检索结果-内蒙古大学图书馆

“Weibing” in traditional Chinese medicine—biological basis and mathematical representation of disease-susceptible state

引用

Acta Pharmaceutica Sinica B 2025年第5期15卷 2363-2371页

作者： Sun, Wanyang Wang, Rong Ouyang, Shuhua Liang, Wanli Duan, Junwei Gong, Wenyong Hu, Lianting Chen, Xiujuan Li, Yifang Kurihara, Hiroshi Yao, Xinsheng Gao, Hao He, Rongrong Institute of Traditional Chinese Medicine and Natural Products College of Pharmacy/Guangdong Engineering Research Center of Traditional Chinese Medicine & Disease Susceptibility/Guangdong Engineering Research Center of Traditional Chinese Medicine & Health Products/International Cooperative Laboratory of TCM Modernization and Innovative Drug Development of Chinese Ministry of Education (MOE)/Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research/State Key Laboratory of Bioactive Molecules and Druggability Assessment Jinan University Guangzhou 510632 China State Key Laboratory of Quality Research in Chinese Medicine Macau University of Science and Technology Macau 999078 China College of Information Science and Technology Jinan University Guangzhou 510632 China Faculty of Data Science City University of Macau Macau 999078 China Medical Big Data Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou 510632 China The Data Center Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital) Tongji Medical College Huazhong University of Science and Technology Wuhan 430016 China

“Weibing” is a fundamental concept in traditional Chinese medicine (TCM), representing a transitional state characterized by diminished self-regulatory abilities without overt physiological or social dysfunction. This perspective delves into the biological foundations and quantifiable markers of Weibing, aiming to establish a research framework for early disease intervention. Here, we propose the “Health Quadrant Classification” system, which divides the state of human body into health, sub-health, disease-susceptible state, and disease. We suggest the disease-susceptible stage emerges as a pivotal point for TCM interventions. To understand the intrinsic dynamics of this state, we propose laboratory and clinical studies utilizing time-series experiments and stress-induced disease susceptibility models. At the molecular level, bio-omics technologies and bioinformatics approaches are highlighted for uncovering intricate changes during disease progression. Furthermore, we discuss the application of mathematical models and artificial intelligence in developing early warning systems to anticipate and avert the transition from health to disease. This approach resonates with TCM's preventive philosophy, emphasizing proactive health maintenance and disease prevention. Ultimately, our perspective underscores the significance of integrating modern scientific methodologies with TCM principles to propel Weibing research and early intervention strategies forward. © 2025 The Authors

关键词： Disease-susceptible state Mathematical modeling Preventive medicine Traditional Chinese medicine Weibing

来源：评论

学校读者我要写书评

暂无评论

keyword-Oriented Multimodal Modeling for Euphemism Identification

arXiv

引用

arXiv 2025年

作者： Hu, Yuxue Li, Junsong Chen, Meixuan Su, Dongyu Wang, Tongguan Sha, Ying College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China

Euphemism identification deciphers the true meaning of euphemisms, such as linking "weed" (euphemism) to "marijuana" (target keyword) in illicit texts, aiding content moderation and combating underground markets. While existing methods are primarily text-based, the rise of social media highlights the need for multimodal analysis, incorporating text, images, and audio. However, the lack of multimodal datasets for euphemisms limits further research. To address this, we regard euphemisms and their corresponding target keywords as keywords and first introduce a keyword-oriented multimodal corpus of euphemisms (KOM-Euph), involving three datasets (Drug, Weapon, and Sexuality), including text, images, and speech. We further propose a keyword-oriented multimodal euphemism identification method (KOM-EI), which uses cross-modal feature alignment and dynamic fusion modules to explicitly utilize the visual and audio features of the keywords for efficient euphemism identification. Extensive experiments demonstrate that KOM-EI outperforms state-of-the-art models and large language models, and show the importance of our multimodal datasets 1 Copyright © 2025, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CMCache: An Adaptive Cross-Level data Placement Method for Multi-Level Cache

引用

IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Zeng, Zhaoyang Tan, Yujuan Ma, Zhulin Li, Jiali Zhao, Sanle Liu, Duo Chen, Xianzhang Ren, Ao Chongqing University College of Computer Science Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China Chongqing Universityof Posts and Telecommunications College of Software Engineering China Chongqing University College of Big Data and Software Engineering Chongqing China

Multi-level cache systems enhance I/O performance by optimizing data placement across various cache levels from a global perspective. However, existing methods often struggle to place data at the optimal cache level promptly due to their reliance on historical access patterns and inflexible placement strategies. These methods face two main challenges: (1) For already cached data with sufficient access history, existing approaches only optimize movement between adjacent cache levels, potentially delaying data arrival at its globally optimal cache level and leading to unnecessary bandwidth consumption and increased latency. (2) For newly entered data without access history, current methods cannot accurately predict their future hotness and simply place them at a fixed cache level (i.e. first or last level), overlooking future accesses of new data and potentially resulting in high cache miss rates or cache pollution. To address these issues, we propose CMCache, an adaptive cross-level data placement method for multi-level cache. CMCache applies distinct placement strategies for cached and new data to reach the optimal level timely, considering their different characteristics. It also logically divides cache space into two sections to manage cached and new data separately, dynamically adjusting section sizes based on access patterns. This approach significantly improves data placement efficiency, achieving up to an 89% reduction in miss rates and a 79% decrease in average response times compared to existing methods. © 2025 IEEE.

关键词： Cache memory

来源：评论

学校读者我要写书评

暂无评论

Effect of high silicon content in enhancing the creep properties of a PM near-α Ti–6Al–2Sn–4Zr–2Mo–0.5Y–0.5Si alloy

引用

Journal of Materials Research and Technology 2025年 36卷 4864-4881页

作者： Xuemei Yu Fuyang Yu Bowen Zhang Xiaogang Wu Hongzhi Niu Deliang Zhang School of Material Science and Engineering Northeastern University Shenyang 110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry (Ministry of Education) Northeastern University Shenyang 110819 China

This study investigates the effect of high Si content on the creep properties and deformation mechanisms of a powder metallurgy (PM) near-α titanium alloy with a composition of Ti–6Al–2Sn–4Zr–2Mo–0.5Y–0.5Si (wt.%) fabricated using high-energy ball milling (HEBM) and thermomechanical powder consolidation (TMC). Under 600 °C/250 MPa, the alloy exhibits a 263 % increase in creep life, a 48 % reduction in steady-state creep rate, and 100 % improvement in creep fracture strain compared with the corresponding titanium alloy containing 0.1 wt% Si under the same fabrication and heat treatment conditions. The superior creep properties are attributed to the stabilized α/β and α/β t interfaces, the uniform dispersion of (Ti, Zr) 6 Si 3 nanoparticles, and the increased Si solubility in both α and β phases, all of which collectively hinder dislocation movement. Additionally, Si addition suppresses the formation of continuous α GB plates, while the presence of higher Si solubility and dispersed nanoscale silicides alleviates stress concentration, delays void and microcrack formation, and ultimately enhances creep fracture strain. These findings underscore the critical role of interface stability, nanoscale silicide dispersion, and enhancing Si solubility in optimizing high-temperature titanium alloys for extreme service environments.

关键词： Near-α titanium alloy Thermomechanical powder consolidation Effect of high silicon content Microstructure Creep property

来源：评论

学校读者我要写书评

暂无评论

LarTap: A Luminance-aware Framework with Text-correlation Priors for Multi-Exposure Image Fusion

引用

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Enlong Li, Jiawei Yan, Tiantian Lei, Jia Zhou, Shihua Wang, Bin Liu, Jinyuan Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi’an Jiaotong University School of Software Engineering Shaanxi Xi’an710049 China Dalian Univer sity of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand

Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Image correlation

来源：评论

学校读者我要写书评

暂无评论

A New Mechanism Model-Assisted Spatiotemporal Information Fusion Quality-Related Fault Diagnosis Method for Large-Scale Industrial Processes

引用

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Hua, Dongjie Dong, Jie Peng, Kaixiang Zhang, Qichun University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China Buckinghamshire New University School of Creative and Digital Industries High WycombeHP11 2JZ United Kingdom

Modern industrial processes with long production lines and multiple operating units exhibit complex spatiotemporal characteristics under the constraints of information flow, material flow, and energy flow. The quality spatial heritability and dynamic-coupled variables of long-term processes bring serious challenges to conventional quality-related fault diagnosis. Against this background, a new mechanism model-assisted spatiotemporal information fusion quality-related fault diagnosis method is proposed. First, the coupling relationships among variables in the mechanism models are intuitively represented as causal correlation networks (CCNs), and large-scale industrial processes are decomposed into multiple subsystems interpretably by CCNs. Second, a novel 1-D convolutional autoencoder with neighborhood preserving embedding in Riemannian space (1DCAE-RNPE) is designed, where 1DCAE is adopted to capture temporal features stably and RNPE mines spatial features in higher dimensions. On this basis, the correlations among spatiotemporal features and quality variables are explored by distributed canonical correlation analysis (DCCA). Bayesian inference is enforced to establish global monitoring statistics. In addition, after a fault occurs, the contributions of the variables are obtained to identify the fault variables and combined with CCNs to recognize the root cause. Finally, the effectiveness and advantages of the proposed method are demonstrated through a practical large-scale industrial process, the hot strip mill process (HSMP). Superior fault diagnosis performance has been attained compared to other competing methods. Meanwhile, root cause recognition is accurately realized. © 1963-2012 IEEE.

关键词： Information fusion

来源：评论

学校读者我要写书评

暂无评论

Sfmfold: A Multi-Modal Contrast Learning Model for Rna Secondary Structure Prediction

SSRN

引用

SSRN 2025年

作者： Li, Ang Liu, Yuanning Zhang, Zhaoyang Li, Shuwang Dong, Hao Lv, Meng 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 College of Software Jilin University Changchun130012 China College of Information Engineering Chang’an University Xi’an710064 China

The secondary structure of RNA can regulate the interaction between RNA and protein and perform various biological tasks through specific folding shapes, which plays an important role in revealing biomolecular mechanisms and developing new vaccines and therapeutic drugs. However, existing RNA secondary structure prediction methods based on thermodynamics and traditional neural network models usually lack the ability to model long-distance dependencies of RNA molecules and cannot handle complex non-nested structures in RNA molecules. In this paper, we propose a multi-modal contrastive learning method SFMFold based on state space model, which captures long-distance interactions in RNA sequences through RNA sequence representation learning part, and models the structure of RNA molecules through RNA structure representation learning part. Finally, the features of the two modalities are aligned and deeply fused to obtain the final RNA molecule prediction map. We test SFMFold on several datasets, and the experiments show that our proposed model shows excellent performance in cross-family prediction and long sequence prediction tasks. In addition, SFMFold can also effectively predict non-nested structures such as pseudoknot with better accuracy than the SOTA methods. © 2025, The Authors. All rights reserved.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space 26

Safe Reinforcement Learning for Social Motor Coordination in...

引用

26th International Conference on Industrial Technology, ICIT 2025

作者： Yang, Fan Huang, Yuening Zhai, Chao Zhang, Hehong China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing HoustonTX United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (纸本)9798331521950

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints. © 2025 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

ATCM: Aerial-Terrestrial LiDAR-based Collaborative Simultaneous Localization and Mapping

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Xu, Yuhang Chen, Chi Yang, Bisheng Wu, Weitong Sun, Shangzhe Wang, Zhiye Li, Liuchun Zou, Qin Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Engineering Research Center for SpatioTemporal Data Smart Acquisition and Application Ministry of Education of China Institute of Geo-Spatial Intelligence Wuhan430079 China Hohai University School of Earth Sciences and Engineering Nanjing211100 China Wuhan University School of Computer Science Wuhan430079 China

Multi-robot collaborative simultaneous localization and mapping (C-SLAM) offers precise scene reconstruction over single-robot SLAM and enables the data fusion from heterogeneous robots. However, heterogeneous C-SLAM faces challenges in both accurate inter-robot loop closure detection and globally consistent data fusion due to inherent viewpoint disparities and heterogeneous data characteristics. This paper introduces ATCM, an Aerial-Terrestrial LiDAR-based C-SLAM method designed for heterogeneous robots without priori initial relative position. ATCM comprises three modules: single-robot front-end employing diverse SLAM methods, multi-robot loop closure detection, and global pose graph optimization. A novel LiDAR-based cross-view global loop descriptor is proposed for scan-to-scan heterogeneous inter-robot loop closure detection. By uniformly mapping cross-view information into the height domain and integrating dynamic height, the loop descriptor automatically achieves viewpoint correction. Additionally, we introduce a bidirectional loop detection algorithm that validates inter-robot loop closures through both forward and reverse detections. Finally, the two-stage global pose graph optimization integrates multi-source measurements, ensuring globally consistent mapping and localization with cross-view data. We have validated the effectiveness of ATCM on campus scenario datasets and the KITTI dataset, achieving a remarkable 21.95% improvement in trajectory accuracy and a 17.00% enhancement in map precision compared to high-precision point cloud maps, surpassing state-of-the-art LiDAR-based odometry methods. In the ablation experiments, the proposed loop descriptor achieved 97% accuracy in recognizing heterogeneous inter-robot loop closures. Moreover, compared to the traditional unidirectional method, the bidirectional loop detection method demonstrates up to a 31.2% improvement in loop closure accuracy. © 2025 IEEE.

关键词： SLAM robotics

来源：评论

学校读者我要写书评

暂无评论

AdvMap: Crafting Adversarial Maps to Counter AI Aimbot in First-Person Shooter Games

引用

IEEE Transactions on Games 2025年

作者： Yan, Kai Chen, Xianyi Cui, Qi Yuan, Haoqin Tan, Zhenshan Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education the School of Computer Science Nanjing210044 China Beijing Jiaotong University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing100044 China

AI-based automatic aiming cheats (a.k.a., AI aimbots) have proliferated in first-person shooter (FPS) games, which grant malicious users an unfair gameplay advantage. Since AI aimbots operate independently of game data and are developed using object detection algorithms, they are difficult to detect with traditional anti-cheating methods. To actively counter AI aimbots, we propose AdvMap, which introduces invisible adversarial perturbations into game scene elements. In optimizing these adversarial perturbations, we design a mixture-of-misleading loss function that increases the total target confidence score within each misleading bounding box. It mitigates the risk of segment missing even when AdvMap is obscured, thereby enhancing the robustness. Besides, an L1-norm constraint with a small scale is employed during each update of the adversarial perturbations, which preserves the fidelity of the game scene. Additionally, to enable effective adaptation to interact with various elements within game environments, we introduce an Image-Subspace-Based Multi-Directional (ISMD) optimization strategy. It enables the adversarial perturbations to adaptively fit into each element by leveraging the mapping relationship between the game's 3D scenes and its corresponding 2D images. Furthermore, we construct a comprehensive benchmark, which includes various FPS games with different graphics styles and perspectives. Extensive experimental results demonstrate the efficacy of our method in countering various AI aimbot tools on different state-of-the-art object detection methods. © 2021 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：