63 research outputs found
The Cut-size Property of Networks and Epidemics Processes
No full textThis master thesis focuses on the particular problem about the cut-size property of different networks. The scope of networks is from trivial network models (e.g., random graph) to real networks (e.g., Power grid network and Facebook network). The Susceptible-Infected-Susceptible (SIS) epidemic model can describe the spreading processes of information or diseases on networks. Within this thesis, we explore the cut-size property of networks and seek the relations between the cut-size property and the spreading behaviors. Our result deduces the cut-size property and the relevant physical meanings of the real networks. In such a way, a deeper understanding of the cut-size would help researchers to obtain insights of real networks. Our results may also contribute to the study of control of dynamic processes on networks
A framework for assessing the resilience of after-sales service supply chains in the medical equipment industry
No full textAs the complexity of global supply chains increases, so does the turbulence and disruption they are suffering from. The medical equipment after-sales service supply chain is closely related to the healthcare industry. Therefore, the ability to provide high levels of customer satisfaction with after-sales services affects not only the performance of the medical equipment companies but also the lives and health of patients. This motivated us to study supply chain resilience in order to help managers in the medical equipment industry improve the resilience of after-sales service supply chains. Through literature research, we found that there is no research specifically on the resilience of the after-sales service supply chain of medical equipment. To fill this gap, we formulated the main research question for this thesis as: “How to assess the resilience of the after-sales service supply chain in the medical equipment industry?”. To answer this research question, we design a supply chain resilience framework by using a mixed-method approach to help managers assess the resilience of medical equipment after-sales service supply chains. And we opted for a mixed-method approach in two phases: (1) Framework development: We identified and synthesize the elements of existing supply chain resilience frameworks found through literature research into a single resilience framework applicable to the after-sales services supply chain for medical equipment. This framework identifies the enablers that need to be maximized and the vulnerabilities that need to be minimized to enhance supply chain resilience and operationalizes these elements as observable indicators. (2) Framework validation: Considering the strong growth of the medical equipment market in China, and the Chinese government ambition to provide safe, effective, convenient, and affordable healthcare services to both rural and urban residents by 2020, we have validated the proposed framework by interviewing professionals working in medical equipment companies located in China. We used questionnaires and semi-structured interviews to determine how participants perceived the importance of the identified elements and indicators. We expect that the resilience framework will help managers to better deal with turbulence and disruptions. The resulting framework identifies 11 enablers and 5 vulnerabilities, operationalized by 43 indicators. As the proposed supply chain resilience framework has not been tested empirically by applying it within a medical equipment company, a first recommendation is to perform such tests. By analysing and summarizing the results of the actual application, the framework can be improved further. Moreover, the expert validation was limited due to research time constraints. The reasons why participants considered some enablers and vulnerabilities to be unimportant could be investigated further, and the indicators can still be operationalized further. Finally, the relative importance of enablers and vulnerabilities could be investigated by using a multi-criteria decision-making method, so as to help managers select only the more important elements to improve the resilience of their supply chain.Complex Systems Engineering and Management (CoSEM
Identification and location of catenary insulator in complex background based on machine vision
No full textЯзыковая репрезентация концепта «скромность» в русской лингвокультуре (на фоне китайской)
No full textДанная работа посвящена исследования взаимосвязи языка и культуры – одному из актуальных направлений в лингвокультурологии. Концепт «скромность» как одобряемое социальное качество личность, отражает особенности культуры и мировидения определенной лингвокультурной общности. Автор предпринял системный анализ настоящего концепта. На основании материалов своего исследования доказала необходимость учета разную языковую картину миру в теории и практике преподавания русского языка как иностранного.This work is devoted to the study of the interrelation between language and culture - one of the current trends in linguoculturology. The concept of "modesty" as an approved social quality personality, reflects the characteristics of culture and worldview of a certain linguistic and cultural community. The author undertook a system analysis of the present concept. Based on the materials of her research, she proved the need to take into account the different linguistic picture of the world in the theory and practice of teaching Russian as a foreign language
RibFrac Dataset: A Benchmark for Rib Fracture Detection, Segmentation and Classification (Test Set)
No full textRibFrac dataset is a benchmark for developping algorithms on rib fracture detection, segmentation and classification. We hope this large-scale dataset could facilitate both clinical research for automatic rib fracture detection and diagnoses, and engineering research for 3D detection, segmentation and classification.
This is the Test Set of RibFrac dataset, including 160 CTs and the corresponding annotations. Files include:
ribfrac-test-images.zip: 160 chest-abdomen CTs in NII format (nii.gz)
Note that only the images are released; the corresponding ground truth labels are not available. Check the MICCAI 2020 RibFrac Challenge website to evaluate your algorithm.
If you find this work useful in your research, please acknowledge the RibFrac project teams in the paper and cite this project as:
Liang Jin, Jiancheng Yang, Kaiming Kuang, Bingbing Ni, Yiyi Gao, Yingli Sun, Pan Gao, Weiling Ma, Mingyu Tan, Hui Kang, Jiajun Chen, Ming Li. Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet. EBioMedicine (2020). (DOI)
or using bibtex
@article{ribfrac2020,
title={Deep-Learning-Assisted Detection and Segmentation of Rib Fractures from CT Scans: Development and Validation of FracNet},
author={Jin, Liang and Yang, Jiancheng and Kuang, Kaiming and Ni, Bingbing and Gao, Yiyi and Sun, Yingli and Gao, Pan and Ma, Weiling and Tan, Mingyu and Kang, Hui and Chen, Jiajun and Li, Ming},
journal={EBioMedicine},
year={2020},
publisher={Elsevier}
}
The RibFrac dataset is a research effort of thousands of hours by experienced radiologists, computer scientists and engineers. We kindly ask you to respect our effort by appropriate citation and keeping data license.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Facial expression recognition and expression intensity estimation
No full textSeventy years ago, psychologist categorized the facial expression into seven categories: angry, disgust, fear, happiness, sadness, surprise and neutral. Through analyzing the expression, psychologists want to predict the emotions behind the expression. Due to all kinds of potential applications on human emotion analysis, automatical analysis of human affective expressions has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. Researchers have done lots of works on this topic in the past thirty years, and proposed lots of promising approaches. Although many works have been done on this topic, these existing methods typically handle deliberately displayed and exaggerated expression of prototypical emotions. There are still some hard problems not solved well for the real system to handle naturally occurring emotions such as exploring discriminative features, time wrapping, and expression intensity estimation. Our work focuses on these real problems, analyzes the challenges in the real system and proposes the sounded solutions for advancing human affect sensing technology.Ph.D.Includes bibliographical referencesIncludes vitaby Peng Yan
WITHDRAWN: HPLC-MS/MS method for plasma concentration and pharmacokinetic studies of ilaprazole enteric-coated tablets on beagle dogs
No full textThis article has been withdrawn at the request of the author(s) and editor. The Publisher apologizes for any inconvenience this may cause.The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy
Years of life lived with disease and years of potential life lost in children who die of cancer in the United States, 2009
No full textCBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010–2014
No full textA TWO-SAMPLE TEST FOR HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO GENE-SET TESTING
No full textWe propose a two-sample test for the means of high-dimensional data when the data dimension is much larger than the sample size. Hotelling's classical T(2) test does not work for this "large p, small n" situation. The proposed test does not require explicit conditions in the relationship between the data dimension and sample size. This offers much flexibility in analyzing high-dimensional data. An application of the proposed test is in testing significance for sets of genes which we demonstrate in an empirical study on a leukemia data set.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000275510800009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Statistics & ProbabilitySCI(E)90ARTICLE2808-8353
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