Multi-Criteria versus Data Envelopment Analysis for Assessing the Performance of Biogas Plants

This paper compares multi-criteria decision aiding (MCDA) and data envelopment analysis (DEA) approaches for assessing renewable energy plants, in order to determine their performance in terms of economic, environmental, and social criteria and indicators. The case is for a dataset of 41 agricultural biogas plants in Austria using anaerobic digestion. The results indicate that MCDA constitutes an insightful approach, to be used alternatively or in a complementary way to DEA, namely in situations requiring a meaningful expression of managerial preferences regarding the relative importance of evaluation aspects to be considered in performance assessment.Multi-criteria decision analysis; DEA; Renewable energy; Biogas

Multicriteria decision analysis for sustainable data centers location

This is a PDF file of an unedited manuscript that has been accepted for publication in International Transactions in Operational Research. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. The final version will be available at: http://dx.doi.org/10.1111/j.1475-3995.2012.00874.

Zero-field Kondo splitting and quantum-critical transition in double quantum dots

Double quantum dots offer unique possibilities for the study of many-body correlations. A system containing one Kondo dot and one effectively noninteracting dot maps onto a single-impurity Anderson model with a structured (nonconstant) density of states. Numerical renormalization-group calculations show that while band filtering through the resonant dot splits the Kondo resonance, the singlet ground state is robust. The system can also be continuously tuned to create a pseudogapped density of states and access a quantum critical point separating Kondo and non-Kondo phases.Comment: 4 pages, 4 figures; Accepted for publication in Physical Review Letter

Heart Rate Variability during Plateau Waves of Intracranial Pressure: a pilot descriptive study

This study aims to describe heart rate variability during the first episode of plateau waves of intracranial pressure (ICP) in Traumatic Brain Injury (TBI) in order to characterize and identify at bedside this cerebrovascular phenomenon. The general behavior of the heart rate variability (HRV) spectral measures expressed in the medians across patients is concordant with an increased HRV in the latter part of the baseline and plateau wave, followed by a decrease after the event and a new increase during the recovery. In low and high frequency bands the same increase is more marked in the parametric analysis. Interpretation of HRV may help clinicians to better identify the plateau waves and allow earlier management

An algorithm for ordinal sorting based on ELECTRE with categories defined by examples

Abstract This work proposes a Progressive Assisted Sorting Algorithm (PASA) based on a multicriteria evaluation ELECTRE-type method. The purpose of the PASA is to aid a decision maker to progressively sort a set of alternatives into a set of categories, which we considered are ordered (ordinal sorting), following a consistency principle. We consider the principle that if an alternative outranks (is as good as) a second one, then it must belong to the same category or to a better category. The set of alternatives already sorted by the decision maker will implicitly define the categories, and will constrain the range of categories where other alternatives may be sorted. We show how the same idea may be used in an aggregation/disaggregation approach, considering some parameters of ELECTRE are not fixed a priori, but are constrained only by the examples provided. In this context, we establish a "convex-shape property" stating that the range of possible categories for an alternative is always an interval of categories. A discussion contrasting this approach with ELECTRE TRI is included in the conclusions

Measurement Of The Electric Energy Storage Capacity In Solar Thermoelectric Generators' Energy Harvesting Modules

Reducing energy consumption is mandatory in self-powered sensor nodes of wireless sensor networks that obtain all their energy from the environment. In this direction, one first step to optimize the network is to accurately measure the total energy harvested, which will determine the power available for sensor consumption. We present here a technique based on an embedded circuit with an ultra-low-power microcontroller to accurately measure the efficiency of flat-panel solar thermoelectric generators operating with environmental temperature gradients. Experimental tests showed that when a voltage of 180 mV (best case in an environmental flat-panel solar thermoelectric generators) is applied to the input of the DC-DC converter, the proposed technique eliminates a measurement error of 33% when compared with the conventional single supercapacitor strategy.13

Arduino-controlled Reflectance Transformation Imaging to the study of cultural heritage objects

Fundacao para a Ciencia e a Tecnologia, Portugal (Grant Nos. UIDB/04349/2020 and UID/FIS/04559/2019)- Private funds. V.C. acknowledges the support from UID/Multi/04349/2019. J.C. acknowledges NOVA.ID.FCT.This article examines the development of a low-cost and portable set-up controlled by an Arduino board to perform Reflectance Transformation Imaging technique, from the information derived from 45 digital photographs of an object acquired using a stationary camera. The set-up consists of 45 high-intensity light emitting diodes (LEDs) distributed over a hemispherical dome of 70 cm in diameter and a digital camera on the top of the dome. The LEDs are controlled by an Arduino board, and the user can individually control the LEDs state (ON or OFF) and duration of illumination. An old manuscript written with iron-gall ink and a set of 1 Euro coins mint in 2002 were photographed with the set-up. The interactive re-lighting and the mathematical enhancement of the object's surface revealed corrosion, loss of material, scratches and other details, which were not perceived in standard images. These unique features, which can be extracted using edge detection processing, have immediate application in different fields such as cultural heritage or forensic studies, where they can be used as fingerprints to identify unique objects, allowing also recognizing the use of tools to alter the surface of coins to increase the price in the market.publishersversionpublishe

A computational approach to forecasting and minimizing electricity costs in the short-term market for distributors in Brazil

In Brazil, the electric power distributors must buy electricity on auctions one, three and five years ahead. If there is inefficiency in the contracting of electric energy, the chamber of Commercialization of Electric Energy, which enables the commercialization, can apply penalties. Thus, this paper proposes a computational approach to forecasting electricity by the class of the consumer using a multi-layer perceptron artificial neural network with a backpropagation algorithm and a prediction using time series techniques through the Bayesian and Akaike selection criteria. The forecast of electricity consumption can serve as support in the purchase of electricity in auctions in the regulated contracting environment and in the process of settlement of differences and for energy management, customer service, and distributor billing. The results show that a multilayer network with a backpropagation algorithm is able to learn the behavior of the data that influences the electric energy consumed by consumption class and can be used to follow the evolution in the demand of each class of consumption and, consequently, to help distributors in the process of contracting of electricity, reduce losses like fines, and reduce the costs of the energy distributor