bluuebunny/crossref_metadata_2025_split huggingface dataset:
Brownian dynamics of Dirac fermions in twisted bilayer graphene
Authors: Yar Abdullah 
Published: August 2023

Abstract
Brownian dynamics of Dirac fermions in twisted bilayer graphene is investigated within the framework of semiclassical relativistic Langevin equations. We find that under the influence of orthogonal, commensurate ac drives in the periodic ratchet potential of a substrate, the charge carriers in the system exhibit pronounced random dynamics, tuned by the twist angle, making twisted bilayer graphene distinct from monolayer graphene. It is shown that as threshold twist angle matches the optimal angle, deterministic running states appear in the limit of weak thermal noise where the diffusion rate is enhanced significantly compared to bare thermal diffusion. Analysis of the real space trajectories and diffusion coefficient illustrates the significant role of thermal noise in the random motion of Dirac fermions. In addition, we find that the Brownian particle shows remarkable ratchet effect as a net current.
Griffiths phase, re-entrant spin-glass behaviour and schottky anomaly in anti-site disordered double perovskite Pr2MnNiO6
Authors: Kumar Rinku , Balasubramanian Padmanabhan , Anas M , Singh Ankita , Chandra Ramesh , Malik V K 
Published: August 2023

Abstract
In the present study, the effect of anti-site disorder on the magnetic properties of Pr2MnNiO6 is explored. Due to anti-site disorder, a reduced T
C preceded by a Griffith phase has been observed. At low temperatures, we also report the development of the spin glass phase in co-existence with the cluster-like ferromagnetic order. The signature of the re-entrant spin glass phase, where the material is ferromagnetic below T
c, and undergoes a transition to the spin glass state, is revealed by the frequency-dependent ac-susceptibility measurements. The spin glass behavior is also supported by the slow decay of thermo-remanent magnetization. The low temperature specific heat shows a small field dependent behaviour which may be attributed to Schottky effects.
Stacking effect on the orbital characters and transition probability of bilayer MoS2
Authors: Mansouri Masoume , Ghalambor Dezfuli Abdol-Mohammad , Salehi Hamdollah 
Published: August 2023

AbstractIn this paper, employing the van der Waals (vdW) corrected density functional method, the electronic and optical properties of bilayer MoS2are calculated for five different stacking patterns (AA,AA′,AB′,AB,andA′B). In this regard, the structural stability and the van der Waals interlayer distance are investigated. The minimum formation energy has indicated the overall trend of relative stability to be asAA′≅AB≅AB′≫AA≅A′B.This meansAA′,AB′,andABhave the most andAA,andA′Bhave the least stable structures. The band structure and probability distribution for finding electrons in atomic orbitals have also been calculated in the Brillouin zone. The results obtained from band structures and orbital characters show the same trend in the three most stable and a different but the same trend for the two least stable orders. In addition, the oscillator strength has been derived from the calculation of the momentum operator matrix elements using first-principles methods. The calculation of transition probabilities from matrix elements indicates thatAA′,AB,andAB′have higher transition probabilities than that ofAAandA′B.The study of transition probability also shows the y-polarized light is stronger than the x-polarized light for all stacking patterns.
Modelling the next-to-leading order matter three-point correlation function using FFTLog
Authors: Guidi M. , Veropalumbo A. , Branchini E. , Eggemeier A. , Carbone C. 
Published: August 2023

Abstract
The study of higher-order statistics, particularly 3-point statistics, of the Large Scale Structure (LSS) of the Universe
provides us with unique information on the biasing relation between luminous and dark matter and on deviations from primordial Gaussianity. As a result, much effort has been put into improving
measurement techniques as well as theoretical modelling,
especially in Fourier space. Comparatively, little progress has been made, instead, in configuration space analyses. This work represents a first step towards filling this gap by proposing a new strategy for modelling 3-point statistics at higher perturbative orders in configuration space. Starting from the next-to-leading order model for the matter bispectrum, we use 2D-FFTLog to generate its counterpart in configuration space. We calibrate the procedure using the leading order predictions for which an analytic model for the 3-point correlation function (3PCF) already exists. Then we assess the goodness of the 3PCF model by comparing its predictions with measurements performed on the matter distribution in collisionless cosmological N-body simulation (DEMNUni). We focus on two redshifts (z = 0.49 and z = 1.05) in the range spanned by current and future galaxy redshift surveys. The χ
2 analysis reveals that the next-to-leading order 3PCF models significantly improve over the leading order one for all triangle configurations in both redshifts, increasing the number of matched configurations at redshift z = 1.05 and z = 0.49, respectively. In particular, a significant improvement is also seen on the Baryonic Acoustic Oscillations (BAO) scale for triangle configurations whose smallest side length is well into the nonlinear regime. The computational cost of the model proposed here is high but not prohibitively large — order of five hours in a 48-core computation — and represents the first step towards a complete 3PC model for the galaxies.
Quasinormal modes of Bardeen black holes with a cloud of strings*
Authors: Liu 刘 Yunlong 云龙 , Zhang 张 Xiangdong 向东 
Published: August 2023

Abstract
We investigate the quasinormal mode and greybody factor of Bardeen black holes with a cloud of strings via the WKB approximation and verify them using the Prony algorithm. We find that the imaginary part of the quasinormal mode spectra is always negative, and the perturbation does not increase with time, indicating that the system is stable under scalar field perturbation. Moreover, the string parameter a has a dramatic impact on the frequency and decay rate of the waveforms. In addition, the greybody factor increases when a and λ increase and when q and l decrease. The parameters λ and l have a significant effect on the tails. In particular, when

, a de Sitter phase appears at the tail.
Highly efficient organic solar cells with improved stability enabled by ternary copolymers with antioxidant side chains
Authors: Song Ao , Huang Qiri , Zhang Chunyang , Tang Haoran , Zhang Kai , Liu Chunchen , Huang Fei , Cao Yong 
Published: August 2023

Abstract
The stability of organic solar cells (OSCs) remains a major concern for their ultimate industrialization due to the photo, oxygen, and water susceptibility of organic photoactive materials. Usually, antioxidant additives are blended as radical scavengers into the active layer. However, it will induce the intrinsic morphology instability and adversely affect the efficiency and long-term stability. Herein, the antioxidant dibutylhydroxytoluene (BHT) group has been covalently linked onto the side chain of benzothiadiazole (BT) unit, and a series of ternary copolymers D18-Cl-BTBHTx (x = 0, 0.05, 0.1, 0.2) with varied ratio of BHT-containing side chains have been synthesized. It was found that the introduction of BHT side chains would have a negligible effect on the photophysical properties and electronic levels, and the D18-Cl-BTBHT0.05: Y6-based OSC achieved the highest power conversion efficiency (PCE) of 17.6%, which is higher than those based active layer blended with BHT additives. More importantly, the unencapsulated device based on D18-Cl-BTBHTx (x = 0.05, 0.1, 0.2) retained approximately 50% of the initial PCE over 30 hours operation under ambient conditions, significantly outperforming the control device based on D18-Cl (90% degradation in PCE after 30 h). This work provides a new structural design strategy of copolymers for OSCs with simultaneously improved efficiency and stability.
High threshold voltage enhancement-mode GaN p-FET with Si-rich LPCVD SiN
x
gate insulator for high hole mobility
Authors: Zhu Liyang , Chen Kuangli , Ma Ying , Cai Yong , Zhou Chunhua , Li Zhaoji , Zhang Bo , Zhou Qi 
Published: August 2023

Abstract
In this work, the GaN p-MISFET with LPCVD-SiN
x
is studied as a gate dielectric to improve device performance. By changing the Si/N stoichiometry of SiN
x
, it is found that the channel hole mobility can be effectively enhanced with Si-rich SiN
x
gate dielectric, which leads to a respectably improved drive current of GaN p-FET. The record high channel mobility of 19.4 cm2/(V∙s) was achieved in the device featuring an Enhancement-mode channel. Benefiting from the significantly improved channel mobility, the fabricated E-mode GaN p-MISFET is capable of delivering a decent-high current of 1.6 mA/mm, while simultaneously featuring a negative threshold-voltage (V
TH) of –2.3 V (defining at a stringent criteria of 10 μA/mm). The device also exhibits a well pinch-off at 0 V with low leakage current of 1 nA/mm. This suggests that a decent E-mode operation of the fabricated p-FET is obtained. In addition, the V
TH shows excellent stability, while the threshold-voltage hysteresis ΔV
TH is as small as 0.1 V for a gate voltage swing up to –10 V, which is among the best results reported in the literature. The results indicate that optimizing the Si/N stoichiometry of LPCVD-SiN
x
is a promising approach to improve the device performance of GaN p-MISFET.
Terahertz phononic crystal in plasmonic nanocavity
Authors: Li Zhenyao , Chang Haonan , Lai Jia-Min , Song Feilong , Yao Qifeng , Liu Hanqing , Ni Haiqiao , Niu Zhichuan , Zhang Jun 
Published: August 2023

Abstract
Interaction between photons and phonons in cavity optomechanical systems provides a new toolbox for quantum information technologies. A GaAs/AlAs pillar multi-optical mode microcavity optomechanical structure can obtain phonons with ultra-high frequency (~THz). However, the optical field cannot be effectively restricted when the diameter of the GaAs/AlAs pillar microcavity decreases below the diffraction limit of light. Here, we design a system that combines Ag nanocavity with GaAs/AlAs phononic superlattices, where phonons with the frequency of 4.2 THz can be confined in a pillar with ~4 nm diameter. The Q
c/V reaches 0.22 nm−3, which is ~80 times that of the photonic crystal (PhC) nanobeam and ~100 times that of the hybrid point-defect PhC bowtie plasmonic nanocavity, where Q
c is optical quality factor and V is mode volume. The optomechanical single-photon coupling strength can reach 12 MHz, which is an order of magnitude larger than that of the PhC nanobeam. In addition, the mechanical zero-point fluctuation amplitude is 85 fm and the efficient mass is 0.27 zg, which is much smaller than the PhC nanobeam. The phononic superlattice-Ag nanocavity optomechanical devices hold great potential for applications in the field of integrated quantum optomechanics, quantum information, and terahertz-light transducer.
Propagation of conformity statements in compliance with the GUM and ISO 17025
Authors: Klauenberg Katy , Greenwood John , Foyer Gisa 
Published: August 2023

Abstract
According to ISO/IEC 17025:2017 statements of conformity must identify the decision rule applied, they must be risk-based and account for uncertainty. In legal metrology and often among testing and calibration laboratories, there is the need to reuse measurement-based conformity statements to disseminate acceptability of measurement results. In particular, decision rules are required that allow the statement of conformity for a linear combination of quantities for which, in turn, conformity statements are available. These decision rules should be simple and use information that is typically available, and they should comply with ISO/IEC 17025:2017, again by accounting for the level of risk and for uncertainty following the suite of documents of the GUM (the Guide to the Expression of Uncertainty in Measurement).
Existing guidance requires the input and evaluation of standard uncertainties, or even of distributions, to derive statements of conformity, and as such may be inapplicable, or the required effort may deter practitioners. After reviewing the existing guidance, this research will identify typical settings which lead to particularly simple decision rules for stating conformity for linear combinations of quantities. These new decision rules are based on the specification limits and on information implicitly available in the decision rules of each input quantity. The rules will be proven, they are generalizable, intended to comply with ISO/IEC 17025:2017 and the GUM documents, and suitable to easily state the risk of not conforming to the weighted sum of the input specifications.
For practitioners, a quick reference on feasible conformity statements for linear combinations of quantities is provided. The applicability of and need for the new decision rules are illustrated by two examples involving the legally regulated weighing of long vehicles and of net loads.
A Study of Chan-Vese Model with the Introduction of Edge Information
Authors: He Ruiying , Han Yajun , Tang Zumiao , Long Sha 
Published: August 2023

Abstract
In image segmentation, the image edge information and the selection of edge-stopping function have a extremely important influence on the segmentation effect. As a classical geometric active contour model, Chan-Vese model completely utilizes image global information for segmentation, which has the problems of inability to handle grayscale uneven images and slow curve evolution. This paper introduces edge information into the Chan-Vese model, chooses a new edge stopping function and simplifies the driving force term to build a new image segmentation model. The new model can detect the target fast, displaying better detection effect on the targets of images with uneven grayscale and the detection effect of the target in the uneven gray image is better
Anomaly Pixel Detection via Dual-Branch Uncertainty Metrics
Authors: Luo Peng , Guo Weizhong 
Published: August 2023

Abstract
Anomaly detection aims to detect abnormal samples from normal data. Existing methods mainly use synthesis-based approach and uncertainty-based approach. Moreover, the above-mentioned methods need to re-train part/all of the deep network, which limits its application scenarios especially real-time scenarios. We propose a novel retraining-free anomaly detection method, which includes a dual-branch anomaly detection mechanism to detect abnormal pixels from two views. In the pseudo-supervised branch, we use the Copy post method to transform the unsupervised anomaly detection task into a supervised binary detection task. In the prototype detection branch, we use prototypes to improve the accuracy of abnormal pixel detection. The anomaly results of the two branches are fused by means of soft voting to produce anomaly detection results with higher recall. Our experiments show that our method significantly outperforms other methods and achieves excellent results on different datasets, which also shows that our method has good generalization ability. Therefore, our proposed method can effectively improve the effectiveness and safety of anomaly detection in real-time scenarios, such as autonomous driving.
High Precision License Plate Recognition Algorithm in Open Scene
Authors: Shu Sen 
Published: August 2023

Abstract
At present, license plate recognition algorithm under restricted conditions is relatively mature and widely used in various license plate recognition system. Due to the influence of factors such as large differences in shooting angles and vehicle motion blur, Chinese license plate recognition is quite challenging. In response to the above problems, this research abandoned the single end-to-end deep learning license plate recognition method, and proposed a step-by-step license plate recognition algorithm that integrated detection and classification, and utilized a level-by-level object detection strategy combined with character classification to predict the characters of the license plate result. On the basis of the above, a multi-anchor character position regression algorithm was proposed to further accurately regress the local area position information of all license plate characters. At the same time, in order to meet the needs of character detection and character classification, as well as the imbalance of the existing license plate datasets, this study contributed a series of supporting license plate datasets. According to the published publications, this study contributed the first large-scale character-level annotated license plate dataset. Extensive experiments show that the method in this study can reach the current state-of-the-art on different datasets. If accepted, the dataset will be publicly available at https://gitee.com/wust30405/lpdataset.
The Research on Remote Sensing Image Change Detection Based on Deep Learning
Authors: Song Xu , Zhou Hongyu , Wu Liang 
Published: August 2023

Abstract
In the past, remote sensing image(RSI) change detection often ignored the importance of shallow information with high resolution and fine grained features, and there were problems of missing detection and rough detection boundary. This caused the uncertainty of the edge pixels of the target and the loss of information of the small target. To solve such problem, we construct a new network under the CDD data set for RSI change detection. In addition, in order to better integrate the information from the bottom and top layers, a channel attention module is introduced to conduct in-depth supervision. The testing results indicated that the accuracy of the evaluation index reaches 88%, the recall rate reaches 86%, and the F1 value reaches 83%, which better solves the problems of the uncertainty of the target edge pixel and the information missing of the small target.
Lunar Dome Detection Method Based on Few-Shot Object Detection
Authors: Sun Chen , Tian Xiaolin 
Published: August 2023

Abstract
Lunar domes have always been one of the important windows to understand lunar volcanic activities, but traditional geological dome identification methods are costly. This study attempts to establish an automatic identification method for lunar volcanic domes through FSOD (Few-Shot Object Detection). Since our previous research has been trying to automatically identify lunar volcanic hills using traditional recognition algorithms, our team will try Few-Shot Object Detection this time. In this study, the researchers first obtained the dome coordinates from the list of known lunar domes and intercepted the data this research need from the corresponding coordinates on the DEM moon images. Subsequently, this research used the existing data to train six traditional recognition algorithms and FSOD and compared their performance to verify the feasibility of this study. This research use AP50 and AP75 to evaluate the performance of the model. Finally, this research found that the AP50 of FSOD can reach 0.76, and the AP75 can reach 0.43. However, although FSOD does perform better than other traditional identification methods, this research believe its accuracy is still far from our expectations. Admittedly, compared with YOLO v5, the performance of FSOD has not been significantly improved, but this study still verifies that it is feasible to apply FSOD to the identification of lunar volcanic domes.
Environment Modeling Method for Intelligent Robots Based on Binocular Vision
Authors: Mi Zengzhen , Zhao Shanshan , Zhang Hongmin 
Published: August 2023

Abstract
With the development of technology, the demand for intelligent mobile robots is growing steadily. Environment perception and modeling, are the key elements to achieve robot intelligence. Therefore, binocular stereo vision sensor is used as the core device to perform operations such as binocular camera calibration, obtaining binocular camera internal/external parameters, and distortion correction. On this basis, a multi-dimensional and all-round environment perception system is constructed, and the dynamic target detection results and 3D scene map reconstruction results are displayed dynamically in real time. The experimental results of binocular camera calibration show that the re-projection error lies in the sub-pixel interval, and the image distortion caused by the camera is basically eliminated to meet the experimental expectation of distortion correction; the experimental results of environment modeling show that the mean value of deviation between the object representation value of 3D scene map and the actual object real value is -0.041m, among which the minimum deviation value is 0.001m and the maximum deviation value is 0.532m, in The experimental results are excellent in the tasks of 3D scene map reconstruction and feature point matching. In addition, the storage space of 3D scene map is significantly reduced, and the real-time performance of the system is significantly better than the traditional method.
Object Detection in Security Inspection Scenarios Based on YOLOv5s: Exploring Experiments
Authors: Wang Shuoyu , Zhang Chenhao , Xiao Zhiren 
Published: August 2023

Abstract
At present, there are many problems with the common methods of manual safety checks. Applying object detection technology to X-ray security inspection can effectively improve efficiency and reduce operating costs. The training parameters of deep neural networks directly affect the performance of the network. Based on YOLOv5s network model, this paper uses the method of control variables to explore the impact of training strategies, data enhancement strategies, and loss function selection on network performance. The experimental results show that a suitable batch size and learning rate can achieve significant performance improvements in the model without changing the network structure. With the increase in strength of the data enhancement strategy, the model performance shows a trend of first improving and then decreasing. The selection of the loss function has a great impact on the model’s performance. The research methods and ideas in this article can provide inspiration for subsequent related research.
Research on an Improved Adaptive Image Enhancement Algorithm
Authors: Chen Xinying , Qiu Yihui 
Published: August 2023

Abstract
Aiming at the image degradation problems that occur during industrial activities and the shortcomings of traditional fuzzy enhancement algorithms in dealing with image degradation problems with high algorithm complexity and poor enhancement results. Based on the tuned tri-threshold fuzzy intensification algorithm, this paper proposes an adaptive image enhancement algorithm, which combines three kinds of degraded images in reality (i.e., images in dusty, night and foggy environments) for simulation verification, and compares the visual effects of the enhanced images and makes objective quantitative evaluation. The experimental results show that: the proposed algorithm can not only effectively improve the contrast of the image, keep the detailed information of the image, and make the image present a more natural visual effect, but also improve the quality evaluation index of the image, and make the variance value is improved by more than 5% compared with other comparison algorithms.