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Revealing the significant role of band structure asymmetry on thermoelectric bipolar conduction

Physical Review B 2025年第4期111卷 045203-045203页

作者： Jiamin Qiu Shizhen Zhi Peng Zhao Jian Wang Xiaojing Ma Sheng Ye Chenhao Lin Xuanhe Zhang Zuoxu Wu School of Materials Science and Engineering and Institute of Materials Genome and Big Data Harbin Institute of Technology Shenzhen 518055 People's Republic of China School of Science and Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen 518055 China

Bipolar conduction, which is widely observed in various materials, plays a deleterious role in the thermoelectric properties. Traditionally, the single-band model is often applied to understand the transport properties of thermoelectric materials. However, it ignores the contribution from minority carriers and is incapable of quantifying the bipolar conduction. Herein, a general and feasible approach for calculating all the temperature-dependent transport properties based on the two-band model has been developed. In addition to the reduced electron Fermi energy and reduced band-gap energy, band structure asymmetry has been identified to play a significant role in bipolar conduction. Importantly, the effect of band structure asymmetry on bipolar conduction has been highlighted by using the Mg3Bi2−xSbx alloy as a typical example. Our results demonstrate that the band structure asymmetry is of great significance to the materials' thermoelectric performance.

关键词： Energy gap

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Networked ISAC Beamforming Design with Capacity-Limited Fronthaul Links

Networked ISAC Beamforming Design with Capacity-Limited Fron...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kexin Zhang Yanqing Xu Tsung-Hui Chang School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Science & Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This study investigates a networked integrated sensing and communication (ISAC) system, where a central processor coordinates multiple ISAC transmitters and a sensing receiver through capacity-limited fronthaul links. The system aims to serve multiple mobile users and simultaneously sense a point target. The primary objective is to minimize the total transmit power while meeting the minimum signal-to-interference-plus-noise ratio (SINR) requirements for both communication and sensing, as well as adhering to the fronthaul capacity constraints. This leads to a complicated joint fronthaul compression and beamforming design (J-FCBD) problem. Although the J-FCBD problem is challenging to solve, we demonstrate that the optimal fronthaul compression variables can be determined in closed form alongside the beamformers. This novel finding has not been previously reported in the literature. Leveraging this insight, we prove that the remaining problem can be globally solved using the semidefinite relaxation (SDR) technique. Simulation results verify the effectiveness of the proposed design.

关键词： Array signal processing Transmitters Simulation System performance Signal processing algorithms Interference Integrated sensing and communication Solids Speech processing Signal to noise ratio

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DeepVec: State-Vector Aware Test Case Selection for Enhancing Recurrent Neural Network

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IEEE Transactions on Software engineering 2025年

作者： Jiang, Zhonghao Yan, Meng Huang, Li Sun, Weifeng Liu, Chao Sun, Song Lo, David Chongqing University School of Big Data and Software Engineering China Chongqing Normal University School of Computer and Information Science China Singapore Management University School of Information Systems Singapore

Deep Neural Networks (DNN) have realized significant achievements across various application domains. There is no doubt that testing and enhancing a pre-trained DNN that has been deployed in an application scenario is crucial, because it can reduce the failures of the DNN. DNN-driven software testing and enhancement require large amounts of labeled data. The high cost and inefficiency caused by the large volume of data of manual labeling, and the time consumption of testing all cases in real scenarios are unacceptable. Therefore, test case selection technologies are proposed to reduce the time cost by selecting and only labeling representative test cases without compromising testing performance. Test case selection based on neuron coverage (NC) or uncertainty metrics has achieved significant success in Convolutional Neural Networks (CNN) testing. However, it is challenging to transfer these methods to Recurrent Neural Networks (RNN), which excel at text tasks, due to the mismatch in model output formats and the reliance on image-specific characteristics. What’s more, balancing the execution cost and performance of the algorithm is also indispensable. In this paper, we propose a state-vector aware test case selection method for RNN models, namely DeepVec, which reduces the cost of data labeling and saves computing resources and balances the execution cost and performance. DeepVec selects data using uncertainty metric based on the norm of the output vector at each time step (i.e., state-vector), and similarity metric based on the direction angle of the state-vector. Because test cases with smaller state-vector norms often possess greater information entropy and similar changes of state-vector direction angle indicate similar RNN internal states. These metrics can be calculated with just a single inference, which gives it strong bug detection and model improvement capabilities. We evaluate DeepVec on five popular datasets, containing images and texts as well as commonl

关键词： Model checking

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Enhancing Fault Diagnosis: A Hybrid Framework Integrating Improved SABO with VMD and Transformer–TELM

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Lubricants 2025年第4期13卷

作者： Yang, Jingzong Li, Xuefeng Mao, Min School of Big Data Baoshan University Baoshan678000 China College of Automobile and Traffic Engineering Nanjing Forestry University Nanjing210037 China Faculty of Information Engineering Quzhou College of Technology Quzhou324000 China

Rolling bearings, as core components in mechanical systems, directly influence the overall reliability of equipment. However, continuous operation under complex working conditions can easily lead to gradual performance degradation and sudden faults, which not only result in equipment failure but may also trigger a cascading failure effect, significantly amplifying downtime losses. To address this challenge, this study proposes an intelligent diagnostic method that integrates variational mode decomposition (VMD) optimized by the improved subtraction-average-based optimizer (ISABO) with transformer–twin extreme learning machine (Transformer–TELM) ensemble technology. Firstly, ISABO is employed to finely optimize the initialization parameters of VMD. With the improved initialization strategy and particle position update method, the optimal parameter combination can be precisely identified. Subsequently, the optimized parameters are used to model and decompose the signal through VMD, and the optimal signal components are selected through a constructed two-dimensional evaluation system. Furthermore, diversified time-domain features are extracted from these components to form an initial feature set. To deeply mine feature information, a multi-layer Transformer model is introduced to refine more discriminative feature representations. Finally, these features are input into the constructed TELM fault diagnosis model to achieve precise diagnosis of rolling bearing faults. The experimental results demonstrate that this method exhibits excellent performance in terms of noise resistance, accurate fault feature capture, and fault classification. Compared with traditional machine learning techniques such as kernel extreme learning machine (KELM), extreme learning machine (ELM), support vector machine (SVM), and Softmax, this method significantly outperforms other models in terms of accuracy, recall, and F1 score. © 2025 by the authors.

关键词： Variational mode decomposition

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An M3RSMA-Based Roadside Cooperative Message Delivery Scheme for Complex Intersection

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IEEE Transactions on Wireless Communications 2025年

作者： Shi, Zhenjiang Liu, Jiajia School of Information Engineering Chang’an University Xi’an710064 China Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710072 China

Traditional single-vehicle intelligence system faces challenges such as blind spots and perception performance bottlenecks due to limitations in sensor perception angles, ranges, and accuracy, which are particularly pronounced in complex intersection. Vehicle-infrastructure cooperative mechanism has been widely recognized as a promising solution to address challenges faced by single-vehicle intelligence. However, against the backdrop of limited spectrum resources and the sharply rising in the number of connected vehicles, how to efficiently deliver cooperative messages from roadside unit to vehicles is often overlooked. Towards this end, we propose a roadside cooperative message delivery scheme based on multicarrier multigroup multicast rate-splitting multiple access, considering the rarely explored case of transmitting messages with limited size under delay constraint. Then we focus on the critical joint optimization problem of message size and power allocation, with consideration for imperfect channel state information at the transmitter. Subsequently, a multi-agent deep reinforcement learning based resource allocation algorithm is designed to solve this joint optimization problem, exhibiting robustness to dynamic changes in vehicle density and message size. Finally, we analyze through extensive numerical results the impacts of various factors on message delivery success probability. © 2002-2012 IEEE.

关键词： Resource allocation

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SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor Diagnosis

arXiv

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arXiv 2025年

作者： Bai, Runci Xu, Guibao Shi, Yanze Institute of Cloud Computing and Big Data China Academy of Information and Communications Technology Beijing100080 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China

Brain tumors can lead to neurological dysfunction, cognitive and psychological changes, increased intracranial pressure, and seizures, posing significant risks to health. The You Only Look Once (YOLO) series has shown superior accuracy in medical imaging object detection. This paper presents a novel SCC-YOLO architecture that integrates the SCConv module into YOLOv9. The SCConv module optimizes convolutional efficiency by reducing spatial and channel redundancy, enhancing image feature learning. We examine the effects of different attention mechanisms with YOLOv9 for brain tumor detection using the Br35H dataset and our custom dataset (Brain_Tumor_dataset). Results indicate that SCC-YOLO improved mAP50 by 0.3% on the Br35H dataset and by 0.5% on our custom dataset compared to YOLOv9. SCC-YOLO achieves state-of-the-art performance in brain tumor detection. Copyright © 2025, The Authors. All rights reserved.

关键词： Health risks

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Movable Antenna Enabled Near-Field Communications: Channel Modeling and Performance Optimization

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IEEE Transactions on Communications 2025年

作者： Zhu, Lipeng Ma, Wenyan Xiao, Zhenyu Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore Beihang University School of Electronic and Information Engineering Beijing100191 China The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China

Movable antenna (MA) technology offers promising potential to enhance wireless communication by allowing flexible antenna movement. To maximize spatial degrees of freedom (DoFs), larger movable regions are required, which may render the conventional far-field assumption for channels between transceivers invalid. In light of it, we investigate in this paper MA-enabled near-field communications, where a base station (BS) with multiple movable subarrays serves multiple users, each equipped with a fixed-position antenna (FPA). First, we extend the field response channel model for MA systems to the near-field propagation scenario. Next, we examine MA-aided multiuser communication systems under both digital and analog beamforming architectures. For digital beamforming, spatial division multiple access (SDMA) is utilized, where an upper bound on the minimum achievable rate across users is derived in closed form. A low-complexity algorithm based on zero-forcing (ZF) is then proposed to jointly optimize the antenna position vector (APV) and digital beamforming matrix (DBFM) to approach this bound. For analog beamforming, orthogonal frequency division multiple access (OFDMA) is employed, and an upper bound on the minimum achievable rate among users is also derived. An alternating optimization (AO) algorithm is proposed to iteratively optimize the APV, analog beamforming vector (ABFV), and power allocation until convergence. For both architectures, we further explore MA design strategies based on statistical channels, with the APV updated less frequently to reduce the antenna movement overhead. Simulation results demonstrate that our proposed algorithms achieve performance close to the derived bounds and also outperform the benchmark schemes using dense or sparse arrays with FPAs. 1972-2012 IEEE.

关键词： Near field communication

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Lattice mismatches caused by intrinsic stacking faults promote cracks initiation in Ni–Cu alloys

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Journal of Materials Research and Technology 2025年 36卷 9504-9517页

作者： Yu Zhou Yongchao Liang Yuanwei Pu Lili Zhou Zean Tian Qian Chen Yunfei Mo Institute of Advanced Optoelectronic Materials and Technology College of Big Data and Information Engineering Guizhou University Guiyang 550025 China Department of Information Engineering Gannan Medical University Ganzhou 341000 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China School of Electronic Information and Electrical Engineering Changsha University Changsha 410003 China

Metals are prone to cracking during processing, and the existence of cracks reduces the mechanical properties and service life. Therefore, molecular dynamics simulation method in this study is used to explore the initiation and propagation mechanism of Ni–Cu alloy cracks, so as to improve their mechanical properties. The results show that the intrinsic stacking faults (ISFs) were generated at the torsional end, resulting in the emission of dislocations and the initiation of shear transition zones (STZs). In the yielding stage, the rapidly growing ISFs led to the initiation of a large number of dislocations. At this time, STZs formed shear bands which caused the model to yield. The ISFs led severe lattice mismatch, increased dislocation density, and formed a large number of dislocation entanglements, thereby promoting the formation of cracks. The number and area of cracks rapidly increased during the plastic deformation stage, which eventually led to irrecoverable fracture of the material. The number and area of cracks are the largest at a high twisting rate. High Ni content can increase the yield strength and reduce the number of cracks. The study employed the construct surface mesh method to establish the relationship between shear bands, ISFs, dislocations, and cracks. The study investigates the mechanisms of crack initiation and propagation, providing a theoretical basis for optimizing the preparation process of Ni–Cu alloy and enhancing its mechanical properties.

关键词： Ni–Cu alloy Molecular dynamics simulation Intrinsic stacking faults Crack initiation Lattice mismatch

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Analysis of converting -circuit into -circuit

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EPJ Quantum Technology 2025年第1期12卷 1-14页

作者： Luo, Qing-bin Ding, Lang Yang, Guo-wu Li, Xiao-yu College of Intelligent Systems Science and Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

A $\mathfrak{C^{*}}$ -circuit, which was proposed in Asiacypt 2022 by Huang and Sun (Advances in cryptology – ASIACRYPT 2022, pp. 614–644, 2022), can directly perform calculations with the existing quantum states, thereby reducing the use of quantum resources in quantum logic synthesis. We theoretically prove how to convert a $\mathfrak{C^{0}}$ -circuit into the corresponding $\mathfrak{C^{*}}$ -circuit through two lemmas and one theorem. The first lemma proves the interchangeability of CNOT gates and NOT gates by using the equivalence of quantum circuits. The second lemma proves that adding CNOT gates to the front of a quantum circuit whose initial states are all $|0\rangle $ s will not change the output states of the circuit. The theorem is used to describe what kind of $\mathfrak{C^{0}}$ -circuit can be transformed into $\mathfrak{C^{*}}$ -circuit, and the correctness of this transformation is proved. Our work will provide a theoretical basis for converting $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit. Then applying the theoretical analysis results to the multiplication over $\text{GF}(2^{8})$ , the constructed quantum circuit needs 27 Toffoli gates and 118 CNOT gates, which is 15 fewer Toffoli gates and 43 CNOT gates than the current best result. This shows that the method of constructing quantum circuits by using the conversion of $\mathfrak{C^{0}}$ -circuit to $\mathfrak{C^{*}}$ -circuit is very efficient.

关键词：

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The Monitoring and Identification of Tomato Early Blight During the Incubation Period by Hyperspectral Image

SSRN

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SSRN 2025年

作者： Bao, Hao Zhang, Yan Huang, Li Guizhou Engineering Research Center for Nondestructive Testing of Agricultural Products Guiyang University Guiyang550005 China School of Big Data and Information Engineering Guizhou University Guiyang550025 China

The Effective detection on pre-symptom characteristics of plant diseases is crucial for precision agriculture. This study focuses on diagnosing the symptom characteristics of tomato early blight during incubation period using hyperspectral imaging and machine learning techniques. Hyperspectral images of tomato leaves were continuously captured after being inoculated with Alternaria solani. A method were proposed in this paper to identify the earliest identifiable time of incubation period based on Euclidean distance, and machine learning models were developed for disease symptom characteristics recognition. Spectral pre-processing was optimized using inter-class and intra-class distance measures, and three characteristic wavelength selection algorithms were tested such as Successive Projections Algorithm, Competitive Adaptive Reweighting Algorithm, and Grey Wolf Optimizer. Four classifiers were evaluated, such as Partial Least Squares Discriminant Analysis, Support Vector Machine, K-Nearest Neighbors, and Light Gradient Boosting Machine. Results showed that the earliest identifiable time of incubation period can be taken as 28 hours after infection for the tomato early blight in this experiment. The GWO-LightGBM model achieved 97.22% accuracy by optimizing with Bayesian tuning, which significantly outperform the full-spectral model. The robustness of GWO-LightGBM model was confirmed with cross-validation and ROC curve analysis. Additionally, some insights can be provided to the microscopic mechanisms of the disease evolution by analysising the 24 characteristic wavelengths being Optimally selected in this paper. This study demonstrates the feasibility and application potential of diagnosing the symptom characteristics of the crops during incubation period by hyperspectral imaging and machine learning. © 2025, The Authors. All rights reserved.

关键词： Fruits

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