Abstract
A significant challenge in asset management is the selection of investment projects for infrastructures, which often relies on subjective judgement and lacks structured decision support methods. This challenge is particularly complex in water systems due to the diverse and heterogeneous nature of the components requiring investment. While the infrastructure value index (IVI) is widely used to characterise assets and support investment decisions in the water sector, its application in optimisation models for generating efficient project portfolios remains unexplored. To address this research gap, this study introduces optimisation models for generating investment portfolio plans in water systems' asset management. The proposed approach includes two mixed-integer linear programming (MILP) models that determine optimal solutions and an evolutionary algorithm that offers sub-optimal alternative investment selection plans to provide decision-makers with additional choices for balancing optimal outcomes. The primary contribution of this research is the combined utilisation of MILP and evolutionary algorithms, integrating the IVI into the decision-making process. These tools provide decision-makers with structured methods for defining investment plans and minimising the subjective elements typically associated with such processes. To illustrate the effectiveness of the models, a case study is presented involving a pumping station of a Portuguese water company. The results demonstrate the practical application and benefits of the proposed approach in optimising investment decisions. This research contributes to advancing asset management practices by integrating quantitative optimisation techniques and leveraging the IVI, thereby enhancing the objectivity and efficiency of investment planning in water systems' asset management.

Abstract
The Single Supervisory Mechanism (SSM) was implemented as a first step towards a Banking Union in November 2014. This paper investigates the impact of the SSM on Eurozone banks' efficiency and position of best-practice frontier. It is based on a balanced panel analysis of 931 European bank-year observations from 2011 to 2017 (133 banks, seven years). The study uses Data Envelopment Analysis and a difference-in-differences approach to explore the evolution of banking performance. We found that the SSM had a negative impact on the efficiency levels of Eurozone banks, particularly in the year after the introduction of the mechanism. Additionally, we observed that the frontier formed by non-Eurozone European Union banks is more productive than the frontier of Eurozone banks in all the years analysed. Both efficiency and frontier position show evidence of a recovery trend in more recent years for both groups. We also found that while Equity-to-Asset Ratio, Return on Average Assets and Gross Domestic Product per capita positively impacted banks' efficiency, domestic credit provided by banks expressed as %GDP had a negative impact on efficiency.

Abstract

Abstract
Jet impingement is widely implemented in a variety of engineering applications and industrial processes where high average heat transfer coefficients and the uniformity of the heat transfer over the impinging surface are required to enhance the process and to avoid local hot (or cold) spots. Multiple jet impingement involves several parameters that interfere with the performance of the process, and there are no universal optimal solutions. To ensure the optimization of the process, it is important to understand the influence of these parameters in the heat transfer over the target surface. To perform this study an experimental research will be performed on a purpose-built test facility which has been commissioned, using a Particle Image Velocimetry system. However, to reduce time and costs associated to the experimental tests, it is important to perform a Design of Experiments, that allows to reduce the number of trials, focusing on the parameters that have a greater influence on the process performance. Taguchi’s method allows the optimization of the process through the selection of the most suitable parameters values. This work presents the method that must be followed before the development of experiments related to the multiple jet impingement over a complex surface, from the design of the experimental setup to the design of the matrix of experiments. © 2019, Springer Nature Switzerland AG.

Abstract
Data clustering combined with multiobjective optimization has become attractive when the structure and the number of clusters in a dataset are unknown. Data clustering is the main task of exploratory data mining and a standard statistical data analysis technique used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. This project analyzes data to extract possible failure patterns in Solar Photovoltaic (PV) Panels. When managing PV Panels, preventive maintenance procedures focus on identifying and monitoring potential equipment problems. Failure patterns such as soiling, shadowing, and equipment damage can disturb the PV system from operating efficiently. We propose a multiobjective evolutionary algorithm that uses different distance functions to explore the conflicts between different perspectives of the problem. By the end, we obtain a non-dominated set, where each solution carries out information about a possible clustering structure. After that, we pursue a-posteriori analysis to exploit the knowledge of non-dominated solutions and enhance the fault detection process of PV panels.

Abstract
As the demand for electric charging accelerates, so does the stress on the relatively insufficient public charging infrastructure. To appropriately manage and scale charging infrastructure, there is a need for support tools capable of predicting the utilization and sales of charging stations, as well as the traffic flow of users from their original location to the charging stations. Therefore, this article proposes a generic methodology for infrastructure placement, namely forecasting demand and predicting its flow to the supply points. The methodology is applied in a case study to the electric charging grid of Portugal with real data, in the context of the needs of a particular charging point operator (CPO). Demand is first forecasted at a high-granularity level with a demand disaggregation model, followed by its capture by the grid of chargers using a parameterized gravity model. Validation is performed by comparing actual with predicted sales per charging station. Adequate visualizations to support decision-making are presented.