Patent Litigation before the New Claims Court

The final chapter was written recently on the United States Court of Claims, a court which from its creation in 1855 had served as the nation\u27s conscience. The existence of this court, which had served long and well in carrying out the task of a sovereign rendering justice against itself, along with the United States Court of Customs and Patent Appeals, was terminated on October 1, 1982, and replaced by the Court of Appeals for the Federal Circuit and the United States Claims Court. It is not the purpose of this paper to outline the history of the Court of Claims as that task has been done adequately by others. Rather, my aim is to discuss the changes which practitioners may expect in bringing patent suits against the United States in the new Claims Court

Patent Litigation before the New Claims Court

The final chapter was written recently on the United States Court of Claims, a court which from its creation in 1855 had served as the nation\u27s conscience. The existence of this court, which had served long and well in carrying out the task of a sovereign rendering justice against itself, along with the United States Court of Customs and Patent Appeals, was terminated on October 1, 1982, and replaced by the Court of Appeals for the Federal Circuit and the United States Claims Court. It is not the purpose of this paper to outline the history of the Court of Claims as that task has been done adequately by others. Rather, my aim is to discuss the changes which practitioners may expect in bringing patent suits against the United States in the new Claims Court

Water Dynamics at Protein Interfaces: Ultrafast Optical Kerr Effect Study

The behavior of water molecules surrounding a protein can have an important bearing on its structure and function. Consequently, a great deal of attention has been focused on changes in the relaxation dynamics of water when it is located at the protein surface. Here we use the ultrafast optical Kerr effect to study the H-bond structure and dynamics of aqueous solutions of proteins. Measurements are made for three proteins as a function of concentration. We find that the water dynamics in the first solvation layer of the proteins are slowed by up to a factor of 8 in comparison to those in bulk water. The most marked slowdown was observed for the most hydrophilic protein studied, bovine serum albumin, whereas the most hydrophobic protein, trypsin, had a slightly smaller effect. The terahertz Raman spectra of these protein solutions resemble those of pure water up to 5 wt % of protein, above which a new feature appears at 80 cm–1, which is assigned to a bending of the protein amide chain

Modelling study for forecasting gaseous pollutants levels in a urban area

Atmospheric pollution is an important topic in environmental sciences. Nowadays the quality and the quantity of the data from air quality monitoring networks are significantly increased, but, for an effective management and assessment of this information, innovative data analysis methodologies have been developed. Approaches coming from advanced statistical methods were introduced in modeling and forecasting procedure to define operational techniques for atmospheric pollutants characterization at different scales. In this paper we present an application of artificial neural networks (ANN) for forecasting atmospheric gaseous pollutants. Starting from hourly data collected in Basilicata (southern Italy), from 1998 to 2007, we select the best dataset in terms of minimum data missing percentage. The applied model is a feed-forward multi-layer perceptron with an only hidden layer. The conjugate gradient learning algorithm is used. The learning capability of the model and the average goodness of the prediction are evaluated by Mean Absolute Percentage Error. The goal is to evaluate the performance the ANN model for forecasting 24-hourly data on the base of only the 24-hourly data collected in the previous day and to quantify the improvement obtained with different input strategies (optimal mix of pollutants defined by data correlation structure analysis). The preliminary results suggest that the dynamical characteristics of the gaseous pollutants may play a fundamental role in the definition of the forecasting procedure. Moreover, results confirm that the correlation structure analysis may be usefully applied for identifying the optimal strategy of data input selection. Nevertheless the quality of data represents the main limit of forecasting techniques at local scale

SWSCE - An Automatic Semantic Web Service Composition Engine

In Proceedings of the 1st International Workshop on Emergent Semantics and cooperaTion in opEn systEMs (ESTEEM), 200

SWSCE -- An Automatic Semantic Web Service Composition Engine

In several scenarios, Semantic Web technologies are gaining momentum as the most promising ones to address the issue of integrating services among different entities, possibly belonging to different location. In particular, Semantic Web Service composition can be used when no individual available service can satisfy a specific client request, but (parts of) available services can be composed and orchestrated in order to do it. In this paper we describe SWSCE, a Semantic Web Service Composition Engine, able to automatically performs the composition of Semantic Web Services