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Obsessive‐compulsive disorder (OCD) commonly affects males and can manifest with a range of urological symptoms directly linked to the content of the OCD. In Part 1 of this article, the authors discussed the recognition and diagnosis of OCD. In Part 2 here, they consider its treatment and related sexual dysfunction.

Obsessive‐compulsive disorder (OCD) commonly affects males and can manifest with a range of urological symptoms directly linked to the content of the OCD, including urinary urgency, urinary frequency, urinary retention and sexual dysfunction. Accurate recognition of OCD in such cases is fundamental to avoid misdiagnosis, risks to patients associated with untreated OCD (including suicidal behaviour), unnecessary physical health investigations and delayed treatment. In Part 1 here, the authors discuss the recognition and diagnosis of OCD. Part 2 will consider its treatment.

Effective monitoring and analysis of network traffic are vital for scientific computing, since scientific applications often require moving massive data from one site to another. A body of statistical and machine learning techniques have been introduced for network traffic monitoring and analysis, but this is considered a highly challenging task due to several reasons, such as unavailability of label information, complication of real-time analysis, and generalisation property of machine learning models. In this paper, we present a novel method to identify continuous time windows of low throughput for the purpose of network performance analysis and anomaly detection, in order to facilitate data transfers for high-performance scientific computing. The presented method is based on supervised learning techniques with an adaptive labelling function that automatically determines if the time window is whether 'slow' or 'normal'. The presented method is validated on real large datasets collected from several data transfer nodes (DTNs) located in Science DMZ. Our experimental results show that the proposed method is able to quickly predict time windows of low performing network transfers, that require attention from network engineers and also to identify the most important features for the classification.

Massive Open Online Courses (MOOC) are the latest online teaching and learning medium through website and are seen as a key factor in future lesson delivery. One well-established and popular MOOC is Coursera which is provided by Stanford University. Online participation and interactions are complex phenomena and the answers to the questions in regard to Courserians' participation gave considerable insights into knowledge construction in discussion forums. The successful construction of knowledge requires active participation. This article is aimed to observe a Coursera community in its natural setting and to investigate the participants' experiences in Coursera discussion forum. The data was derived from over 60 online interview forums with Coursera learners. Field note data was extracted from 160 days of interaction with the participants. Analysis of the qualitative data revealed some insights about learners' feelings, challenges, ideas and recommendations.

To understand the impact of a new pricing structure on residential electricity peak demands, we need a baseline model that captures every factor other than the new price. The gold standard baseline is a randomised control trial, however, control trials are hard to design. The alternative to learn a baseline model from the past measurements fails to make reliable predictions about the daily peak usage values next summer. To overcome these shortcomings, we propose several new methods. Among these methods, the one named LTAP is particularly promising. It accurately predicts future usages of the control group. It also predicts the reductions of the peak demands to remain the same, while previous studies have found the reduction to be diminishing over time. We believe that LTAP is capturing the self-selection bias of the treatment groups better than techniques used in previous studies and are looking for opportunities to confirm this feature.

Massive Open Online Courses (MOOC) are becoming increasingly difficult to ignore in online education. Recent developments in MOOC have heightened the need for understanding the MOOC. The issue of an in-depth understanding of the MOOC setting has been a key subject within the field of qualitative research. However, far too little attention has been paid to the MOOC setting. This paper seeks to put the Netnography methodology into context by presenting a description of the Coursera community to better understand this community. It contributes by providing details about Coursera community through our engagement in this community.

In this study, 441 Universiti Teknologi Malaysia students participated as respondents. Through a questionnaire respondents were asked how frequently they engage in specific behaviors, including hacking, using pirated software, music and movies in addition to use of gambling and pornography sites. The aim was to investigate which individual characteristics including age, gender, religion, citizenship, level of education, ego strength and locus of control are among determinants of these behaviors. The results of these questions were used to report the ratio of frequent users of these materials based on different categories. Logistic regression was used to see what the influential factors in the investigated behaviors are. The results suggest that males are more engaged with almost all of the investigated behaviors. In addition, citizenship, ego strength, age and religion reflected a statistically significant difference among people in certain activities. Level of education and locus of control did not reflect any significance in engaging in aforementioned behaviors.

One of the most significant discussions in education and information technology is cloud computing. Over the past six years, researchers have shown an increased interest in cloud computing for educational contexts. However, these rapid changes in information technology have a serious effect on teaching frameworks. Concerns have been raised about how cloud computing can be integrated with teaching frameworks and if it can improve teaching. In this paper, the Danielson Teaching Framework, which focuses on key aspects of teaching, was used. This framework has divided complex teaching activities into components such as communicating with students, students engagement and others which all might be improved by cloud computing; using cloud computing has capability to enhance performance of teaching. This paper seeks to address the domains of teaching frameworks and introduce an initial version of a cloud–based teaching framework that considers cloud computing can provides insight to future research on teaching frameworks.

Recently researchers have shown an increased interest in cloud computing technology. It is becoming increasingly difficult to ignore cloud computing technology in education context. However rapid changes in information technology are having a serious effect on teaching framework designs. So far, however, there has been little discussion about cloud computing benefits in domains of teaching frameworks which propels us to study and redesign teaching frameworks considering cloud computing. The purpose of this paper is to review recent research into cloud computing and features which can be improved with this new technology. This paper studied several researches through literature to determine the main impact of cloud computing on “planning and preparation” and “instruction” domains as two main domains of teaching framework. Light will be then shed on the impact and potential benefits of cloud computing on teaching framework. The paper closes by proposing to design an evaluation table based on cloud computing artifacts to enhance teaching practice and highlights its offerings for educational institutions.

This paper seeks to address one of the current issues of large organizations; it is rapid growth of data without any quick way to extract worthwhile and hidden knowledge from considerable huge volume of data. It seems that management of higher education institutes interest to the best method for solving this problem and making a good decisions and strategies. Contrast to the authors’ initial sample consisted of five medical universities of Tehran, in this paper a large sample was chosen because of the expected valuable discovered knowledge. This data was collected from 65 universities of Iran based 18 years. The data is in Persian. The present paper confirms the authors’ previous findings and contributes additional discovered knowledge related to the major group of program with different geographical, the main factor of sharp increase in the number of students and preferred learning style, study mode and programs and considerable growth of female students after 1996. The findings of this study have a number of important implications for future planning of higher education to improve ranking of universities. Another important practical implication is that other researchers can use them in their studies on higher education.