Abstract
The shotcrete process has been extensively used for many years in different civil and mining operations. Nevertheless, it is still either applied by an operator which controls the shotcrete nozzle manually or through a remote control. In either case, the operation is entirely controlled by the operator. Automating the shotcrete process involves developments in different parts of the process, such as the tunnel scanning for 3D model generation and the shotcrete path automatic generation and execution. This paper describes the work developed for this last part, namely the automatic generation and execution of a shotcrete path, given the mesh of a tunnel and a set of input parameters, for application in railway tunnels. The developed path also considers specificities of the concrete projection process, such as the uncontrolled flow variation due to the pumping systems, generating a trajectory that aims at minimizing this effect. Results are shown using a realistic simulator and an uneven railway tunnel, using an industrial robot mounted on a railway wagon.

Abstract
Educational chatbots are digital tools designed to assist learners in various educational settings. These chatbots use natural language processing (NLP) and machine learning algorithms to simulate human conversation and respond to user queries in a way that facilitates learning. They can be integrated into various educational platforms such as learning management systems, educational apps, and websites to provide learners with a personalized and interactive learning experience. Our paper discusses different scenarios for educational purposes and suggests in total four scenarios for educational needs.

Abstract
Given the relevance of the textile industry, over the years, for the portuguese economy, we intend to evaluate the economic performance of companies belonging to CAE 14131 through the indicators ROA, ROE, ROS and EVA/employees. Through the DEA technique, the BoD model is used to aggregate the various indicators in order to determine the composite indicator of 5.397 companies observed over the years 2011 to 2020, in order to deepen the knowledge about the Portuguese business economic textile sector. Through data analysis there is a progressive improvement of the indicators studied over the years which can be explained by the technological evolution occurred in this industry, although the sector under study uses mostly intensive labour. In each year, the efficient frontier is defined mostly by micro and small enterprises, which are predominantly located in the North of Portugal. © 2023 ITMA.

Abstract
Sectorisation refers to dividing a whole into smaller parts, the sectors, to facilitate an activity or achieve some goals. The paper proposes a new matrix form genetic encoding system, called matrix form binary grouping (MFBG), specifically designed for sectorisation and related problems. In MFBG representation, the columns and rows represent sectors and nodes, respectively. As a solution procedure, we followed NSGA-II by contemplating adapted measures for three commonly used criteria (equilibrium, compactness, contiguity) for sectorisation problems. The performance of the MFBG within the NSGA-II is tested from two perspectives: 1) through several experiments on the set of instances; 2) by its comparison with the group-oriented genetic encoding system under the grouping GA. Results showed that the MFBG could find good quality solutions and outperforms the GGA. This confirms that the MFBG is an innovative procedure for dealing with sectorisation problems and an excellent contribution as an alternative encoding technique. © 2023 Inderscience Enterprises Ltd.

Abstract
This paper explores the application of Deep Learning techniques to forecast electricity market prices. Three Deep Learning (DL) techniques are tested: Dense Neural Networks (DNN), Long Short-Term Memory Networks (LSTM) and Convolutional Neural Networks (CNN); and two non-DL techniques: Multiple Linear Regression and Gradient Boosting (GB). First, this work compares the forecast skill of all techniques for electricity price forecasting. The results analysis showed that CNN consistently remained among the best performers when predicting the most unusual periods such as the Covid19 pandemic one. The second study evaluates the potential application of CNN for automatic feature extraction over a dataset composed by multiple explanatory variables of different types, overcoming part of the feature selection challenges. The results showed that CNNs can be used to reduce the need for a variable selection phase.

Abstract
Evaluating the efficiency of surface treatments is a problem of paramount importance for the cork stopper industry. Generically, these treatments create coatings that aim to enhance the impermeability and lubrification of cork stoppers. Yet, current methods of surface analysis are typically time-consuming, destructive, have poor representativity or rely on indirect approaches. In this work, the use of a laser-induced breakdown spectroscopy (LIBS) imaging solution is explored for evaluating the presence of coating along the cylindrical surface and in depth. To test it, several cork stoppers with different shaped areas of untreated surface were analyzed by LIBS, making a rectangular grid of spots with multiple shots per spot, to try to identify the correspondent shape. Results show that this technique can detect the untreated area along with other features, such as leakage and holes, allowing for a high success rate of identification and for its performance at different depths, paving the way for future industry-grade quality control solutions with more complex surface analysis.