Abstract
This study focuses on the assessment of ground vibrations due to pile driving activities. Given the likelihood of excessive vibration due to the driving process, it is imperative to predict vibration levels during the design phase. The primary goal of this work is to integrate machine learning techniques, specifically Extreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANNs) for real-time vibration prediction. The training dataset was generated using a validated numerical model and the trained models were validated based on experimental results. This validation process highlights the efficiency and accuracy of Extreme Gradient Boosting in predicting the-free-field response of the ground.

Abstract
Tabulation is a well-known technique for improving the efficiency of recursive functions with redundant function calls. A key point in the application of this technique is to identify a suitable representation for the table. In this paper, we propose the use of zippers as tables in the tabulation process. Our approach relies on a generic function zipWithZipper, that makes strong use of lazy evaluation to traverse two zippers in a circular manner. The technique turns out to be particularly efficient when the arguments to recursive calls are closely situated within the function domain. For example, in the case of natural numbers this means function calls on fairly contiguous values. Likewise, when dealing with tree structures, it means functions calls on immediate sub-trees and parent nodes. This results in a concise and efficient zipper-based embedding of attribute grammars.

Abstract
A presente obra, “O Habitar de Ensinar e do Aprender OnLIFE em Tempos de Ecologias Inteligentes”, dá continuidade às discussões sobre o Habitar do Ensinar e do Aprender realizadas nas obras anteriores a saber: “O Habitar do Ensinar e do Aprender OnLIFE: Vivências na Educação Contemporânea” (2021); “O Habitar do Ensinar e do Aprender: Desafios para/na/da Educação OnLIFE” (2022); e “O Habitar do Ensinar e do Aprender: as diferentes dimensões da/na Educação OnLIFE” (2023). Assim, embora publicada no ano de 2024, a obra está intrinsecamente ligada à produção do IV RIEOnLIFE e do VIII WLC em 2023, que ocorreram no Instituto Federal do Norte de Minas Gerais (IFNMG). Para tanto, damos continuidade às discussões sobre o Habitar do Ensinar e do Aprender OnLIFE, o qual se desenvolve num tempo constituído por inteligências diversas (humanas e não humanas), que conectadas formam o que denominamos de “ecologias inteligentes”.

Abstract

Abstract
Nowadays ransomware attacks have become one of the main problems organizations face. The threat of ransomware attacks, with their capacity to paralyze entire organizations, creates the need to develop a ransomware recovery utility function to help further prepare for the impact of such attacks and enhance the organization's knowledge and perception of risk. This work proposes a ransomware recovery utility function that aims to estimate the impact of a ransomware attack measured in manpower hours till recovery and taking into account different devices and different scenarios.

Abstract
In the dynamic realm of nowadays manufacturing, integrating digital technologies has become paramount for enhancing operational efficiency and decision-making processes. This article presents a novel system architecture that integrates a Simulation-based Digital Twin (DT) with emerging trends in manufacturing to enhance decision-making, accompanied by a detailed technical approach encompassing protocols and technologies for each component. The DT leverages advanced simulation techniques to model, monitor, and optimize production processes in real time, facilitating both strategic and operational decision-making. Complementing the DT, trending technologies such as artificial intelligence, additive manufacturing, collaborative robots, autonomous vehicles, and connectivity advancements are strategically integrated to enhance operational efficiency and facilitate the adoption of the Manufacturing as a Service (MaaS) paradigm. A case study within a MaaS supplier context, deployed in an industrial laboratory with advanced robotic systems, demonstrates the practical application of optimizing dynamic job-shop configurations using Simulation-based DT, showcasing strategies to improve operational efficiency and resource utilization. The results of the industrial experiment were highly encouraging, underscoring the potential for extension to more intricate industrial systems, with particular emphasis on incorporating sustainability and remanufacturing principles.