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Practical bearing fault diagnosis faces several key challenges: noise interference, performance degradation under varying operating conditions, and slow diagnostic speed. For such engineering problems, this article combines deeply separable convolution to establish three lightweight neural network fault diagnosis models—MobileNet-DLCNN, ShuffleNet-DLCNN and SqueezeNet-DLCNN. At the same time, the failure mechanism of rolling bearings is studied. This article incorporates colored noise into the standard bearing failure datasets from Case Western Reserve University (CWRU) and the American Society for Machinery Failure Prevention Technology (MFPT), and conducts noise resistance training under variable adaptive characteristics, compares the diagnostic ability with the datasets before adding colored noise. Then, the identification and classification abilities of the three lightweight convolution models proposed under each dataset, as well as the transfer learning adaptive ability of the three models between different datasets and working conditions, are verified through comparative experiments. The experimental results show that SqueezeNet-DLCNN has the best diagnostic performance among the three models, and it can complete the recognition and classification of all data in about one minute with an accuracy of 97%. The lightweight convolution designed in this article has the advantages of strong noise resistance, high efficiency, and fast speed.

We have developed a novel mediated biochemical oxygen demand (BOD) biosensor based on immobilized Bacillus subtilis (B. subtilis) on three-dimensional (3D) porous graphene-polypyrrole (rGO-PPy) composite. The 3D porous rGO-PPy composite was prepared using hydrothermal method following with electropolymerization. Then the 3D porous rGO-PPy composite was used as a support for immobilizing negatively charged B. subtilis denoted as rGO-PPy-B through coordination and electrostatic interaction. Further, the prepared rGO-PPy-B was used as a microbial biofilm for establishing a mediated BOD biosensor with ferricyanide as an electronic acceptor. The indirect determination of BOD was performed by electrochemical measuring ferrocyanide generated from a reduced ferricyanide mediator using interdigited ultramicroelectrode array (IUDA) as the working electrode. The experimental results suggested a good linear relationship between the amperometric responses and BOD standard concentrations from 4 to 60 mg/L, with a limit detection of 1.8 mg/L (S/N ≥ 3). The electrochemical measurement of real water samples showed a good agreement with the conventional BOD5 method, and the good anti-interference as well as the long-term stability were well demonstrated, indicating that the proposed mediated BOD biosensor in this study holds a potential practical application of real water monitoring.

A novel electrochemical sensor based on electropolymerized ion imprinted poly (o-phenylenediamine) PoPD/electrochemical reduced graphene (ERGO) composite on glass carbon electrode (GCE) was fabricated for selective and sensitive determination of trace Cd(II) in water. ERGO was first deposited on the surface of GCE by electrochemical cyclic voltammetry (CV) scanning to enhance the electron transport activity at electrode surface. The ion imprinted polymer (IIP) of imprinted PoPD was then in situ electropolymerized on ERGO via CV scanning with oPD as functional monomer and Cd(II) ions as template, following removal of the template using electrochemical peroxidation method. The obtained imprinted PoPD/RERGO composites were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray energy spectroscopy (EDS) for the observation of their morphologies and components. The electrochemical behavior of the imprinted PoPD/ERGO/GCE was performed by CV and SWASV. The fabricated sensor of the imprinted PoPD/ERGO/GCE showed a good selectivity toward target Cd(II) ions in the presence of other heavy metal ions. Under the optimized experimental conditions, the sensor exhibited a good linear relationship between SWASV stripping peak values and Cd(II) concentration in the range of 1 to 50 ng/mL, with the limit of detection as 0.13 ng/mL (S/N = 3). The proposed electrochemical sensor of imprinted PoPD/ERGO/GCE was successfully applied for trace Cd(II) determination in real water samples.

Optical and synthetic aperture radar (SAR) images exhibit non-negligible intensity differences due to their unique imaging mechanisms, which makes it difficult for classical SIFT-based algorithms to obtain sufficiently correct correspondences when processing the registration of these two types of images. To tackle this problem, an accurate optical and SAR image registration algorithm based on the SIFT algorithm (OS-PSO) is proposed. First, a modified ratio of exponentially weighted averages (MROEWA) operator is introduced to resolve the sudden dark patches in SAR images, thus generating more consistent gradients between optical and SAR images. Next, we innovatively construct the Harris scale space to replace the traditional difference in the Gaussian (DoG) scale space, identify repeatable key-points by searching for local maxima, and perform localization refinement on the identified key-points to improve their accuracy. Immediately after that, the gradient location orientation histogram (GLOH) method is adopted to construct the feature descriptors. Finally, we propose an enhanced matching method. The transformed relation is obtained in the initial matching stage using the nearest neighbor distance ratio (NNDR) and fast sample consensus (FSC) methods. And the re-matching takes into account the location, scale, and main direction of key-points to increase the number of correctly corresponding points. The proposed OS-PSO algorithm has been implemented on the Gaofen and Sentinel series with excellent results. The superior performance of the designed registration system can also be applied in complex scenarios, including urban, suburban, river, farmland, and lake areas, with more efficiency and accuracy than the state-of-the-art methods based on the WHU-OPT-SAR dataset and the BISTU-OPT-SAR dataset.

Zinc-ion batteries (ZIBs) are receiving increasing research attention due to their high energy density, resource abundance, low-cost, intrinsic high-safety properties, and the appropriate plating/stripping voltage. Gel-state electrolytes possess merits of having a wide electrochemical window, good flexibility, superior water retainability, and excellent compatibility with aqueous electrolytes, which makes them potential candidates for flexible batteries. However, the practical applications of ZIBs with gel-state electrolytes still have some issues of water content easily dropping, poor mechanical stability, and the interface problem. Therefore, the application of hydrogel-based, self-healing gel, gel polymer, thermos-reversible, and other additional functions of gel electrolytes in ZIBs are discussed in this review. Following that, the design of multi-functional gel-state electrolytes for ZIBs is proposed. Finally, the prospect and the challenges of this type of battery are described.

Three-dimensional (3D) porous graphene-based materials have displayed attractive electrochemical catalysis and sensing performances, benefiting from their high porosity, large surface area, and excellent electrical conductivity. In this work, a novel electrochemical sensor based on 3D porous reduced graphene (3DPrGO) and ion-imprinted polymer (IIP) was developed for trace cadmium ion (Cd(II)) detection in water. The 3DPrGO was synthesized in situ at a glassy carbon electrode (GCE) surface using a polystyrene (PS) colloidal crystal template and the electrodeposition method. Then, IIP film was further modified on the 3DPrGO by electropolymerization to make it suitable for detecting Cd(II). Attributable to the abundant nanopores and good electron transport of the 3DPrGO, as well as the specific recognition for Cd(II) of IIP, a sensitive determination of trace Cd(II) at PoPD-IIP/3DPrGO/GCE was achieved. The proposed sensor exhibited comprehensive linear Cd(II) responses ranging from 1 to 100 μg/L (R2 = 99.7%). The limit of detection (LOD) was 0.11 μg/L, about 30 times lower than the drinking water standard set by the World Health Organization (WHO). Moreover, PoPD-IIP/3DPrGO/GCE was applied for the detection of Cd(II) in actual water samples. The satisfying recoveries (97–99.6%) and relative standard deviations (RSD, 3.5–5.7%) make the proposed sensor a promising candidate for rapid and on-site water monitoring.

The large random errors in Micro-Electro-Mechanical System (MEMS) gyros are one of the major factors that affect the precision of inertial navigation systems. Based on the indoor inertial navigation system, an improved wavelet threshold de-noising method was proposed and combined with a gradient radial basis function (RBF) neural network to better compensate errors. We analyzed the random errors in an MEMS gyroscope by using Allan variance, and introduced the traditional wavelet threshold methods. Then, we improved the methods and proposed a new threshold function. The new method can be used more effectively to detach white noise and drift error in the error model. Finally, the drift data was modeled and analyzed in combination with the RBF neural network. Experimental results indicate that the method is effective, and this is of great significance for improving the accuracy of indoor inertial navigation based on MEMS gyroscopes.

The study of matching degree between energy equipment manufacturing and market demand is crucial for energy enterprises to adjust business strategies, expand market share, and develop sustainably. Considering that the current electricity market evaluation indicators are rarely selected from a global perspective and a single evaluation method may lead to one-sided results, this article takes the technology and equipment related to electric energy as the research object and selects six indicators, including technical standards, qualification certification, export methods, after-sales service, market concentration, and product concentration. By analyzing the supply and demand characteristics of major global regional markets and the situation of Chinese power enterprises in these markets, we propose matching model cluster including osculating value method, rank-sum ratio method, ideal point method, entropy value method, and efficacy coefficient method to conduct the matching degree study. The results show that the overall market matching degree of Chinese power companies is good, especially in Southeast Asia, Central Asia, and Africa. For markets with a low degree of matching, we analyze the reasons based on the matching indicators values to provide companies with corresponding strategies and recommendations.

This study aims to investigate the effectiveness and implementation strategies of project-based teaching in the course of \"CNC Technology\" based on the cultivation of applied talents. Through project practice, cultivate students’ practical abilities and application skills to improve their overall quality and employment competitiveness. The expected research results can verify the adaptability and effectiveness of the teaching mode based on the cultivation of applied talents in the course of \"CNC Technology\", and determine the positive impact of project-based teaching on the cultivation of students’ practical ability and application skills. The research will propose design and implementation strategies suitable for project-based teaching in the course of \"CNC Technology\", providing reference and reference for teachers’ teaching. Through the promotion of research, experience and guidance can be provided for the teaching reform and talent cultivation of related courses.

The demand for high-performance underwater thrusters in marine engineering is increasing. The concealed, mobile, and efficient underwater ability of fish provides many directions for research. The black ghost knifefish uses only wavy ventral fins to swim and can hover and roll in the water. Based on the physiological and morphological characteristics of the black ghost knifefish, we explored the structure and movement mode of the ventral fin, so as to establish a two-degree of freedom (2-DOF) structural model and kinematic model. We reveal the motion mechanism of the undulating fin propulsion through the constructed model and computational fluid dynamics. It is found that when the fin surface fluctuates, a pair of vortices with opposite directions will be formed on the concave side of the fin surface. These vortices will produce a central jet on the fin surface, provide a reverse impulse for the ventral fin, and make the fin obtain power. In addition, we found that the propulsive force of the ribbon fin along the body direction is positively correlated with the swing amplitude and frequency of the fin movement, and the propulsive torque of the ribbon fin to realize the maneuvering movement increases first and then decreases with the increase of the torsion angle. The research on the structure and motion mechanism of the ribbon fin of the black ghost knifefish provides a basis for the development of a bionic prototype of multi-DOF motion and the control strategy of high-mobility motion.