Abstract

Abstract
Renewable energy is an essential driver of the energy transition towards a more sustainable world. However, sustainability requires the coordination of the economic, environmental, and social dimensions, turning it into a complex objective. The aim of this study is to review the state of the art of the articles that analyze economic, environmental, and social metrics that can be used to evaluate the impact of renewable. In addition, this work also classifies metrics into two main approaches: macro-studies, corresponding to those that evaluate based on global and aggregated impacts, and micro-studies, corresponding to those that focus on regional and local impacts. A systematic literature review was used to identify and define these main metrics, based on common research databases. Seven metrics were found and described for the environmental impact, four for the economic impact and five for the social impact. The main finding revealed that micro-studies are more prevalent in comparison to macro-studies. Moreover, the systematic literature review allows achieving the objective and highlighting the proposed sustainability assessment framework as crucial for gauging and evaluating impact metrics across the economic, social, and environmental dimensions. The difficulty in isolating and measuring each metric may be attributed to the challenges involved in studying the corresponding impact, whether at the micro or macro level. More targeted studies can help in a more efficient energy transition. © 2023 The Authors, published by EDP Sciences.

Abstract

Abstract
Companies focus on achieving high service levels and need to combine short service times with the dynamics between cost and quality. Their transportation systems are therefore a fundamental part; they must be reliable and efficient. This study was implemented in a company of the marine industry, and its final product has special characteristics that require special transportation, i.e., they need a truck with a special structure to be able to transport the boats. This situation causes the vehicle to return empty to the company, a route that the company must support economically. The company has already approached several options with logistic service providers (3PL) without obtaining positive solutions. It is in this sense that the present project arises, which aims to develop a tool for the creation of round-trip circuits, given that in the current context the company intends to acquire a vehicle with reduced environmental impact. In a first phase we analyze the company’s needs based on the unique characteristics of the final product, then we study the existing options on the market. Culminating in the proposal of a vehicle that allows performing a circuit in round trip (distribute the final product and return with raw material and not empty) powered by renewable energy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Morphing attacks keep threatening biometric systems, especially face recognition systems. Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown. At the same time, there is a constant concern regarding the lack of interpretability of deep learning models. Balancing performance and interpretability has been a difficult task for scientists. However, by leveraging domain information and proving some constraints, we have been able to develop IDistill, an interpretable method with state-of-the-art performance that provides information on both the identity separation on morph samples and their contribution to the final prediction. The domain information is learnt by an autoencoder and distilled to a classifier system in order to teach it to separate identity information. When compared to other methods in the literature it outperforms them in three out of five databases and is competitive in the remaining. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

Abstract