Applied Sciences

The diaphragm is a key component of the lithium-ion battery and largely determines its performance. Currently, commercial diaphragms suffer from poor thermal stability, low porosity, and low liquid absorption rate. In this study, we prepared a polyurethane/polyacrylonitrile (PU/PAN) lithium-ion battery diaphragm using a centrifugal spinning method with PU as the main substrate and PAN as the additive. The results showed that the PU/PAN nanofiber diaphragm prepared by centrifugal spinning had a 3D porous structure, and when using 18% PU:PAN = 7:3, the porosity of the fiber diaphragm was 83.9%, the liquid absorption rate was 493%, and the ionic conductivity was 1.79 mS/cm. The battery system had good electrochemical performance and thermal stability, with an electrochemical stability window of 5.2 V. The diaphragm did not shrink when heated at 160 °C. In a lithium-ion battery system with lithium iron phosphate (LiFePO₄) as the cathode material, the capacity remained at 147.1 mAh/g after 50 cycles at a 0.2 C rate, with a capacity retention rate of 95.8%. This indicated excellent cycle stability and a multiplicative performance with good application potential. Full article

Surface cracks are typical defects in high-speed rail (HSR) slab tracks, which can cause structural deterioration and reduce the service reliability of the track system. However, the question of how to effectively detect and quantify the surface cracks remains unsolved at present. In this paper, a novel crack-detection method based on infrared thermography is adopted to quantify surface cracks on rail-track slabs. In this method, the thermogram of a track slab acquired by an infrared camera is first processed with the non-subsampled contourlet transform (NSCT)-based image-enhancement algorithm, and the crack is located via an edge-detection algorithm. Next, to quantitatively detect the surface crack, a pixel-locating method is proposed, whereby the crack width, length, and area can be obtained. Lastly, the detection accuracy of the proposed method at different temperatures is verified against a laboratory test, in which a scale model of the slab is poured and a temperature-controlled cabinet is used to control the temperature-change process. The results show that the proposed method can effectively enhance the edge details of the surface cracks in the image and that the crack area can be effectively extracted; the accuracy of the quantification of the crack width can reach 99%, whilst the accuracy of the quantification of the crack length and area is 85%, which essentially meets the requirements of HSR-slab-track inspection. This research could open the possibility of the application of IRT-based track slab inspection in HSR operations to enhance the efficiency of defect detection. Full article

Existing deep learning-based methods for detecting fake news are uninterpretable, and they do not use external knowledge related to the news. As a result, the authors of the paper propose a graph matching-based approach combined with external knowledge to detect fake news. The approach focuses on extracting commonsense knowledge from news texts through knowledge extraction, extracting background knowledge related to news content from a commonsense knowledge graph through entity extraction and entity disambiguation, using external knowledge as evidence for news identification, and interpreting the final identification results through such evidence. To achieve the identification of fake news containing commonsense errors, the algorithm uses random walks graph matching and compares the commonsense knowledge embedded in the news content with the relevant external knowledge in the commonsense knowledge graph. The news is then discriminated as true or false based on the results of the comparative analysis. From the experimental results, the method can achieve 91.07%, 85.00%, and 89.47% accuracy, precision, and recall rates, respectively, in the task of identifying fake news containing commonsense errors. Full article

The circadian and stress-realizing systems are interconnected, and the balance of their interaction determines the state of human health. The objective of this study was to investigate the modulating effect of activating the circadian system on heart rate variability in female medical students with high anxiety while performing a cognitive task. After 20 min of adequate stimulation of circadian photoreceptors with a monochromatic blue light, the cognitive task performance resulted in a decrease in sympathetic impact. During the session of monochromatic blue light exposure, a trend of increasing heart rate variability was observed in a state of relative rest (especially in the first 5 min). A comparative analysis of the parameters of the letter cancellation test before and after light exposure revealed a statistically significant increase in the indexes reflecting mental productivity, work accuracy, and concentration of attention. The results suggest an essential physiological role of the human circadian system in modulating the autonomic and psycho-emotional conditions, as well as cognitive functions of individuals with high anxiety. Our findings indicate the possibility of quickly correcting the balance in human systemic regulatory mechanisms using the activation of retinal circadian photoreceptors by blue light. Full article

Considering the technological enhancements nowadays, antennas tend to be smaller in order to be easily integrated in devices. The most used antennas today in small high-tech devices close to the human body are planar antennas. In this paper, a Yagi Uda planar antenna operating at a frequency of 2.4 GHz is HF analyzed and optimized by increasing its bandwidth and gain while maintaining its initial dimensions. The methods used to optimize the antenna’s operation are the use of different dielectrics, different numbers of directors, and different dimensions for directors, placing new conductor elements, all while keeping the same dimensions for its implementation on the planar device. The optimized structure of the planar Yagi Uda antenna has a 10% increase in bandwidth and a 30% increase in gain, reaching a peak value of 4.84 dBi. In our daily activities, we use devices with such antennas very often, so an analysis of the antenna’s influence on the human body is performed: the SAR, electric and magnetic field and radiation power density are determined, represented and reported to the standards in force. For the frequency considered, the SAR should be below 4 W/kg for the head/torso when the exposure is more than six minutes, which is a value exceeded by the antenna in its near vicinity. The calculated maximum electric field limit is 0.349 V/m and the maximum magnetic field value is 28.441 V/m for an exposure between 6 and 30 min values, which is also exceeded in the immediate vicinity of the antenna. The results allow us to suggest that such an antenna should be placed further from the human body, or some protection should be placed between the body and the antenna. From the radiation power density point of view for the modeled antenna, it can be said that a distance from the antenna greater than 0.5 m is considered to be safe. Full article

Vehicle passing angles are critical metrics for evaluating the geometric passability of vehicles. The accurate measurement of these angles is essential for route planning in complex terrain and in guiding the production of specialized vehicles. However, the current measurement methods cannot meet the requirements of efficiency, convenience and robustness. This paper presents a novel measurement method by building and measuring the point cloud of a vehicle chassis. Based on this method, a novel measurement system is designed and its effectiveness is verified. In the system, a wheeled robot acquires and processes data after passing underneath the vehicle. Then, we introduce a new approach to reduce the main sources of error when building point clouds beneath the vehicle, achieved by modifying the extraction algorithm and the proportion of different feature points in each frame. Additionally, we present a fast geometric calculation algorithm for calculating the passing angles. The simulation experiment results demonstrate deviations of 0.06252%, 0.01575%, and 0.003987% when comparing the calculated angles to those of the simulated vehicle. The experimental results show that the method and system are effective at acquiring the point cloud of the vehicle and calculating the parameters of passing angles with good data consistency, exhibiting variances of 0.12407, 0.12407, and 0.69804. Full article

Cell-based therapy is an excellent therapeutic modality that involves cell transplantation into patients; however, given that most transplanted cells die immediately post-transplantation, the application of this strategy remains limited. Cell encapsulation is a promising technique for prolonging the survival of transplanted cells, although a definitive encapsulation protocol is yet to be established. Herein, we selected sodium alginate as a polymer for cell encapsulation and optimized the structure and function of cell-encapsulating alginate capsules. First, alginate capsules were prepared using various concentrations of sodium alginate and calcium chloride solution. The NanoLuc luciferase (Nluc)-expressing murine mesenchymal stem cell line C3H10T1/2 was used to prepare the alginate capsules, and cell survival was evaluated after transplantation into mice. The structural properties of the alginate capsules were dependent on the preparation conditions. Capsules with adequate hardness were obtained using 1% sodium alginate and 10% calcium chloride solutions. Alginate capsules encapsulating 5 × 10³ C3H10T1/2/Nluc cells/10 μL maintained a constant cell number over time under in vitro culture conditions. After transplantation into mice, C3H10T1/2/Nluc cells encapsulated in alginate capsules exhibited a significantly longer survival (≥40 days) than suspended cells. Based on these findings, cell-encapsulating alginate capsules with optimal properties can be used for long-term cell-based therapies. Full article

Diamond cutting-tool wear has a direct impact on the processing accuracy of the machined surface in ultra-precision diamond cutting. It is difficult to monitor the tool’s condition because of the slight wear amount. This paper proposed a hybrid deep learning model for tool wear state prediction in ultra-precision diamond cutting. The cutting force was accurately estimated and the wear state of the diamond tool was predicted by using the hybrid deep learning model with the motion displacement, velocity, and other signals in the machining process. By carrying out machining experiments, this method can classify diamond-tool wear condition with an accuracy of more than 85%. Meanwhile, the effectiveness of the proposed method was verified by comparing it with a variety of machine learning models. Full article

To perform indicator selection and verification for the on-orbit fault reconstruction of a giant satellite swarm, a hybrid multi-objective fault reconstruction algorithm is proposed and then verified by Monte Carlo analysis. First, according to the on-orbit failure analysis of the satellite swarm, several optimization indicators, such as the health state of the satellite swarm, the total energy consumption of reconstruction, and the balance of fuel consumption, are proposed. Then, a hybrid multi-objective fitness function is constructed, and a hybrid multi-objective genetic algorithm is used to optimize the objective function to obtain the optimal reconstruction strategy. Finally, the algorithm is statistically verified by Monte Carlo analysis. The simulation results not only show the algorithm’s validity but also reveal the relationship between the number of satellite faults and the health of the satellite swarm. From this, the maximum number of faulty satellites allowed in the giant satellite swarm is calculated, which is significant for assessing the swarm’s health. Full article

Increasing development of infrastructure in Indonesia has driven the need for effective ground improvement methods to accelerate the consolidation of soft soil, which is estimated to occupy around 10% of the country’s land area. A prefabricated vertical drain combined with vacuum preloading is among the most effective methods for this purpose. However, the prefabricated vertical drain creates a smear zone in the surrounding soil area during installation. This study examines the effectiveness of a newly developed mandrel system in reducing the smear zone during prefabricated vertical drain installation. Large-scale consolidation tests at a macro level and microstructure analysis using scanning electron microscopy at a micro level were employed to investigate the effect of soil water content and shear strength. The results show that the water content and shear strength of the soft soil gradually increased in the inner smear zone and transition zone, while both decreased in the radial distance. Furthermore, the soil structure underwent a transformation in which the particle area and pore area became a closed flake structure, and apparent agglomeration occurred. The test results indicate that the newly developed mandrel system can effectively reduce the smear zone. The macro to micro test results demonstrated that the mandrel system is successful in reducing the smear zone effect. Full article

One significant source of decoherence in superconducting circuits is known as two-level systems (TLSs), found in amorphous oxide layers. These circuits can, however, also be utilized as spectral and temporal TLS probes. Comprehensive investigations on the physics of TLSs are now possible thanks to recent advancements in superconducting qubits. Here, we simultaneously measure the tunable Xmon qubit decoherence time as well as the resonance frequency for more than 3 days to investigate stochastic fluctuations. Time-domain Allan deviation and frequency-domain power spectral density analysis indicate that two TLSs in near resonance with the qubit are responsible for the fluctuations. From the extracted oscillation in $T_1$ decay, we locate the two TLSs near the junctions. Full article

This paper focuses on the implementation of nfstream, an open source network data analysis tool and machine learning model using the TensorFlow library for HTTP attack detection. HTTP attacks are common and pose a significant security threat to networked systems. In this paper, we propose a machine learning-based approach to detect the aforementioned attacks, by exploiting the machine learning capabilities of TensorFlow. We also focused on the collection and analysis of network traffic data using nfstream, which provides a detailed analysis of network traffic flows. We pre-processed and transformed the collected data into vectors, which were used to train the machine learning model using the TensorFlow library. The proposed model using nfstream and TensorFlow is effective in detecting HTTP attacks. The machine learning model achieved high accuracy on the tested dataset, demonstrating its ability to correctly identify HTTP attacks while minimizing false positives. Full article

The circular-hole rotating core fog nozzle has excellent atomization performance and has been widely used in the realm of spray dust. As part of this study, a mathematical model was developed for predicting the Sauter mean diameter (SMD) of nozzles of this type. The coaction between the SMD of the nozzle and the three influencing factors of axial distance, water supply pressure, and outlet diameter was investigated based on the customized spray’s experimental platform and orthogonal design method. According to the comparative analysis of the size range, the axial distance, outlet diameter and water supply pressure are three parameters that affect the SMD of the nozzle, and the degree of influence is axial distance > outlet diameter > water supply pressure. On this basis, a mathematical model was developed using the multiple regression method to predict the SMD of the nozzle. We analyzed the results and compared them to the SMD value predicted by the multiple regression mathematical model and the orthogonal experiment results. The change trend was the same, the values were essentially the same, and the average relative error was just 16.11%. Accordingly, the mathematical model presented in this paper may be used for the prediction and calculation of the droplet size for circular-hole rotating core micro-fog nozzles. Full article

During the operational phase of a bridge, the crossbeam, acting as a supporting member, plays an important role in keeping the cross-sectional shape constant in addition to resisting against various lateral and longitudinal loads and distributing the dead and the live loads to the adjacent main girders. The complex functional requirements lead to a complex internal force composition of the crossbeam. When subjected to torque, the two main beams of the twin I-girder bridge will have deformation in opposite longitudinal directions (known as warping deformation) to counteract the torque. The existing research has not considered the impact of main beam warping deformation on the internal force of the crossbeam. Based on the existing research, this article further considers the impact of main beam warping deformation on the internal force of the crossbeam, making the calculation of the internal force of the crossbeam more accurate. The results show that the torsional characteristics of the continuous twin I-girder bridge can be calculated using Vlasov’s theory of thin-walled structures combined with the displacement method. As for the effect of the crossbeam on the torsional stiffness of the structure, it can be managed by making the crossbeam stiffness continuous; however, in general, the equivalent stiffness is small compared to the stiffness of the main beam and it can be ignored. The crossbeam can be simplified to a bar with two solid ends for the internal force calculation whose formula is proposed in this paper, based on the existing frame model, and it can further consider the influence of warping deformation of the main beam on the internal force of the beam, and the calculation accuracy is high. Full article

This study evaluated dental implant stability, vertical bone gain, bone density, and crestal bone loss using different crestal sinus lift techniques (osteotomy, Densah burs, and piezosurgery). A total of 21 patients were randomly divided into three groups: Group 1: patients were treated using a Densah drill crestal sinus lift, Group 2: patients were treated using a piezoelectric crestal sinus lift and Group 3: patients were treated using an osteotome crestal sinus lift. The patients in all three groups underwent bone grafting and implant placement. An Osstell device was used to determine the implant stability by recording the values of the implant stability quotient (ISQ). CBCT was performed before and 6 months after implant placement for radiographic evaluation and comparison among the groups. All dental implants were completely successful, and statistically significant differences from baseline to 6 months were noted in all groups (p < 0.05). The Densah burs technique resulted in the best implant stability of all groups, while the osteotome technique demonstrated better vertical bone augmentation. However, the values for bone density and crestal bone loss showed no significant difference among all treated groups (p > 0.05). All three techniques were successful for crestal sinus lifts with good clinical outcomes at a 6-month follow-up. The Densah group demonstrated better implant stability, shorter surgery time, and fewer complications; however, the vertical bone gain was greater with the osteotome technique. Full article

Ear recognition is a biometric identification technology based on human ear feature information, which can not only detect the human ear in the picture but also determine whose human ear it is, so human identity can be verified by human ear recognition. In order to improve the real-time performance of the ear recognition algorithm and make it better for practical applications, a lightweight ear recognition method based on YOLOv5s is proposed. This method mainly includes the following steps: First, the MobileNetV3 lightweight network is used as the backbone network of the YOLOv5s ear recognition network. Second, using the idea of the Ghostnet network, the C3 module and Conv module in the YOLOv5s neck network are replaced by the C3Ghost module and GhostConv module, and then the YOLOv5s-MG ear recognition model is constructed. Third, three distinctive human ear datasets, CCU-DE, USTB, and EarVN1.0, are collected. Finally, the proposed lightweight ear recognition method is evaluated by four evaluation indexes: mAP value, model size, computational complexity (GFLOPs), and parameter quantity (params). Compared with the best results of YOLOv5s, YOLOv5s-V3, YOLOv5s-V2, and YOLOv5s-G methods on the CCU-DE, USTB, and EarVN1.0 three ear datasets, the params, GFLOPS, and model size of the proposed method YOLOv5s-MG are increased by 35.29%, 38.24%, and 35.57% respectively. The FPS of the proposed method, YOLOv5s-MG, is superior to the other four methods. The experimental results show that the proposed method has the performance of larger FPS, smaller model, fewer calculations, and fewer parameters under the condition of ensuring the accuracy of ear recognition, which can greatly improve the real-time performance and is feasible and effective. Full article

This paper employed an electrical circuit simulator to investigate an electrodynamic suspension system (EDS) for passenger rail transport applications. Focusing on a null-flux suspension system utilizing figure-eight-shaped coils (8-shaped coils), the aim was to characterize the three primary electromagnetic forces generated in an EDS and to compare the findings with existing literature. The dynamic circuit theory (DCT) approach was utilized to model the system as an electrical circuit with lumped parameters, and mutual inductance values between the superconducting (SC) coil and the upper and lower loops of the 8-shaped coil were calculated and inputted into the simulator. The results were compared with experimental data obtained from the Yamanashi test track. The comparison demonstrated close alignment between the theoretical expectations and the obtained experimental curves, validating the accuracy of the proposed model. The study highlights the advantages of this new approach, including faster computation times and efficient implementation of modifications. Overall, this work contributes to the ongoing development and optimization of null-flux suspension Maglev systems. Full article

In order to meet the requirements of high accuracy and fast algorithm for numerical heat transfer simulation, an iterative scheme of Proper Orthogonal Decomposition (for short, POD) dimension reduction based on the classical central difference Galerkin spectral method is proposed for solving two-dimensional transient heat conduction problems. The POD dimension reduction spectral method model is constructed by taking the calculation results of classical central difference Galerkin spectral method as sample data. The numerical algorithm characteristics of flow and heat transfer are studied by using a partial differential equation as a mathematical model, and the error estimation is given. Finally, different time intervals are used as parameters to simulate experiments. The results show that the POD method is applicable to transient nonlinear heat conduction problems, and the maximum average relative error of the reconstructed temperature field is 0.89675%. Moreover, the POD method not only has a high calculation accuracy, but also has an average calculation speed as high as 310.25 times that of the central difference Galerkin algorithm. It can be seen that under the condition that the error between the solution of POD dimension reduction extrapolation algorithm and the solution of classical central difference Galerkin spectrum method is small enough, the POD method can greatly reduce the calculation amount, shorten the running time, and ensure a high accuracy of the calculation results, thus verifying the effectiveness and feasibility of the algorithm. Full article