Abstract
Predictive maintenance (PDM) is emerging as a strong transformative tool within Industry 4.0, enabling significant improvements in the sustainability and efficiency of manufacturing processes. This in-depth literature review, which follows the PRISMA 2020 framework, examines how PDM is being implemented in several areas of the manufacturing industry, focusing on how it is taking advantage of technological advances such as artificial intelligence (AI) and the Internet of Things (IoT). The presented in-depth evaluation of the technological principles, implementation methods, economic consequences, and operational improvements based on academic and industrial sources and new innovations is performed. According to the studies, integrating CDM can significantly increase machine uptime and reliability while reducing maintenance costs. In addition, the transition to PDM systems that use real-time data to predict faults and plan maintenance more accurately holds out promising prospects. However, there are still gaps in the overall methodologies for measuring the return on investment of PDM implementations, suggesting an essential research direction.

Abstract
Este artigo apresenta um framework cujo intuito é orientar a criação de práticas pedagógicas em auditoria governamental, fundamentado no Paradigma da Educação OnLIFE. Esse framework está estruturado em três elementos interconectados: o ecossistema conectivo inventivo, as competências a serem desenvolvidas e as missões a serem realizadas. Essas práticas pedagógicas são fundamentais para a formação em auditoria governamental, permitindo que os estudantes desenvolvam uma compreensão integrada das competências técnicas e comportamentais necessárias à profissão. Apoiado no Paradigma da Educação OnLIFE, o framework visa coengendrar atores humanos e não humanos no processo formativo, estimulando interações que promovam a cocriação entre esses elementos. Dessa forma, o framework não apenas identifica um conjunto de competências, mas também estabelece um ecossistema propício ao desenvolvimento dessas competências por meio de missões práticas e colaborativas. Ao propor práticas pedagógicas inventivas, a abordagem busca tornar a formação em Auditoria Governamental mais adaptativa e alinhada às demandas contemporâneas, potencializando a inventividade e a problematização no contexto educacional.

Abstract
Virtual Reality applications increasingly require methods to effectively guide users to important elements within the virtual environment. Central visual cues are the most common method, which have proven effective for directing attention, yet often compromise on level of immersion. This work explored whether peripheral visual cues could serve as an alternative approach that supports attention guidance while preserving sense of presence. We performed a user study with 24 participants to compare four visual cues: two central cues (Floating Text and Floating Arrow) and two peripheral cues (Edge Lighting and Swarm). Users completed a visual search task of 7 objects for each visual cue, with data collected on performance through reaction time, round time, and total errors. Additionally, presence and workload were evaluated through the IGROUP Presence Questionnaire and NASA Task Load Index, respectively. No statistically significant differences were found between peripheral and central cues for presence, however performance and workload varied significantly based on specific cue implementation rather than type of positioning. Our findings indicate that peripheral positioning does not inherently provide attention guidance advantages over central placement. Instead, thoughtful cue design, with a simple yet clear appearance and behavior appears to be the critical factor for achieving effective attention guidance while preserving presence in IVEs. These results provide valuable insights for VR content creators to facilitate the design process of VR experiences. © 2025 Copyright held by the owner/author(s).

Abstract

Abstract
The increasing penetration of inverter-based resources (IBR) in medium voltage (MV) networks presents significant challenges for traditional overcurrent (OC) protection systems, particularly in ensuring selectivity, reliability, and fault isolation. This paper presents an adaptive protection system (APS) that dynamically adjusts protection settings based on real-time network conditions, addressing the challenges posed by distributed energy resources (DER). The methodology builds on ongoing research and development efforts, combining an offline phase, where operational scenarios are simulated using historical data, clustered with fuzzy c-means (FCM), and optimized with evolutionary particle swarm optimization (EPSO), and an online phase. To overcome the static nature of conventional schemes, a machine learning (ML)-based classifier is integrated into the APS, enabling real-time adaptation of protection settings. In the online phase, a centralized substation protection controller (CPC) leverages real-time measurements, communicated via IEC 61850 standard protocols, to classify network conditions using a support vector machine (SVM) classifier and activate the appropriate protection settings. The proposed APS has been validated on a Hardware-in-the-Loop (HIL) platform, demonstrating significant improvements in fault detection times, selectivity, and reliability compared to traditional OC protection systems. As part of a continued effort to refine and expand the system's capabilities, this work highlights the potential of integrating artificial intelligence (AI) and real-time/online decision-making to enhance the adaptability and robustness of MV network protection in scenarios with high DER penetration. © 2025 Elsevier B.V., All rights reserved.

Abstract
Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.