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Synergistic Effect of Combined Hydrothermal Carbonization of Fenton's Reagent and Biomass Enhances the Adsorption and Combustion Characteristics of Sludge towards Eco-Friendly and Efficient Sludge Treatment

SSRN

引用

SSRN 2021年

作者： Li, Yeqing Yang, Xingru Zhu, Mingyu Dong, Liming Jiang, Hao Xu, Quan Zhou, Hongjun Han, Yongming Feng, Lu State Key Laboratory of Heavy Oil Processing Beijing Key Laboratory of Biogas Upgrading Utilization College of New Energy and Materials China University of Petroleum Beijing Beijing102249 China Key Laboratory of Cleaner Production and Integrated Resource Utilization of China National Light Industry Beijing Technology and Business University Beijing100048 China College of Information Science & Technology Beijing University of Chemical Technology Beijing100029 China Department of Biological and Chemical Engineering Aarhus University Blichers Allé 20 Tjele8830 Denmark NIBIO Norwegian Institute of Bioeconomy Research P.O. Box 115 ÅsN-1431 Norway

The amount of lignocellulose biomass and sludge is enormous, so it is of great significance to find a treatment combining the two substances. Co-hydrothermal carbonization (Co-HTC) has emerged as an efficient approach to dispose sludge. However, the improvement of sludge upgrading and combustion performance remains an important challenge during the Co-HTC of sludge. In this work, the Co-HTC of sludge and Fenton’s reagent at different mixing ratios was proposed to achieve sludge reduction. Moreover, the addition of two kinds of biomass improved the adsorption capacity and combustion performance of hydrochars. When sludge and sawdust were the Co-HTC at the mass ratio of 1:3, the liquid phase Pb concentration decreased notably to 18.06%. Furthermore, the adsorption capacity of hydrochars was further improved by modification, which was in accordance with pseudo-second-order kinetics. Particularly, the hydrochars derived from the Co-HTC had higher heating value (HHV) and could be used as a clean fuel. This study proposed a new technical route of combining the HTC with Fenton’s reagent and lignocellulose biomass, which could be served as a cleaner and eco-friendly treatment of sludge. © 2021, The Authors. All rights reserved.

关键词： Biomass

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Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

arXiv

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arXiv 2023年

作者： Chen, Jinbiao Zhang, Zizhen Cao, Zhiguang Wu, Yaoxin Ma, Yining Ye, Te Wang, Jiahai School of Computer Science and Engineering Sun Yat-sen University China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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An oxygen-deficient vanadium oxide@N-doped carbon heterostructure for sodium-ion batteries: insights into the charge storage mechanism and enhanced reaction kinetics

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Journal of Materials Chemistry A 2020年第6期8卷 3450-3458页

作者： Qingyuan Ren Ning Qin Bin Liu Yuan Yao Xu Zhao Zhao Deng Yu Li Yucheng Dong Dong Qian Bao-Lian Su Wenjun Zhang Hong-En Wang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology Wuhan China Department of Materials Science and Technology Southern University of Science and Technology (SUSTC) Shenzhen China Centre of Super Diamond & Advanced Films (COSDAF) Department of Materials Science and Engineering City University of Hong Kong HKSAR China Beijing National Laboratory of Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China Institute of Chemical Materials Chinese Academy of Engineering Physics Mianyang 621900 China College of Chemistry and Chemical Engineering Central South University Changsha 410083 China Laboratory of Inorganic Materials Chemistry (CMI) University of Namur 61 rue de Bruxelles Belgium College of Physics and Electronics Information Yunnan Normal University Kunming 650500 China

Vanadium oxides are a class of promising anode candidates for sodium-ion batteries (SIBs) with high theoretical capacity and low cost. However, their practical application has been impeded by the low electronic conductivity, sluggish ionic transport and large volume change upon sodiation. Herein, a novel, high-performance anode for SIBs based on an oxygen-deficient vanadium oxide (VO/V₂O₃) heterostructure embedded in porous nitrogen-doped carbon (denoted as VNC) derived by a very simple and localized phase transition from a vanadium glycolate precursor has been demonstrated. The strong synergy of the hierarchically porous framework, VO/V₂O₃ phase heterojunctions with oxygen vacancy defects, and interfacial coupling with N-doped carbon (NC) efficiently promotes the electron/ion transport and alleviates the volume change during the sodiation/de-sodiation process. Electrochemical evaluation reveals that this novel VNC anode material manifests high sodium-ion storage capability and stability. Ex situ X-ray diffraction (XRD), transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS) analyses show that (a portion of) the vanadium oxides can be converted into vanadium metal and sodium oxides. First-principles density functional theory (DFT) calculations reveal that coupling with NC effectively enhances the interfacial interactions and charge transfer between vanadium oxides and carbon, as well as the conversion reaction kinetics during sodiation/desodiation. The present work offers a viable strategy for the rational design and exploration of novel heterostructure composite electrodes for a wide array of beyond-lithium-ion batteries.

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Van der Waals spin-orbit torque antiferromagnetic memory

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Physical Review B 2024年第22期110卷 L220409-L220409页

作者： Lishu Zhang Zhengping Yuan Jie Yang Jun Zhou Yanyan Jiang Hui Li Yongqing Cai Evgeny Y. Tsymbal Yuan Ping Feng Department of Physics National University of Singapore Singapore 117542 Singapore School of Information Science and Technology ShanghaiTech University Shanghai 201210 China Key Laboratory of Material Physics School of Physics and Microelectronics Ministry of Education Zhengzhou University Zhengzhou 450001 China Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (ASTAR) 2 Fusionopolis Way Innovis #08-03 Singapore 138634 Singapore Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials Ministry of Education Shandong University Jinan 250061 China Institute of Applied Physics and Materials Engineering University of Macau Taipa Macau SAR China Department of Physics and Astronomy & Nebraska Center for Materials and Nanoscience University of Nebraska Lincoln Nebraska 68588-0299 USA Center for Advanced 2D Materials National University of Singapore Singapore 117546 Singapore

The technique of conventional ferromagnet/heavy-metal spin-orbit torque (SOT) offers significant potential for enhancing the efficiency of magnetic memories. However, it faces fundamental physical limitations, including shunting effects from the metallic layer, broken symmetry for enabling antidamping switching, spin scattering caused by interfacial defects, and sensitivity to stray magnetic fields. To address these issues, we here propose a van der Waals (vdW) field-free SOT antiferromagnetic memory using a vdW bilayer LaBr2 (an antiferromagnet with perpendicular magnetic anisotropy) and a monolayer Td phase WTe2 (a Weyl semimetal with broken inversion symmetry). By systematically employing density functional theory in conjunction with nonequilibrium Green's function methods and macrospin simulations, we demonstrate that the proposed vdW SOT devices exhibit remarkably low critical current density approximately 10MA/cm2 and rapid field-free magnetization switching in 250 ps. This facilitates excellent write performance with extremely low energy consumption. Furthermore, the device shows a significantly low read error rate, as evidenced by a high tunnel magnetoresistance ratio of up to 4250%. The superior write and read performance originates from the unique strong on-site (insulating phase) and off-site (magnetic phase) Coulomb interactions in electride LaBr2, a large nonzero z-component polarization in WTe2, and the proximity effect between them.

关键词： Van der Waals forces

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Deformation Mechanisms of Β-Li in Mg-Li Dual-Phase Alloy and Effects of Surface Oxide Layer

SSRN

引用

SSRN 2023年

作者： Li, Jing Jin, Li Yi, Sangbong Zhang, Xin Dong, Jie Luo, Ming Key Laboratory of High Performance Manufacturing for Aero Engine Northwestern Polytechnical University Ministry of Industry and Information Technology Xi’an710072 China Engineering Research Center of Advanced Manufacturing Technology for Aero Engine Ministry of Education Northwestern Polytechnical University Xi’an710072 China National Engineering Research Center of Light Alloy Net Forming State Key Laboratory of Metal Matrix Composite School of Materials Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Materials and Process Design Helmholtz-Zentrum Hereon Geesthacht21502 Germany State Key Laboratory of Solidification Processing China

This study investigates the deformation mechanisms of β-Li micropillar prepared from a Mg-7Li dual-phase alloy with or without surface oxide layer. The oxide layer improves the strength of micropillar due to the dislocation trapping effect. As the oxide layer having a nanoporous structure becomes relatively thinner with increasing the micropillar size, the strengthening effect resulting from the oxide layer decreases with increasing the micropillar size. Regardless of the existence of the surface oxide layer, all β-Li micropillars show continuous deformation characteristics. This is ascribed to the interactions between pre-existing dislocations and non-planar dislocations activated during compression, which induces low-angle grain boundary and effectively traps the dislocations at the central area. © 2023, The Authors. All rights reserved.

关键词： Grain boundaries

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Jinan250353 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

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Deep Learning Methods for Calibrated Photometric Stereo and Beyond

arXiv

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arXiv 2022年

作者： Ju, Yakun Lam, Kin-Man Xie, Wuyuan Zhou, Huiyu Dong, Junyu Shi, Boxin School of Electrical and Electronic Engineering Nanyang Technological University Singapore The Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong The Research Institute for Future Media Computing Shenzhen University Shenzhen China The Department of Informatics University of Leicester Leicester United Kingdom The Faculty of Information Science and Engineering The Institute for Advanced Ocean Study Ocean University of China Qingdao China The National Key Laboratory for Multimedia Information Processing National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models. Copyright © 2022, The Authors. All rights reserved.

关键词： Light sources

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A Deep Reinforcement Learning-based Contention Window Avoidance Scheme for Distinguishing Network Service Priorities

A Deep Reinforcement Learning-based Contention Window Avoida...

引用

International Conference on Computing, Networking and Communications (ICNC)

作者： Xiaowei Nie Demin Wang Yuqing Zhang Xiaoduo Pan Zhibin Zuo National Tibetan Plateau Data Center (TPDC) State Key Laboratory of Tibetan Plateau Earth System Environment and Resources (TPESER) Institute of Tibetan Plateau Research Chinese Academy of Sciences Beijing China Science Center of Lingshan Forum of Guangdong Province Guangzhou China School of Ecology and Environment Tibet University Lhasa China College of Information Science and Engineering Henan University of Technology Zhengzhou China Henan International Joint Laboratory of Grain Information Processing Zhengzhou China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

This study introduces a Deep Reinforcement Learning-based Contention Window Avoidance Scheme aimed at improving service priority differentiation and network throughput under high load conditions within wireless networks that utilize the Network Distributed Coordination Function (DCF) mechanism. This novel approach involves two key strategies: firstly, it enables the differentiation of service priorities within DCF-based channel access by categorizing them in accordance with the varying data types and streams transmitted by different stations; secondly, it leverages Deep Reinforcement Learning to dynamically evaluate the network state through analysis of collision probabilities derived from channel observations. By doing so, the scheme dynamically modulates the ranges of contention windows in alignment with the service priorities, responding to the immediate network circumstances. Empirical evidence indicates that the proposed scheme substantially mitigates collision frequencies and augments data transmission throughput, thus significantly advancing the overall performance of networks that require service priority differentiation.

关键词： Frequency modulation Wireless networks Throughput Deep reinforcement learning Data communication Streams Optimization

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Direct speech-to-image translation

arXiv

引用

arXiv 2020年

作者： Li, Jiguo Zhang, Xinfeng Jia, Chuanmin Xu, Jizheng Zhang, Li Wang, Yue Ma, Siwei Gao, Wen Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China National Engineering Laboratory for Video Technology School of Electronic Engineering and Computer Science Peking University Beijing100871 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Institute of Digital Media School of Electronic Engineering and Computer Science Peking University Beijing100871 China Peng Cheng Lab Shenzhen China Bytedance Inc

Direct speech-to-image translation without text is an interesting and useful topic due to the potential applications in human-computer interaction, art creation, computer-aided design. etc. Not to mention that many languages have no writing form. However, as far as we know, it has not been well-studied how to translate the speech signals into images directly and how well they can be translated. In this paper, we attempt to translate the speech signals into the image signals without the transcription stage. Specifically, a speech encoder is designed to represent the input speech signals as an embedding feature, and it is trained with a pretrained image encoder using teacher-student learning to obtain better generalization ability on new classes. Subsequently, a stacked generative adversarial network is used to synthesize high-quality images conditioned on the embedding feature. Experimental results on both synthesized and real data show that our proposed method is effective to translate the raw speech signals into images without the middle text representation. Ablation study gives more insights about our method. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Federated Multimedia Recommendation Systems with Privacy Protection

Federated Multimedia Recommendation Systems with Privacy Pro...

引用

IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Shih Yu Chang Hsiao-Chun Wu Kun Yan Scott Chih-Hao Huang Yiyan Wu Department of Applied Data Science San Jose State University San Jose CA USA School of Electrical Engineering and Computer Science Louisiana State University Baton Rouge LA USA Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin Guangxi China Department of Information and Telecommunication Guilin University of Electronic Technology Guilin Guangxi China Department of Electrical Engineering National Tsinghua University Hsinchu Taiwan

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

In this big data era, distributed machine learning and global data pooling have become quite critical in practical implementation of any multimedia data-science technique. The emerging federated multimedia recommendation systems (FeMRSs) offer a promising approach to address the need of personalized recommendations subject to both users’ privacy and data security. However, there exists no quantitative method for assessing the discrepancy between the large original rating matrix stored on the server and the dimensionality-reduced rating matrix produced on each client’s equipment. Meanwhile, there lacks a systematic means to evaluate the differential privacy (DP), which is a critical security measure in many distributed systems, particularly when sensitive data are processed. In this work, we first introduce a novel personalized dimensionalityreduction algorithm utilizing the matrix sketching technique. This new algorithm effectively control the difference between the original rating matrix on the server and the dimensionality-reduced rating matrix on each client’s equipment. Moreover, a randomized DP matrix factorization algorithm is designed to be executed at each client’s equipment. The theoretical proof is also carried out to show how much DP can be attained by use of the aforementioned randomized DP matrix factorization algorithm. Finally, extensive numerical studies are presented to evaluate the effectiveness of our proposed novel algorithms using both simulated and real datasets for building the FeMRSs.

关键词： Dimensionality reduction Digital multimedia broadcasting Privacy Systematics Distributed databases Filtering algorithms Servers

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