FORGE: An eLearning Framework for Remote Laboratory Experimentation on FIRE Testbed Infrastructure

The Forging Online Education through FIRE (FORGE) initiative provides educators and learners in higher education with access to world-class FIRE testbed infrastructure. FORGE supports experimentally driven research in an eLearning environment by complementing traditional classroom and online courses with interactive remote laboratory experiments. The project has achieved its objectives by defining and implementing a framework called FORGEBox. This framework offers the methodology, environment, tools and resources to support the creation of HTML-based online educational material capable accessing virtualized and physical FIRE testbed infrastruc- ture easily. FORGEBox also captures valuable quantitative and qualitative learning analytic information using questionnaires and Learning Analytics that can help optimise and support student learning. To date, FORGE has produced courses covering a wide range of networking and communication domains. These are freely available from FORGEBox.eu and have resulted in over 24,000 experiments undertaken by more than 1,800 students across 10 countries worldwide. This work has shown that the use of remote high- performance testbed facilities for hands-on remote experimentation can have a valuable impact on the learning experience for both educators and learners. Additionally, certain challenges in developing FIRE-based courseware have been identified, which has led to a set of recommendations in order to support the use of FIRE facilities for teaching and learning purposes

Intelligent data analysis to interpret major risk factors for diabetic patients with and without ischemic stroke in a small population

This study proposes an intelligent data analysis approach to investigate and interpret the distinctive factors of diabetes mellitus patients with and without ischemic (non-embolic type) stroke in a small population. The database consists of a total of 16 features collected from 44 diabetic patients. Features include age, gender, duration of diabetes, cholesterol, high density lipoprotein, triglyceride levels, neuropathy, nephropathy, retinopathy, peripheral vascular disease, myocardial infarction rate, glucose level, medication and blood pressure. Metric and non-metric features are distinguished. First, the mean and covariance of the data are estimated and the correlated components are observed. Second, major components are extracted by principal component analysis. Finally, as common examples of local and global classification approach, a k-nearest neighbor and a high-degree polynomial classifier such as multilayer perceptron are employed for classification with all the components and major components case. Macrovascular changes emerged as the principal distinctive factors of ischemic-stroke in diabetes mellitus. Microvascular changes were generally ineffective discriminators. Recommendations were made according to the rules of evidence-based medicine. Briefly, this case study, based on a small population, supports theories of stroke in diabetes mellitus patients and also concludes that the use of intelligent data analysis improves personalized preventive intervention

3-D object mesh geometry compression with vector quantization

IEEE 12th Signal Processing and Communications Applications Conference -- APR 28-30, 2004 -- Kusadasi, TURKEYWOS: 000225861200076In this study, the objective is to develop a new combined method for efficient compression of classical 3-D object mesh representation. This can be realized in two primary steps: Mesh connectivity coding and data (geometry) compression. For realizing the first step, the algorithm of Isenburg [1] has been employed. For the second step, vector quantization methods have been used to compress the vertex coordinate. The difference between our study and the others is that our study uses ECVQ method for vertex coordinate compression to improve the results.IEEE, Tubitak, Istanbul Teknik Univ, Aselsan, Profile Telre, TURCom, Sgi, Datacore, Divi

Combining multiple clusterings for protein structure prediction.

Computational annotation and prediction of protein structure is very important in the post-genome era due to existence of many different proteins, most of which are yet to be verified. Mutual information based feature selection methods can be used in selecting such minimal yet predictive subsets of features. However, as protein features are organised into natural partitions, individual feature selection that ignores the presence of these views, dismantles them, and treats their variables intermixed along with those of others at best results in a complex un-interpretable predictive system for such multi-view datasets. In this paper, instead of selecting a subset of individual features, each feature subset is passed through a clustering step so that it is represented in discrete form using the cluster indices; this makes mutual information based methods applicable to view-selection. We present our experimental results on a multi-view protein dataset that are used to predict protein structure

Effects of parenteral papaverine and piracetam administration on cochlea following acoustic trauma

Introduction: Noise exposure, the main cause of hearing loss in countries with lot of industries, may result both in temporary or permanent hearing loss. The goal of this study was to investigate the effects of parenteral papaverine and piracetam administration following an acoustic trauma on hearing function with histopathologic correlation. Materials and Methods: Eighteen Wistar albino rats exposed to noise for 8 h in a free environment were included. We divided the study population into three groups, and performed daily intraperitoneal injections of papaverine, piracetam, and saline, respectively, throughout the study. We investigated the histopathologic effects of cellular apoptosis on inner hair cells (IHCs) and outer hair cells (OHCs) and compared the distortion product otoacoustic emissions (DPOAEs) thresholds among the groups. Results and Discussion: On the 3rd and 7th days, DPOAE thresholds at 8 kHz were significantly higher both in papaverine and piracetam groups compared with the control group (P = 0.004 for 3rd day, P = 0.016 and P = 0.028 for 7th day, respectively). On the 14th day, piracetam group had significantly higher mean thresholds at 8 kHz (P = 0.029); however, papaverine group had similar mean thresholds compared to the control group (P = 0.200). On the 3rd and 7th days following acoustic trauma, both IHC and OHC loss were significantly lower in both papaverine and piracetam groups. On the 7th day, the mean amount of apoptotic IHCs and OHCs identified using Caspase-3 method were significantly lower in both groups, but the mean amount identified using terminal deoxynucleotidyl transferase dUTP nick end labeling method were similar in both groups compared to the control group. Conclusion: We demonstrated the effects of papaverine and piracetam on the recovery of cochlear damage due to acoustic trauma on experimental animals using histopathologic and electrophysiologic examinations