We report a combined experimental and theoretical investigation of dissociative electron attachment (DEA) to the nucleobase uracil. Using ion momentum imaging experiments employing a DEA reaction microscope we have measured 3-dimensional momentum distributions of specific anionic fragments following DEA to uracil by 6 eV electrons. From the measured anion fragment kinetic energy we determine th...

There has been an ever growing concern to measure efficiency of decision making units (DMUs). Parametric approaches have been the popular methods for measuring the same. Data Envelope Analysis (DEA) is an addition in this domain. This paper is an attempt to understand the concept of DEA approach. DEA is a Linear Programming Problem that provides a means of calculating apparent efficiency levels...

Data Envelopment Analysis (DEA) is traditionally based on the axiom of convex production possibility sets. In many research situations this axiom is considered overly restrictive, and recent research has focused on finding suitable ways of relaxing it. Unfortunately, there currently is no consensus in the DEA field on why and how to account for non-convexities. This paper explores empirical evi...

In a relatively short period of time Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. DEA has been successfully applied to a host of different types of entities engaged in a wide variety of activities in many contexts worldwide. This chapter discusses the fundamental DEA models and some of their extensions.

Data envelopment analysis (DEA) is a nonparametric method from the area of operations research that measures the relationship of produced outputs to assigned inputs and determines an efficiency score. This efficiency score can be interpreted as a performance measure in investment analysis. Recent literature contains intensive discussion of using DEA to measure the performance of hedge funds, as...

In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researchers, practitioners, and reviewers may have con...

Data Envelopment Analysis (DEA) is the methodology for evaluating the relative productive efficiency of decision making units (DMUs) that produce multiple-outputs using multiple-inputs. DEA was proposed for the first time in 1957 by Farrell; nonetheless, the wide usage of this method begun with its generalization and the linear programming formulation which is due to Charnes et al. (1978) (for ...

The inherent elasticity of dielectric elastomer actuators (DEAs) gives this technology great potential in energy efficient locomotion applications. In this work, a modular double cone DEA is developed with reduced manufacturing and maintenance time costs. This actuator can lift 45 g of mass (5 times its own weight) while producing a stroke of 10.4 mm (23.6% its height). The contribution of the ...

Data Envelopment Analysis (DEA) is a relatively new “data oriented” non-parametric approach for evaluating the performance of complex entities called Decision Making Units (DMUs) which convert multiple inputs into multiple outputs. DEA as a linear programming procedure computes a comparative ratio of outputs to inputs for each DMU, which is reported as the relative efficiency score. In a relati...

This paper describes the DEA-based investment strategy for constructing of a stock portfolio in the Croatian stock market. The relative efficiency of the DMUs, which are in this case the selected stocks from Zagreb Stock Exchange, is obtained from the output oriented CCR and BCC models. The set of inputs consists of risk measures, namely return variance, Value at Risk (VaR) and beta coefficient...