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
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. 2 5 and 0. 1 9, and Root Mean Square Error (RMSE) values of 0.41 and 0.35, for SBP (m m H g) and D B P( m H g), 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. © 2025 IEEE.

Abstract

Abstract
The ever-changing landscape of distance learning AI and learning analytics transforms engagement and efficiency in education. AI systems analyze behavior and performance data to provide real-time feedback for improved outcomes. Learning analytics further help educators to identify at-risk students while fostering better teaching strategies. By integrating AI with learning analytics, distance education becomes more inclusive, ensuring learners receive the support necessary to thrive in an increasingly digital and knowledge-driven world. AI and Learning Analytics in Distance Learning explores the development of distance learning. It examines the challenges of using these systems and integrating them with distance learning. The book covers topics such as AI, distance learning technology, and management systems, and is an excellent resource for academicians, educators, researchers, computer engineers, and data scientists. © 2025 by IGI Global Scientific Publishing. All rights reserved.

Abstract
Olive cultivation is a pillar of Mediterranean agriculture, deeply rooted in both tradition and economic importance. This paper presents a novel two-phase methodology for the automated preprocessing of olive leaf images to facilitate accurate cultivar classification. Leveraging the state-of-the-art YOLO11 framework, two models (YOLO11n and YOLO11s) were employed for detection and segmentation tasks. A comprehensive dataset, combining in-situ captured images with publicly available data, was meticulously annotated using both manual and semi-automatic processes. The detection model identifies individual olive leaves, while the segmentation model isolates the leaves by replacing the background with a uniform white, thereby simulating laboratory conditions. Experimental results demonstrate that YOLO11n outperforms YOLO11s in terms of mean Average Precision and F1-score, confirming the feasibility of deploying the system on mobile devices for real-time, in-field classification.