Azorean Agriculture Efficiency by PARThe producers always aspire at increasing the efficiency of their production process. However, they do not always succeed in optimizing their production. In the last years, the interest on Data Envelopment Analysis (DEA) as a powerful tool for measuring efficiency has increased. This is due to the large amount of data sets collected to better understand the phenomena under study, and, at the same time, to the need of timely and inexpensive information. The "Productivity Analysis with R" (PAR) framework establishes a user-friendly data envelopment analysis environment with special emphasis on variable selection and aggregation, and summarization and interpretation of the results. The starting point is the following R packages: DEA (Diaz-Martinez and Fernandez-Menendez, 2008) and FEAR (Wilson, 2007). The DEA package performs some models of Data Envelopment Analysis presented in (Cooper et al., 2007). FEAR is a software package for computing nonparametric efficiency estimates and testing hypotheses in frontier models. FEAR implements the bootstrap methods described in (Simar and Wilson, 2000). PAR is a software framework using a portfolio of models for efficiency estimation and providing also results explanation functionality. PAR framework has been developed to distinguish between efficient and inefficient observations and to explicitly advise the producers about possibilities for production optimization. PER framework offers several R functions for a reasonable interpretation of the data analysis results and text presentation of the obtained information. The output of an efficiency study with PAR software is selfexplanatory. We are applying PAR framework to estimate the efficiency of the agricultural system in Azores (Mendes et al., 2009). All Azorean farms will be clustered into homogeneous groups according to their efficiency measurements to define clusters of "good" practices and cluster of "less good" practices. This makes PAR appropriate to support public policies in agriculture sector in Azores. ABSTRACTThe producers always aspire at increasing the efficiency of their production process. However, they do not always succeed in optimizing their production. In the last years, the interest on Data Envelopment Analysis (DEA) as a powerful tool for measuring efficiency has increased. This is due to the large amount of data sets collected to better understand the phenomena under study, and, at the same time, to the need of timely and inexpensive information.The "Productivity Analysis with R" (PAR) framework establishes a user-friendly data envelopment analysis environment with special emphasis on variable selection and aggregation, and summarization and interpretation of the results. The starting point is the following R packages: DEA (Diaz-Martinez and Fernandez-Menendez, 2008) and FEAR (Wilson, 2007). The DEA package performs some models of Data Envelopment Analysis presented in (Cooper et al., 2007). FEAR is a software package for computing nonparametric efficiency estimates ...

Some typical cases of intelligent handling of weather forecasts such as translation, visualization, etc. are decomposed into two subprocesses ~ analysis and synthesis. Specific techniques are presented for analysis and synthesis of weather forecast texts as well as for generation of weather maps. These techniques deal with the weather forecasts at different levels ~ syntactic, discourse and semantic. They are based on a conceptual model underlying weather forecasts as well as on formal descriptions of the means of expression used in particular natural and cartographic sublanguages.

The purpose of this study is to offer a method for prediction the result of treatment of gingival recessions using coronally advanced flap (CAF) and platelet rich fibrin membrane (PRFm) with CAF and connective tissue graft (CTG). The reported work gets answers to the following open questions: Is the treatment of mucogingival defects a predictable procedure? Is the jaw a factor affecting the outcome of coronally advanced flap root coverage procedure? To achieve this goal the authors followed the Creeping Attachment clinical parameter six months postoperatively.

In this paper we consider the problem of building models that have high sentiment classification accuracy without the aid of a labeled dataset from the target domain. For that purpose, we present and evaluate a novel method based on level of abstraction of nouns. By comparing high-level features (e.g. level of affective words, level of abstraction of nouns) and low-level features (e.g. unigrams, bigrams), we show that, high-level features are better to learn subjective language across domains. Our experimental results present accuracy levels across domains of 71.2% using SVMs learning models.

A major challenge in computational linguistics is to uncover word interactions in linguistic expressions. In this paper a new framework for discovering interaction between the words constituting multi-word relevant expressions is proposed. This framework is built on an algorithm for relevant expression extraction called Lo-calMaxs algorithm, partitioning round medoids clustering method and Bayesian networks. Bayesian networks are attractive for their ability to represent dependencies and to learn from observations. This new technology facilitates text comprehension. It may also enable control of highly ambiguous text input.