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954 results on '"LIU Haiping"'

1. Research Progress on Animal Models of Intrauterine Growth Restriction

Author

HU Qiwen, BI Zheng, LIU Haiping, DONG Zhihua, ZHUYanlin, and WANG Jinhua

Subjects
animal model, intrauterine growth restriction, research progress, Medicine
Abstract

The occurrence of intrauterine growth restriction (IUGR) may be related to maternal malnutrition, abnormal placental function, immune abnormalities, genetically related problems as well as other diseases, but the mechanism is still unclear. Therefore, the study of IUGR and the development of its animal model are critical issues in obstetrics. IUGR models are mainly based on laboratory rodents, such as mice and rats, and other mammals such as pigs, rabbits and sheep. This article introduced several common IUGR animal models, including nutrition restriction model, high-altitude pregnancy model, natural selection model, and nicotine exposure model, and also described the construction methods of IUGR models and the comparison of their advantages and disadvantages.

Published
2022
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2. Study on the genetic damage caused by cadmium sulfide quantum dots in human lymphocytes

Author

Liu Haiping, Liu Huajie, Liu Haiyan, Zhang Duo, Wang Qian, Li Shuang, Cao Yanhua, and Li Qingzhao

Subjects
cadmium sulfide quantum dots, dna damage, comet test, micronucleus test, chromosome aberration test, Biology (General), QH301-705.5
Abstract

Cadmium sulfide quantum dots (CdS QDs) are being developed for sensors, fluorescent probes, and other platforms and are attracting increasing attention. Given the growing demand for QDs, it is clear that there is a need to understand their potential toxicity to organisms. However, little is known regarding the genotoxicity of CdS QDs to humans. Therefore, this study used CdS QDs as the research object, cultured human peripheral blood lymphocytes, and randomly divided them into a control group, CdS I group (CdS QDs), and CdS II group (CdS QDs coated with thioglycolic acid). After cultivation, we measured the olive tail distance, tail length, tail DNA%, lymphocyte micronucleus rate, and aneuploid rate. The comet test results indicated that the indices of the QD group were significantly larger than those of the control group (P < 0.05). The results of the micronucleus and chromosome aberration tests showed that the lymphocyte micronucleus rate and chromosome aneuploid rate in the QD group were significantly increased (P < 0.05) compared with those in the control group. In conclusion, CdS QDs have certain genotoxicity to human peripheral blood lymphocytes, and the DNA damage caused by CdS QDs encapsulated with thioglycolic acid is less severe than that caused by nonencapsulated CdS QDs.

Published
2022
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3. Alleviating Hyperparameter-Tuning Burden in SVM Classifiers for Pulmonary Nodules Diagnosis with Multi-Task Bayesian Optimization

Author

Chi, Wenhao, Liu, Haiping, Dong, Hongqiao, Liang, Wenhua, and Liu, Bo

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

In the field of non-invasive medical imaging, radiomic features are utilized to measure tumor characteristics. However, these features can be affected by the techniques used to discretize the images, ultimately impacting the accuracy of diagnosis. To investigate the influence of various image discretization methods on diagnosis, it is common practice to evaluate multiple discretization strategies individually. This approach often leads to redundant and time-consuming tasks such as training predictive models and fine-tuning hyperparameters separately. This study examines the feasibility of employing multi-task Bayesian optimization to accelerate the hyperparameters search for classifying benign and malignant pulmonary nodules using RBF SVM. Our findings suggest that multi-task Bayesian optimization significantly accelerates the search for hyperparameters in comparison to a single-task approach. To the best of our knowledge, this is the first investigation to utilize multi-task Bayesian optimization in a critical medical context., Comment: 12 pages, 4 figures, 37 references

Published
2024

5. A NEW PREDICTION METHOD FOR RUL PREDICTION OF CRUSHER ROLL IN NON STATIONARY PARTIALLY OBSERVED STATE

Author

WU JianJun, LIU HaiPing, and YE Xiang

Subjects
Partially observable state, Correlation analysis, Wavelet analysis, RUL Prediction, Support vector regression, Mechanical engineering and machinery, TJ1-1570, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Accurate prediction of the remaining useful life（RUL） of the roller with double toothed roll crusher is an important basis for maintenance personnel to make scientific maintenance decision. In engineering practice, some state information is often observed. In order to solve the problem of the remaining useful life of the roller sleeve is difficult to predict accurately in the non-linear and non-stationary state, a new method of remaining useful life prediction based on wavelet transform and SVR fusion is proposed. Firstly, the correlation analysis is used to select the characteristic value of the vibration signal of the roller. Secondly, wavelet technology is used to eliminate the noise of the collected vibration signals. Then, the relationship between the residual life and the characteristic value is established, and the nonlinear dynamic change of the roll sleeve is described. Finally through the SVR model to predict the remaining useful life of the crusher roller. Prediction results show that the method can effectively solve the remaining useful life prediction of partially observable state, thereby reducing the maintenance cost, and has strong engineering applicability and popularization value.

Published
2018
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11. Study on Thermal Comfort of Radar Electronic Cabin Operator Based on BP Neural Network

Author

Liu, Haiping, Dang, Sina, Wen, Tao, Qu, Jue, Wang, Wei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Long, Shengzhao, editor, Dhillon, Balbir S., editor, and Ye, Long, editor

Published
2024
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12. Experimental Study on Influence of Thermal Environment of Radar Electronic Shelter on Thermal Comfort of Operators

Author

Zhang, Jiali, Dang, Sina, Liu, Haiping, Wang, Qingli, Zeng, Fan, Xiao, Jun, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Long, Shengzhao, editor, Dhillon, Balbir S., editor, and Ye, Long, editor

Published
2024
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13. Multi-objective Optimization of Piezoelectric Energy Harvester Combined with Euler Buckled Beams Type Nonlinear Energy Sink

Author

Zhang, Jun, Liu, Haiping, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Rui, Xiaoting, editor, and Liu, Caishan, editor

Published
2024
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14. Study on the Dynamic Performance of X-shaped Vibration Isolator with Friction Damping Based on Incremental Harmonic Balance Method

Author

Yang, Zhongren, Liu, Haiping, Li, Hongbo, Wang, Tian, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Jing, Xingjian, editor, Ding, Hu, editor, Ji, Jinchen, editor, and Yurchenko, Daniil, editor

Published
2024
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48. Evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support: three decades development course and future prospect

Author

Liu, Bo, Chi, Wenhao, Li, Xinran, Li, Peng, Liang, Wenhua, Liu, Haiping, Wang, Wei, and He, Jianxing

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret the massive volume of chest images generated daily, computer-assisted diagnosis of pulmonary nodule has opened up new opportunities to relax the limitation from physicians' subjectivity, experiences and fatigue. And the fair access to the reliable and affordable computer-assisted diagnosis will fight the inequalities in incidence and mortality between populations. It has been witnessed that significant and remarkable advances have been achieved since the 1980s, and consistent endeavors have been exerted to deal with the grand challenges on how to accurately detect the pulmonary nodules with high sensitivity at low false-positives rate as well as on how to precisely differentiate between benign and malignant nodules. There is a lack of comprehensive examination of the techniques' development which is evolving the pulmonary nodules diagnosis from classical approaches to machine learning-assisted decision support. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the computer-assisted nodules detection and benign-malignant classification techniques developed over 3 decades, which have evolved from the complicated ad hoc analysis pipeline of conventional approaches to the simplified seamlessly integrated deep learning techniques. This review also identifies challenges and highlights opportunities for future work in learning models, learning algorithms and enhancement schemes for bridging current state to future prospect and satisfying future demand., Comment: We have substantially revised the article. The previous version had 74 pages and 2 figures, and the lateset version had 66 pages and 6 figures

Published
2019
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