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RBFK cipher:a randomized butterfly architecture‑based lightweight block cipher for IoT devices in the edge computing environment

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Cybersecurity 2023年第3期6卷 18-36页

作者： Sohel Rana M.Rubaiyat Hossain Mondal Joarder Kamruzzaman Institute of Information and Communication Technology Bangladesh University of Engineering and TechnologyDhakaBangladesh centre for smart analytics Federation University AustraliaGippslandVIC 3842Australia

Internet security has become a major concern with the growing use of the Internet of Things(IoT)and edge computing *** though data processing is handled by the edge server,sensitive data is generated and stored by the IoT devices,which are subject to *** most IoT devices have limited resources,standard security algorithms such as AES,DES,and RSA hamper their ability to run *** this paper,a lightweight symmetric key cipher termed randomized butterfly architecture of fast Fourier transform for key(RBFK)cipher is proposed for resource-constrained IoT devices in the edge computing *** butterfly architecture is used in the key scheduling system to produce strong round keys for five rounds of the encryption *** RBFK cipher has two key sizes:64 and 128 bits,with a block size of 64 *** RBFK ciphers have a larger avalanche effect due to the butterfly architecture ensuring strong *** proposed cipher satisfies the Shannon characteristics of confusion and *** memory usage and execution cycle of the RBFK cipher are assessed using the fair evaluation of the lightweight cryptographic systems(FELICS)*** proposed ciphers were also implemented using MATLAB 2021a to test key sensitivity by analyzing the histogram,correlation graph,and entropy of encrypted and decrypted *** the RBFK ciphers with minimal computational complexity provide better security than recently proposed competing ciphers,these are suitable for IoT devices in an edge computing environment.

关键词： Avalanche effects Block ciphers Butterfly architecture Edge computing FELICS IoT Lightweight cryptosystems MATLAB

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Voter model on adaptive networks

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Chinese Physics B 2022年第5期31卷 780-790页

作者： Jinming Du Key Laboratory of Data analytics and Optimization for smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Institute of Industrial and Systems Engineering College of Information Science and EngineeringNortheastern UniversityShenyang 110819China Liaoning Engineering Laboratory of Operations analytics and Optimization for smart Industry Northeastern UniversityShenyang 110819China

Voter model is an important basic model in statistical *** recent years,it has been more and more used to describe the process of opinion formation in *** real complex systems,the interactive network of individuals is dynamically adjusted,and the evolving network topology and individual behaviors affect each ***,we propose a linking dynamics to describe the coevolution of network topology and individual behaviors in this paper,and study the voter model on the adaptive *** theoretically analyze the properties of the voter model,including consensus probability and *** evolution of opinions on dynamic networks is further analyzed from the perspective of evolutionary ***,a case study of real data is shown to verify the effectiveness of the theory.

关键词： statistical physics opinion dynamics evolutionary game theory complex systems

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Visualization and simulation of steel metallurgy processes

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International Journal of Minerals,Metallurgy and Materials 2021年第8期28卷 1387-1396页

作者： Te Xu Guang Song Yang Yang Pei-xin Ge Li-xin Tang Key Laboratory of Data analytics and Optimization for smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Liaoning Engineering Laboratory of Operations analytics and Optimization for smart Industry Northeastern UniversityShenyang 110819China Liaoning Key Laboratory of Manufacturing System and Logistics Shenyang 110819China Institute of Industrial&Systems Engineering Northeastern UniversityShenyang 110819China

Steel production involves the transfer and transformation of material and energy at different levels, structures, and scales, and this process incurs substantial information in the material and energy dimensions. Given the black-box feature of iron and steel production processes, process visualization plays an important role and inevitably benefits parameter correction, technical support decision-making, personnel training, and other aspects of the steel metallurgy industry. The technological characteristics of the entire process in the steel industry were analyzed in this study, a visualization technology route based on virtual reality(VR) was built, and the important components of visual simulation system for steel industry and the important technical points needed to realize the system were proposed. On the foundation, a visual simulation model for the process scheduling of the iron and steel enterprise raw materials' field, slab, and hot rolling processes was built, and a visualization simulation platform of the iron and steel metallurgy plant-wide process, including ironmaking, steelmaking, hot rolling, and cold rolling, was developed. Lastly, the effectiveness of platform was illustrated by practical application.

关键词： virtual reality steel metallurgy plant-wide visualization

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Forecast for cosmological parameter estimation with gravitational-wave standard sirens from the LISA-Taiji network

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Science China(Physics,Mechanics & Astronomy) 2022年第1期65卷 1-13页

作者： Ling-Feng Wang Shang-Jie Jin Jing-Fei Zhang Xin Zhang Department of Physics College of SciencesNortheastern UniversityShenyang 110819China MOE Key Laboratory of Data analytics and Optimization for smart Industry Northeastern UniversityShenyang 110819China

LISA and Taiji are expected to form a space-based gravitational-wave(GW)detection network in the *** this work,we make a forecast for the cosmological parameter estimation with the standard siren observation from the LISA-Taiji *** simulate the standard siren data based on a scenario with configuration angle of 40°between LISA and *** models for the population of massive black hole binary(MBHB),i.e.,popⅢ,Q3d,and Q3nod,are considered to predict the events of MBHB *** find that,based on the LISA-Taiji network,the number of electromagnetic(EM)counterparts detected is almost doubled compared with the case of single Taiji ***,the LISA-Taiji network’s standard siren observation could provide much tighter constraints on cosmological *** example,solely using the standard sirens from the LISA-Taiji network,the constraint precision of H;could reach 1.3%.Moreover,combined with the CMB data,the GW-EM observation based on the LISA-Taiji network could also tightly constrain the equation of state of dark energy,e.g.,the constraint precision of w reaches about 4%,which is comparable with the result of CMB+BAO+*** is concluded that the GW standard sirens from the LISA-Taiji network will become a useful cosmological probe in understanding the nature of dark energy in the future.

关键词： LISA-Taiji network gravitational-wave standard siren cosmological parameter estimation massive black hole binary electromagnetic counterpart

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Enhancing Telemarketing Success Using Ensemble-Based Online Machine Learning

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Big Data Mining and analytics 2024年第2期7卷 294-314页

作者： Shahriar Kaisar Md Mamunur Rashid Abdullahi Chowdhury Sakib Shahriar Shafin Joarder Kamruzzaman Abebe Diro Department of Information Systems and Business analytics RMIT UniversityMelbourne 3000Australia School of Engineering and Technology Central Queensland UniversityRockhampton 4700Australia Faculty of Engineering Computer and Mathematical ScienceUniversity of AdelaideAdelaide 5005Australia centre for smart analytics Federation University AustraliaBallarat 3350Australia School of Accounting Information Systems and Supply ChainRMIT UniversityMelbourne 3000Australia

Telemarketing is a well-established marketing approach to offering products and services to prospective *** effectiveness of such an approach,however,is highly dependent on the selection of the appropriate consumer base,as reaching uninterested customers will induce annoyance and consume costly enterprise resources in vain while missing interested *** introduction of business intelligence and machine learning models can positively influence the decision-making process by predicting the potential customer base,and the existing literature in this direction shows promising ***,the selection of influential features and the construction of effective learning models for improved performance remain a ***,from the modelling perspective,the class imbalance nature of the training data,where samples with unsuccessful outcomes highly outnumber successful ones,further compounds the problem by creating biased and inaccurate ***,customer preferences are likely to change over time due to various reasons,and/or a fresh group of customers may be targeted for a new product or service,necessitating model retraining which is not addressed at all in existing works.A major challenge in model retraining is maintaining a balance between stability(retaining older knowledge)and plasticity(being receptive to new information).To address the above issues,this paper proposes an ensemble machine learning model with feature selection and oversampling techniques to identify potential customers more accurately.A novel online learning method is proposed for model retraining when new samples are available over *** newly introduced method equips the proposed approach to deal with dynamic data,leading to improved readiness of the proposed model for practical adoption,and is a highly useful addition to the *** experiments with real-world data show that the proposed approach achieves excellent results in all cases(e.g.,98.6

关键词： machine learning online learning oversampling telemarketing imbalanced dataset ensemble model

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Electrochemical derusting in molten Na_(2)CO_(3)–K_(2)CO_(3)

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International Journal of Minerals,Metallurgy and Materials 2021年第4期28卷 637-643页

作者： Dong-yang Zhang Xue Ma Hong-wei Xie Xiang Chen Jia-kang Qu Qiu-shi Song Hua-yi Yin Key Laboratory for Ecological Metallurgy of Multimetallic Mineral of Ministry of Education School of MetallurgyNortheastern UniversityShenyang 110819China Key Laboratory of Data analytics and Optimization for smart Industry of Ministry of Education Northeastern UniversityShenyang 110819China

The formation of a rust layer on iron and steels surfaces accelerates their degradation and eventually causes material *** addition to fabricating a protective layer or using a sacrificial anode, repairing or removing the rust layer is another way to reduce the corrosion rate and extend the lifespans of iron and ***, an electrochemical healing approach was employed to repair the rust layer in molten Na_(2)CO_(3)-K_(2)CO_(3).The rusty layers on iron rods and screws were electrochemically converted to iron in only several minutes and a metallic luster *** electron microscopy(SEM) and energy dispersive X-ray spectroscopy(EDS) analyses showed that the structures of the rust layer after healing were slightly porous and the oxygen content reached a very low ***, high-temperature molten-salt electrolysis may be an effective way to metalize iron rust of various shapes and structures in a short time, and could be used in the repair of cultural relics and even preparing a three-dimensional porous structures for other applications.

关键词： molten salt electro-deoxidation rust stainless steel derusting

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Electrolytic alloy-type anodes for metal-ion batteries

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Rare Metals 2021年第2期40卷 329-352页

作者： Xian-Yang Li Jia-Kang Qu Hua-Yi Yin Key Laboratory for Ecological Metallurgy of Multimetallic Mineral of Ministry of Education School of MetallurgyNortheastern UniversityShenyang110819China Key Laboratory of Data analytics and Optimization for smart Industry Ministry of EducationNortheastern UniversityShenyang110819China

Alloy-type metals/alloys hold the promise of increasing the energy density of metal-ion batteries(MIBs)because of their theoretical high gravimetrical *** and semimetal-analogs are typical alloy-type ***,the large-scale extraction of semimetals(Si,Ge)and semimetal-analogs(Sb,Bi,Sn)by traditional metallurgical routes highly relies on using reducing agents(e.g.,carbon,hydrogen,reactive metals),which consumes a large number of fossil fuels and produces greenhouse gas *** addition,the common metallurgical methods for extracting semimetals involve relatively high operating temperatures and therefore produce bulk metal ingots solidified from the liquid ***,the commonly used electrode materials in batteries are fine ***,directly producing semimetal powders would be more energy *** addition,semimetals are good candidates to host alkali/alkaline-earth ions through the alloying process because the electronegativity of semimetals is ***,preparing semimetal powders via an environment-sound manner is of great interest to provide sustainable anode materials for MIBs while reducing the ecological ***-cost and high-output capacity anode powder materials,as well as straightforward and environmental-benign synthetic methods,play key roles in enabling the energy conversion and storage technologies for real applications of *** technologies offer new strategies to extract semimetals using electrons as the reducing agent that comes from renewable ***,the morphologies and structures of the electrolytic products can be rationally tailored by tuning the electrode potentials,electrolytes,and operating *** this regard,using the one-step green electrochemical method to prepare high-capacity and cheaper alloy-type metalloids for MIB anodes can fulfill the requirements for developing *** review critically overviews recent developments and advances in the elect

关键词： Electrochemical extraction Alloy-type anodes Metal-ion batteries Manufacture strategies Morphologies and structures

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An artificial intelligence framework for explainable drift detection in energy forecasting

Energy and AI

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Energy and AI 2024年第3期17卷 368-379页

作者： Chamod Samarajeewa Daswin De Silva Milos Manic Nishan Mills Harsha Moraliyage Damminda Alahakoon Andrew Jennings centre for Data analytics and Cognition La Trobe UniversityAustralia Department of Computer Science Virginia Commonwealth UniversityRichmondUSA

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building Science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.

关键词： Building energy consumption forecasting Explainable drift detection Energy embedding Dimensional modeling

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Gravitational wave standard sirens and cosmological parameter measurement

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Science China(Physics,Mechanics & Astronomy) 2019年第11期62卷 146-150页

作者： Xin Zhang Department of Physics College of Sciences Northeastern University Ministry of Education Key Laboratory of Data analytics and Optimization for smart Industry Northeastern University Center for High Energy Physics Peking University

The first detection of gravitational waves (GWs) produced by the binary neutron star (BNS) merger in August 17, 2017[1] is fairly meaningful because it initiated the new era of multi-messenger astronomy. In this BNS m... 详细信息

关键词： CDM Gravitational wave standard sirens and cosmological parameter measurement

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Annihilating the Formation of Silicon Carbide:Molten Salt Electrolysis of Carbon-Silica Composite to Prepare the Carbon-Silicon Hybrid for Lithium-lon Battery Anode

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Energy & Environmental Materials 2020年第2期3卷 166-176页

作者： Xianbo Zhou Hongwei Xie Xiao He Zhuqing Zhao Qiang Ma Muya Cai Huayi Yin Key Laboratory for Ecological Metallurgy of Multimetallic Mineral of Ministry of Education School of metallurgyNortheastern University110819Shenyang China Key Laboratory of Data analytics and Optimization for smart Industry(Northeastern University) Ministry of Education110819Shenyang China

Silicon(Si)and carbon(C)composites hold the promise for replacing the commercial graphite anode,thus increasing the energy density of lithium-ion batteries(LIBs).To mitigate the formation of SiC,this paper reports a molten salt electrolysis approach to prepare C-Si composite by the electrolysis of C-SiO2 *** the conventional way of making a C coating on Si,C-SiO2 composites were prepared by pyrolyzing the low-cost sucrose and *** electrochemical deoxidation of the C-SiO2 composites not only produces nanostructured Si inside the C matrix but also introduces voids between the C and Si owing to the volume shrinkage from converting SiO2 to *** importantly,the use of Mg ion-containing molten salts precludes the generation of SiC,and the electrolytic Si@C composite anode delivers a capacity of about 1500 mAh g-1 after 100 cycles at a current density of 500 mA ***,the Si@C‖LiNi0.6Co0.2Mn0.2O2 full cell delivers a high energy density of 608 Wh ***,the molten salt approach provides a one-step electrochemical way to convert oxides@C to metals@C functional materials.

关键词： anode material carbon lithium-ion batteries molten salt electrolysis silicon

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