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Preoperative Contrast-Enhanced Ultrasound Predicts Microvascular Invasion in Hepatocellular Carcinoma as Accurately as Contrast-Enhanced MR

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JOURNAL OF ULTRASOUND IN MEDICINE 2024年第3期43卷 439-453页

作者： Wang, Cuiwei Zhang, Teng Sun, shuwen Ye, Xinhua Wang, Yali pan, Minhong Shi, Haibin Nanjing Med Univ Affiliated Hosp 1 Dept Ultrasound Nanjing Peoples R China Nanjing Med Univ Affiliated Hosp 1 Dept Radiol Nanjing Peoples R China Nanjing Med Univ Affiliated Hosp 1 Dept Pathol Nanjing Peoples R China Nanjing Med Univ Affiliated Hosp 1 Dept Intervent Radiol Nanjing Jiangsu Provinc Peoples R China

Objectives-Both contrast-enhanced ultrasound (CEUS) and contrast-enhanced magnetic resonance (CEMR) are important imaging methods for hepatocellular carcinoma (HCC). This study aimed to establish a model using preoperative CEUS parameters to predict microvascular invasion (MVI) in HCC, and compare its predictive efficiency with that of CEMR ***-A total of 93 patients with HCC (39 cases in MVI positive group and 54 cases in MVI negative group) who underwent surgery in our hospital from January 2020 to June 2021 were retrospectively analyzed. Their clinical and imaging data were collected to establish CEUS and CEMR models for predicting MVI. The predictive efficiencies of both models were ***-By the univariate and multivariate regression analyses of patients' clinical information, preoperative CEUS static and dynamic images, we found that serrated edge and time to peak were independent predictors of MVI. The CEUS prediction model achieved a sensitivity of 92.3%, a specificity of 83.3%, and an accuracy of 84.6% (Az: 0.934). By analyzing the clinical and CEMR information, we found that tumor morphology, fast-in and fast-out, peritumoral enhancement, and capsule were independent predictors of MVI. The CEMR prediction model achieved a sensitivity of 97.4%, a specificity of 77.8%, and an accuracy of 83.2% (Az: 0.900). The combination of the two models achieved a sensitivity of 84.6%, a specificity of 87.0%, and an accuracy of 86.2% (Az: 0.884). There was no significant statistical difference in the areas under the ROC curve of the three ***-The CEUS model and the CEMR model have similar predictive efficiencies for MVI of HCC. CEUS is also an effective method to predict MVI before operation.

关键词： contrast-enhanced magnetic resonance contrast-enhanced ultrasound hepatocellular carcinoma microvascular invasion

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面向新工科的移动机器人教育体系及实践平台构建

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创新创业理论研究与实践 2022年第23期5卷 79-81页

作者：刘艳张伟李艳君王雪洁陈琢潘树文浙大城市学院信息与电气工程学院浙江杭州310015 浙江经济职业技术学院数字信息技术学院浙江杭州310018

该文提出了面向新工科的自动化类专业移动机器人教育体系及实践平台构建,实现了教育模式从学科知识导向转变为产业需求导向、从专业分割转型跨界交叉、从适应服务转向支撑引领的转变;采用“以做促学、以赛促学,以研促学”模式驱动该体... 详细信息

该文提出了面向新工科的自动化类专业移动机器人教育体系及实践平台构建,实现了教育模式从学科知识导向转变为产业需求导向、从专业分割转型跨界交叉、从适应服务转向支撑引领的转变;采用“以做促学、以赛促学,以研促学”模式驱动该体系的实施,通过统筹建设不同层级难度的课程内容和实践平台,使得课程体系网状化、教学内容和实践平台紧跟市场需求,提高了移动机器人方向专业人才的培养质量。

关键词：新工科教育移动机器人教育体系实践平台

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Research on Seismic Signal Identification and Magnitude Prediction Model Based on Sample Entropy and Machine Learning 2

Research on Seismic Signal Identification and Magnitude Pred...

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2nd IEEE International Conference on Sensors, Electronics and Computer Engineering, ICSECE 2024

作者： pan, Li Liu, Mingyuan Chen, Rong Ma, shuwen Lanzhou Vocational Technical College Lanzhou730070 China Institute for Optimization and Decision Analytics Liaoning Technical University Fuxin123000 China

ISBN: (纸本)9798350373646

Based on advanced artificial intelligence technology, this study explores for earthquake early warning and magnitude prediction. By analyzing seismic signals through CEEMDAN, constructing sample entropy feature vectors and applying various machine learning models, natural seismic events are successfully identified. In terms of magnitude prediction, combining LSTM and other models, we effectively extracted seismic wave features and realized accurate prediction. Through data analysis and model optimization, the association between features and magnitude is deeply explored, providing new ideas for earthquake research. The study also explores the connection between reservoir attributes and magnitude. This paper innovatively reveals the CEEMDAN sample entropy analysis method, which brings new ideas and methods to the field of seismology. These results are of great significance to the development of earthquake early warning technology and earthquake prevention measures and provide useful references for future related research. © 2024 IEEE.

关键词： Prediction models

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Female frogs communicate with males through blinking

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CURRENT BIOLOGY 2024年第5期34卷 R191-R192页

作者： Chen, pan Liu, shuwen Wu, Yatao Liu, Guoqing Jin, Yingying Zhang, Fang Anhui Normal Univ Coll Life Sci Anhui Prov Key Lab Conservat & Exploitat Biol Reso Wuhu 241000 Peoples R China Anhui Normal Univ Sch Geog & Tourism Wuhu 241000 Peoples R China

Blinking is a behavior unique to animal taxa with motile eyelids, such as most amphibians and reptiles as well as all birds and mammals1 . Eyelid movement has physiological functions, such as lubricating the cornea and washing away dust, but its potential signaling functions are not well understood1 , 2 . The use of eyeblinks as a social signal is currently thought to be restricted to some primates, especially humans and their companion animals, but has not been verified in other taxa1 , 3 , 4 . Here, through field observation and experiments, we demonstrate that female concave-eared torrent frogs (Odorrana tormota ), which inhabit noisy streams, use eyeblinks to communicate with males to urge them to initiate amplexus for mating. Our findings reveal that eyeblinks may serve as a social signal in non-primate species.

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A Kalman Filtering for Linear Discrete-time System with Unknown Inputs 30

A Kalman Filtering for Linear Discrete-time System with Unkn...

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30th Chinese Control and Decision Conference (CCDC)

作者： Li, Weiran pan, Jie Li, Yanjun pan, shuwen Liu, Yan Zhejiang Univ City Coll Sch Informat & Elect Engn Key Discipline Automat Hangzhou 310015 Peoples R China Univ Western Australia Crawley WA 6009 Australia

ISBN: (纸本)9781538612446

This paper focuses on the joint input and state estimation for stochastic discrete-time systems. To obtain unique estimators that are optimal in the sense of being both least-squares and minimum-variance unbiased (MVU) and their recursive solution, an objective function of the sum of squared errors of outputs is first minimized with respect to an extended state vector including states and unknown inputs. A recursive solution of the extended state vector is then derived with the aid of matrix manipulations. Finally, appropriate matrix decomposition and transformation are used to extract the recursive solutions only involving the current states and unknown inputs from the recursive solution obtained in the previous step, This approach avoids excessive computation due to the dimensions of the matrices that increase with time. As a result, the resulting recursive solution is referred to as a general Kalman filter with unknown inputs (GKF-UI), which covers the most general case of unknown inputs so far in the literature without resorting to transforming the outputs and/or unknown inputs. The unknown inputs to be estimated by the proposed GKF-UI could be arbitrary signals without any prior information and the properties of the proposed GKF-UI are also examined. A numerical example with partial feedthrough from three unknown inputs is used to demonstrate the effectiveness and accuracy of the proposed algorithm.

关键词： Kalman filtering unknown input estimation minimum-variance unbiased filter least-squares estimation

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Handling dependencies among performance shaping factors in SPAR-H through DEMATEL method

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NUCLEAR ENGINEERING AND TECHNOLOGY 2023年第8期55卷 2897-2904页

作者： Xu, Zhihui Shang, shuwen Su, Xiaoyan Qian, Hong pan, Xiaolei State Key Lab Nucl Power Safety Monitoring Technol Shenzhen 518172 Peoples R China Shanghai Univ Elect Power Sch Automat Engn Shanghai 200090 Peoples R China

The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method is a widely used method in human reliability analysis (HRA). Performance shaping factors (PSFs) refer to the factors that may influence human performance and are used to adjust nominal human error probabilities (HEPs) in SPAR-H. However, the PSFs are assumed to be independent, which is unrealistic and can lead to unreasonable estimation of HEPs. In this paper, a new method is proposed to handle the dependencies among PSFs in SPAR-H to obtain more reasonable results. Firstly, the dependencies among PSFs are analyzed by using decision-making trial and evaluation laboratory (DEMATEL) method. Then, PSFs are assigned different weights according to their dependent relationships. Finally, multipliers of PSFs are modified based on the relative weights of PSFs. A case study is illustrated that the proposed method is effective in handling the dependent PSFs in SPAR-H, where the duplicate calculations of the dependent part can be reduced. The proposed method can deal with a more general situation that PSFs are dependent, and can provide more reasonable results.

关键词： Human reliability analysis Dependence Performance shaping factors SPAR-H Decision-making trial and evaluation laboratory

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Contrastive Active Learning Under Class Distribution Mismatch

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第4期45卷 4260-4273页

作者： Du, pan Chen, Hui Zhao, Suyun Chai, shuwen Chen, Hong Li, Cuiping Renmin Univ China Sch Informat Beijing 100872 Peoples R China Renmin Univ China Sch Stat Beijing 100872 Peoples R China

Active learning(AL) has been successful based on the premise that labeled and unlabeled data come from the same class distribution. However, its performance undergoes a severe deterioration under class distribution mismatch, wherein the unlabeled data contain numerous instances out of the class distribution of labeled data. In this article, we solve this practical yet rarely studied problem by minimizing the AL error, which is formally defined and decomposed as the valid query error and invalid query error. Specifically, the invalid query error is associated with the queries from unknown categories, and the valid query error is attributed to less informative queries from target categories. In light of this discovery, we propose a contrastive AL framework, named ConAL, to simultaneously learn the semantics and distinctiveness of the instances by contrastive techniques, thereby reducing the invalid query error and valid query error, respectively. Theoretically, we prove that the AL error of ConAL has a tight upper bound. Experimentally, ConAL achieves superior performance on two benchmark datasets, CIFAR10 and CIFAR100, and a cross-dataset with class distribution across multi-datasets. Furthermore, we validate that the ConAL technique performs admirably even on the realistic dataset. To the best of our knowledge, ConAL is the first AL work for class distribution mismatch.

关键词： Semantics Dogs Annotations Data models Costs Task analysis Supervised learning Active learning class distribution mismatch contrastive learning machine learning

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Health characteristics and factors associated with transition shock in newly graduated registered nurses: a latent class analysis

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BMC NURSING 2024年第1期23卷 211页

作者： Wang, pan Zhou, Juan Shen, Xin Ge, Yaping Li, Yanran Ge, Hui Li, shuwen Southeast Univ Zhong Da Hosp Dept Cardiac Surg Dingjiaqiao 87 Nanjing 210009 Peoples R China Anhui Med Univ Sch Nursing Feicui Rd 15 Hefei 230032 Peoples R China

Background Transition shock occurs at a vulnerable time in newly graduated registered nurses' careers and has a clear impact on both newly graduated registered nurses' productivity and patient recovery outcomes. Identifying classification features of transition shock and targeting interventions to support newly graduated registered nurses is imperative. The study aimed to explore potential transition shock subgroups of newly graduated registered nurses and further explore the impact of population characteristics and two indices of health on transition *** A descriptive, cross-sectional design was conducted. An online questionnaire was sent via WeChat to newly graduated registered nurses who started work in 2021 at seven hospitals between August and November 2021, and 331 nurses filled out the questionnaire. Latent class analysis was used to identify the potential class of the transition shock of newly graduated registered nurses, and multinomial logistic regression analyses were used to determine the factors of potential *** The study identified four classes of transition shock in newly graduated registered nurses, namely, "high transition shock", "physical fatigue-lack of knowledge", "development adaptation" and "low transition shock-worry" groups. Newly graduated registered nurses who urinated less than 4 times per day (OR = 0.051, 95% CI = 0.005-0.502) were likely to be in the "high transition shock" group. Newly graduated registered nurses who did not delay urination (OR = 4.267, 95% CI = 1.162-11.236) were more likely to belong to the "low transition shock-worry" group. Newly graduated registered nurses without sleep disturbance were more likely to be in the "physical fatigue - lack of knowledge" (OR = 3.109, 95% CI = 1.283-7.532), "development adaptation" (OR = 8.183, 95% CI = 2.447-27.066), and "low transition shock-worry" (OR = 8.749, 95% CI = 1.619-47.288) groups than in the 'high transition shock' *** This study h

关键词： Transition shock Newly graduated registered nurses Latent class analysis

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缓解职业人员热应激用个体冷却服的研究进展

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现代丝绸科学与技术 2021年第4期36卷 35-40页

作者：陈曦潘姝雯戴宏钦胡珏陈慧臻苏州大学纺织与服装工程学院江苏苏州215021

不论是在航空、消防、医疗,还是在危险品处理领域,防护服的使用,通常都将削弱人体对环境的散热能力,增加人体热应激和高热的风险。通过对国内外个体冷却服的分类、研究现状以及测试评价方法的介绍,提出未来个体冷却服设计应该在服装面... 详细信息

不论是在航空、消防、医疗,还是在危险品处理领域,防护服的使用,通常都将削弱人体对环境的散热能力,增加人体热应激和高热的风险。通过对国内外个体冷却服的分类、研究现状以及测试评价方法的介绍,提出未来个体冷却服设计应该在服装面料与结构、冷源的选择与位置分布、动力源、纳米技术等方面进行创新,同时结合温控系统实现智能调温,为有针对性地缓解职业环境下劳动者热应激、保障劳动者的职业安全与生产力提供参考。

关键词：防护服热应激冷却人体热平衡职业安全

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Power Flow for Grids with Unfixed Topologies by Using Deep Neural Network 37

Power Flow for Grids with Unfixed Topologies by Using Deep N...

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37th Chinese Control Conference (CCC)

作者： Ye, Qianying Li, Yanjun pan, shuwen Xiang, Ji Zhejiang Univ City Coll Sch Informat & Elect Engn Hangzhou 310015 Zhejiang Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9789881563958

The power flow problem is critical in power systems, providing fundamental and vital information in system planning, operational management, and control strategy. The mathematical model of power flow problem is composed of several non-linear equations conventionally solved by the Newton-Raphson method. In this paper, the deep neural network which is called slacked auto-encoder (SAE), has been used to solve the power flow problem. Compared to the traditional neural network, stacked auto-encoder is a more effective method in terms of getting better local optimum and solving the problem of gradient diffusion. The resulted deep neural network can deal with the grids with various topologies corresponding to various line or bus faults. After a detailed description, the proposed method is evaluated by a comparison with a traditional neural network by Matlab simulation.

关键词： Power Flow Artificial Neural Network Stacked Auto-Encoder

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