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Oliveira, M. M., Camanho, A. S., and Gaspar, M. B. 2010. Technical and economic efficiency analysis of the Portuguese artisanal dredge fleet. - ICES Journal of Marine Science, 67: 1811-1821. An efficiency analysis of the commercial dredge fleet operating along the south coast of Portugal between 2005 and 2007 sought to determine the efficiency of the vessels using data envelopment analysis models, considering fixed inputs (vessel power, length, tonnage, and an indicator of stock biomass) and a variable input (number of days at sea). The annual quota per vessel was also included in the model as a contextual factor. In the technical-efficiency (TE) analysis, outputs were defined by the catch weight for each of the three target species (bivalves). Using price data for each species in the wholesale market, revenue efficiency was also estimated to complement the TE analysis. The advantage of the approach lies in the ability to separate technical aspects from allocative aspects in the efficiency assessment, allowing two-dimensional graphic representation of vessel performance. The procedure allows the identification of benchmark vessels, which maximized the catch weight of the species landed, given their inputs, as well as the vessels that selected the appropriate target species to maximize the revenue of the fishing activity, given output prices. The approach also allowed the specification of targets for inefficient vessels that correspond to the catch by species, permitting revenue maximization from fishing.

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In this paper, we incorporate fuzzy reasoning in fractional-order PD controllers. The resulting fuzzy fractional PD controller is investigated in terms of its digital implementation and robustness. The combined features of both algorithms will result in a better control system performance. Also, the effectiveness and robustness of the new methodology is illustrated through an application example.

Abstract
Today, organisations suffer a tremendous pressure to become more agile, efficient, profitable and dynamic. The market presents itself as an area of severe competition, where the margin between success and failure is becoming tenuous. Some authors have noticed the importance of meaningful and reliable information as a prerequisite of successful management and high-quality decision-making. Multiple factors, in the business environment were shown to cause impact in the organisational performance, such as the market attractiveness, the resources availability, clockspeed-industry, the environmental predictability, decision velocity and market opportunity discovery. The purpose of developing this research work was essentially to respond the following questions: Can the organisation improve performance by monitoring the market dynamics, by compiling information for the strategic positioning identification and market opportunities discovery? Can the information gathered agile the decision-making and the strategic alignment with the market? The market attractiveness index calculation phase intends to quantify the data collected from the multiple indicators, and then determine an index, for each market in the evaluation process. This technique is divided in three main steps, which consist of: (1) Calculating the mass density by category (Macro-Environment, Micro-Environment and Organisation), followed by (2) the product calculation of each category by the main market and, finally, (3) the prioritization of the different markets in study. The development of a methodology for assessing the market attractiveness is considered to be very relevant in the global market scenario. It can help organisations to gain a greater awareness of market dynamics, speed up the decision process and increase its consistency, thus enabling the strategic alignment and the improvement of the organisational performance.

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This chapter introduces the volume on Data Mining (DM) for Business Applications. The chapters in this book provide an overview of some of the major advances in the field, namely in terms of methodology and applications, both traditional and emerging. In this introductory paper, we provide a context for the rest of the book. The framework for discussing the contents of the book is the DM methodology, which is suitable both to organize and relate the diverse contributions of the chapters selected. The chapter closes with an overview of the chapters in the book to guide the reader.

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Peak voltage sensors, namely diode based detectors, have been used for in-circuit testing of RF circuits. These detectors are simple to implement but provide a limited accuracy. On the other hand, the power inferred from these measurements assumes that impedance is known. This work presents a comparison between measurements obtained with peak voltage and true power detectors. The trade-off between observing one and the other are discussed, namely as far as information concerning the output load is concerned. Simulation results, acquired with the model of a prototype demonstration chip, show that more accurate information is obtained with power rather than voltage measurements in case load impedance variations occur. Experimental results obtained with a prototype chip are currently being obtained. ©2010 IEEE.

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