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Combining permissioned blockchain and Bayesian best-worst method for transparent supplier selection in supply chain management

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science China Technological sciences 2024年第8期67卷 2579-2593页

作者： LIU JiaJun ZHANG Jie LENG JieWu Shanghai Engineering research center of Industrial Big data and Intelligent System Institute of Artificial Intelligence Donghua University State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology

Supplier selection is an important business activity in order to realize the purchasing function in supply chain management. The supplier selection process includes four stages, i.e., bidding inviting, bidding, group decision-making, and results disclosure,involving the participation of manufacturing service demanders(MSDs), manufacturing service suppliers(MSSs), and decisionmakers. Nowadays, all the participants have raised concerns about the increased transparency in supplier selection. Therefore,this study proposes a transparent supplier selection method by considering the engagement of suppliers. In this method, the Bayesian best-worst method(Bayesian BWM) is used to aggregate decision-makers' preferences into the overall optimal weights of the alternative MSSs, and the MSS with the largest weight is considered the suitable MSS for MSDs. Furthermore,blockchain is introduced to record the decision-making process information about supplier selection through a customized smart contract, where MSSs act as supervisors to supervise the decision-making process through the distributed consensus mechanism rather than directly participate in the decision-making process. Finally, a case study of supplier selection in purchasing vibration acceleration sensors is presented. The result shows that the proposed method can support MSDs in selecting suitable MSS from alternative MSSs by aggregating decision-makers' preferences, and blockchain can provide credible information about the supplier selection process for MSSs, MSDs, and decision-makers. In this way, the transparency of supplier selection is enhanced.

关键词： blockchain supplier selection supply chain transparency Bayesian BWM group decision-making

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First Passage Density of Brownian Motion with Two-sided Piecewise Linear Boundaries

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Acta Mathematica Sinica,English Series 2024年第6期40卷 1505-1520页

作者： Zhen YU Mao Zai TIAN center for Applied Statistics School of StatisticsRenmin University of ChinaBeijing 100872P.R.China School of Mathematics and data science Changji UniversityChangji 831100P.R.China

The first passage time has many applications in fields like finance,econometrics,statistics,and ***,explicit formulas for the first passage density have only been obtained for a few *** paper derives an explicit formula for the first passage density of Brownian motion with twosided piecewise continuous boundaries which may have some points of *** are used to obtain a simplified formula for estimating the first passage ***,the results are also generalized to the case of two-sided general nonlinear *** can be easily carried out with Monte Carlo method and it is demonstrated for several typical two-sided boundaries that the proposed approximation method offers a highly accurate approximation of first passage density.

关键词： Boundary non-crossing probability first density passage density two-sided piecewise continuous boundaries Brownian motion

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Flocking fragmentation formulation for a multi-robot system under multi-hop and lossy ad hoc networks

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Frontiers of Information Technology & Electronic Engineering 2024年第8期25卷 1057-1076页

作者： Silan LI Shengyu ZHANG Tao JIANG The research center of 6G Mobile Communications School of Cyber Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China

We investigate the impact of network topology characteristics on focking fragmentation for a multi-robot system under a multi-hop and lossy ad hoc network,including the network's hop count features and information's successful transmission probability(STP).Specifically,we first propose a distributed communication calculation execution protocol to describe the practical interaction and control process in the ad hoc network based multi-robot system,where focking control is realized by a discrete-time Olfati-Saber model incorporating STP-related ***,we develop a fragmentation prediction model(FPM)to formulate the impact of hop count features on fragmentation for specific focking *** model identifies the critical system and network features that are associated with *** considering general focking scenarios affected by both hop count features and STP,we formulate the flocking fragmentation probability(FFP)by a data fitting model based on the back propagation neural network,whose input is extracted from the *** FFP formulation quantifies the impact of key network topology characteristics on fragmentation *** results verify the effectiveness and accuracy of the proposed prediction model and FFP formulation,and several guidelines for constructing the multi-robot ad hoc network are concluded.

关键词： Multi-robot flocking Flocking fragmentation probability Fragmentation prediction Multi-robot communication networks

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data-driven evacuation and rescue traffic optimization with rescue contraflow control

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Journal of Safety science and Resilience 2024年第1期5卷 1-12页

作者： Zheng Liu Jialin Liu Xuecheng Shang Xingang Li School of Management Engineering Zhengzhou University of AeronauticsZhengzhou 450046China Collaborative Innovation center for Aviation economy Development of Henan Province Zhengzhou University of AeronauticsZhengzhou 450046China School of Systems science Beijing Jiaotong UniversityBeijing 100044China Key Laboratory of Transport Industry of Big data Application Technologies for Comprehensive Transport Beijing Jiaotong UniversityBeijing 100044China School of Transportation Engineering Shandong Jianzhu UniversityJinan 250101China

In response to local sudden disasters,e.g.,high-rise office or residential building fire disasters,road occupation can cause conflicts,and traffic directions may be opposite between evacuation vehicles and rescue vehicles;moreover,lane contraflow can be adopted to meet these surge traffic ***,lane contraflow that provides more roads for rescue vehicles reduces the traffic supply in the evacuation *** is unclear how to control the number of contraflow roads used by rescue vehicles to coordinate evacuation and rescue traffic ***,we adjust the critical rescue traffic volume of reversing the normal road traffic direction to control rescue ***,we propose a multiobjective mixed integer linear programming formulation for evacuation and rescue traffic ***,considering that the upper limit of the critical rescue traffic volume is unknown and that the proposed formulation includes multiple objectives and multi-priority vehicle classes,a three-stage solving algorithm is ***,a large-scale evacuation and rescue traffic optimization result dataset is obtained for the Nguyen–Dupuis road network,and the impact of different rescue contraflow control plans on evacuation and rescue traffic is studied based on data-driven sta-tistical *** results show that by adjusting the optimal rescue traffic route,the critical rescue traffic volume for reversing the normal road traffic direction can reduce the interference of rescue traffic to evacuation traffic operation performance without reducing rescue traffic operation performance,and can be used to coor-dinate evacuation and rescue traffic operation under rescue contraflow.

关键词： Evacuation and rescue traffic optimization Rescue contraflow control Multiobjective mixed integer linear programming Three-stage solving algorithm data-driven statistical analysis

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data-driven slicing for dimension reduction in regressions:A likelihood-ratio approach

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science China Mathematics 2024年第3期67卷 647-664页

作者： Peirong Xu Tao Wang Lixing Zhu Department of Statistics Shanghai Jiao Tong UniversityShanghai 200240China center for Statistics and data science Beijing Normal University at ZhuhaiZhuhai 519087China

To efficiently estimate the central subspace in sufficient dimension reduction,response discretization via slicing its range is one of the most used methodologies when inverse regression-based methods are ***,existing slicing schemes are almost all ad hoc and not widely ***,how to define datadriven schemes with certain optimal properties is a longstanding problem in this *** research described here is then ***,we introduce a likelihood-ratio-based framework for dimension reduction,subsuming the popularly used methods including the sliced inverse regression,the sliced average variance estimation and the likelihood acquired ***,we propose a regularized log likelihood-ratio criterion to obtain a data-driven slicing scheme and derive the asymptotic properties of the estimators.A simulation study is carried out to examine the performance of the proposed method and that of existing methods.A data set concerning concrete compressive strength is also analyzed for illustration and comparison.

关键词： full-likelihood approach adaptive slicing regularization second-order method

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X-Ray Source Classification Using Machine Learning: A Study with EP-WXT Pathfinder LEIA

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research in Astronomy and Astrophysics 2024年第8期24卷 177-197页

作者： Xiaoxiong Zuo Yihan Tao Yuan Liu Yunfei Xu Wenda Zhang Haiwu Pan Hui Sun Zhen Zhang Chenzhou Cui Weimin Yuan National Astronomical Observatories Chinese Academy of Sciences University of Chinese Academy of sciences National Astronomical data center

X-ray observations play a crucial role in time-domain *** Einstein Probe (EP),a recently launched X-ray astronomical satellite,emerges as a forefront player in the field of time-domain astronomy and high-energy *** a focus on systematic surveys in the soft X-ray band,EP aims to discover high-energy transients and monitor variable sources in the *** achieve these objectives,a quick and reliable classification of observed sources is *** this study,we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder—Lobster Eye Imager for Astronomy (LEIA) and EP-WXT *** proposed Random Forest classifier,built on selected features derived from light curves,energy spectra,and location information,achieves an accuracy of approximately 95%on EP simulation data and 98%on LEIA observational *** classifier is integrated into the LEIA data processing pipeline,serving as a tool for manual validation and rapid classification during *** paper presents an efficient method for the classification of X-ray sources based on single observations,along with implications of most effective features for the *** work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.

关键词： methods: data analysis stars: variables: general X-rays: binaries X-rays: bursts

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Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

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research in Astronomy and Astrophysics 2024年第3期24卷 283-296页

作者： Zi-Yi You Yun-Rong Pan Zhi Ma Li Zhang Shuo Xiao Dan-Dan Zhang Shi-Jun Dang Ru-Shuang Zhao Pei Wang Ai-Jun Dong Jia-Tao Jiang Ji-Bing Leng Wei-An Li Si-Yao Li School of Physics and Electronic science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and data Processing Guizhou Normal UniversityGuiyang 550025China College of Big data and Information Engineering Guizhou UniversityGuiyang 550025China CAS Key Laboratory of FAST National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China Key Laboratory of Information and Computing Guizhou Province Guizhou Normal UniversityGuiyang 550001China School of Big data and Computer science Guizhou Normal UniversityGuiyang 550025China Guizhou Software Engineering research center Guiyang 550000China

Pulsar search is always the basis of pulsar navigation,gravitational wave detection and other research ***,the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope(FAST)shows an explosive growth rate that has brought challenges for its pulsar candidate filtering ***,the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification *** this study,a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented,which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for *** on both High Time Resolution Universe SurveyⅡ(HTRU2)and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars:On HTRU2,the precision and recall rates of its parallel mode reach0.981 and 0.988 *** FAST data,those of its parallel mode reach 0.891 and 0.961,meanwhile,the running time also significantly decreases with the increment of parallel nodes within ***,we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.

关键词： methods data analysis-surveys-methods numerical

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Added Benefit of the Early-Morning-Orbit Satellite Fengyun-3E on the Global Microwave Sounding of the Three-Orbit Constellation

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Advances in Atmospheric sciences 2024年第1期41卷 39-52页

作者： Juan LI Zhengkun QIN Guiqing LIU Jing HUANG CMA Earth System Modeling and Prediction Centre Beijing 100081China State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China Joint center for data Assimilation research and Applications School of Atmospheric SciencesNanjing University of Information Science&TechnologyNanjing 210044China

The three-orbit constellation can comprehensively increase the spatial coverage of polar-orbiting satellites,but the polar-orbiting satellites currently in operation are only mid-morning-orbit and afternoon-orbit ***-3E(FY-3E)was launched successfully on 5 July 2021 in *** an early-morning-orbit satellite,FY-3E can help form a complete three-orbit observation system together with the mid-morning and afternoon satellites in the current mainstream operational *** this study,we investigate the added benefit of FY-3E microwave sounding observations to the midmorning-orbit Meteorological Operational satellite-B(Met Op-B)and afternoon-orbit Fengyun-3D(FY-3D)microwave observations in the Chinese Meteorological Administration global forecast system(CMA-GFS).The results show that the additional FY-3E microwave temperature sounder-3(MWTS-3)and microwave humidity sounder-2(MWHS-2)data can increase the global coverage of microwave temperature and humidity sounding data by 14.8% and 10.6%,*** enables the CMA-GFS to achieve nearly 100% global coverage of microwave-sounding observations at each analysis ***,after effective quality control and bias correction,the global biases and standard deviations of the differences between observations and model simulations are also *** on the Advanced Microwave Sounding Unit A and the Microwave Humidity Sounder onboard Met Op-B,and the MWTS-2 and MWHS-2 onboard FY-3D,adding the microwave sounding data of FY-3E can further reduce the errors of analysis results and improve the global prediction skills of CMA-GFS,especially for the southern-hemisphere forecasts within 96 hours,all of which are significant at the 95% confidence level.

关键词： data assimilation microwave temperature sounder MWTS-3

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Intelligent proportioning model of converter scrap based on optimization algorithm

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Progress in Natural science:Materials International 2024年第1期34卷 37-44页

作者： Chaojie Zhang Jinjun Cheng Liqiang Zhang Yingxue Wang Wei Zhang Jianwei Yuan Haichuan Wang School of Metallurgical Engineering Anhui University of Technology National Engineering research center for Public Safety Risk Perception and Control by Big data(RPP) China Academic of Electronics and Information Technology Wuhu Xinxing Pipes Co. Ltd.

Scrap is one of the main raw materials in converter steelmaking. Due to the wide variety of scrap and the significant differences in its prices, the proportioning of scrap presents a challenge in converter steelmaking. This paper explores the optimal proportioning method of scrap for converters using an optimization mathematical model. Initially, the melting characteristics of scrap after being added to the converter's molten iron were investigated using finite element numerical simulations. The study found that when the weight of the scrap is fixed, both the shape and type of the scrap significantly influence the melting time. Subsequently, employing mathematical optimization methods and considering comprehensive process parameters of the 210t converter smelting, including the composition and unit price of the scrap, as well as the requirements of the converter steelmaking process for scrap, an optimization model for the proportioning of scrap in converters was *** model was solved using computer programming languages. Through this optimization model, it is possible to obtain the lowest cost per ton of molten steel in the converter smelting process by determining the optimal proportions of various types of scrap.

关键词： scrap proportioning Optimization model Converter steelmaking mathematical model numerical simulation

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering research center for Cyberspace data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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