Virtual Reality Exposure Therapy is a method of cognitive behavioural therapy that aids in the treatment of anxiety disorders by making therapy practical and cost-efficient. It also allows for the seamless tailoring of the therapy by using objective, continuous feedback. This feedback can be obtained using biosensors to collect physiological information such as heart rate, electrodermal activity and frontal brain activity. As part of developing our objective feedback framework, we developed a Virtual Reality adaptation of the well-established emotional Stroop Colour–Word Task. We used this adaptation to differentiate three distinct levels of anxiety: no anxiety, mild anxiety and severe anxiety. We tested our environment on twenty-nine participants between the ages of eighteen and sixty-five. After analysing and validating this environment, we used it to create a dataset for further machine-learning classification of the assigned anxiety levels. To apply this information in real-time, all of our information was processed within Virtual Reality. Our Convolutional Neural Network was able to differentiate the anxiety levels with a 75% accuracy using leave-one-out cross-validation. This shows that our system can accurately differentiate between different anxiety levels.

This paper presents some theoretical results on the smaller number Nk(a, b) of sensors to achieve k coverage for the rectangular area [0, a] × [0, b]. The first properties show the numbers Nk(a, b) are sub-additive and increasing on each variable. Based on these results, some lower and upper bounds for Nk(a, b) are introduced. The main result of the article proves that the minimal density of sensors to achieve k-coverage is (k) ≤ k/2 improving a previous result of Ammari and Das [2]. Finally, the numbers N1(a, b) are tabled for some small values of a, b.

The Mobile Message Passing Interface is a library which implements MPI functionality on Bluetooth enabled mobile phones. It provides many of the functions available in MPI, including point-to-point and global communication. The main restriction of the library is that it was designed to work over Bluetooth piconets. Piconet based networks provide for a maximum of eight devices connected together simultaneously. This limits the libraries usefulness for parallel computing. A solution to solve this problem is presented that provides the same functionality as the original Mobile MPI library, but implemented over a Bluetooth scatternet. A scatternet may be defined as a number of piconets interconnected by common node(s). An outline of the scatternet design is explained and its major components discussed.

Proteins and the networks they determine, called interactome networks, have received attention at an important degree during the last years, because they have been discovered to have an influence on some complex biological phenomena, such as problematic disorders like cancer. This paper presents a contribution that aims to optimize the detection of protein communities through a greedy algorithm that is implemented in the C programming language. The optimization involves a double improvement in relation to protein communities detection, which is accomplished both at the algorithmic and programming level. The resulting implementation’s performance was carefully tested on real biological data and the results acknowledge the relevant speedup that the optimization determines. Moreover, the results are in line with the previous findings that our current research produced, as it reveals and confirms the existence of some important properties of those proteins that participate in the carcinogenesis process. Apart from being particularly useful for research purposes, the novel community detection algorithm also dramatically speeds up the proteomic databases analysis process, as compared to some other sequential community detection approaches, and also to the sequential algorithm of Newman and Girvan.

In this chapter, we will discuss the design and development of a patient passport mHealth application for Cystic Fibrosis adults from ideation to app-store release. By allowing the patients access to their own unique data, it is anticipated that it will be of benefit when travelling abroad and between CF centres. The design process followed a pipeline we developed that is informed by patient and healthcare professional input. The app structure resembles an Irish patient file and is divided into three categories: “My CF Info”, “My Medical History”, and “My Clinical Appointments”. My CF Info allows the patient to store personal information such genotype, medical team contact information, physiotherapy, allergies, and medications. My Medical History allows the user to record information such as CF renal disease, CF diabetes, and the insertion/removal of a portacath/gastrostomy tube. My Clinical Appointments allows the user to record the type of appointment (annual assessment, clinic, other) and all information that would ordinarily be inserted into a patient file such as weight, height, spirometry and other comments. Weight and lung function are also displayed in a plot graph. The app has undergone pilot testing with five CF adults before being rolled out onto the Google Play Store.