Self-regulation is a key skill for academic success, and to measure self-regulation strategies is especially important in the context of engineering education. However, measuring self-regulation strategies in engineering students has been a challenge due to the lack of validated instruments in Spanish. The objective of this study is developing an exploratory factor analysis (EFA) to identify the factors of the instrument the Self-Regulation Strategy Inventory - Self Report (SRSI-SR) to measure self-regulation strategies in engineering students. The design was instrumental, with the participation of 140 Chilean engineering students. The results show adequate validity and reliability indices, being made up of 16 items represented in 4 factors: 1) Organization of the environment, 2) Organization of the task, 3) Information search and 4) Inadequate regulation habits. We suggest that our adaptation of the SRSI-SR instrument to Spanish can be useful to measure self-regulation strategies in engineering students.

Chile, Japan, and Mexico are among the world’s most seismically active regions, each representing a distinct subduction setting. This work extends prior studies on earthquake classification and evaluates the effectiveness of sequence-based methods across these different subduction zones. We frame the task as classifying events into foreshock, mainshock, and aftershock classes. Our pipeline comprises three stages: first, spatio-temporal clustering to group related seismic events; second, labeling each event to identify the mainshock and assign foreshock/aftershock roles; and finally, sequence classification using deep learning models, including Long Short-Term Memory (LSTM), Transformer, and Spatio-Temporal Transformer (STTN) architectures. Our results across the three regions show that the approach is effective: mainshock detection is consistently strong, aftershock classification achieves intermediate performance, and foreshocks remain the most challenging class. Among the tested architectures, the Transformer model exhibits the most consistent and competitive performance across regions. These findings underscore the effectiveness of the approach across distinct subduction zones while highlighting persistent challenges in foreshock recognition and opportunities for further improvement.

In the Multi-Objective Shortest Path Problem (MO-SPP), one has to find paths on a graph that simultaneously minimize multiple objectives. It is not guaranteed that there exists a path that minimizes all objectives, and the problem thus aims to find the set of Pareto-optimal paths from the start to the goal vertex. A variety of multi-objective A*-based search approaches have been developed for this purpose. Typically, these approaches maintain a front set at each vertex during the search process to keep track of the Pareto-optimal paths that reach that vertex. Maintaining these front sets becomes burdensome and often slows down the search when there are many Pareto-optimal paths. In this article, we first introduce a framework for MO-SPP with the key procedures related to the front sets abstracted and highlighted, which provides a novel perspective for understanding the existing multi-objective A*-based search algorithms. Within this framework, we develop two different, yet closely related approaches to maintain these front sets efficiently during the search. We show that our approaches can find all cost-unique Pareto-optimal paths, and analyze their runtime complexity. We implement the approaches and compare them against baselines using instances with three, four and five objectives. Our experimental results show that our approaches run up to an order of magnitude faster than the baselines.

Multi-objective search (MOS) has emerged as a unifying framework for planning and decision-making problems where multiple, often conflicting, criteria must be balanced. While the problem has been studied for decades, recent years have seen renewed interest in the topic across AI applications such as robotics, transportation, and operations research, reflecting the reality that real-world systems rarely optimize a single measure. This paper surveys developments in MOS while highlighting cross-disciplinary opportunities, and outlines open challenges that define the emerging frontier of MOS

This paper provides a theoretical study on MultiObjective Heuristic Search. We frst classify states in the state space into must-expand, maybe-expand, and never-expand states and then transfer these definitions to nodes in the search tree. We then formalize a framework that generalizes A* to MultiObjective Search. We study different ways to order nodes under this framework and its relation to traditional tie-breaking policies and provide theoretical fndings. Finally, we study and empirically compare different ordering functions.

This work addresses the challenge of improving the accuracy of vehicle classification at toll booths on Chilean interurban highways through the application of machine learning techniques. The study focuses on a highway in the Bío Bío region, where a set of representative data is collected and prepared, including detailed information on vehicles registered and classified by the concessionaire company. The total data used consists of 961,915 records. Four machine learning models are evaluated: K-Nearest Neighbors (KNN), Multilayer Neural Network (MLP), Support Vector Machine (SVM), and Decision Tree Classifier (DTS). The models are trained and evaluated using standard metrics such as accuracy, precision, recall, and F1-Score. Additionally, confusion matrix, learning curve, ROC curve, and Precision-Recall curve are assessed. The experiments show that KNN and MLP are the models with the best results, achieving an accuracy greater than 98.6% and an F1-Score close to 97.9%, thereby improving the accuracy of the current toll booth system and exceeding the 95% accuracy required by the Chilean Ministry of Public Works