This paper investigates a special type of recurrent expressions, lexical bundles, defined as a sequence of three or more words that co-occur frequently in a particular register (Biber et al., 1999). Considering the importance of this group of multi-word sequences in academic prose, this study explores the forms and syntactic structures of three- and four-word bundles in English abstracts writte...

In this paper we present an efficient system for independent speaker speech recognition based on neural network approach. The proposed architecture comprises two phases: a preprocessing phase which consists in segmental normalization and features extraction and a classification phase which uses neural networks based on nonparametric density estimation namely the general regression neural networ...

Abstract. We introduce a methodology for generalizing Data Envelopment Analysis to incorporate the role and impact of constituencies in the classification of the model’s attributes. Constituencies determine whether entities’ attributes in a DEA study are treated as desirable or undesirable. This extension of DEA is the basis for a methodology to answer questions that arise such as: Which consti...

We consider the problem of allocating personal protective equipment, namely surgical and respiratory masks, to health centers under extremely limited supply. formulate a multi-objective multi-period non-linear resource allocation model for this with objectives minimizing number infected workers, patients deprivation cost function defined over shortages. solve resulting using ε-constraint algori...

The objective of this work is to estimate the production frontier and technical efficiency levels of Côte d’Ivoire firms. The methodology used is closely linked to the recent nonparametric frontier models, as for example Cazals, Florens and Simar (2002), Aragon, Daouia and Thomas-Agnan (2003) or Florens and Simar (2001). The main advantage compared to the classical nonparametric methods (DEA, F...

We propose new families of models and algorithms for high-dimensional nonparametric learning with joint sparsity constraints. Our approach is based on a regularization method that enforces common sparsity patterns across different function components in a nonparametric additive model. The algorithms employ a coordinate descent approach that is based on a functional soft-thresholding operator. T...

This study proposes a multi-criteria design optimization of cam mechanisms. approach is based on the multistage interconnection different tools: MATLAB, ADAMS, and mode frontier. It consists three main issues: (i) The polynomial representation splines given by checkpoints defining specific motion follower; (ii) connection these degrees fitting in order to combine them entire profile (iii) inclu...