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Fatemeh Shirazi

Bio: Fatemeh Shirazi is an academic researcher from Shiraz University of Medical Sciences. The author has contributed to research in topics: Medicine & Qualitative research. The author has an hindex of 12, co-authored 34 publications receiving 336 citations. Previous affiliations of Fatemeh Shirazi include Graduate University of Advanced Technology & Katholieke Universiteit Leuven.

Papers
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Journal ArticleDOI
TL;DR: According to the findings, the critical thinking skills score of students was unacceptably low, and it is essential to pay more attention to improving critical thinking in academic lesson planning.
Abstract: Background:Academic achievement is one of the most important indicators in evaluating education. Various factors are known to affect the academic achievement of students.Purpose:This study was performed to assess the relationship between critical thinking skills and learning styles and the a

72 citations

Journal ArticleDOI
TL;DR: In this article, the authors survey previous research on designing, developing, and deploying systems for anonymous communication and provide important insights about the differences between the existing classes of anonymous communication protocols.
Abstract: The Internet has undergone dramatic changes in the past 2 decades and now forms a global communication platform that billions of users rely on for their daily activities. While this transformation has brought tremendous benefits to society, it has also created new threats to online privacy, such as omnipotent governmental surveillance. As a result, public interest in systems for anonymous communication has drastically increased. In this work, we survey previous research on designing, developing, and deploying systems for anonymous communication. Our taxonomy and comparative assessment provide important insights about the differences between the existing classes of anonymous communication protocols.

51 citations

Journal ArticleDOI
TL;DR: This is the first work that provides a detailed multi-faceted study of the collateral information collection of the applications on Facebook and that analyses the threat of user profiling by application providers.

29 citations

Proceedings ArticleDOI
03 Nov 2014
TL;DR: It is found that people interviewed did not proactively try to prevent being identified and tracked, and the findings indicate that security being compromised, resulting in losing money for example, is more concrete and more easily brought to mind than privacy-related problems.
Abstract: Recent revelations about surveillance by several institutions and de-identification can be expected to have increased public awareness of identification- and tracking-related privacy threats. It is reasonable to expect that the general public has started using corresponding privacy protection mechanisms. Our goal with this research was to determine whether they actually do this. If not, we wanted to explore possible explanations for not uptaken such privacy-protecting countermeasures. We interviewed 20 (mainly lay) people and found that our interviewees did not proactively try to prevent being identified and tracked. We identified seven different types of explanations. Including a number of misconceptions which might explain this puzzling level of apathy. The participants demonstrated confusion between different kinds of sensitive data; and displayed a confusion between the semantics of `privacy' and `security'. The findings also indicate that security being compromised, resulting in losing money for example, is more concrete and more easily brought to mind than privacy-related problems. In terms of the consequences of surveillance, the most commonly cited outcome is the receipt of personalized advertisements, which many consider beneficial. Potentially negative impacts of identification and tracking is often assumed to not occur to them. Our interviews also pointed out a gap between passive and active knowledge about identification and tracking techniques, their impact on privacy and countermeasures against them.

27 citations

Journal ArticleDOI
TL;DR: The findings indicated significant effect of PRP and PPP on VEGFR2 and CD34 expression by human umbilical vein endothelial cells, which was greater in latter.
Abstract: Platelet-rich plasma (PRP) has been established as an autologous source for therapeutic angiogenesis. The purpose of this study was to evaluate PRP angiogenic effects compared to platelet-poor plasma (PPP) in vitro and in vivo. The effects of PRP on vascular endothelial growth factor receptor-2 (VEGFR2) and CD34 expression were evaluated using real-time PCR, flow cytometry, western blot, immunocytochemistry and pathological study, as were carried out in both human umbilical endothelial cell culture and rat skin. Our findings indicated significant effect of PRP and PPP on VEGFR2 and CD34 expression by human umbilical vein endothelial cells, which was greater in latter. These effects, however, were confirmed by demonstrating an earlier angiogenic effect of PPP in vivo when compared to PRP. The findings of the present study as the first comparative study of PRP versus PPP are novel. Nevertheless, further studies are needed to clarify the underlying mechanism of these findings to improve the therapeutic effects of PRP and PPP.

26 citations


Cited by
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Journal ArticleDOI
TL;DR: The practical guide for medical teachers is one book that the authors really recommend you to read, to get more solutions in solving this problem.
Abstract: The title of this excellent book uses ‘Medical’ in the broadest possible sense, as the information contained within the book is generic to all healthcare teachers. It is a multi-author book, with t...

489 citations

Posted Content
TL;DR: A literature review on blockchain interoperability is conducted, by collecting 262 papers, and 70 grey literature documents, constituting a corpus of 332 documents, showing that Blockchain interoperability has a much broader spectrum than cryptocurrencies.
Abstract: Blockchain interoperability is emerging as one of the crucial features of blockchain technology, but the knowledge necessary for achieving it is fragmented. This fact makes it challenging for academics and the industry to seamlessly achieve interoperability among blockchains. Given the novelty and potential of this new domain, we conduct a literature review on blockchain interoperability, by collecting 262 papers, and 70 grey literature documents, constituting a corpus of 332 documents. From those 332 documents, we systematically analyzed and discussed 80 documents, including both peer-reviewed papers and grey literature. Our review classifies studies in three categories: Cryptocurrency-directed interoperability approaches, Blockchain Engines, and Blockchain Connectors. Each category is further divided into sub-categories based on defined criteria. We discuss not only studies within each category and subcategory but also across categories, providing a holistic overview of blockchain interoperability, paving the way for systematic research in this domain. Our findings show that blockchain interoperability has a much broader spectrum than cryptocurrencies. The present survey leverages an interesting approach: we systematically contacted the authors of grey literature papers and industry solutions to obtain an updated view of their work. Finally, this paper discusses supporting technologies, standards, use cases, open challenges, and provides several future research directions.

178 citations

Journal Article

162 citations

Journal ArticleDOI
TL;DR: This work has put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification, and described the involvement of the conventional Neural Network, Logic Based classifiers such as the Random Forest algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods.
Abstract: Breast cancer is one of the largest causes of women's death in the world today. Advance engineering of natural image classification techniques and Artificial Intelligence methods has largely been used for the breast-image classification task. The involvement of digital image classification allows the doctor and the physicians a second opinion, and it saves the doctors' and physicians' time. Despite the various publications on breast image classification, very few review papers are available which provide a detailed description of breast cancer image classification techniques, feature extraction and selection procedures, classification measuring parameterizations, and image classification findings. We have put a special emphasis on the Convolutional Neural Network (CNN) method for breast image classification. Along with the CNN method we have also described the involvement of the conventional Neural Network (NN), Logic Based classifiers such as the Random Forest (RF) algorithm, Support Vector Machines (SVM), Bayesian methods, and a few of the semisupervised and unsupervised methods which have been used for breast image classification.

94 citations