Role In Applications Research Articles

Numerical Calculation of the Oil Flow in a Truck Rear Axle Transmission and the Influence of Gear-Induced Air Flow

Abstract For the reliable operation of a gearbox, consistently sufficient lubrication of machine elements is necessary. Thus, the gearbox fluid flow plays an important role. The state of the art indicates that computational fluid dynamics (CFD) enables targeted support of the gearbox development process at an early stage. Computational time plays a prominent role in practical applications. The particle-based smoothed particle hydrodynamics (SPH) shows great potential for time-efficient calculations, especially with a simplified single-phase modeling approach. This study first examines the influence of gear-induced air flow on gearbox oil flow, focusing on a test gearbox to identify a suitable modeling approach for a truck rear axle transmission. The results indicate that the gear-induced air flow mainly impacts gearbox oil flow at higher circumferential speeds. For lower circumferential speeds, a single-phase model yields good results with significantly reduced computation times compared to a two-phase model. Applying the single-phase model to the truck rear axle transmission and comparing numerical results with experimental findings demonstrates a reliable representation of the oil flow characteristics.

Interaction Length Analysis of Hypersonic Shock–Boundary-Layer Interaction Including High-Enthalpy Effects

Shock-induced flow separation plays an important role in practical applications of hypersonic flows, like intake unstart and unsteady aeroelastic phenomena. The size of the shock–boundary-layer interaction (SBLI) region determines the extent of choking in engine inlet ducts. Recent scaling studies have shown that interaction length is a function of shock-induced pressure jump, upstream Mach number, Reynolds number, wall heating/cooling, and flow deflection angle at the shock wave. In this work, we analyze the interaction length scaling for different hypersonic SBLI cases at varying flow enthalpies. We perform Reynolds-averaged Navier–Stokes (RANS) simulations, using a shock-strength-dependent turbulence model, of hypersonic SBLI flows over a range of Mach numbers and at flow enthalpies where vibrational nonequilibrium starts to be important. The numerical results closely match the experimental data, validating the RANS predictions. The size of the separation bubble in such high-enthalpy cases is found to not follow the known scaling relations. We propose a modified scaling that collapses a wide range of hypersonic Mach numbers and freestream enthalpy conditions. We also derive the high-Mach-number limit to demonstrate that wall cooling/heating and geometry are the main factors affecting the size of the SBLI region in hypersonic flows.

Research on the Performance and Computational Fluid Dynamics Numerical Simulation of Plate Air Gap Membrane Distillation Module.

Membrane distillation (MD) is widely used in the field of seawater desalination. Among its various sub-categories, air gap membrane distillation (AGMD) stands out due to its high thermal efficiency and compatibility with low-grade heat sources. This study delves into the impact of varying operating conditions on AGMD performance, employing numerical simulations which are grounded in experimental validation. The objective was to enhance the performance of AGMD, mitigate polarization phenomena, and provide a reference for optimizing membrane component design. The results show that the agreements between the simulated and the experimental values were high. When increasing the feed temperature and decreasing the coolant temperature, the impact of polarization phenomena on the performance of AGMD was reduced. The mass flux, Total Permeate Concentration (TPC), and heat flux increased by 81.69%, 36.89%, and 118.01%, respectively, when the feed temperature was increased from 50 °C to 75 °C. When the coolant temperature decreased from 22 °C to 7 °C, the mass flux increased by 37.06%. The response surface analysis revealed that the feed temperature has significant influence on AGMD performance, and there is a noticeable interaction between the feed temperature and coolant temperature. These findings will play key roles in practical applications.

First principles calculation and experimental study of Ln0.125Sr0.875Co0.875Ta0.125O3-δ(Ln = La, Sm, and Nd) as potential cathode materials for intermediate-temperature solid oxide fuel cells

Solid oxide fuel cells (SOFCs), as important energy equipment, have excellent characteristics, including low pollution and high catalytic activity. The cathode, which is the key component of an SOFC, plays a vital role in practical applications. Herein, we study the properties of Ln0.125Sr0.875Co0.875Ta0.125O3-δ (Ln = La, Sm, and Nd, abbreviated as LSCT, SSCT, and NSCT, respectively) cathode materials both theoretically and experimentally. The conductivity of LnSCT gradually decreased with decreasing Ln3+ ionic radius. Density of states (DOS) analysis indicated that LSCT has the highest conductivity owing to the band center of Co 3d being closer to O 1s. Density functional theory (DFT) calculations showed that the SSCT sample has the lowest oxygen vacancy formation energy, which facilitated the formation of oxygen vacancies. The binding energy results show that SSCT and NSCT have relatively good structural stability compared to LSCT. The polarization impedance values of LSCT, SSCT, and NSCT at 700 °C are 0.105, 0.069, and 0.304 Ω cm2, respectively. The CO2 sensitivity of LSCT, SSCT, and NSCT was evaluated by calculating the CO2 adsorption energy and performing impedance tests under different CO2 concentrations. These results indicate that LSCT and SSCT have better CO2 tolerance than NSCT. At 700 °C, for Ln = La, Sm, and Nd, the maximum power of NiCu-SDC/SDC/LSGM/LnSCT electrolyte-supported fuel cell reached 430, 462, and 382 mW cm−2, respectively. These findings provide a reference for the future design and development of SOFCs cathode material.

Analysis of the one-decoy-state SARG04 quantum cryptography protocol in the presence of afterpulse effects

The SARG04 quantum key distribution protocol can offer greater robustness against photon number splitting attacks than the BB84 protocol that is implemented with weak pulses. In this paper, we propose a tight key analysis for the SARG04 protocol, by considering the one-decoy method and investigating its performance under the influence of a detector afterpulse. Our results demonstrate that an increase in block size leads to a slight increase in both the secure key rate and the maximum transmission distance. Importantly, the detector afterpulse plays a crucial role in practical applications and has a more pronounced effect on the SARG04 protocol compared to the BB84 protocol.

Case study Based on Ogane's Problem-solving Process

In the 1980s, there were problems in the teaching of problem-solving in mathematics in the former Soviet Union, such as formulaic solutions and a lack of emphasis on teaching mathematical thinking. Therefore, Oganesian believes that in the traditional education system and in a large number of mathematical teaching practices, the role of mathematical exercises in developing students' mathematical level is limited. Mathematical exercises are difficult to test students' other aspects of mathematical development and ideological education factors. Introducing new teaching models is an urgent need for teaching mathematical problem-solving. Based on this, Oganesian proposed four steps for solving problems in his book "Teaching Methods for Primary and Secondary School Mathematics": understanding the problem conditions-developing a solution plan - implementing the solution plan - researching the obtained solutions. The article takes the "theorem for determining the perpendicularity of a straight line to a plane" in mathematics as an example, and analyzes its application based on the process of solving problems using the Oganesian method. By introducing mathematical problems into the Oganesian phase problem-solving process, gradually solving problems, delving into the vertical relationship between lines and planes, and analyzing their significance and role in practical applications. Assist students in cultivating thinking methods and problem-solving strategies for mathematical problem-solving systems, guide them to solve mathematical problems more deeply and accurately, enhance the problem-solving ability of mathematical learners, and optimize their thinking logic.

Carbon materials derived by crystalline porous materials for capacitive energy storage

Abstract The controlled synthesis of precise carbon nanostructures with high electron conductivity, high reaction activity, and structural stability plays a significant role in practical applications yet largely unmet. Metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and coordination polymers (CPs) as crystalline porous materials (CPMs) have shown extraordinary porosity, tremendous structural diversity, and highly ordered pores, offering a platform for precise controlled carbon materials (CMs) with regular porous structures and high performances. Some recent studies have shown that CMs derived from CPMs with high specific surface area, superior chemical stability, excellent electrical conductivity offer a great opportunity for electrochemical energy storage and conversion. In this review, we summarize recent milestones of CPMs derived CMs in the field of capacitive energy storage. We hope the more precise design and control at the atomic level of CPMs could provide us a constructive view of the structure-activity relationship between CMs and electrochemical capacitors, as well as future trends and prospects.

Insect‐scale fast moving and ultra‐load‐carrying robot

AbstractThe load capacity of small robots plays a crucial role in practical applications. Drawing inspiration from biological load‐carrying mechanisms, a crawling robot based on piezoelectric bending is proposed. It is an insect‐scale piezoelectric‐driven crawling robot with a load capacity of 100.4 times its weight, which is the highest measured among published insect‐scale robots. The robot was designed and fabricated in a monolithic manner, enhancing the assembly efficiency of the robot. It weighs only 0.68 g and has a length of only 40 mm. The robot can achieve rapid movement and jumping by using two different driving methods. Its maximum forward speed is 7.77 BL/s (body lengths per second), and it can reach a forward speed of 1 BL/s under a load of 41.6 g, the maximum load of the robot is 68.24 g. It can move rapidly, climb slopes, traverse ground obstacles, and pass through pipes, similar to an insect.

High Electrical and Mechanical Properties Obtained in a Polyimide‐Based Nanocomposite with Sandwich Structure via Multidimensional Design

AbstractAs an excellent polymer material, polyimide (PI) plays an important role in practical applications. However, traditional filler doping modification is hard to simultaneously improve the dielectric properties and mechanical properties of the film. Here, PI–boron nitride (BN) nanosheets/PI–sodium titanate nanotubes (STNs)/PI–BN composite films with sandwich structure are prepared by in situ polymerization and multilayer coating processes, which significantly enhance the electrical and mechanical properties of dielectric films. With only 0.5 wt% fillers doping, the AC breakdown strength of the composite film can reach 183.7 kV mm−1, which is 16.41% higher than that of pure PI. When the filler doping is 1.5 wt%, the corona aging time of the composite film achieves 311.7% higher than that of pure PI film. In addition, the composite film exhibits outstanding elongation at break (58%) and tensile strength (138.92 MPa) performance. The 2D BN of the outer layer and 1D STNs of the middle layer effectively block the injection of charge carriers while improving the internal polarization response. A mechanism related to microstructure and interface is proposed to explain the improvement of electrical and mechanical properties in detail. This work provides a useful idea for the accurate design of high‐performance dielectric films.

Research on reinforcement learning based on PPO algorithm for human-machine intervention in autonomous driving

<abstract> <p>Given the current limitations in intelligence and processing capabilities, machine learning systems are yet unable to fully tackle diverse scenarios, thereby restricting their potential to completely substitute for human roles in practical applications. Recognizing the robustness and adaptability demonstrated by human drivers in complex environments, autonomous driving training has incorporated driving intervention mechanisms. By integrating these interventions into Proximal Policy Optimization (PPO) algorithms, it becomes possible for drivers to intervene and rectify vehicles' irrational behaviors when necessary, during the training process, thereby significantly accelerating the enhancement of model performance. A human-centric experiential replay mechanism has been developed to increase the efficiency of utilizing driving intervention data. To evaluate the impact of driving intervention on the performance of intelligent agents, experiments were conducted across four distinct intervention frequencies within scenarios involving lane changes and navigation through congested roads. The results demonstrate that the bespoke intervention mechanism markedly improves the model's performance in the initial stages of training, enabling it to overcome local optima through timely driving interventions. Although an increase in intervention frequency typically results in improved model performance, an excessively high intervention rate can detrimentally affect the model's efficiency. To assess the practical applicability of the algorithm, a comprehensive testing scenario that includes lane changes, traffic signals, and congested road sections was devised. The performance of the trained model was evaluated under various traffic conditions. The outcomes reveal that the model can adapt to different traffic flows, successfully and safely navigate the testing segment, and maintain speeds close to the target. These findings highlight the model's robustness and its potential for real-world application, emphasizing the critical role of human intervention in enhancing the safety and reliability of autonomous driving systems.</p> </abstract>

Global precise consensus tracking control for uncertain multiagent systems in cooperation-competition networks

The multiagent systems (MASs) play a crucial role in practical applications, and the global precise control of MASs under cooperative-competitive networks presents significant challenges. This paper addresses the global precise consensus tracking control problem for uncertain MASs in cooperation-competition networks. A novel framework of distributed event-based prescribed-time observer (EPTO) is proposed for the followers, which allows to accurately estimate the leader's state within a prescribed time interval. Moreover, the design of event-triggered conditions for each EPTO reduces the communication load between neighboring agents by avoiding the continuous transmission of neighbor states. Using the observed values after triggering within the backstepping method framework, a global bipartite control scheme is then developed to expedite the convergence time, enhance the convergence performance, and perform precise tracking control for high-order uncertain MASs. The distinctive feature of the proposed global bipartite control scheme lies in the design of a novel controller, which improves the semi-global control result of the existing schemes. Finally, two simulation examples are presented to demonstrate the effectiveness of the EPTO-based bipartite control scheme.

Wettability of Quartz by Ethanol, Rhamnolipid and Triton X-165 Aqueous Solutions with Regard to Its Surface Tension

The wettability of quartz by different liquids and solutions plays a very important role in practical applications. Hence, the wetting behaviour of ethanol (ET), rhamnolipid (RL) and Triton X-165 (TX165) aqueous solutions with regard to the quartz surface tension was investigated. The investigations were based on the contact angle measurements of water (W), formamide (F) and diiodomethane (D) as well as ET, RL and TX165 solutions on the quartz surface. The obtained results of the contact angle for W, F and D were used for the determination of quartz surface tension as well as its components and parameters using different approaches, whereas the results obtained for the aqueous solution of ET, RL and TX165 were considered with regard to their adsorption at the quartz–air, quartz–solution and solution–air interfaces as well as the solution interactions across the quartz–solution interface. The considerations of the relations between the contact angle and adsorption of solution components at different interfaces were based on the components and parameters of the quartz surface tension. They allow us to, among other things, establish the mechanism of the adsorption of individual components of the solution at the interfaces and standard Gibbs surface free energy of this adsorption.

Analysis and application of key technologies in digital signal processing

With the uncreasing reform of times and the continuous advancement of my science and technology, in recent years, my overall national strength is strengthening gradually. I have entered an information age, one digital age, the use of digital signal processing is becoming more and more frequent and important. Therefore, this paper mainly analyzes the application of DSP, the digital signal processing technology show to make the exposition. Digitalization is a major trend in today's society and the main theme of the development of the times, while digital signal processor is a component born with the times. The so-called digital signal processing is to use a universal digital signal chip to extract useful information from the signal by digital calculation. In recent years, in the wake of the constantly improving of science and technology and the continuous reform and innovation of technology in China, DSP is more and more close to people's production and life, and plays an important role in practical applications. Therefore, it is necessary to discuss and analyze its development.

High-quality computational ghost imaging with multi-scale light fields optimization

High-quality computational ghost imaging under low sampling rates has always attracted much attention and plays an important role in practical applications. In this paper, a novel optical field optimization method based on multi-scale light fields singular value decomposition which can greatly reduce the number of computational ghost imaging measurements is proposed. The computational ghost imaging measurement matrix is derived from the components obtained by singular value decomposition of self-designed special measurement matrices. When the measurement matrix is fully sampled, high-quality reconstructed image can be obtained. Similarly, when the measurement matrix is under-sampled, it is still possible to obtain high-quality reconstructed image and show the performance of multi-resolution imaging. Simulation and experimental results show that our method can obtain high-quality computational ghost imaging, even at low sampling rates, and as the number of splicing matrices increases, the number of measurements is further reduced.

Stochastic Gradient Perturbation: An Implicit Regularizer for Person Re-Identification

Generalization of the person re-identification (ReID) model plays an important role in practical application, and we discuss a simple yet effective regularizer to improve it inspired by Adversarial Training (AT). AT has been indicated as an advanced regularizer due to its adversarial mechanism, ability to mine hard samples, and nature of data augmentation. However, serving as an augmentation-based regularizer, AT shows low diversity of the perturbation, excessive computational cost, and the optimization dilemma between adversarial robustness and accuracy for ReID task, and is thus suboptimal. To tackle these limitations and get a more effective regularizer for ReID, we rethink the nature of AT and unveil that the adversarial data augmentation is essentially reflected by gradients. Based on this, a novel implicit regularizer, named Stochastic Gradient Perturbation (SGP), is proposed, which naturally brings three merits: 1) Better diversity of the perturbation due to the proposed non-directional stochastic perturbations rather than directional adversarial perturbations. 2) Lower computational cost due to the proposed implicit gradient augmentation rather than explicitly additional data. 3) The optimization dilemma of the adversarial robustness and generalization is naturally overcome since SGP contains the adversarial gradient perturbation. Further, we put forward a perspective that the generalization and adversarial robustness may have an inter unity. Experiments on the baseline and SOTA models demonstrate powerful performances of the plugged-played SGP, and both generalization and adversarial robustness can be guaranteed.

Novel Land Cover Change Detection Deep Learning Framework with Very Small Initial Samples Using Heterogeneous Remote Sensing Images

Change detection with heterogeneous remote sensing images (Hete-CD) plays a significant role in practical applications, particularly in cases where homogenous remote sensing images are unavailable. However, directly comparing bitemporal heterogeneous remote sensing images (HRSIs) to measure the change magnitude is unfeasible. Numerous deep learning methods require substantial samples to train the module adequately. Moreover, the process of labeling a large number of samples for land cover change detection using HRSIs is time-consuming and labor-intensive. Consequently, deep learning networks face challenges in achieving satisfactory performance in Hete-CD due to the limited number of training samples. This study proposes a novel deep-learning framework for Hete-CD to achieve satisfactory performance even with a limited number of initial samples. We developed a multiscale network with a selected kernel-attention module. This design allows us to effectively capture different change targets characterized by diverse sizes and shapes. In addition, a simple yet effective non-parameter sample-enhanced algorithm that utilizes the Pearson correlation coefficient is proposed to explore the potential samples surrounding every initial sample. The proposed network and sample-enhanced algorithm are integrated into an iterative framework to improve change detection performance with a limited number of small samples. The experimental results were achieved based on four pairs of real HRSIs, which were acquired with Landsat-5, Radarsat-2, and Sentinel-2 satellites with optical and SAR sensors. Results indicated that the proposed framework could achieve competitive accuracy with a small number of samples compared with some state-of-the-art methods, including three traditional methods and nine state-of-the-art deep learning methods. For example, the improvement rates are approximately 3.38% and 1.99% compared with the selected traditional methods and deep learning methods, respectively.

Optical Lines in Europium and Terbium-Activated Yttrium Tantalate Phosphor: Combined Experimental and Group-Theoretical Analysis

The rare-earth ions in crystals such as terbium (YTaO4:Tb3+) and europium (YTaO4:Eu3+)-activated yttrium tantalate phosphors have a number of attractive features that predetermine their crucial role in practical application in contemporary optoelectronic devices. In this article, we employ the group-theoretical arguments aimed to reveal the group-theoretical classification of the crystal field levels and selection rules for the allowed optical transition between the crystal field components of Tb3+ and Eu3+ of the low symmetry crystal field in the activated yttrium tantalate phosphors. We also establish possible polarization rules for the lines corresponding to the allowed transitions. We deduce the symmetry-assisted results for the selection rules in the optical transitions accompanied by the absorption/emission of the vibrational quanta. The selection rules for the vibronic satellites of the zero-phonon lines are expected to be useful for the identification of the lines in the spectra of rare-earth ions with a weak vibronic coupling. The results of the low-temperature measurements of photoluminescence under the 325 nm excitation are in compliance with the group-theoretical analysis. The aim of the paper is to establish symmetry-assisted results that are the background of the quantitative crystal field theory based on the quantum-mechanical consideration.

Effective Fermi-Level Modulation of Two-Dimensional Conjugated Covalent Organic Frameworks Achieved by Introducing p-Type Organic Dopants

Covalent organic frameworks (COFs) are a new emerging class of two-dimensional functional materials, and their unique electronic character plays a key role in practical applications. Here, to provide a useful strategy to tune the electronic character of COFs, doping effects with organic molecular acceptors, TCNQ, F4TCNQ, and TCNE, on the frontier orbital energy levels of sp2c-COF and COF366 mono- and bilayers are explored. Due to the charge transfer from COFs to dopants, the Fermi level is shifted to a lower energy and drops into the top valence band of sp2c-COF and COF366, and the VBM/CBM energy level of COFs is also decreased. Moreover, the increased electron density shifts the LUMO energy level of dopants to a higher energy. Then, two electronic states, the top valence band of COFs and the LUMO of the p-type dopants, are pinned around the Fermi level. It means that the organic molecular acceptor serves as an effective p-type dopant for COFs. In addition, it is confirmed that the stronger the electron-accepting ability of p-type dopants or the higher the surface density of the dopants, the larger the variation of the frontier energy levels of COF monolayers will be. Thereby, an overall linear correlation between the electronic property variations of COFs and the charge transfer amount from COFs to p-type dopants is observed. Our results proved that surface doping with organic molecular acceptors is a reliable approach to modulate the frontier energy level of COFs, which provides an effective strategy to optimize the performance of COF-based devices.

Research on Blockchain Anonymous Communication Based on Key Derivation

Abstract With the continuous development of the Internet and communication technologies, network communication provides convenience but also brings security problems such as exposure of users' personal privacy information and theft, private tampering, and forgery of false information. Modern cryptography technology is an important safeguard against message eavesdropping and tampering, while the rapidly developing anonymous communication technology in this century makes it difficult for attackers to infer user's personal information and communication relationships. In response to the potential threats of traditional centralized systems such as central nodes being vulnerable to attacks and data storage being tampered with, this paper proposes a blockchain anonymous communication algorithm KDAC based on key derivation, which takes advantage of the decentralization, data immutability, consensus mechanism and anonymity of blockchain and combines the ECC cryptographic derivation algorithm and anonymous communication technology to realize the key-at-a-time, one-address-at-a-time The key derivation scheme ensures the message integrity and tamper-evident while effectively hiding the identity information of both communication parties. In addition, this paper also optimizes the blockchain anonymous communication system with key derivation. Users only need the initial key of blockchain nodes to join the network for communication, and the information transmission is difficult to trace based on the blockchain network, which can effectively guarantee communication security and anonymity. The experimental results show that the efficiency of the derived key algorithm is roughly in the same order of magnitude as that of the 256-bit AES symmetric encryption algorithm, which can play a better role in practical applications. On the other hand, the derived key generated based on the algorithm has complete randomness in association verification, and it is impossible to reverse the initial parameters, which can well guarantee the anonymity of user identity.

Impact dynamics of droplets on the well-designed wrinkled surfaces: Enhancement of bounding ability

Designing the nanostructured substrates with efficiently executing self-cleaning functions such as anti-icing, anti-fogging, anti-flaming, and anti-corrosion is always a hot scientific research topic and plays an essential role in practical applications. Herein, a novel substrate decorated with wrinkled structures is investigated. Results show that it has a large improvement in the reduction of contact time compared with the smooth substrate, and could affect the critical velocity of transition between retraction rebound and non-reaction transition. Effects of geometrical parameters of the wrinkled structures on droplet rebounding ability are detailed discussed, such as angles, intervals, and arrangement gradient. Most importantly, a well-designed substrate decorated with wrinkled structures and two confinement walls is proposed, which is found that the contact time could be largely reduced due to the limitation of spreading behaviors of the impacting droplets resulting from the heavy restriction effect of the confined walls. The findings of this work are expected to provide potential implications for many multidisciplinary fields, such as aerospace engineering, industrial manufacturing, interfacial heat transfer, environment, energy storage, and so forth.