-speed, high-acceleration and long journey movement. This paper presents a new robot configuration that can address these problems. This paper also conducts an all-around characteristic analysis of this new parallel robot, including kinematics, dynamics and structure, to better study the robot and improve its performance. This paper optimizes an algorithm to make it more suitable for rehabilitation training. Finally, the performance improvements brought by optimization are verified by simulations.We report the use of a novel technology based on optical photothermal infrared (O-PTIR) spectroscopy for obtaining simultaneous infrared and Raman spectra from the same location of the sample allowing us to study bacterial metabolism by monitoring the incorporation of 13C- and 15N-labeled compounds. Infrared data obtained from bulk populations and single cells via O-PTIR spectroscopy were compared to conventional Fourier transform infrared (FTIR) spectroscopy in order to evaluate the reproducibility of the results achieved by all three approaches. Raman spectra acquired were concomitant with infrared data from bulk populations as well as infrared spectra collected from single cells, and were subjected to principal component analysis in order to evaluate any specific separation resulting from the isotopic incorporation. Similar clustering patterns were observed in infrared data acquired from single cells via O-PTIR spectroscopy as well as from bulk populations via FTIR and O-PTIR spectroscopies, indicating full incorporation of heavy isotopes by the bacteria. Satisfactory discrimination between unlabeled (viz. 12C14N), 13C14N- and 13C15N-labeled bacteria was also obtained using Raman spectra from bulk populations. In this report, we also discuss the limitations of O-PTIR technology to acquire Raman data from single bacterial cells (with typical dimensions of 1 × 2 µm) as well as spectral artifacts induced by thermal damage when analyzing very small amounts of biomass (a bacterium tipically weighs ~ 1 pg).To stabilize the detection signal of palladium-based hydrogen sensors on paper substrates, a graphite intermediate layer was painted on the surface of paper. The graphite-on-paper (GOP) substrate offers advantages such as good thermo-electrical conductivity, low cost, and uncomplicated preparation technology. Quasi-1-dimensional palladium (Pd) thin films with 8 nm and 60 nm thicknesses were deposited on the GOP substrates using the vacuum evaporation technique. Thanks to the unique properties of the GOP substrate, a continuous Pd microfiber network structure appeared after deposition of the ultra-thin Pd film. Additionally, the sensing performance of the palladium-based hydrogen sensor was not affected, whether using GOP or paper substrate at 25 °C. Surprisingly, heating-induced loss of sensitivity was restrained due to the increased electrical conductivity of the GOP substrate at 50 °C.
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