bluuebunny/crossref_metadata_2025_split huggingface dataset:
How does the operating bandwidth of non-foster negative capacitor affect its stability properties?
Authors: Zanic Dominik , Krois Igor , Hrabar Silvio 
Published: May 2024

The comparative analysis of the stability properties of three generic types of negative non-Foster capacitors (ideal non-dispersive type, low-pass type, and band-pass type) is presented. It is shown that the range of external RC loads that ensure stable operation for a band-pass negative capacitor significantly exceeds the ranges required for both a low-pass negative capacitor and an ideal dispersionless negative capacitor. Furthermore, the rigorous inequality is derived, which connects the operating bandwidth and a range of permissible external loads, thus enabling a straightforward design of practical stable non-Foster systems. Finally, the RF hardware demonstrator (100 kHz–100 MHz) of the band-pass negative capacitor was fabricated, and all measurement results were found to be in very good agreement with the theoretical predictions.
Probing the influence of cation distribution on the vibrational dynamics and phase transitions in Mg-doped zinc ferrites: Quantum mechanical study
Authors: Almutairi Tahani Saad 
Published: May 2024

The assignment of vibrational modes in spinel ferrites using infrared (IR) and Raman spectroscopy is a task of considerable complexity due to the intricate vibrational behavior exhibited by these materials. This complexity is further compounded by the presence of a variety of metal cations occupying distinct positions in the crystal lattice. In the present study, we have undertaken a detailed analysis of the vibrational spectrum of Mg–Zn ferrite utilizing an ab initio periodic quantum-mechanical approach. We employed a localized Gaussian-type function basis set in conjunction with the B3LYP hybrid Hamiltonian to achieve this. The obtained results indicate that the ground state of Mg–Zn ferrite is an open-shell system with a mixed structure, characterized by magnesium atoms predominantly occupying octahedral sites, zinc atoms at the tetrahedral site, and iron atoms distributed across both sites. We report a discernible free energy difference of 0.07 eV, which includes zero-point energy, corresponding to 812 K, between the antiferromagnetic ground state and a ferromagnetic arrangement within the normal structure. The variations in IR and Raman frequencies identified in this research, as compared to the existing literature, hold significant potential for determining the phase of Mg–Zn ferrites in experimental spectra. This advancement offers a vital step toward a deeper understanding of the vibrational properties and potential applications of these ferrites.
A stable and robust fault diagnosis method for bearing using lightweight batch normalization-free residual network
Authors: Zhu Bao , He Chunmeng 
Published: May 2024

Abstract
The conventional deep learning-based bearing fault diagnosis methods tend to utilize denoising modules to improve the fault diagnosis performance in noisy scenes. However, the addition of denoising modules will increase expensive computational costs, leading to a delayed acquisition of fault diagnosis results. This work proposed a lightweight batch normalization (BN)-free residual network without any denoising modules for bearing fault diagnosis which properly rescaled the weights in a standard initialization instead of BN to avoid the exploding gradient problem and vanishing gradient problem at the beginning of training for deep neural networks. Therefore, it prevents the undesirable properties caused by BN. Compared with other methods, the fault diagnosis performance of the proposed method can maintain a high level with different input sizes and batch sizes. Especially in noisy scenes, the testing accuracy of fault diagnosis on different bearing datasets can be improved by 13.54% and 7.74% using fewer parameters and floating point operations on different bearing datasets.
In-beam PET monitoring of proton therapy: a method for filtering prompt radiation events
Authors: Ma Qiuhui , Yang Zhiyong , Mu Dengyun , Gao Min , Zhang Ruilin , Wan Lin , Qiu Ao , Xie Qingguo 
Published: May 2024

Abstract

Objective. In-beam positron emission tomography (PET) is a promising technology for real-time monitoring of proton therapy. Random coincidences between prompt radiation events and positron annihilation photon pairs can deteriorate imaging quality during beam-on operation. This study aimed to improve the PET image quality by filtering out the prompt radiation events. Approach. We investigated a prompt radiation event filtering method based on the accelerator radio frequency phase and assessed its performance using various prompt gamma energy thresholds. An in-beam PET prototype was used to acquire the data when the 70 MeV proton beam irradiated a water phantom and a mouse. The signal-to-background ratio (SBR) indicator was utilized to evaluate the quality of the PET reconstruction image. Main results. The selection of the prompt gamma energy threshold will affect the quality of the reconstructed image. Using the optimal energy threshold of 580 keV can obtain a SBR of 1.6 times for the water phantom radiation experiment and 2.0 times for the mouse radiation experiment compared to those without background removal, respectively. Significance. Our results show that using this optimal threshold can reduce the prompt radiation events, enhancing the SBR of the reconstructed image. This advancement contributes to more accurate real-time range verification in subsequent steps.
Impact of reduced differential transform under heat-moisture coupling on the hygrothermoelastic properties in cartesian coordinates
Authors: Sutar Chandrashekhar S , Patil Sandip N , Chaudhari Kamini K 
Published: May 2024

Abstract
The objective of this work is to analyze the effects of combined temperature and moisture in a rectangular plate under hygrothermal loading using hygrothermoelastic model. The reduced differential transform method is used to obtain the solution of temperature, moisture and stress. We have demonstrated the efficacy of the reduced differential transform approach to examine several aspects of the Hygrothermoelastic model. The results are illustrated numerically and graphically for porous composite material. From the study, it is observed that temperature shows a negative effect while moisture increases in a positive direction along spatial direction under the assumed initial and boundary conditions. The stresses are affected due to hygrothermal loading.
EfficientSkinSegNet: a lightweight convolutional neural network for accurate skin lesion segmentation
Authors: Deng Shuangcheng , Li Zhiwu , Zhang Jinlong , Hua Junfei , Li Gang , Yang Yang , Li Aijing , Wang Junyang , Song Yuting 
Published: May 2024

Abstract
Accurate segmentation of skin lesions is crucial for the early detection and treatment of skin cancer. In this study, we propose EfficientSkinSegNet, a novel lightweight convolutional neural network architecture specifically designed for precise skin lesion segmentation. EfficientSkinSegNet incorporates efficient feature extraction encoders and decoders, leveraging multi-head convolutional attention and spatial channel attention mechanisms to extract and enhance informative features while eliminating redundant ones. Furthermore, a multi-scale feature fusion module is introduced in the skip connections to facilitate effective fusion of features at different scales. Experimental evaluations on benchmark datasets demonstrate that EfficientSkinSegNet outperforms state-of-the-art methods in terms of segmentation accuracy while maintaining a compact model size. The proposed network shows promise for practical clinical diagnostic applications, providing a balance between segmentation performance and computational efficiency. Future research will focus on evaluating EfficientSkinSegNet’s performance on diverse semantic segmentation tasks and optimizing it for medical image analysis.
A multiple directions turning vehicle counting method at intersections based on arbitrary-oriented detection and stack Long Short-Term Memory
Authors: Li Shuang , Liu Chunsheng 
Published: May 2024

Abstract
The traffic flow parameters of turning vehicles at urban road intersections play an important role in the intelligent traffic system. Traffic road monitoring is difficult to cover large intersection scenes. With the application of unmanned aerial vehicles in traffic, drone-based intersection monitoring has great potential due to the large perspective. But there is rarely research on multiple turning vehicle counting at intersections based on drones. A few existing multiple turning vehicle counting methods are based on vehicle detection and tracking methods. However, the vehicles at the intersection have various orientations, the accurate information of the vehicle cannot be obtained by detection, and the tracking process is complicated and redundant for the counting task. Based on the traffic video at intersections taken by unmanned aerial vehicles, we propose a eight turnings vehicle counting model without complex tracking. Firstly, a spatial attention and channel adaptive attention model net is proposed for arbitrary-oriented vehicle detection to get the orientation and position of vehicles. Secondly, a turning spatio-temporal counting feature for different turning vehicles and its extraction method are proposed. Finally, the stack Long Short-Term Memory net based counting model is designed to process the turning spatio-temporal counting feature and counting eight different turnings vehicles at intersection. The experiments show that the proposed method can realize counting eight different turnings vehicles simultaneously in drone-based traffic video at the intersection without relying on complex multi-target tracking. The average counting accuracy has reached 98.18%.
Analytical modeling of nucleation and growth of graphene layers on CNT array and its application in field emission of electrons
Authors: Sharma Shruti , Sharma Suresh C 
Published: May 2024

Abstract
Carbon Nanotube (CNT) arrays and graphene have undergone several investigations to achieve efficient field emission (FE) owing to CNT’s remarkable large aspect ratio and graphene’s exceptional FE stability. However, when dense CNT arrays and planar graphene layers were used as field emitters, their field enhancement factor reduced dramatically. Therefore, in this paper, we numerically analyze the growth of a dense CNT array with planar graphene layers (PGLs) on top, resulting in a CNT-PGL hybrid and the associated field enhancement factor. The growth of the CNT array is investigated using Plasma Enhanced Chemical Vapor Deposition (PECVD) chamber in C2H2/NH3 environment with variable C2H2 flow, Ni catalyst film thickness, and substrate temperature followed by PGL precipitation on its top at an optimized cooling rate and Ni film thickness. The analytical model developed accounts for the number density of ions and neutrals, various surface elementary processes on catalyst film, CNT array growth, and PGLs precipitation. According to our investigation, the average growth rate of CNTs increases and then decreases with increasing C2H2 flow rate and catalyst film thickness. CNTs grow at a faster rate when the substrate temperature increases. Furthermore, as the chamber temperature is lowered from 750 °C to 250 °C in N2 environment and Ni film thickness grows, the number of the graphene layers increases. The field enhancement factors for the CNT array and hybrid are then calculated based on the optimal parameter values. The average height of the nanotubes, their spacing from one another, and the penetration of the electric field due to graphene coverage are considered while computing the field enhancement factor. It has been found that adding planar graphene layers to densely packed CNTs can raise its field enhancement factor. The results obtained match the current experimental observations quite well.
Effect of MWCNTs surface functionalization on the characterization of PVA/MWCNTs nanocomposites
Authors: Le Thi Mai Hoa , Vo Nguyen Da Huyen , Dang Dung My Thi , Doan Tin Chanh Duc 
Published: May 2024

Abstract
The present study aims to put forward the role of surface functionalization of multi-walled carbon nanotubes (MWCNTs) on the enhancement thermal and hydrophilicity properties of new nanocomposite based poly(vinyl alcohol)/surface functionalized MWCNTs (PVA/MWCNTs), prepared by a facile phase inversion process. In this study, the fabrication of PVA/MWCNTs nanocomposites is carried out using functionalized MWCNTs (f-MWCNTs) with different functional groups (–OH, –COOH, –NH2). The structural, morphological, thermal, hydrophilicity and physico-chemical properties of PVA/MWCNTs nanocomposites are characterized by XRD, FE-SEM, FT-IR, TGA and contact angle measurement. The FT-IR results confirm the formation of a hydrogen bond between MWCNTs and PVA chain. XRD analysis indicates an improvement in the crystallinity of the PVA/MWCNTs nanocomposite. TGA results reveal that the PVA/f-MWCNTs nanocomposites show higher thermal stability than pure PVA. It is revealed that PVA/MWCNTs nanocomposites based functionalized MWCNTs remarkably increase the degradation temperature, and thus enhancing the thermal properties. The highest weight loss (30.7 wt%) and degradation temperature (520 °C) values are obtained with PVA/f-MWCNTs(0.5 wt%). The contact angle measurement confirms that the hydrophobic properties of pure PVA became hydrophilic because of the functional groups of MWCNTs. The hydrophilicity of PVA/MWCNTs nanocomposites is increased with the increase in wt% of MWCNTs embedded in the nanocomposite.
Development of beamline control system for Hefei Advanced Light Facility
Authors: Liu Qun , Chen Liuguo , Liu Gongfa , Wang Xiao , Li Fang , He Bo 
Published: May 2024

Abstract
The design and the control scheme of beamline control system
for Hefei Advanced Light Facility (HALF;Hefei, People's Republic of
China) which is under construction is described in this paper.It
mainly includes, equipment protection safety, personal protection
safety,fast signal interlock and status monitoring and fault
diagnosis system. In this control system ,the human presence
perception function has been added to the personal protection safety
system. Multi-parameter status monitoring of key equipment on
beamline station can automatically alarm equipment failures and send
relevant personnel timely notifications. At the same time, it can
also provide early warning on the health status of key equipment on
the beamline. The system not only improves the safety and
reliability of beamline control, but also increases the degree of
automation and intelligence, reduces the workload of operation and
maintenance personnel, and improves operation and maintenance
efficiency.
Geometry of spinors: doubly-chiral plane-wave expansion
Authors: Fabbri Luca 
Published: May 2024

Abstract
We employ the polar re-formulation of spinor fields to see in a new light their classification into regular and singular spinors, these last also called flag-dipoles, further splitting into the sub-classes of dipoles and flagpoles: in particular, we will study the conditions under which flagpoles may be solutions of the Dirac field equations. We argue for an enlargement of the plane-wave expansion.
Investigation of JGS frost susceptibility testing method
Authors: Zheng Gong , Jin Hyunwoo , Ryu Byung Hyun , Lee Jangguen 
Published: May 2024

Abstract
Frost heave is a type of frost action that occurs when the soil temperature is below freezing temperature. Construction in cold regions is challenging and can cause severe damage to structures due to frost heave. The segregation potential (SP) is widely accepted in engineering design and for frost heave predictions as an indicator for frost susceptibility classification. The purpose of this study is to investigate the frost heave experiments in the JGS method and to evaluate the JGS frost susceptibility testing results for different soil mixtures with different SP through numerical simulation with a Thermal-Hydraulic coupled model. The results show good agreement between numerical simulation and test results for different degrees of frostsusceptible soils. This model is expected to provide accurate predictions of the frost heave behavior of different soils.
Evaluating wave force and interface fluid velocity distribution at the vicinity of a breakwater by considering consolidation of porous media
Authors: Zou S , Shi Z , Huang M 
Published: May 2024

Abstract
Previous studies have successfully simulated the interaction between wave and porous media using volume-averaged Reynolds-averaged Navier-Stokes (VARANS) equations. However, the consolidation of porous media subject to wave loading affects the progressing wave, which is crucial to computation of wave force and wave-porous media interface fluid velocity. Research on this aspect is scarce. This study adopted the
u
-p approximation for Biot dynamic consolidation theory to quantify the consolidation of porous media. The continuity condition on the interfaces between the wave and porous seabed was considered to achieve their interaction. Coupling algorithms of wave and porous seabed were validated against experimental measurements in the literature. An engineering case of wave traveling over a breakwater was examined, and computation results of wave pressures and interface fluid velocities at the vicinity of a breakwater by VARANS equations and wave-porous seabed coupling algorithm of this study were compared to explore the influence of the consolidation effect on the progressing wave.
3D SPH implementation to large deformation of granular mass using an advanced hypoplastic constitutive model
Authors: Zhu Chengwei , Wu Wei , Wang Shun , Peng Chong 
Published: May 2024

Abstract
In this study, an advanced hypoplastic model incorporating the critical state is integrated into smoothed particle hydrodynamics (SPH) for the numerical investigation of geotechnical large-deformation problems. Moreover, a return mapping strategy is proposed to regulate the stress state to avoid calculation failure. Furthermore, a closed-form solution to predicting the failure surface implicitly incorporated in the hypoplastic model is derived. The proposed SPH method is examined with element tests such as the oedometer test and biaxial compression test to observe a good performance of the SPH model. Finally, the SPH model is applied to numerically reproducing sand column collapse to investigate the effect of the initial void ratio on the behavior of soil.
Constitutive modeling of anisotropic small-strain stiffness behavior of Shanghai soft clay
Authors: Chen Zhengyi , Gu Xiaoqiang , Shi Zhenhao , Yang Min 
Published: May 2024

Abstract
Small strain stiffness plays an important role in determining the deformation of geotechnical engineering in soft soils. To model the anisotropic small strain stiffness behavior of soft clay, an enhancement to the bounding surface plasticity model is proposed. This enhancement involves incorporating the anisotropy in soil stiffness at very small strains and its non-linear degradation. Small strains are considered via cross-anisotropy elasticity, while the concept of intergranular strain is used to reflect the non-linear degradation of stiffness with strains and the effect of recent stress history. Using the proposed model, the triaxial test results on K0 consolidated Shanghai clay specimens are simulated to investigate the model’s performance. In the simulation, three typical shear stress paths are adopted. The comparison of the model simulations against experimental evidence is discussed, where the effects of anisotropy in initial soil stiffness and stress-induced anisotropy on the variation of small-strain stiffness, pore pressure, and shear-volume coupling response are highlighted.
Integrated database of granular soils under triaxial shear and its application in the prediction of stress–strain relationship
Authors: Xiong W , Wang J , Cheng Z 
Published: May 2024

Abstract
This study presents a novel data generation framework that generates a large database for machine learning (ML)-based soil model predictions. The dataset comprised 216 sets of triaxial tests on morphologically mutated and gene-decayed granular samples. This database was then estimated using five widely utilized ML algorithms to predict the stress-strain relationship of granular soils. They include the support vector machine (SVM), bagged trees, Gaussian process regression (GPR), and back propagation neural network (BPNN) algorithms. Following the hyperparameter settlement, model training, and testing, all the trained models captured the effects of the multiscale particle morphology, initial packing state, and confining stress. The excellent training and testing performances indicate the superior quality of the generated dataset. The fine tree, exponential GPR, and BPNN outperformed the Gaussian SVM and bagged trees in terms of the predictive performance. Among them, the exponential GPR exhibits the best model performance in reflecting the particle morphology effect, whereas the fine tree and BPNN generally exhibit good predictive performance for missing local information. Furthermore, all the models are tested by the micro-tomography (μCT) experimental data. The findings of this study were validated through a comparison between the DEM and model prediction results.
The impact of initial fabric anisotropy on cyclic liquefaction behavior with polygonal particles using the discrete element method (DEM)
Authors: Ajamnorouzi M , Hosseininia E S 
Published: May 2024

Abstract
Liquefaction is one of the most significant phenomena that can occur in soil during an earthquake. The destructive effects of this phenomenon on earth structures have captured the attention of many geotechnical engineers. Additionally, the gradual increase in pore water pressure during the cyclic loading induced by the propagation of earthquake waves, known as cyclic liquefaction, is a crucial factor that might lead to the complete loss of shear strength in soils. A better understanding of cyclic liquefaction is essential for improving earthquake hazard analysis and mitigating structural damage. Factors influencing liquefaction behavior include particle size, particle grading, and soil fabric. By “soil fabric,” we refer to the arrangement and positioning of particles with each other, particle shape, and the shape of voids among particles. This research aims to focus on the effects of fabric anisotropy on cyclic liquefaction of granular soils with angular particles. To carry out this investigation, the discrete element method has been employed. In the discrete element method, the geometry of particles and, generally, the soil fabric can be considered. In this research, specimens with varying particle orientation angles were produced and subjected to cyclic loading tests under undrained conditions. By examining the stress paths of specimens during cyclic loading, it is observed that the initial fabric and inherent anisotropy significantly influence the behavior of the specimens. Inherent anisotropy leads to varying numbers of cycles required for the initial liquefaction of each specimen. Furthermore, it is observed that as the particle orientation angle increases, the localization of axial strains shifts from tensile to compressive directions. The effect of inherent anisotropy plays a substantial role in the initial liquefaction (ru = 1). If the liquefaction of specimens is of interest at lower values of ru, the effect of inherent anisotropy can be disregarded.