Abstract
The rise of environmental crimes has become a major concern globally as they cause significant damage to ecosystems, public health and result in economic losses. The availability of vast sensor data provides an opportunity to analyze environmental data proactively. This helps to detect irregularities and uncover potential criminal activities. This paper highlights the critical role played by machine learning (ML) and remote sensing technologies in the continuously evolving scenarios of environmental crime. By examining some case studies on detecting illegal fishing, illegal oil spills, illegal landfills, and illegal logging, we delve into the practical implementation of data-driven approaches for environmental crime detection. Our goal with this study is to provide an overview of the existing research in this area and foster the use of ML and data science techniques to enhance environmental crime detection.

Abstract
Gastric cancer, ranking as the sixth most prevalent cancer globally and a leading cause of cancer-related mortality, follows a sequential progression known as Correa's cascade, spanning from chronic gastritis to eventual malignancy. Although endoscopy exams using NarrowBand Imaging are recommended by internationally accepted guidelines for diagnostic Gastric Intestinal Metaplasia, the lack of endoscopists with the skill to assess the NBI image patterns and the disagreement between endoscopists when assessing the same image, have made the use of biopsies the gold standard still used today. This proposal doctoral thesis seeks to address the challenge of developing a Computer-Aided Diagnosis solution for GIM detection in NBI endoscopy exams, aligning with the established guidelines, the Management of Epithelial Precancerous Conditions and Lesions in the Stomach. Our approach will involve a dataset creation that follows the standardized approach for histopathological classification of gastrointestinal biopsies, the Sydney System recommended by MAPS II guidelines, and annotation by gastroenterology experts. Deep learning models, including Convolutional Neural Networks, will be trained and evaluated, aiming to establish an internationally accepted AI-driven alternative to biopsies for GIM detection, promising expedited diagnosis, and cost reduction.

Abstract
The transition to the water circular economy (WCE) requires several stakeholders' awareness, articulation, and action involving complex governance concerns. As a participatory approach to identifying problems, designing solutions, and implementing strategic actions, the co-creation process should support stakeholder involvement to adjust existing institutional arrangements to foster the WCE. This article designs and applies a co-creation process to analyse the perception of key stakeholders about institutional challenges for water reuse and explore their contributions to innovate policy, planning, and governance for the implementation of new water reuse technology in Almendralejo (Spain), Lecce (Italy), Omis (Croatia), and Eilat (Israel). The findings indicate that implementing a new water loop encounters complex institutional and production-related obstacles, which different stakeholders address in varying ways. Moreover, the proposed solutions to the on-site issues identified emphasise the need for actions that foster engagement and collaboration, particularly to enhance awareness, training, and regulation. Addressing these challenges associated with adopting new water loops, even when technical, may depend on non-technical solutions regarding the institutional framework. The co-creation processes highlight the importance of focusing on institutional arrangements and stakeholder awareness while implementing new water loops to ensure and promote symbiotic territories that consider the policy, producers', and users' strategies.

Abstract
Neonatal seizures represent a critical medical issue that requires prompt diagnosis and treatment. Typically, at-risk newborns undergo a Magnetic Resonance Imaging (MRI) brain assessment followed by continuous seizure monitoring using multichannel EEG. Visual analysis of multichannel electroencephalogram (EEG) recordings remains the standard modality for seizure detection; however, it is limited by fatigue and delayed seizure identification. Advances in machine and deep learning have led to the development of powerful neonatal seizure detection algorithms that may help address these limitations. Nevertheless, their performance remains relatively low and often disregards the non-stationary attributes of EEG signals, especially when learned from weakly labeled EEG data. In this context, the present paper proposes a novel deep-learning approach for neonatal seizure detection. The method employs rigorous preprocessing to reduce noise and artifacts, along with a recently developed time-frequency distribution (TFD) derived from a separable compact support kernel to capture the fast spectral changes associated with neonatal seizures. The high-resolution TFD diagrams are then converted into RGB images and used as inputs to a pre-trained ResNet-18 model. This is followed by the training of an attention-based multiple-instance learning (MIL) mechanism. The purpose is to perform a spatial time-frequency analysis that can highlight which channels exhibit seizure activity, thereby reducing the time required for secondary evaluation by a doctor. Additionally, per-instance learning (PIL) is performed to further validate the robustness of our TFD and methodology. Tested on the Helsinki public dataset, the PIL model achieved an area under the curve (AUC) of 96.8%, while the MIL model attained an average AUC of 94.1%, surpassing similar attention-based methods.

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
Engagement in the ongoing energy transition is particularly challenging for energy-poor citizens. As such, there is a pressing need for a better understanding of their experiences and for strategies that enable their engagement. In this study, we identify different groups of citizens based on their energy poverty issues and examine their engagement behaviors (seeking information, proactive managing, sharing feedback, helping others, and advocating). Using cluster analysis and multiple correspondence analysis, we analyzed a sample of 915 citizens from eight European cities participating in a Horizon2020 EU project (Alkmaar-NL, Bari-IT, Celje-SI, Evora-PT, Granada-ES, Hvidovre-DK, Ioannina-GR, & Uacute;jpest-HU). Several groups of citizens reported either multiple energy issues, a single issue (energy bills, insulation, cooling, heating), or no issues, and the statistical tests showed significant differences across these groups in terms of engagement in seeking information, helping, and advocating. Moreover, we identified that certain groups tend to have specific levels of engagement (high, medium, low) and that sharing feedback generally has a low level of engagement. Overall, this study provides empirical insights into how energy-poor citizens exercise agency through engagement behaviors and offers actionable insights for designing measures to mitigate energy poverty in complementarity with technical and economical solutions.