Qualitative and quantitative methodological approach for evaluating public R&D funding

This paper presents and demonstrates a qualitative and quantitative methodological approach to the evaluation of public Research and Development (R&D) funding. This approach aims to support rather than replace traditional evaluation approaches, by focusing on the input, output and behavioural effects of public R&D funding. This study employs as an evaluation model an adapted impact analysis model of the Finnish Funding Agency for Technology and Innovation (TEKES), including additionality theory and other evaluation methods. In addition, an evaluation tool is proposed in which R&D indicators are arranged in portfolios. Furthermore, a data repository mechanism is designed, and a computational tool for computing and displaying evaluation results is employed - a customised web-based application centred by an ontological evaluation model. The methodological approach is articulated, explained, illustrated and discussed by employing R&D programmes granted by the Greek funding agency. The results display both a standardised approach that can be applied to R&D funding evaluation, as well as a flexible and modular approach that can be adapted according to the objectives and policies of funding agency. The main benefits of this methodology are the ease of decoding quantitative and qualitative evaluation attributes in an automated way with low cost; and the ability to evaluate results objectively, in addition to producing outcomes in a broad and comparative manner. The challenge in this methodological approach is focused on three key areas: the exploitation of new evaluation tools, the elevation of hidden information, and the formulation of appropriate questions beneficial to revealing the effectiveness of government R&D funding mechanism

Intelligent, integrated, diabetes mellitus patient management system

In the course of this thesis an integrated Diabetes Management System for Diabetes Mellitus (DM) patients was developed, especially those suffering from Diabetes Type I. While designing and developing this system the needs of all relevant users was taken into consideration and at the same time the most advanced technological tools were used. The main aim of the developed system was i) the use of user friendly technology (DM patients, their families, carers and physicians), ii) the design of a system whose users will capitalize on the benefits offered by technology advances while their everyday life and habits remain unchanged, iii) the improvement of DM patients‘ quality of everyday life, while at the same time offer an increased feeling of health security. The system, which is supported by the appropriate telecommunication platform for the remote monitoring and management of diabetes patients, comprises of the following components: i) Diabetes Management System (DMS), ii) Mobile Phone Application, iii) website for the news updates and the management of the DMS. DMS consists of a central database system, an application for patient data to be managed by physicians, an application for patient data to be managed by the DM patients themselves and a number of interfaces between the DMS and other subsystems. Emphasis has been given to the use of data integrity mechanisms as well as to the secure transmission and storage of the data and its analysis results. Via this system an individualized approach to consulting DM patients, on a treatment and daily needs level, is achieved. More specifically, the DM patient‘s data is transmitted to the physician either via the website or via the mobile phone. The physician using the appropriate tools, analyses the data and issues to the point, early and efficient interventions in order to improve the individual‘s health status. These interventions, that range from simple ones, such as how to use the syringe or follow a diet regime, to complex ones such as changing the insulin dosage or even alerting the patient that he/she requires medical attention, when available, immediately update all the DM patient, relevant applications. Moreover the system allows for the automatic support of the patient‘s treatment using an appropriate computational model. This model, using historical patient data suggests the optimal insulin dosage. During its pilot the system was used to suggest the dosage of injected insulin. Finally, the system supports the secure and timely update of the patient electronic health record, by inserting on a continuous basis, with vital information that will help him/her with his disease guidance as well as in future reports

Feeder bus network design with modular transit vehicles

Modular transit vehicles have the potential to transform public transport systems. Indeed, the possibility of dynamically adjusting capacity by assembling and disassembling multiple modular pods allows for improved transit service flexibility and efficiency, reducing operator and passenger costs. As such, modular buses are considered particularly advantageous for settings with large variations in passenger demand, permitting en-route capacity adjustment and seamless transfers. In this context, this study presents a model for the design of a feeder bus network operated by autonomous modular buses, accounting for en-route transfers, and a Genetic Algorithm to solve the non-linear mixed integer programming problem arising. Results for a case study in Athens, Greece for an area served by three metro lines are presented and discussed

Neural Networks Modelling after Myocardial Infarction in Rats

Cardiac function is reduced after acute myocardial infarction due to myocardial injury and to changes in the viable non-ischemic myocardium, a process known as cardiac remodeling. Current treatment of patients with acute myocardial infarction (AMI) reduces infarct size, preserves left ventricular function, and improves survival. However, it does not prevent remodeling which leads to heart failure. The aim of the present study was to model the echocardiographically estimated data with respect to the surgically collected data using Neural Networks. In particular, we attempted to analyze the relationship between cardiac remodeling variables obtained from echo and the infarct variables obtained from surgical data using neural networks. Towards that purpose, 199 rats were separated in two groups. The first group was subjected to coronary artery ligation, while the second underwent a sham operation. Echocardiography was used for rat monitoring. Scar weight and area were estimated after surgical incision. It appeared that several factors could be modelled with neural networks. Such modeling approaches could be developed to enable the simulation of the pathophysiological process after an Acute Myocardial Infarction (AMI) and predict with accuracy the effects of novel or current treatments that act via modulation of tissue injury, Left Ventricular dilation, geometry and hypertrophy

E-Health towards ecumenical framework for personalized medicine via Decision Support System

The purpose of the present manuscript is to present the advances performed in medicine using a Personalized Decision Support System (PDSS). The models used in Decision Support Systems (DSS) are examined in combination with Genome Information and Biomarkers to produce personalized result for each individual. The concept of personalize medicine is described in depth and application of PDSS for Cardiovascular Diseases (CVD) and Type-1 Diabetes Mellitus (T1DM) are analyzed. Parameters extracted from genes, biomarkers, nutrition habits, lifestyle and biological measurements feed DSSs, incorporating Artificial Intelligence Modules (AIM), to provide personalized advice, medication and treatment

Regressions of Clustered Gene Expression Data Manifest Tumor-Specific Genes in Urinary Bladder Cancer

Bladder cancer or urinary bladder cancer, is a common neoplasm of the urinary tract, with higher prevalence in men aged 60 to 70 years. In the present work we have used gene expression microarray data both from in-house experimentation, as well as from publicly available microarray data. We have used bioinformatics analyses as well as regression methodologies, in order to find common gene expression profiles with respect to tumor subtypes and differentiation. Our approach included gene clustering with k-means, and gene functional annotation. We have found several gene groups that manifest common expression profiles and also we have identified clusters of genes that manifested an ascending or descending pattern with respect to tumor differentiation and subtype. Such approaches could prove useful to the identification of noel gene targets that could be utilized as prognostic, diagnostic and therapeutic targets

Neural network based glucose - insulin metabolism models for children with Type 1 diabetes

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN