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3D/4D additive-subtractive manufacturing of heterogeneous ceramics

Journal of Materials science & technology 2024年第34期201卷 210-221页

作者： Guo Liu Xinya Lu Xiaofeng Zhang Yan Zhao Shenghui Yi Jingjun Xu Yuqi Zhan Jianan Yin Chengcheng Feng Zhifeng Zhou Peiyu Wang Zhou Chen Yunhu He Siyao Chen Pengchao Liu Jun Zuo Yu Dai Jian Wu Sida Liu Jian Lu Centre for Advanced Structural Materials City University of Hong Kong Shenzhen Research InstituteGreater Bay Joint DivisionShenyang National Laboratory for Materials ScienceShenzhen 518057China CityU-Shenzhen Futian Research Institute Shenzhen 518045China Hong Kong Branch of National Precious Metals Material Engineering Research Center City University of Hong KongHong Kong 999077China Department of Mechanical Engineering City University of Hong KongHong Kong 999077China Department of Materials Science and Engineering City University of Hong KongHong Kong 999077China National Engineering Laboratory for Modern Materials Surface Engineering Technology&The Key Lab of Guangdong for Modern Surface Engineering Technology Institute of New MaterialsGuangdong Academy of ScienceGuangzhou 510650China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China Faculty of Materials Science Shenzhen MSU-BIT UniversityShenzhen 518100China Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China School of Physics and Materials Nanchang UniversityNanchang 330031China

The manufacturing of heterogeneous high-temperature material components is challenging for use in practical ***-dimensional(3D)printing provides solutions to programmable constructing ceramic ***,the development of heterogeneous ceramics is limited by low flexibil-ity of heterogeneity,geometrical complexity,structural resolution,manufacturing efficiency,and mate-rial *** this study,we demonstrated flexible and rapid approaches for fabricating complicated and precise heterogeneous ceramics by shape-changing(4D)or shape-keeping(3D)additive-subtractive manufacturing(ASM)*** shape-changing strategy for heterogeneous ceramics was achieved by global ceramization of heterogeneous precursors,while the shape-keeping strategy for heterogeneous ceramics was achieved by local receramization of homogeneous *** element analysis(FEA)simulations of the influence of the thermal shrinkage dominant in the shape-changing strategy on the shape deformation of heterogeneous ceramics could be valuable predictions of the experimental *** 3D/4D ASM methods are generic for high-temperature materials and extendable to metallic and dia-mond materials.

关键词： 3D/4D printing Additive-subtractive manufacturing Heterogeneous ceramics Elastomer-derived ceramics Local receramization

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Intelligent Approach for Clustering Mutations’ Nature of COVID-19 Genome

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computers, Materials & Continua 2022年第9期72卷 4453-4466页

作者： Ankur Dumka Parag Verma Rajesh Singh Anuj Bhardwaj Khalid Alsubhi Divya Anand Irene Delgado Noya Silvia Aparicio Obregon Computer Science and Engineering Women Institute of TechnologyUttarakhand248007India Chitkara University Institute of Engineering and Technology Chitkara University PunjabPunjab140401India Division of Innovation&Entrepreneurship Lovely Professional UniversityPunjab144411India Computer Science and Engineering Chandigarh UniversityPunjab140413India Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah37848Saudi Arabia Computer Science and Engineering Lovely Professional UniversityPunjab144411India Higher Polytechnic School Universidad Europea del AtlánticoSantander39011Spain Universidad Internacional Iberoamericana Campeche24560MexicoC.P Universidade Internacional do Cuanza Bairro Kaluanda BiéAngola

In December 2019,a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology *** outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9,2020,named Novel Coronavirus 2019(nCoV-2019).This nCoV-2019 is now known as *** is a big list of infections of this coronavirus which is present in the form of a big *** virus can cause several diseases that usually develop with a serious *** to the World Health Organization(WHO),2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome(SARS)and Middle East Respiratory Syndrome(MERS)coronaviruses,so COVID-19 can repeatedly change its internal genome structure to extend its *** and accurately predicting the mutational properties of the genome structure of COVID-19 can form a good leadership role in preventing and fighting against *** this research paper,an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses’complete *** method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary *** paper identifies five main clusters of mutations with k=5 as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses.

关键词： nCoV-2019 SARS-CoV-2 COVID-19 genome structure etiology COVID-19 mutations COVID-19 genomes

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Effects of term weighting approach with and without stop words removing on Arabic text classification 9

Effects of term weighting approach with and without stop wor...

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9th International Conference on Optimization and Applications, ICOA 2023

作者： Alhenawi, Esra'a Khurma, Ruba Abu Castillo, Pedro A. Arenas, Maribel G. Al-Hinawi, Ayat Mahmoud Al-Ahliyya Amman University Faculty of Information Technology Department of Software Engineering Amman Jordan Middle East University Meu Research Unit Faculty of Information Technology Department of Computer Science Amman11831 Jordan University of Granada Granada Spain Hashemite University Department of Allied Engineering Sciences Zarqa Jordan

ISBN: (纸本)9798350312546

Text classification is a technique for grouping articles into pre-defined categories. Prior to categorization, text documents have to be set up and organised in a form that is compatible with the data mining techniques. That has produced many term weighting techniques have been developed in the literature to improve the functionality of text categorization systems. This paper compares techniques for text classification based on when stop words are eliminated from the documents once and when they are not. We used an Arabic data set of 322 papers grouped into six subjects (health, politics, science, education, agriculture and finance) to research which is the influence of prior weighting of features approaches on classification outcomes with regard to accuracy, recall, precision, and F-measure values. Each document subset includes 50 documents, except the health one that includes 61. We carried out four experiments and checked results getting that only for precision, the term frequency feature weighting approach with stop word removal are more or less the same than for binary method. Moreover the conclusion for accuracy, recall and F-measure is that Feature weighting approach with stop word outperforms the Binary one. Additionally, if the phrase for weighting is the same for both methods, to remove stop word increases classification accuracy for all the experiments. © 2023 IEEE.

关键词： Classification (of information)

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Model of Visualization and Analytics for Open Data (Case: Election Voters & Kids Disability Category) 1

Model of Visualization and Analytics for Open Data (Case: El...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Wibowo, Ripto Mukti Fakieh, Bahjat Ramzan, Muhammad Sher Alzahrani, Ahmed Saeed Siddiqui, Muazzam Alzahrani, Bander King Abdulaziz University Faculty of Computing and Information Technology Department of Information Systems Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Department of Computer Science Saudi Arabia Indiana University Luddy School of Informatics Computing and Engineering Bloomington United States

ISBN: (纸本)9781665472753

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities. © 2023 IEEE.

关键词： Open Data

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MatrixWorld: A Pursuit-Evasion Platform for Safe Multi-agent Coordination and Autocurricula

arXiv

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arXiv 2023年

作者： Sun, Lijun Chang, Yu-Cheng Lyu, Chao Lin, Chin-Teng Shi, Yuhui Department of Computer Science and Engineering Southern University of Science and Technology China Faculty of Engineering and Information Technology University of Technology Sydney Australia College of Computer and Information Science Southwest University China

Multi-agent reinforcement learning (MARL) achieves encouraging performance in solving complex tasks. However, the safety of MARL policies is one critical concern that impedes their real-world applications. Popular multi-agent benchmarks focus on diverse tasks yet provide limited safety support. Therefore, this work proposes a safety-constrained multi-agent environment: MatrixWorld5, based on the general pursuit-evasion game. Particularly, a safety-constrained multi-agent action execution model is proposed for the software implementation of safe multi-agent environments based on diverse safety definitions. It (1) extends the vertex conflict among homogeneous / cooperative agents to heterogeneous / adversarial settings, and (2) proposes three types of resolutions for each type of conflict, aiming at providing rational and unbiased feedback for safe MARL. Besides, MatrixWorld is also a lightweight co-evolution framework for the learning of pursuit tasks, evasion tasks, or both, where more pursuit-evasion variants can be designed based on different practical meanings of safety. As a brief survey, we review and analyze the co-evolution mechanism in the multi-agent setting, which clearly reveals its relationships with autocurricula, self-play, arms races, and adversarial learning. Thus, MatrixWorld can also serve as the first environment for autocurricula research, where ideas can be quickly verified and well understood. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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Automatic navigation and self-driving technology in agricultural machinery: A state-of-the-art systematic review

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IEEE Access 2025年 13卷 94370-94401页

作者： Hossain, Md Sabbir Rahman, Mostafijur Rahman, Ashifur Kabir, Md Mohsin Mridha, M.F. Huang, Jie Shin, Jungpil Western Illinois University MacombIL61455 United States Washington University of Science and Technology AlexandriaVA22314 United States Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh Mälardalens University School of Innovation Design and Engineering Västerås722 20 Sweden American International University-Bangladesh Department of Computer Science and Engineering Dhaka1229 Bangladesh The University of Aizu School of Computer Science and Engineering Aizu-wakamatsu965-8580 Japan

The advent of automatic navigation and self-driving technology in agricultural machinery has transformed modern farming by enhancing efficiency and safety. This systematic review aims to evaluate the development, implementation, and impact of self-driving technology in agriculture. We synthesized recent advancements and research findings on the integration of Global Positioning System (GPS), machine learning algorithms, and sensor technologies in agricultural machinery. Key applications of self-driving machinery in planting, harvesting, and field monitoring were explored, with findings showing improvements in farm productivity, reduced operational costs, and enhanced worker safety. Despite the benefits, challenges such as technological barriers, regulatory issues, and infrastructure limitations remain. This review informs stakeholders about the future potential of self-driving technology in promoting sustainable and precision farming practices. © 2013 IEEE.

关键词： Harvesters

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Powering IoT Systems with 5G Wireless Communication: A Comprehensive Review 8

Powering IoT Systems with 5G Wireless Communication: A Compr...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Palarimath, Suresh Pyingkodi, M. Thenmozhi, K. Maqsood, Mohammed Salam, Mohammed Abdul Palarimath, Roopa Devi University of Techonology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Governors State University Information Technology Department University Park United States Mansarovar Global University Faculty of Computer Science Bhopal India

ISBN: (纸本)9798350396638

In today's modern world, IoT plays an important and multifaceted function in all industries. 5th Generation Wireless Systems - 5G is the primary technical platform on which the Internet of Things plays a major role in smart technologies. Incorporating 5G technology into the architecture of an IoT system is now simple. Using plug-and-play technology, remote access to configuration and control is possible. Smart technology always results in faster data transfer rates, more bandwidth, greater capacity, lower latency, and a quicker output response. Based on this notion, IoT brings about a dramatic shift in heterogeneous networks. So, there is a need to implement 5G-IoT technologies. In the age of the Internet of Things, the fifth-generation (5G) cellular networks give the smart and complex technical environment. 5G Cellular Networks offer great growth potential for IoT technologies. This article gives a thorough study of the role of 5G Cellular Networks in the expansion of IoT technologies across all industries. Since the inclusion of renewable energy emerges as a new trend in the digital world, 5G is very beneficial in the age of the Internet of Things. To achieve the link between 'Things and People,' 'Things and Things,' and 'People and People' in power Systems, it is necessary to combine IoT with 5G in power systems to a high degree of success in various power-based industry-based systems. This article demonstrates a smart scenario and provides a thorough analysis of "how 5G drives the IoT System intelligently in every way and how IoT affects as the "most promising technology"with this smart 5G Wireless Communication System."In addition, the IoT era 5G application possibilities were demonstrated. © 2023 IEEE.

关键词： Internet of things

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Investigation of Task Scheduling in Cloud Computing by using Imperialist Competitive and Crow Search Algorithms

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Procedia computer science 2023年 230卷 879-889页

作者： Jayavadivel Ravi Rajkumar N Viji C Loganathan D K S N Sushma Stalin M Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance University Bangalore Karnataka India Department of Information Science & Engineering Cambridge Institute of Technology Bangalore Karnataka India Research Scholar School of Computer Science and Engineering REVA University Bangalore Karnataka India

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner.... 详细信息

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner. The main function of the data processing server is to show how many users are being investigated and provide accurate, efficient and efficient information. Important Algorithm Editing Players in the Cloud Defines the virtual machine (VM) required for this purpose. The role of editing the algorithm reduces the effect of the schedule. Naturally affected algorithms have recently been used to quickly comply with the recent traditional algorithms. Given many consumers of many cloud computing services, many researchers may have a serious explanation that many researchers take and discuss the complex themes of NP. Some sites use imperialist algorithms (ICA) and birds. The purpose of the proposed project is to develop intelligent scientific algorithms that focus on the integration of ICA and CSA to obtain data. CSA is concentrated on the corner of food habits. The crow is looking for his friends to get enough food for today's food. This will help the CSA find suitable VMs for these machines and complete the equipment. Cloud Sim is used to calculate CSA output with minmin and ant algorithms. The simulation results show that the CSA is extra powerful than the MinMin and Ant procedures .

关键词： Crow search algorithm Imperialist competitive algorithm. Task scheduling Nature-inspired and Cloud computing

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Comparative Analysis of Convolutional Neural Networks Performance in Remote Sensing for Solar Panel Detection

Comparative Analysis of Convolutional Neural Networks Perfor...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Seyedeh Fatemeh Khakzad Tahere Taghizade Firozjaee Reza Mortazavi Erfan Khoshnevisan Sina Khoshnevisan Faculty of Civil Engineering Shahrood University of Technology Shahrood Iran School of Engineering Damghan University Damghan Iran Faculty of Computer Science Shahrood University of Technology Shahrood Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

As the importance of solar energy continues to grow as a pivotal renewable resource, the accurate identification of solar panels within satellite imagery becomes indispensable for both resource assessment and strategic planning. The increasing utilization of solar power signifies a shift towards cleaner and renewable energy sources and serves as a critical measure in mitigating the adverse impacts of climate change, which is predominantly fueled by the widespread use of fossil fuels. This study employs a high-quality, manually labeled dataset comprising 250 satellite images of solar panels in California. Four convolutional neural network (CNN) architectures—DeepLabV3+ VGG16, ResU-Net, UNet3+, and SegNet—are evaluated for their efficacy in detecting solar panels. The models were trained for 50 epochs using a learning rate of 0.0003, the Binary Cross-Entropy loss function, and the Adam optimizer. Performance was assessed using the Dice Coefficient, Recall, and Precision metrics, with DeepLabV3+VGG16 achieving the highest performance with a Dice Coefficient of 94.62% and Recall of 94.23%. These findings provide valuable insights for researchers in selecting appropriate network architectures for accurate semantic segmentation tasks and effective solar panel detection and estimation.

关键词： Training Climate change Accuracy Semantic segmentation Solar energy Satellite images Solar panels Convolutional neural networks Reliability Testing Climate change Solar panels

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

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IEEE Network 2025年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab Nanyang Technological University College of Computing and Data Science Singapore Guangdong University of Technology School of Automation Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia National University of Singapore College of Design and Engineering Department of Electrical and Computer Engineering Singapore

The rapid development of generative artificial intelligence (AI) technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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