Development of Cotton Picker Fire Monitoring System Based on GA-BP Algorithm.

Due to the characteristics of the cotton picker working in the field and the physical characteristics of cotton, it is easy to burn during the operation, and it is difficult to be detected, monitored, and alarmed. In this study, a fire monitoring system of cotton pickers based on GA optimized BP neural network model was designed. By integrating the monitoring data of SHT21 temperature and humidity sensors and CO concentration monitoring sensors, the fire situation was predicted, and an industrial control host computer system was developed to monitor the CO gas concentration in real time and display it on the vehicle terminal. The BP neural network was optimized by using the GA genetic algorithm as the learning algorithm, and the data collected by the gas sensor were processed by the optimized network, which effectively improved the data accuracy of CO concentration during fires. In this system, the CO concentration in the cotton box of the cotton picker was validated, and the measured value of sensor was compared with the actual value, which verified the effectiveness of the optimized BP neural network model with GA. The experimental verification showed that the system monitoring error rate was 3.44%, the accurate early warning rate was over 96.5%, and the false alarm rate and the missed alarm rate were less than 3%. In this study, the fire of cotton pickers can be monitored in real time and an early warning can be made in time, and a new method was provided for accurate monitoring of fire in the field operation of cotton pickers.

MicroRNA-223 downregulation promotes HBx-induced podocyte pyroptosis by targeting the NLRP3 inflammasome.

Hepatitis B virus (HBV) and its related protein, HBV X (HBx), play an important role in podocyte injury in HBV-associated glomerulonephritis (HBV-GN). The microRNA MiR-223 is expressed in several diseases, including HBV-associated disease, while the nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome plays a major role in pyroptosis. In this study, we investigated the function and mechanism of action of miR-223 in HBx-induced podocyte pyroptosis. A quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) assay showed that miR-223 was downregulated in HBx-transfected podocytes. Transfection with an miR-223 mimic abolished the expression of the NLRP3 inflammasome and the cytokines that are released as a result of NLRP3 overexpression. Moreover, transfection with HBx and NLRP3 overexpression plasmids increased the expression of pyroptosis-related proteins, especially in the presence of miR-223 inhibitors. Thus, miR-223 downregulation plays an important role in HBx-induced podocyte pyroptosis by targeting the NLRP3 inflammasome, suggesting that miR-223 is a potential therapeutic target for alleviating HBV-GN inflammation.

A Novel Intelligent Fault Diagnosis Method for Rolling Bearings Based on Wasserstein Generative Adversarial Network and Convolutional Neural Network under Unbalanced Dataset.

Rolling bearings are widely used in industrial manufacturing, and ensuring their stable and effective fault detection is a core requirement in the manufacturing process. However, it is a great challenge to achieve a highly accurate rolling bearing fault diagnosis because of the severe imbalance and distribution differences in fault data due to weak early fault features and interference from environmental noise. An intelligent fault diagnosis strategy for rolling bearings based on grayscale image transformation, a generative adversative network, and a convolutional neural network was proposed to solve this problem. First, the original vibration signal is converted into a grayscale image. Then more training samples are generated using GANs to solve severe imbalance and distribution differences in fault data. Finally, the rolling bearing condition detection and fault identification are carried out by using SECNN. The availability of the method is substantiated by experiments on datasets with different data imbalance ratios. In addition, the superiority of this diagnosis strategy is verified by comparing it with other mainstream intelligent diagnosis techniques. The experimental result demonstrates that this strategy can reach more than 99.6% recognition accuracy even under substantial environmental noise interference or changing working conditions and has good stability in the presence of a severe imbalance in fault data.

An Analysis Scheme of Balancing Energy Consumption with Mobile Velocity Control Strategy for Wireless Rechargeable Sensor Networks.

Wireless Rechargeable Sensor Networks (WRSN) are not yet fully functional and robust due to the fact that their setting parameters assume fixed control velocity and location. This study proposes a novel scheme of the WRSN with mobile sink (MS) velocity control strategies for charging nodes and collecting its data in WRSN. Strip space of the deployed network area is divided into sub-locations for variant corresponding velocities based on nodes energy expenditure demands. The points of consumed energy bottleneck nodes in sub-locations are determined based on gathering data of residual energy and expenditure of nodes. A minimum reliable energy balanced spanning tree is constructed based on data collection to optimize the data transmission paths, balance energy consumption, and reduce data loss during transmission. Experimental results are compared with the other methods in the literature that show that the proposed scheme offers a more effective alternative in reducing the network packet loss rate, balancing the nodes' energy consumption, and charging capacity of the nodes than the competitors.

Composite poly(ethylene oxide)-based solid electrolyte with consecutive and fast ion transport channels constructed by upper-dimensional MIL-53(Al) nanofibers.

Novel structural designs for metal organic frameworks (MOFs) are expected to improve ion-transport behavior in composite solid electrolytes. Herein, upper-dimensional MIL-53(Al) nanofibers (MNFs, MIL-53 belongs to the MIL (Material Institute Lavoisier) group) with flower-like nanoflake structures have been designed and constructed via modified hydrothermal coordination. The optimized MNFs with high surface area and porosity can form abundant interfaces with poly(ethylene oxide) (PEO) matrix. The plasticization of MNFs to the PEO matrix will facilitate segmental movement of PEO chains to facilitate Li+ conduction. The unsaturated open metal centers of MNFs can effectively capture bis(trifluoromethanesulfonyl)imide anions (TFSI-) to deliver more free lithium ions for transfer. Moreover, the upper-dimensional nanofiber structure endows lithium ions with a long-range and consecutive transport pathway. The obtained composite solid electrolyte (MNFs@PEO) presents a high ionic conductivity of 4.1 × 10-4 S cm-1 and a great Li+ transference number of 0.4 at 60 °C. The electrolyte also exhibits a stable Li plating/stripping behavior over 1000 h at 0.1 mA cm-1 with inhibited Li dendrite growth. Furthermore, the Li/LiFePO4 and Li/LiNi0.8Mn0.1Co0.1O2 batteries with MNFs@PEO as electrolytes both display great cycling stabilities with high-capacity retention, indicating their potential applications in lithium metal batteries. The study will put forward new inspirations for designing advanced MOF-based composite solid electrolytes.

Microstructure of long period stacking ordered phase in an as-cast Mg-Ni-Zn-Dy alloy.

An as-cast Mg-5Ni-4Zn-1Dy (wt.%) alloy was prepared by traditional ingot metallurgy. Transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) were used to study the microstructural characteristics of 14H LPSO phase in the as-cast Mg-5Ni-4Zn-1Dy (wt.%) alloy. Selected-area electron diffraction, convergent-beam electron diffraction, energy dispersive spectrum and high-angle annular dark-field (HAADF) scanning transmission electron microscopy (STEM) and atomic-resolution energy dispersive spectrum (EDS) mapping were used to study the microscopic characteristics of the 14H LPSO phase. The unit cell of 14H LPSO phase has two ABCA-type building blocks and the Zn, Ni and Dy elements were enriched on the ABCA-type stacking sequence, especially on the B and C layers. Space group of the 14H LPSO phase was determined to be P63mc and atomic structural model was constructed.