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
Pharmaceuticals have emerged as contaminants in aquatic ecosystems, challenging the water quality concept. These compounds enter wastewater treatment plants, where inefficient treatments pose concerns for long-term river and tap water quality, consequently impacting environmental and human health. Considering this, the present study first reports the simultaneous quantification of paracetamol, salicylic acid, and carbamazepine by electrochemistry with carbon screen-printed electrodes, as well as liquid chromatography-tandem mass spectrometry (LC-MS/MS). At pH 7.4 and by optimized DPV, LODs were 0.783, 1.53, and 0.113 mu M for paracetamol, salicylic acid, and carbamazepine, respectively. The recovery values obtained by LC-MS/MS in tap water are not satisfactory regarding the data obtained in river water with DPV electrochemical experiments. Moreover, in both analytical methods, the highest sensitivity was obtained for carbamazepine, with the lowest RSD values. These analytical data highlight the remarkable sensitivity and detection skills of DPV and LC-MS/MS analysis. Developing portable potentiostats for in situ pharmaceutical detection and monitoring outside the labs is crucial for ensuring environmental and health safety. Herein two portable approaches are tested: commercial SensitSmart (R) and homemade electrochemical Internet-of-Things (IoT)-enabled devices. The results of SensitSmart (R) are reliable but lower than those obtained with a benchtop potentiostat and require a USB connection with PCs, tablets, or smartphones. The practical application of a homemade IoT device was validated with potassium ferricyanide with an output similar to benchtop potentiostat, which represents a proof of concept. In the future, these IoT devices will operate without external components or specific software.

Abstract
The efficiency of wastewater treatment systems must be reflected in the removal of the pollutant load from the influent and the optimal energy performance of electrical equipment. Wastewater Treatment Plants (WWTPs) are part of the Intensive Energy Consumption Management System (SGCIE) and are therefore subject to mandatory energy audits. This article aims to assess the impact of an energy audit in a WWTP, according to ISO 50001:2018 and the Plan-Do-Check-Act (PDCA) methodology, to identify and quantify both persistent and transient energy inefficiencies. According to the results, the energy audit contributed to an approximate 10.8% reduction in electrical energy consumption. During the assessment, several challenges were identified that may compromise the effectiveness of audits in improving energy performance. The complexity of the treatment model, aging infrastructure and equipment, the lack of real-time data, and a limited number of indicators hinder the proper management of inefficiency phenomena, particularly transient ones.

Abstract
Road safety is a global issue, with road-related accidents being one of the biggest leading causes of death. Motorcyclists are especially susceptible to injuries and death when there is an accident, due to the inherent characteristics of motorcycles. Accident prevention is paramount. To improve motorcycle safety, this paper discusses and proposes a preliminary architecture of a system composed of various sensors, to assist and warn the rider of potentially dangerous situations such as front and back collision warnings, pedestrian collision warnings, and road monitoring.

Abstract
Object detection is a fundamental task of computer vision that is constantly evolving, with a wide range of applications in fields such as security, medicine, and autonomous driving. This work presents an interactive self-learning course dedicated to exploring some crucial concepts for beginners in object detection. The course offers educational resources, including the possibility to follow a simple tutorial on the operation of an object detection model and definitions of the main concepts related to object detection technology. Users also have a brief description of object detection algorithms such as YOLO (You Only Look Once), R-CNN (Region-based Convolutional Neural Networks), and SSD (Single Shot Detector) and the possibility to learn more about these in a tutorial prepared on a Google Colab notebook. The course aims to provide a learning experience accessible to beginners in the field of object detection, who want to take the first step in their learning about the subject. After completing the tutorial, the user answers a questionnaire, with the goal of analyzing the learning outcomes and extracting the user's impression of the website in general. With this paper, we want to show the advantages of using tools of this nature to foster learning regarding object detection.

Abstract
Medical imaging is vital for diagnosing and treating colorectal cancer (CRC), a leading cause of mortality. Classifying colorectal polyps and CRC precursors remains challenging due to operator variability and expertise dependence. Deep learning (DL) models show promise in polyp classification but face adoption barriers due to their 'black box' nature, limiting interpretability. This study presents an example-based explainable artificial intehlligence (XAI) approach using Pix2Pix to generate synthetic polyp images with controlled size variations and LIME to explain classifier predictions visually. EfficientNet and Vision Transformer (ViT) were trained on datasets of real and synthetic images, achieving strong baseline accuracies of 94% and 96%, respectively. Image quality was assessed using PSNR (18.04), SSIM (0.64), and FID (123.32), while classifier robustness was evaluated across polyp sizes. Results show that Pix2Pix effectively controls image attributes like polyp size despite limitations in visual fidelity. LIME integration revealed classifier vulnerabilities, underscoring the value of complementary XAI techniques. This enhances DL model interpretability and deepens understanding of their behaviour. The findings contribute to developing explainable AI tools for polyp classification and CRC diagnosis. Future work will improve synthetic image quality and refine XAI methodologies for broader clinical use.