Detecting Chronic Diseases from Sleep-Wake Behaviour and Clinical Features

The file attached to this record is the author's final peer reviewed version.Many chronic diseases show evidence of correlations with sleep-wake behaviour, and there is an increasing interest in making use of such correlations for early warning systems. This research presents an approach towards early chronic disease detection by mining sleep-wake measurements using deep learning. Specifically, a Long-Short-Term-Memory network is applied on actigraph data enriched with clinical history of patients. Experiments and analysis are performed targeting detection at an early and advanced disease stage based on different clinical data features. The results show for disease detection an averaged accuracy of 0:62, 0:73, 0:81, 0:77 for hypertension, diabetes, sleep apnea and chronic kidney disease, respectively. Early detection performs with an averaged accuracy of 0:49 for sleep apnea and 0:56 for diabetes. Nevertheless, compared to existing work, our approach shows an improvement in performance and demonstrates that predicting chronic diseases from sleep-wake behavior is feasible, though further investigation will be needed for early prediction

Electron-positron momentum distributions and positron lifetime in semiconductors in the generalized gradient approximation

The positron annihilation characteristics have been calculated taking the electron-positron correlation in the generalized gradient approximation (GGA). The calculated electron-positron momentum distributions in Si along the [110] direction in the GGA scheme agree very well with the experiment. The comparison of anisotropies of the momentum distributions along different crystal directions with the theory shows that only the GGA scheme gives the exact values. The enhancement factor for the valence electrons in the electron-positron momentum density is found to be weakly dependent on the momentum. The positron lifetimes in group IV, III-V, and II-VI semiconductors agree very well with the previous calculations and the experiment.published_or_final_versio

A Systematic Mapping Study on Off-The-Shelf-based Software Acquisition

Acquiring software from external suppliers and developing less software in-house can help software-developing organizations improve operational efficiency by reducing costs, time and reusing current technologies. Software projects increasingly use Off-The-Shelf (OTS) products. From the acquirer perspective, there is a need to understand in more detail OTS-based software acquisition processes, because they are different to and less well-understood than those for the acquisition of custom software. In this paper we have undertaken a systematic mapping study on OTS-based software acquisition. The study compares and contrasts OTS-based software acquisition and non-OTS-based software acquisition, and identifies factors influencing decision making in OTS-based software acquisition. We find that the main difference is that there is a relationship between determining the software requirements and OTS selection in OTS-based software acquisition. For commercial OTS software, the major factors are functionality and quality of the software, but for open-source OTS software, cost was the most important factor

Real-Time Sensor Observation Segmentation For Complex Activity Recognition Within Smart Environments

The file attached to this record is the author's final peer reviewed versionActivity Recognition (AR) is at the heart of any types of assistive living systems. One of the key challenges faced in AR is segmentation of the sensor events when inhabitant performs simple or composite activities of daily living (ADLs). In addition, each inhabitant may follow a particular ritual or a tradition in performing different ADLs and their patterns may change overtime. Many recent studies apply methods to segment and recognise generic ADLs performed in a composite manner. However, little has been explored in semantically distinguishing individual sensor events and directly passing it to the relevant ongoing/new atomic activities. This paper proposes to use the ontological model to capture generic knowledge of ADLs and methods which also takes inhabitant-specific preferences into considerations when segmenting sensor events. The system implementation was developed, deployed and evaluated against 84 use case scenarios. The result suggests that all sensor events were adequately segmented with 98% accuracy and the average classification time of 3971ms and 62183ms for single and composite ADL scenarios were recorded, respectively

Inference of gene regulatory networks from genome-wide knockout fitness data

Motivation: Genome-wide fitness is an emerging type of high-throughput biological data generated for individual organisms by creating libraries of knockouts, subjecting them to broad ranges of environmental conditions, and measuring the resulting clone-specific fitnesses. Since fitness is an organism-scale measure of gene regulatory network behaviour, it may offer certain advantages when insights into such phenotypical and functional features are of primary interest over individual gene expression. Previous works have shown that genome-wide fitness data can be used to uncover novel gene regulatory interactions, when compared with results of more conventional gene expression analysis. Yet, to date, few algorithms have been proposed for systematically using genome-wide mutant fitness data for gene regulatory network inference. Results: In this article, we describe a model and propose an inference algorithm for using fitness data from knockout libraries to identify underlying gene regulatory networks. Unlike most prior methods, the presented approach captures not only structural, but also dynamical and non-linear nature of biomolecular systems involved. A state–space model with non-linear basis is used for dynamically describing gene regulatory networks. Network structure is then elucidated by estimating unknown model parameters. Unscented Kalman filter is used to cope with the non-linearities introduced in the model, which also enables the algorithm to run in on-line mode for practical use. Here, we demonstrate that the algorithm provides satisfying results for both synthetic data as well as empirical measurements of GAL network in yeast Saccharomyces cerevisiae and TyrR–LiuR network in bacteria Shewanella oneidensis

Synthesis of Multi-Wall Carbon Nanotubes Using Unseeded Hydrocarbon Diffusion Flames

A method is provided for synthesizing carbon nanotubes from unseeded methane-air diffusion flames. A novel stainless steel and Ni—Cr wire probe is also provided for collecting carbon nanotubes from those diffusion flames

Reality and Perception: Activity monitoring and data collection within a real-world smart home

The file attached to this record is the author's final peer reviewed version.Smart home technologies have been developing rapidly in the last few years. However, there is still a lack of annotated rich datasets that can be used for different analysis purposes by researchers. The motivation for this study is driven by the need of self-management for chronic disease patients and the often neglected privacy aspects. The study describes the extension of an existing smart home environment at Great Northern Haven (GHN) with ambient and wearable devices. The discussed principles include the design of the experiment, data collection strategies and encountered challenges in regards to the sensors, connection problems and occupation with multiple inhabitants