Scientific and technological performance evaluation of the Spanish Council for Scientific Research (CSIC) in the field of Biotechnology

An evaluation of the Spanish CSIC performance in Biotechnology, as compared with those of the French CNRS and the Italian CNR, has been carried out to determine the balance between the generation of scientific knowledge and the transfer of technology. This study shows a high scientific productivity mostly in journals with moderate impact factor, a low generation of patents and an insufficient transfer of knowledge to the Spanish companies. Other indicators confirm the existence of competitive human resources in biotechnological research producing scientific knowledge of interest for the development of patents and that cooperates successfully at European level.     

Spectral domain analysis of conductor losses in a multiconductorsystem via the incremental inductance rule

Spectral domain analysis is adapted to analyse the conductor losses of a multiconductor system embedded in a multilayered medium using a quasi-TEM approach and assuming a strong skin effect. After adapting the incremental rule to a multiconductor system, the conductor losses are computed in terms of the derivative of the inductance matrix     

Electrochemical,FTIR and morphological study of polypyrrole—Polystyrenesulphonate conducting films

The electrochemical behaviour, FTIR spectrum and the morphology of polypyrrole-polystyrenesulphonate (PPy-PSS) films obtained potentiostatically at 0.6 V in a 0.05m Py + 1.7 × 10^?2m NaPSS medium have been analysed. Different electrochemical parameters have been modified in order to establish the first polaron formation, the reversibility of the redox process and the doping mechanism. The FTIR spectrum confirms the existence of C?O groups in the film structure and two possible explanations are suggested. SEM reveals a higher homogeneity in these films than in PPy films doped with smaller counterions.     

Enantiospecific synthesis fromd-fructose of (2S,5R)- and (2R,5R)-2-methyl-1,6-dioxaspiro[4.5]decane [the odor bouquet minor components ofParavespula vulgaris (L.)]

The synthesis of (2S,5R)-(1) and (2R,5R)-2-methyl-1,6-dioxaspiro [4.5]decane (2) from (2RS,5R,8R,9R,10S)-8,9,10-trihydroxy-2-methyl-1, 6-dioxaspiro[4.5]decane (8), obtained in five steps fromd-fructose using Wittig's methodology, reduction, and spiroketalation, has been accomplished by a Corey dideoxygenation at C-8,9, followed by a Barton deoxygenation at C-10, of the appropriately protected derivatives.Enantiospecific synthesis of spirocetals. Part V. For Part IV, see Izquierdo et al. (1992).     

Enantiospecific synthesis of (R)-1,7-dioxaspiro [5.5]undecane [major component of olive fruit fly (Dacus Oleae) sex pheromone] from d-Fructose

The synthesis of the title compound13 has been carried out through the preparation of its precursor, (3R,4R,5S,6R)-3,4,5-trihydroxy-1,7-dioxaspiro[5.5]undecane (6), obtained fromd-fructose using Wittig's methodology, reduction, and spiroketalation. Compound6 was transformed into13 by a Barton deoxygenation at C-5 followed by a Corey dideoxygenation at C-3,4 of the appropriately protected derivatives.Enantiospecific synthesis of spiroacetals. Part II. For Part I, see Izquierdo and Plaza (1990).     

On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images

In this work, neural network-based models involved in hyperspectral image spectra separation are considered. Focus is on how to select the most highly informative samples for effectively training the neural architecture. This issue is addressed here by several new algorithms for intelligent selection of training samples: (1) a border-training algorithm (BTA) which selects training samples located in the vicinity of the hyperplanes that can optimally separate the classes; (2) a mixed-signature algorithm (MSA) which selects the most spectrally mixed pixels in the hyperspectral data as training samples; and (3) a morphological-erosion algorithm (MEA) which incorporates spatial information (via mathematical morphology concepts) to select spectrally mixed training samples located in spatially homogeneous regions. These algorithms, along with other standard techniques based on orthogonal projections and a simple Maximin-distance algorithm, are used to train a multi-layer perceptron (MLP), selected in this work as a representative neural architecture for spectral mixture analysis. Experimental results are provided using both a database of nonlinear mixed spectra with absolute ground truth and a set of real hyperspectral images, collected at different altitudes by the digital airborne imaging spectrometer (DAIS 7915) and reflective optics system imaging spectrometer (ROSIS) operating simultaneously at multiple spatial resolutions.     

State of the art of frameworks and middleware for facilitating mobile and ubiquitous learning development

The emergence of mobile and ubiquitous technologies as important tools to complement formal learning has been accompanied by a growing interest in their educational benefits and applications. Mobile devices can be used to promote learning anywhere and anytime, to foster social learning and knowledge sharing, or to visualize augmented reality applications for learning purposes. However, the development of these applications is difficult for many researchers because it requires understanding many different protocols; dealing with distributed schemas, processes, platforms, and services; learning new programming languages; and interacting with different hardware sensors and drivers. For that reason, the use of frameworks and middleware that encapsulate part of this complexity appears to be fundamental to the further development of mobile learning projects. This study analyzes the state of the art of frameworks and middleware devoted to simplifying the development of mobile and ubiquitous learning applications. The results can be useful to many researchers involved in the development of projects using these technologies by providing an overview of the features implemented in each of these frameworks.     

Team performance and information system implementation

Although the Earned Value Method (EVM), a multi-dimensional project control system, has been widely used since 1960, there has been little research focused specifically on its application in IS implementation. The management of IS projects—including the accurate forecasting of project duration—is complicated by the strong impact of the learning curve on the effectiveness of EVM. This study makes new contributions by advancing formulas for the accurate prediction of project duration, and by developing a decision support model in which the learning curve is fully integrated with EVM, and performance changes due to learning are isolated from other factors affecting project duration. The study makes three contributions to the understanding and use of EVM in IS implementation: (1) It provides an extended version of EVM, (2) it provides an illustration of the application of EVM to a real project situation based on empirical data from an ERP implementation project, and (3) it provides a theoretical basis for empirical studies of IS project control techniques. The decision support model can be used to determine both the learning curve coefficient and the project completion date during the early stages of a project, and it thus offers a significant practical contribution to the management of IS projects.