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Surface-enhanced Raman scattering on nanomaterials for cancer and pathogens diagnosis

Nano Research 2025年第2期18卷 650-674页

作者： Jingjing Wu Baohong Zhang Lei Xu Yanyu Zhao Tao Chen Jie Lin Anran Li School of Engineering Medicine Beihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology of the People’s Republic of ChinaBeihang UniversityBeijing 100191China School of Optics and Photonics Beijing Institute of TechnologyBeijing 100089China Shanxi Lu'an Taihang Lubricant Technology Co. Ltd.Changzhi 046204China Cixi Institute of Biomedical Engineering Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China

Surface-enhanced Raman scattering(SERS)is a non-invasive spectroscopic technique that provides specific chemical fingerprint information for biomarkers in cancer and pathogen ***,the SERS strategies are limited by the non-specific interactions between substrates and co-existing substances in biological matrices and the challenges of obtaining molecular fingerprint information from the complex vibrational *** recent years,the rapid development of novel substrates with high SERS activity has opened up new opportunities for their applications in cancer and pathogen *** aim of this review is to present the recent progress and perspectives of novel SERS-based substrates for cancer and pathogen diagnostic ***,we will introduce recently developed SERS-active nanomaterials and discuss the influencing factors of the SERS ***,the advantages of SERS in the diagnosis of cancer and pathogens will be ***,we will review the latest breakthroughs in cancer and pathogen detection research with SERS technology,as well as the new opportunities for SERS applications brought about by artificial intelligence(Al)*** addition,the novel microfluidic-SERS platforms for cancer and pathogens diagnosis will also be ***,we will summarize the challenges and future perspectives of SERS technology in the field of early cancer diagnosis and rapid pathogen *** is highly expected that this review could benefit a comprehensive understanding of the research status of the SERS-active nanomaterials and arouse the research enthusiasm for them,leading to accelerated clinical translation of SERS technology in cancer and pathogen diagnosis.

关键词： surface-enhanced Raman scattering(SERS) SERS-active nanomaterials cancer and pathogen diagnosis artificial intelligence(Al)technology

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Solidification modes and delta-ferrite of two types of 316L stainless steels:a combination of as-cast microstructure and HT-CLSM research

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Journal of Iron and Steel Research International 2025年第2期32卷 426-436页

作者： Yang Wang Chao Chen Xiao-yu Yang Zheng-rui Zhang Jian Wang Zhou Li Lei Chen Wang-zhong Mu College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024ShanxiChina Department of Materials Science and Engineering KTH Royal Institute of TechnologyStockholm 10044Sweden College of Artificial Intelligence and Big Data for Medical Sciences Shandong First Medical University and Shandong Academy of Medical SciencesJinan 250117ShandongChina College of Mechanical Engineering Taiyuan University of TechnologyTaiyuan 030024ShanxiChina Key Laboratory of Electromagnetic Processing of Materials(Ministry of Education) Northeastern UniversityShenyang 110819LiaoningChina

In 316L austenitic stainless steel,the presence of ferrite phase severely affects the non-magnetic properties.316L austenitic stainless steel with low-alloy type(L-316L)and high-alloy type(H-316L)has been *** microstructure and solidification kinetics of the two as-cast grades were in situ observed by high temperature confocal laser scanning microscopy(HT-CLSM).There are significant differences in the as-cast microstructures of the two 316L stainless steel *** L-316L steel,ferrite morphology appears as the short rods with a ferrite content of 6.98%,forming a dual-phase microstructure consisting of austenite and ***,in H-316L steel,the ferrite appears as discontinuous network structures with a content of 4.41%,forming a microstructure composed of austenite and sigma(σ)*** alloying elements in H-316L steel exhibit a complex distribution,with Ni and Mo enriching at the austenite grain ***-CLSM experiments provide the real-time observation of the solidification processes of both 316L specimens and reveal distinct solidification modes:L-316L steel solidifies in an FA mode,whereas H-316L steel solidifies in an AF *** differences result in ferrite and austenite predominantly serving as the nucleation and growth phases,*** solidification mode observed by experiments is similar to the thermodynamic calculation *** L-316L steel solidified in the FA mode and showed minimal element segregation,which lead to a direct transformation of ferrite to austenite phase(δ→γ)during phase transformation after ***,the H-316L steel solidified in the AF mode and showed severe element segregation,which lead to Mo enrichment at grain boundaries and transformation of ferrite into sigma and austenite phases through the eutectoid reaction(δ→σ+γ).

关键词： 316L austenitic stainless steel As-cast microstructure High-temperature confocal laser scanning microscopy Solidification mode Ferrite Characterization

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An Improved Multi-objective Particle Swarm Optimization Algorithm with Reduced Initial Search Space 21st

An Improved Multi-objective Particle Swarm Optimization Algo...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Zhiguang, Chu Yingchen, Fan Xiaolei, Zhang Ruyan, Zhang Xing, Zhang School of Electronics and Information Engineering Liaoning University of Technology Jinzhou121000 China Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Jinzhou121000 China

ISBN: (纸本)9789819601240

As data dimensions continue to increase, the challenges of data storage become more severe. While feature selection can reduce the complexity of high-dimensional data, existing methods are prone to local optima and high computational costs. To address these issues, this paper proposes a hybrid feature selection method based on MOPSO (Multi-Objective Particle Swarm Optimization). This method first uses a filter approach to narrow the search space, then employs a particle swarm algorithm to generate particles, incorporating mutation, crossover, and selection strategies during particle updates to enhance their search capabilities. Additionally, an archive set is established, utilizing grid-based and density-based methods during the update process to avoid local optima. Finally, the proposed method is compared with two single-objective optimization methods and two multi-objective optimization methods on multiple datasets. Experimental results demonstrate that the proposed method significantly reduces classification error rates and effectively reduces the number of features, providing an efficient solution for feature selection in high-dimensional data and addressing the challenges in high-dimensional data analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Particle swarm optimization (PSO)

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StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal Large Language Models 31

StoryLLaVA: Enhancing Visual Storytelling with Multi-Modal L...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Yang, Li Xiao, Zhiding Huang, Wenxin Zhong, Xian Hubei Key Laboratory of Big Data Intelligent Analysis and Application School of Computer Science and Information Engineering Hubei University China Hubei Key Laboratory of Transportation Internet of Things School of Computer Science and Artificial Intelligence Wuhan University of Technology China

ISBN: (纸本)9798891761964

The rapid development of multi-modal large language models (MLLMs) has positioned visual storytelling as a crucial area in content creation. However, existing models often struggle to maintain temporal, spatial, and narrative coherence across image sequences and frequently lack the depth and engagement of human-authored stories. To address these challenges, we propose Story with Large Language and Vision Assistant (StoryLLaVA), a novel framework for enhancing visual storytelling. Our approach introduces a Topic-Driven Narrative Optimizer (TDNO) that improves both the training data and MLLM models by integrating image descriptions, topic generation, and GPT-4-based refinements. Furthermore, we employ a preference-based ranked story sampling method that aligns model outputs with human storytelling preferences through positive-negative pairing. These two phases of the framework differ in their training methods: the former uses supervised fine-tuning, while the latter incorporates reinforcement learning with positive and negative sample pairs. Experimental results demonstrate that StoryLLaVA outperforms current models in visual relevance, coherence, and fluency, with LLM-based evaluations confirming the generation of richer and more engaging narratives. The enhanced dataset and model are available at https://***/XxxZzD/StoryLLaVA. © 2025 Association for Computational Linguistics.

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Privacy-Preserving Fingerprint Recognition via Federated Adaptive Domain Generalization

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Computers, Materials & Continua 2025年第3期82卷 5035-5055页

作者： Yonghang Yan Xin Xie Hengyi Ren Ying Cao Hongwei Chang Henan Key Laboratory of Big Data Analysis and Processing Computer and Information EngineeringHenan UniversityKaifeng475004China College of Information Science and Technology Nanjing Forestry UniversityNanjing210037China Henan Branch China Life Insurance Co.Ltd.Zhengzhou450000China

Fingerprint features,as unique and stable biometric identifiers,are crucial for identity ***,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data *** Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security ***,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across *** heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse *** address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated *** algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature ***,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model *** conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.

关键词： Fingerprint recognition privacy protection federated learning adaptive weight adjustment

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OmniSQL: Synthesizing High-quality Text-to-SQL data at Scale

arXiv

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arXiv 2025年

作者： Li, Haoyang Wu, Shang Zhang, Xiaokang Huang, Xinmei Zhang, Jing Jiang, Fuxin Wang, Shuai Zhang, Tieying Chen, Jianjun Shi, Rui Chen, Hong Li, Cuiping Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China ByteDance Inc China

Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements in large language models (LLMs) have significantly enhanced text-to-SQL performance, existing approaches face notable limitations in real-world text-to-SQL applications. Prompting-based methods often depend on closed-source LLMs, which are expensive, raise privacy concerns, and lack customization. Fine-tuning-based methods, on the other hand, suffer from poor generalizability due to the limited coverage of publicly available training data. To overcome these challenges, we propose a novel and scalable text-to-SQL data synthesis framework for automatically synthesizing large-scale, high-quality, and diverse datasets without extensive human intervention. Using this framework, we introduce SynSQL-2.5M, the first million-scale text-to-SQL dataset, containing 2.5 million samples spanning over 16,000 synthetic databases. Each sample includes a database, SQL query, natural language question, and chain-of-thought (CoT) solution. Leveraging SynSQL-2.5M, we develop OmniSQL, a powerful open-source text-to-SQL model available in three sizes: 7B, 14B, and 32B. Extensive evaluations across nine datasets demonstrate that OmniSQL achieves state-of-the-art performance, matching or surpassing leading closed-source and open-source LLMs, including GPT-4o and DeepSeek-V3, despite its smaller size. We release all code, datasets, and models to support further research. Copyright © 2025, The Authors. All rights reserved.

关键词： Structured Query Language

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Decentralized Unlearning for Trustworthy AI-Generated Content (AIGC) Services

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IEEE Network 2025年第3期39卷 285-292页

作者： Lin, Yijing Du, Hongyang Gao, Zhipeng Yao, Jing Jiang, Bingting Niyato, Dusit Li, Ruidong Zhang, Ping Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China The University of Hong Kong Department of Electrical and Electronic Engineering Pokfulam Hong Kong Central South University School of Computer Science and Engineering Changsha410083 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore Kanazawa University Institute of Science and Engineering Kanazawa920-1192 Japan

The widespread adoption of AI-Generated Content (AIGC) has captured remarkable interests from academia and industry. However, this advancement brings forth challenges in data ownership, as emphasized by the General data Protection Regulation’s right to be forgotten. The right to be forgotten allows participants to erase their data contributions from well-trained AIGC models. Training AIGC models demands an extensive amount of data and computations, often surpassing the capabilities of individual participants. As a solution, Federated Learning (FL) is employed to collaboratively train an AIGC model without sharing raw data directly. To protect the right to be forgotten in FL, "federated unlearning" has been introduced to eradicate the data contributions of unlearning participants from fully trained AIGC models, achieved by deleting historical model updates. However, given that historical updates are distributed among various participants, they may still hold contributions from the unlearning participants. This presents a challenge to the trustworthiness of federated unlearning. In this paper, we first provide an overview of both centralized and federated learning and unlearning methods. We survey relevant works and classify four main categories for federated unlearning. Subsequently, we introduce a comprehensive decentralized unlearning architecture designed for trustworthy AIGC services. To address the challenge of participants retaining historical contributions from unlearning participants, we integrate a coded computing-based interaction mechanism, ensuring both scalability and security. We further present a case study using NanoGPT to demonstrate the effectiveness of the proposed framework for decentralized unlearning. We conclude by identifying potential future research directions. © 1986-2012 IEEE.

关键词： Network security

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Effective Instruction Parsing Plugin for Complex Logical Query Answering on knowledge Graphs 34

Effective Instruction Parsing Plugin for Complex Logical Que...

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34th ACM Web Conference, WWW 2025

作者： Zhuo, Xingrui Wang, Jiapu Wu, Gongqing Pan, Shirui Wu, Xindong Hefei University of Technology Anhui Hefei China Beijing University of Technology Beijing China Griffith University Gold Coast QLD Australia The Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology China

ISBN: (纸本)9798400712746

knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. To enhance the generalization of KGQE models, recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries. The whole process is commonly referred to as Query Pattern Learning (QPL). However, current QPL methods typically suffer from the pattern-entity alignment bias problem, leading to the learned defective query patterns limiting KGQE models’ performance. To address this problem, we propose an effective Query Instruction Parsing Plugin (QIPP) that leverages the context awareness of Pre-trained Language Models (PLMs) to capture latent query patterns from code-like query instructions. Unlike the external information introduced by previous QPL methods, we first propose code-like instructions to express FOL queries in an alternative format. This format utilizes textual variables and nested tuples to convey the logical semantics within FOL queries, serving as raw materials for a PLM-based instruction encoder to obtain complete query patterns. Building on this, we design a query-guided instruction decoder to adapt query patterns to KGQE models. To further enhance QIPP’s effectiveness across various KGQE models, we propose a query pattern injection mechanism based on compressed optimization boundaries and an adaptive normalization component, allowing KGQE models to utilize query patterns more efficiently. Extensive experiments demonstrate that our plug- and-play method¹ improves the performance of eight basic KGQE models and outperforms two state-of-the-art QPL methods. © 2025 Copyright held by the owner/author(s).

关键词： Complex Query Answering knowledge Graph Pre-trained Language Model Query Pattern Learning

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Research on the detection and analysis of weak signals with low SNR based on the Welch algorithm 24

Research on the detection and analysis of weak signals with ...

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2nd International Conference on Communication Networks and Machine Learning, CNML 2024

作者： Wu, Bin Zhang, Wanqing Zhang, Jun Yu, Jing Huangfu, Taoyu Hebei Key Laboratory of Marine Perception Network and Data Processing School of Computer and Communication Engineering Northeastern University at Qinhuangdao Hebei Qinhuangdao China Southeast University Jiangsu Nanjing China

ISBN: (纸本)9798400711688

Submarines are vital for military defense and national security in strategic communication systems. Power Spectral Density (PSD) characterizes signal spectrum frequency dependence and is crucial for analyzing broadband random signals. This study proposes a simplified PSD estimation method using the Welch algorithm for detecting weak signals in noisy environments, particularly in submarine communications. MATLAB simulations demonstrate that the Welch algorithm with a Blackman window function and 512-point overlap improves variance performance and resolution, especially in low signal-to-noise ratio conditions. The results underscore the algorithm’s effectiveness in enhancing signal detection in submarines. © 2024 Copyright held by the owner/author(s).

关键词： MATLAB

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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

作者： Miao, Haonan Zhao, Peizheng Sun, Yuqi Nan, Fang Zhang, Xiaolong Wu, Yaqiang Tian, Feng School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350368741

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students' gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods. © 2025 IEEE.

关键词： classroom depth-enhanced 2D space dual-view gaze target estimation spatial transformation

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