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Control Logic Routing for Continuous-Flow Microfluidic Biochips Based on Deep Reinforcement Learning

Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2025年第4期62卷 950-962页

作者： Cai, Huayang Huang, Xing Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

With the advancement of electronic design automation, continuous-flow microfluidic biochips have become one of the most promising platforms for biochemical experiments. This chip manipulates fluid samples in milliliters or nanoliters by utilizing internal microvalves and microchannels, and thus automatically performs basic biochemical experiments, such as mixing and detection. To achieve the correct bioassay function, the microvalves deployed inside the chip are usually managed by a multiplexer-based control logic, and valves receive control signals from a core input through the control channel for accurate switching. Since biochemical reactions typically require high sensitivity, the length of control paths connecting each valve needs to be reduced to ensure immediate signal propagation, and thus to reduce the signal propagation delay. In addition, to reduce the fabrication cost of chips, a vital issue to be addressed in the logic architecture design is how to effectively reduce the total channel length within the control logic. To address the above issues, we propose a deep reinforcement learning-based control logic routing algorithm to minimize the signal propagation delay and total control channel length, thereby automatically constructing an efficient control channel network. The algorithm employs the dueling deep Q-network architecture as the agent of the deep reinforcement learning framework to evaluate the tradeoff between signal propagation delay and total channel length. Besides, the diagonal channel routing is implemented for the first time for control logic, thus fundamentally improving the efficiency of valve switching operations and reducing the fabrication cost of the chip. The experimental results demonstrate that the proposed algorithm can effectively construct a high-performance and low-cost control logic architecture. © 2025 Science Press. All rights reserved.

关键词： Deep reinforcement learning

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VECLLF: A vehicle-edge collaborative lifelong learning framework for anomaly detection in VANETs

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Computer Networks 2025年 265卷

作者： Wang, Yingqing Liang, Yanhua Huang, Yue Qin, Guihe Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Jilin University Center for Computer Fundamental Education Changchun130012 China

With the rapid development of intelligent connected vehicles, Vehicular Ad Hoc Network (VANET) is widely used in intelligent transportation systems. VANETs are vulnerable to attacks in their operating environments, such as denial of service (DoS) attacks and injection attacks. Thus, the development of an efficient anomaly detection framework is a promising solution. Most existing methods can only detect known attacks, are less efficient in detecting unknown attacks, and fail to deal with them. To solve these problems, the lifelong learning framework (LLF) is introduced, which aims to store and label suspicious data by applying blockchain and then retrain the model. However, existing LLFs are overly idealized as they do not adequately consider the key issues of label budget and storage space. To improve the practicality of existing methods, this paper proposes a novel LLF based on the existing LLF, called Vehicle Edge Collaborative Lifelong Learning Framework (VECLLF), which can deploy the model update task on the edge server. Specifically, the proposed anomaly detection module based on ensemble active learning is used to achieve high-performance detection under a limited label budget, the proposed incremental learning module based on a sample library continuously learns the model by replaying representative samples within limited storage space, the proposed optimization-based feature selection module is used to remove redundant and interfering features, and the explainable artificial intelligence (XAI) module is used to explain the model to ensure its interpretability. In addition, the blockchain-based database (BC-DB) module is used to help store suspicious data and interact with security agencies to label it. We conducted experiments on two public datasets, AWID2 and CICIDS2017, and the results show that VECLLF outperforms state-of-the-art anomaly detection models and existing LLF, with F1-scores exceeding 97% on both datasets. Furthermore, we collect data from th

关键词： Rapid transit

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SAHSD: Enhancing Hate Speech Detection in LLM-Powered Web Applications via Sentiment Analysis and Few-Shot Learning 34

SAHSD: Enhancing Hate Speech Detection in LLM-Powered Web Ap...

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34th ACM Web Conference, WWW 2025

作者： Wang, Yulong Li, Hong Wei, Ni State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China College of Big Data and Information Engineering Guizhou University Guiyang China School of Communication Science and Engineering Fudan University Shanghai China

ISBN: (纸本)9798400712746

As large language models (LLMs) increasingly power web applications, including social networks, the challenge of moderating hate speech has become a critical concern for the Web. These LLM-powered applications, while offering near-human interaction capabilities, are vulnerable to harmful or biased content due to imperfect training data scraped from the Web. Current hate speech detection methods often struggle with limited annotated data, especially for real-time moderation on these platforms. This paper introduces Sentiment-Aided Hate Speech Detection (SAHSD), a novel approach designed to enhance hate speech detection specifically in LLM-powered web applications. By treating hate speech detection as a few-shot learning task, SAHSD utilizes sentiment analysis to refine pre-trained language models (LM) for improved accuracy in recognizing harmful content. SAHSD first employs publicly available sentiment datasets to train a sentiment analysis model, which is then fine-tuned by merging sentiment prompts with hate speech prompts, enabling efficient and accurate detection even with limited training samples. The effectiveness of SAHSD is demonstrated through experiments on widely used web-sourced datasets like SBIC and HateXplain. SAHSD achieves an exceptional F1-score of 0.99 with only 64 training samples and outperforms advanced techniques such as ToKen, MRP, and HARE, with significant improvements of 33% on SBIC and 95% on HateXplain. SAHSD surpasses GPT-4 in generalization performance across multiple datasets, showing an 8% improvement when trained on equal-sized samples. These results underscore SAHSD’s potential to enhance content moderation in LLM-driven web platforms, contributing to a safer, more inclusive and accountable Web ecosystem. © 2025 Copyright held by the owner/author(s).

关键词： Hate Speech Large Language Model Machine Learning Security

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A Hierarchical Encrypted Compression Scheme for Intra-Vehicle Network

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Shang, Chao Cao, Jin Li, Zejian Niu, Ben Lam, Kwok-Yan Chi, Chi-Hung Li, Hui Xidian University State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xi'an710126 China Nanyang Technological University College of Computing and Data Science National Digital Trust Centre Singapore639798 Singapore Chinese Academy of Sciences State Key Laboratory of Information Security Institute of Information Engineering Beijing100094 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Nanyang Technological University National Digital Trust Centre Singapore639798 Singapore

The CAN bus is the most widely used bus for intra-vehicle communication due to its high transmission stability, excellent real-time communication capability, and relatively low cost. As the number of ECUs grows, the CAN bus load increases and thus raises the possibility of data transmission delays and errors. Message compression based on the differential algorithm has been proposed to reduce the CAN bus load. However, current works do not consider the security problems of the CAN bus. Attackers can manage to acquire the original messages before compression and disturb the message statistics to decrease compression rate by injecting malicious frames. In this paper, we propose a secure compression mechanism for the intra-vehicle network, including an improved compression algorithm, a stream key distribution scheme, and a hierarchical encryption scheme. Formal verification results show that the proposed scheme can achieve mutual authentication, message confidentiality and integrity, resist replay attacks, and support secure compression. Evaluations using real vehicle data on 16 MHz boards show the average communication overhead can be reduced by 46.38% compared to the original messages. Performance analysis results show our scheme can reduce computational overhead on compression by 31.82% and 19.43% on decompression compared to related schemes. © 2000-2011 IEEE.

关键词： Buses

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Stratified strategies for enhancing thermal comfort through multidimensional compactness optimization in urban built-up areas during heatwaves

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Sustainable Cities and Society 2025年 127卷

作者： Ji, Yifeng Liu, Ying Tang, Hongyu Li, Zhitao Bai, Yihang Feng, Tao Urban and Data Science Lab of Advanced Science and Engineering Hiroshima University Hiroshima Higashi739-8529 Japan Smart Transport Key Laboratory of Hunan Province School of Traffic and Transportation Engineering Central South University Changsha410075 China School of Geography University of Leeds Leeds United Kingdom

Thermal comfort (TC) in built-up areas with varying levels of compactness is unevenly affected during heatwaves (HW). However, identifying zones that should prioritize compactness optimization to effectively enhance TC is often overlooked. This study constructed a research framework for enhancing TC through stratified planning strategies by identifying key compactness-optimized areas and patterns during HW. Taking the built-up area of Shenyang, China, as an example, the compactness index containing spatial, functional and socio-economic dimensions and the TC index were first constructed based on multi-source data. Afterwards, different types of compactness-optimized areas, dominant compactness in different regions, and trade-off and synergy patterns among various dimensions of compactness were revealed using a geographically weighted regression model (GWRM) and local bivariate spatial autocorrelation analysis. The results show that compactness decreases from the center to the periphery of the built-up area, while TC follows the opposite trend. A total of 10 types of compactness-optimized areas are identified, including 8 key types covering 40.472 % of the built-up area. Based on the trade-offs and synergies between different compactness dimensions, 8 optimization patterns are revealed, with synergistic optimization across all three dimensions representing the largest share (71.256 %). Furthermore, 4 optimization categories with different priorities are identified, each exhibiting distinct spatial patterns and targeted optimization strategies. These findings support hierarchical resource allocation and strategic intervention to enhance thermal comfort and promote climate-resilient cities. © 2025 The Author(s)

关键词： Resource allocation

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Two-stage optimal scheduling strategy for community integrated energy system based on uncertainty and integrated demand response model

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Renewable Energy 2025年 251卷

作者： Wu, Shengcheng Pang, Aiping Electrical Engineering College Guizhou University Guiyang550025 China Key Laboratory of "Internet+" Collaborative Intelligent Manufacturing in Guizhou Provence Guizhou Guiyang550025 China Bank of Communications Data Center Shanghai 201112 China

This study presents an optimization management framework for community integrated energy systems. First, the output scenarios of renewable energy are generated by the Monte Carlo method, and the K-means method is used to reduce the scenarios. According to the spatio-temporal characteristics of electric vehicles, this study proposes a charging/discharging decision-making method based on the fuzzy theory. Moreover, an integrated demand response model based on the real-time price mechanism and a two-stage optimization scheduling strategy is formulated. The results show that, compared with two traditional scenarios, the proposed strategy reduces comprehensive operating costs on typical days by 8.81% and 3.22% in summer and 6.55% and 3.33% in winter, respectively, while ensuring overall satisfaction. This study also analyzes the impact of the number of electric vehicles and the weight of the objective function on the optimization scheduling results. © 2025 Elsevier Ltd

关键词： Risk perception

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Prompt-guided orthogonal multimodal fusion for cancer survival prediction

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Information Sciences 2025年 715卷

作者： Huang, Lan Guo, Shuyu Bai, Tian Zhao, Ruihong Tao, Ke College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Gastroenterology The First Hospital of Jilin University Changchun130000 China

Cancer survival prediction can assist clinicians in developing personalized treatment plans for patients. Comprehensive cancer diagnosis and treatment require integrating macroscopic and microscopic imaging. However, significant discrepancies in the spatial resolution and anatomical scale between imaging modalities hinder existing multimodal fusion methods from effectively learning correlated semantic features with limited datasets. In this work, we introduce a prompt-guided orthogonal multimodal fusion strategy (POMF) for fusing multimodal medical images across anatomical scales. POMF utilizes modality-specific prompts to fine-tune pretrained models, facilitating bias adaptation to medical imaging features while ensuring more computationally efficient training. A modality consistency-discrepancy prototype is designed as the modality-inherent prompt in POMF, disentangling the multimodal features and bridging the potential correlations across the orthogonal modalities. POMF is validated on a glioma survival prediction task using paired radiology and pathology images. The experiment results suggest that POMF achieves superior C-index with existing full-tuning and prompt-tuning methods. Additionally, the ablation studies demonstrate that POMF is adaptable to various architectures of pretrained encoders and multiple multimodal fusion strategies on cross-scale medical images. © 2025 Elsevier Inc.

关键词： Mammography

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FDAC: Federated Domain Adaptation via Dual Contrastive Learning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Yi, Chang'an Chen, Haotian Zhang, Yifan Xu, Yonghui Zhou, Yan Cui, Lizhen Yu, Han Foshan University Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology School of Electronic and Information Engineering Foshan China Jinan China School of Computing Singapore Singapore College of Computing and Data Science Singapore Singapore

Federated domain adaptation (FDA) aims to transfer knowledge collaboratively from multiple source domains to related but different unlabelled target domains. The data of each domain are locally maintained, and various domain gaps exist among them, resulting in extreme challenges in simultaneously mitigating diverse distribution shifts and preserving discriminative knowledge without accessing the source data. Many existing works have failed to fully explore different source models to measure domain shifts and leverage semantic knowledge, resulting in skewed alignment and partial preservation of discriminative information. In this paper, we propose a novel approach named FDAC to address Federated Domain Adaptation by thoroughly investigating source models via dual Contrastive mechanisms. FDAC contrastively increases the data diversity to align features across domains in a fine-grained manner by manipulating the latent deep architecture and compensating for knowledge from each source domain;simultaneously, it contrastively utilizes the comprehensive semantic knowledge of different source domains to guide the adaptation process. Extensive experiments on several real datasets demonstrate that FDAC outperforms all comparative methods under most conditions. Furthermore, FDAC only needs approximately half of the communication rounds compared with the state-of-the-art methods, indicating that FDAC can significantly improve communication efficiency, which is another key factor in the federated setting. The source code is publicly available at https://***/ycarobot/FDAC. © 1991-2012 IEEE.

关键词： Contrastive learning Domain adaptation Federated learning Vision transformer

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Gradient neural network models for approximate Tucker decomposition of time-dependent tensors

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Neurocomputing 2025年 640卷

作者： Che, Maolin Wei, Yimin Yan, Hong State Key Laboratory of Public Big Data and School of Mathematics and Statistics Guizhou University Guiyang550025 China Center for Intelligent Multidimensional Data Analysis Hong Science Park Shatin Hong Kong School of Mathematical Sciences and Key Laboratory of Mathematics for Nonlinear Sciences Fudan University Shanghai200433 China Department of Electrical Engineering and Center for Intelligent Multidimensional Data Analysis City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong

In this paper, we introduce a gradient neural network model for approximating the Tucker decomposition of time-dependent tensors with a specified multilinear rank bound. The behavior of this model is governed by a system of ordinary differential equations. First, based on the Karush–Kuhn–Tucker (KKT) conditions of the optimization problem for the approximate Tucker decomposition of a constant tensor, a gradient neural network model is proposed, demonstrating local stability according to Lyapunov stability theory. Next, we design a gradient neural network model specifically tailored for time-dependent tensors. Finally, using the Taylor-type difference rules, we discretize the proposed neural network models, ensuring zero stability for the implementation of digital hardware. We illustrate the effectiveness and characteristics of our approach through numerical examples. © 2025 Elsevier B.V.

关键词： Lyapunov methods

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Ionic Effect Enhanced the Adsorption of Phosphorus-Containing Organics from Complex Wastewater

SSRN

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SSRN 2025年

作者： Li, Zhiqiang Wang, Rongyue Jia, Wenting Ning, Pengge Cao, Hongbin State Key Laboratory of Biochemical Engineering Key Laboratory of Biopharmaceutical Preparation and Delivery Chinese Academy of Sciences Beijing100049 China Chemistry and Chemical Engineering Data Center Chinese Academy of Sciences Beijing100049 China National Engineering Research Center of Green Recycling for Strategic Metal Resources Chinese Academy of Sciences Beijing100049 China Institute of Process Engineering Chinese Academy of Science Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Achieving the complete adsorption of phosphorus-containing organics from the complex wastewater generated during the recycling process of waste batteries is a critical environment challenge. The research findings of this work reveal that in complex solution systems, ions can significantly alter the hydrophilicity or structural morphology of phosphorus-containing organic compounds, thereby influencing their adsorption efficacy. Based on these insights, an innovative adsorption strategy based on ion-regulation was proposed for the complete removal of phosphorus-containing organics from complex wastewater. By precisely modulating the concentration of H3O+ ions within actual cobalt extraction wastewater prior to the adsorption process, we achieved a removal rate exceeding 99.98% for phosphorus-containing organics. This breakthrough offers innovative perspectives for the comprehensive removal of organic pollutants in complex wastewaters within the waste battery recycling sector and provides important guarantees for the industry’s environmental health and sustainable development. © 2025, The Authors. All rights reserved.

关键词： Sustainable development

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