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Life cycle environmental impacts of the electrothermal magnesium production: a comparative assessment with the Pidgeon and electrolysis methods

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Journal of Material Cycles and Waste Management 2025年 1-12页

作者： Mohanasundaram, Madan Vasan, A. Keerthi Dhopte, Shilpa M. Sheikh, Rehan R. Seth, Disilwa Meshram, Rohit B. CSIR-National Metallurgical Laboratory Jamshedpur India Computer Science and Engineering Department MIT School of Computing MIT Art Design and Technology University Pune India

The demand for magnesium is steadily progressing in the fields of structural and non-structural applications, but especially in automobiles, because its overall weight advantage results in increased fuel efficiency. The primary goal of the current study is to quantify cradle-to-gate environmental burdens associated with the developed electrothermal process for 1 ton magnesium production using GaBi software. The developed process is compared with the Pidgeon process of China and the electrolysis process of Israel and India. The collected data is analysed using the CML 2001 method. The primary impacts include abiotic depletion potential fossil of about 7.93E04 MJ, global warming potential of about 7.82E03 kg CO2 eq., human toxicity potential of about 2.87E03 kg DCB eq., and marine aquatic ecotoxicity potential of 1.11E07 kg DCB eq. Few insignificant impacts, such as an acidification potential of 96.7 kg SO2 eq., a eutrophication potential of 4.15 kg phosphate eq., and a terrestrial ecotoxicity potential of 21.1 kg DCB eq., are also observed. Electricity contributes approximately 98% to the total environmental impacts across all categories. The life cycle analysis to produce primary magnesium via the Pidgeon process in China led to higher environmental damage potential compared to the developed electrothermal process and electrolysis process. The renewable energy sources and recycling can significantly reduce the environmental impact of primary magnesium production, making it more eco-friendly material.

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LAMeTA: Intent-Aware Agentic Network Optimization via a Large AI Model-Empowered Two-Stage Approach

arXiv

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arXiv 2025年

作者： Liu, Yinqiu Liu, Guangyuan Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Sun, Geng Xiong, Zehui Han, Zhu College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Technology Jilin University China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering University of Houston Houston United States Department of Computer Science and Engineering Kyung Hee University Korea Republic of

Nowadays, Generative AI (GenAI) reshapes numerous domains by enabling machines to create content across modalities. As GenAI evolves into autonomous agents capable of reasoning, collaboration, and interaction, they are increasingly deployed on network infrastructures to serve humans automatically. This emerging paradigm, known as the agentic network, presents new optimization challenges due to the demand to incorporate subjective intents of human users expressed in natural language. Traditional generic Deep Reinforcement Learning (DRL) struggles to capture intent semantics and adjust policies dynamically, thus leading to suboptimality. In this paper, we present LAMeTA, a Large AI Model (LAM)-empowered Two-stage Approach for intent-aware agentic network optimization. First, we propose Intent-oriented Knowledge Distillation (IoKD), which efficiently distills intent-understanding capabilities from resource-intensive LAMs to lightweight edge LAMs (E-LAMs) to serve end users. Second, we develop Symbiotic Reinforcement Learning (SRL), integrating E-LAMs with a policy-based DRL framework. In SRL, E-LAMs translate natural language user intents into structured preference vectors that guide both state representation and reward design. The DRL, in turn, optimizes the generative service function chain composition and E-LAM selection based on real-time network conditions, thus optimizing the subjective Quality-of-Experience (QoE). Extensive experiments conducted in an agentic network with 81 agents demonstrate that IoKD reduces mean squared error in intent prediction by up to 22.5%, while SRL outperforms conventional generic DRL by up to 23.5% in maximizing intent-aware QoE. © 2025, CC BY.

关键词： Deep reinforcement learning

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Robotic pendant drop:containerless liquid for μs-resolved,AI-executable XPCS

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Light(science & Applications) 2023年第9期12卷 1857-1866页

作者： Doga Yamac Ozgulbas Don Jensen Jr. Rory Butler Rafael Vescovi Ian T.Foster Michael Irvin Yasukazu Nakaye Miaoqi Chu Eric M.Dufresne Soenke Seifert Gyorgy Babnigg Arvind Ramanathan Qingteng Zhang Data Science and Learning Division Argonne National LaboratoryLemontIL 60439USA X-ray Science Division Argonne National LaboratoryLemontIL 60439USA Departement of Computer Science University of Chicago5801 S Ellis AveChicagoIL 60637USA XRD Design and Engineering Department Rigaku Corporation 3-9-12 Matsubara-choAkishima-shiTokyo 196-8666Japan Bioscience Division Argonne National LaboratoryLemontIL 60439USA

The dynamics and structure of mixed phases in a complex fluid can significantly impact its material properties,such as ***-angle X-ray Photon Correlation Spectroscopy(SA-XPCS)can probe the spontaneous spatial fluctuations of the mixed phases under various in situ environments over wide spatiotemporal ranges(10−6–103 s/10−10–10−6 m).Tailored material design,however,requires searching through a massive number of sample compositions and experimental parameters,which is beyond the bandwidth of the current coherent X-ray *** 3.7-μs-resolved XPCS synchronized with the clock frequency at the Advanced Photon Source,we demonstrated the consistency between the Brownian dynamics of~100 nm diameter colloidal silica nanoparticles measured from an enclosed pendant drop and a sealed *** electronic pipette can also be mounted on a robotic arm to access different stock solutions and create complex fluids with highly-repeatable and precisely controlled composition *** closed-loop,AI-executable protocol is applicable to light scattering techniques regardless of the light wavelength and optical coherence,and is a first step towards high-throughput,autonomous material discovery.

关键词： resolved sealed mounted

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Feature-based Intrusion Detection System with Support Vector Machine 1

Feature-based Intrusion Detection System with Support Vector...

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1st IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2022

作者： Khodaskar, Manish Medhane, Darshan Ingle, Rajesh Buchade, Amar Khodaskar, Anuja Pune Institute of Computer Technology Dept. of Information Technology Pune India MVPS's K.B.T. College of Engineering Dept. of Computer Engineering Nashik India Dept. of Computer Science & Engineering Naya Raipur India Mit School of Engineering Mit Art Design and Technology University Department of Computer Science & Engineering Pune India Sipna College of Engineering and Technology Dept. of Computer Engineering Amaravati India

ISBN: (数字)9781665428323

ISBN: (纸本)9781665428323

Today billions of people are accessing the internet around the world. There is a need for new technology to provide security against malicious activities that can take preventive/ defensive actions against constantly evolving attacks. A new generation of technology that keeps an eye on such activities and responds intelligently to them is the intrusion detection system employing machine learning. It is difficult for traditional techniques to analyze network generated data due to nature, amount, and speed with which the data is generated. The evolution of advanced cyber threats makes it difficult for existing IDS to perform up to the mark. In addition, managing large volumes of data is beyond the capabilities of computer hardware and software. This data is not only vast in scope, but it is also moving quickly. The system architecture suggested in this study uses SVM to train the model and feature selection based on the information gain ratio measure ranking approach to boost the overall system's efficiency and increase the attack detection rate. This work also addresses the issue of false alarms and trying to reduce them. In the proposed framework, the UNSW-NB15 dataset is used. For analysis, the UNSW-NB15 and NSL-KDD datasets are used. Along with SVM, we have also trained various models using Naive Bayes, ANN, RF, etc. We have compared the result of various models. Also, we can extend these trained models to create an ensemble approach to improve the performance of IDS. © 2022 IEEE.

关键词： Support vector machines

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Deep Learning-Based ECG Analysis for Anomaly Detection and Classification Using DCNN 1

Deep Learning-Based ECG Analysis for Anomaly Detection and C...

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1st International Conference on Emerging Technologies and Innovation for Sustainability, EmergIN 2024

作者： Pasha, Moghal Yaseen Sharma, Nitin Priyanka Verma, Vipul Kumar Choudhary, Amar Sharma, Rahul Department of CSE-DS CVR College of Engineering Hyderabad India Department of Information Technology Graphic Era Hill University Ajay Kumar Garg Engineering College Ghaziabad India Department of Computer Applications Chandigarh University Gharuan India Department Computer Science and Engineering United College Of Engineering & Research Prayagraj India Department Electronics and Communication Engineering Alliance College of Engineering and Design (ACED) Alliance University Bengaluru India Department of Computer Science and Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (纸本)9798350391268

This investigation shows how effective deep learning-based Electrocardiogram (ECG) analysis for anomaly detection and classification can be realized in an IoT-enabled health monitoring system, employing Deep Convolutional Neural Networks (DCNN). In this research ECG data from IoT sensors, transmitting to the cloud storage and are preprocessed for the study. Dataset contains 3200 records, and of this data 70% is used for training, whereas the rest (30%) are test instances. After that preprocessing the raw ECG signals are denoised and artefact is corrected, normalized then segmented. Different models like DCNN, LSTM networks, SVM and RF were trained & tested. The highest accuracy with the DCNN model was 96.88%, along with showing 97.20, 96.50, and 96.85 for precision, recall as well as F1 score respectively in giving us a very high understanding that it can accurately predict steriod induced cardiac anomalies. The LSTM model also had a well performance with 94.5% accuracy, and SVM about 91.2%, RF was the less predictable one achieving on average only 88.98%. Confusion matrix also attested the generalizability of DCNN with 460 true positives and 515 true negatives. This study illustrates the promise of deep learning models to model even more nuanced features from ECG signals and produce trustworthy, truthful forecasts. This research could be integrated into IoT health monitoring solutions to provide continuous real-time cardiac health surveillance, early anomaly detection and timely medical intervention resulting in better patient care and outcomes for clinical as well as home healthcare. © 2024 IEEE.

关键词： cardiac health monitoring DCNN deep learning ECG analysis IoT

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Enhancing ESG Reporting Analysis: Leveraging GPT and Resampling Methods for Improved Multi-Label Classification 4

Enhancing ESG Reporting Analysis: Leveraging GPT and Resampl...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Wu, Jun-Wei Lin, Lydia Hsiao-Mei Tsai, Richard Tzong-Han National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan National Taipei University of Technology Department of Interaction Design Taipei Taiwan Center for Gis Research Center for Humanities and Social Sciences Academia Sinica Taipei Taiwan

ISBN: (纸本)9798350360721

ESG reporting, covering environmental, social, and governance aspects, is a crucial resource for investors, companies, and governments to understand a company's value. However, the sheer volume of data and information in the reports makes it difficult to retrieve specific information on ESG. ESG-BERT models are developed to categorize the content of ESG reports based on the relevant field. However, the accurate prediction of multiple classifications is difficult due to unevenly distributed labels. To address this problem, we used a self-made assistant based on GPT to label the data and then resampling technology to adjust the balance of the data set. After the data set was balanced, we adjusted to ESG-BERT to improve its multi-label classification accuracy. We evaluated multiple resampling methods and determined the most suitable strategy for classifying ESG report content. Thus, the accuracy of ESG content analysis was improved through more refined model adjustment and preprocessing methods. The results indicated that balancing the data for improved classification ensures fairness and objectivity in distributing content for ESG reports with multiple labels. © 2024 IEEE.

关键词： Classification (of information)

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Joint Automatic Speech Recognition And Structure Learning For Better Speech Understanding

Joint Automatic Speech Recognition And Structure Learning Fo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hu, Jiliang Li, Zuchao Shen, Mengjia Ai, Haojun Li, Sheng Zhang, Jun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China National Institute of Information and Communications Technology Japan

ISBN: (纸本)9798350368741

Spoken language understanding (SLU) is a structure prediction task in the field of speech. Recently, many works on SLU that treat it as a sequence-to-sequence task have achieved great success. However, This method is not suitable for simultaneous speech recognition and understanding. In this paper, we propose a joint speech recognition and structure learning framework (JSRSL), an end-to-end SLU model based on span, which can accurately transcribe speech and extract structured content simultaneously. We conduct experiments on name entity recognition and intent classification using the Chinese dataset AISHELL-NER and the English dataset SLURP. The results show that our proposed method not only outperforms the traditional sequence-to-sequence method in both transcription and extraction capabilities but also achieves state-of-the-art performance on the two datasets. © 2025 IEEE.

关键词： Information Extraction Speech Recognition Spoken Language Understanding

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Remote Monitoring System of Aquaculture Parameters Based on STM32 10

Remote Monitoring System of Aquaculture Parameters Based on ...

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Lei, Shanrui Li, Baoying Pan, Zhenxiang Wang, Yuru Shen, Mingfei School of Information Science and Engineering Dalian Polytechnic University China School of Design Jiannan University China School of Computer Science and Technology Nanjing University of Technology China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

In order to detect aquaculture environmental conditions faster and more accurately, this paper designs an aquaculture environmental parameter detection system composed of multiple sensors with STM32 microcontroller as the core controller, One NET cloud platform as the remote monitoring platform. The main function is to use the sensor node to collect water quality parameter information in the breeding area, and use the least square method to establish temperature compensation model for the PH value. Then, the water quality information is uploaded to the One NET cloud platform through the Raspberry Pi 4B+, so as to meet the needs of users for remote real-time monitoring. Experiment shows that the system can meet the basic requirements of farmers to check the water quality parameters of the aquaculture area anytime and anywhere, and meet the expected assumptions. © 2022 IEEE.

关键词： Aquaculture

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A Survey on Resource Management in Joint Communication and Computing-Embedded SAGIN

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IEEE Communications Surveys and Tutorials 2025年第3期27卷 1911-1954页

作者： Chen, Qian Guo, Zheng Meng, Weixiao Han, Shuai Li, Cheng Quek, Tony Q. S. Harbin Institute of Technology School of Electronics and Information Engineering Harbin150001 China Simon Fraser University School of Engineering Science BurnabyBCV5A 1S6 Canada Memorial University Electrical and Computer Engineering St. John’sNLA1B 3X5 Canada Singapore University of Technology and Design Tampines 487372 Singapore Yonsei University Yonsei Frontier Lab Seoul03722 Korea Republic of

The advent of the 6G era aims for ubiquitous connectivity, with the integration of non-terrestrial networks (NTN) offering extensive coverage and enhanced capacity. As manufacturing advances and user demands evolve, space-air-ground integrated networks (SAGIN) with computational capabilities emerge as a viable solution for services requiring low latency and high computational power. Resource management within joint communication and computing-embedded SAGIN (JCC-SAGIN) presents greater complexity than traditional terrestrial networks. This complexity arises from the spatiotemporal dynamics of network topology and service demand, the interdependency of large-scale resource variables, and intricate tradeoffs among various performance metrics. Thus, a thorough examination of resource management strategies in JCC-SAGIN is crucial, emphasizing the role of non-terrestrial platforms with processing capabilities in 6G. This paper begins by reviewing the architecture, enabling technologies, and applications in JCC-SAGIN. Then, we offer a detailed overview of resource management modeling and optimization methods, encompassing both traditional optimization approaches and learning-based intelligent decision-making frameworks. Finally, we outline the prospective research directions in JCC-SAGIN. © 1998-2012 IEEE.

关键词： Decision making

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Transmission Line Routing Based on Multi-Source Geographic Information and Improved Grey Wolf Algorithm 5

Transmission Line Routing Based on Multi-Source Geographic I...

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5th International Conference on Machine Learning and computer Application, ICMLCA 2024

作者： Yang, Shu Xia, Yudong Zhu, Lankang Chen, Hao Cui, Li Pan, Wenwu Li, Ali He, Liang Jiaxing Hengchuang Electric Power Design and Research Institute Co. Ltd Jiaxing Hengchuang Jiaxing China School of Computer Science and Technology Xinjiang University Urumqi China Tsinghua University Center for Information Science and Technology Department of Electronic Engineering and Beijing National Research Beijing China

ISBN: (纸本)9798331530334

The design and selection of transmission lines are crucial for the security, economy and reliability of power systems. Traditional planning methods mainly rely on field visits and empirical judgment of grid researchers and have many limitations. This paper proposes a transmission line path planning method using multi-source geographic information and the improved grey wolf algorithm to reduce some costs. We utilize the geographic information system (GIS) for data processing and apply triangular fuzzy number hierarchical analysis to assess cost weights. By incorporating crossover and mutation operators, we enhance the grey wolf algorithm for optimized path planning. A local area in Jiaxing City, Zhejiang Province, is used for case study, which demonstrates that our method reduces costs compared to traditional grey wolf optimization to a certain extent. The results indicate that our approach offers a more reasonable path planning solution. © 2024 IEEE.

关键词： Power transmission lines

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