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The effect of silica nanoparticle (Nano-SiO2) dopants on insulation paper cellulose, and the interaction between them, was investigated using molecular dynamics simulations. The mechanical properties, interactions, and cellulose-Nano-SiO2 compatibility of composite models of cellulose doped with Nano-SiO2 were studied. An increase in Nano-SiO2 size leads to a decrease in the mechanical properties, and a decrease in the anti-deformation ability of the composite model. The binding energies and bond energies per surface area of the composite models indicate that the bonding interaction between spherical Nano-SiO2 and cellulose is the strongest among the four different Nano-SiO2 shapes that are investigated. The solubilities of the four composite models decrease with increasing Nano-SiO2 size, and the difference between the solubility of pure cellulose and those of the composite models increases with increasing Nano-SiO2 size. Good doping effects with the highest cellulose-Nano-SiO2 compatibility are achieved for the cellulose model doped with spherical Nano-SiO2 of 10 Å in diameter. These findings provide a method for modifying the mechanical properties of cellulose by doping, perhaps for improving insulation dielectrics. [ABSTRACT FROM AUTHOR]

With the development of electronic technology, semiconductor memory is gradually shifting toward smaller area with less power consumption. SOT-MRAM is one of the competitive substitutes for DRAM and SRAM due to its superior endurance and switching speed. In contrast to STT-MRAM, the separation of read and write routes allows SOT-MRAM to have a lower error rate and higher lifetime, but this comes at the expense of the memory density. In recent years, vertical integrated circuits have relied on TSV to complete 3D stacking to ease the pressure of Moore's Law on scaling circuits. SOT-MRAM can take advantage of 3D stacking to reduce power and latency. More importantly, the density of SOT-MRAM can be improved at the same time. In the paper, simulation is conducted based on DESTINY, with the TSV model supplemented to NVSIM to evaluate the performance of MRAM 3D structures. The 3D SOT-MRAM structure in DESTINY can be implemented with a bus layer and interconnect structure between layers, which greatly reduces the expense of area. However, the 3D structure requires a more complex interconnect structure to truly meet the requirements of high density. For this reason, 3D model of unit interconnection using TSV is presented in the paper. Memory has several components, of which the memory array is the one with the largest area share. This paper explores the spatial structure of the array and proposes a new model which allows more complex interconnect structures to be accomplished on the same area. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the adaptability and enhancement methods of ignition chips in complex electromagnetic environments. In this paper, combined with simulation and calculation technology, we first establish the electromagnetic–electrical–thermal coupling simulation model of ignition chips and analyze the influence of two electromagnetic environments, continuous electromagnetic wave and nuclear electromagnetic pulse, on the electrical–thermal coupling in the bridge area of the ignition chip. The results show that a continuous electromagnetic wave field strength of 1200 V/m, irradiated for 5 s, a nuclear electromagnetic pulse with a rise time of 5 ns, a half-height width of 24 ns, and a leg wire length of 350 mm will cause the ignition chip to fire accidently. In order to suppress its accidental firing, the electromagnetic protection is enhanced by using parallel Transient Voltage Suppressor (TVS) diodes. It has been shown that parallel TVS diodes provide good protection against both continuous electromagnetic waves and nuclear electromagnetic pulses. In particular, the peak induced current decreases by more than 64.7% during the action of continuous electromagnetic waves, and the maximum temperature in the bridge area decreases by 54.34% during the action of nuclear electromagnetic pulses. [ABSTRACT FROM AUTHOR]

In this paper, we use tracked vehicles as an example to study complex systems and surrogate model building method. Due to the high cost and long development cycle associated with the traditional development approach, involving static design and experimental verification, there is an urgent need to explore an effective dynamic optimization design method to enhance vibration reduction performance. This paper introduces a methodology for constructing a model of complex equipment surrogate, aimed at streamlining subsequent parameter optimization processes. The dynamics model of the tracked vehicle is utilized, and the root-mean-square value of plumb vertical acceleration at the center of mass is selected as the dynamic performance index. The impact of structure parameters on dynamic performance is analyzed, and a range of design parameters are determined. The optimal Latin hypercube experimental design method is employed to generate sample data. A radial basis function neural network, optimized by particle swarm optimization, is used to construct a dynamic surrogate model for the tracked vehicle. The surrogate model constructed in this paper greatly reduces the time cost in optimizing complex equipment parameters. In the past, parameter optimization required conducting numerous simulations to collect data, with each simulation taking about 700 s. However, the surrogate model introduced in this paper only requires ∼100 s for completion. This significant reduction in time not only saves costs but also maintains a high level of accuracy. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of photovoltaic (PV) power generation has led to an increased focus on accurate forecasting of PV power output. Interval forecasting, which provides uncertainty measurement information for forecasting results, has become a hot research topic in this field. However, the accuracy of single models or traditional multi-model forecasting methods is often insufficient for meeting the forecasting requirements. In addition, interval forecasting places higher demands on the learning and generalization capabilities of forecasting models. To address these issues, this paper proposes an intelligent multi-model forecasting method based on output features clustering and convolutional neural network–long short term memory (CNN–LSTM) for PV power interval forecasting. First, multiple feature indices are constructed to represent the differences in PV output features for different months. The intelligent clustering method is then employed to achieve the multi-model clustering for forecasting model. Finally, CNN–LSTM is utilized to implement the PV power interval forecasting. The combination of CNN and LSTM effectively improves the modeling accuracy of the intelligent forecasting model. Based on actual data from PV power stations, the method described in this paper narrows down the forecasting interval compared with the single model, reducing prediction interval normalized average width by more than 4%. [ABSTRACT FROM AUTHOR]

A frequency and beam reconfigurable antenna based on encoded reflectors for Wi-Fi and IoT applications is proposed in this paper. By modifying the traditional Alford antenna, extending the transmission line, adding radiation arms, and loading PIN diodes, the frequency reconfigurable function can be realized. Then, the three-layer reflectors are loaded around the antenna. The pattern reconfigurable function of the antenna can be achieved by encoding the operating states of the PIN diodes on the reflectors. The antenna is fed from the bottom via a coaxial transmission line, and the dimension of the antenna is 0.99λ0 × 0.99λ0. The simulated and measured results show that the working frequencies of the antenna can be switched between 1.65, 2, and 2.4 GHz with bandwidths of 2.4%, 4.4%, and 1.2%, gains of 3, 4.5, and 2 dBi, and efficiencies of 48.8%, 75.0%, and 49.6% in directional mode, respectively. The antenna works in omnidirectional radiation mode with a scanning accuracy of 10°, bandwidths of 6.6%, 4.9%, and 0.8%, gains of 0.3, 0.52, and −2.8 dBi, and efficiencies of 73.1%, 74.1%, and 51.9% at 1.65, 2, and 2.4 GHz, respectively, when the PIN diodes are tuned in off states. The antenna proposed in this paper offers several advantages, including a wider frequency band, a broader angular range, and higher precision compared to other frequency and pattern reconfigurable antennas. The antenna can be utilized for Wi-Fi and IoT communication, thereby reducing the number of antennas, enhancing communication quality and accuracy, and improving device integration. [ABSTRACT FROM AUTHOR]

In this paper, aiming at the current distribution network with power operation in the presence of outdoor bright light, the operating scene is complex and not fixed, the transmission lines and lead samples are small and uneven, leading to poor accuracy of machine learning and other methods of distribution network with power operation robot identification and localization, this paper selects three-dimensional LIDAR to design a set of support vector machine (SVM) classifier and correlation analysis based on the target recognition method. This paper selects three-dimensional LIDAR to design a set of support vector machine (SVM) classifier and correlation analysis based on the target recognition method. First, in the context of power distribution network operations, we have explored various filtering methods suitable for preprocessing transmission line data. These methods aim to remove outliers and clutter from point clouds, thus enhancing both point cloud quality and processing speed. This, in turn, improves the efficiency of real-time on-site detection. We conducted experimental comparisons of various clustering algorithms and opted for a region-based point cloud clustering algorithm to achieve the segmentation of individual point clouds. Secondly, we proposed a multi-feature composite approach based on the Viewpoint Feature Histogram (VFH) features, which helps maintain the scale-invariant characteristics of the features. We then introduced a multi-feature composite criterion based on VFH features to extract features from segmented point clouds. Subsequently, an SVM classifier based on this composite feature criterion was developed to achieve target identification and classification. Experimental results have demonstrated improved accuracy in target identification. Finally, we integrated this system with a bucket-arm vehicle for coordinate conversion and precise navigation of the robot to the target station. This method significantly improves the overall operational efficiency of power distribution tasks. [ABSTRACT FROM AUTHOR]

Minimizing the offloading latency of agricultural drip irrigation and fertilization tasks has long been a pressing issue in agricultural drip irrigation and fertilization wireless sensor networks (AIFWSNs). The introduction of edge computing as a robust and practical aid to cloud computing in AIFWSNs can significantly improve the execution speed of agricultural drip irrigation and fertilization tasks and effectively reduce the task offloading latency. Therefore, this paper investigates the optimization method of drip irrigation and fertilization task offloading delay in AIFWSNs based on edge computing and proposes a new edge task offloading method for AIFWSNs, namely, Quantum Chaotic Genetic Optimization Algorithm (QCGA). This paper introduces a novel quantum operator in QCGA, comprising a quantum non-gate and a quantum rotation gate, to improve the algorithm's global search capability. The quantum operator accomplishes the updating of quantum rotating gates without querying the quantum rotation angle table, which reduces the computational complexity of introducing quantum optimization into the task offloading problem of AIFWSNs. This paper proposes a new chaotic operator to make the initial solution more uniformly distributed in the search space by chaotic mapping. This paper's simulation experiments compared QCGA and snake optimizer (SO), genetic algorithm (GA), particle swarm optimization (PSO), sequential offloading, and random offloading methods. Simulation results showed that, compared with SO, GA, PSO, sequential offloading, and random offloading methods, the average delay of QCGA was reduced by 9.96%, 26.78%, 29.31%, 44.67%, and 61.24%. [ABSTRACT FROM AUTHOR]

TEAM Problem 35, a benchmark problem in the optimization of electrical equipment, is a multi-objective, multi-variable optimization problem involving uniform field optimization. This paper proposes a novel guided optimization algorithm to address this problem and achieve optimal magnetic field regulation. First, the deep learning method is used to predict the magnetic field of the solenoid; second, the generated magnetic field cloud map is screened to extract the magnetic field information of the more optimal solution with uniform magnetic field distribution by calculating the variance of the generated magnetic field map; finally, the magnetic field features are combined with the particle swarm optimization algorithm; specifically, the variance of the calculated magnetic field map is added to the objective function of the optimization algorithm to strengthen the optimization process. This integration improves the convergence speed of the optimization process and ensures the continuity and stability of the obtained Pareto solutions. To optimize the solenoid, this paper considers the uniformity of magnetic field distribution and power loss as the primary optimization objectives. The radius of the coil is chosen as the design variable. The optimization results are compared with those obtained by conventional optimization algorithms, demonstrating superior optimization performance. [ABSTRACT FROM AUTHOR]

Sound source localization has a wide range of application prospects in many fields, such as smart home and audio monitoring. Traditional methods are difficult to achieve accurate location in the face of multi-path reflection, reverberation, and ambient noise. In this paper, a complex mapping conversion method for sound source location is proposed. By using complex-valued convolutional neural networks to fuse the amplitude and phase information of the data, a more accurate and comprehensive analysis can be carried out to improve its robustness and realize the accurate location of the sound source. The sound source location method based on complex-valued convolutional neural networks is studied, and the complex mapping principle is analyzed. Simulation and experimental studies were carried out, and the results of simulation and experiment are basically consistent. In the experiment, the positioning accuracy of the complex mapping method is 9.49% higher than that of the absolute value method and 15.81% higher than that of the phase angle method. In addition, its localization success rate, respectively, increased by 4.9% and 8.6% compared to two other methods. This paper opens up a new way for the application of complex-valued convolutional neural networks in sound source localization. [ABSTRACT FROM AUTHOR]

This document is a retraction notice from AIP Advances regarding four manuscripts that have been retracted due to evidence of manipulation in the publication and peer review processes. The editors of AIP Advances have retracted these papers after questions were raised about the data processing and analysis. The retraction is based on the discovery of identical reviewer reports and requests for revisions to add citations to unrelated works by the authors, indicating peer review manipulation. The authors maintain that the data support the validity of the papers, but the editors believe that the issues with the peer review process undermine their confidence in the validity of the papers. [Extracted from the article]

This document is an erratum for a published paper titled "Computation of the masses of the elementary particles." The author has identified two errors in the original paper and provides corrections for them. The corrections involve replacing equations, code tables, and graph figures. The corrected results are qualitatively the same as the original paper, but not quantitatively. The author suggests that using high-performance computing and refining the integrations may result in predicted particle masses closer to the measured values. The erratum includes tables, code listings, and graphs to support the corrections. [Extracted from the article]

In traditional diamond cutting, the cutting force is usually large and it will affect tool life and machining quality. Elliptical vibration cutting (EVC) as one of the ultra-precision machining technologies has a lot of advantages, such as reduces cutting force, extend tool life and so on. It’s difficult to predict the transient cutting force of EVC due to its unique elliptical motion trajectory. Study on chip formation will helpfully to predict cutting force. The geometric feature of chip has important effects on cutting force, however, few scholars have studied the chip formation. In order to investigate the time-varying cutting force of EVC, the geometric feature model of chip is established based on analysis of chip formation, and the effects of cutting parameters on the geometric feature of chip are analyzed. To predict transient force quickly and effectively, the geometric feature of chip is introduced into the cutting force model. The calculated results show that the error between the predicted cutting force in this paper and that in the literature is less than 2%, which proves its feasibility. [ABSTRACT FROM AUTHOR]

Potential transfer is an essential link in equipotential live-line work (EPLW), which has a potential safety hazard. To ensure the safety of the linemen, this paper conducted experimental research on this process. First, to investigate the discharge characteristics of the potential transfer gap in EPLW, this paper developed a simplified experimental platform to examine the relationship between the transfer voltage and gap distance, as well as the electrode structure, during the potential transfer process. Second, to explore the discharge development process of potential transfer, this paper built a discharge observation experimental platform to observe the discharge process and explore the effects of gap distance and type of the floating electrode on the discharge development process and potential transfer current. The research results are valuable for practical projects and linemen safety. [ABSTRACT FROM AUTHOR]

Magnesium alloy shows promise of becoming a new generation of degradable biomaterials because of its good biocompatibility and mechanical properties. This paper details the preparation of magnesium alloy by adding calcium and zinc elements to pure magnesium at a specific ratio and strengthened by solid solution treatment and extrusion. The magnesium alloy is subjected to micro-motion wear testing, in vitro degradation product analysis, and in vitro biocompatibility testing and demonstrates excellent performance. The study demonstrates that the prepared Mg-2.0Zn-1.6Ca alloy friction ring has an elliptical shape with a low friction coefficient, which notably enhances the wear resistance of the material. Furthermore, the corrosion of magnesium alloy products in simulated body fluids does not adversely affect the human body. This is because the surface of the magnesium alloy favors cell growth and has excellent antimicrobial properties. The purpose of this paper is to enhance the preparation, surface friction properties, and biocompatibility of magnesium alloys and to provide theoretical and practical guidance for the preparation, processing, and application of high-performance medical magnesium alloys. [ABSTRACT FROM AUTHOR]

In order to obtain a flexible strain sensor with higher sensitivity, a resistive flexible strain sensor based on the AgNWs-graphene-PDMS sensitive layer is prepared in this paper. This paper presents the preparation of a flexible sensitive layer, the preparation and encapsulation of the flexible strain sensor, and the performance tests of the sensor prepared with AgNWs-graphene (AgNWs-Gr) mixtures at different concentrations. The test results indicate that the prepared sensor has a wide detection range with a maximum stretchable length of 118%. The flexible strain sensor prepared with the AgNWs-Gr mixture at a concentration of 5% has the highest sensitivity and the maximum strain sensitivity of up to 236. It also has good repeatability, stability, and rapid response, and it has been tested for practical applications. The results show that the flexible strain sensor has a broad application prospect in fields such as wearable devices and intelligent robots. [ABSTRACT FROM AUTHOR]

The star sensor is the most accurate measurement instrument in the spacecraft attitude measurement system, and the accurate centroid of the star point is the basis for ensuring the performance of the star sensor. Currently, the centroid of the gray method is the most widely used centroid extraction method in practice. Systematic errors caused by the centroid of the gray method and random noise in the detector imaging process are the main factors contributing to the deviation of the star centroiding coordinates. Considering the relationship between the point spread function and the pixel gray value, this paper proposes a centroiding method to reduce the star point centroiding error by fusing a priori information and energy distribution. The star charts are first preprocessed using a curvature filter and Gaussian blur to reduce the random noise. Then the complexity of the point spread function is considered, and the pixel gray values are corrected based on a priori information and gray value fuzzy processing. Finally, the symmetry of the one-dimensional energy distribution is used to quickly determine the sub-pixel deviation to get the star centroid coordinates. Through simulation and physical simulation experiments, the method was verified to be effective, and the extraction accuracy met the requirements of high-precision star sensors. The night sky observation test results demonstrate that the method in this paper can improve the measurement accuracy of the star sensors. [ABSTRACT FROM AUTHOR]

A surface defect detection method for hot-rolled steel strips was proposed to address the challenges of detecting small target defects, significant differences in morphology, and unclear defect characteristics. This method is based on multiscale feature perception and adaptive feature fusion. First, based on the spatial distribution characteristics of the steel strip image, redundant background interference is removed using automatic gamma correction and Otsu thresholding. Second, based on the characteristics of surface defects in steel strips, this paper proposes TDB-YOLO (YOLO with a small target detection layer), a Bidirectional Feature Pyramid Network (BiFPN), and Double Cross Stage Partial (CSP) Bottleneck with three convolutions (DC3). To detect small object defects, a small target detection layer with a smaller receptive field focuses on fine-grained features, reducing the model's probability of missed detection. In terms of feature extraction, DC3 enhances the interaction of feature information from different spatial scales, enabling the model to effectively handle features of varying scales. In terms of feature fusion, the BiFPN is used to adaptively fuse deep-level and shallow-level feature information, enhancing the semantic richness of the feature information. Ultimately, the proposed model in this paper achieved an accuracy of 90.3% and a recall rate of 88.0% for surface defects in steel strips. The mean average precision was 90.4%, and the frames per second was 33. The detection performance of this model outperformed those of other detection models, demonstrating its ability to effectively meet the real-time detection requirements of surface defects in industrial scenarios on steel strips. [ABSTRACT FROM AUTHOR]

With the goal of "carbon peak and carbon neutrality," the need for environmentally friendly gases to replace SF6, a high greenhouse effect gas, is urgent. C4F7N, as an environmentally friendly gas with the greatest potential to replace SF6 as an arc extinguishing medium in circuit breakers, can be mixed with CO2 to greatly improve the shortcomings of its high liquefaction temperature, and the calculation of the physical parameters of the mixed gas plasma is a prerequisite for the computational simulation of the arc process in the opening of circuit breakers. Because solving the physical parameters is expensive, based on the system of differential equations, this paper adopts several machine learning algorithms by mining the relationship between the data using the known physical parameter data to predict the results of the physical parameters to be solved under certain conditions, which greatly reduces the cost of computation. The machine learning algorithms used in this paper are K-nearest-neighbor regression, decision tree, random forest, support vector machine, and gradient boosting regression, of which for the support vector machine, hyperparameters find it difficult to determine the problem of optimization using the gray wolf algorithm. The prediction results of several algorithms show that they are more accurate and that the problem can be solved better by using the method of machine learning. Finally, the comparison results show that the support vector machine exhibits better performance in most cases and that the gray wolf algorithm can make the results of the support vector machine more accurate. [ABSTRACT FROM AUTHOR]

Electrical breakdown behaviors of nanopaper prepared from nanofibrillated cellulose (NFC) were investigated. Compared to conventional insulating paper made from micro softwood fibers, nanopaper has a dramatically enhanced breakdown strength. Breakdown field of nanopaper is 67.7 kV/mm, whereas that of conventional paper is only 20 kV/mm. Air voids in the surface of conventional paper are observed by scanning electron microscope (SEM). Further analyses using mercury intrusion show that pore diameter of conventional paper is around 1.7 μm, while that of nanopaper is below 3 nm. Specific pore size of nanopaper is determined to be approximately 2.8 nm by the gas adsorption technique. In addition, theoretical breakdown strengths of nanopaper and conventional paper are also calculated to evaluate the effect of pore size. It turns out that theoretical values agree well with experimental data, indicating that the improved strength in nanopaper is mainly attributed to the decreased pore size. Due to its outstanding breakdown strength, this study indicates the suitability of nanopaper for electrical insulation in ultra-high voltage convert transformers and other electrical devices. [ABSTRACT FROM AUTHOR]

In response to the challenges in mobile robot path tracking using model predictive control, where the predictive model weakens the controller’s ability to respond to sudden changes in the reference path curvature and heading, this paper proposes a composite control strategy suitable for agricultural robots. The strategy combines the maximum torque per ampere control and an Extended State Observer (ESO). The paper initially establishes a mathematical model for a Permanent Magnet Synchronous Motor (PMSM) considering aggregated disturbances. It designs a position tracking controller based on a non-singular terminal sliding mode and convergence law. This controller, employing a non-cascaded structure, replaces traditional position and velocity loop controllers and is proven to be stable with finite-time convergence through Lyapunov’s theorem. To enhance the system’s disturbance rejection capabilities further, the paper introduces an ESO to estimate system disturbances and applies it for feedforward compensation. The paper concludes by providing stability proof for the overall PMSM servo system in agricultural robots. Finally, the paper conducts simulations and experimental verifications based on the designed controller, demonstrating that the controller exhibits excellent path tracking performance, fast convergence, and robustness against external disturbances. [ABSTRACT FROM AUTHOR]

The propagation of online rumors is rapid, and its propagation mechanism has always been a research difficulty. In this paper, the network rumors are compared to the nuclear fission process, and a network rumors propagation model is constructed. First, the initial online rumors are compared to neutrons, uranium nuclei are compared to individual rumor receivers, and fission barriers are compared to individual active propagation thresholds. Second, the process of nuclear fission is analyzed and the degree of energy accumulation is used to compare the social impact of online rumors. Finally, the online rumor propagation model based on nuclear fission is constructed. Through experimental simulation, it has been shown that compared to classic infectious disease models, this model can better describe the propagation process of rumors in social networks. Through research and analysis, suggestions for suppressing rumors have been proposed, providing new ideas and references for future researchers. [ABSTRACT FROM AUTHOR]

This paper deals with self-absorption in a cylindrical high-pressure Hg–TℓI discharge for which local thermodynamic equilibrium can be assumed. Calculations of the densities of species, especially thallium, have been performed using a parabolic temperature profile and a constant mercury/thallium ratio throughout the discharge tube. This paper focuses on studying the self-absorption thallium lines by comparing them to those of mercury. Particular attention has been paid to the study of the additive effects on the self-absorption of spectral intensities. [ABSTRACT FROM AUTHOR]

Infrared thermography technology, leveraging its unique ability to capture temperature features, has significantly improved the precision of high-temperature target localization. However, infrared imaging technology is limited by issues such as low image contrast, difficulty in distinguishing object categories, and limited image clarity. To enable intelligent detection of high-temperature objects that may cause fires in warehouses, this paper proposes an innovative method that integrates deep learning image segmentation with infrared and visible light image technology. We developed a new image segmentation model based on improved Fully Convolutional Networks and Deconvolutional Networks, introducing a batch normalization layer to accelerate convergence and employing the PReLU activation function to prevent neuron death, thereby enhancing convergence speed and accuracy. Through a feature dynamic image registration method combining a joint model and a cross-modulation strategy, we achieved efficient image fusion. In addition, a game theory-based strategy was adopted to correct localization results, ensuring accuracy. Experimental results demonstrate that the improved model achieves localization accuracy and precision rates of up to 89.30% and 88.00%, respectively, in real-world warehouse heat source scenarios, representing a significant improvement of 9.90% and 2.85% compared to the pre-improvement model, fully validating its advancement and effectiveness. [ABSTRACT FROM AUTHOR]

Wireless Power Transmission (WPT) is widely used. In practical engineering applications, the transmitter and receiver of a stable and reliable WPT system need to establish not only a power transmission channel but also a data transmission channel to realize closed-loop control and information interaction. In this paper, a novel simultaneous wireless power and data transfer system based on a series–parallel (SP) resonant network is proposed. The system integrates an SP higher-order resonant network with series carrier injection, which makes power transmission and data transmission share a magnetic coupling channel. The isolation between power and data is realized by exploiting the frequency characteristics of the compensation network, thus realizing efficient power transmission and data transmission. In this paper, accurate and detailed mathematical modeling of the system is conducted, and the gain characteristics of the power transmission channel and the data transmission channel are demonstrated. Meanwhile, the mutual interference between the two channels is analyzed, and a feasible and effective parameter design method is developed. Finally, an experimental prototype with an output power of 16 W and a backward data rate of 100 Kbps is constructed to verify the feasibility of the proposed system and the correctness of the parameter design. [ABSTRACT FROM AUTHOR]

In complex environments, such as mines, ports, and pipelines, wireless power transfer technology is a convenient and safe power supply method. However, the offset and change in direction of the receiving coil greatly weaken the power transfer performance. To address this issue, this paper proposes a variable direction wireless power transfer device for mining based on metamaterials. According to the relationship between incident angle, refracted angle, and permeability, three different metamaterials are selected to fabricate a metamaterial slab arranged in 3 × 3 using printed circuit board technology. Experimental results show that when the transmitting coil and receiving coil are perpendicular to and deviate from each other, the designed power transfer system with the metamaterial slab can effectively increase power transfer by 117.9%. The proposed variable direction transfer system only needs a metamaterial slab without an additional power supply or control structure, and it is convenient for large-scale manufacturing and application. [ABSTRACT FROM AUTHOR]

Under turbulent conditions, the images of objects may be severely blurred and become unrecognizable because of the complex aberrations introduced by turbulence. Using adaptive optics (AO) to compensate for these wavefront aberrations can theoretically solve the problem of image blurring under turbulent conditions. However, due to its small field of view and high cost, the AO system is difficult to widely use. In recent years, using the technology of light field photography to correct turbulence images has been proposed. However, the current studies are either based on the structure of the non-focused light field camera, which makes it hard to achieve good image quality, or on the need to redesign the core sensor of the non-focused light field camera, which greatly increases the cost and complexity. We believe that the structure of a focused light field camera has a good potential for turbulence image correction and propose a new method of achieving a corrected focused image using a focused light field camera in this paper. We propose to use phase estimation instead of depth estimation for a focused light field camera and phase maps instead of a depth map as the basis for achieving a focused image. By the proposed method, a corrected focused image with a large field of view under turbulent conditions is achieved without using independent wavefront correction devices or redesigning the light field camera. The experiments are given to verify the effectiveness of the proposed method under strong turbulent conditions in the laboratory and real turbulent conditions in the outdoors. The proposed method in this paper provides a new and low-cost solution for image correction for optical imaging systems under turbulent conditions, so it has a high application value. [ABSTRACT FROM AUTHOR]

Recently, lithium niobate thin film laterally excited bulk wave resonators (XBARs) have attracted much attention because of their advantage of high frequency and outstanding electromechanical coupling factor (K2) enabling wide filter bandwidths. These can satisfy the increasing 5G communication demand. However, their large temperature coefficient limits their development to some extent. To improve their temperature stability with little K2 reduction, this paper has proposed a sandwiched structure for temperature compensated XBARs (TC XBARs). This structure includes the top silicon dioxide (SiO2) layer and the bottom SiO2 layer. One layer can improve temperature stability, and the other layer can increase K2. The optimized XBARs have a temperature coefficient of frequency (TCF) of −90.77 ppm⁄°C. The common two-layer TC XBARs can achieve a TCF of −22 ppm⁄°C sacrificing K2 to 8%. However, the proposed sandwiched TC XBARs can achieve a K2 of 12.15 and a TCF of −28.94 ppm⁄°C simultaneously, with a large FoM. Meanwhile, spurious modes can be suppressed in the sandwiched structure. Thus, this sandwiched structure can provide a good solution for the high performance of XBARs. [ABSTRACT FROM AUTHOR]

Inter-turn short circuit fault (ISCF) is the main fault type of dry-type air-core reactors (DARs), and the reactor burnout event caused by winding ISCF seriously threatens the safe and stable operation of the electric power system. In order to effectively curb the further worsening of ISCF in DARs, a fault detection method based on frequency response analysis was proposed. First, the effect of ISCF on the frequency response curve of the windings was investigated based on the distributed parameter model of DARs. Then, four sets of eigenvalues were proposed for ISCF diagnosis based on the influence law and curve data analysis theory. Finally, based on the eigenvalues, the support vector machine model optimized by the sparrow search algorithm was used to effectively classify the degree of ISCF of the reactor, and the accuracy reached 96%. Furthermore, a test platform was built based on the theoretical analysis for experimental verification, and the test results are consistent with the simulation results. Hence, the method proposed in this paper could effectively classify the ISCF and its severity in DARs. The research in this paper could provide effective guidance for the diagnosis of ISCF in DARs. [ABSTRACT FROM AUTHOR]

This document is a retraction notice from AIP Advances regarding four manuscripts that have been retracted due to evidence of manipulation in the publication and peer review processes. The editors of AIP Advances have retracted these papers after questions were raised about the data processing and analysis. The retraction is based on the discovery of identical reviewer reports and requests for revisions to add citations to unrelated works by the authors, indicating peer review manipulation. The authors maintain that the data support the validity of the papers, but the editors believe that the issues with the peer review process undermine their confidence in the validity of the papers. [Extracted from the article]

This document is a retraction notice from AIP Advances regarding four manuscripts that have been retracted due to evidence of manipulation in the publication and peer review processes. The editors of AIP Advances have determined that critical steps in the peer review process were manipulated, including the return of identical reviewer reports and author-suggested reviewers requesting revisions to add citations to unrelated works by the authors. As a result, the validity of these papers has been called into question and they are considered potentially unreliable. The authors maintain that the data support the validity of the papers, but the editors believe that the issues with the peer review process undermine confidence in their validity. [Extracted from the article]

This document is a retraction notice from AIP Advances regarding a manuscript titled "Exploring the wave solutions of a nonlinear non-local fractional model for ocean waves." The manuscript, along with three others, has been retracted due to evidence of manipulation in the publication and peer review processes. AIP Publishing requests that readers consider these works potentially unreliable as they have not undergone a credible peer-review process. The editors of AIP Advances have retracted the papers after questions were raised about the data processing and analysis. The authors maintain that the data support the validity of the papers, but the issues with the peer review process have undermined confidence in their validity. [Extracted from the article]

The performance requirements of motors are gradually increasing, and anisotropic soft magnetic materials have been applied in motors to improve performance, The magnetostriction of anisotropic soft magnetic materials is one of the main contributors in extensive research on anisotropic soft magnetic material transformers. Compared to transformers, the magnetic field in motors has more complex harmonic and multi-directional characteristics. In this paper, the magnetic field of motors stator teeth under both no-load and load conditions is calculated and analyzed to research magnetostriction of anisotropic soft magnetic materials in motor magnetic field and predict the NVH performance of anisotropic soft magnetic material motors accurately. Furthermore, the magnetostriction in different magnetization angles is investigated for the multi-directional characteristics, and the result is explained from microscopic principles. This paper focuses on a splicing oriented silicon steel (OSS) motor, the magnetization angle of OSS in motor varying from 0° to 90°. Firstly, the equivalent formulas of magnetic field are obtained under the analytical and finite element calculations. After that, the frequency and direction of experiment magnetic field are confirmed and the magnetostriction of OSS in different magnetization angle and frequency is measured. In the end, the variation character of magnetostriction of OSS is summarized. [ABSTRACT FROM AUTHOR]

Functional magnetic materials are used in a wide range of "green" applications, from wind turbines to magnetic refrigeration. Often the magnetic materials used contain expensive and/or scarce elements, making them unsuitable for long term solutions. Further, traditional material discovery is a slow and costly process, which can take over 10 years. Material informatics is a growing field, which combines informatics, machine learning (ML) and high-throughput experiments to rapidly discover new materials. To prove this concept, we have devised a material informatics workflow and demonstrated the core components of natural language processing (NLP) to extract data from research papers to create a functional magnetic material database, machine learning with semi-heuristic models to predict compositions of soft magnetic materials, and high-throughput experimental evaluation using combinatorial sputtering and high-throughput magneto-optic Kerr effect (MOKE) magnetometry. This material informatics workflow provides a quicker, cheaper route to functional magnetic materials discovery. [ABSTRACT FROM AUTHOR]

With the rapid development of portable computing devices and users' demand for high-quality graphics rendering, embedded Graphics Processing Units (GPU) systems for graphics processing are increasingly turning into a key component of computer architecture to enhance computability. The cache system based on traditional static random access memory (SRAM) plays a crucial role in GPUs. But high leakage, low lifetime and poor integration problems deeply plague the science and engineering field. In the paper, a novel magnetic random access memory (MRAM) based cache architecture of GPU systems is proposed for highly efficient graphics processing and computing accelerating, with the merits of high speed, long endurance, strong interference resistance, and ultra-low power consumption. Spin transfer torque-MRAM and spin orbit torque-MRAM are utilized in off-chip and on-chip caches, respectively. A controller design scheme with prefetching modules and optimized cache coherency protocols are adopted. After testing and evaluating with multiple loads, neural network models and datasets, the simulation results show that the proposed system can achieve up to 28%, 56%, and 66.45% optimizations mostly in terms of speed, energy and leakage power, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a method to generative design bionic bone structures wing ribs by simulating the formation process of soap bubble boundary. In this method, the position of the central control point is optimized by the genetic algorithm to simulate the moving process of the bubble, and the control point is simplified by the K-means classification method to improve the computational efficiency. The boundary formed by the extrusion of bubbles is filled with a bone-like grid structure to realize the optimal design of a bone-like rib structure. Compared with the traditional honeycomb sandwich structure, the bone-like rib structure can distribute the stress evenly throughout the structure, thus reducing the stress concentration and improving the bearing capacity of the structure. The method proposed in this paper provides a new idea for a multilevel structure design. [ABSTRACT FROM AUTHOR]

Designing advanced combustors that operate at high temperatures and produce little pollution, especially in the absence of primary and dilution holes, is a difficult task that may bring significant challenges. In this regard, this paper introduces a Kriging surrogate model approach to optimize the outlet temperature distribution of the combustor to achieve such advanced low-pollution combustors. Building upon previous research, this study explores the effects of the swirler blade installation angle on the outlet temperature distribution of the combustor without primary or dilution holes. Traditional methods, such as the control variable method using computational fluid dynamics (CFD) for numerical simulation, are limited in application due to the complex coupling of flow, heat transfer, mass transfer, and combustion processes. In contrast, surrogate models, especially the Kriging model, offer a rapid and efficient alternative to extensive CFD simulations that provide accurate predictions and error estimates for the solution of the problem. In summary, this paper details the process of generating sample points through three-dimensional numerical simulations, develops a Kriging surrogate model through Latin hypercube sampling, and optimizes the model to identify the most uniform outlet temperature distribution achievable by adjusting the installation angle of the swirl blade. The optimal design parameters, which are quickly obtained through the Kriging model, showed a significant reduction in the overall temperature distribution function and the radial temperature distribution function by 21% and 27.14%, respectively. [ABSTRACT FROM AUTHOR]

Friction plays a key role in the assessment of the safety and stability of mechanical systems (such as superconducting magnet quench explosion, aerospace vehicle bearing wear, etc.). Due to the closeness of the interfaces in engineering structures and the randomness of the contact surfaces, existing methods for measuring static friction force are unable to measure it at the contact interfaces of engineering structures under service conditions. In this paper, a new method for measuring the static friction force at the interface based on electrical signals is proposed. This method enables the measurement of the static friction force at interfaces of complex engineering structures under service conditions solely through electrical signals. The results indicate that the contact resistance gradually decreases with the increase in tangential load during the static friction stage until a monotonic behavior of macroscopic sliding occurs. The evolution of contact resistance is linked to the evolution of the real contact area, and this monotonic behavior can be explained as the deformation form of contact points. The accuracy of the proposed electrical measurement method is verified by comparison with experimental results (with an error of less than 9%). The indirect measurement method of friction force proposed in this paper can effectively measure the static friction force at the interfaces of engineering structures under service conditions, and it is expected to be applied to the detection of friction performance at engineering structure interfaces in extreme service environments. [ABSTRACT FROM AUTHOR]

Support Vector Machines (SVMs) have achieved significant success in the field of power transformer fault diagnosis. However, challenges such as determining SVM hyperparameters and their suitability for binary classification still exist. This paper proposes a novel method for power transformer fault diagnosis, called ECOC-WSO-SVM, which utilizes a White Shark Optimizer (WSO) and error correcting output codes to optimize SVMs. First, t-distributed Stochastic Neighbor Embedding (t-SNE) is employed to reduce the dimensionality of Dissolved Gas Analysis (DGA) features constructed using the correlation ratio method, from 26 dimensions. In addition, to effectively solve the hyperparameters of SVMs, a multi-strategy fusion method is proposed to improve the WSO, incorporating tent chaos initialization, elite opposite learning, and selection strategies, forming TEWSO, and its superior optimization performance is validated using IEEE CEC2021 test functions. Furthermore, to address the limitations of SVMs as a binary classifier, an error correcting output code fusion SVM is introduced, thus constructing a multi-class SVM model. Finally, the diagnostic performance of the ECOC-TEWSO-SVM model is validated using real-world data. Results demonstrate that the proposed model exhibits the best diagnostic performance compared to traditional models and those in the literature, thereby proving the significance and effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

The high temperature superconductors enhanced magnetoplasmadynamic thruster is a promising device for various space mission scenarios. However, a critical issue hindering the enhancement of the thruster's performance is the problem of anode power deposition. Previous studies have identified the anode fall voltage as a major contributor to this power deposition. This paper introduces an approximate analytical formula for calculating anode power deposition resulting from anode fall voltage. The paper further analyzes and calculates the variation of anode fall voltage with an applied magnetic field and discharge current. The findings reveal a direct correlation between applied magnetic field and both anode fall voltage and power, indicating an increase with higher magnetic field values. In contrast, while the anode fall voltage experiences a slight decline with increased discharge current, the anode power deposition remains elevated due to the transition from field ionization to thermal ionization. To gain a comprehensive understanding of anode power deposition, an experiment was conducted to obtain measurements under different operating conditions, involving varying levels of applied magnetic field and discharge current. The experimental results align with the theoretical predictions. These insights have the potential to serve as a valuable reference for enhancing the efficiency of magnetoplasmadynamic thrusters, bringing us closer to unlocking the full potential of these remarkable propulsion systems in the realm of space exploration. [ABSTRACT FROM AUTHOR]

A rod fastened rotor (RFR), which contains serial disks, rods (or a rod), and a torque tube, is the main form of heavy duty gas turbines and light duty aero engines. The roughness of the interface is the main parameter affecting the bending stiffness of RFR, and the jumpiness of the disks is the intuitive representation of the unbalance of the rotor. In this paper, a multi-optimization algorithm, NSGA-Ⅱ, was used to improve the vibration behavior of the RFR-bearing system. First, the quantitative relation between the bending stiffness of the rotor and the roughness of the interface was set up by combining the Persson contact theory and the Greenwood–Williamson model, and the nondimensional bending stiffness (NS) was proposed as the optimization objective of roughness. Then, the optimization objective of the jumpiness of the disk, (A + B), was rendered based on the minimum unbalance force. Finally, the multi-optimization on the roughness and jumpiness of the RFR was carried out, and the vibration behavior was improved greatly. In addition, this multi-optimization method was applied to a practical RFR, and the experimental results showed that the vibration amplitude was reduced by about 82%. The work in this paper can provide the guidance for vibration reduction of the rotor system. [ABSTRACT FROM AUTHOR]

To enhance the utilization of clean energy sources, such as wind power and photovoltaic within virtual power plants, and mitigate carbon emissions, this paper proposes a virtual power plant participation in the electricity carbon joint market optimization operation model based on ensemble empirical mode decomposition–improved bat algorithm (IBA)–Markov chain new energy output prediction. First, complementary set empirical mode decomposition is performed on historical data to construct a Markov chain based wind power and photovoltaic prediction model optimized by IBA. Second, this prediction model is used to predict the daily generation power of wind power and photovoltaic power. Finally, with the optimization goals of maximizing the benefits and minimizing the carbon costs of virtual power plants, a virtual power plant system participating in the electricity carbon joint market model based on wind power and photovoltaic output prediction results is constructed. At the same time, demand response factors are introduced and solved using the NSGA-II algorithm. Taking a certain park as an example for simulation analysis, the research results show that the combined effect of carbon market and demand response can achieve 99.82% of new energy consumption in virtual power plants without significantly reducing profits, basically achieving complete new energy consumption, demonstrating the effectiveness of the proposed model in this paper. [ABSTRACT FROM AUTHOR]

The SF6/N2 gas mixture as a preliminary alternative to SF6 is used in the power industry gas insulation medium. At present, the application of the 30% SF6/70% N2 gas mixture has made some progress, but the influence law of microwater content on its discharge decomposition characteristics is still unclear. In this paper, we simulate the defects of metal protrusions in the equipment through needle-plate electrodes, carry out SF6/N2 96hPD and its decomposition characteristic test under different microwater contents, and find that the increase of the microwater content in the SF6/N2 gas mixture will exacerbate the phenomenon of PD, resulting in the enhancement of the average discharge, and under the action of prolonged PD, more characteristic decomposition products will be generated, of which the SOF2 content is the highest, which can be as one of the characteristic components for fault severity judgment. This study provides a certain reference for the specific requirements of the microwater content of the SF6/N2 gas mixture in power equipment. [ABSTRACT FROM AUTHOR]

Multi-UAV cluster technology is a hot topic in modern electronic countermeasures research. Based on the "OODA loop" theory of electronic countermeasures, this paper analyzes the effectiveness evaluation method of multi-UAV collaborative electronic countermeasures. We establish an electronic countermeasures correlation state analysis model based on CRITIC weighting and improved gray correlation theory. This method combines multi-UAV collaborative scene examples and use the gray state correlation analysis method based on the dynamic resolution coefficient. We simulate the effects of electronic countermeasures and various influencing factors on the expected task objectives and verify the effectiveness of the proposed method. The results show that this method can better adapt to the dynamic electronic countermeasures scenarios of multi-UAV clusters and has certain participation value for comprehensively and systematically improving the effectiveness evaluation of electronic countermeasures. [ABSTRACT FROM AUTHOR]

Rotor imbalances in wind turbines present safety risks and lead to economic losses, and a method to diagnose rotor imbalances is urgently needed. A diagnostic method for rotor imbalances is proposed in this paper. First, a signal reconstruction method is proposed, and a novel index is used to determine the number of components used in signal decomposition in order to effectively address the interference by noise on the sensor. Second, an entropy calculation method is proposed, and the Gaussian kernel function is used to replace the fuzzy functions. The results indicate significant differences for different types of rotor imbalances. Moreover, it exhibits good noise robustness and a low dependence on the data length. Third, a support vector machine with multiscale kernels is proposed, and kernel functions with various characteristics and scales are combined. It has a well-distributed hyperplane and better classification performance, and it is robust to wind conditions. Finally, the method is tested and verified with varying levels of noise and turbulence. The results demonstrate satisfactory performance because the proposed method can effectively identify rotor imbalances under different noise and wind conditions. [ABSTRACT FROM AUTHOR]