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Rare earth element Ce enables high thermoelectric performance in n-type SnSe polycrystals

Journal of Materials Science & Technology 2023年第12期143卷 234-241页

作者： Shan Li Li Yin Yijie Liu Xiaodong Wang Chen Chen Qian Zhang School of Civil Aviation Northwestern Polytechnical UniversityXi’an 710072China School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of TechnologyShenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

SnSe exhibits excellent thermoelectric performance in both n-and p-type single crystals,but its n-type polycrystals are restricted because of the lower electrical ***,we dually introduced rare earth element Ce and PbTe to optimize the thermoelectric properties of n-type SnSe *** is demonstrated that Ce is an effective cationic dopant to convert SnSe from p-to n-type conductor,and an enhanced peak zT value of∼0.9 at 823 K was obtained in Sn 0.97 Ce 0.03 Se due to the improved power ***,PbTe alloying not only reduced the band gap to increase the carrier concentration,but also enhanced the density-of-states effective mass,and hence further increased the power factor in the whole measured temperature ***,the lattice thermal conductivity was significantly reduced owing to the enhanced phonon scattering by the mass and strain *** a result,the peak zT value was increased to∼1.3 for Sn 0.9 Pb 0.07 Ce 0.03 Se 0.93 Te 0.07 together with a high average zT value of∼0.52 in the temperature range of 300 to 823 K.

关键词： Thermoelectric N-type SnSe Ce doping PbTe alloying

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PriFU: Capturing Task-Relevant Information Without Adversarial Learning 24

PriFU: Capturing Task-Relevant Information Without Adversari...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Bi, Xiuli Hu, Yang Liu, Bo Li, Weisheng Cosman, Pamela Xiao, Bin Chongqing Key Laboratory of Image Cognition Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China Department of Electrical and Computer Engineering University of California San Diego San Diego United States

ISBN: (纸本)9798400706868

As machine learning advances, machine learning as a service (MLaaS) in the cloud brings convenience to human lives but also privacy risks, as powerful neural networks used for generation, classification or other tasks can also become privacy snoopers. This motivates privacy preservation in the inference phase. Many approaches for preserving privacy in the inference phase introduce multi-objective functions, training models to remove specific private information from users' uploaded data. Although effective, these adversarial learning-based approaches suffer not only from convergence difficulties, but also from limited generalization beyond the specific privacy for which they are trained. To address these issues, we propose a method for privacy preservation in the inference phase by removing task-irrelevant information, which requires no knowledge of the privacy attacks nor introduction of adversarial learning. Specifically, we introduce a metric to distinguish task-irrelevant information from task-relevant information, and achieve more efficient metric estimation to remove task-irrelevant features. The experiments demonstrate the potential of our method in several tasks. Our code will be available at: https://***/iwhoyoung/PriFU. © 2024 ACM.

关键词： Adversarial machine learning

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Propolis inspired sunscreens for efficient UV-protection and skin barrier maintenance

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Nano Research 2022年第9期15卷 8237-8246页

作者： Yu Fu Ruoyu Wan Lei Yang Lidan Xiong Junfei Hu Jie Tang Hailun He Zhipeng Gu Li Li Yiwen Li College of Polymer Science and Engineering State Key Laboratory of Polymer Materials EngineeringSichuan UniversityChengdu 610065China Cosmetics Safety and Efficacy Evaluation Center Department of DermatologyWest China HospitalSichuan UniversityChengdu 610041China NMPA Key Laboratory for Human Evaluation and Big Data of Cosmetics Chengdu 610041China

The direct use of naturally occurring,small molecular ingredients in bioinspired sunscreens has raised several concerns due to the instability,photocytotoxicity,and potential blood toxicity of those *** this work,we have employed natural ultraviolet(UV)-blocking molecule caffeic acid phenethyl ester(CAPE)from propolis to prepare poly(CAPE)nanoparticles(NPs)as the main bioactive ingredient to fabricate propolis-inspired hydrogel *** with small molecular CAPE,poly(CAPE)NPs exhibited better dispersion and stability in water,as well as lower physiological toxicity and skin *** the resulting composite hydrogels demonstrated promising properties including water-resistant whereas can be easily erased by warm water as well as safety when interacting with *** importantly,the hydrogel sunscreens showed excellent UV protection properties both in vitro and in vivo,and the positive effects in maintaining skin barrier *** work provides new strategies towards the facile construction of nature-inspired robust sunscreens in the future.

关键词： bioinspired sunscreen composite hydrogel ultraviolet protection

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Enhancing Drug-Drug Interaction Prediction by knowledge Graph Embedding 23

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Liu, Chao Wang, Xizhao Liu, Qiang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518000 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug). © 2024 IEEE.

关键词： knowledge graph

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification 23

Domain Adaptation Random Vector Functional Link for White Bl...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wang, Qin Wang, Xi-Zhao Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798331528041

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells. © 2024 IEEE.

关键词： Deep learning

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Optimizing Indoor Network Element Layout for Enhanced Signal Coverage and Security in Location-Based Services 7th

Optimizing Indoor Network Element Layout for Enhanced Signa...

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7th International Conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024

作者： Yu, Xiaomin Yu, Xiaokun College of Computer and Control Engineering Qiqihar University Qiqihar China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University Qiqihar China Heilongjiang Jiaotong Polytechnic Qiqihar China

ISBN: (纸本)9783031855924

Recently, with the rapid development of communication technology, location-based services (LBS) in indoor environments have penetrated into various aspects of people’s daily lives. Wireless local area networks (WLAN) have become the preferred choice for location-based services due to their broad deployment and low cost. However, the typical non-line-of-sight characteristics of indoor environments result in severe fluctuations in received signal strength (RSS), signal coverage vulnerabilities can exacerbate environmental interference, making positioning results more vulnerable to attacks or tampering. The layout of indoor network elements is a key factor that constrains signal coverage rate. This paper focuses on the optimization of indoor network element layout, proposes an indoor network element layout optimization model based on signal coverage and quality in indoor environments and introduces an Adaptive Simulation Annealing Non-inertial Opposite Particle Swarm Optimization algorithm (ASA-NRPSO). Firstly, a reverse strategy is introduced into the particle flight process of the Particle Swarm Optimization (PSO) algorithm, and high-quality particles are selected for the next generation iteration. In the selection of the reverse strategy, the simulated annealing idea is integrated to adaptively select the reverse strategy based on the needs of the particles at different stages, which avoids the particles falling into local optima. The inertia term in the particle swarm velocity update formula is replaced with group information for non-inertial updates. This can more fully utilize the global information of the population to guide the movement of the next generation of particles and improve the convergence speed of the particle swarm. The elite mutation strategy is used to increase the diversity of particles and improve the global search capability of the algorithm. This method ensures the rapid generation of network element layout to improve the signal coverage rate

关键词： Location based services

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Grasp Control Method for Robotic Manipulator Based on Federated Reinforcement Learning 7

Grasp Control Method for Robotic Manipulator Based on Federa...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Wang, Yue Zhong, Shida Yuan, Tao College Of Electronics And Information Engineering Shenzhen University State Key Laboratory Of Radio Frequency Heterogeneous Integration Shenzhen China Shenzhen University Guangdong-Hong Kong Joint Laboratory For Big Data Imaging And Communication Shenzhen China

ISBN: (纸本)9798350361445

With development of mobile edge computing (MEC) and the Artificial Intelligent Internet of Things (AIoT), how to use intelligent devices to better apply to industrial scenarios has become a critical factor in promoting industry to reduce costs, increase efficiency, and enable development. In this paper, we analysis the problem of robotic manipulators working in dynamic environments and propose a model based on federated reinforcement learning (FRL) to control robotic manipulator grasping. In the proposed FRL framework, the optimal control policy is learned independently by multiple agents on their respective AIoT devices. Also, we use federated learning to ensure the safety of data and improve the reward function to accelerate the learning of model. We evaluate the performance of the proposed method and compare it with the traditional method based on RL. The experimental results show that proposed algorithm significantly improves the model training convergence speed by about 64% and the task average reward performance index by about 36% compared with baseline. © 2024 IEEE.

关键词： Reinforcement learning

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Resilient multi-objective mission planning for UAV formation:A unified framework integrating task pre-and re-assignment

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Defence Technology(防务技术) 2025年第3期45卷 203-226页

作者： Xinwei Wang Xiaohua Gao Lei Wang Xichao Su Junhong Jin Xuanbo Liu Zhilong Deng Department of Engineering Mechanics State Key Laboratory of Structural AnalysisOptimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalianLiaoning 116024China School of Mathematics and Big Data Anhui University of Science and TechnologyHuainanAnhui 232001China School of Mathematical Science Dalian University of TechnologyDalianLiaoning 116024China Department of Airborne Vehicle Engineering Naval Aeronautical and Astronautical UniversityYantai 264001China

Combat effectiveness of unmanned aerial vehicle(UAV)formations can be severely affected by the mission execution *** the practical execution phase,there are inevitable risks where UAVs being destroyed or targets failed to be *** improve the mission reliability,a resilient mission planning framework integrates task pre-and re-assignment modules is developed in this *** the task pre-assignment phase,to guarantee the mission reliability,probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization *** an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient *** in the task-reassignment phase,possible trigger events are first analyzed.A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency *** the dual objective used in the former phase is adapted into a single objective to keep a consistent combat *** cases of different scales demonstrate that the two modules cooperate well with each *** the one hand,the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is *** the other hand,the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a *** corresponding animation is accessible at ***/video/BV12t421w7EE for better illustration.

关键词： Cooperative mission planning UAV formation Mission reliability Evolutionary algorithm Contract net protocol

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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