316 stainless steel is a widely used structural material in industrial applications, and its corrosion remains a critical issue when exposed to humid environments over prolonged periods. In this study, we developed a facile one-step electrodeposition method to fabricate a robust cerium nitrate hexahydrate/myristic acid composite hydrophobic coating on 316 stainless steel. The coating exhibits excellent hydrophobicity and effectively lowers the corrosion potential of the substrate. By optimizing the electrodeposition time and voltage, the hydrophobic performance and structural stability of the composite coating were significantly improved. The hydrophobicity and microstructure of the coating were systematically characterized using water contact angle measurements, scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD). Results show that the static water contact angle of the optimized hydrophobic coating reaches 141.5 ± 1.2°. Electrochemical tests demonstrate that the coated sample exhibits significantly enhanced electrochemical corrosion resistance compared to bare 316 stainless steel, with a notable decrease in corrosion current density and positive shift in corrosion potential.

The reflectivity performance of radar absorbing materials is usually studied through testing and simulation. Based on testing system and principle of the far-field RCS method, bow method and portable device rapid detection method, the advantages and disadvantages of the three methods were analyzed. The reflectance data of the three methods were tested and compared. The results show that the far-field RCS method and the bow method are consistent, while the portable device's rapid detection is similar to the others, but exits some differences. In the simulation study, the electromagnetic parameters measured and calculated by the free-space method were simulated and calculated by using the time domain solver or frequency domain solver of the full-wave algorithm in the simulation software (CST Microwave Studio). The simulation results show that the calculation results of the two solvers are almost identical, and they are not much different from the far-field RCS measurement results.

D-galactose was used to establish a model of chronic oxidative liver injury in rats, and to study the metabolomics mechanism of Longdan Xiegan Decoction（LXD） in treating chronic oxidative liver injury in rats. After 6 weeks of continuous modelling and drug administration, urine and faecal samples were collected. The relevant markers in urine and faeces were screened and identified based on GC-MS technology coupled with metabolomics methods. The results showed that the treatment of chronic oxidative liver injury in rats by LXD was associated with the effects of trans-aconitic acid, sebacic acid, lauric acid, ketoglutaric acid, nicotinic acid, indoleglycolide, fenugreek acid, nicotinamide, fatty acids, malic acid, L-aspartic acid, uridine, quinolenic acid, citric acid, O-acetylserine, stearic acid, isocitric acid, chondroitinic acid, phenylacetic acid, alanine, 3-phosphoglycerol, arginine, 6- Glucose phosphate, 2hydrouracil, 2-hydroxybutyric acid, hydroxylamine, 2-hydroxyglycolic acid, and 27 biomarkers and pathways related to the TCA cycle, glycerol catabolism, and fatty acid catabolism. 

 To ensure the safety and efficiency of the battery during use, it is crucial to accurately calculate the remaining battery charge (State of Charge, SOC) in real time. To address the shortcomings of sudden situation tracking, prior information, and dynamic environments, an SOC estimation method based on the Adaptive Strong Tracking Unscented Kalman Filter (ASTFUKF) is proposed. Considering that the accuracy of SOC estimation is highly dependent on the accuracy of the battery model, the ASTFUKF and Extended Kalman Filter (EKF) algorithms are subsequently used to jointly estimate the battery SOC and SOH (State of Health). Distinguishing and processing different time scales based on the time-varying characteristics of SOC and SOH can optimize the calculation strategy and save resources. Experimental comparisons of different algorithms in dynamic environments show that the proposed algorithm has an average error of 0.27% and a maximum error of 0.48% under the HWFET condition, and an average error of 0.44% and a maximum error of 0.91% under the UDDS condition, significantly improving the estimation accuracy of battery SOC and SOH.

The T-shaped welding process of 5754 aluminum alloy with 2mm thickness was carried out by melting inert gas shielded welding. The changes of macroscopic morphology, microstructure and microhardness of welded joint were analyzed by welding current, voltage and speed. The results show that increasing the welding current, voltage or reducing the welding speed will increase the welding heat input, increase the weld penetration or even produce penetration phenomenon, and lead to uneven coaling or cracking of the weld and heat affected zone, which will reduce the hardness of most areas of the welded joint, thereby reducing the welding quality. However, when the heat input is low, the welded joint will produce non-fusion phenomenon and a large number of pores, so that the joint hardness is reduced and the welding performance is affected. Through the test analysis, the joint hardness distribution is uniform, the macroscopic defects are significantly reduced, the micro structure is refined, and the comprehensive performance is improved after the optimized process parameters are used for welding.

In order to solve the problems of low precision, local optimization and slow convergence of subtraction average optimizer algorithm, a multi-strategy improvement method is proposed. Firstly, the population members are initialized using Logistic chaotic mapping to make the population distribution more uniform. Secondly, a nonlinear weight factor is introduced to reduce the influence of randomness on the optimization of the algorithm. Finally, the combination of variable spiral and Cauchy variation strategy updates the position of search agents, increases the diversity of particles, helps the algorithm to jump out of the local optimal region, explore a broader search space, improve computing efficiency, and find a balance between global search and local search. The performance of the improved VCSABO algorithm is tested and evaluated by eight benchmark test functions. The experimental results show that the convergence speed, calculation accuracy and optimization ability of the improved subtraction average optimizer algorithm are greatly improved, and the effectiveness of the improved strategy is verified. At the same time, VCSABO algorithm is used to optimize the variational mode decomposition algorithm to process CWRU fault signals. The results show that the key parameter combination (K, α) can be obtained adaptively by this method, and the characteristic components in the signal can be effectively decomposed, which shows significant technical advantages in the processing of weak fault signals of rotating machinery and validates the engineering application value of VCSABO algorithm.

 To study the dispersion performance, high-temperature stability and phase transformation behavior of composite materials, the high-temperature oxidation resistance of carbon nanotube-reinforced ceramic matrix composites is investigated. Carbon nanotubes were treated with acidic liquid. The treated carbon nanotubes were combined with alumina ceramic matrix materials to prepare powder materials, and the powder was sintered to form carbon nanotube-reinforced ceramic matrix composite material blocks. The increase in oxidation mass and the thermogravimetric loss rate of the composite material under different amounts of carbon nanotubes were analyzed. The high-temperature oxidation resistance of the composite material under the changes of sintering temperature and ablation distance was determined through microscopic analysis. The test results show that when the dosage of carbon nanotubes is 35%, the increase value of oxidation mass and the thermogravimetric loss rate of the composite material are both relatively low, and the high-temperature oxidation resistance performance is good. When the sintering temperature is 1500 ℃, the interior of the composite material is relatively compact and oxidation is not likely to occur. When the ablation distance is relatively close, it will lead to the destruction of the high-temperature oxidation resistance of this material. Therefore, attention should be paid to the usage environment of this material.

With the rapid development of wireless communication technologies and the increasing prevalence of smart mobile devices, location-based services have been widely applied. In recent years, keyword related location queries have become one of the hot research topics. For example, given a query point q and a set of keywords T, the query returns the data object point that contains all keywords and is closest to the query point q. For spatial keyword queries with nearest neighbor constraints in such road network, the road network is first partitioned, and then an STG-Tree index structure is constructed based on G-tree. Then, invalid data object points are pruned with the shortest distance between subgraphs as the lower bound, and an effective query algorithm is proposed. Finally, the query performance of the algorithm is verified through a large number of experimental experiments.

The ocean contains renewable energy with a high energy density, offering significant potential for energy harvesting. Collecting abundant wave energy and utilizing it to power electrical facilities such as sensors for cross-sea bridges not only provides a sustainable energy solution but also paves the way for the development of green energy. To address the self-powering issue of cross-sea bridge sensors, a piezoelectric wave energy harvesting scheme based on a capsule structure is proposed. This scheme captures ocean wave energy and converts it into electricity to power the sensors. Modeling and simulation results demonstrate that the piezoelectric wave energy harvester can achieve an output voltage of up to 15V and a power of 38mW, verifying the feasibility of the proposed wave energy harvesting scheme for self-powering low-power monitoring sensors in cross-sea bridge applications.

The inverted pendulum system is a typical unstable nonlinear system. A mathematical model for a first-order inverted pendulum was established using Newtonian mechanics, and system simulation was implemented through the Simulink module in MATLAB. Building upon state feedback control, to reduce the settling time of full-order observer-based state feedback control, this study proposes a reduced-order observer-based state feedback control method. This approach converts high-dimensional data into low-dimensional data, simplifying system structure while demonstrating improved efficiency and faster settling speed compared with full-order observers.

Based on the principles of electrochemical reactions and semi-empirical methods, an output characteristic model for proton exchange membrane fuel cell (PEMFC) stacks is constructed. A modified transient search optimization (MTSO) algorithm is proposed, which initializes the search agent group using the tent chaotic mapping strategy and enhances global optimization capability through nonlinear decreasing and global reverse learning strategies. It is utilized for identifying unknown parameters in the semi-empirical model of PEMFC. Simulation results demonstrate that, compared to the transient search optimization (TSO) algorithm, enhanced transient search optimization (ETSO) algorithm, and harris hawks optimization (HHO) algorithm, the MTSO algorithm exhibits superior performance in parameter identification of the PEMFC model. It provides valuable insights for optimizing the design and control of fuel cell systems.

Traffic speed prediction is challenging due to its complex dynamic spatial-temporal characteristics. To deeply explore the latent information in traffic data, this paper proposes a multi-scale spatial-temporal graph convolutional network for traffic speed prediction. The method integrates modules that capture different temporal-scale features, as well as dynamic temporal and spatial features, into a unified neural network framework. Specifically, the model first employs multi-scale temporal partitioning to learn traffic patterns at different time scales, including short-term, medium-term, and long-term scales. Then, it combines adaptive graph convolution and dilated causal convolution to capture dynamic spatiotemporal dependencies at various scales. Next, a parameter matrix-based approach is used to fuse the outputs from different scales, followed by stacked activation functions and linear layers to generate the final prediction. Experimental results on multiple real-world datasets demonstrate that the proposed method improves prediction accuracy. Additionally, ablation studies validate the effectiveness of each module.

In this study, benzo[a]pyrene (BaP) was used as the polluted background in PC12 cell culture studies.To track changes in cell phenotypic, proliferation rate, ROS value, and cell survival rate, oestradiol and NGF were introduced. Using the ggplot2 program and macrotranscriptome sequencing, a number of analyses were conducted on the number of genes, changes in gene expression, and gene expression between pathways.The findings demonstrated that when exogenous Nerve Growth Factor (NGF) was present, oestradiol promoted cells' endogenous NGF secretion. It stopped the overexpression of the anti-apoptotic protein Bcl-2 by fortifying the MAPK signaling pathway. Cell differentiation was induced downstream of the MAPK signaling pathway.Because the findings of the transcriptome test indicated that the PTPRO protein and the E2Fs transcription factor family were highly expressed. We confirmed the appropriate mRNA levels and focused on E2F1 to control PTPRO protein-induced neuronal differentiation based on our previous research.Lastly, we demonstrated the cytotoxicity of BaP and oestradiol by simulating their denaturation of the PTPRO protein using computer molecular docking simulation technologies. Currently, there is a dearth of research on the mechanism of interference between PAHs and estrogen. In order to address PAHs in the environment, we believe that our study will generate fresh research ideas.

Fiber can provide strong clues and supportive evidence for relevant cases of criminal offence,having been extensively studied in forensic investigations.This experiment tested different kinds of fiber by FTIR-ATR method.According to different absorption peaks of infrared spectra, we may differentiate fbers.The method is a nondestructive analytical technique, which needs a small amount of sample and rapid testing, has good repeatability, therefore can be used to characterize fibers from crime scene.