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Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model
sensors Article Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model Baoping Xiong 1,2 , Nianyin Zeng 3,*, Yurong Li 4 , Min Du 1,5,*, Meilan Huang 1, Wuxiang Shi 1, Guojun Mao 2 and Yuan Yang 6,* 1 College of Physics and Information Engineering, Fuzhou University, Fuzhou City 350116, Fujian Province, China; [email protected] (B.X.); [email protected] (M.H.); [email protected] (W.S.) 2 Department of Mathematics and Physics, Fujian University of Technology, Fuzhou City 350118, Fujian Province, China; [email protected] 3 Department of Instrumental and Electrical Engineering, Xiamen University, Fujian 361005, China 4 Fujian Key Laboratory of Medical Instrumentation & Pharmaceutical Technology, Fuzhou University, Fuzhou City 350116, Fujian Province, China; [email protected] 5 Fujian provincial key laboratory of eco-industrial green technology, Wuyi University, Wuyishan City 354300, Fujian Province, China 6 Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL 60208, USA * Correspondence: [email protected] (N.Z.); [email protected] (M.D.); [email protected] (Y.Y.) Received: 15 January 2020; Accepted: 19 February 2020; Published: 21 February 2020 Abstract: Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. -
World Higher Education Database Whed Iau Unesco
WORLD HIGHER EDUCATION DATABASE WHED IAU UNESCO Página 1 de 438 WORLD HIGHER EDUCATION DATABASE WHED IAU UNESCO Education Worldwide // Published by UNESCO "UNION NACIONAL DE EDUCACION SUPERIOR CONTINUA ORGANIZADA" "NATIONAL UNION OF CONTINUOUS ORGANIZED HIGHER EDUCATION" IAU International Alliance of Universities // International Handbook of Universities © UNESCO UNION NACIONAL DE EDUCACION SUPERIOR CONTINUA ORGANIZADA 2017 www.unesco.vg No paragraph of this publication may be reproduced, copied or transmitted without written permission. While every care has been taken in compiling the information contained in this publication, neither the publishers nor the editor can accept any responsibility for any errors or omissions therein. Edited by the UNESCO Information Centre on Higher Education, International Alliance of Universities Division [email protected] Director: Prof. Daniel Odin (Ph.D.) Manager, Reference Publications: Jeremié Anotoine 90 Main Street, P.O. Box 3099 Road Town, Tortola // British Virgin Islands Published 2017 by UNESCO CENTRE and Companies and representatives throughout the world. Contains the names of all Universities and University level institutions, as provided to IAU (International Alliance of Universities Division [email protected] ) by National authorities and competent bodies from 196 countries around the world. The list contains over 18.000 University level institutions from 196 countries and territories. Página 2 de 438 WORLD HIGHER EDUCATION DATABASE WHED IAU UNESCO World Higher Education Database Division [email protected] -
Eliminating Blood Oncogenic Exosomes Into the Small Intestine with Aptamer-Functionalized Nanoparticles
Corrected: Publisher correction ARTICLE https://doi.org/10.1038/s41467-019-13316-w OPEN Eliminating blood oncogenic exosomes into the small intestine with aptamer-functionalized nanoparticles Xiaodong Xie1,5, Huifang Nie1,5, Yu Zhou1, Shu Lian1, Hao Mei1, Yusheng Lu2, Haiyan Dong1,3, Fengqiao Li1, Tao Li1, Bifei Li1, Jie Wang1, Min Lin1, Chaihung Wang1, Jingwei Shao1, Yu Gao 1, Jianming Chen2, Fangwei Xie4 & Lee Jia1,2* 1234567890():,; There are disease-causing biohazards in the blood that cannot be treated with modern medicines. Here we show that an intelligently designed safe biomaterial can precisely identify, tow and dump a targeted biohazard from the blood into the small intestine. Positively charged mesoporous silica nanoparticles (MSNs) functionalized with EGFR-targeting apta- mers (MSN-AP) specifically recognize and bind blood-borne negatively charged oncogenic exosomes (A-Exo), and tow A-Exo across hepatobiliary layers and Oddi’s sphincter into the small intestine. MSN-AP specifically distinguish and bind A-Exo from interfering exosomes in cell culture and rat and patient blood to form MSN-AP and A-Exo conjugates (MSN-Exo) that transverse hepatocytes, cholangiocytes, and endothelial monolayers via endocytosis and exocytosis mechanisms, although Kupffer cells have been shown to engulf some MSN-Exo. Blood MSN-AP significantly decreased circulating A-Exo levels, sequentially increased intestinal A-Exo and attenuated A-Exo-induced lung metastasis in mice. This study opens an innovative avenue to relocate blood-borne life-threatening biohazards to the intestine. 1 Cancer Metastasis Alert and Prevention Center, College of Chemistry; Fujian Provincial Key Laboratory of Cancer Metastasis Chemoprevention and Chemotherapy, Fuzhou University, Minjiang University, Fuzhou, Fujian 350116, China. -
Face Recognition Research Based on Fully Convolution Neural Network
2017 2nd International Conference on Mechatronics and Information Technology (ICMIT 2017) Face Recognition Research Based on Fully Convolution Neural Network YangWang1,a,Jiachun Zheng1,b,* 1Information Engineering College,Jimei University,Xiamen 361021,China [email protected], [email protected]. Keywords: Face recognition; Caffe; CNN; feature extraction Abstract: Face recognition technology has always been a hot topic, and has a widely application in our daily life. In recent years, with the rapid development of deep learning and the birth of various frameworks, face recognition is provided a new platform and ushered in a new opportunity. In this paper, a fully Convolution Neural Network(CNN) based on the Caffe framework and GPU is put forward for face recognition. The accuracy rate of it reaches 99% in the test set. Compared with the traditional extraction feature combined with the classifier method, CNN has a unique advantage, and doesn’t need to manually extract features. 1. Introduction In 2006, Geoffrey Hinton, a professor of machine learning, published a paper on the world's top academic journal Science, which led to the development of deep learning in the field of research and application. In 2011, Microsoft proposed a voice recognition system based on neural network, the existing results completely changed by the results of voice recognition system. Google and Baidu also launched their own network model. The NEC Research Institute first proposed neural networks into natural language processing. In 2012, Alex used the deeper convolution neural network model to achieve the best results in the Image Net game, reducing the original error rate by 9%, making a big step forward in the study of image recognition. -
An Chengri an Chengri, Male, Born in November, 1964.Professor. Director
An Chengri , male, born in November, 1964.Professor. Director of Institute of International Studies, Department of Political Science, School of philosophy and Public Administration,Heilongjiang University. Ph. D student of Japanese politics and Diplomacy History, NanKai University,2001.Doctor(International Relations History), Kokugakuin University,2002. Research Orientation: Japanese Foreign Relations, International Relation History in East Asia Publications: Research on contemporary Japan-South Korea Relations(China Social Science Press,October,2008);International Relations History of East Asia(Jilin Science Literature Press,March,2005) Association: Executive Director of China Institute of Japanese History , Director of China Society of Sino-Japanese Relations History Address: No.74 Xuefu Road, Nangang District, Haerbin, Heilongjiang, Department of Political Science, School of philosophy and Public Administration,Heilongjiang University. Postcode: 150080 An shanhua , Female, born in July,1964. Associate Professor, School of History, Dalian University. Doctor( World History),Jilin University,2007. Research Orientation: Modern and contemporary Japanese History, Japanese Foreign Relations, Political Science Publications: Comparative Studies on World Order View of China Korea and Japan and their Diplomatic in Modern Time ( Japanese Studies Forum , Northeast Normal University, 2006); Analysis of Japan's anti-system ideology towards the international system ( Journal of Changchun University of Science and Technology , Changchun University,2006) -
Cross-Complementary Subsets in Paratopological Groups
Hacettepe Journal of Hacet. J. Math. Stat. Volume 49 (1) (2020), 1 – 7 Mathematics & Statistics DOI : 10.15672/HJMS.2018.647 Research Article Cross-complementary subsets in paratopological groups Weihua Lin1,2, Lianhua Fang3, Li-Hong Xie∗4 1 College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350007, P.R. China 2 School of Mathematics and Statistics Minnan Normal University, Zhangzhou 363000, P.R. China 3 General Education Center, Quanzhou University of Information Engineering, Quanzhou, 362000, P.R. China 4 School of Mathematics and Computational Science, Wuyi University, Jiangmen 529020, P.R. China Abstract The following general question is considered by A.V. Arhangel’skiˇı[Perfect mappings in topological groups, cross-complementary subsets and quotients, Comment. Math. Univ. Carolin. 2003]. Suppose that G is a topological group, and F, M are subspaces of G such that G = MF . Under these general assumptions, how are the properties of F and M related to the properties of G? Also, A.V. Arhangel’skiˇıand M. Tkachenko [Topological Groups and Related Structures, Atlantis Press, World Sci., 2008] asked what is about the above question in paratopological groups [Open problem 4.6.9, Topological Groups and Related Structures, Atlantis Press, World Sci. 2008]. In this paper, we mainly consider this question and some positive answers to this question are given. In particular, we find many A.V. Arhangel’skiˇı’sresults hold for k-gentle paratopological groups. Mathematics Subject Classification (2010). 22A05, 54H11, 54D35, 54D60 Keywords. paratopological groups, k-gentle paratopological groups, perfect mappings, paracompact p-spaces, metrizable groups, countable tightnesses 1. Introduction Recall that a semitopological group is a group with a topology such that the multi- plication in the group is separately continuous. -
Public Space Analysis on Spontaneously Formed University
International Conference on Humanities and Social Science (HSS 2016) Public Space Analysis on Spontaneously Formed University Towns on the Basis of Students’ Behaviors to Attend and Dismiss the Class —A Case Study of Shigulu Block in Jimei District, Xiamen City Min-feng YAO1, 2, Sha LIU2 and Run-shen LIU2 1School of Transportation Engineering, TongJi University, Shanghai, China 2School of Architecture, Huaqiao University, Xiamen, Fujian, China Keywords: Behaviors, University town, Jimei School Village, Shigulu Block. Abstract. Jimei School Village is a university town with a history of nearly one hundred years. Its “spontaneous” formation is distinctly different from that of the prevailing “planning-construction” university towns. Under the turning point of public space optimization in Jimei District, this paper took the behaviors of students in the university town to attend and dismiss the class as the breakthrough point to analyze characteristics in the block, so as to understand the demands of students in this special university town for public space. The paper summarized possible shortcomings in Jimei School Village and regarded this as the basis for optimization and reconstruction of the school village. It provided a case for researches on “spontaneous” university towns. History and Current Status of Jimei School Village and Shegulu Blocks Jimei School Village Jimei School Village is located in Jimei District, Xiamen City. It is the general term for all types of schools and cultural institutions in Jimei. The name of Jimei School Village originated in the 1920s. In order to avoid adverse impacts of warlords on students, schools in Jimei made a petition to the State Department to admit Jimei schools as “school villages with permanent peace”, so as to ensure a peaceful learning environment. -