Abstract
Obstructive sleep apnea is a heterogeneous sleep disorder with varying phenotypes. Several studies have already performed cluster analyses to discover various obstructive sleep apnea phenotypic clusters. However, the selection of the clustering method might affect the outputs. Consequently, it is unclear whether similar obstructive sleep apnea clusters can be reproduced using different clustering methods. In this study, we applied four well-known clustering methods: Agglomerative Hierarchical Clustering; K-means; Fuzzy C-means; and Gaussian Mixture Model to a population of 865 suspected obstructive sleep apnea patients. By creating five clusters with each method, we examined the effect of clustering methods on forming obstructive sleep apnea clusters and the differences in their physiological characteristics. We utilized a visualization technique to indicate the cluster formations, Cohen's kappa statistics to find the similarity and agreement between clustering methods, and performance evaluation to compare the clustering performance. As a result, two out of five clusters were distinctly different with all four methods, while three other clusters exhibited overlapping features across all methods. In terms of agreement, Fuzzy C-means and K-means had the strongest (kappa = 0.87), and Agglomerative hierarchical clustering and Gaussian Mixture Model had the weakest agreement (kappa = 0.51) between each other. The K-means showed the best clustering performance, followed by the Fuzzy C-means in most evaluation criteria. Moreover, Fuzzy C-means showed the greatest potential in handling overlapping clusters compared with other methods. In conclusion, we revealed a direct impact of clustering method selection on the formation and physiological characteristics of obstructive sleep apnea clusters. In addition, we highlighted the capability of soft clustering methods, particularly Fuzzy C-means, in the application of obstructive sleep apnea phenotyping.

Abstract
This paper explores the integration of SolidWorks, LabVIEW, and Arduino as a comprehensive and cost-effective approach to teaching robotics to undergraduate students. In scenarios where real hardware is unavailable or prohibitively expensive, this methodology offers significant advantages. SolidWorks enables students to design and simulate robotic components in a virtual environment, fostering a deep understanding of mechanical design and engineering principles. LabVIEW provides an intuitive graphical interface for programming and control, allowing students to develop and test their algorithms. Finally, Arduino, as an open-source hardware platform, bridges the gap between virtual simulations and physical implementation, offering a hands-on experience with minimal financial investment. Together, these tools create a robust educational framework that enhances theoretical knowledge through practical application, encourages innovation, and prepares students for real-world engineering challenges. The paper concludes that this integrated approach not only mitigates the limitations of resource constraints but also enriches the learning experience by providing a versatile and accessible platform for robotics education.

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
Force sensors are essential elements of actuator systems, providing measurement and force control in different domains. This literature review discusses its applications in the industry, academic research, and educational domains. In an industrial setup, force sensors enhance efficiency, safety, and reliability within automation systems, predominantly robotic arms and assembly lines. In the academic environment, using such sensors fosters innovation within robotics and biomechanical studies, allowing for testing theoretical models and new methodologies. In education, force sensors help students understand basic concepts about mechanics and robotics from practical work. Understanding this diverse application allows one to design effective actuator systems, promoting technological advances and improved learning experiences. With this literary review, the aim is to gain an understanding of the state of the art in force sensor actuators applied in various areas, such as academia, education, and industry.

Abstract
Artificial intelligence (AI) holds significant potential for enhancing quality of gastrointestinal (GI) endoscopy, but the adoption of AI in clinical practice is hampered by the lack of rigorous standardisation and development methodology ensuring generalisability. The aim of the Quality Assessment of pre-clinical AI studies in Diagnostic Endoscopy (QUAIDE) Explanation and Checklist was to develop recommendations for standardised design and reporting of preclinical AI studies in GI endoscopy. The recommendations were developed based on a formal consensus approach with an international multidisciplinary panel of 32 experts among endoscopists and computer scientists. The Delphi methodology was employed to achieve consensus on statements, with a predetermined threshold of 80% agreement. A maximum three rounds of voting were permitted. Consensus was reached on 18 key recommendations, covering 6 key domains: data acquisition and annotation (6 statements), outcome reporting (3 statements), experimental setup and algorithm architecture (4 statements) and result presentation and interpretation (5 statements). QUAIDE provides recommendations on how to properly design (1. Methods, statements 1-14), present results (2. Results, statements 15-16) and integrate and interpret the obtained results (3. Discussion, statements 17-18). The QUAIDE framework offers practical guidance for authors, readers, editors and reviewers involved in AI preclinical studies in GI endoscopy, aiming at improving design and reporting, thereby promoting research standardisation and accelerating the translation of AI innovations into clinical practice.

Abstract
This chapter examines how Artificial Intelligence (AI) and Learning Analytics (LA) are transformingdistanceeducation, accelerated by the COVID-19 shift toe-learning. By using data from Learning Management Systems (LMS), these technologies can personalize learning, improve student retention, and automate tasks. AI, particularly machine learning, enables dynamic adaptation to student needs, while LA provides valuable insights for informed instructional decisions. However, ethical concerns, including data privacy and algorithmic bias, must be addressed to ensure equitable access and fair learning outcomes. The future of distance learning lies in responsible integration of AI and LA, creating immersive and inclusive educational experiences. © 2025 by IGI Global Scientific Publishing. All rights reserved.