Mobile Apparel Shopping: Application to Innovation Theory

Our mobile devices have changed the way we think about apparel shopping. Today you can flip on your phone and buy a pair of jeans in under a minute. Mobile technology is becoming increasingly attractive as it converts traditional electronic commerce (e-commerce) into mobile commerce (m-commerce) (Min, Dong, & Chin, 2012) allowing consumers to conduct online transactions via devices such as smart phones and tablets

Symptoms-Based Fuzzy-Logic Approach for COVID-19 Diagnosis

The coronavirus (COVID-19) pandemic has caused severe adverse effects on the human life and the global economy affecting all communities and individuals due to its rapid spreading, increase in the number of affected cases and creating severe health issues and death cases worldwide. Since no particular treatment has been acknowledged so far for this disease, prompt detection of COVID-19 is essential to control and halt its chain. In this paper, we introduce an intelligent fuzzy inference system for the primary diagnosis of COVID-19. The system infers the likelihood level of COVID-19 infection based on the symptoms that appear on the patient. This proposed inference system can assist physicians in identifying the disease and help individuals to perform self-diagnosis on their own cases

SnapCatch: Automatic Detection of Covert Timing Channels Using Image Processing and Machine Learning

With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (CTC) have become an imminent network security risk that continues to grow in both sophistication and utilization. These types of channels utilize inter-arrival times to steal sensitive data from the targeted networks. CTC detection relies increasingly on machine learning techniques, which utilize statistical-based metrics to separate malicious (covert) traffic flows from the legitimate (overt) ones. However, given the efforts of cyber attacks to evade detection and the growing column of CTC, covert channels detection needs to improve in both performance and precision to detect and prevent CTCs and mitigate the reduction of the quality of service caused by the detection process. In this article, we present an innovative image-based solution for fully automated CTC detection and localization. Our approach is based on the observation that the covert channels generate traffic that can be converted to colored images. Leveraging this observation, our solution is designed to automatically detect and locate the malicious part (i.e., set of packets) within a traffic flow. By locating the covert parts within traffic flows, our approach reduces the drop of the quality of service caused by blocking the entire traffic flows in which covert channels are detected. We first convert traffic flows into colored images, and then we extract image-based features for detection covert traffic. We train a classifier using these features on a large data set of covert and overt traffic. This approach demonstrates a remarkable performance achieving a detection accuracy of 95.83% for cautious CTCs and a covert traffic accuracy of 97.83% for 8 bit covert messages, which is way beyond what the popular statistical-based solutions can achieve

A Data-Driven Password Strength Meter for Cybersecurity Assessment and Enhancement

Password-based authentication is the most popular authentication mechanism over insecure networks due its simplicity and convenience. To ensure the security of this authentication mechanism, measuring the strength of users’ passwords becomes a crucial task to guide users to create stronger passwords. However, password strength meters are only helpful if they are accurate. Passwords meters that do not provide accurate scores that reflect the actual passwords strengths, e.g., providing a high score for a weak password, may misinform users and hinder the overall security of password-based authentication mechanisms. While many password strength meters were proposed in the literature, the lack of a standardized method to measure password strengths and comparing the accuracy of different password meters, selecting the most appropriate password meter will remain a difficult and unclear process. In this paper, we propose and implement a data-driven password meter that scrapes and collects large datasets to be used by the proposed password strength meter to help provide more accurate scores. Also, we measured the influence of the proposed meter at guiding users to create stronger passwords by tracking their eye movements. To do this, we conducted a user study on a testing web service and monitored the eye movements of our users using an eye tracking tool. Our results exhibited a significant improvement by influencing 88% of users to create an average of 150 years for password cracking-time

Pomegranate Juice Prevents the Formation of Lung Nodules Secondary to Chronic Cigarette Smoke Exposure in an Animal Model

Background. Cigarette smoke (CS) induces an oxidative stress, DNA damage, and lung cancer. Pomegranate juice (PJ) possess potent antioxidant activity attributed to its polyphenols. We investigated whether PJ supplementation would prevent the formation of lung nodules, attenuate mitotic activity, and reduce hypoxia-inducible factor-1α (HIF-1α) expression secondary to CS exposure in an animal model. Methods. Mice were divided into: Control group, CS group, CS + PJ group, and PJ-only group. CS and CS + PJ were exposed to CS, 5 days per week, for a total of 5 months. Animals were then housed for additional four months. CS + PJ and PJ groups received PJ throughout the experiment period while others received placebo. At the end of the experiment, the incidence of lung nodules was assessed by (1) histological analysis, (2) mitotic activity [measurement of PHH3 antibodies], and (3) measurement of HIF-1α expression. Results. The incidence of lung nodules was significantly increased in CS. CS exposure significantly increased PHH3 and HIF-1α expression. PJ supplementation attenuated the formation of lung nodules and reduced PHH3 and HIF-1α expression. Conclusion. PJ supplementation significantly decreased the incidence of lung cancer, secondary to CS, prevented the formation of lung nodules, and reduced mitotic activity and HIF-1α expression in an animal model

Factors That Determine The Outcome of Valvular Disease Among Patients, Based On The Type Of Hospital, Location Of Patient, And Type Of Insurance.

Valvular disease affects the heart\u27s valves and can lead to complications if left untreated. In 2017, about 2.7% (U. S) of the population had a valvular disease. The Centers for Disease Control and Prevention (CDC) also estimated that about 2500 Americans die yearly due to valvular disease. Several factors, such as the type of valvular disease, can affect the outcome of this disease. However, the hospital type, insurance status, and location of the patients may determine the quality of care and valvular disease outcome. Teaching hospitals are often in urban regions and house various well-grounded specialists as well as tools and equipment that may be a significant contributory factor to the outcome of Valvular heart disease. This study aims at determining the importance of quality of healthcare access in the outcome of valvular disease. At the bivariate analysis level, it was hypothesized that the type of hospital, location of patients, and age at diagnosis are significantly related to the outcome of valvular disease. At the multivariate level, it was hypothesized that after controlling for every other variable, the predictor variables were significantly related to the outcome of valvular disease. Data analysis was conducted on cross-2012 sectional National Inpatient Survey (NIS) data. The Core, severity, and hospital data were used for this analysis. Descriptive statistics and bivariate and multivariate logistic regressions were conducted to assess the association between the outcome of valvular disease and the type of hospital (teaching or non-teaching), patient location, age at diagnosis, insurance, income, and sex. All analysis was performed using the Statistical Analysis System (SAS). The results of the descriptive study showed about 2.9% of patients had comorbidity from valvular disease. Patients attending teaching hospitals had a 0.3% comorbidity present (P =.001). At the multivariate analysis level, patients at the teaching hospital were less likely to have comorbidity compared to individuals at non-teaching (AOR = 0.735; CI = 0.549, 0.970, P = 0.0303). Patients with public or no insurance were less likely to have a comorbidity of valvular disease as compared to patients with private insurance (AOR =0.596, AOR =0.288; CI = 0.393, 0.904 CI= 0.120, 0.692 P= 0.0149 P= 0.0054 respectively). Also, males were less likely to have valvular heart disease comorbidity as compared to females. All other variables not mentioned were not significant in the multivariate analysis. Accreditation programs can ensure that non-teaching hospitals have the necessary resources, equipment, and personnel to manage the valvular disease. Furthermore, providing incentives, such as financial support or performance-based incentives, can encourage non-teaching hospitals to invest in the necessary resources and personnel to manage valvular heart disease. We also recommend awareness campaigns and screening programs for patients in rural regions

Referral Physicians’ Knowledge of Radiation Dose: A Cross-sectional Study

AIM: The purpose of the study was to evaluate the knowledge of referring physicians of general practitioners, residents, and medical specialists in Jordan and the Middle East on radiation dose and its impact on vulnerable patients. MATERIALS AND METHODS: The Institutional Review Board approved this study before data collection. A cross-sectional study employed questionnaire that was distributed to respondents (n = 293) of general practitioners, residents, specialists, and therapists. The questionnaire consisted of 29 questions. Nine questions concerned with demographics and the remaining 20 questions were divided into five sections: Radiation dose, ionizing radiation, pediatric radiation, pregnant women radiation, and radiation risks. The mean score was computed out of 20. Chi-squared test of independence was utilized to analyze each question. To compare the responses between the demographic variables groups, Kruskal–Wallis and Mann–Whitney tests were used. RESULTS: Out of the 293 respondents, 128 (43.7%) were aware of radiation. The average score of the questionnaire was 9.5 out of 20 (47.5%). Within each section, the level of knowledge varied. Physicians had the highest level of knowledge in radiation risk (85.7%) followed by ionizing radiation (62.1%). The questionnaire revealed lower levels of knowledge in the areas of pediatric radiation, pregnant women radiation, and radiation dose. The percentages of respondents, (with fair to good level of knowledge), were 47.1%, 34.5%, and 24.6%, respectively. CONCLUSION: The results of this study were consistent with previous studies that demonstrated a poor level of general knowledge in referring physicians regarding radiation dose, ionizing radiation, pediatric radiation, pregnant women radiation, and radiation risks

Acute exposure to cigarette smoking followed by myocardial infarction aggravates renal damage in an in vivo mouse model

Cigarette smoking (S) is a risk factor for progressive chronic kidney disease, renal dysfunction, and renal failure. In this study, the effect of smoking on kidney function was investigated in a mouse model of myocardial infarction (MI) using 4 groups: control (C), smoking (S), MI, and S+MI. Histological analysis of S+MI group showed alterations in kidney structure including swelling of the proximal convoluted tubules (PCTs), thinning of the epithelial lining, focal loss of the brush border of PCTs, and patchy glomerular retraction. Molecular analysis revealed that nephrin expression was significantly reduced in the S+MI group, whereas sodium-hydrogen exchanger-1 (NHE-1) was significantly increased, suggesting altered glomerular filtration and kidney functions. Moreover, S+MI group, but not S alone, showed a significant increase in the expression of connective tissue growth factor (CTGF) and fibrotic proteins fibronectin (FN) and α-smooth muscle actin (SMA), in comparison to controls, in addition to a significant increase in mRNA levels of IL-6 and TNF-α inflammatory markers. Finally, reactive oxygen species (ROS) production was significantly accentuated in S+MI group concomitant with a significant increase in NOX-4 protein levels. In conclusion, smoking aggravates murine acute renal damage caused by MI at the structural and molecular levels by exacerbating renal dysfunction.This work was supported by grants from the Medical Practice Plan (MPP) at AUB (grant title "Effect of Second Hand Smoking (SHS) on Cardiac and Vascular Smooth Muscle Remodeling: A Targeted and Global Approach." Lead PI: Firas Kobeissy, co-PIs: Asad Zeidan and Ahmad Husari), from Lebanese National Council for Scientific Research (Kazem Zibara), from AUB URB (Firas Kobeissy), and from Lebanese University grant (Kazem Zibara).Scopu