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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan khaled sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineering and also with K.A.CARE Energy Research and Innovation Center King Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer Engineering Faculty of Engineering and also with K.A.CARE Energy Research and Innovation Center King Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete (HDI) data hyper-parameter adaptation latent factor analysis (LFA) particle swarm optimization (PSO)

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Disassembly Sequence Planning:A Survey

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1308-1324页

作者： Xiwang Guo MengChu Zhou Abdullah Abusorrah Fahad Alsokhiry khaled sedraoui the College of Computer and Communication Engineering Liaoning Shihua UniversityFushun 113001China the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering Faculty of Engineeringand the Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be *** contain reusable resource that can be recycled and used to generate desired economic ***,performing their efficient disassembly is highly important in green manufacturing and sustainable economic *** typical examples are electronic appliances and electromechanical/mechanical *** paper presents a survey on the state of the art of disassembly sequence *** can help new researchers or decision makers to search for the right solution for optimal disassembly *** reviews the disassembly theory and methods that are applied for the processing,repair,and maintenance of obsolete/discarded *** paper discusses the recent progress of disassembly sequencing planning in four major aspects:product disassembly modeling methods,mathematical programming methods,artificial intelligence methods,and uncertainty *** survey should stimulate readers to be engaged in the research,development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

关键词： Artificial intelligence(AI) disassembly sequence planning(DSP) intelligent optimization modeling methods multiobjective optimization single-objective optimization uncertainty.

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Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 402-411页

作者： Xin Luo Wen Qin Ani Dong khaled sedraoui MengChu Zhou the School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808 Hengrui(Chongqing)Artificial Intelligence Research Center Department of Big Data Analyses TechniquesCloudwalkChongqing 401331China the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the Department of Computer and Information Science City College of Dongguan University of TechnologyDongguan 523419China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning ***,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial *** at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training *** it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality *** results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

关键词： Big data industrial application industrial data latent factor analysis machine learning parallel algorithm recommender system(RS) stochastic gradient descent(SGD)

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan khaled sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1952-1966页

作者： Meiji Cui Li Li MengChu Zhou Jiankai Li Abdullah Abusorrah khaled sedraoui IEEE the School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China the Department of Electronics and Information Engineering Tongji UniversityShanghai 201804China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction *** search operation conducted in this low space facilitates the population with fast convergence towards the *** strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary ***,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence *** proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to *** indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base *** with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

关键词： Autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization

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A PID-incorporated Latent Factorization of Tensors Approach to Dynamically Weighted Directed Network Analysis

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 533-546页

作者： Hao Wu Xin Luo MengChu Zhou Muhyaddin J.Rawa khaled sedraoui Aiiad Albeshri Chongqing Institute of Green and Intelligent Technology Chinese Academy of SciencesChongqing 400714and also with the University of Chinese Academy of SciencesBeijing 100049China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA IEEE Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia College of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A large-scale dynamically weighted directed network(DWDN)involving numerous entities and massive dynamic interaction is an essential data source in many big-data-related applications,like in a terminal interaction pattern analysis system(TIPAS).It can be represented by a high-dimensional and incomplete(HDI)tensor whose entries are mostly *** such an HDI tensor contains a wealth knowledge regarding various desired patterns like potential links in a DWDN.A latent factorization-of-tensors(LFT)model proves to be highly efficient in extracting such knowledge from an HDI tensor,which is commonly achieved via a stochastic gradient descent(SGD)***,an SGD-based LFT model suffers from slow convergence that impairs its efficiency on large-scale *** address this issue,this work proposes a proportional-integralderivative(PID)-incorporated LFT *** constructs an adjusted instance error based on the PID control principle,and then substitutes it into an SGD solver to improve the convergence *** studies on two DWDNs generated by a real TIPAS show that compared with state-of-the-art models,the proposed model achieves significant efficiency gain as well as highly competitive prediction accuracy when handling the task of missing link prediction for a given DWDN.

关键词： Big data high dimensional and incomplete(HDI)tensor latent factorization-of-tensors(LFT) machine learning missing data optimization proportional-integral-derivative(PID)controller

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Experimental and numerical investigation of a solar eductorassisted low-pressure water desalination system

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Science Bulletin 2016年第12期61卷 959-973页

作者： Mehran Ahmadi Poovanna Thimmaiah Majid Bahrami khaled sedraoui Hani H.Sait Ned Djilali Laboratory for Alternative Energy Conversion (LAEC) School of Mechatronic Systems Engineering Simon Fraser University Renewable Energy Research Group King Abdulaziz University Department of Mechanical Engineering and Institute for Integrated Energy Systems University of Victoria

Greenhouse solar-energy driven desalination technology is potentially well suited for supplying water and small scale irrigation in remote and/or rural areas, and for avoiding over-exploitation of available water resources. The efficiency and productivity of these systems are however low, in part because the heat of evaporation has to be transferred as waste heat to ambient air during condensation. In order to maximize energy regeneration during condensation we propose an educator based system that lowers the evaporation process temperature by reducing pressure. The feasibility of the educator assisted passive solar desalination system is investigated using a detailed computational fluid dynamics analysis complemented by experiments. The study focuses in particular on the ability of the new design to lower the required evaporation temperature and thereby reduce the energy intensity of the process. Two configurations, with open and closed educator, are investigated and a detailed analysis of the thermofluid processes is presented. The configuration with a closed educator installed outside the evaporation chamber shows very promising performance. The proposed system can maintain the maximum temperature and pressure in theevaporation chamber below the desirable temperature and pressure thresholds （30 ℃ and 5 kPa）. The analysis and experimental data also show it is possible to further reduce energy requirements by reducing the motive water flow rates.

关键词： Desalination. Solar energy EvaporationHumidification Dehumidification Regenerative heating

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Calculus bovis in hepatocellular carcinoma:Tumor molecular basis,Wnt/β-catenin pathway role,and protective mechanism

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World Journal of Gastroenterology 2024年第35期30卷 3959-3964页

作者： khaled Mohamed Mohamed Koriem Department of Medical Physiology Medical Research and Clinical Studies InstituteNational Research CentreGiza 12622Egypt

In this editorial,we comment on the recent article by Huang et *** editorial focuses specifically on the molecular mechanisms of hepatocellular carcinoma(HCC),mechanism of Wnt/β-catenin pathway in HCC,and protective mechanism of Calculus bovis(CB)in *** cancer is the fourth most common cause of cancer-related deaths *** most prevalent kind of primary liver cancer,HCC,is typically brought on by long-term viral infections(hepatitis B and C),non-alcoholic steatohepatitis,excessive alcohol consumption,and other conditions that can cause the liver to become chronically inflamed and *** is a wellknown traditional remedy in China and Japan and has been used extensively to treat a variety of diseases,such as high fever,convulsions,and *** in lipid metabolism,cholesterol metabolism,bile acid metabolism,alcohol metabolism,and xenobiotic detoxification lead to fatty liver disease and liver ***,which is a tricarboxylic acid cycle intermediate,is vital to energy production and mitochondrial *** is also thought to be a signaling molecule in metabolism and in the development and spread of liver *** Wnt/β-catenin pathway is made up of a group of proteins that are essential for both adult tissue homeostasis and embryonic *** is frequently caused by the dysregulation of the Wnt/β-catenin signaling *** HCC liver carcinogenesis,Wnt/β-catenin signaling is activated by the expression of downstream target *** between the liver and the gut exists via the portal vein,biliary tract,and systemic ***"gutliver axis"controls intestinal *** of the main factors contributing to the development,progression,and treatment resistance of HCC is the abnormal activation of the Wnt/β-Catenin signaling ***,understanding this pathway is essential to treating *** ingredients of CB,particularly oleanolic acid,ergosterol,and ursolic acid,have anti-p

关键词： Hepatocellular carcinoma MicroRNAs Wnt/β-catenin pathway Calculus bovis Apoptosis

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Comparative analysis on the performance of different types of input-and command-shaping controllers in minimizing payload residual vibration of an overhead crane with an inclined supporting track

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International Journal of Mechanical System Dynamics 2024年第1期4卷 22-33页

作者： Mohammed Alfares khaled Alhazza Mechanical Engineering Department College of Engineering and PetroleumKuwait UniversityKuwait CityKuwait

Reducing the effects of external disturbance on overhead crane systems is crucial,as they can impair the controller performance and cause excessive vibrations or oscillations of the *** such external disturbance is the inclination of the supporting track of the crane trolley,which causes the system dynamics model to *** open-loop control strategy is widely utilized to control the payload sway motion and generally does not require any alterations in the physical structure of a system or the installation of sensors and/or *** and command shaping are two common open-loop control techniques applied to control overhead *** this paper,the effect of moving an overhead crane system along an inclined supporting track is *** addition,the ability of different types of input-and command-shaping control schemes to suppress the residual vibrations due to trolley track inclination is *** types of input-shaping controllers,which are double-step,zero vibration,and one command waveform(WF)shaper based on a trigonometric function,are used and tested.A linear equation of motion of the overhead crane resting on an inclined surface is developed to simulate the overhead crane and payload *** effectiveness of the different types of open-loop controllers to suppress residual vibrations is verified by both simulation and experimental *** addition,a new WF command shaper is proposed and designed to overcome track inclination while eliminating payload residual vibration.A comprehensive comparative analysis,both numerically and experimentally,is performed on the new proposed shaper to measure its effectiveness in handling inclination when compared to other types of open-loop *** new shaper outperforms other controllers in eliminating payload residual vibration for a wider range of inclination angles.

关键词： overhead cranes open-loop control input and command shaping payload residual vibrations

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A review of mechanism of action of outdoor exposure in preventing myopia incidence and progression

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International Journal of Ophthalmology(English edition) 2024年第6期17卷 1144-1155页

作者： Chikezie Grand Ihesiulor khaled Alzahrani Hema Rhadhakrishnan Division of Pharmacy&Optometry School of Health SciencesFaculty of BiologyMedicine and HealthThe University of ManchesterManchester M139PLUnited Kingdom Department of Ophthalmology King Fahad Armed Forces HospitalJeddah 23311Saudi Arabia

Various studies have suggested several environmental,pharmacological,medical,and optical interventions and some are in use but their efficacy in myopia control may be transient,and the cellular,molecular,and biochemical mechanisms involved *** exposure is currently regarded as an effective and enduring strategy in the control of myopia development and ***,the mechanism behind the effect of outdoor exposure and its association with genetic predisposition and other relatively more significant environmental factors on myopia is still a *** review focuses on survey-based and intervention-based studies carried out to propose a mechanism that accounts for myopia development and important for its control.

关键词： outdoor exposure daylight exposure myopia control myopia

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