Abstract
The autonomous navigation of unmanned ground vehicles (UGVs) in unstructured environments, such as agricultural or forestry settings, has been the subject of extensive research by various investigators. The navigation capability of a UGV in unstructured environments requires considering numerous factors, including the quality of data reception that allows reliable interpretation of what the UGV perceives in a given environment, as well as the use these data to control the UGV's navigation. This article aims to study different PID control algorithms to enable autonomous navigation on a robotic platform. The robotic platform consists of a forestry tractor, used for forest cleaning tasks, which was converted into a UGV through the integration of sensors. Using sensor data, the UGV's position and orientation are obtained and utilized for navigation by inputting these data into a PID control algorithm. The correct choice of PID control algorithm involved the study, analysis, and implementation of different controllers, leading to the conclusion that the Vector Field control algorithm demonstrated better performance compared to the others studied and implemented in this paper.

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
Type 2 diabetes mellitus (T2DM) is a chronic disease characterized by hyperglycemia, which could alter metabolic, vascular and hematological parameters. A low-carbohydrate, high-fat diet (LCHF) diet benefits glycemic and blood pressure control. In turn, exercise in hypoxia (EH) is known to improve insulin sensitivity, erythropoiesis and angiogenesis. LCHF combined with EH seem to be a potential therapeutic strategy for T2DM and hypertension (HTN), but evidence is still scarce. The aim of this study was to evaluate the effects of eight weeks of EH combined with a LCHF on hematological and lipid profiles, inflammation and blood pressure in older patients with T2DM and coexistent HTN. Diabetic patients with HTN (n=42) were randomly assigned to a (1) control group: control diet (high-carbohydrate and low-fat diet) + exercise in normoxia; (2) EH group: control diet + EH; (3) intervention group: LCHF + EH. Baseline and eight-week measurements included systolic, diastolic, and mean blood pressure (SBP, DBP, MAP, respectively), and hematological and lipid profiles and inflammation biomarkers. Blood pressure decreased after interventions (p<0.001), with no differences among groups (SBP: p=0.151; DBP: p=0.124 and MAP: p=0.18). There were no differences in lipid profile and C-reactive protein (p>0.05). Mean corpuscular hemoglobin (MCH) increased in the EH group (p=0.027), while MCH concentration decreased in the EH+LCHF group (p=0.046). In conclusion, there is no additional benefit in adding hypoxia to exercise or restricting carbohydrates, on blood pressure in patients with T2DM and coexisting HTN. Further elucidation of the mechanisms underlying hematological adaptations is imperative. Trial registration number: NCT05094505.

Abstract
Conflict-free Replicated Data Types (CRDTs) allow optimistic replication in a principled way. Different replicas can proceed independently, being available even under network partitions and always converging deterministically: Replicas that have received the same updates will have equivalent state, even if received in different orders. After a historical tour of the evolution from sequential data types to CRDTs, we present in detail the two main approaches to CRDTs, operation-based and state-based, including two important variations, the pure operation-based and the delta-state based. Intended for prospective CRDT researchers and designers, this article provides solid coverage of the essential concepts, clarifying some misconceptions that frequently occur, but also presents some novel insights gained from considerable experience in designing both specific CRDTs and approaches to CRDTs.

Abstract
This article focuses on the creation and evaluation of synthetic data to address the challenges of imbalanced datasets in machine learning (ML) applications, using fake news detection as a case study. We conducted a thorough literature review on generative adversarial networks (GANs) for tabular data, synthetic data generation methods, and synthetic data quality assessment. By augmenting a public news dataset with synthetic data generated by different GAN architectures, we demonstrate the potential of synthetic data to improve ML models' performance in fake news detection. Our results show a significant improvement in classification performance, especially in the underrepresented class. We also modify and extend a data usage approach to evaluate the quality of synthetic data and investigate the relationship between synthetic data quality and data augmentation performance in classification tasks. We found a positive correlation between synthetic data quality and performance in the underrepresented class, highlighting the importance of high-quality synthetic data for effective data augmentation.

Abstract
[No abstract available]