Abstract
Most human time is spent indoors, and due to the pandemic, monitoring indoor air quality (IAQ) has become more crucial. In this study, an IoT (Internet of Things) architecture is implemented to monitor IAQ parameters, including CO2 and particulate matter (PM). An ESP32-C6-based device is developed to measure sensor data and send them, using the MQTT protocol, to a remote InfluxDBv2 database instance, where the data are stored and visualized. The Python 3.11 scripting programming language is used to automate Flux queries to the database, allowing a more in-depth data interpretation. The implemented system allows to analyze two measured scenarios during sleep: one with the door slightly open and one with the door closed. Results indicate that sleeping with the door slightly open causes CO2 levels to ascend slowly and maintain lower concentrations compared to sleeping with the door closed, where CO2 levels ascend faster and the maximum recommended values are exceeded. This demonstrates the benefits of ventilation in maintaining IAQ. The developed system can be used for sensing in different environments, such as schools or offices, so an IAQ assessment can be made. Based on the generated data, predictive models can be designed to support decisions on intelligent natural ventilation systems, achieving an optimized, efficient, and ubiquitous solution to moderate the IAQ.

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
The growing demand for highly maneuverable mobile robots today drives Mecanum-wheeled robot popularity in industrial automation, logistics, and service robotics. These omnidirectional robots have features that enable them to run appropriately in conditions requiring exact motion control. Nevertheless, low-level control techniques for these robots are still challenging to apply because of their nonlinear dynamics and external perturbations, including wheel friction and slip-page.

Abstract
Problem: Portable ECG/sEMG acquisition systems for telemedicine often lack application flexibility (e.g., limited configurability, signal validation) and efficient wireless data handling. Methodology: A modular biosignal acquisition system with up to 8 channels, 24-bit resolution and configurable sampling (1-4 kHz) is proposed, featuring per-channel gain/source adjustments, internal MUX-based reference drive, and visual electrode integrity monitoring; Bluetooth (R) transmits data via a bit-wise packet structure (83.92% smaller than JSON, 7.28 times faster decoding with linear complexity based on input size). Results: maximum 6.7 mu V-rms input-referred noise; harmonic signal correlations >99.99%, worst-case THD of -53.03 dBc, and pulse wave correlation >99.68% in frequency-domain with maximum NMSE% of 6e-6%; and 22.3-hour operation (3.3 Ah battery @ 150 mA). Conclusion: The system enables high-fidelity, power-efficient acquisition with validated signal integrity and adaptable multi-channel acquisition, addressing gaps in portable biosensing.

Abstract
This paper examines a series of bachelor's and master's thesis projects from the supervisor's perspective, focusing on how Augmented Reality (AR) and Mixed Reality (MR) can enhance industrial robotics engineering education. While industrial robotics systems continue to evolve and the need for skilled robotics engineers grows, teaching methods have not changed. Mostly, higher education in robotics engineering still relies on funding industrial robots or otherwise on traditional 2D tools that do not effectively represent the complex spatial interactions involved in robotics. This study presents a comparative analysis of seven thesis projects integrating MR technologies to address these challenges. All projects were supervised by the lead author and showcase different approaches and learning outcomes, building on insights from previous work. This comparison outlines the benefits and challenges of using MR for robotics engineering education. Additionally, it shares key takeaways from a supervisory standpoint as an evolutionary process, offering practical insights for fellow educators/supervisors guiding MR-based robotics education projects.

Abstract
Humans spend most of their time indoors, where air quality and comfort are crucial to health and well-being. Elevated CO2 levels in buildings can reduce cognitive function, discomfort, and health issues. Indoor CO2 monitoring has emerged as a key focus in the literature, particularly in residential buildings, as it can play a vital role in helping to maintain adequate ventilation rates. The growing smart home market demands seamless integration and control, which are essential for implementing IAQ sensing devices. However, interoperability barriers between platforms and devices continue to hinder smart home adoption. To address these challenges, Matter protocol is starting to appear in the market. In this work, a wireless CO2 sensor is developed based on ESP32-C6 and SCD40 and integrated into a created Matter-enabled ecosystem formed with the Home Assistant open-source platform. The utilized hardware and software enable the usage of two different wireless communication technologies, WiFi and Thread, enhancing compatibility. The study highlights the rapid and seamless onboarding of the developed CO2 monitoring device into smart home ecosystems using the Matter protocol. As a result, once the device is successfully added to the ecosystem, the measurements can be accessed and analyzed through a mobile application, forming an IoT environment.