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Circular Economy and Sustainability of the Clothing and Textile Industry

Materials Circular Economy 2021年第1期3卷 1-9页

作者： Chen, Xuandong Memon, Hifza A. Wang, Yuanhao Marriam, Ifra Tebyetekerwa, Mike College of Civil and Architecture Engineering Guangxi Key Laboratory of New Energy and Building Energy Saving and Guangxi Engineering Research Center for Solid Waste Resources Development and Application Guilin University of Technology Guilin People’s Republic of China Department of Textile Engineering Mehran University of Engineering and Technology Jamshoro Pakistan School of Mechanical Medical and Process Engineering Queensland University of Technology (QUT) Brisbane Australia School of Engineering College of Engineering and Computer Science The Australian National University Canberra Australia

Textiles are essential to humans in a variety of ways, especially clothing. However, the speed at which they end up in landfills is astonishing (one garbage truck per second), posing a severe risk to the environment, if the trend continues. Governments and responsible organizations are starting to make calls to different stakeholders to redesign the textile chain from linear to circular economy. In this perspective, we highlight some of the possible approaches to be undertaken including the need for the creation of renewable raw materials sources, rethinking production, maximum use and reuse of textile products, reproduction, and recycling strategies, redistribution of textiles to new and parallel markets, and improvising means to extend the textiles lifetime.

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Heliocentric Distance Dependence of Zodiacal Light Observed by Hayabusa2#

arXiv

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arXiv 2023年

作者： Tsumura, Kohji Matsuura, Shuji Sano, Kei Iwata, Takahiro Yano, Hajime Kitazato, Kohei Takimoto, Kohji Yamada, Manabu Morota, Tomokatsu Kouyama, Toru Hayakawa, Masahiko Yokota, Yasuhiro Tatsumi, Eri Matsuoka, Moe Sakatani, Naoya Honda, Rie Kameda, Shingo Suzuki, Hidehiko Cho, Yuichiro Yoshioka, Kazuo Ogawa, Kazunori Shirai, Kei Sawada, Hirotaka Sugita, Seiji Department of Natural Sciences Faculty of Science and Engineering Tokyo City University Tokyo158-8557 Japan Department of Physics School of Science and Technology Kwansei Gakuin University Hyogo669-1337 Japan Department of Space Systems Engineering School of Engineering Kyushu Institute of Technology Fukuoka804-8550 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Kanagawa252-5210 Japan Department of Space and Astronautical Science The Graduate University of Advanced Studies Kanagawa240-0193 Japan Department of Computer Science and Engineering The University of Aizu Fukushima965-8580 Japan Planetary Exploration Research Center Chiba Institute of Technology Chiba275-0016 Japan Department of Earth and Planetary Science University of Tokyo Tokyo113-0033 Japan Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Tokyo135-0064 Japan Instituto de Astrofisica de Canarias University of La Laguna Tenerife Spain Geological Survey of Japan National Institute of Advanced Industrial Science and Technology Ibaraki305-8567 Japan Center for Data Science Ehime University Ehime790-8577 Japan Rikkyo University Tokyo171-8501 Japan Meiji University Kanagawa214-8571 Japan Department of Complexity Science and Engineering University of Tokyo Chiba277-8561 Japan JAXA Space Exploration Center Japan Aerospace Exploration Agency Kanagawa252-5210 Japan Kobe University Hyogo657-8501 Japan

Zodiacal light (ZL) is sunlight scattered by interplanetary dust particles (IDPs) at optical wavelengths. The spatial distribution of IDPs in the Solar System may hold an important key to understanding the evolution of the Solar System and material transportation within it. The number density of IDPs can be expressed as n(r) ∼ r−α, and the exponent α ∼ 1.3 was obtained by previous observations from interplanetary space by Helios 1/2 and Pioneer 10/11 in the 1970s and 1980s. However, no direct measurements of α based on ZL observations from interplanetary space outside Earth’s orbit have been performed since then. Here, we introduce initial results for the radial profile of the ZL at optical wavelengths observed over the range 0.76-1.06 au by ONC-T aboard the Hayabusa2# mission in 2021-2022. The ZL brightness we obtained is well reproduced by a model brightness, although there is a small excess of the observed ZL brightness over the model brightness at around 0.9 au. The radial power-law index we obtained is α = 1.30 ± 0.08, which is consistent with previous results based on ZL observations. The dominant source of uncertainty arises from the uncertainty in estimating the diffuse Galactic light (DGL). © 2023, CC BY.

关键词： Orbits

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Evaluation of OpenAI o1: Opportunities and Challenges of AGI

arXiv

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arXiv 2024年

作者： Zhong, Tianyang Liu, Zhengliang Pan, Yi Zhang, Yutong Zhou, Yifan Liang, Shizhe Wu, Zihao Lyu, Yanjun Shu, Peng Yu, Xiaowei Cao, Chao Jiang, Hanqi Chen, Hanxu Li, Yiwei Chen, Junhao Hu, Huawen Liu, Yihen Zhao, Huaqin Xu, Shaochen Dai, Haixing Zhao, Lin Zhang, Ruidong Zhao, Wei Yang, Zhenyuan Chen, Jingyuan Wang, Peilong Ruan, Wei Wang, Hui Zhao, Huan Zhang, Jing Ren, Yiming Qin, Shihuan Chen, Tong Li, Jiaxi Zidan, Arif Hassan Jahin, Afrar Chen, Minheng Xia, Sichen Holmes, Jason Zhuang, Yan Wang, Jiaqi Xu, Bochen Xia, Weiran Yu, Jichao Tang, Kaibo Yang, Yaxuan Sun, Bolun Yang, Tao Lu, Guoyu Wang, Xianqiao Chai, Lilong Li, He Lu, Jin Sun, Lichao Zhang, Xin Ge, Bao Hu, Xintao Zhang, Lian Zhou, Hua Zhang, Lu Zhang, Shu Liu, Ninghao Jiang, Bei Kong, Linglong Xiang, Zhen Ren, Yudan Liu, Jun Jiang, Xi Bao, Yu Zhang, Wei Li, Xiang Li, Gang Liu, Wei Shen, Dinggang Sikora, Andrea Zhai, Xiaoming Zhu, Dajiang Zhang, Tuo Liu, Tianming Department of Mathematical and Statistical Sciences University of Alberta Edmonton Canada School of Computing University of Georgia GA United States Institute of Medical Research Northwestern Polytechnical University Xi'An China College of Arts and Sciences University of Georgia Athens United States Institute of Plant Breeding Genetics & Genomics University of Georgia AthensGA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States The Lamar Dodd School of Art University of Georgia GA United States School of Computer Science Northwestern Polytechnical University Xi'An China School of Automation Northwestern Polytechnical University Xi'An China University of California Los AngelesCA United States Department of Radiology The Second Xiangya Hospital Central South University Changsha China Clinical Research Center for Medical Imaging in Hunan Province Changsha China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guanghua School of Management Peking University Beijing China Department of Radiation Oncology Mayo Clinic PhoenixAZ United States Second Language Acquisition and Teaching University of Arizona TucsonAZ United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China The First Hospital of Hebei Medical University Hebei Shijiazhuang China School of Computer and Cyber Sciences Augusta University AugustaGA United States School of Physics and Information Technology Shaanxi Normal University Xi'An China School of Future Technology South China University of Technology Guangzhou China Department of Radiology and BRIC University of North Carolina Chapel HillNC United States Department of Educational Psychology University of Georgia AthensGA United States Johns Hopkins University BaltimoreMD United States School of Architecture Tsinghua Universit

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: • 83.3% success rate in solving complex competitive programming problems, surpassing many human experts. • Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. • 100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. • Advanced natural language inference capabilities across general and specialized domains like medicine. • Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. • Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. • Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. • Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence. This evaluation not only highlights o1-preview's current strengths and limitations but also identifies crucial areas for future development, including multi-modal integratio

关键词： Emotion Recognition

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Conversation to Automation in Banking Through Chatbot Using Artificial Machine Intelligence Language

Conversation to Automation in Banking Through Chatbot Using ...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Sasha Fathima Suhel Vinod Kumar Shukla Sonali Vyas Ved Prakash Mishra Bachelor of Science Information Technology Amity University Dubai UAE Department of Engineering and Architecture Amity University Dubai UAE Department of Computer Application University of Petroleum and Energy Studies Dehradun India

ISBN: (数字)9781728170169

ISBN: (纸本)9781728170176

Artificial Machine Intelligence is a very complicated topic. It involves creating machines that are capable of simulating knowledge. This paper examines some of the latest AI patterns and activities and then provides alternative theory of change in some of the popular and widely accepted postulates of today. Based on basic A.I. (Artificial Intelligence) structuring and working for this, System-Chatbots are made (or chatter bots). The paper shows that A.I is ever improving. As of now there isn't enough information on A.I. however this paper provides a new concept which addresses machine intelligence and sheds light on the potential of intelligent systems. The rise of chatbots in the finance sector is the latest disruptive force that has changed the way customers interact. In the banking industry, the introduction of Artificial Intelligence has driven chatbots and changed the face of the interaction between bank and customers. The banking sector plays an important role in development into any country. It also explores the existing usability of chatbot to assess whether it can fulfill customers ever-changing needs.

关键词： Business Machine learning Online banking Natural language processing

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Proactive user engagement via friendly survey and data-driven methodologies

Proactive user engagement via friendly survey and data-drive...

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International Conference on Data Engineering Workshops

作者： Paolo Bethaz Riccardo Callà Tania Cerquitelli Arianna Montorsi Claudia De Giorgi Department of Control and Computer Engineering Politecnico di Torino Turin Italy Department of Applied Science and Technology Politecnico di Torino Turin Italy Department of Architecture and Design Politecnico di Torino Turin Italy

ISBN: (数字)9781728142661

ISBN: (纸本)9781728142678

In this paper, we present CAROL (unConventionAl suRvey methOdoLogy) to collect users' opinions, to analyze them effectively, and to graphically provide interesting summaries in near-real-time. CAROL has been designed to collect citizens' opinions, needs, and a perception that can be very useful to redesign cities and drive the innovation process. To this aim, particular importance is given to the architecture implemented to collect the data, which involves sending voice messages instead of text, allowing users greater freedom of expression. Moreover, to make the survey more friendly, an initial analysis is done in real-time thanks to an intuitive dashboard, which shows the first results in the form of graphs and charts, updated after each message. Finally, a more in-depth analysis can be carried out at the end of the survey, thanks to text-mining techniques adapted to the context. As use case, the proposed methodology has been tested in a public event organized by Politecnico di Torino and addressed to female students, with the aim to collect their ideas about new services/opportunities that can be offered to support women in STEM.

关键词： Real-time systems Servers computer architecture Technological innovation Data visualization Smart cities

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A preliminary approach to composition classification of ultra-high energy cosmic rays 3rd

A preliminary approach to composition classification of ultr...

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3rd International Conference on Applied Physics, System Science and computers, APSAC 2018

作者： Guillén, Alberto Todero, Carlos Martínez, José Carlos Herrera, Luis Javier Computer Architecture and Technology Department University of Granada Granada Spain University of São Paulo Escola de Engenharia de Lorena São Paulo Brazil

ISBN: (纸本)9783030215064

The study of cosmic rays remains as one of the most challenging research fields in Physics. There are several reasons why it is a hot topic as it can reveal information on Galaxy life-cycle and the mystery of how these rays arrive to the Earth with such high energy values (beyond 1020 eV). The work presented in this paper tries to answer a first question regarding to the type of primary particle that hits the atmosphere. Starting from a subset of known variables, we will try to perform a classification using Deep Learning models. We also compare two different approaches: classical classification and continuous output (like in regression). The results obtained are quite encouraging as we were able to obtain high classification accuracy even with a very reduced subset of variables. © Springer Nature Switzerland AG 2019.

关键词： Cosmic rays

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Special Section Guest Editorial: Advances in Breast Imaging

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Journal of Medical Imaging 2019年第3期6卷 031401-031401页

作者： Krupinski, Elizabeth A. Astley, Sue Marti, Robert Tornai, Martin Zwiggelaar, Reyer Emory University Department of Radiology and Imaging Sciences AtlantaGA United States University of Manchester Division of Informatics Imaging and Data Science Manchester United Kingdom University of Girona Department of Architecture and Computer Technology Girona Spain Duke University Departments of Radiology and Biomedical Engineering DurhamNC United States Aberystwyth University Department of Computer Science Aberystwyth United Kingdom

This guest editorial introduces the special section on Advances in Breast Imaging.

关键词： Digital breast tomosynthesis Breast imaging Breast cancer Breast Cancer Digital mammography computer simulations Mammography Optical filters Radiology

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Benchmarking SpMV Methods on Many-Core Platforms 1st

Benchmarking SpMV Methods on Many-Core Platforms

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1st International Symposium on Benchmarking, Measuring, and Optimization, Bench 2018

作者： Xie, Biwei Jia, Zhen Bao, Yungang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Computer Science Princeton University Princeton United States University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030328122

SpMV is an essential kernel existing in many HPC and data center applications. Meanwhile, the emerging many-core hardware provides promising computational power, and is widely used for acceleration. Many methods and formats have been proposed aiming at better performance of SpMV on many-core platforms. However, there is still lack of a comprehensive comparison of SpMV methods to show their performance difference on sparse matrices with various sparse patterns. Moreover, there is still no systematic work to bridge the gap between SpMV performance and sparse pattern. In this paper, we investigate the performance of 27 SpMV methods with 1500+ sparse matrices on two many-core platforms: Intel Xeon Phi (Knights Landing 7250) and Nvidia GPGPU (Tesla M40). Our work shows that no single SpMV methods is optimal for all sparse patterns, but some methods can achieve approximately the best performance on most sparse matrices. We further select 13 features to describe the sparse pattern and analyze their correlations to the performance of each SpMV method. Our observations should help other researchers and practitioners to better understand the SpMV performance and provide implications to guide the selection of suitable SpMV method. © 2019, Springer Nature Switzerland AG.

关键词： Benchmarking

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Corrigendum to “An analysis of urban sprawl growth and prediction using remote sensing and machine learning techniques” [J. S. Am. Earth Sci. Volume 142 , 15 August 2024, 104988]

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Journal of South American Earth Sciences 2025年 152卷

作者： Alanoud Al Mazroa Mashael Maashi Fadoua Kouki Kamal M. Othman Nahla Salih Mohamed Ahmed Elfaki Sabarunisha Begum S Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University (PNU) P.O. Box 84428 Riyadh 11671 Saudi Arabia Department of Software Engineering College of Computer and Information Sciences King Saud University Po box 103786 Riyadh 11543 Saudi Arabia Department of Financial and Banking Sciences Applied College at Muhail Aseer King Khalid University Saudi Arabia Department of Electrical Engineering College of Engineering and Islamic Architecture Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Applied College Imam Abdulrahman Bin Faisal University Saudi Arabia Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia Department of Biotechnology P.S.R. Engineering College Sivakasi 626140 India

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Research on the surface water quality in the Huaihe River Basin and the gensis based on multivariate statistical analysis

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Scientific reports 2025年第1期15卷 19763页

作者： Shuzhen Feng Chaokai Zhang Jiaheng Yan Ke Ren Ningbo Peng Wei Jiang Shouhua Liu Nanjing Vocational Institute of Railway Technology Nanjing 210031 China. Faculty of Architecture and Civil Engineering Huaiyin Institute of Technology Huaian 223003 China. HYIT-CREC Institute of Science and Technology for Conservation of Cultural Heritage Huaiyin Institute of Technology Huaian 223003 China. Key Scientific Research Base of the State Administration of Cultural Heritage for Integrated Technology and Application of Grotto Cultural Heritage Protection Engineering Department Chengdu 610036 China. Faculty of Architecture and Civil Engineering Huaiyin Institute of Technology Huaian 223003 China. jiahengyan@***. HYIT-CREC Institute of Science and Technology for Conservation of Cultural Heritage Huaiyin Institute of Technology Huaian 223003 China. jiahengyan@***. Key Scientific Research Base of the State Administration of Cultural Heritage for Integrated Technology and Application of Grotto Cultural Heritage Protection Engineering Department Chengdu 610036 China. jiahengyan@***. Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian 223003 China.

The Huaihe River Basin (HRB) is an important water system in eastern China, and its water quality has received widespread attention. This study explored the latest spatial variation patterns of surface water quality in the HRB to cope with the increasingly severe challenges of water resource management. By integrating multidimensional water quality data from surface water monitoring stations, including dissolved oxygen (DO), chemical oxygen demand (COD and COD), biochemical oxygen demand (BOD), ammonia nitrogen (NH-N), total phosphorus (TP), and total nitrogen (TN), this study utilized a cluster analysis technique to categorize the water quality data and reveal changes in the geographic variability of water quality. Among the 382 monitoring stations in the HRB, 258 stations had TN content lower than Class V, which was the highest among all monitoring indicators. The entropy weight method used to assess the comprehensive water quality showed that there were 157 and 163 monitoring stations belonging to Class III and IV, respectively, and stations with poor water quality were distributed downstream in the river network and estuary area. Correlation and cluster analyses indicated that agricultural and organic matter pollution were the two main factors affecting water quality in the HRB, particularly in the downstream area, and the high loading of nutrient salts such as TP and NH-N reflected the significant influence of agricultural activities. In addition, the study examined the potential driving role of factors such as topography, geomorphology, and human activities on water quality changes and visualized the relationship between water quality class and cluster categories through spatial distribution maps and Sankey diagrams to clarify the regional patterns of water quality problems.

关键词： Agricultural pollution Cluster analysis Huaihe River Basin Spatial change rules Surface water quality

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