Self-Directed Learning, A Guide for Learners and Teachers

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...

A Practical Guide for Medical Teachers.

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.

A Survey on Blockchain Interoperability: Past, Present, and Future Trends

The Code of Ethics for Nurses.

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.

/pdf/involvement-of-machine-learning-for-breast-cancer-image-58svea1vk8.pdf

Involvement of Machine Learning for Breast Cancer Image Classification: A Survey

Nursing students' and educators' views about nursing education in iran

Third-party apps enable a personalized experience on social networking platforms; however, they give rise to privacy interdependence issues. Apps installed by a user’s friends can collect and potentially misuse her personal data inflicting collateral damage on the user while leaving her without proper means of control. In this paper, we present a multi-faceted study on the collateral information collection of apps in social networks. We conduct a user survey and show that Facebook users are concerned about this issue and the lack of mechanisms to control it. Based on real data, we compute the likelihood of collateral information collection affecting users; we show that the probability is significant and depends on both the friendship network and the popularity of the app. We also show its significance by computing the proportion of exposed user attributes including the case of profiling, when several apps are offered by the same provider. Finally, we propose a privacy dashboard concept enabling users to control the collateral damage.

/pdf/collateral-damage-of-facebook-apps-friends-providers-and-2tasuefxzi.pdf

Collateral Damage of Facebook Apps: Friends, Providers, and Privacy Interdependence

Urolithiasis is the third most common urinary disease and evidence shows that the incidence of urolithiasis has increased continually during the past decades. The relationship between urinary stones and diet has been known to some extent but there are different opinions about it. This research was done to determine the relationship between dietary habit, food resources intake and urinary stone disease. This is a case–control study on 161 adult patients with urinary stone disease who referred to Hasheminejad Urology Center and 254 healthy subjects who were matched with the cases for age and gender. All the subjects were interviewed through a questionnaire. Data were analysed using spss, version 14. An independent t-test, χ2-test and regression analysis were used to examine the differences. The findings showed that the intake of resources of calcium (p = 0·048), phosphorus (p = 0·001), potassium (p < 0·0001), vitamin A (p < 0·0001), vitamin D (p < 0·0001) and vitamin C (p = 0·004) was significantly lower in patients in comparison with the healthy subjects, but the intake of resources of oxalate, magnesium and vitamin B6 was not significantly different. Results showed that among food resources that were significantly different in the two groups, the resources of vitamins A, C, D, resources of calcium and potassium and liquid were significantly different, as shown by the results of the regression test. Thus findings suggest that urinary stone formation has a relationship with dietary habit. Considering the challenges of treatment and the costs of this disease, dietary recommendations may be an easy and cost-effective way to reduce the health care burden of renal stone.

The role of dietary habits in urinary stone disease

Optimization methods have been widely used in image processing and computer vision. In this paper, k-nearest neighbor (KNN) and real-valued gravitational search algorithm (RGSA) are used to detect the breast cancer tumors in mammography images. GSA is used as a tool for optimization of the features weighting (FW) and tuning the classifier. FW-KNN based on GSA is employed to enhance the K-NN classification accuracy. The weighted features and the tuned K-NN classifier are utilized for detecting tumors. The obtained results show good efficiency of GSA-based FW-KNN classification for breast cancer tumor detection.

Fatemeh Shirazi

Papers

Nursing students' and educators' views about nursing education in iran

Collateral Damage of Facebook Apps: Friends, Providers, and Privacy Interdependence

The role of dietary habits in urinary stone disease

Feature weighting for cancer tumor detection in mammography images using gravitational search algorithm

Personal Information Dashboard: Putting the Individual Back in Control