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Observation of acoustic hybrid topological phases induced by the p-d orbital interactions

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science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 132-139页

作者： Xiao Xiang Feng Gao Yu-Gui Peng Peng Wu Zong-Lin Li Xue-Feng Zhu school of Physics and Innovation Institute Huazhong University of Science and Technology

In spinless systems,growing attention has recently been attracted to synthetic gauge fields,which redefine the fundamental crystal symmetries by utilizing the projective algebraic ***,synthetic gauge fields have been commonly investigated in single orbital systems,and explorations on hybrid orbitals remain elusive in ***,we report the experimental observation of hybrid topological phases induced by hybrid synthetic gauge flux,which is formed by the interaction between p and d orbitals embedded in acoustic *** breaking the translation symmetries of Lxand/or Ly,we unambiguously demonstrate the first-order M?bius edge states and higher-order corner *** work establishes a platform for seeking exotic topological phases induced by hybrid-orbitals interactions and initializing the framework of hybrid-orbitalsrelated topological *** applications can be anticipated in scenarios such as high-quality sensing and robust wave trapping due to the stepwise energy distribution of the hybrid topological phase.

关键词： topological insulator synthetic gauge field projective symmetry corner states

The optimal control theory——a scientific approach to fundamentally solving control problems

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science China(Information sciences) 2025年第1期68卷 389-391页

作者： Huanshui ZHANG school of Electrical and Automation Engineering Shandong University of Science and Technology school of Control science and Engineering Shandong University

The maximum principle has bridged mathematical optimization to optimal control,ushering in significant developments and refinements in optimal control theory,notably during the 1960s with the advent of linear quadratic (LQ)control and linear quadratic estimation (LQE).This progression propelled optimal control theory into further advancements,encompassing stochastic control,robust/H-infinity control,model predictive control (MPC),networked control,and reinforcement learning *** control,established upon a rigorous mathematical foundation,extends static optimization theory to dynamic systems,exhibiting scientific essence,unity,and ***,since its inception,optimal control theory has served as an indispensable core role across all control-related domains,including communication-constrained control in networked systems,consensus control,cooperative control,and reinforcement learning control.

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First observation results of Macao science Satellite-1 on lightning-induced electron precipitation

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science China Earth sciences 2025年第1期68卷 1-10页

作者： Yongping WANG Yixin SUN Qiugang ZONG Gaopeng LU Xudong GU Ze-Jun HU Bin LI Key Laboratory for Polar science Ministry of Natural Resources Polar Research Institute of China Institute of Space Physics and Applied technology Peking University State Key Laboratory of Lunar and Planetary science Macau University of Science and Technology school of Earth and Space sciences University of Science and Technology of China school of Electronic Information Wuhan University

The Medium-energy Electron Spectrometers(MES) is a space-borne instrument onboard Macao science Satellite-1(MSS-1) dedicated to monitoring the typical charged particle radiation characteristics in the satellite orbit and the process of their occurrence and development, including short bursts of lightning-induced electron precipitation(LEP).This paper presents the first results of the LEP measurements by the MSS-1.47 LEP events are identified with the routine operation for 3 months since satellite launch, all within the range of 1.5

关键词： Lightning-induced electron precipitation Inner radiation belts Energetic electrons Macao science Satellite-1

Privacy-preserving explainable AI: a survey

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science China(Information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN school of Information and Communication technology Griffith University school of Computer and Communication sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer science University of Bremen Faculty of Information technology HUTECH University Department of Computer science Hanoi University of Science and Technology school of Electrical Engineering and Computer science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

Co/Co_(7)Fe_(3)heterostructures with controllable alloying degree on carbon spheres as bifunctional electrocatalyst forrechargeable zinc-air batteries

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International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 476-487页

作者： Junkang Chen Yongyue Zhuang Yanxin Qiao Yu Zhang Aihua Yuan Hu Zhou school of Materials science and Engineering Jiangsu University of Science and TechnologyZhenjiang 212003China school of Environmental and Chemical Engineering Jiangsu University of Science and TechnologyZhenjiang 212003China

Exploring efficient and nonprecious metal electrocatalysts of oxygen reduction reaction(ORR)and oxygen evolution reaction(OER)is crucial for developing rechargeable zinc-air batteries(ZABs).Herein,an alloying-degree control strategy was employed to fabricate nitrogen-doped carbon sphere(NCS)decorated with dual-phase Co/Co_(7)Fe_(3)heterojunctions(CoFe@NCS).The phase composition of materials has been adjusted by controlling the alloying *** optimal CoFe_(0.08)@NCS electrocatalyst displays a half-wave potential of 0.80 V for ORR and an overpotential of 283 mV at 10 mA·cm^(-2)for OER in an alkaline *** intriguing bifunctional electrocatalytic activity and durability is attributed to the hierarchically porous structure and interfacial electron coupling of highly-active Co_(7)Fe_(3)alloy and metallic Co *** the CoFe_(0.08)@NCS material is used as air-cathode catalyst of rechargeable liquid-state zinc-air battery(ZAB),the device shows a high peak power-density(157 mW·cm^(-2))and maintains a stable voltage gap over 150 h,outperforming those of the benchmark(Pt/C+RuO_(2))-based *** particular,the as-fabricated solid-state flexible ZAB delivers a reliable compatibility under different bending *** work provides a promising strategy to develop metal/alloy-based electrocatalysts for the application in renewable energy conversion technologies.

关键词： bifunctional electrocatalysts oxygen reduction reaction oxygen evolution reaction zinc-air battery metal/alloy carbon sphere

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A Generative Model-Based Network Framework for Ecological Data Reconstruction

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Computers, Materials & Continua 2025年第1期82卷 929-948页

作者： Shuqiao Liu Zhao Zhang Hongyan Zhou Xuebo Chen school of Electronic and Information Engineering University of Science and Technology LiaoningAnshan114051China school of Computer science and Software Engineering University of Science and Technology LiaoningAnshan114051China

This study examines the effectiveness of artificial intelligence techniques in generating high-quality environmental data for species introductory site selection *** Strengths,Weaknesses,Opportunities,Threats(SWOT)analysis data with Variation Autoencoder(VAE)and Generative AdversarialNetwork(GAN)the network framework model(SAE-GAN),is proposed for environmental data *** model combines two popular generative models,GAN and VAE,to generate features conditional on categorical data embedding after SWOT *** model is capable of generating features that resemble real feature distributions and adding sample factors to more accurately track individual sample *** data is used to retain more semantic information to generate *** model was applied to species in Southern California,USA,citing SWOT analysis data to train the *** show that the model is capable of integrating data from more comprehensive analyses than traditional methods and generating high-quality reconstructed data from them,effectively solving the problem of insufficient data collection in development *** model is further validated by the Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS)classification assessment commonly used in the environmental data *** study provides a reliable and rich source of training data for species introduction site selection systems and makes a significant contribution to ecological and sustainable development.

关键词： Convolutional Neural Network(CNN) VAE GAN TOPSIS data reconstruction

Post-tunable salt gel electrolytes toward flexible supercapacitor switches

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Journal of Energy Chemistry 2025年第1期100卷 114-124页

作者： Hui Wang Shuang Chen Xuming Liu Qin Zhang Xin Liu Polymeric and Soft Materials Laboratory School of Chemistry and Life Science and Advanced Institute of Materials Science Changchun University of Technology Polymeric and Soft Materials Laboratory School of Chemical Engineering and Advanced Institute of Materials Science Changchun University of Technology

Convertible hydrogel supercapacitors have emerged as promising energy storage devices in switches,diodes, and transistors. However, inherent weaknesses in ionic conductivity, mechanical properties,and water retention of hydrogel electrolytes seriously hinder their development. Inspired by the hardness conversion of sea cucumber skin, a conductivity and mechanics dual-tunable salt gel electrolyte is successfully designed. The salt gel presents a reversible switching of conductors-insulators and a mechanical regulation between softness and hardness via the dissolution-crystallization transition of sodium acetate trihydrate（SAT）. Meanwhile, the salt gels spontaneously grow a layer of “armor” through saturated phase-change salt crystals effectively reducing water evaporation of hydrogel ***, this phase-change soft-rigid conversion strategy will expand the capabilities of gel-based flexible supercapacitors(area capacitance: 258.6 mF cm-2), and the capacitance retention rate could still reach 86.9% after 3000 cycles at high temperatures. Moreover, the salt gel supercapacitor is potentially used in over-heat alarm systems. It is anticipated that the strategy of conductivity and mechanics of dual-tunable salt gel would provide a new perspective on the development of energy storage devices,wearable electronics, and flexible robots.

关键词： Capacitance

Pulsed dynamic electrolysis enhanced PEMWE hydrogen production:Revealing the effects of pulsed electric fields on protons mass transport and hydrogen bubble escape

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Journal of Energy Chemistry 2025年第1期100卷 201-214页

作者： Xuewei Zhang Wei Zhou Yuming Huang Liang Xie Tonghui Li Huimin Kang Lijie Wang Yang Yu Yani Ding Junfeng Li Jiaxiang Chen Miaoting Sun Shuo Cheng Xiaoxiao Meng Jihui Gao Guangbo Zhao school of Energy science and Engineering Harbin Institute of Technology China Datang technology Innovation Co. Ltd

The transition of hydrogen sourcing from carbon-intensive to water-based methodologies is underway,with renewable energy-powered proton exchange membrane water electrolysis （PEMWE） emerging as the preeminent pathway for hydrogen *** remarkable advancements in this field,confronting the sluggish electrochemical kinetics and inherent high-energy consumption arising from deteriorated mass transport within PEMWE systems remains a formidable *** impediment stems primarily from the hindered protons mass transfer and the untimely hydrogen bubbles *** address these challenges,we harness the inherent variability of electrical energy and introduce an innovative pulsed dynamic water electrolysis *** to constant voltage electrolysis (hydrogen production rate:51.6 m L h-1,energy consumption:5.37 kWh Nm-3H2),this strategy (hydrogen production rate:66 m L h-1,energy consumption:3.83 kWh Nm-3H2) increases the hydrogen production rate by approximately 27%and reduces the energy consumption by about 28%.Furthermore,we demonstrate the practicality of this system by integrating it with an off-grid photovoltaic （PV） system designed for outdoor operation,successfully driving a hydrogen production current of up to 500 mA under an average voltage of approximately 2 *** combined results of in-situ characterization and finite element analysis reveal the performance enhancement mechanism:pulsed dynamic electrolysis （PDE） dramatically accelerates the enrichment of protons at the electrode/solution interface and facilitates the release of bubbles on the electrode *** such,PDE-enhanced PEMWE represents a synergistic advancement,concurrently enhancing both the hydrogen generation reaction and associated transport *** promising technology not only redefines the landscape of electrolysis-based hydrogen production but also holds immense potential for broadening its application across a diverse spectrum of electrocatalytic endeavors.

关键词： Electrolysis

Deep learning-based software engineering: progress,challenges, and opportunities

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science China(Information sciences) 2025年第1期68卷 57-144页

作者： Xiangping CHEN Xing HU Yuan HUANG He JIANG Weixing JI Yanjie JIANG Yanyan JIANG Bo LIU Hui LIU Xiaochen LI Xiaoli LIAN Guozhu MENG Xin PENG Hailong SUN Lin SHI Bo WANG Chong WANG Jiayi WANG Tiantian WANG Jifeng XUAN Xin XIA Yibiao YANG Yixin YANG Li ZHANG Yuming ZHOU Lu ZHANG school of Journalism and Communication Sun Yat-sen University school of Software technology Zhejiang University school of Software Engineering Sun Yat-sen University school of Software Dalian University of Technology school of Computer science and technology Beijing Institute of Technology Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of EducationSchool of Computer Science Peking University State Key Laboratory for Novel Software technology Nanjing University school of Computer science and Engineering Beihang University Institute of Information Engineering Chinese Academy of Sciences school of Computer science Fudan University State Key Laboratory of Complex & Critical Software Environment (CCSE) School of Software Beihang University school of Computer and Information technology Beijing Jiaotong University school of Computer science and technology Harbin Institute of Technology school of Computer science Wuhan University Huawei Technologies

Researchers have recently achieved significant advances in deep learning techniques, which in turn has substantially advanced other research disciplines, such as natural language processing, image processing, speech recognition, and software engineering. Various deep learning techniques have been successfully employed to facilitate software engineering tasks, including code generation, software refactoring, and fault localization. Many studies have also been presented in top conferences and journals, demonstrating the applications of deep learning techniques in resolving various software engineering tasks. However,although several surveys have provided overall pictures of the application of deep learning techniques in software engineering,they focus more on learning techniques, that is, what kind of deep learning techniques are employed and how deep models are trained or fine-tuned for software engineering tasks. We still lack surveys explaining the advances of subareas in software engineering driven by deep learning techniques, as well as challenges and opportunities in each subarea. To this end, in this study, we present the first task-oriented survey on deep learning-based software engineering. It covers twelve major software engineering subareas significantly impacted by deep learning techniques. Such subareas spread out through the whole lifecycle of software development and maintenance, including requirements engineering, software development, testing, maintenance, and developer collaboration. As we believe that deep learning may provide an opportunity to revolutionize the whole discipline of software engineering, providing one survey covering as many subareas as possible in software engineering can help future research push forward the frontier of deep learning-based software engineering more systematically. For each of the selected subareas,we highlight the major advances achieved by applying deep learning techniques with pointers to the available datasets i

关键词： deep learning software engineering software benchmark software artifact representation survey