bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of copper and graphene-assembled films in 5G wireless communication and THz electromagnetic-interference shielding
Authors: Song Rongguo , Mao Boyang , Wang Zhe , Hui Yueyue , Zhang Ning , Fang Ran , Zhang Jingwei , Wu Yuen , Ge Qi , Novoselov Kostya S. , He Daping 
Published: February 2023

Since first developed, the conducting materials in wireless communication and electromagnetic interference (EMI) shielding devices have been primarily made of metal-based structures. Here, we present a graphene-assembled film (GAF) that can be used to replace copper in such practical electronics. The GAF-based antennas present strong anticorrosive behavior. The GAF ultra-wideband antenna covers the frequency range of 3.7 GHz to 67 GHz with the bandwidth (BW) of 63.3 GHz, which exceed ~110% than the copper foil-based antenna. The GAF Fifth Generation (5G) antenna array features a wider BW and lower sidelobe level compared with that of copper antennas. EMI shielding effectiveness (SE) of GAF also outperforms copper, reaching up to 127 dB in the frequency range of 2.6 GHz to 0.32 THz, with a SE per unit thickness of 6,966 dB/mm. We also confirm that GAF metamaterials exhibit promising frequency selection characteristics and angular stability as flexible frequency selective surfaces.
Predicted novel Janus γ-Ge2
XY (

X/Y=

S, Se, Te) monolayers with Mexican-hat dispersions and high carrier mobilities
Authors: Vu Tuan V , Phuc Huynh V , Nhan Le C , Kartamyshev A I , Hieu Nguyen N 
Published: February 2023

Abstract
This work is motivated by the recent fabrication of a new four-atom-thick hexagonal polymorph from group IV monochalcogenide, so-called γ-GeSe (Lee et al 2021 Nano Lett.
21 4305). In this paper, we propose and examine the structural characteristics, electronic properties, and carrier mobility of monolayers Janus γ-Ge2
XY (

X

/

Y
=

S, Se, or Te) based on comprehensive first-principles calculations. Monolayers γ-Ge2
XY are confirmed to be structurally stable. Our calculations reveal that γ-Ge2
XY monolayers are indirect semiconductors with Mexican-hat-like dispersions in the top valence band. While the effect of the electric field on the energy band dispersions of γ-Ge2
XY monolayers is weak, the energy band dispersions are changed drastically in the presence of strain, especially compressive strain. Interestingly, a structural phase transition from semiconductor to metal is observed in γ-Ge2
XY under compressive strain. γ-Ge2STe and γ-Ge2SeTe possess high electron mobility with values of

3.22
×

10
3

and

8.33
×

10
3

cm2 V−1 s−1, respectively. Our findings not only explore the fundamental physical properties of γ-Ge2
XY but also open up new opportunities in the design of high-performance electronic nanodevices based on layered nanomaterials with Mexican-hat-like dispersions.
Effects of an external electric field on the electronic properties and optical excitations of germanane and silicane monolayers
Authors: Zaabar F , Mahrouche F , Mahtout S , Rabilloud F , Rezouali K 
Published: February 2023

Abstract
Using density functional theory in conjunction with many-body perturbation theory, we theoretically investigated the electronic structures of monolayers germanane and silicane in an applied out-of-plane uniform electric field. Our results show that although the band structures of both monolayers are affected by the electric field, the band gap width cannot be reduced to zero even for high field-strengths. Moreover, excitons are shown to be robust under electric fields, so that Stark shifts for the fundamental exciton peak is only of the order of a few meV for fields of 1 V Å−1. The electric field has also no significant effect on electron probability distribution, as the exciton dissociation into free electron–hole pairs is not observed even at high electric field strengths. Franz-Keldysh effect is also studied in monolayers germanane and silicane. We found that, due to the shielding effect, the external field is prevented to induce absorption in the spectral region below the gap and only above-gap oscillatory spectral features are allowed. One can benefit from such a characteristic where the absorption near the band edge is not altered by the presence of an electric field, especially since these materials have excitonic peaks in the visible range.
A grounded perspective on new early dark energy using ACT, SPT, and BICEP/Keck
Authors: Cruz Juan S. , Niedermann Florian , Sloth Martin S. 
Published: February 2023

Abstract
We examine further the ability of the New Early Dark Energy model (NEDE) to resolve the current tension between the Cosmic Microwave Background (CMB) and local measurements of H
0 and the consequences for inflation. We perform new Bayesian analyses, including the current datasets from the ground-based CMB telescopes Atacama Cosmology Telescope (ACT), the South Pole Telescope (SPT), and the BICEP/Keck telescopes, employing an updated likelihood for the local measurements coming from the SH
0ES collaboration. Using the SH
0ES prior on H
0, the combined analysis with Baryonic Acoustic Oscillations (BAO), Pantheon, Planck and ACT improves the best-fit by Δχ
2 = -15.9 with respect to ΛCDM, favors a non-zero fractional contribution of NEDE, fNEDE > 0, by 4.8σ, and gives a best-fit value for the Hubble constant of H
0 = 72.09 km/s/Mpc (mean 71.49 ± 0.82 with 68% C.L.). A similar analysis using SPT instead of ACT yields consistent results with a Δχ
2 = -23.1 over ΛCDM, a preference for non-zero f
NEDE of 4.7σ and a best-fit value of H0
= 71.77 km/s/Mpc (mean 71.43 ± 0.85 with 68% C.L.). We also provide the constraints on the inflation parameters r and ns
coming from NEDE, including the BICEP/Keck 2018 data, and show that the allowed upper value on the tensor-scalar ratio is consistent with the ΛCDM bound, but, as also originally found, with a more blue scalar spectrum implying that the simplest curvaton model is now favored over the Starobinsky inflation model.
The Atacama Cosmology Telescope: limits on dark matter-baryon interactions from DR4 power spectra
Authors: Li Zack , An Rui , Gluscevic Vera , Boddy Kimberly K. , Bond J. Richard , Calabrese Erminia , Dunkley Jo , Gallardo Patricio A. , Guan Yilun , Hincks Adam , Huffenberger Kevin M. , Kosowsky Arthur , Louis Thibaut , Madhavacheril Mathew S. , Moodley Kavilan , Page Lyman A. , Partridge Bruce , Qu Frank J. , Salatino Maria , Sherwin Blake , Sifón Cristóbal , Vargas Cristian , Wollack Edward J. 
Published: February 2023

Abstract
Diverse astrophysical observations suggest the existence of cold dark matter that
interacts only gravitationally with radiation and ordinary baryonic matter. Any nonzero coupling
between dark matter and baryons would provide a significant step towards understanding the
particle nature of dark matter. Measurements of the cosmic microwave background (CMB) provide
constraints on such a coupling that complement laboratory searches. In this work we place upper
limits on a variety of models for dark matter elastic scattering with protons and electrons by
combining large-scale CMB data from the Planck satellite with small-scale information from
Atacama Cosmology Telescope (ACT) DR4 data. In the case of velocity-independent scattering, we
obtain bounds on the interaction cross section for protons that are 40% tighter than previous
constraints from the CMB anisotropy. For some models with velocity-dependent scattering we find
best-fitting cross sections with a 2σ deviation from zero, but these scattering models are
not statistically preferred over ΛCDM in terms of model selection.
Functional connectivity based machine learning approach for autism detection in young children using MEG signals
Authors: Barik Kasturi , Watanabe Katsumi , Bhattacharya Joydeep , Saha Goutam 
Published: February 2023

Abstract

Objective. Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD. Approach. We recorded resting-state magnetoencephalogram signals from thirty children with ASD (4–7 years) and thirty age and gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used artificial neural network (ANN) and support vector machine (SVM) classifiers in the machine learning framework with a five-fold CV technique. Main results. To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50–100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1–4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity. Significance. Our findings support the weak central coherency theory in autism detection. Further, despite its lower complexity, we show that region-wise COH analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.
EEG-based major depressive disorder recognition by selecting discriminative features via stochastic search
Authors: Chang Hongli , Zong Yuan , Zheng Wenming , Xiao Yushun , Wang Xuenan , Zhu Jie , Shi Mengxin , Lu Cheng , Yang Hao 
Published: February 2023

Abstract

Objective. Major depressive disorder (MDD) is a prevalent psychiatric disorder whose diagnosis relies on experienced psychiatrists, resulting in a low diagnosis rate. As a typical physiological signal, electroencephalography (EEG) has indicated a strong association with human beings’ mental activities and can be served as an objective biomarker for diagnosing MDD. Approach. The basic idea of the proposed method fully considers all the channel information in EEG-based MDD recognition and designs a stochastic search algorithm to select the best discriminative features for describing the individual channels. Main results. To evaluate the proposed method, we conducted extensive experiments on the MODMA dataset (including dot-probe tasks and resting state), a 128-electrode public EEG-based MDD dataset including 24 patients with depressive disorder and 29 healthy controls. Under the leave-one-subject-out cross-validation protocol, the proposed method achieved an average accuracy of 99.53% in the fear-neutral face pairs cued experiment and 99.32% in the resting state, outperforming state-of-the-art MDD recognition methods. Moreover, our experimental results also indicated that negative emotional stimuli could induce depressive states, and high-frequency EEG features contributed significantly to distinguishing between normal and depressive patients, which can be served as a marker for MDD recognition. Significance. The proposed method provided a possible solution to an intelligent diagnosis of MDD and can be used to develop a computer-aided diagnostic tool to aid clinicians in early diagnosis for clinical purposes.
IDP-PGFE: an interpretable disruption predictor based on physics-guided feature extraction
Authors: Shen C. , Zheng W. , Ding Y. , Ai X. , Xue F. , Zhong Y. , Wang N. , Gao L. , Chen Z. , Yang Z. , Chen Z. , Pan Y. 
Published: February 2023

Abstract
Disruption prediction has made rapid progress in recent years, especially in machine learning (ML)-based methods. If a disruption prediction model can be interpreted, it can tell why certain samples are classified as disruption precursors. This allows us to tell the types of incoming disruption for disruption avoidance and gives us insight into the mechanism of disruption. This paper presents a disruption predictor called interpretable disruption predictor based on physics-guided feature extraction (IDP-PGFE) and its results on J-TEXT experiment data. The prediction performance of IDP-PGFE with physics-guided features is effectively improved (true positive rate = 97.27%, false positive rate = 5.45%, area under the ROC curve = 0.98) compared to the models with raw signal input. The validity of the interpretation results is ensured by the high performance of the model. The interpretability study using an attribution technique provides an understanding of J-TEXT disruption and conforms to our prior comprehension of disruption. Furthermore, IDP-PGFE gives a possible mean on inferring the underlying cause of the disruption and how interventions affect the disruption process in J-TEXT. The interpretation results and the experimental phenomenon have a high degree of conformity. The interpretation results also gives a possible experimental analysis direction that the resonant magnetic perturbations delays the density limit disruption by affecting both the MHD instabilities and the radiation profile. PGFE could also reduce the data requirement of IDP-PGFE to 10% of the training data required to train a model on raw signals. This made it possible to be transferred to the next-generation tokamaks, which cannot provide large amounts of data. Therefore, IDP-PGFE is an effective approach to exploring disruption mechanisms and transferring disruption prediction models to future tokamaks.
Learning-based Similarity Join for Power Data
Authors: Sun Shiming , Shan Xin , Wei Xueyun , Tai Chunliang , Liu Chao 
Published: February 2023

Abstract
The increasing instrumentation of physical and computing processes has given us unprecedented capabilities to collect massive volumes of time series. Power data is a typical kind of time series. Considering that the original time series data has ineluctable limitations such as uneven distribution, non-uniform length, poor sampling rate and noisy, we propose a learning=based similarity join for power data consisting of RNN encoder and matrix model. In addition, we develop the partition techniques by grouping process nodes following the matrix join model, ensuring the accuracy and efficiency of similarity join for data series. We conduct experiments on real data-set to evaluate the performance of our approach, demonstrating the effectiveness and scalability of our method.
Image dehazing algorithm based on ViTGAN and its application
Authors: Liu Jianghao 
Published: February 2023

Abstract
In recent years, many methods based on deep learning have emerged in the field of image dehazing. The ViTGAN generation adversarial network based on Transformer is used to design image dehazing algorithm.In ViTGAN, the generator is improved to achieve the desired effect.The first half of the collaterals can be used as a data encoder to extract the features of the original image. In this part, a lower sampling layer is placed behind each density block.Generate network can regard as the second half of a corresponding to the first encoder decoder, in this part, each layer of the sampling density behind block placed a, for the first half is passed on the image characteristics of sampling, can be reduced by the sampling image characteristics to enlarge to the image of the original size, to ensure that the network output is correct.The feature map of the decoder and the symmetric encoder can be fused to ensure that the features of the decoder can be properly represented.
Optical Characteristics Simulation of Wheat Stripe Rust Urediospores
Authors: Chen Xu , He Dongjian 
Published: February 2023

Abstract
Stripe rust is one of the most common diseases challenging the safe production of wheat. Rapid identification and analysis of urediospores, responsible for disease transmission, is the key to preventing and controlling stripe rust. In this study, combined with chemical analysis, spectral analysis method and finite element simulation of the intrinsic characteristics of urediospore were studied in details. Firstly, a comparative analysis of the urediospore components was carried out by HPLC-MS, and a total of 31 components were extracted. On this basis, a 3D urediospore model was established by using FEM software, the characteristic frequencies and modes were calculated. The results shown that and the resonance frequencies and modes of the elliptical structure were lower and more diverse. The method and conclusion can lay a theoretical foundation for the accurate monitoring and early control of wheat stripe rust urediospores.
Robust fractional-order fast terminal sliding mode control of aerial manipulator derived from a mutable inertia parameters model
Authors: Zheng Wenlei , Li Zhan , Xiu Bingkai , Zhao Bingliang , Guo Zhigang 
Published: February 2023

Abstract
The coupling disturbance between the manipulator and the unmanned aerial vehicle (UAV) deteriorates the control performance of system. To get high performance of the aerial manipulator, a robust fractional order fast terminal sliding mode control (FOFTSMC) strategy based on mutable inertia parameters is proposed in this paper. First, the dynamics of aerial manipulator with consideration of the coupling disturbance is derived by utilizing mutable inertia parameters. Then, based on the dynamic model, a robust FOFTSMC algorithm is designed to make the system fly steadily under coupling disturbance. Furthermore, stability analysis is conducted to prove the convergence of tracking errors. Finally, comparative simulation results are given to show the validity and superiority of the proposed scheme.
Design and Deployment of Django-based Housing Information Management System
Authors: Yu Xiya , Li Xianhe , Wu Changping , Xu Gongyou 
Published: February 2023

Abstract
With the further development of diversification and nationalization of the housing market, the demand of housing enterprises for digitalization and refinement of housing management is growing. The original web service system has problems of low development efficiency, long development cycle and difficulty in changing requirements, which can hardly meet the current growing and changing requirements. Based on the existing technology, we built a lightweight and agile development system based on Django framework. Based on the comprehensive study of MVC design pattern and MTV design pattern, we analyzed the requirements of the listing management system, wrote the data interaction logic using Django framework, used MySQL database and Redis, OSS, SMS and SMTP middleware. The core design of the housing information management system is optimized, unifying user login and registration behavior, fine-grained decentralization of system users, unifying front-end and back-end data exchange standards, and making dynamic back-end settings, recording behavior logs and developing multi-environment configuration optimization for the system, bringing positive contribution to the digitalization and fine-grained management of housing enterprises’ listings.
YOLOCa: Center aware yolo for dense object detection
Authors: Gao Zixiang 
Published: February 2023

Abstract
In this report, we propose a fully convolutional one-stage object detector (YOLOCa) with a novel decoupled head, an improved label assignment strategy, and a new loss computation method for dense object detection. By comprehensively considering the IoU, score and position connection between the prediction frame and the ground truth in the sample allocation, and by introducing some current mainstream loss function calculation methods such as Focal loss, DIOU nms, etc. Through the above techniques, we have effectively improved the AP. We conduct experiments on the global wheat detection dataset, and as shown in figure 1, our method achieves an AP of 49.9%, which is a 14.5% improvement over the baseline yolov3 (35.4%). This is still 0.8% AP higher than yolox which also uses the advanced label assignment strategy, and we still maintain the speed efficiency of the first-level object detector. We hope our work can provide some useful tips for developers and researchers, the source code is at https://github.com/gaozixiang13/YOLOCa.
Image detection of insulator defects in complex environments
Authors: Zhang Wancai , Zhang Nan , Yang Wenqing , Wang Tao 
Published: February 2023

Abstract
Aiming at the practical problems of low detection accuracy and low detection speed for the detection of power insulators and insulator defects in a complex environmental context, a method is proposed to improve the YOLOv4 algorithm for the detection of power insulator images and insulators with defects. By making a dataset of electric power insulators and insulators with defects, the K-means algorithm is used to cluster the electric power insulator image samples to obtain different sizes of prior box parameters; then a weighting factor is introduced by improving the balanced cross-entropy to increase the contribution degree of the loss function; finally, the depth of the network is deepened by adding convolution layers before and after the spatial pyramidal pooling structure. The experimental results show that the single detection time of the improved model is 3.27S, and the average detection accuracy for insulator defects is improved by 24.36% compared with the original YOLOv4 algorithm. Also the value of the mean average accuracy on the test set by the improved YOLOv4 algorithm is 84.05%, which is 17.83% better than the original YOLOv4 algorithm, fully demonstrating the ability to locate and identify the defects existing in power insulator images well.
An Energy Efficient Task Scheduling for a Cloud-edge System
Authors: Cheng Xinyun , Yu Jinghang , Zhang Mingming , Xia Fei , Zhang Zichen , Ji Mingtao 
Published: February 2023

Abstract
With the widespread popularity of edge computing, the energy consumption of edge devices has attracted lots of research interests. This paper studies the cloud-edge system under the solar energy supply scenario, and optimizes the system parameter configuration according to the energy supply of photovoltaic power generation, to reduce the system energy consumption and prolong the system running time. Specifically, we built an edge video detection system based on Raspberry Pi, and deeply analyzed the energy consumption model of the system. Based on this, the problem of dynamically adjusting the video detection operating parameters is modeled as a long-term optimization problem. In order to solve this problem effectively, the long-term optimization problem is transformed to an instantaneous optimization problem, so that the problem can be solved efficiently in real time, and the rationality of this transformation is proved. Finally, we propose a low-energy-consumption scheduling algorithm to optimize the task scheduling and energy consumption of the system. Through simulation experiments, the performance of the proposed algorithm is evaluated. The results show that the scheduling algorithm can reduce the energy consumption of the system by an average of 18%.