检索结果-内蒙古大学图书馆

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

引用

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

关键词：

来源：评论

学校读者我要写书评

暂无评论

An efficient algorithm for hamilton cycle based on the enlarged rotation-extension technique

An efficient algorithm for hamilton cycle based on the enlar...

引用

International MultiConference of Engineers and Computer Scientists, IMECS 2014

作者： Du, Lizhi College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan430065 China

ISBN: (纸本)9789881925336

Algorithm studies on the Hamilton cycle are mainly based on the Rotation-Extension method developed by Posa. However, due to the deficiency of Posa's method, all these products are only efficient for much denser graphs or sparse but regular graphs. By many years' study, we developed the "Enlarged Rotation-Extension" technique which utterly changed and expanded the Posa's original one and can surmount its deficiency. Based on this technique, our algorithm can quickly calculate randomly produced un-directed graphs with up to ten thousand vertices on personal computer, no matter dense or sparse, the correctness is one hundred percent. We also calculated the data of hamilton cycles on a famous web site and we still got one hundred percent correctness.

关键词： Computational complexity

来源：评论

学校读者我要写书评

暂无评论

Station-keeping control method for GEO satellite based on relative orbit dynamics

Station-keeping control method for GEO satellite based on re...

引用

World Congress on Intelligent control and Automation (WCICA)

作者： Wenbo Yang Shaoyuan Li Ning Li Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University Shanghai CN

Based on the analysis of relative orbit dynamics for GEO (geostationary orbit, GEO) satellite and LQG (Linear Quadratic Gaussian, LQG) algorithm, this paper proposed a station-keeping control method for GEO satellite, which realized north-south station-keeping and west-east station-keeping simultaneously. To further verify the effectiveness of the proposed method, corresponding mathematical simulation is carried out. Simulation results show that the proposed method can obtain good control precision compared with conventional station-keeping control method of GEO satellite. Thus, the proposed method is suitable for station-keeping control of GEO satellite.

关键词： Satellites Orbits Equations Mathematical model Acceleration Vectors control systems

来源：评论

学校读者我要写书评

暂无评论

Robust visual tracking based on local kernelized representation

Robust visual tracking based on local kernelized representat...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Qiaozhe Li Yu Qiao Jie Yang Key Laboratory of Ministry of Education for System Control and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781479973989

Visual tracking, when regarded as a classification problem, usually need sufficient amount of data for online learning. Current approaches usually employ multiple binary labeled samples to train or update the appearance model at each frame. However, most positive samples collected by perturbation of the target location are equally treated as the estimated target when used for updating. The exact target information might be ambiguous. In this paper, we propose a robust visual tracking algorithm based on local kernelized representation. The use of kernels incorporates the target information to better represent each samples. Local kernel scores, which measure the similarity between samples and target templates in specific feature space, are used to form feature vector to represent the entire region. A neural network is then deployed into the particle Alter framework to estimate target location. Experimental results on a variety of challenging sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.

关键词： Conferences Robots Biomimetics

来源：评论

学校读者我要写书评

暂无评论

An information Diffusion Model Based on Individual Characteristics

An Information Diffusion Model Based on Individual Character...

引用

第三十三届中国控制会议

作者： Jun Hua Lin Wang Xiaofan Wang Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information ProcessingMinistry of Education

ISBN: (纸本)9781479947249

We proposes a model of information diffusion over social networks, in which individuals can be classified into innovative ones and conformable ones according to their reactions to new information. By simulations over various networks,we find that both the network structure and the composition of population have apparent effect on the extent of information diffusion. We also find that, in different stages of diffusion, innovative individuals and conformable individuals play different roles to promote the spread of information. Furthermore, initial conditions such as which kind of individual is initially activated also has impact on the diffusion.

关键词： Complex networks,information diffusion,Individual characteristics

来源：评论

学校读者我要写书评

暂无评论

Structure reconstruction for linear network systems with dynamical structure functions

Structure reconstruction for linear network systems with dyn...

引用

第三十三届中国控制会议

作者： Tongcai Wang Lin Wang Xiaofan Wang Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education

ISBN: (纸本)9781479947249

The state-space model for linear network systems which represents the structure information, in general, cannot be recovered from the dynamical input-output data, since typically the realization problem does not have a unique *** structure functions have been introduced to represent the structural information only between the *** though, it has shown that for a given network composed of p measured individuals, in order to completely recover the relationships between the measurements, p experiments with each experiment independently controlling a measured individual must be performed. This paper will investigate the structure reconstruction for a kind of linear networks, in which all states are measurable, but only partial states can be directly controlled by external inputs. Therefore, the network structure cannot be inferred directly by making use of the dynamical structure function. A new algorithm is proposed, in which the relations between the nodes with input are obtained from the dynamical structure function;and then, the influences from the nodes with control input to the nodes without are deduced;and finally, a convex optimization algorithm with a sparsity assumption is used to infer the full structure information.

关键词： Network reconstruction,Linear systems,Dynamical structure function,Transfer function,Convex optimization

来源：评论

学校读者我要写书评

暂无评论

A Noise Removal Approach for Object-based Classification of VHR Imagery via Post-classification

A Noise Removal Approach for Object-based Classification of ...

引用

International Conference on Audio, Language and Image processing

作者： Laiwen Zheng Lihong Wan Hong Huo Tao Fang Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (纸本)9781479939046

The pixel-based classification of remotely sensed images always produces a large amount of "speckled" or "salt and pepper" noises. Both post-classification smoothing and object-based classification techniques have been proposed to tackle this problem. However, most of them are not adequate to deal with the noises in object-based classification of very high resolution (VHR) remote sensing imagery, because a lot of noisy regions will be produced by image segmentation and the existing post-classification approaches generally are tailored towards pixel-based classification. This paper proposes a novel noise removal approach for object-based classification of VHR imagery via post-classification. It includes four phases: firstly, an image is segmented into homogeneous regions;secondly, all regions are classified according to their spectral and texture features;thirdly, noisy regions are distinguished by using shape features. Finally, the noisy regions are removed by using contextual features. Experimental results show the proposed approach is effective and can improve the overall accuracy of classification of VHR remote sensing imagery.

关键词： VHR Remote Sensing Imagery Object-based Classification Noise Removal Post-classification

来源：评论

学校读者我要写书评

暂无评论

A soft-sensing method with online correction based on semi-supervised learning

A soft-sensing method with online correction based on semi-s...

引用

第25届中国过程控制会议

作者： Qifeng Tang Dewei Li Yugeng Xi Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information ProcessingMinistry of Education

Soft sensing has been widely used in chemical industry to build an online monitor of the variables which are unmeasurable online or measurable online but with a high ***,one inherent difficulty is insufficiency of the training samples because the labeled data is *** this,the traditional soft-sensing structure has no online correction *** forecasting result may be incorrect if the working condition is *** this work,a semi-supervised method is proposed to build the soft-sensing model by use of the unlabeled ***,an online correction mechanism is proposed to establish a soft-sensing approach,which estimates the input variables at each step by a prediction model and calibrates the output variables by a compensation *** experimental results show that the proposed method has better prediction accuracy and generalization ability than other approaches.

关键词： soft-sensing,semi-supervised learning(SSL),online correction,neural network(NN).

来源：评论

学校读者我要写书评

暂无评论

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

引用

arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Energy-aware effectiveness analysis in two-link MIMO networks

Energy-aware effectiveness analysis in two-link MIMO network...

引用

World Congress on Intelligent control and Automation (WCICA)

作者： Zhenzhe Sun Jing Wu Chengnian Long Dept. of Automation Key Lab. of System Control and Information Processing

This paper investigates energy effectiveness problem for 802.11 n+ protocol in heterogeneous MIMO networks. Specifically, we consider a two-link network, where a 2 × 2 MIMO link transmits concurrently with an 1 × 1 link. We show that the completely cancelling out all the interference may result in an ineffective transmission for the incoming link. It consumes much energy on eliminating interference but achieving low transmission rate. Based on the observation, we propose a compromising nulling approach to mitigate the energy effectiveness problem. Compromising nulling takes an energy metric for transmission and interference design, which can be converted into an optimal pre-coding design problem. Numerical results show that compromising nulling strikes a balance between interfering other transmissions and maintain its own transmission effectiveness.

关键词： Interference Measurement Signal to noise ratio MIMO Blogs Antennas Receivers

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：