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7th International Conference on Educational Technology Management, ICETM 2024

作者： Huang, Mingjing Cheong, Ngai Liu, Jiaqi Zhang, Zhuofan Faculty of Applied Sciences Macao Polytechnic University China Intelligent Transportation Engineering Institute Guangdong Communication Polytechnic Guangdong Guangzhou China

ISBN: (纸本)9798400717468

With the rapid development of information technology, personalized education recommendation systems have gained widespread attention and rapid development in China’s education sector. These systems provide customized learning resources and course recommendations to meet the individual needs of different students by analyzing their learning behaviors, preferences, and performance. This study comprehensively searched 45, 623 literatures from 2002 to 2022, focusing on the current status, key technologies, and application practices of educational recommendation systems in China. It first investigates the basic deep learning recommendation methods used in current personalized learning recommendation systems, and then summarizes the current status of machine learning techniques applied in personalized learning recommendation systems for higher education in China. Finally, through a comprehensive analysis of the existing literature, we discuss the current challenges and future development direction of China’s educational recommendation systems, and put forward improvement suggestions for domestic educational recommendation systems, aiming to fill the research gaps and promote technological progress and innovation. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Out-of-order enabled operating system for uncertain planning, scheduling and execution in aviation maintenance

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Journal of Manufacturing Systems 2025年 80卷 824-840页

作者： Yang, Fan Ren, Cheng Liu, Wei Li, Mingxing Li, Ming Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic University Hong Kong School of Intelligent Systems Science and Engineering Jinan University Zhuhai China Research Institute for Advanced Manufacturing The Hong Kong Polytechnic University Hung Hom Hong Kong Research Centre The Hong Kong Polytechnic University Hung Hom Hong Kong Research Centre for Digital Transformation of Tourism The Hong Kong Polytechnic University Hung Hom Hong Kong PolyU Academy for Artificial Intelligence The Hong Kong Polytechnic University Hong Kong

Maintenance has long been a concern in manufacturing, both in the production and product-service phases. As a type of large product, aviation maintenance produces a collection of services to ensure that aircrafts or aircraft systems, components, and structures meet airworthiness standards. Planning, scheduling, and execution (PSE) is important for maintenance systems to optimize resource utilization and job sequencing through decision-making at different time cycles. However, stochastic uncertainty always exists, affecting the stability of the entire maintenance process. Therefore, in this study, which was inspired by operating systems (i.e., Windows, Android, etc.) for processing uncertain user actions with high efficiency, an out-of-order enabled operation system in aviation maintenance (OoO-AMOS) is designed to mitigate the influence of uncertainties that exist in the PSE procedure. Two key components, namely, thread manager and resource manager, are proposed at the kernel level of the OoO-AMOS. The concept of out-of-order (OoO) is deployed for the thread manager to dynamically select the optimal order sequence based on task dependencies and feasibility. A finite state machine (FSM) model is integrated as the operation validation mechanism to formulize the resource states and their transitions. Finally, a case study is conducted to evaluate the effectiveness of the proposed OoO-AMOS. The results show that OoO-AMOS presents significant advantages over traditional approaches. In uncertain environments, the total setup time was reduced by more than 55 %, whereas the maintenance makespan, average order tardiness, and hangar turnover rate achieved improvements of more than 22 %, 31 %, and 23 %, respectively. © 2025 The Authors

关键词： Decision making

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Vision-language model-based human-robot collaboration for smart manufacturing:A state-of-the-art survey

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Frontiers of engineering Management 2025年第1期12卷 177-200页

作者： Junming FAN Yue YIN Tian WANG Wenhang DONG Pai ZHENG Lihui WANG Department of Industrial and Systems Engineering The Hong Kong Polytechnic UniversityHong Kong 999077China Department of Production Engineering KTH Royal Institute of TechnologyStockholmSweden

human-robot collaboration(HRC)is set to transform the manufacturing paradigm by leveraging the strengths of human flexibility and robot *** recent breakthrough of Large Language Models(LLMs)and Vision-Language Models(VLMs)has motivated the preliminary explorations and adoptions of these models in the smart manufacturing ***,despite the considerable amount of effort,existing research mainly focused on individual components without a comprehensive perspective to address the full potential of VLMs,especially for HRC in smart manufacturing *** fill the gap,this work offers a systematic review of the latest advance-ments and applications of VLMs in HRC for smart manu-facturing,which covers the fundamental architectures and pretraining methodologies of LLMs and VLMs,their applications in robotic task planning,navigation,and manipulation,and role in enhancing human-robot skill transfer through multimodal data ***,the paper discusses current limitations and future research directions in VLM-based HRC,highlighting the trend in fully realizing the potential of these technologies for smart manufacturing.

关键词： vision-language models large language models human-robot collaboration smart manufacturing

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Rapid acquisition of a unique combination of strength and ductility in La-Fe-Co-Si magnetocaloric alloy: Eutectic interface construction by selective laser melting

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Journal of Manufacturing Processes 2025年 141卷 1098-1108页

作者： Zhang, Ruochen Yuan, Bo Qian, Mingfang Zhang, Xuexi Liu, Zhengxian School of Materials Science and Engineering Shenyang Ligong University Shenyang110159 China Institute of Intelligent Manufacturing Technology Shenzhen Polytechnic University Guangdong Shenzhen518055 China School of Materials Science and Engineering Harbin Institute of Technology Harbin150001 China Beijing System Design Institute of Electro-mechanic Engineering Beijing100854 China

In this work, La,Cu,Si-rich eutectic interfaces consisting of the refined α-Fe and LaCuSi phases are primarily constructed via adding Cu by selective laser melting to rapidly acquire a combination of strength and ductility in La-Fe-Co-Si alloy. As a result, the ingot exhibits a decreased porosity from 2.52 vol% to 1.84 vol%. It is also the lowest porosity that has been reported among the La-Fe-Si-based ingots fabricated by laser additive manufacturing until now compared to those without eutectic interfaces， so these as-built ingots exhibit the enhanced hardness. More importantly, due to the ductility of α-Fe and LaCuSi phases, the ductility of the ingot is significantly increased to 6 % and a yield stage is first discovered with a yield strength 549.43 MPa and ductility 6 % in the as-built ingot. Simultaneously, the compression strength is as large as 583.88 MPa. After a short annealing process at 1323 K for 12 h, large amounts of the refined magnetocaloric La(Fe,Co,Si)13 phases are formed. The interfacial phases are transformed to LaCu2 phases semi-coherent to La(Fe,Co,Si)13 phases and ductile LaCuSi phases. It causes an extra 36 % enhancement of compression strength to 918.98 MPa without any ductility sacrifice thanks to the combined impact of the refinement and second-phase strengthening. It is the highest strength that has ever been reported in this material. Simultaneously, a wide working temperature interval 300 K–340 K with high magnetocaloric cyclicity is obtained. © 2025 The Society of Manufacturing Engineers

关键词： Silicon alloys

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Robust Networked Control Adopting Prediction-Compensation Mechanism for IoT-Based Building Field-Level Control Concerning Network Uncertainties

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IEEE Internet of Things Journal 2025年第11期12卷 15818-15827页

作者： Li, Xinyue Li, Hangxin Cao, Jiannong Wang, Shengwei The Hong Kong Polytechnic University Dept. of Bldg. Environ. and Ener. Eng. and Research Institute for Sustainable Urban Development Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Building Environment and Energy Engineering Hong Kong Hong Kong The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen China

Internet of Things (IoT) technologies offer great potential benefits to the development of smart buildings. However, the functionalities of IoT applications in buildings, especially those involving time-critical control tasks, are still limited due to the strict real-Time and reliability requirements. These tasks could be easily affected by network uncertainties in the IoT environment. Current optimization methods aimed at mitigating network impacts have limitations in their applications and often overlook the impacts in real engineering cases. This study, therefore, proposes a robust networked control adopting the prediction-compensation mechanism to improve the robustness of building field-level controls implemented in the IoT-enabled building automation system. The control mainly consists of a predictor to estimate the controlled variable, and a compensator to evaluate the uncertainties. To assess the performance and the improvement on control robustness, a typical time-critical building field-level control task is implemented in a networked building field-level control simulation platform, considering network uncertainties. The proposed robust control is adopted for implementing the control task. The results show that the proposed robust networked control is a promising option due to its significant improvement in the control robustness when affected by network constraints, especially in critical conditions of the control process. © 2014 IEEE.

关键词： Networked control systems

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Investigating Niobium Coating Removal Techniques from Tool Steel Substrates 8th

Investigating Niobium Coating Removal Techniques from Tool S...

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8th installment of the REWAS conference series held at the TMS Annual Meeting and Exhibition focuses on circular economy for the energy transition, 2025

作者： Gupta, Akanksha Mishra, Brajendra Department of Material Science and Engineering Worcester Polytechnic Institute 100 Institute Rd WorcesterMA01609 United States

ISBN: (纸本)9783031808913

NiobiumNiobium is a critical and strategic metal with high economic importance in various industriesIndustries like aerospace, defense, and healthcare sectors, as well as due to its limited supply risk. Brazil accounts for nearly 90% of total niobiumNiobium production. It is necessary to use secondary resources to meet global niobiumNiobium needs and contribute to circular economyCircular economy. One such secondary source for niobiumNiobium is in the form of metal coating for applications including chemical equipment’s, aerospace components, and biomedical implants due to its superior corrosion resistance, biocompatibility, and high-temperature properties. The objective is the efficient removal of niobiumNiobium coating without affecting the steel substrate. Several coating removalCoating removal processes are reviewed with prime focus on electrochemical methods which are accompanied by empirical results. The material characteristics of the bimetallic compositeBimetallic composite are first analyzed to check the thickness of the coating, composition of coating and substrate, and interdiffusion interface in order to investigate the most efficient coating removalCoating removal process. Chemical leachingLeaching and electrochemical coating removalCoating removal of niobiumNiobium is investigated in alkaline medium, and the electrochemical analysis is conducted using open-circuit measurements and potentiodynamic behaviorPotentiodynamic behavior of pure niobiumNiobium anodes. Thermal and mechanical methods for niobiumNiobiumCoating removal are also evaluated. Metallic niobiumNiobium coating can be removed from steel substrate by alkaline leachingLeaching in the form of soluble metal niobiumNiobium complex, oxidized to niobiumNiobium pentoxide at high temperatures, or removed mechanically by abrasive blasting. © The Minerals, Metals & Materials Society 2025.

关键词： Corrosion resistance

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Parametric Study of a Radio Frequency Plasma Cathode

Parametric Study of a Radio Frequency Plasma Cathode

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Gutierrez, Rafael Blandino, John J. Aerospace Engineering Dept. Worcester Polytechnic Institute WorcesterMA01609 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

A modular radio frequency plasma cathode has been designed to assess the sensitivity of performance to several geometric parameters, operating conditions, and propellants. The plasma cathode is designed to operate with argon, simulated VLEO air, and water vapor. Results of preliminary experiments with argon are reported. A global model was also developed to examine performance trends using these propellants. The global modeling results are presented for a single set of geometric parameters for the different propellants considered. Specifically, the electron density, electron temperature, neutral species temperature, electron current extracted, and current extraction cost are calculated and reported. Results from preliminary experiments are also presented comparing the ideal electron current extraction calculated using the global model with measurements. © 2025, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Propellants

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Multi-Stage Product Control Decision-Making Based on Monte Carlo and Bayesian Methods

Multi-Stage Product Control Decision-Making Based on Monte C...

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2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Tong, He Li, Linjie Lin, Shicong College of Aeronautical Engineering Beijing Polytechnic Beijing China Army Aviation Institute Beijing China

ISBN: (纸本)9798331504205

Product production control decision-making refers to the decision-making on the organization of the production process that is closely related to product production. Enterprises face a key challenge in optimizing spare parts, semi-finished products, and finished products in complex production situations involving multiple processes and components. Detecting problems while minimizing production and logistics losses is crucial. To address this, we adopt a multi-stage decision-making approach using dynamic programming optimization. Specifically, we apply sequential sampling, Monte Carlo, and Bayesian methods to infer and optimize various segments. Our decision-making method covers issues such as whether to inspect spare parts, inspect finished products, or dismantle unqualified finished products. Experimental data show that the model can simulate different combinations of inspection strategies, providing optimal decisions for multi-stage control of product quality. © 2025 IEEE.

关键词： Inspection

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Energy Efficiency Prediction of Assembled Concrete Buildings Based on Deep Recurrent Neural Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 477-490页

作者： Shen, Cun-Li Zhang, Huan-Bin Architectural Engineering Institute Chongqing Industry & Trade Polytechnic Chongqing408000 China College of Information Science Adamson University Ermita Metro Manila 1000 Philippines

Assembled concrete buildings usually have better thermal insulation and sealing properties, which can effectively reduce building energy consumption and carbon emissions during the production phase. By studying and predicting the energy-saving performance of assembled concrete buildings, it can provide designers with a more accurate assessment of energy consumption. However, the energy-saving performance of assembled concrete buildings is affected by a variety of factors, and the prediction of energy-saving needs to take into account the interactions of multiple factors, which requires a high level of model accuracy. Therefore, this work proposes a deep recurrent neural network-based energy efficiency prediction for assembled concrete buildings. First, the construction process of assembled buildings is divided into five stages according to the modelling principles. According to the characteristics of each stage, the calculation methods of carbon emissions of the five sub-parts were established, and the carbon emission factors corresponding to different subjects were sorted out, which were used as the theoretical basis of the subsequent carbon emission prediction model. Then, Antisymmetric Recursive Neural Network (ARNN) is used to build a deep learning environment for building energy efficiency prediction. Meanwhile, after analysing the working principle of the attention mechanism in deep learning, it is proposed that Global Attention Mechanism (GAM) is introduced in ARNN, so that the model pays more attention to the part of the input sequence with important information. Finally, the proposed GAM-ARNN model is compared with a variety of other prediction models through 15 assembly building case base data. The experimental results show that the proposed GAM-ARNN model is the optimal model, and the error between its predicted data and the measured data of the carbon emission factor method is 1.19%, which is significantly lower than that of other prediction models. © 20

关键词： Prediction models

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Liquid metal assistant self-propagating high-temperature synthesis of S-containing high-entropy MAX-phase materials

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材料科学技术（英文版） 2025年第6期209卷 1-8页

作者： Donglong Bai Qiang Wang Bin Deng Yang Li Ao Huang Zitong Cheng Yun Zhao Jing Li Wei Yao Jianguang Xu School of Materials and Energy Shanghai Key Laboratory of Engineering Materials Application and EvaluationShanghai Polytechnic UniversityShanghai 201209China Shanghai Thermophysical Properties Big Data Professional Technical Service Platform Shanghai Engineering Research Center of Advanced Thermal Functional MaterialsShanghai 201209China School of Materials Science and Engineering Yancheng Institute of TechnologyYancheng 224000China School of Materials and Energy Shanghai Key Laboratory of Engineering Materials Application and EvaluationShanghai Polytechnic UniversityShanghai 201209China

Due to their high-entropy effects,the high-entropy(HE)MAX-phase materials improve the comprehen-sive performance of MAX phases,opening up more possibilities for practical engineering ***,it is still challenging to obtain S-containing high-entropy MAX phases because of the high volatilization behavior of sulfur,suffering from issues such as high reaction temperature and long re-action time of traditional synthesis *** paper proposes a novel process named as liquid metal assistant self-propagating high-temperature synthesis(LMA-SHS)for efficient synthesis of high-purity S-containing high-entropy MAX-phase ***-melting-point metal(Sn or In)has been introduced into the raw mixture and melted into a liquid phase during the early stage of the SHS *** serv-ing as a"binder"between transition metal atoms of the M-site due to the negative mixing enthalpy,this liquid phase can accelerate mass and heat transfer during the SHS process,ensuring a uniform solid solution of each element and realizing the synthesis of high-purity(TiNbVZr)2SC in an extremely short *** synthesis method for high-entropy MAX-phase materials developed in this study,i.e.,LMA-SHS,showing very short reaction time,low energy consumption,high yield,and low cost,has the promise to be a general energy-and resource-efficient route towards high-purity HE materials.

关键词： High-entropy S-containing MAX phases Liquid metal LMA-SHS

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