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.

Abstract

Abstract
Many strategies have been developed to monitor the volume of volume of Above Ground Biomass (AGB) in forest areas as a fundamental step for managing carbon concentration. This study explores the use of use of Light Detection and Ranging (LiDAR) data obtained through Unmanned Aerial Vehicles (UAVs) to estimate height values in a vegetation colony composed of oaks (Quercus pyrenaica Willd.) in northern Portugal. The extraction of pertinent information from LiDAR data was facilitated by using the LAStools extension within the Quantum Geographic Information System (QGIS) software framework. The generated raster and image information were used to calculate the height values of the vegetation. Following this extraction, the information was meticulously organized into datasets, which were then employed in Deep Learning (DL) algorithms. The VGG16 model was selected as the underlying framework for the present study. Height predictions were made using dimensions of 16 x 16, 32 x 32, and 64 x 64 pixels for the Red, Green and Blue (RGB) images. The data was estimated and compared using both the standard format of the VGG16 model and a superficially adapted version of its convolution layers. The algorithm's efficacy was validated by comparing the forecast results with the data obtained from QGIS, which revealed minimal discrepancies. It was observed that using 64 x 64 pixel scale images yielded enhanced accuracy, resulting in reduced values for the Mean Absolute Error (MAE). The study demonstrates the viability of applying DL techniques to accurately capture information about a forest area using RGB images.

Abstract
User experience and performance in Virtual Reality (VR) training solutions can be influenced by various factors, including the presence or absence of contextual elements in the environment. This study investigates the impact of environmental context on user experience and training performance by comparing a contextualised and a non-contextualised VR environment. To this end, key metrics were assessed: usability, sense of presence, user workload, user experience, and training performance. A between-subjects study was conducted with 30 participants tasked with assembling a car antenna on a virtual factory assembly line. The sample was divided into two conditions: a neutral environment containing only essential equipment, and a contextualised environment simulating a detailed factory floor. Results indicate comparable levels of usability, low user workload, and high overall presence across both conditions. Spatial presence and Completion Time regarding training performance metrics showed a statistically significant difference, with higher scores observed in the contextualised environment. The results suggest that a contextual environmental design is worthwhile for creating a more effective and efficient immersive training solution.

Abstract
This work presents a method for fabricating multiple identical Fabry-Perot cavities using White Light Interferometry (WLI) to monitor the length tuning of the structure in real time. This fabrication approach enables the actual optical path difference to be tracked during the process, utilizing the same interrogator intended for sensor operation. As a proof of concept, three identical cavities were successfully fabricated. These cavities were confirmed to be identical using the WLI system and were further characterized by measuring their dimensions under a microscope. This technique enabled the production of three Fabry-Perot cavities with a measured length of 499.60 +/- 0.01 mu m.

Abstract
Continuous, non-invasive Blood Pressure (BP) monitoring remains a key challenge in preventive cardiovascular healthcare. In this case study, we explore the potential of cuffless BP estimation using Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals acquired from the thighs of just one subject via a smart toilet seat equipped with built-in sensors. This unobtrusive setup enables passive data collection during routine bathroom use. We extracted timedomain and morphology-based features from the ECG and PPG signals, and trained three artificial intelligence regression models - Support Vector Regression (SVR), Random Forest (RF), and XGBoost - to estimate Systolic (SBP) and Diastolic (DBP) BP. The SVR model achieved the best performance, with Mean Absolute Error (MAE) values of 0.25 and 0.19, and Root Mean Square Error (RMSE) values of 0.41 and 0.35, for SBP (mmHg) and DBP(mmHg), respectively. The Pearson Correlation Coefficient (PCC) exceeded 0.99 for both measures, indicating strong agreement between estimated and reference values. These findings support the integration of passive BP monitoring systems into everyday environments, promoting accessible and scalable solutions for long-term cardiovascular risk assessment.