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Preparation of sulfur-containing functional group-modified resin and its tungsten‑molybdenum adsorption and separation performance with application to waste-alloy leachate

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Hydrometallurgy 2025年

作者： Tiantian Shi Liwen Ma Na Chen Xiaoli Xi Zuoren Nie Collaborative Innovation Center of Capital Resource-Recycling Material Technology College of Materials Science and Engineering Beijing University of Technology Beijing 100124 China National Engineering Laboratory for Industrial Big-data Application Technology College of Materials Science and Engineering Beijing University of Technology Beijing 100124 China Key Laboratory of Advanced Functional Materials Ministry of Education College of Materials Science and Engineering Beijing University of Technology Beijing 100124 China

Since W and Mo are strategic metals in China, the efficient, clean, and value-added utilization of secondary W and Mo resources is important. Ion-exchange methods using resins for the adsorption of Mo after sulfidation can effectively separate W and Mo, but these methods cause sulfide pollution. In this study, the macroporous anion exchange resin D301, which consists of a styrene-divinylbenzene copolymer matrix with tertiary amine groups, was modified by ethyl (methylthio) acetate (EA) of molecular formula C 5 H 10 O 2 S to form resin EA-D301. This resin has the following properties: (i) enhanced the efficiency of W and Mo adsorption and separation, (ii) exhibited a dense structure and well-developed pores, (iii) maximum adsorption capacity (Q W ) of 1062 mg/g for W, and (iv) maximum separation factor ( β Mo W ) of 6.95 at pH 7.5 and 25 °C in a W Mo solution with equal metal concentrations of 0.05 mol/L. The adsorption of both W and Mo by EA-D301 was hardly affected by other anions, and Q W remained as high as 908 mg/g after ten adsorption-desorption cycles, indicating good stability. The adsorption mechanism was an ion-exchange reaction followed by coordination reactions. When EA-D301 was used to treat actual W Mo waste alloy leachate, a Q W of 647 mg/g and a β Mo W of 4.03 were achieved, demonstrating good performance in industrial applications. The EA-D301 resin is a promising W adsorbent with advantages such as simple synthesis, high adsorption capacity, and environmental friendliness. This resin provides an effective way to extract W and separate W and Mo from various resources.

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Leader-Following Consensus of Stochastic Dynamical Multi-Agent Systems Under PI Control

Leader-Following Consensus of Stochastic Dynamical Multi-Age...

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Annual Conference of Industrial Electronics Society

作者： Haibo Gu Kexin Liu Jinhu Lu Zhang Ren School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781728154145

ISBN: (纸本)9781728154152

With the development of swarm intelligence in last decades, consensus problem of multi-agent systems has been attracting much attention. To reveal the inherent mechanism of leader-following consensus in multi-agent systems with stochastic dynamics, PI control protocols are designed in this paper. Owing to the stochastic analysis techniques, algebraic graph theory, and constructing appropriate Lyapunov function, it is proved that the leader-following consensus of nonlinear stochastic dynamical multi-agent systems can be reached in mean square. Sufficient condition are deduced to select the PI control protocol parameters. Finally, the theoretical results are demonstrated through a simulation example.

关键词： Protocols Topology Stochastic processes Network topology Multi-agent systems Laplace equations Eigenvalues and eigenfunctions

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Efficient Adaptive Label Refinement for Label Noise Learning

arXiv

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arXiv 2025年

作者： Zhang, Wenzhen Cheng, Debo Lu, Guangquan Zhou, Bo Li, Jiaye Zhang, Shichao School of Computer Science and Engineering Guangxi Normal University Guangxi Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guangxi Guilin541004 China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guangxi Guilin541004 China UniSA STEM University of South Australia AdelaideSA5095 Australia Guangxi Collaborative Innovation Center of Modern Sericulture and Silk Hechi University Guangxi Hechi546300 China The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou310027 China

Deep neural networks are highly susceptible to overfitting noisy labels, which leads to degraded performance. Existing methods address this issue by employing manually defined criteria, aiming to achieve optimal partitioning in each iteration to avoid fitting noisy labels while thoroughly learning clean samples. However, this often results in overly complex and difficult-to-train models. To address this issue, we decouple the tasks of avoiding fitting incorrect labels and thoroughly learning clean samples and propose a simple yet highly applicable method called Adaptive Label Refinement (ALR). First, inspired by label refurbishment techniques, we update the original hard labels to soft labels using the model’s predictions to reduce the risk of fitting incorrect labels. Then, by introducing the entropy loss, we gradually ‘harden’ the high-confidence soft labels, guiding the model to better learn from clean samples. This approach is simple and efficient, requiring no prior knowledge of noise or auxiliary datasets, making it more accessible compared to Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks

arXiv

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arXiv 2021年

作者： Li, Jianxin Peng, Hao Cao, Yuwei Dou, Yingtong Zhang, Hekai Yu, Philip S. He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China the Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States School of Information Science and Engineering Yanshan University Qinhuangdao066004 China the Department of Computer Science and Engineering Lehigh University BethlehemPA18015 United States

Graph neural networks (GNNs) have been widely used in deep learning on graphs. They can learn effective node representations that achieve superior performances in graph analysis tasks such as node classification and node clustering. However, most methods ignore the heterogeneity in real-world graphs. Methods designed for heterogeneous graphs, on the other hand, fail to learn complex semantic representations because they only use meta-paths instead of meta-graphs. Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors. We propose a novel Higher-order Attribute-Enhancing (HAE) framework that enhances node embedding in a layer-by-layer manner. Under the HAE framework, we propose a Higher-order Attribute-Enhancing Graph Neural Network (HAEGNN) for heterogeneous network representation learning. HAEGNN simultaneously incorporates meta-paths and meta-graphs for rich, heterogeneous semantics, and leverages the self-attention mechanism to explore content-based nodes’ interactions. The unique higher-order architecture of HAEGNN allows examining the first-order as well as higher-order neighborhoods. Moreover, HAEGNN shows good explainability as it learns the importances of different meta-paths and meta-graphs. HAEGNN is also memory-efficient, for it avoids per meta-path based matrix calculation. Experimental results not only show HAEGNN’s superior performance against the state-of-the-art methods in node classification, node clustering, and visualization, but also demonstrate its superiorities in terms of memory efficiency and explainability. © 2021, CC BY.

关键词： Graph neural networks

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A New Method for Quantifying Workload of Drivers and Application in Analyzing Stabilization Time after Critical Events in Varying Automation Levels

SSRN

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SSRN 2022年

作者： Zhai, Junda Lu, Guangquan Chen, Facheng Liu, Miaomiao School of Transportation Science and Engineering Beihang University Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Traffic Management School People’s Public Security University of China Beijing100038 China Beijing100088 China

The workload of drivers increases rapidly after the critical events and the period of time for workload to stabilize is worthy to investigate due to the close relationship between the workload and driving performance of drivers. It is yet unclear if the stabilization time of drivers varies across the different automation levels. In previous studies, a number of measurements have been utilized in evaluating the workload of drivers while most of the existing methods have limitations in studying the stabilization time. Therefore, this study introduces a new method for quantifying the cognitive workload of drivers with a measurement frequency of 2 seconds based on the performance of drivers to a newly designed calculating NDRT. A driving simulator experiment containing three automated driving levels and two critical events was conducted for the verification and application of the quantification method. data were collected from 52 participants. The workload before and after critical events was analyzed and the results of quantified workload, the measurement acquired through the quantification method, showed a high similarity with heart rate and pupil diameter, which supported that the measurement could well reflect the workload of drivers with and without critical events. Moreover, the quantification method was applied to analyzing stabilization time after the critical situations and it was observed that quantified workload was a more appropriate measurement in determining the stabilization time of workload compared to heart rate and pupil diameter. In addition, the analysis results based on the workload quantification method showed that the effect of automation levels on the stabilization time of drivers after the critical events occurred was not significant. © 2022, The Authors. All rights reserved.

关键词： Automobile drivers

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End-to-end Visual Object Tracking with Motion Saliency Guidance

End-to-end Visual Object Tracking with Motion Saliency Guida...

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第三十九届中国控制会议

作者： Yucheng Zhang Kexin Liu Tian Wang School of Automation Science and Electrical Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

In recent years, deep learning-based object tracking methods have achieved excellent performance. Most of existing object tracking algorithms only focus on target appearance features and ignore motion features. However, most of tracked targets are moving, motion features are important in object tracking tasks. In this work, we are committed to extracting deep features with motion saliency to highlight targets from the background and improve tracking accuracy. To this end, a new object tracking network combining correlation filters, siamese network and optical flow network is proposed. In the proposed network, we apply optical flow to extract motion information of targets, and employ an attention network to integrate motion features and appearance features. The evaluations on OTB2013, OTB2015 and UAV123 demonstrate that the proposed method can track targets accurately while ensuring speed.

关键词： Object tracking Motion saliency Optical flow Correlation filters Siamese network

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Reusability of lactic acid on hydrolysis of nanocrystalline cellulose from pepper waste (piper nigrum L.)

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Case Studies in Chemical and Environmental engineering 2024年 10卷

作者： Holilah, Holilah Suryanegara, Lisman Bahruji, Hasliza Hamid, Zuratul Ain Abdul Wahyudi, Muhammad Sapto Masruchin, Nanang Asranudin, Asranudin Faradilla, RH. Fitri Syafri, Edi Melenia, Alvina Tata Jovita, Stella Nugraha, Reva Edra Prasetyoko, Didik Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong 16911 Indonesia Research Collaboration Center for Nanocellulose BRIN-Andalas University Padang 25163 Indonesia Centre of Advanced Material and Energy Sciences Universiti Brunei Darussalam Jl. Tungku Link BE 1410 Brunei Darussalam School of Materials & Mineral Resources Engineering Universiti Sains Malaysia Nibong Tebal Pulau Pinang 14300 Malaysia Department of Food Science and Technology Faculty of Agriculture Halu Oleo University Southeast Sulawesi Kendari Indonesia Department of Agricultural Technology Politeknik Pertanian Negeri Payakumbuh West Sumatra 26271 Indonesia Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya 60294 Indonesia

Lactic acid recovery and reusability for hydrolysis of cellulose offers an economical method and produces fewer waste streams. Successful lactic acid recovery up to three recycle times efficiently hydrolyzed NCs with rod-shape morphology. However, after four cycles, the nanocrystalline cellulose yield reduced from 72.3 % to 48.2 %, forming large nanocelluloses with high crystalline index. Large-size nanocellulose with high zeta potential and thermal stability were obtained with increasing cycles due to inefficient acid diffusion into the rigid crystalline network compared to amorphous cellulose. This study offers a green pathway for hydrolysis nanocellulose using a less corrosive and low toxicity lactic acid than inorganic acids. © 2024 The Authors

关键词： acid recovery hydrolysis lactic acid Nanocrystalline cellulose organic acid

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Tempered enthusiasm by interviewed experts for synthetic data and ELSI checklists for AI in medicine

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AI and ethics 2025年第3期5卷 3241-3254页

作者： Laura Y Cabrera Jennifer Wagner Sara Gerke Daniel Susser Department of Engineering Science and Mechanics Pennsylvania State University W-316 Millennium Science Complex University Park PA 16802 USA. Department of Philosophy Pennsylvania State University University Park PA 16802 USA. Huck Institutes of the Life Sciences Pennsylvania State University University Park PA 16802 USA. Rock Ethics Institute Pennsylvania State University University Park PA 16802 USA. Bioethics Program Pennsylvania State University University Park PA 16802 USA. School of Engineering Design and Innovation Pennsylvania State University University Park PA 16802 USA. Department of Anthropology Pennsylvania State University University Park PA 16802 USA. Department of Biomedical Engineering Pennsylvania State University University Park PA 16802 USA. Institute for Computational and Data Sciences Pennsylvania State University University Park PA 16802 USA. College of Law and European Union Center University of Illinois Urbana-Champaign Champaign IL 61820 USA. Department of Information Science Cornell University Ithaca NY 14853 USA.

Synthetic data are increasingly being used in data-driven fields. While synthetic data is a promising tool in medicine, it raises new ethical, legal, and social implications (ELSI) challenges. There is a recognized need for well-designed approaches and standards for documenting and communicating relevant information about artificial intelligence (AI) research datasets and models, including consideration of the many ELSI challenges. This study investigates the ethical dimensions of synthetic data and explores the utility and challenges of ELSI-focused computational checklists for biomedical AI via semi-structure interviews with subject matter experts. Our results suggest that AI experts have tempered views about the promises and challenges of both synthetic data and ELSI-focused computational checklists. Experts discussed a number of ELSI issues covered by previous literature on the topic, such as issues of bias and privacy, yet other less discussed ELSI issues, such as social justice implications and issues of trust were also raised. When discussing ELSI-focused computational checklists our participants highlighted the challenges connected to developing and implementing them.

关键词： Artificial intelligence Computational checklists ELSI Synthetic data

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A two-stage multi-fidelity design optimization for K-mer-based pattern recognition (KPR) in image processing

A two-stage multi-fidelity design optimization for K-mer-bas...

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ASME 2020 International Design engineering Technical Conferences and Computers and Information in engineering Conference, IDETC-CIE 2020

作者： Yao, Yu-Ta Wu, Yu-Wei Lin, Po Ting Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei11031 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei11031 Taiwan Center for Cyber-Physical System Innovation National Taiwan University of Science and Technology Taipei10607 Taiwan

ISBN: (纸本)9780791884010

Pattern recognition has been widely used in various applications of image processing. It is used to extract meaningful image features from the given image samples and to build classification systems with the intelligence of human recognition. Convolutional Neural Network (CNN) [1] has been one of the most popular and widely used methods for image pattern recognition applications. However, CNN was known not to be rotation-invariant to image patterns. It usually required a larger amount of training image dataset with greater variations in positions and orientations, or additional numerical treatments of spatial transformations [2]. On the other hand, K-mer-based Pattern Recognition (KPR) [3] has been developed to apply an unique way of rotation-invariant sampling to the inspected image pattern and analyze the frequency of the captured pattern features. A classification system was then built based on the Kmer frequency for the desired pattern recognition. In this paper, a series of tests and verifications of the KPR was done. It was found that finding the appropriate design parameters of the KPR for a specific application of image pattern recognition could be costly. A two-stage multi-fidelity design optimization was utilized to improve the efficiency of finding the parameters of KPR. In each iteration of the multi-fidelity design optimization procedure, the first stage was to evaluate the accuracy and efficiency of the design parameters in the K-mer-based pattern classification based on a full set of the given images. The second stage was to find a newer set of design parameters that performed the best based on a smaller set of images, which provided a classification set with lower fidelity than the original one. As a result, the proposed strategy of the multi-fidelity design optimization was more efficient than finding the optimal design parameters based on the full set of the given images. Copyright © 2020 ASME.

关键词： Iterative methods

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The effect of organic acid hydrolysis on the properties of nanocellulose from edamame husk (Glycine max (L.) merill)

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Case Studies in Chemical and Environmental engineering 2025年 11卷

作者： Wahyudi, Muhammad Sapto Holilah Bahruji, Hasliza Prasetyoko, Didik Asranudin Pratama, Agus Wedi Indriani, Dina Wahyu Suryanegara, Lisman Faradilla, RH Fitri Mahardika, Melbi Wardani, Ratih Kusuma Piluharto, Bambang Knight, Victor Feizal Norrrahim, Mohd Nor Faiz Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong Indonesia Research Collaboration Center for Nanocellulose BRIN-Andalas University Padang Indonesia Study Program of Chemistry Faculty of Science and Technology UIN Sunan Kalijaga Yogyakarta Indonesia Centre of Advanced Material and Energy Sciences Universiti Brunei Darussalam Jl. Tungku Link BE 1410 Brunei Darussalam Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Research Center for Applied Microbiology National Research and Innovation Agency (BRIN) Cibinong Indonesia Department of Biosystem Engineering Faculty of Agricultural Technology Universitas Brawijaya Jalan Veteran Malang Indonesia Department of Food Science and Technology Faculty of Agriculture Universitas Halu Oleo Kendari Indonesia Academy Pharmacy of Surabaya Ketintang Gayungan Surabaya 60231 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences University of Jember Jember Indonesia Research Center for Chemical Defence Defence Research Institute Universiti Pertahanan Nasional Malaysia Kem Sungai Besi Kuala Lumpur 57000 Malaysia

The increasing demand for sustainable materials has highlighted nanocellulose as a promising candidate due to its exceptional mechanical strength, thermal stability, and superior barrier properties. These attributes enable its application in diverse fields, including packaging, biomedical devices, and nanocomposites. This study investigates the valorization of edamame bean husks, a byproduct of the globally consumed legume snack, as a sustainable feedstock for nanocellulose production. Cellulose was extracted from the husks via delignification and bleaching, followed by a green and facile acid hydrolysis method employing organic acids (maleic acid, lactic acid, citric acid, and formic acid) to produce nanocellulose. The resulting nanocellulose exhibited a fibrillar morphology with particle sizes ranging from 7.59 to 17.51 nm, depending on the acid used. X-ray diffraction analysis revealed a crystallinity index ranging from 63.49 % to 67.24 %. Notably, acid hydrolysis significantly enhanced the thermal stability of the nanocellulose compared to both the raw husk and isolated cellulose. These findings demonstrate the potential of edamame agricultural waste as a valuable resource for sustainable and eco-friendly nanocellulose production, contributing to a circular bioeconomy. © 2025 The Authors

关键词： Characterization Edamame husk waste Green synthesis Nanocellulose Organic acids

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