Abstract
This paper introduces two machine learning optimization algorithms to significantly enhance position estimation in Reconfigurable Intelligent Surface (RIS) aided localization for mobile user equipment in Non-Line-of-Sight conditions. Leveraging the strengths of these algorithms, we present two methods capable of achieving extremely high accuracy, reaching sub-centimeter or even sub-millimeter levels at 3.5 GHz. The simulation results highlight the potential of these approaches, showing significant improvements in indoor mobile localization. The demonstrated precision and reliability of the proposed methods offer new opportunities for practical applications in real-world scenarios, particularly in Non-Line-of-Sight indoor localization. By evaluating four optimization techniques, we determine that a combination of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) results in localization errors under 30 cm in 90 % of the cases, and under 5 mm for close to 85 % of cases when considering a simulated room of 10 m by 10m where two of the walls are equipped with RIS tiles. © 2023 IEEE.

Abstract
This research investigates the implementation of lean practices in services in order to identify those that have a greater influence on company performance. Regression analysis with data from a systematic literature review was the basis to study the relationship between lean and performance. For this purpose, a total of 104 case studies were considered. A main finding was that some lean practices, such as “voice of the customer” and “cross-functional teams” have a significant positive influence on performance. Also, the results suggest that the more engaged managers are and the more they invest in training, the better company performance will be. Finally, one may also conclude that knowledge about the determinants of lean management will allow managers to be aware of what is decisive to improve company performance. © 2023, World Scientific and Engineering Academy and Society. All rights reserved.

Abstract
Power transformers are one of the main elements of a power grid, and their downtime impacts the entire network. Repairing their failures can be very costly, so sophisticated maintenance techniques are necessary. To attempt to solve this problem, we developed a mixed-integer nonlinear optimization model that, focusing on a single power transformer, both schedules this maintenance and also decides how much of the hourly demand it will satisfy. A high level of load on a power transformer increases its temperature, which increases its degradation, and so these two decisions have to be carefully balanced. We also consider that power transformers have several components that degrade differently. Our model becomes very difficult to solve even in reasonably sized instances, so we also present an iterative refinement heuristic.

Abstract
This study aims to explore how higher education institutions respond to the challenge of incorporating soft skills into their curricula. It employs a mixed-methods approach in which the quantitative analysis of the disciplines addressing this issue is complemented by a thematic analysis of semi-structured interviews conducted with four higher education institutions in Portugal. The findings indicate that although the number of subjects specifically addressing soft skills is small, there is a growing concern to incorporate soft skills in pedagogical and evaluation methodologies in each course. Several challenges, good practices, and future perspectives are also explored in this work.

Abstract

Abstract
Evaluating predictive models is a crucial task in predictive analytics. This process is especially challenging with time series data because observations are not independent. Several studies have analyzed how different performance estimation methods compare with each other for approximating the true loss incurred by a given forecasting model. However, these studies do not address how the estimators behave for model selection: the ability to select the best solution among a set of alternatives. This paper addresses this issue. The goal of this work is to compare a set of estimation methods for model selection in time series forecasting tasks. This objective is split into two main questions: (i) analyze how often a given estimation method selects the best possible model; and (ii) analyze what is the performance loss when the best model is not selected. Experiments were carried out using a case study that contains 3111 time series. The accuracy of the estimators for selecting the best solution is low, despite being significantly better than random selection. Moreover, the overall forecasting performance loss associated with the model selection process ranges from 0.28 to 0.58%. Yet, no considerable differences between different approaches were found. Besides, the sample size of the time series is an important factor in the relative performance of the estimators.