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Non-Hermitian polarization quantized by time-reversal symmetry

science China(Physics,Mechanics & Astronomy) 2024年第12期67卷 141-147页

作者： Jing Cheng Jinbing Hu Zhigao Hu Lin Chen Minghui Lu Songlin Zhuang Technical Center for Multifunctional Magneto-Optical Spectroscopy(Shanghai) Engineering Research Center of Nanophotonics&Advanced Instrument(Ministry of Education)Department of Physics and Electronic ScienceEast of China Normal UniversityShanghai200241China College of Optical-Electrical Information and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Wuhan National Laboratory for Optoelectronics and School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan430074China National Laboratory of Solid State Microstructures Science and Engineering Nanjing UniversityNanjing210093China

It is well known that in one-dimensional(1D) crystalline insulators,the electric polarization is a manifestation of Berry phase,which can not be quantized by time-reversal symmetry(TRS) as in Hermitian physics TRS does not induce any topological phase in one *** this paper we report that even though associated with complex eigenenergies a 1D non-Hermitian insulator obeying only TRS is capable of presenting quantized bulk *** underlying physical reason is unveiled:TRS guarantees the complex energies to come in pair(E,E*),and the corresponding decaying and amplifying wave functions also come in pair and have the same variation rate,hence,giving rise to a stable wannier *** electron transport is performed by means of charge pumping process,which verifies the physical mechanism *** last,we discuss the possible experimental implementation of the proposed model by means of twisted-π gauge flux.

关键词： non-Hermitian polarization time-reversal symmetry complex hopping

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Designing Adaptive Multiple Dependent State Sampling Plan for Accelerated Life Tests

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computer Systems science & engineering 2023年第8期46卷 1631-1651页

作者： Pramote Charongrattanasakul Wimonmas Bamrungsetthapong Poom Kumam Division of Mathematics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology KrungthepBangkok10120Thailand Division of Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiPathum Thani12110Thailand Center of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research Laboratory Room SCL 802 Fixed Point LaboratoryScience Laboratory BuildingDepartments of MathematicsFaculty of ScienceKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand

A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multiple dependent state sampling plan(MDSSP)*** accelerated conditions,the lifetime of a product follows the Weibull distribution with a known shape parameter,while the scale parameter can be determined using the acceleration factor(AF).The Arrhenius model is used to estimate AF when the damaging process is *** economic design of the proposed sampling plan was also considered for the ALT.A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number(ASN)and total cost of inspection(TC)under both producer’s and consumer’s *** results are presented to support the AMDSSP for the ALT,while performance comparisons between the AMDSSP,the MDSSP and a single sampling plan(SSP)for the ALT are *** indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated *** AMDSSP also had a higher operating characteristic(OC)curve than both the existing sampling *** real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.

关键词： Accelerated life test acceleration factor adaptive of multiple dependent state sampling plan average sample number total cost of inspection weibull distribution

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Multi-Task Learning for Alzheimer's Disease Diagnosis and Mini-Mental State Examination Score Prediction

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Big Data Mining and Analytics 2024年第3期7卷 828-842页

作者： Jin Liu Xu Tian Hanhe Lin Hong-Dong Li Yi Pan Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Xinjiang Engineering Research Center of Big Data and Intelligent Software School of SoftwareXinjiang UniversityUrumqi 830091China School of Science and Engineering University of DundeeDundeeDD14HNUK Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.

关键词： multi-task learning Alzheimer's disease diagnosis mini-mental state examination score prediction

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WindVibraTransformer: A Foundational Model for Precise and Robust Wind Turbine Condition Monitoring via Vibration Signals 23

WindVibraTransformer: A Foundational Model for Precise and R...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Wakayama, Takuya Inoue, Taiki Ogata, Jun Iida, Makoto Ogawa, Tetsuji Waseda University Department of Communications and Computer Engineering Japan Japan Research Center for Advanced Science and Technology The University of Tokyo Japan

ISBN: (纸本)9798350374889

We aimed to develop WindVibraTransformer, a foundational model based on Transformers applied to vibration spectrograms, to derive precise and robust feature representations for wind turbine condition monitoring. In anomaly detection for wind turbine equipment, it is common to employ inlier modeling, where models are built solely on data from normal operation, with deviations classified as anomalies. Acquiring data for wind turbines, especially from the specific turbine being monitored, is challenging, often resulting in limited availability. However, this limitation can increase susceptibility to false alarms due to environmental variations. Thus, in this study, we explored the use of a vision Transformer trained with self-supervised learning to predict masked spectra conditioned on unmasked ones from vibration spectrograms, serving as the precise and robust feature extractor. Since this model aims to capture relationships within the context of the surroundings rather than solely the overview of the spectrogram, the resulting feature representations are expected to be complex yet less directly influenced by environmental variations. The complexity of these feature representations is addressed by constructing the normal state model using highly expressive normalizing flows. Experimental comparisons conducted on anomaly detection using real wind turbine vibration signals demonstrated that our approach enables highly reliable detection compared to existing unsupervised auto-encoder-based and supervised wind turbine classifier-based feature extraction methods. © 2024 IEEE.

关键词： Spectrographs

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Mechanistic insight into the synergy between nickel single atoms and nanoparticles on N-doped carbon for electroreduction of CO_(2)

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Journal of Energy Chemistry 2025年第1期100卷 327-336页

作者： Mingdong Sun Wenwen Guan Cailing Chen Chao Wu Xiaoling Liu Biao Meng Tao Chen Yu Han Jun Wang Shibo Xi Yu Zhou State Key Laboratory of Materials-Oriented Chemical Engineering College of Chemical EngineeringNanjing Tech UniversityNanjing 211816JiangsuChina Advanced Membranes and Porous Materials Center Physical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Institute of Sustainability for Chemicals Energy and EnvironmentA*STAR(Agency for ScienceTechnology and Research)Jurong IslandSingapore 627833Singapore College of Materials Science and Engineering Sichuan UniversityChengdu 610065SichuanChina

The synergy of single atoms(SAs)and nanoparticles(NPs)has demonstrated great potential in promoting the electrocatalytic carbon dioxide reduction reaction(CO_(2)RR);however,the rationalization of the SAs/NPs proportion remains one challenge for the catalyst ***,a Ni2+-loaded porous poly(ionic liquids)(PIL)precursor synthesized through the free radical self-polymerization of the ionic liquid monomer,1-allyl-3-vinylimidazolium chloride,was pyrolyzed to prepare the Ni,N co-doped carbon materials,in which the proportion of Ni SAs and NPs could be facilely modulated by controlling the annealing *** catalyst Ni-NC-1000 with a moderate proportion of Ni SAs and NPs exhibited high efficiency in the electrocatalytic conversion of CO_(2)into *** Ni K-edge X-ray absorption near-edge structure(XANES)spectra and theoretical calculations were conducted to gain insight into the synergy of Ni SAs and *** charge transfer from Ni NPs to the surrounding carbon layer and then to the Ni SAs resulted in the electron-enriched Ni SAs active *** the electroreduction of CO_(2),the coexistence of Ni SAs and NPs strengthened the CO_(2)activation and the affinity towards the key intermediate of*COOH,lowering the free energy for the potential-determining*CO_(2)→*COOH step,and therefore promoted the catalysis efficiency.

关键词： Single atom catalyst Synergy effect cO2 reduction Carbon catalyst Electrocatalysis

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Regulation of cryogenic mechanical behaviors of C-added non-equiatomic CoCrFeNiMo ferrous medium-entropy alloy via control of initial microstructure

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材料科学技术（英文版） 2025年第5期208卷 141-151页

作者： Ji Yeong Lee Hyeonseok Kwon Jae Heung Lee Jihye Kwon Jaemin Wang Jae Wung Bae Jongun Moon Byeong-Joo Lee Yoon-Uk Heo Hyoung Seop Kim Department of Materials Science and Engineering Pohang University of Science and Technology(POSTECH)Pohang37673Republic of Korea Department of Metallurgical Engineering Pukyong National UniversityBusan 48513Republic of Korea Division of Advanced Materials Engineering Center for Advanced Powder Materials and PartsKongju National UniversityCheonanChungnam31080Republic of Korea Graduate Institute of Ferrous and Eco Materials Technology Pohang University of Science and Technology77 Cheongam-RoNam-GuPohang 37673Republic of Korea Department of Materials Science and Engineering Pohang University of Science and Technology(POSTECH)Pohang37673Republic of Korea Graduate Institute of Ferrous and Eco Materials Technology Pohang University of Science and Technology77 Cheongam-RoNam-GuPohang 37673Republic of Korea Advanced Institute for Materials Research(WPI-AIMR) Tohoku UniversitySendai980-8577Japan Institute for Convergence Research and Education in Advanced Technology Yonsei UniversitySeoul03722Republic of Korea

This study demonstrated the potential for customizing the desired properties of the Co18.5Cr12Fe55Ni9Mo3.5C2(at.％)ferrous medium-entropy alloy by manipulating the deformation-induced martensite transformation(DIMT)behavior at liquid nitrogen *** was achieved by modify-ing various initial microstructures through annealing at temperatures ranging from 900 to 1200 ℃.The variations in DIMT kinetics were analyzed based on two main factors.(1)Inducing carbide precipitation by annealing at 900 and 1000 ℃ results in changes in the composition within the matrix,which may affect the stability of the face-centered cubic *** with a higher volume fraction of the carbide precipitates exhibit lower ΔGFCC→BCC and faster DIMT kinetics.(2)The onset and kinetics of DIMT are also affected by the use of martensite nucleation sites,which may vary depending on the presence of non-recrystallized regions or the grain *** fine-grained structures,martensite primarily nucleated in the non-recrystallized regions and grain ***,in coarse-grained microstructures,martensite mainly nucleated along the in-grain shear bands and their *** precise control of the microstructure results in superior *** samples annealed at 900 and 1000 ℃ with carbide precipitates and fine grains exhibit ultrahigh ultimate tensile strength,which may reach elevated values up to～1.8 GPa,while those annealed at 1100 and 1200 ℃ with larger grains and no precipitates exhibit a uniform elongation that exceeds 100％.

关键词： Ferrous medium-entropy alloy Deformation-induced martensitic transformation Microstructures Phase stability Precipitation

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Personality-based and trust-aware products recommendation in social networks

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Applied Intelligence 2023年第1期53卷 879-903页

作者： Vatani, Nasim Rahmani, Amir Masoud Javadi, Hamid Haj Seyyed Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics and Computer Science Shahed University Tehran Iran

In recent years, with the development of technology, the shopping approach of people has moved towards pervasive online social shopping. As a result, how to create a recommendation algorithm that offers products based on the personal and different needs and tastes of people on social networks is a significant research issue. This article proposes a personality-based and trust-aware probabilistic product recommendation algorithm in social networks. We present a dynamic method for determining how similar people in social networks are. For this purpose, we consider the personality-based features of recommendation attributes of products in social networks. Then, the level of trust of products and types of correlations among the products is considered to create a probabilistic matrix of product recommendation. Moreover, for solving the cold start problem of products, we consider qualitative aspects of products while exploiting personality-based user behavior regarding their purchases. At last, the empirical experiments are conducted to analyze the impact of the algorithm’s different influence factors using the Amazon dataset. Moreover, the results of comprehensive experiments adopted to verify the proposed personalized recommendation algorithm’s effectiveness show that the proposed algorithm has the appropriate effectiveness and the higher accuracy. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Behavioral research

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A Comparative Study on the Application of Text Mining in Cybersecurity

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Recent Advances in computer science and Communications 2023年第3期16卷 80-93页

作者： Barik, Kousik Misra, Sanjay Konar, Karabi Kaushik, Manju Ahuja, Ravin Department of Computer Science JIS Institute of Advanced Studies & Research JIS University Kolkata India Department of Computer Science and Communication Ostfold University Halden Norway Department of Computer Engineering Amity University Jaipur India Center of ICT/ICE Research CUCRID Covenant University Ota Nigeria

Aims: This paper aims to conduct a Systematic Literature Review (SLR) of the relative applications of text mining in cybersecurity. Objectives: The amount of data generated worldwide has been attributed to a change in different activities associated with cyber security, and demands a high automation level. Methods: In the cyber security domain, text mining is an alternative for improving the usefulness of various activities that entail unstructured data. This study searched databases of 516 papers from 2015 to 2021. Out of which, 75 papers are selected for analysis. A detailed evaluation of the selected studies employs sources, techniques, and information extraction on cyber security applications. Results: This study extends gaps for future studies, such as text processing, availability of datasets, innovative methods, and intelligent text mining. Conclusion: This study concludes with interesting findings of employing text mining in cybersecurity applications;the researchers need to exploit all related techniques and algorithms in text mining to detect and protect the organization from Cybersecurity applications. © 2023 Bentham science Publishers.

关键词： Data mining

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PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

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Journal of Systems engineering and Electronics 2024年第4期35卷 965-975页

作者： JIANG Haipeng WU Guoqing SUN Mengdan LI Feng SUN Yunfei FANG Wei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and TechnologyJiangnan UniversityWuxi 214122China China Ship Scientific Research Center Wuxi 214082China Taihu Laboratory of Deepsea Technological Science Wuxi 214082China Department of Mathematics Nanjing UniversityNanjing 210023China

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search *** have good running and mining performance,but they still require huge computational resource and may miss many *** to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded *** show that the mining performance of PHUI-GA outperforms the existing *** mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

关键词： high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance

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