Intermodal Network Model of Coal Distribution in the United States

2015 ◽  
Vol 2479 (1) ◽  
pp. 69-77
Author(s):  
Benjamin Blandford ◽  
Ted Grossardt ◽  
Michael Shouse ◽  
John Ripy
Keyword(s):  
United States ◽  
Network Model ◽  
The United States

scholarly journals How advocacy affects Twitter migraine conversations: A pilot cross-sectional survey of Northeast American “migraine” landscape on Twitter from May to June 2020

2020 ◽  
Vol 3 ◽  
pp. 251581632097208
Author(s):  
Pengfei Zhang ◽  
Santosh Bhaskarabhatla
Keyword(s):  
United States ◽  
Social Media ◽  
Network Analysis ◽  
Network Model ◽  
Large Scale ◽  
The United States ◽  
Closeness Centrality ◽  
Control Group ◽  
Annual Conference ◽  
Sources Of Information

Background: Twitter is a leading microblogging platform, with over 126 million daily active users as of 2019, which allows for large-scale analysis of tweets related to migraine. June 2020 encompassed the National Migraine and Headache Awareness Month in the United States and the American Headache Society’s virtual annual conference, which offer opportunities for us to study online migraine advocacy. Objective: We aim to study the content of individual tweets about migraine, as well as study patterns of other topics that were discussed in those tweets. In addition, we aim to study the sources of information that people reference within their tweets. Thirdly, we want to study how online awareness and advocacy movements shape these conversations about migraine. Methods: We designed a Twitter robot that records all unique public tweets containing the word “migraine” from May 8th, 2020 to June 23rd, 2020, within a 400 km radius of New Brunswick, New Jersey, United States. We built two network analysis models, one for the months of May 2020 and June 2020. The model for the month of May served as a control group for the model for the month of June, the Migraine Awareness Month. Our network model was developed with the following rule: if two hashtag topics co-exist in a single tweet, they are considered nodes connected by an edge in our network model. We then determine the top 30 most important hashtags in the month of May and June through applications of degree, between-ness, and closeness centrality. We also generated highly connected subgraphs (HCS) to categorize clusters of conversations within each of our models. Finally, we tally the websites referenced by these tweets during each month and categorized these websites according to the HCS subgroups. Results: Migraine advocacy related tweets are more popular in June when compared to May as judged by degree and closeness centrality measurements. They remained unchanged when judged by between-ness centralities. The HCS algorithm categorizes the hashtags into a large single dominant conversation in both months. In each of the months, advocacy related hashtags are apart of each of the dominant conversation. There are more hashtag topics as well as more unique websites referenced in the dominant conversation in June than in May. In addition, there are many smaller subgroups of migraine-related hashtags, and in each of these subgroups, there are a maximum of two websites referenced. Conclusion: We find a network analysis approach to be fruitful in the area of migraine social media research. Migraine advocacy tweets on Twitter not only rise in popularity during migraine awareness month but also may potentially bring in more diverse sources of online references into the Twitter migraine conversation. The smaller subgroups we identified suggest that there are marginalized conversations referencing a limited number of websites, creating a possibility of an “echo chamber” phenomenon. These subgroups provide an opportunity for targeted migraine advocacy. Our study therefore highlights the success as well as potential opportunities for social media advocacy on Twitter.

scholarly journals Outbreak dynamics of COVID-19 in China and the United States

2020 ◽  
Author(s):  
Mathias Peirlinck ◽  
Kevin Linka ◽  
Francisco Sahli Costabal ◽  
Ellen Kuhl
Keyword(s):  
United States ◽  
Network Model ◽  
Global Economy ◽  
The United States ◽  
Mitigation Strategies ◽  
Global Network ◽  
World Health ◽  
Infectious Period ◽  
Contact Period ◽  
Disease Specific

AbstractOn March 11, 2020, the World Health Organization declared the coronavirus disease 2019, COVID19, a global pandemic. In an unprecedented collective effort, massive amounts of data are now being collected worldwide to estimate the immediate and long-term impact of this pandemic on the health system and the global economy. However, the precise timeline of the disease, its transmissibility, and the effect of mitigation strategies remain incompletely understood. Here we integrate a global network model with a local epidemic SEIR model to quantify the outbreak dynamics of COVID-19 in China and the United States. For the outbreak in China, in n = 30 provinces, we found a latent period of 2.56±0.72 days, a contact period of 1.47±0.32 days, and an infectious period of 17.82±2.95 days. We postulate that the latent and infectious periods are disease-specific, whereas the contact period is behavior-specific and can vary between different provinces, states, or countries. For the early stages of the outbreak in the United States, in n = 50 states, we adopted the disease-specific values from China, and found a contact period of 3.38±0.69 days. Our network model predicts that–without the massive political mitigation strategies that are in place today–the United states would have faced a basic reproduction number of 5.3±0.95 and a nationwide peak of the outbreak on May 10, 2020 with 3 million infections. Our results demonstrate how mathematical modeling can help estimate outbreak dynamics and provide decision guidelines for successful outbreak control. We anticipate that our model will become a valuable tool to estimate the potential of vaccination and quantify the effect of relaxing political measures including total lock down, shelter in place, and travel restrictions for low-risk subgroups of the population or for the population as a whole.

scholarly journals Epidemiological geographic profiling for a meta-population network

2020 ◽  
Author(s):  
Yoshiharu Maeno ◽  
Keyword(s):  
United States ◽  
Hong Kong ◽  
Statistical Method ◽  
Geographical Distribution ◽  
Network Model ◽  
The United States ◽  
Ranking List ◽  
Geographic Profiling

Epidemiological geographic profiling is a statistical method for making inferences about likely areas of a source from the geographical distribution of patients. Epidemiological geographic profiling algorithms are developed to locate a source from the dataset on the number of new cases for a meta-population network model. It is found from the WHO dataset on the SARS outbreak that Hong Kong remains the most likely source throughout the period of observation. This reasoning is pertinent under the restricted circumstance that the number of reported probable cases in China was missing, unreliable, and incomprehensive. It may also imply that globally connected Hong Kong was more influential as a spreader than China. Singapore, Taiwan, Canada, and the United States follow Hong Kong in the likeliness ranking list

Correlative microscopy in distrubuted (client/server) computing environments: tools for catalyzing the rapid dissemination of information about the cell biology of the human prostate

1995 ◽  
Vol 53 ◽  
pp. 682-683
Author(s):  
A. Hakam ◽  
J.T. Gau ◽  
M.L. Grove ◽  
B.A. Evans ◽  
M. Shuman ◽  
...  
Keyword(s):  
United States ◽  
Cell Biology ◽  
Tissue Bank ◽  
The United States ◽  
Prostate Adenocarcinoma ◽  
Correlative Microscopy ◽  
Client Server ◽  
Critical Elements ◽  
Transplant Organ

Prostate adenocarcinoma is the most common malignant tumor of men in the United States and is the third leading cause of death in men. Despite attempts at early detection, there will be 244,000 new cases and 44,000 deaths from the disease in the United States in 1995. Therapeutic progress against this disease is hindered by an incomplete understanding of prostate epithelial cell biology, the availability of human tissues for in vitro experimentation, slow dissemination of information between prostate cancer research teams and the increasing pressure to “ stretch” research dollars at the same time staff reductions are occurring.To meet these challenges, we have used the correlative microscopy (CM) and client/server (C/S) computing to increase productivity while decreasing costs. Critical elements of our program are as follows:1) Establishing the Western Pennsylvania Genitourinary (GU) Tissue Bank which includes >100 prostates from patients with prostate adenocarcinoma as well as >20 normal prostates from transplant organ donors.

Digital image acquisition, processing, and analysis in a polymer microscopy laboratory

1994 ◽  
Vol 52 ◽  
pp. 454-455
Author(s):  
Vinod K. Berry ◽  
Xiao Zhang
Keyword(s):  
United States ◽  
Image Analysis ◽  
Digital Image ◽  
Image Acquisition ◽  
Image Transmission ◽  
The United States ◽  
Quantitative Image Analysis ◽  
Quantitative Image

In recent years it became apparent that we needed to improve productivity and efficiency in the Microscopy Laboratories in GE Plastics. It was realized that digital image acquisition, archiving, processing, analysis, and transmission over a network would be the best way to achieve this goal. Also, the capabilities of quantitative image analysis, image transmission etc. available with this approach would help us to increase our efficiency. Although the advantages of digital image acquisition, processing, archiving, etc. have been described and are being practiced in many SEM, laboratories, they have not been generally applied in microscopy laboratories (TEM, Optical, SEM and others) and impact on increased productivity has not been yet exploited as well.In order to attain our objective we have acquired a SEMICAPS imaging workstation for each of the GE Plastic sites in the United States. We have integrated the workstation with the microscopes and their peripherals as shown in Figure 1.

Silence of the Land: On the Historical Irrelevance of Territory to Congressional Districting and Political Representation in the United States

2001 ◽  
Vol 15 (01) ◽  
pp. 53-87 ◽  
Author(s):  
Andrew Rehfeld
Keyword(s):  
United States ◽  
House Of Representatives ◽  
Political Representation ◽  
The United States ◽  
Representative Government ◽  
Congressional District ◽  
Congressional Districts ◽  
Communities Of Interest

Every ten years, the United States “constructs” itself politically. On a decennial basis, U.S. Congressional districts are quite literally drawn, physically constructing political representation in the House of Representatives on the basis of where one lives. Why does the United States do it this way? What justifies domicile as the sole criteria of constituency construction? These are the questions raised in this article. Contrary to many contemporary understandings of representation at the founding, I argue that there were no principled reasons for using domicile as the method of organizing for political representation. Even in 1787, the Congressional district was expected to be far too large to map onto existing communities of interest. Instead, territory should be understood as forming a habit of mind for the founders, even while it was necessary to achieve other democratic aims of representative government.

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