Utilising semantic technologies for decision support in dementia care

The main objective of this work is to discuss our experience in utilising semantic technologies for building decision support in Dementia care systems that are based on the non-intrusive on the non-intrusive monitoring of the patient’s behaviour. Our approach adopts context-aware modelling of the patient’s condition to facilitate the analysis of the patient’s behaviour within the inhabited environment (movement and room occupancy patterns, use of equipment, etc.) with reference to the semantic knowledge about the patient’s condition (history of present of illness, dependable behaviour patterns, etc.). The reported work especially focuses on the critical role of the semantic reasoning engine in inferring medical advice, and by means of practical experimentation and critical analysis suggests important findings related to the methodology of deploying the appropriate semantic rules systems, and the dynamics of the efficient utilisation of complex event processing technology in order to the meet the requirements of decision support for remote healthcare systems

Echo state network for occupancy prediction and pattern mining in intelligent environment

Pattern analysis and prediction of sensory data is becoming an increasing scientific challenge and a massive economical interest supports the need for better pattern mining techniques. The aim of this paper is to investigate efficient mining of useful information from a sensor network representing an ambient intelligence environment. The goal is to extract and predict behavioral patterns of a person in his/her daily activities by analyzing the time series data representing the behaviour of the occupant, generated using occupancy sensors. There are various techniques available for analysis and prediction of a continuous time series signal. However, the occupancy signal is represented by a binary time series where only discrete values of a signal are available. To build the prediction model, recurrent neural networks are investigated. They are proven to be useful tools to solve the difficulties of the temporal relationships of inputs between observations at different time steps, by maintaining internal states that have memory. In this paper, a special form of recurrent neural network, the so-called Echo State Network (ESN) is used in which discrete values of time series can be well processed. Then, a model developed based on ESN is compared with the most popular recurrent neural net-works; namely Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL). The results showed that ESN provides better prediction results compared with BPTT and RTRL. Using ESN, large datasets are learnt in only few minutes or even seconds. It can be concluded that ESN are efficient and valuable tools in binary time series prediction. The results presented in this paper are based on simulated data generated from a simulator representing a person in a 1 bedroom flat

Secure and privacy-aware proxy mobile IPv6 protocol for vehicle-to-grid networks

Vehicle-to-Grid (V2G) networks have emerged as a new communication paradigm between Electric Vehicles (EVs) and the Smart Grid (SG). In order to ensure seamless communications between mobile EVs and the electric vehicle supply equipment, the support of ubiquitous and transparent mobile IP communications is essential in V2G networks. However, enabling mobile IP communications raises real concerns about the possibility of tracking the locations of connected EVs through their mobile IP addresses. In this paper, we employ certificate-less public key cryptography in synergy with the restrictive partially blind signature technique to construct a secure and privacy-aware proxy mobile IPv6 (SP-PMIPv6) protocol for V2G networks. SP-PMIPv6 achieves low authentication latency while protecting the identity and location privacy of the mobile EV. We evaluate the SP-PMIPv6 protocol in terms of its authentication overhead and the information-theoretic uncertainty derived by the mutual information metric to show the high level of achieved anonymity

Silver Nanoparticles in Poultry Production

Nanoparticles of silver (nano-Ag) is an emerging alternative feed supplement for poultry and likely for medical applications. As a result of nanosilver special characteristic of killing bacteria, antimicrobial materials containing nanosilver are becoming increasingly important because of their wide range of applications. Despite the widespread use of nanosilver products, relatively few studies have been undertaken to determine the biological effects of nano- silver exposure. The ultimate objective of this paper is to clarify the potential of nano-Ag as an alternative growth promoting supplement for chicken

Development of an Optical System for Measuring Fluorescence Lifetimes

This project presents the design of a cost-effective, portable, and simplified fluorescence detection system for measuring fluorescence lifetime decay as an alternative to the available methods currently in use. There are multiple systems available to detect fluorescence of a sample, but they contain multiple parts and require expensive equipment in order to function. Due to the number of parts needed, the cost of implementing those fluorescence lifetime decay measuring systems are high. The fluorescence measuring system will be simplified into four main components that are interchangeable based on the application needs.In this project, a simple fluorescence measuring system will be used to detect a sample of quantum dots and a sample of an organic based dye and examine the results for system feasibility. Future works may include testing of various time decaying quantum dots, and testing of various other light detection devices, such as other avalanche photodiodes (APD) or photomultiplier tubes (PMTs)

Bubble Growth Models in Saturated Pool Boiling of Water on a Smooth Metallic Surface: Assessment and a New Recommendation

Prediction of bubble growth rate is very important for the development of accurate models for bubble departure diameter and thus the heat transfer rates in nucleate boiling. This paper presents an evaluation study to the existing homogeneous and heterogeneous bubble growth models using our experimental data for bubble growth in saturated pool boiling of deionized water on a plain copper surface. The experiments were conducted at pressures 1, 0.5 and 0.15 bar and superheat in the range 5.1 – 19.5 K. To start with, the paper presents a critical review on bubble growth models in homogeneous and heterogeneous boiling. It was found that homogeneous growth models achieved some partial agreement with the experimental data at some conditions and thus they should be used carefully in heterogeneous boiling. There was a good agreement between some of the models that were suggested based on the assumption that bubble growth occurs due to evaporation from the superheated boundary layer around the bubble. The models based on microlayer evaporation only could not explain the experimental data, i.e. partial agreement at some conditions. The model that predicted the data very well at all conditions was the “relaxation boundary layer” model by Van Stralen [25]. This model was generalized in the current study by suggesting two new empirical models for the departure diameter and departure time.Engineering and Physical Sciences Research Council of the UK, under Grant: EP/S019502/1