Virtual reality (VR) has emerged as a promising tool for transforming science, technology, engineering, and mathematics (STEM) education, yet its adoption in K–12 classrooms remains uneven and often limited to short-term pilots. While prior studies highlight VR’s potential to increase engagement and support conceptual understanding, questions persist about scalability, sustainability, and equity in implementation. This paper addresses these gaps by synthesizing recent scholarship and proposing a structured framework of best practices for integrating VR into K–12 STEM education. Drawing on academic literature, U.S. policy reports, and case studies, we identify persistent challenges that include high costs, lack of teacher preparation, infrastructure disparities, and overlooked accessibility concerns. We use these findings to inform a phased implementation roadmap. Our framework emphasizes assessment and planning, technical integration, teacher preparation, student implementation, and iterative evaluation, providing actionable strategies for schools and districts. Results of this synthesis indicate that successful VR adoption requires coordinated attention to pedagogy, funding, professional development, and equity. We conclude that moving VR from isolated novelty projects to sustainable and equitable tools in STEM classrooms depends on aligning technology with curricular goals, investing in teacher pipelines, and embedding VR within long-term evaluation and improvement cycles.

While most industrial maintenance strategies are centered on optimizing machine runtime and cost reduction, the condition-based maintenance (CBM) strategy distinguishes itself from others in its use of real-time operational data from machines to help engineers make informed decisions. The introduction of machine learning (ML) into a CBM strategy can increase its effectiveness, enabling more accurate predictions and making the decision-making process more efficient. In this review paper, we seek to provide a comprehensive overview of the role ML plays in modern CBM systems, beginning by outlining the core concepts and historical development of CBM and briefly introducing various ML techniques being employed in industry today. We then review numerous real-world cases where ML-based CBM systems have been implemented and discuss some of the technological, human, and ethical challenges faced by organizations seeking to integrate sophisticated ML models into existing CBM systems. We end by highlighting some of the current limitations of ML-based CBM systems, paving the way for a discussion on emerging trends and future research directions in this area.

Human activity recognition and prediction systems are crucial to the safety of autonomous vehicles. Specifically, a challenge that has been the subject of much research is the ability to differentiate between adult and child pedestrians. Failure to correctly identify the type of pedestrian can cause accidents. In this paper, we propose a novel multiple object tracking system for autonomous vehicles that is designed to overcome the challenges associated with differentiating between adult and child pedestrians. Our system uses modern machine learning based object detection and tracking methods to identify the type of pedestrian and measure various characteristics about their behavior, such as their speed and trajectory. Results from experiments to evaluate the effectiveness of our proposed framework show that our system is able to correctly differentiate between adult and child pedestrians and can provide useful information about their movements to the autonomous vehicle. Overall, our research demonstrates the potential of our multiple object tracking system to improve the safety and performance of autonomous vehicles.

In modern advanced manufacturing systems, the use of smart sensors and other Internet of Things (IoT) technology to provide real-time feedback to operators about the condition of various machinery or other equipment is prevalent. A notable issue in such IoT-based advanced manufacturing systems is the problem of connectivity, where a dropped Internet connection can lead to the loss of important condition data from a machine. Such gaps in the data, which we call irregular truncated signals, can lead to incorrect assumptions about the status of a machine and other flawed decision-making processes. This paper presents an adaptive data imputation framework based on machine learning (ML) algorithms to assess whether the missing data in a signal is missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR) and automatically select an appropriate ML-based data imputation model to deal with the missing data. Our results demonstrate the potential for applying ML algorithms to the challenge of irregularly truncated signals, as well as the capability of our adaptive framework to intelligently solve this issue.

The presence of silica in iron ore concentrate can have significant negative impacts on the efficiency and quality of steel production. As such, providing engineers with early and reliable information about the purity of iron ore concentrate is crucial for smooth mining operations. This paper reports on the development of a long short-term memory (LSTM) network and an ensemble data interpolation technique to enhance quality prediction in the froth flotation process of an iron ore mine. Our results demonstrate the ability of our model to accurately predict the silica content of iron ore concentrate on a minute-by-minute basis, as well as the ability to forecast hours in advance.