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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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A novel hybrid optimization algorithm and its application in solving complex problem

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International Journal of Hybrid information technology 2015年第2期8卷 1-10页

作者： Jia, Hao Department of Electrical Engineering Dalian Institute of Science and Technology Dalian116052 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China

Ant colony optimization (ACO) algorithm is a new heuristic algorithm which has been demonstrated a successful technology and applied to solving complex optimization problems. But the ACO exists the low solving precision and premature convergence problem, particle swarm optimization (PSO) algorithm is introduced to improve performance of the ACO algorithm. A novel hybrid optimization (HPSACO) algorithm based on combining collaborative strategy, particle swarm optimization and ant colony optimization is proposed for the traveling salesman problems in this paper. The HPSACO algorithm makes use of the exploration capability of the PSO algorithm and stochastic capability of the ACO algorithm. The main idea of the HPSACO algorithm uses the rapidity of the PSO algorithm to obtain a series of initializing optimal solutions for dynamically adjusting the initial pheromone distribution of the ACO algorithm. Then the parallel search ability of the he ACO algorithm are used to obtain the optimal solution of solving problem. Finally, various scale TSP are selected to verify the effectiveness and efficiency of the proposed HPSACO algorithm. The simulation results show that the proposed HPSACO algorithm takes on the better search precision, the faster convergence speed and avoids the stagnation phenomena. © 2015 SERSC.

关键词： Traveling salesman problem

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Secure bilinear pairing outsourcing made more efficient and flexible 15

Secure bilinear pairing outsourcing made more efficient and ...

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10th ACM Symposium on information, computer and Communications Security, ASIACCS 2015

作者： Tian, Haibo Zhang, Fangguo Ren, Kui School of Information Science and Technology Sun Yat-sen University China Guangdong Provincial Key Laboratory of Information Security Guangzhou510006 China Department of Computer Science and Engineering University at Buffalo State University of New York NY14260-2500 United States

ISBN: (纸本)9781450332453

The increasing availability of cloud computing allows more and more mobile devices to outsource expensive computations. Among these computations, bilinear pairing is very fundamental and frequently-used by many modern cryptographic protocols. Currently, the most efficient outsourcing algorithm of bilinear pairings requires about 5 point additions in G1 and G2 and 4 multiplications in GT under the one-malicious version of a two-untrusted-program assumption. And the result of the algorithm is checkable with a probability about 1=2. In this paper, we improve the state-of-the-art by proposing two new outsourcing algorithms for bilinear pairings. One is a more efficient outsourcing algorithm under the same assumption with the same checkability. The other is more exible under a two-untrusted-program assumption with improved checkability. Both algorithms are better suited to various applications where on-line computations are strictly limited due to the lack of available computing resources. Copyright © 2015 ACM.

关键词： Outsourcing

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Ambient carbon monoxide and daily mortality: a global time-series study in 337 cities

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The Lancet. Planetary health 2021年第4期5卷 e191-e199页

作者： Kai Chen Susanne Breitner Kathrin Wolf Massimo Stafoggia Francesco Sera Ana M Vicedo-Cabrera Yuming Guo Shilu Tong Eric Lavigne Patricia Matus Nicolás Valdés Haidong Kan Jouni J K Jaakkola Niilo R I Ryti Veronika Huber Matteo Scortichini Masahiro Hashizume Yasushi Honda Baltazar Nunes Joana Madureira Iulian Horia Holobâcă Simona Fratianni Ho Kim Whanhee Lee Aurelio Tobias Carmen Íñiguez Bertil Forsberg Christofer Åström Martina S Ragettli Yue-Liang Leon Guo Bing-Yu Chen Shanshan Li Ai Milojevic Antonella Zanobetti Joel Schwartz Michelle L Bell Antonio Gasparrini Alexandra Schneider Department of Environmental Health Sciences Yale School of Public Health New Haven CT USA. Electronic address: kai.chen@yale.edu. Institute of Epidemiology Helmholtz Zentrum München-German Research Center for Environmental Health Neuherberg Germany Institute for Medical Information Processing Biometry and Epidemiology Ludwig-Maximilians-Universität München Munich Germany. Institute of Epidemiology Helmholtz Zentrum München-German Research Center for Environmental Health Neuherberg Germany. Department of Epidemiology Lazio Regional Health Service Rome Italy. Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK Department of Statistics Computer Science and Applications "G. Parenti" University of Florence Florence Italy. Institute of Social and Preventive Medicine University of Bern Bern Switzerland Oeschger Center for Climate Change Research University of Bern Bern Switzerland. Climate Air Quality Research Unit School of Public Health and Preventive Medicine Monash University Melbourne VIC Australia. Shanghai Children's Medical Center Shanghai Jiao Tong University School of Medicine Shanghai China School of Public Health Institute of Environment and Population Health Anhui Medical University Hefei China School of Public Health and Social Work Queensland University of Technology Brisbane QLD Australia Center for Global Health School of Public Health Nanjing Medical University Nanjing China. Air Health Science Division Health Canada Ottawa ON Canada School of Epidemiology and Public Health University of Ottawa Ottawa ON Canada. Department of Public Health Universidad de los Andes Santiago Chile. Faculty of Nursing and Midwifery Universidad de los Andes Santiago Chile. School of Public Health Key Lab of Public Health Safety of the Ministry of Education and Key Lab of Health Technology Assessment of the Ministry of Health Fudan University Shanghai China Shanghai Key Laboratory of Atmospheric Part

BACKGROUND:Epidemiological evidence on short-term association between ambient carbon monoxide (CO) and mortality is inconclusive and limited to single cities, regions, or countries. Generalisation of results from previous studies is hindered by potential publication bias and different modelling approaches. We therefore assessed the association between short-term exposure to ambient CO and daily mortality in a multicity, multicountry setting. METHODS:We collected daily data on air pollution, meteorology, and total mortality from 337 cities in 18 countries or regions, covering various periods from 1979 to 2016. All included cities had at least 2 years of both CO and mortality data. We estimated city-specific associations using confounder-adjusted generalised additive models with a quasi-Poisson distribution, and then pooled the estimates, accounting for their statistical uncertainty, using a random-effects multilevel meta-analytical model. We also assessed the overall shape of the exposure-response curve and evaluated the possibility of a threshold below which health is not affected. FINDINGS:Overall, a 1 mg/m increase in the average CO concentration of the previous day was associated with a 0·91% (95% CI 0·32-1·50) increase in daily total mortality. The pooled exposure-response curve showed a continuously elevated mortality risk with increasing CO concentrations, suggesting no threshold. The exposure-response curve was steeper at daily CO levels lower than 1 mg/m, indicating greater risk of mortality per increment in CO exposure, and persisted at daily concentrations as low as 0·6 mg/m or less. The association remained similar after adjustment for ozone but was attenuated after adjustment for particulate matter or sulphur dioxide, or even reduced to null after adjustment for nitrogen dioxide. INTERPRETATION:This international study is by far the largest epidemiological investigation on short-term CO-related mortality. We found significant associations between ambient

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MPSICA: An intelligent routing recovery scheme for heterogeneous wireless sensor networks

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information Sciences 2015年 308卷 49-60页

作者： Ding, Yongsheng Hu, Yifan Hao, Kuangrong Cheng, Lijun Engineering Research Center of Digitized Textile and Apparel Technology Ministry of Education Donghua University Shanghai 201620 China College of Information Sciences and Technology Donghua University Shanghai 201620 China Shandong Computer Science Center (National Supercomputer Center in Jinan) Jinan 250014 China Shandong Provincial Key Laboratory of Computer Network Jinan 250014 China

To address the fault-tolerant recovery problem in a heterogeneous wireless sensor network consisting of several resource-rich supernodes, and a large number of energy-constrained ordinary sensor nodes, we propose a multi-particle-swarm immune cooperative algorithm (MPSICA) to provide intelligent routing recovery scheme. The MPSICA could maintain K disjoint paths from each source node to the nearest supernode and the available path from supernodes to the sink. Moreover, it could investigate the optimal alternative routing strategies, and solve the problem with cloning, high-frequency mutation, clone selection operations, which can improve the fault tolerant ability and reliability of inter-cluster and intra-cluster data transmission. Using this scheme, we can efficiently repair the broken path of intercluster supernodes or intracluster ordinary nodes network. Finally, we discuss the implementation of the MPSICA based fault-tolerant routing protocol and present the performance evaluation through experiments. The simulation results have verified that the MPSICA based protocol can provide reliable communication with intelligent bio-heuristic routing recovery scheme, thus extend the lifetime of WSNs. © 2015 Elsevier Inc. All rights reserved.

关键词： Heterogeneous sensor networks Immune cooperation Master-slave swarm Routing recovery

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Building cognizance rule knowledge for fault diagnosis based on fuzzy rough sets

Building cognizance rule knowledge for fault diagnosis based...

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作者： He, Xi-Xu Chen, Lei-Ting Jia, Hai-Tao School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China Dongguan Institute of Information Engineering University of Electronic Science and Technology of China Sichuan China Center of Information University of Electronic Science and Technology of China Chengdu Sichuan China Provincial Key Laboratory of Digital Media Chengdu Sichuan China

With the continuous development of huge systems, dependence on the system is continually increasing. The failure of such systems will cause huge losses. The reason for system failure is often unclear, so that inconsistency and uncertainty between fault data will appear. In the actual application process, there is a process of change. If it is possible to predict the failure probability from the monitoring parameters, it will be very beneficial to system troubleshooting. Therefore, this paper proposes a new recognition algorithm based on fuzzy rough sets, in order to adapt to the processing of uncertain fault detection data. Additionally, the optimal direction of the dynamic information entropy increment is used to predict the fault information. This can quickly find the faults and provide important information for fault detection. It is verified that the proposed algorithm can improve the early warning and the accuracy of fault diagnosis information systems in the fault simulation analysis of a diesel engine. © 2015 - IOS Press and the authors. All rights reserved.

关键词： Failure analysis

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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The Recognition of Maize seeds Based on Multi-scale Feature Fusion and Extreme Learning Machine

The Recognition of Maize seeds Based on Multi-scale Feature ...

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2015 3rd International Conference on Mechanical Engineering and Intelligent Systems(ICMEIS 2015)

作者： Mingzhi Du Xiao Ke Mingke Zhou College of Mathematics and Computer Science Fuzhou University Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing (Fuzhou University)

In recognizing traditional crops seeds like maize seeds, we usually use electrophoresis assay method, fluorescence scanning method and chemical assay method. These methods are destructive methods. They take a long time to detect and are demanding of professional background knowledge and hardware conditions etc. What's more, these methods, based on BP neural network and support vector machine(SVM)while taking a long time to detect are less accurate in process of classification. In this paper, based on the computer vision technology, we proposed a new method for the classification of maize seeds, a method based on multi-scale feature fusion and extreme learning machine. First, we extract the multi-scale fusion feature of maize seeds. Second, based on extreme learning machine, we construct the classifier model of maize seed. Third, because of the window of image in the case of multi-scale detection has the problem of capturing the same object seed with many overlapping windows, we put forward a kind of window fusion algorithm to solve it. The simulation results show that: The method is able to identify the maize seeds accurately. Using this method the accuracy of classification of maize seeds can reached 97.66% and the error rate is less than 0.1%. Compared with the traditional methods, the method we proposed can improve the speed of detection and the accuracy of classification, and has no strict hardware requirements.

关键词： extreme learning machine feature fusion object recognition

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A Runtime Architecture Based Framework Managing Hybrid Clouds

A Runtime Architecture Based Framework Managing Hybrid Cloud...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Xuee Zeng Xingtu Lan Xing Chen Wenzhong Guo College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing

Cloud management becomes increasingly complex and brings high costs, especially with the advent of hybrid cloud. In a hybrid cloud, numerous resources like Virtual Machines (VMs) and Physical Machines in different clouds have to be managed together to make the whole hybrid cloud work cost-effectively. For controlling the management cost, in particular the manual management cost, many programs have been developed to take over manual management tasks or reduce their complexity and difficulty. These programs are usually hard-coded by languages like Java and C++, which bring enough capability and flexibility but also cause high programming effort and cost. As the architecture-based runtime model is causally connected with the corresponding running system automatically, constructing a hybrid cloud management system based on the architecture-based runtime models of clouds can benefit from the model-specific natures, and thus reduce the development workload. This paper proposes a runtime architecture based approach to developing the management programs in a simple but powerful enough manner. First of all, the manageability (such as APIs, configuration files and scripts) of different kinds of clouds, is abstracted as a runtime architecture based model of cloud software architecture, which can automatically and immediately propagate any observable runtime changes of the target platforms to the corresponding architecture models, and vice versa. Second, we provide a unified model of cloud software architecture, according to the domain knowledge of current cloud platforms, such as Cloud Stack, Open Stack and Eucalyptus. Third, the synchronization between the unified model and cloud runtime models is ensured through model transformation, thus, all the management tasks of the hybrid cloud, could be carried out through executing programs on the unified model, which decreases the complexity of use and management. The experiment on a real-world hybrid cloud demonstrates the feasibil

关键词： computers Software Conferences

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A Novel Quantum-behaved Particle Swarm Optimization Algorithm

A Novel Quantum-behaved Particle Swarm Optimization Algorith...

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第十四届分布式计算及其应用国际学术研讨会

作者： Jing Zhao Hong Liu School of Information Qilu University of Technology Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology College of Information Science and Engineering Shandong Normal University

A novel Quantum-behaved Particle Swarm Optimization algorithm with probability(P-QPSO) is introduced to improve the global convergence property of QPSO. In the proposed algorithm, all the particles keep the original evolution with large probability, and do not update the position of particles with small probability, and re-initialize the position of particles with small probability. Seven benchmark functions are used to test the performance of P-QPSO. The results of experiment show that the proposed technique can increase diversity of population and converge more rapidly than other evolutionary computation methods.

关键词： particle swarm optimization algorithm quantum-behaved probability benchmark function

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