bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structural design and simulation of herringbone groove bearing lubricated with liquid metal
Authors: Fu Zhenlei , Wang Wen 
Published: July 2024

Abstract
We designed a novel herringbone groove sliding bearing suitable for gallium-based liquid metal lubrication, taking advantage of the excellent fluidity, high thermal conductivity, and strong stability of gallium-based liquid metal at room temperature. We used the finite element method to analyze how eccentricity affected the bearing pressure variation and chose the best structural parameters by comparing how different groove parameters affected the peak pressure and carrying capacity. The results show that the herringbone groove reduces bearing friction resistance and causes the bearing pressure variation to exhibit a herringbone pattern. The herringbone groove structural parameters have a significant impact on the peak pressure and carrying capacity of the bearing. The gallium-based liquid metal bearing works well when the herringbone angle is 50°, the groove depth is between 15 μm and 25 μm, and the groove ratio is between 0.7 and 0.9.
Simulation and analysis of short circuit fault in ship power system based on simulink
Authors: Li Zhaoqiang , Yang Haijun , Wang Lingling , Ji Wendong 
Published: July 2024

Abstract
In this paper, a short-circuit fault simulation of a ship’s electrical power system is carried out based on Simulink. According to the power system sketch of the ship, a simulation model is built, initial conditions are set, two-phase short-circuit faults and three-phase short-circuit faults are simulated, and the simulation results are analyzed. The results show that the short-circuit fault has a large impact on both the generator side and the load side, in which the impact on the load side is more serious. At the generator side, the voltage of the corresponding phase drops by about 1/3-1/2, and the current increases by about 1.5 times. At the load side, when a two-phase short circuit occurs, the voltage drops to half of the original one, and when a three-phase short circuit occurs, the voltage drops to 0. The short-circuit current has the largest value at the beginning of the short circuit, then oscillates and decays, and finally stabilizes at about 15 times the normal working current.
An aging simulation method based on the change of input vector inside the critical path
Authors: Sang Qianqian , Yang Xinhuan , Wang Liang , Wang Shuo , Wang Chuanzheng , Yu Mingyan , Zhao Yuanfu 
Published: July 2024

Abstract
With the large-scale expansion and process shrinking of modern integrated circuits, aging has become one of the main factors threatening circuit reliability. To evaluate the impact of aging on circuit life, it is important to investigate aging simulation methods. Aiming at the problem of slow speed and insufficient accuracy of large-scale digital circuit aging simulation methods, a circuit aging simulation method based on the change of input vector inside the critical path (CICP) is proposed in this paper. First, the critical path (CP) with the tightest timing is extracted by static timing analysis (STA), and SPICE simulation is carried out to accurately predict the aging of the CP of the circuit. Second, the aging simulation is carried out considering the change of the internal input vector of the CP. The simulation results show that after changing the internal input signal, the aging path delay increases up to 3.5%.
Clustering statistical method of high dimensional sparse data based on fuzzy data
Authors: Wang Hu 
Published: July 2024

Abstract
Developing effective clustering and statistical methods for high-dimensional sparse data presents unique challenges compared to traditional low-dimensional data. To address this, a novel approach is proposed, leveraging fuzzy data principles to enhance the clustering and statistical performance of high-dimensional sparse datasets. The method builds upon the fuzzy C-means clustering algorithm, introducing key modifications for better suitability to high-dimensional sparse data. One crucial enhancement involves tackling the local optimization problem by optimizing the initial clustering center, significantly reducing clustering statistical time. Replacing the original Euclidean distance with cosine distance improves the clustering and statistical performance of high-dimensional sparse data. Experimental results have shown that this method has superior clustering statistical performance when the data dimensions are different. When the data dimension is low, and the blocking ratio is 10%, the clustering statistical effect is optimal. When the data dimension is high, and the blocking ratio is 40%, the clustering statistical effect is optimal. This method has higher hit rates and clustering statistical efficiency at different sparsity levels.
Research on topology optimization of carbon fiber reinforced composite electric vehicle helmet based on deep learning
Authors: Zhang Xiangkui , Wei Wei , Hu Ping , Han Chengxiang 
Published: July 2024

Abstract
To study the topology optimization of carbon fiber composite helmets, a method of optimizing the three-dimensional (hereinafter referred to as 3D) model based on a deep learning network after dimensionality reduction is proposed. In this method, the 3D shell model is expanded and tiled into two-dimensional (hereinafter referred to as 2D) grids based on the one-step inverse forming method. The variable density method (Isotropic Material Penalty Density method) is used to collect the data set required for deep learning training, and the U-Net neural network is built to complete the training. To obtain the input tensor of the neural network, the mechanical analysis of the 3D model is carried out, and the physical properties are imported into the network for learning. In this way, the 2D optimization results of the helmet model are obtained and then reflected in the 3D model according to the unit topology relationship to obtain the final optimization results. Then, the ordinary helmet and the optimized helmet are simulated and compared under the same quality to test their mechanical properties. The results show that the stiffness performance and energy absorption effect of the optimized helmet are stronger when the quality of the two helmets is the same.
Evaluation method of software reliability index
Authors: Wang Jia , Liu Fusheng , Shen Ying , Xu Dan 
Published: July 2024

Abstract
Software reliability refers to the ability of software not to cause system failure under specified conditions and within the specified time. With the increasing demand for equipment systems for software systems, the software system has become an important part of determining the success or failure of combat force system missions. The reliability assessment of the software system is becoming more and more important. Aiming at the problem of software reliability evaluation, this paper discusses the choice of reliability evaluation model. The model parameters are determined. On this basis, according to the selected reliability evaluation model, the model parameters are estimated through the collected data. Finally, the reliability evaluation analysis is carried out.
Prediction model for very high frequency communication interruption based on random forest regression algorithm
Authors: Han Chunyong , Li Yawei , Su Zhongzhi 
Published: July 2024

Abstract
The onboard Very High Frequency (VHF) communication is susceptible to interruption due to antenna radiation signal blockage caused by the aircraft body at certain angles resulting from changes in the aircraft’s attitude. To establish a prediction model for communication interruptions, data on the radiation power level, attitude angle 1, and attitude angle 2 of the antenna were initially collected. Subsequently, a machine learning model was constructed to predict the radiation power at two specific frequency points of a VHF communication antenna for helicopters. The model was built by using the random forest (RF) regression algorithm. For the prediction model of frequency point 1, the Goodness of Fit (R2) and Mean Squared Error (MSE) were 0.997 and 0.023, respectively, with feature importance scores of 0.98 for attitude angle 1 and 0.02 for attitude angle 2. For the prediction model of frequency point 2, the R2 and MSE were 0.999 and 0.008, respectively, with feature importance scores of 0.91 for attitude angle 1 and 0.09 for attitude angle 2. The study results indicate that the variation in attitude angle 1 significantly impacts the antenna’s radiation power. The RF regression model can effectively predict the onboard VHF communication capability, thereby providing technical support for reducing the potential risk of communication interruption.
Performance research of edge computing nodes based on heterogeneous environment
Authors: Cheng Wei , Lin Bing , Cheng Liming 
Published: July 2024

Abstract
Edge computing realizes real-time parallel storage and computing in a distributed way, which can reduce the data transmission cost. The edge nodes, which are composed of different heterogeneous physical devices and hardware resources, assume the computing and storage capacity of the edge network. At present, the resource characteristics and performance of heterogeneous edge nodes are difficult to characterize. Therefore, a resource-aware performance feature detection model for heterogeneous edge compute nodes is proposed in this study. This model is based on a resource utilization matrix and resource relative performance quantization algorithm to extract the performance characteristics of the same type of resources of the edge nodes, to realize multi-dimensional and systematic feature detection and description of heterogeneous edge nodes. On this basis, a weight-based edge node task scheduling algorithm is designed. The final verification results show that the CPU relative performance ratios of Node 1-Node 2, Node 1-Node 3, and Node 2-Node 3 are 6.23, 3.81, and 0.81 respectively. The performance relationship among the three nodes is Node 1> Node 3> Node 2. The test group and the verification group tend to be consistent overall. This method has a good application prospect.
Dynamic iterative imputation algorithm based on rough fuzzy C-Means clustering
Authors: Gong Zheng , Yan Chun , Liu Wei 
Published: July 2024

Abstract
Missing data is a very common phenomenon in daily life. To mitigate its adverse impact on data analysis, this paper proposes a dynamic iterative imputation algorithm based on the rough fuzzy C-means algorithm. The algorithm incorporates dynamic approximations to partition and iterate the cluster assignment of missing sample points, utilizing information from complete sample points within the same cluster for iterative imputation. The imputation results are evaluated by using MAE and RMSE metrics, and the optimal number of iterations and imputation values are determined by minimizing these metrics. To verify the effectiveness of the proposed algorithm, 6 UCI public datasets are selected for imputation tests, and the change curves of metric value with iterations are generated under different missing proportions in each dataset. The optimal imputation result is obtained based on the lowest point of the curve. Subsequently, the performance of the proposed algorithm is compared with three cluster-based and five machine learning-based imputation algorithms. The results demonstrate the significant superiority of the proposed algorithm over the other methods, achieving a more approximate imputation of missing values in the dataset.
Shock waves in Interstellar Cloud-Cloud and Wind-Cloud Collisions
Authors: Navarrete S. , Pinargote B. J. , Banda-Barragán W. E. 
Published: July 2024

Abstract
The interstellar medium (ISM) is a key ingredient of galaxies and their evolution, consisting of multiphase, turbulent dust and gas. Some of the star-forming regions in our Galaxy originate from cloud-cloud and wind-cloud collisions, which generate shock waves that change the physical and chemical properties of the gas. We utilise our own python-based shock-finding algorithm to study the properties and distribution of shocks in interstellar collisions. Such interactions are studied via 3D numerical simulations with different initial conditions: Cloud-cloud collisions (CCc): We identify four stages of evolution: pre-collision, compression, pass-through, and dissipation. We also vary the size of one of the colliding clouds. Larger clouds facilitate cloud erosion and the formation of more and stronger shocks at early stages. Shock distributions are also time-dependent, as strong shocks are only produced during the early stages. As the collisions evolve, turbulent kinetic energy is rapidly dissipated, so most perturbations become subsonic waves at late times. Wind-cloud collisions (WCc): we identify four stages: compression, stripping, expansion, and break-up. We study the evolution of several diagnostics in these clouds: energies (thermal and kinetic), temperature, displacement of the centre of mass, and mass-weighted averages of the cloud density and acceleration. We show, that the geometry of the cloud impact the diagnostic parameters, for example, smoothing the edges of the cloud leads to enhanced mass losses and dispersion, but has little impact on the shock distribution.
Lane-Emden equations for Relativistic Anisotropic Polytropes
Authors: Fernando Bustos Oscar 
Published: July 2024

Abstract
In this research work, spherically symmetric compact objects with anisotropic matter are studied within the framework of general relativity. For this purpose, the equation of state of the master polytrope P = Kργ
+ αρ − β is employed, which prevents the divergence of the tangential velocity at the object’s surface and generalizes the treatment of the equation of state. The corresponding Lane-Emden equation is derived and integrated using an anisotropy function that encompasses conformally flat polytropes and vanishing compactness factor. Furthermore, the physical acceptability conditions of the solutions are analyzed.
Stellar models with like-Wyman IIa complexity factor
Authors: Andrade J. , Andrade D. 
Published: July 2024

Abstract
The goal of this work is to build a new family of stellar interior solutions in the anisotropic regime of pressure using the framework of gravitational decoupling via minimal geometric deformation. For such purpose, we use a generalization of the complexity factor of the well-known Wyman IIa (n = 1) interior solution in order to close the Einstein’s Field Equations, as well we use the Wyman IIa, Tolman IV, and Heintzmann IIa and Durgapal IV models as seeds solutions. These models fulfill the fundamental physical acceptability conditions for the compactness factor of the system 4U 1820-30. Stability against convection and against collapse are also studied.
Virtual Acoustic Characterization of a Multipurpose Sports Facility: A Case Study of the Coliseum at “Unidad Educativa Municipal Sebastián de Benalcázar”, Quito, Ecuador
Authors: Jácome L S , Simbaña P , Cadena G 
Published: July 2024

Abstract
This study presents a virtual acoustic characterization of a multipurpose sports facility, focusing on a case study of the coliseum at “Unidad Educativa Municipal Sebastián de Benalcázar” in Quito, Ecuador. Utilizing a non-intrusive, simulation-based approach, an initial detailed survey of the coliseum’s interior was conducted. AutoCAD and SketchUp software were utilized to generate a three-dimensional (3D) model based on the acquired data. This model was then exported to ODEON software for acoustic simulation. Important acoustic parameters, including reverberation duration, clarity, STI (Speech Transmission Index), and RASTI (Rapid Speech Transmission Index), were obtained from the simulation. These parameters were instrumental in characterizing the sports facility’s acoustic quality and comparing it with established reference values. Special emphasis was given to the initial parameters of reverberation time and STI, crucial for assessing the acoustic quality of the space. This virtual characterization provides significant insights into the acoustical properties of the coliseum, highlighting its potential and limitations as a multifunctional sports venue. The present study makes a significant contribution to the field of virtual acoustic assessments by providing a comprehensive framework for future improvements in the acoustic design and sound quality of similar environments. The study specifically addresses the distinct geographical and architectural context of Quito, Ecuador.
First model of industrial noise propagation in the vicinity of the Yasuní National Park
Authors: Velasco Geoconda M. , Vásconez C. L. , Xulvi-Brunet R. , Dueñas-Vidal A. , Vizcarra S. , de la Cruz I. , Rivera-Parra P. , Rivera-Parra J. L. 
Published: July 2024

Abstract
This work addresses aspects of industrial noise produced by the facilities of oil extraction near Yasuní National Park, located in the Ecuadorian Amazon region. The acoustic sources within this kind of facilities could influence in the wildlife behavior, which could impact negatively on the species. The acoustic wave radial propagation model in an open field is proposed through a geometric divergence attenuation, atmospheric absorption effect, dispersion effects due to obstacles, and soil effects. The initial model without obstacles makes predictions based on an algorithm considering that the data are associated to a GPS position, the first propagation model without obstacles determines the level of sound pressure for each quasi-horizontal measurement (x, y). Then, dispersion factors are incorporated through the NORD2000 model to consider the local flora that introduces reflection and dispersion phenomena which are in the environment considering geometric measurements estimated in situ. The pressure level of the model without obstacles decreases as the frequency increases, but with less intensity compared to the pressure level of the model with obstacles. As the frequency increases, there are important fluctuations since the attenuating factors influence more than the correction factors.
Martin-Puplett interferometer for studying THz and subTHz radiation
Authors: Nazhmudinov R M , Kishin I A , Yu Kidanova E , Karataev P V 
Published: July 2024

Abstract
The design of a portable terahertz and subterahertz interferometer, manufactured according to the Martin-Puplett scheme, is presented. The possibility of using film polypropylene polarizers in interferometers of this type has been demonstrated. The provided possibility of using various types of radiation detectors makes it possible to perform measurements in high-performance or high-sensitivity modes. The presented interferometer is proposed to be used to measure the spectra of terahertz and subterahertz radiation produced during the interaction of bunched and continuous beams of charged particles with solid targets.
Effect of ultrasound exposure time in the size distribution of lecithin liposomes in the nanoscale
Authors: Barros J L , Rodríguez V A , Bayas M V 
Published: July 2024

Abstract
The characteristics of liposome suspensions subjected to ultrasound were studied using the Nanoparticle Trajectory Analysis (NTA) technique. These were prepared with either DPPC or soy lecithin using the thin layer method. For each preparation protocol, the size and ζ potential probability densities were determined by averaging the densities obtained in several measurements. Ultrasound exposure changed the characteristics of the size and ζ potential distributions. For the size distribution, the exposure resulted in an increment of the number of liposomes with diameters in the range of ∼ 100 nm, the corresponding size distributions became slimmer, with mean diameters smaller than the ones without exposure. This is reflected in the reduction of the polydispersity index experienced by the liposome suspensions after sonication. For DPPC liposomes, it changed from ≈ 0.26 without exposure to ≈ 0.19 after 10 min sonication whereas for soy lecithin liposomes it changed from ≈ 0.30 without exposure to ≈ 0.21 after 10 min sonication. For the ζ potential, after 10 min exposure, the distributions became wider, with mean values more negative than the ones without exposure. The mean ζ potential, for DPPC liposomes, changed from ≈ −35 mV without exposure to ≈ −39 mV after 10 min sonication whereas for soy lecithin liposomes it changed from ≈ −38 mV without exposure to ≈ −42 mV after 10 min sonication. Therefore, our results show that exposure to ultrasound reduced the polydispersity and increased the stability of the liposome suspensions.
Enhanced fluorescence emission of a single quantum dot in a porous silicon photonic crystal—plasmonic hybrid resonator
Authors: Granizo Evelyn , Kriukova Irina , Escudero-Villa Pedro , Samokhvalov Pavel , Nabiev Igor 
Published: July 2024

Abstract
Currently, much interest is attracted to investigating the potential of hybrid systems that exhibit plasmon-induced photoluminescence (PL) enhancement of quantum emitters in terms of optoelectronics and biosensing applications. The implementation of these systems based on photonic microcavities offers benefits due to a stronger localization of the field within the resonant cavity. Porous silicon is one of interesting materials for engineering such microcavities thanks to the simplicity of its fabrication and the possibility to embed emitters from the solution into a ready-made resonator. In this theoretical study, the fluorescence enhancement of a quantum dot (QD) in a hybrid system based on a porous silicon microcavity (pSiMC) and silver nanoplatelets (AgNPs) was investigated using finite element method (FEM) numerical simulations. For this purpose, infinite arrays were simulated by using a periodic unit cell. The pSiMC was designed as two λ/4 distributed Bragg reflectors with alternating refractive indices and a cavity layer of a double thickness between them. For comparison, simulations were also performed for an AgNP and a QD in a reference monolayer with a constant refractive index without a microcavity structure. The results show QD fluorescence enhancement in the AgNP/pSiMC hybrid system, mainly due to the higher excitation rate.
Quantum fluctuation dynamics of open quantum systems with collective operator-valued rates, and applications to Hopfield-like networks
Authors: Fiorelli Eliana 
Published: July 2024

Abstract
We consider a class of open quantum many-body systems that evolves in a Markovian fashion, the dynamical generator being in GKS-Lindblad form. Here, the Hamiltonian contribution is characterized by an all-to-all coupling, and the dissipation features local transitions that depend on collective, operator-valued rates, encoding average properties of the system. These types of generators can be formally obtained by generalizing, to the quantum realm, classical (mean-field) stochastic Markov dynamics, with state-dependent transitions. Focusing on the dynamics emerging in the limit of infinitely large systems, we build on the exactness of the mean-field equations for the dynamics of average operators. In this framework, we derive the dynamics of quantum fluctuation operators, that can be used in turn to understand the fate of quantum correlations in the system. We then apply our results to quantum generalized Hopfield associative memories. Here we show that, asymptotically and at the description level of quantum fluctuations, only a very weak amount of quantum correlations, in the form of quantum discord, emerges beyond classical correlations.