Editorial

Abstract

We are pleased to share Volume 11, Issue 2 of the Latin American Journal of Computing (LAJC) with you. This edition includes a selection of pioneering research articles that demonstrate the latest advancements in the computer science field. Each paper included in this volume represents rigorous academic research and innovative problem-solving methods. We believe that the insights and discoveries presented here will significantly contribute to the field, stimulate insightful discussions, and inspire future innovations.

This issue begins with three articles that explore advanced methodologies in process monitoring, heat transfer, and robotics. The first article investigates the use of Echo State Networks (ESNs) to create digital twins for nonlinear dynamic chemical processes, demonstrating the potential of ESNs in generating efficient surrogate models for real-time process monitoring and control. The second article addresses the inverse problem in heat transfer modeling using the Transition Markov Chain Monte Carlo method, showcasing its effectiveness in estimating spatially variable thermophysical properties. Next, Janarthanan et al. explore the potential of data generated by robots, specifically focusing on ROS Bag files used in the Robot Operating System (ROS). The study highlights security concerns, such as unauthorized access and data theft, due to plain text communication in legacy ROS systems.

This issue also delves into the critical applications of artificial intelligence and machine learning in various scientific and industrial domains. The fourth article presents the ANN-MoC approach for solving inverse transient transport problems, showcasing its potential in engineering and medical fields by accurately estimating absorption coefficients from scalar flux measurements. Next, another study explores the impact of data balance on short-term rainfall forecasts using Artificial Neural Networks (ANNs) with data from the Amazon Tall Tower Observatory (ATTO). This research emphasizes the necessity of balanced data to improve the accuracy and reliability of meteorological models, highlighting the broader implications for environmental monitoring and prediction. Additionally, the volume includes an innovative fault classification model for industrial processes, merging Decision Trees with Genetic Programming to enhance preventive and corrective measures.

Finally, we explore financial markets and technological advancements. One article compares the Brazilian stock market with cryptocurrencies like Bitcoin, Ethereum, and Solana, using the Kolmogorov-Smirnov test to examine their relationships and potential investment opportunities. The last study uses machine learning and the Grey Wolf Optimization meta-heuristic to predict Brazil's electricity demand, showcasing advanced regression models for accurate energy consumption forecasting.

We hope that the diverse range of topics and innovative approaches presented in this volume will inspire your own research endeavors. The advancements in computational intelligence, machine learning, and data analysis showcased here underscore the transformative potential of these technologies in addressing real-world challenges. As we continue to explore the frontiers of computer science, we invite you to join us in pushing the boundaries of knowledge within our scientific community. Together, we can drive progress and make meaningful contributions to the field.