People News Research Articles

People News - Winter 2022

This issue’s People News includes an award-winning photo by David J. Lockwood, a thank you to Kailash Mishra, and In Memoriams for Michael J. Graham and Royce W. Murray

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From the Editor: What? You’re a human, too?!

As you read this latest rant, I am preparing my talk for the Gordon Research Conference (GRC) on Aqueous Corrosion, chaired this year by the illustrious Jamie Noël of Western University (see more about him in People News).

People News - Fall 2022

Fall 2022 People News includes a report on the Gordon Research Conference on Aqueous Corrosion and an In Memoriam article honoring Eliezer Gileadi

Hospital Mortality in COVID-19 Patients with Chronic Diseases: A Retrospective Cohort Study in Sirjan, Iran, in 2021

Background: The news of several people being affected by unusual pneumonia at the beginning of the year 2020 in China led to the introduction of a new type of coronavirus as the cause of a new respiratory disease. With the rapid spread of the disease in China and then to other parts of the world, the new coronavirus became scientifically known as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) and the resulting disease as coronavirus disease 2019 (COVID-19). This disease has a variety of symptoms and can cause SARS. Objectives: This study aimed to investigate the chronic diseases affecting the mortality rate of this disease. Methods: This retrospective cohort study was performed on 7222 eligible patients admitted to hospitals in Sirjan, Iran, from March 2020 to June 2021 with a positive polymerase chain reaction for COVID-19. The researcher extracted information from the patients' files using the questionnaire of demographic characteristics, clinical signs and symptoms, and the history of underlying diseases. The data were analyzed by the SPSS software using descriptive and analytical statistical tests, namely chi-squared, independent samples t-test, and univariate and multivariate logistic regression tests. Results: Out of 7,222 patients with COVID-19, 294 died, of whom 152 were male and 142 were female (OR = 1.23, CI: 0.97 - 1.55). Moreover, 220 people out of 294 dead cases were over 60 years old (OR = 8.92, CI: 6.82 - 11.67). The most common diseases were hypertension (OR = 3.63, CI: 2.81 - 4.69) and diabetes (OR = 3.16, CI: 2.38 - 4.20). The most common symptoms among those who died were respiratory distress (51.4%) and cough (34.7%). Conclusions: The mortality rate is directly related to the underlying diseases in COVID-19, and the most common underlying disease in the statistical population of this study was hypertension. In addition, respiratory distress is a common symptom in dead patients, which can be used as a predictor of death.

Non-Uniform Influence Blocking Maximization in Social Network

Online Social Network (OSN) is one of the most popular internet services. It also has become the main source of news for many people. Despite all the benefits, OSN significantly increases the rate of rumor spreading among people. Influence Blocking Maximization (IBM) aims to limit the propagation of rumor by broadcasting anti-rumor information. In IBM problem, the users are treated equally, however, they may have dissimilar worthiness. In this paper, we introduce Non-Uniform IBM (NU-IBM) where each user has its own weight. As a case study for NU-IBM, we present Distance-Aware IBM (DA-IBM) where determines the users’ weight based on the geographical distance from the Rumor Targeted Location (RTL). In order to handle non-identical weight for users, we develop a sampling-based method called NU-IBM-Solver. Through attentively analyzing the sample size, our proposed method is able to return a (1−1/e−ϵ) approximation solution similar to greedy algorithm. As well as theoretical analysis, we perform extensive experiments over four real-world networks in various conditions. The evaluations also confirm that NU-IBM-Solver is similar to greedy in terms of effectiveness, while it is thousands of times faster.

Temporal Rumor Blocking in Online Social Networks: A Sampling-Based Approach

Online social networks (OSNs) are one of the most popular services available on the Internet. They are also considered as the primary source of news for many people. In spite of all the advantages of OSNs, dissemination of rumors has become a major concern. Influence blocking maximization (IBM) is the problem of determining savior users to limit the spread of misinformation as much as possible. Although the IBM problem has been widely studied, most of existing works neglect the effect of deadline and delay. In this article, we study Temporal IBM and propose a sampling-based approach (TIBM-M) with both theoretical guarantee and practical runtime efficiency. We perform extensive experiments on four real-world datasets and both experimentally and theoretically show that TIBM-M consistently matches the greedy algorithm in terms of effectiveness while significantly improves the efficiency. The results also indicate that TIBM-M has better blocking effect than other methods in all conditions.

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CSA NewsVolume 67, Issue 6 p. 28-28 MEMBERSHIP People News First published: 25 May 2022 https://doi.org/10.1002/csan.20713Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume67, Issue6June 2022Pages 28-28 RelatedInformation

The Effectiveness of Pfizer-BioNTech and Oxford-AstraZeneca Vaccines to Prevent Severe COVID-19 in Costa Rica: Nationwide, Ecological Study of Hospitalization Prevalence.

BackgroundThe Costa Rican COVID-19 vaccination program has used Pfizer-BioNTech and Oxford-AstraZeneca vaccines. Real-world estimates of the effectiveness of these vaccines to prevent hospitalizations range from 90%-98% for two doses and from 70%-91% for a single dose. Almost all of these estimates predate the Delta variant.ObjectiveThe aim of this study is to estimate the dose-dependent effectiveness of COVID-19 vaccines to prevent severe illness in real-world conditions in Costa Rica, after the Delta variant became dominant.MethodsThis observational study is a secondary analysis of hospitalization prevalence. The sample is all 3.67 million adult residents living in Costa Rica by mid-2021. The study is based on public aggregated data of 5978 COVID-19–related hospital records from September 14, 2021, to October 20, 2021, and 6.1 million vaccination doses administered to determine hospitalization prevalence by dose-specific vaccination status. The intervention retrospectively evaluated is vaccination with Pfizer-BioNTech (78%) and Oxford-AstraZeneca (22%). The main outcome studied is being hospitalized.ResultsVaccine effectiveness against hospitalization (VEH) was estimated as 93.4% (95% CI 93.0-93.9) for complete vaccination and 76.7% (95% CI 75.0-78.3) for single-dose vaccination among adults of all ages. VEH was lower and more uncertain among older adults aged ≥58 years: 92% (95% CI 91%-93%) for those who had received full vaccination and 64% (95% CI 58%-69%) for those who had received partial vaccination. Single-dose VEH declined over time during the study period, especially in the older age group. Estimates were sensitive to possible errors in the population count used to determine the residual number of unvaccinated people when vaccine coverage is high.ConclusionsThe Costa Rican COVID-19 vaccination program that administered Pfizer-BioNTech and Oxford-AstraZeneca vaccines seems to be highly effective at preventing COVID-19–related hospitalization after the Delta variant became dominant. Even a single dose seems to provide some degree of protection, which is good news for people whose second dose of the Pfizer-BioNTech vaccine was postponed several weeks to more rapidly increase the number of people vaccinated with a first dose. Timely monitoring of vaccine effectiveness is important to detect eventual failures and motivate the public to get vaccinated by providing information regarding the effectiveness of the vaccines.

Radial Restricted Boltzmann Machines with Functional Neural Network for Classification of the Fake and Real News Analysis

News methods are the main source of news for people around the world. However, there was a surprising rise in social media spread in later periods due to the significant increase in technology and updates. Fake news is as old as the news industry - misinformation, hoax, propaganda and satire have been around for a long time. So fake news is untrue and false information without proper evidence. Auto-detection technology is being built and studied on artificial intelligence and deep learning to address fake news’s rise and spread. This method’s efforts have been made to automate the process of fake news detection. This existing machine learning method has less classification accuracy, and multiple combination Machine learning methods have not consumed memory usage; it provides a higher false rate. Overcome this problem by introducing a deep learning method, namely Radial Restricted Boltzmann Machines with Functional Neural Network (R2BM-FNN), to reduce memory usage from a large set of training data. Initially, to apply the Count Victories distribution for data preprocessing to remove the stop words and unrelated text from data sets. In this proposed method neural network to train the neurons with the help of stochastic gradient descent. This proposed GRU-RLU method results in classification accuracy, precision, sensitivity, specificity, and execution time to evaluate system performance.

Googling Alzheimer Disease: An Infodemiological and Ecological Study

Introduction: Understanding the emergent role of the internet on the health-seeking behavior of people is critical not only in the areas of medicine and public health but also in the field of infodemiology. Methods: Using Google Trends, data on global search queries for Alzheimer disease (AD) between January 2004 and April 2021 were analyzed. The relationship between online interest, as reflected by search volume index (SVI), and measures of disease burden, namely prevalence, deaths, and disability-adjusted life years, was evaluated. Results: There was a reduction in the tendency to search for AD during the past two decades. SVI peaks corresponded to news of famous people with AD and awareness months. Symptoms, causes, and differences with the term dementia were central queries for persons interested in AD. No notable overall correlation between SVI and measures of disease burden was found due to competing results. Sub-group analyses, however, showed that these correlations may be influenced by socioeconomic development, with strong negative significant associations observed in lower middle-income countries. Conclusion: Online interest in AD may represent a more complex metric influenced by socioeconomic factors. Awareness of the impact of celebrity diagnosis and awareness months on online search behavior may prove useful in the planning of public health campaigns for AD.

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CSA NewsVolume 66, Issue 11 p. 33-33 MEMBERSHIP People News First published: 27 October 2021 https://doi.org/10.1002/csan.20608Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume66, Issue11November 2021Pages 33-33 RelatedInformation

People News - Fall 2021

In this edition of People News we honor those who have earned promotions and awards, and remember members Kathryn Bullock, Sergio Trasatti, Forrest Trumbore, and Petr Zuman.

A Semi-Automatic Method for Efficient Detection of Stories on Social Media

Twitter has become one of the main sources of news for many people. As real-world events and emergencies unfold, Twitter is abuzz with hundreds of thousands of stories about the events. Some of these stories are harmless, while others could potentially be life-saving or sources of malicious rumors. Thus, it is critically important to be able to efficiently track stories that spread on Twitter during these events. In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter. We ran a user study with 25 participants, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can track stories about real-world events.

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CSA NewsVolume 66, Issue 8 p. 25-25 MEMBERSHIP People News First published: 29 July 2021 https://doi.org/10.1002/csan.20536Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume66, Issue8August 2021Pages 25-25 RelatedInformation

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People News - Summer 2021

In this edition of People News we honor those who have earned promotions and awards, and remember members Isamu Akasaki, C. Charles Austen Angell, Susan Anne Odom, Waldfried Plieth, Morton Schwartz, and George Edward Thompson.

Span identification and technique classification of propaganda in news articles

Propaganda is a rhetorical technique designed to serve a specific topic, which is often used purposefully in news article to achieve our intended purpose because of its specific psychological effect. Therefore, it is significant to be clear where and what propaganda techniques are used in the news for people to understand its theme efficiently during our daily lives. Recently, some relevant researches are proposed for propaganda detection but unsatisfactorily. As a result, detection of propaganda techniques in news articles is badly in need of research. In this paper, we are going to introduce our systems for detection of propaganda techniques in news articles, which is split into two tasks, Span Identification and Technique Classification. For these two tasks, we design a system based on the popular pretrained BERT model, respectively. Furthermore, we adopt the over-sampling and EDA strategies, propose a sentence-level feature concatenating method in our systems. Experiments on the dataset of about 550 news articles offered by SEMEVAL show that our systems perform state-of-the-art.

Deep Cross-Modal Face Naming for People News Retrieval

How to integrate multimodal information sources for face naming in multimodal news is a hot and yet challenging problem. A novel deep cross-modal face naming scheme is developed in this paper to facilitate more effective people news retrieval for large-scale multimodal news. This scheme integrates deep multimodal analysis, cross-modal correlation learning, and multimodal information mining, in which the efficient naming mechanism aims to cluster the deep features of different modalities into a common space to explore their inter-related correlations, and a special Web mining pattern is designed to optimize the name-face matching for rare non-celebrity. Such a cross-modal face naming model can be treated as a problem of bi-media semantic mapping and modeled as an inter-related correlation distribution over deep representations of multimodal news, in which the most important is to create more effective cross-modal name-face correlation and measure to what degree they are correlated. The experiments on a large number of public data from Yahoo! News have obtained very positive results and demonstrated the effectiveness of the proposed model.