Catálogo de publicaciones - libros

The goal of this study was to estimate the ability of biceps femoris (BF) muscle, a hamstring muscle crucial for biarticulate movement, to adapt to changed functional demands. For this purpose and due to ethical reasons, in a group of healthy sedentary men and of 15 sprinters, a non-invasive mechanomyography (MMG) method was used to measure the muscle twitch contraction times (Tc). These correlate with the proportions of slow and fast fibers in the muscle. To further elucidate the data obtained by MMG method and to obtain reference data for the muscle, the fiber type proportions in autoptic samples of BF in sedentary young men were determined according to histochemical reaction for myofibrillar adenosine triphosphatase (mATPase). With MMG we indirectly demonstrated that biceps femoris muscle has a strong potential to transform into a faster contracting muscle after sprint training, since the average Tc in sprinters was much lower (19.5± 2.3ms) than in the sedentary group (30.25± 3.5ms). The results of the histochemical analysis of BF muscle also imply a high adapting potential of this muscle. Beside type 1, 2a and 2× (2b) fibers a relatively high proportion of intermediate type 2c fibers (5.7%± 0.7), which co-expressed MyHC-1 and -2a, was found. Therefore, type 2c might represent a potential pool of fibers, capable of transformation either to slow type 1 or to fast type 2a in order to tune the functional response of BF muscle according to the actual functional demands of the organism.

In today’s information age there is a large availability of repositories storing various types of information about individuals. Data mining technology has emerged as a means of identifying patterns and trends from large quantities of data. The application of data mining technology to identify interesting patterns from these repositories leads to serious privacy concerns. Despite its benefit in a wide range of applications, data mining techniques also have raised a number of ethical issues. Some such issues are privacy, data security, intellectual property rights and many others. In this paper, we address the privacy problem against unauthorized secondary use of information. We focus primarily on privacy preserving data clustering on categorical data. In the proposed method, the categorical data is converted into binary data and it is transformed using geometric data transformation method. Then, clustering using conventional clustering algorithm is done on the transformed data to ensure privacy.

High Performance Computers are essential tools of trade in modern day science. The current research problems scientists and engineers are attempting to solve are becoming increasingly complex and are beyond the reach of traditional laboratory style experimental methodologies. Access to supercomputers makes a great deal of difference as they help in dealing with very high resolution models which result in accurate simulations of the phenomena that one is trying to investigate, in reasonable timeframes. This, in turn helps in cutting down the number of experiments that one has to conduct and also in arriving at accurate inferences at a faster rate. The Computer Division of the Bhabha Atomic Research Centre (BARC) has been developing high performance parallel computers for its scientists and engineers for over a decade. The latest in the ANUPAM series of parallel supercomputers is called ‘Anupam-Ameya’, a 512-processor cluster running Linux. This paper describes the design goals, architecture, components and benchmarking figures of the Anupam-Ameya cluster.

Scenarios are a popular focus for the acquisition and validation of system requirements and in the generation of system-based test-cases. However, generating scenarios manually is a tedious process, which may introduce errors or produce incomplete scenario sets. This paper discusses an approach to scenario capture, in support of requirements engineering that can then be used for test-scenario capture and test-case generation. The approach has been automated successfully, producing usage-scenarios from UML (Unified Modelling Language) Activity Diagrams without the need for manual intervention. The paper walks through the approach - with a specific focus on the processing of concurrent regions - and compares the results with other approaches applied to the same UML activity model.

– The paper presents Framework for eXecutable UML (FXU). FXU transforms UML models into programming code and supports execution of the resulting application according to the behavioral model. The code generation and execution is based on UML classes and their state machines. The FXU framework takes into account all concepts of state machines defined by the UML 2.0 specification. Ambiguities of UML state machine interpretation had to been resolved within the framework in order to obtain an executable application. During the runtime execution separate state machines and orthogonal regions are run as parallel threads. All kinds of events, states, pseudostates and activities are processed, as well. The framework was implemented and tested for C# code. The tool supports model-driven development of high quality applications.

In this work the flow control in a single stage absorption heat pump (SSAHP) is shown, in particular case for the water – Carrol ™ absorption pair, and the operating conditions of the system are calculated. The dimensionless tendencies of powers are shown as a function of the final temperature of revaluation from waste energy. The used software is described for the temperature sensors calibration, as well as the software for in - line calculations of powers and mass flows into the thermodynamic cycle. Finally, a correlation for working fluid control into the Laboratory absorption heat pump is shown.

In recent years, peer-to-peer data integration systems have attracted significant attention for their ability to communicate, collaborate and share information in a networked environment. One of the main problems that arises in such systems is how to exploit their mappings in order to answer queries posed to one peer. Our proposed framework can be used to exploit the existing mapped data together with its data location information for defining a peer’s data view. This data view is expected to produce query results based on peer preferences rather than using standard query processing at the super-peer level, as practiced in current super-peer P2P systems. Our framework consists of two major components: a mediated knowledge-base at the super-peer and user-defined data views at the peer.

The notion of intelligent split menu for web browser is to demonstrate the experimental domain model that is associated with a user interface to improve data entry. A split menu becomes popular recently in computer application. Now, it is extended into the web browser for data entry purposes. The objective of this study is to investigate on user perception of integrating intelligent split menu on web browser data entry. Therefore, a prototype web browser model is used to embed the web browser model with a data model in order to gather feedback on the user perceptions. Results were mainly positive, as many are keen on the idea of having split menu and integrating it on the web browser. It shows how a web browser can be enhanced to be more useful to the users.

The paper presents an enhanced version of an existing wiki platform (XWiki), in order to integrate knowledge, based on various Semantic Web technologies. In particular, we describe how metadata, microformats, ontologies are meaningfully used in this context, and we show the utility of our approach via two use cases.

— In this paper a novel technique of content based video retrieval is presented. The proposed technique uses Dual Tree Complex Wavelet Transform (DTCWT) based features of video frames for the purpose of shot change detection, key frame selection and video indexing. For shot change detection consecutive frame difference is computed, shot change is reported when the difference exceeds a certain threshold. For keyframe selection a frame is to be selected which is not part of shot transition using k-mean clustering of DTCWT feature vectors. Video shots are indexed using DTCWT features of the selected keyframes. Video query is processed by comparing the features of shot with the features database of the shots. For the purpose of features similarity we have used correlation based distance metric as it produced better results for this kind of feature similarity. The results are compared the results with classical techniques and it is shown how dual tree complex wavelet transform based features performed better. The whole framework uses similar kind of feature which makes it simple and efficient.— CBVR, Video Indexing, Shot Boundaries, Key Frames