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Zhao B, Yu Z, Wang H, Shuai C, Qu S, Xu M. Data Science Applications in Circular Economy: Trends, Status, and Future. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6457-6474. [PMID: 38568682 DOI: 10.1021/acs.est.3c08331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights. Despite the exciting scientific advances in this field, there still lacks a comprehensive review on this topic to summarize past achievements, synthesize knowledge gained, and navigate future research directions. In this paper, we try to summarize how DS accelerated the transition to CE. We conducted a critical review of where and how DS has helped the CE transition with a focus on four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency and optimizing product design, (3) extending product lifetime through repair, and (4) facilitating waste reuse and recycling. We also introduced the limitations and challenges in the current applications and discussed opportunities to provide a clear roadmap for future research in this field.
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Affiliation(s)
- Bu Zhao
- School for Environment and Sustainability, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Zongqi Yu
- College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Hongze Wang
- School of Professional Studies, Columbia University, New York, New York 10027, United States
| | - Chenyang Shuai
- School of Management Science and Real Estate, Chongqing University, Chongqing, 40004, China
| | - Shen Qu
- School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China
- Center for Energy & Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China
| | - Ming Xu
- School of Environment, Tsinghua University, Beijing, 100084, China
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Li Z, Deng Q, Liu P, Bai J, Gong Y, Yang Q, Ning J. An intelligent identification and classification system of decoration waste based on deep learning model. WASTE MANAGEMENT (NEW YORK, N.Y.) 2024; 174:462-475. [PMID: 38113671 DOI: 10.1016/j.wasman.2023.12.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 12/04/2023] [Accepted: 12/11/2023] [Indexed: 12/21/2023]
Abstract
Efficient sorting and recycling of decoration waste are crucial for the industry's transformation, upgrading, and high-quality development. However, decoration waste can contain toxic materials and has greatly varying compositions. The traditional method of manual sorting for decoration waste is inefficient and poses health risks to sorting workers. It is therefore imperative to develop an accurate and efficient intelligent classification method to address these issues. To meet the demand for intelligent identification and classification of decoration waste, this paper applied the deep learning method You Only Look Once X (YOLOX) to the task and proposed an identification and classification framework of decoration waste (YOLOX-DW framework). The proposed framework was validated and compared using a multi-label image dataset of decoration waste, and a robot automatic sorting system was constructed for practical sorting experiments. The research results show that the proposed framework achieved a mean average precision (mAP) of 99.16 % for different components of decoration waste, with a detection speed of 39.23 FPS. Its classification efficiency on the robot sorting experimental platform reached 95.06 %, indicating a high potential for application and promotion. This provides a strategy for the intelligent detection, identification, and classification of decoration waste.
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Affiliation(s)
- Zuohua Li
- School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Shenzhen 518055, China
| | - Quanxue Deng
- School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Shenzhen 518055, China.
| | - Peicheng Liu
- School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Shenzhen 518055, China
| | - Jing Bai
- The Institute for Sustainable Development, Macau University of Science and Technology, Macau 999078, China
| | - Yunxuan Gong
- School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Shenzhen 518055, China
| | - Qitao Yang
- School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Shenzhen 518055, China
| | - Jiafei Ning
- School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen 518055, China; Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Shenzhen 518055, China
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Jakšić Z, Devi S, Jakšić O, Guha K. A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics. Biomimetics (Basel) 2023; 8:278. [PMID: 37504166 PMCID: PMC10807478 DOI: 10.3390/biomimetics8030278] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 07/29/2023] Open
Abstract
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.
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Affiliation(s)
- Zoran Jakšić
- Center of Microelectronic Technologies, Institute of Chemistry, Technology and Metallurgy, National Institute of the Republic of Serbia University of Belgrade, 11000 Belgrade, Serbia;
| | - Swagata Devi
- Department of Electronics and Communication Engineering, B V Raju Institute of Technology Narasapur, Narasapur 502313, India;
| | - Olga Jakšić
- Center of Microelectronic Technologies, Institute of Chemistry, Technology and Metallurgy, National Institute of the Republic of Serbia University of Belgrade, 11000 Belgrade, Serbia;
| | - Koushik Guha
- Department of Electronics and Communication Engineering, National Institute of Technology Silchar, Silchar 788010, India;
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