Effect of Environmental Pollutants PM2.5, CO, NO2, and O3 on the Incidence and Mortality of SARS-CoV-2 Infection in Five Regions of the USA.

In recent decades, environmental pollution has become a significant international public problem in developing and developed nations. Various regions of the USA are experiencing illnesses related to environmental pollution. This study aims to investigate the association of four environmental pollutants, including particulate matter (PM2.5), carbon monoxide (CO), Nitrogen dioxide (NO2), and Ozone (O3), with daily cases and deaths resulting from SARS-CoV-2 infection in five regions of the USA, Los Angeles, New Mexico, New York, Ohio, and Florida. The daily basis concentrations of PM2.5, CO, NO2, and O3 were documented from two metrological websites. Data were obtained from the date of the appearance of the first case of (SARS-CoV-2) in the five regions of the USA from 13 March to 31 December 2020. Regionally (Los Angeles, New Mexico, New York, Ohio, and Florida), the number of cases and deaths increased significantly along with increasing levels of PM2.5, CO, NO2 and O3 (p < 0.05), respectively. The Poisson regression results further depicted that, for each 1 unit increase in PM2.5, CO, NO2 and O3 levels, the number of SARS-CoV-2 infections significantly increased by 0.1%, 14.8%, 1.1%, and 0.1%, respectively; for each 1 unit increase in CO, NO2, and O3 levels, the number of deaths significantly increased by 4.2%, 3.4%, and 1.5%, respectively. These empirical estimates demonstrate an association between the environmental pollutants PM2.5, CO, NO2, and O3 and SARS-CoV-2 infections, showing that they contribute to the incidence of daily cases and daily deaths in the five different regions of the USA. These findings can inform health policy decisions about combatting the COVID-19 pandemic outbreak in these USA regions and internationally by supporting a reduction in environmental pollution.

Smartphone users: Understanding how security mechanisms are perceived and new persuasive methods.

Protecting smartphones against security threats is a multidimensional problem involving human and technological factors. This study investigates how smartphone users' security- and privacy-related decisions are influenced by their attitudes, perceptions, and understanding of various security threats. In this work, we seek to provide quantified insights into smartphone users' behavior toward multiple key security features including locking mechanisms, application repositories, mobile instant messaging, and smartphone location services. To the best of our knowledge, this is the first study that reveals often unforeseen correlations and dependencies between various privacy- and security-related behaviors. Our work also provides evidence that making correct security decisions might not necessarily correlate with individuals' awareness of the consequences of security threats. By comparing participants' behavior and their motives for adopting or ignoring certain security practices, we suggest implementing additional persuasive approaches that focus on addressing social and technological aspects of the problem. On the basis of our findings and the results presented in the literature, we identify the factors that might influence smartphone users' security behaviors. We then use our understanding of what might drive and influence significant behavioral changes to propose several platform design modifications that we believe could improve the security levels of smartphones.

Analysis of Web Spam for Non-English Content: Toward More Effective Language-Based Classifiers.

Web spammers aim to obtain higher ranks for their web pages by including spam contents that deceive search engines in order to include their pages in search results even when they are not related to the search terms. Search engines continue to develop new web spam detection mechanisms, but spammers also aim to improve their tools to evade detection. In this study, we first explore the effect of the page language on spam detection features and we demonstrate how the best set of detection features varies according to the page language. We also study the performance of Google Penguin, a newly developed anti-web spamming technique for their search engine. Using spam pages in Arabic as a case study, we show that unlike similar English pages, Google anti-spamming techniques are ineffective against a high proportion of Arabic spam pages. We then explore multiple detection features for spam pages to identify an appropriate set of features that yields a high detection accuracy compared with the integrated Google Penguin technique. In order to build and evaluate our classifier, as well as to help researchers to conduct consistent measurement studies, we collected and manually labeled a corpus of Arabic web pages, including both benign and spam pages. Furthermore, we developed a browser plug-in that utilizes our classifier to warn users about spam pages after clicking on a URL and by filtering out search engine results. Using Google Penguin as a benchmark, we provide an illustrative example to show that language-based web spam classifiers are more effective for capturing spam contents.

Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances.

In recent years, indoor positioning has emerged as a critical function in many end-user applications; including military, civilian, disaster relief and peacekeeping missions. In comparison with outdoor environments, sensing location information in indoor environments requires a higher precision and is a more challenging task in part because various objects reflect and disperse signals. Ultra WideBand (UWB) is an emerging technology in the field of indoor positioning that has shown better performance compared to others. In order to set the stage for this work, we provide a survey of the state-of-the-art technologies in indoor positioning, followed by a detailed comparative analysis of UWB positioning technologies. We also provide an analysis of strengths, weaknesses, opportunities, and threats (SWOT) to analyze the present state of UWB positioning technologies. While SWOT is not a quantitative approach, it helps in assessing the real status and in revealing the potential of UWB positioning to effectively address the indoor positioning problem. Unlike previous studies, this paper presents new taxonomies, reviews some major recent advances, and argues for further exploration by the research community of this challenging problem space.