Joint Spectrum Sensing and Resource Allocation for OFDM-based Transmission with a Cognitive Relay

In this paper, we investigate the joint spectrum sensing and resource allocation problem to maximize throughput capacity of an OFDM-based cognitive radio link with a cognitive relay. By applying a cognitive relay that uses decode and forward (D&F), we achieve more reliable communications, generating less interference (by needing less transmit power) and more diversity gain. In order to account for imperfections in spectrum sensing, the proposed schemes jointly modify energy detector thresholds and allocates transmit powers to all cognitive radio (CR) subcarriers, while simultaneously assigning subcarrier pairs for secondary users (SU) and the cognitive relay. This problem is cast as a constrained optimization problem with constraints on (1) interference introduced by the SU and the cognitive relay to the PUs; (2) miss-detection and false alarm probabilities and (3) subcarrier pairing for transmission on the SU transmitter and the cognitive relay and (4) minimum Quality of Service (QoS) for each CR subcarrier. We propose one optimal and two sub-optimal schemes all of which are compared to other schemes in the literature. Simulation results show that the proposed schemes achieve significantly higher throughput than other schemes in the literature for different relay situations.Comment: EAI Endorsed Transactions on Wireless Spectrum 14(1): e4 Published 13th Apr 201

Central auditory functions in primary school children with and without phonological awareness problems

AbstractObjectiveThe primary objective of this study was to assess central auditory functions in a group of primary school children with dyslexia mainly phonological awareness problems and to compare their performance with children with good phonological awareness ability.DesignA group of 52 students with phonological awareness problems (according to their performance in phonological awareness subtest of Arabic Reading Test (ART)) and 31 age- and sex matched students without phonological awareness problems participated in the study. All children were free from any neurological problems, had normal distant visual acuity, normal peripheral hearing sensitivity in both ears and IQ equal or above 90. The children from both groups were subjected to central auditory tests (CAT). Comparison between both groups in their performance in CAT was done and the correlations between CAT and items of phonological awareness subtest were examined.ResultsThe students with phonological awareness problems as a group performed significantly poorer than controls on all central auditory tests. Also, there was a significant correlation between the speech perception in noise test (SPIN) and phonological awareness in the left ear mainly for (Recognition of the middle sound of the word, Deletion of the middle sound of the word and Addition of a sound to the word).ConclusionsThe group of children with phonological awareness problem showed clinically significant diminished performance compared to the group without phonological awareness problem, reflecting difficulties in the processing of auditory information

Reporting an Experience on Design and Implementation of e-Health Systems on Azure Cloud

Electronic Health (e-Health) technology has brought the world with significant transformation from traditional paper-based medical practice to Information and Communication Technologies (ICT)-based systems for automatic management (storage, processing, and archiving) of information. Traditionally e-Health systems have been designed to operate within stovepipes on dedicated networks, physical computers, and locally managed software platforms that make it susceptible to many serious limitations including: 1) lack of on-demand scalability during critical situations; 2) high administrative overheads and costs; and 3) in-efficient resource utilization and energy consumption due to lack of automation. In this paper, we present an approach to migrate the ICT systems in the e-Health sector from traditional in-house Client/Server (C/S) architecture to the virtualised cloud computing environment. To this end, we developed two cloud-based e-Health applications (Medical Practice Management System and Telemedicine Practice System) for demonstrating how cloud services can be leveraged for developing and deploying such applications. The Windows Azure cloud computing platform is selected as an example public cloud platform for our study. We conducted several performance evaluation experiments to understand the Quality Service (QoS) tradeoffs of our applications under variable workload on Azure.Comment: Submitted to third IEEE International Conference on Cloud and Green Computing (CGC 2013

FTIR SPECTROSCOPIC TRENDS AND DNA DAMAGE IN RABBIT LENS DUE TO LONG RUN OF TAMOXIFEN TREATMENT

Objective: The aim of the present work is to evaluate the molecular structure changes of the lens of rabbits and DNA damage of epithelial cells due to tamoxifen administration. Methods: Twenty four healthy New Zealand white rabbits were divided into 2 main groups. The first group is served as control (n=12) kept untreated, second one is Tamoxifen administrative group (n=12) received orally daily dose of 15 mg/kg. Rabbits were decapitated after 2, 4, 6 and 8 mo, respectively. Using fourior transform infrared (FTIR) to study the molecular structure changes due to tamoxifen and comet assay analysis for discovering DNA damage. Results: FTIR data indicated that tamoxifen affects structural components in NHOH and fingerprint region. Increases of β-turns of the protein secondary structure while, reducing the content of both α-helix after 8 mo and Turns appeared for all periods of administrative tamoxifen were observed. On the other hand tamoxifen induced a statistically significant increase in comet assay parameters as tail moment compared to control animals that indicated DNA damage due to single or double strand break. Conclusion: Tamoxifen uses for more than 6 mo may lead to changes in the molecular structure of the lens and damage of DNA cells. An ophthalmic baseline examination prior to anti-cancer treatment may help detect any pre-existing ocular condition and lead to reduction of ocular side effects when predisposed patients are screened and examined regularly during and after chemotherapeutic therapy

Supporting Ambulance Crews Electronically through the Provision of ‘On-Demand’ Patient Health Information

The North East Ambulance Service (NEAS) does not have direct access to any information regarding patient health history, current medication, allergies, etc. that might assist an ambulance crew when they are dispatched to an emergency incident. Therefore, an ambulance crew responding to a call-out usually travels to the incident ‘information blind’ regarding the patient’s general health status. What makes the ambulance service in general, and the ambulance crew in particular, unique from other healthcare organizations is the spectrum of exposure to a diversity of health organizations (care homes, GPs, hospitals, etc.), and none-health organizations (police, social services, fire forces,etc.). This thesis describes an investigation into the feasibility of implementing a software Information Broker (IB) prototype, that has the ability to provide ‘on-demand’ electronic health information to ambulance crews while on scene, by accessing a set of autonomous databases containing patient records. This is to support the ambulance crew with reliable patient information in order to assist their decision-making process, therefore, reduce unnecessary patients’ conveyance to the Emergency Department (ED). The thesis also examines the sociotechnical issues surrounding health information transfer between and within the National Health Service (NHS) in the United Kingdom (UK) for patients with epilepsy (PWE), specifically in the North East region of England. A case study approach was employed as an overarching framework for the feasibility study of the IB prototype. This case study was centred upon studying the needs of people with epilepsy (PWE), as this group has been identified by NEAS as frequent users of the ambulance service. In many cases, if the ambulance crew are given adequate information to support their decision-making, they do not need to convey patients to a hospital ED unless necessary. Within the case study, a phenomenological approach was employed for the set of perspectives used for investigating the sociotechnical issues surrounding the IB. The three perspectives were the perceptions of NEAS operational and management staff, those of the JCUH staff and PWE/carers, and finally, the perceptions of the ambulance crew. The prototype IB technology has demonstrated the feasibility of using an information transfer broker to transfer information from autonomous organizations to the ambulance crew on scene. Overcoming technical challenges alone is not sufficient for this success. Stakeholders’ requirements, organization collaboration, compliance with national standards and targets, social and technical aspects, and so forth, are other issues that have been considered. Involvement of potential stakeholders in stages of any Health and Information Technology (HIT) development is an essential element to be included, as much as possible, to satisfy those requirements and needs of end-users. Improving the data availability to the ambulance crews on scene via an IB, means that they can perform better decision-making while on scene with a patient. The demonstration of the IB prototype has shown its potential for transferring patient health information from an autonomous database to ambulance crews. To increase opportunities of success, shared incentives and aims of the intra- and inter-organizational communication and collaboration should facilitate the implementation of HIT. Facilitating incremental improvements of systems and technologies may have an effect on the organization as a whole in terms of robustness of systems and technologies

SYNTHESIS, ANTICANCER EVALUATION AND MOLECULAR MODELING OF SOME SUBSTITUTED THIAZOLIDINONYL AND THIAZOLYL PYRAZOLE DERIVATIVES

Objective: The present work aimed to synthesize some new substituted thiazoles incorporated to pyrazole moiety starting from 1-(3-chlorophenyl)-3-(4-methoxyphenyl)-1H-pyrazole-4-carboxaldehyde (1) in order to evaluate their anticancer activity and GSTP1 inhibition in a trail to explore new potential GST inhibitors and prevent the resistance of cells to anticancer drugs. In addition, investigate the probability of the most promising cytotoxic compounds to inhibit GSTP1 enzyme via molecular docking study.Methods: The carboxaldehyde 1 was treated with substituted thiosemicarbazide in absolute ethanol to give the corresponding thiosemicarbazone derivatives 2a–d. Cyclization of 2a-d either by ethyl bromoacetate, phenacyl bromide or maleic acid anhydride furnished new thiazole derivatives 3, 4 and 5, respectively. These target compound 2-5 were screened for their GSTP1 inhibition and cytotoxic activity against HEPG-2 (human liver carcinoma), A549 (human lung carcinoma) and PC3 (human prostate carcinoma). Finally, molecular docking study of the most promising cytotoxic compounds against GSTP1 (PDB ID: 3GUS) is discussed.Results: Compounds 4a, 4b, and 4d were found to be highly active against HEPG-2 and PC-3 cancer cell lines with IC50 values ranging from 0.2±0.81 to 9.3±2.08 μM compared to doxorubicin with IC50= 37.8±1.50 and 41.1±2.01 μM, respectively. Screening of 4a, 4b and 4d against GSTP1 showed higher inhibition activity with IC50 ranging from 1.5±0.18 to 4.3±0.29 μM. Docking studies revealed the promising binding affinities of the latter compounds which match with the binding mode of the ligand, NBDHEX toward the active site of GSTP1.Conclusion: Compounds 4a, 4b and 4d were distinguished by the higher anticancer activity against HEPG-2, A-549 and PC-3 cell lines of tumor and the remarkable inhibitory activity against GSTP1

Modelling atmospheric ozone concentration using machine learning algorithms

Air quality monitoring is one of several important tasks carried out in the area of environmental science and engineering. Accordingly, the development of air quality predictive models can be very useful as such models can provide early warnings of pollution levels increasing to unsatisfactory levels. The literature review conducted within the research context of this thesis revealed that only a limited number of widely used machine learning algorithms have been employed for the modelling of the concentrations of atmospheric gases such as ozone, nitrogen oxides etc. Despite this observation the research and technology area of machine learning has recently advanced significantly with the introduction of ensemble learning techniques, convolutional and deep neural networks etc. Given these observations the research presented in this thesis aims to investigate the effective use of ensemble learning algorithms with optimised algorithmic settings and the appropriate choice of base layer algorithms to create effective and efficient models for the prediction and forecasting of specifically, ground level ozone (O3). Three main research contributions have been made by this thesis in the application area of modelling O3 concentrations. As the first contribution, the performance of several ensemble learning (Homogeneous and Heterogonous) algorithms were investigated and compared with all popular and widely used single base learning algorithms. The results have showed impressive prediction performance improvement obtainable by using meta learning (Bagging, Stacking, and Voting) algorithms. The performances of the three investigated meta learning algorithms were similar in nature giving an average 0.91 correlation coefficient, in prediction accuracy. Thus as a second contribution, the effective use of feature selection and parameter based optimisation was carried out in conjunction with the application of Multilayer Perceptron, Support Vector Machines, Random Forest and Bagging based learning techniques providing significant improvements in prediction accuracy. The third contribution of research presented in this thesis includes the univariate and multivariate forecasting of ozone concentrations based of optimised Ensemble Learning algorithms. The results reported supersedes the accuracy levels reported in forecasting Ozone concentration variations based on widely used, single base learning algorithms. In summary the research conducted within this thesis bridges an existing research gap in big data analytics related to environment pollution modelling, prediction and forecasting where present research is largely limited to using standard learning algorithms such as Artificial Neural Networks and Support Vector Machines often available within popular commercial software packages