Abstract
The Beninar Dam, located in Southeastern Spain, is an earth-fill dam that has experienced filtration issues since its construction in 1985. Despite the installation of various monitoring systems, the data collected are sparse and inadequate for the dam's lifetime. The present research integrates Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) and dam modeling to validate the monitoring of this dam, opening the way to enhanced integrated monitoring systems. MT-InSAR was proved to be a reliable and continuous monitoring system for dam deformation, surpassing previously installed systems in terms of precision. MT-InSAR allowed the almost-continuous monitoring of this dam since 1992, combining ERS, Envisat, and Sentinel-1A/B data. Line-of-sight (LOS) velocities of settlement in the crest of the dam evolved from maximums of -6 mm/year (1992-2000), -4 mm/year (2002-2010), and -2 mm/year (2015-2021) with median values of -2.6 and -3.0 mm/year in the first periods (ERS and Envisat) and -1.3 mm/year in the Sentinel 1-A/B period. These results are consistent with the maximum admissible modeled deformation from construction, confirming that settlement was more intense in the dam's early stages and decreased over time. MT-InSAR was also used to integrate the monitoring of the dam basin, including critical slopes, quarries, and infrastructures, such as roads, tracks, and spillways. This study allows us to conclude that MT-InSAR and dam modeling are important elements for the integrated monitoring systems of embankment dams. This conclusion supports the complete integration of MT-InSAR and 3D modeling into the monitoring systems of embankment dams, as they are a key complement to traditional geotechnical monitoring and can overcome the main limitations of topographical monitoring.

Abstract
Globalisation, urbanisation and the recent COVID-19 pandemic has been raising the demand for logistic activities. This change is affecting the entire supply chain, especially the last-mile step. This step is considered the most expensive and ineffective part of the supply chain and a source of negative economic, environmental and social externalities. This article aims to characterise the sustainable urban last-mile logistics research field through a systematic literature review (N = 102). This wide and holistic review was organised into six thematic clusters that identified the main concepts addressed in the different areas of the last-mile research and the existence of 14 solutions, grouped into three types (vehicular, operational, and organisational solutions). The major findings are that there are no ideal last-mile solutions as their limitations should be further explored by considering the so-called triple bottom line of sustainability; the integration and combination of multiple last-mile alternative concepts; or by establishing collaboration schemes that minimise the stakeholders' conflicting interests.

Abstract
Globally, insect pests are the primary reason for reduced crop yield and quality. Although pesticides are commonly used to control and eliminate these pests, they can have adverse effects on the environment, human health, and natural resources. As an alternative, integrated pest management has been devised to enhance insect pest control, decrease the excessive use of pesticides, and enhance the output and quality of crops. With the improvements in artificial intelligence technologies, several applications have emerged in the agricultural context, including automatic detection, monitoring, and identification of insects. The purpose of this article is to outline the leading techniques for the automated detection of insects, highlighting the most successful approaches and methodologies while also drawing attention to the remaining challenges and gaps in this area. The aim is to furnish the reader with an overview of the major developments in this field. This study analysed 92 studies published between 2016 and 2022 on the automatic detection of insects in traps using deep learning techniques. The search was conducted on six electronic databases, and 36 articles met the inclusion criteria. The inclusion criteria were studies that applied deep learning techniques for insect classification, counting, and detection, written in English. The selection process involved analysing the title, keywords, and abstract of each study, resulting in the exclusion of 33 articles. The remaining 36 articles included 12 for the classification task and 24 for the detection task. Two main approaches-standard and adaptable-for insect detection were identified, with various architectures and detectors. The accuracy of the classification was found to be most influenced by dataset size, while detection was significantly affected by the number of classes and dataset size. The study also highlights two challenges and recommendations, namely, dataset characteristics (such as unbalanced classes and incomplete annotation) and methodologies (such as the limitations of algorithms for small objects and the lack of information about small insects). To overcome these challenges, further research is recommended to improve insect pest management practices. This research should focus on addressing the limitations and challenges identified in this article to ensure more effective insect pest management.

Abstract
The world wine sector is a multi-billion dollar industry with a wide range of economic activities. Therefore, it becomes crucial to monitor the grapevine because it allows a more accurate estimation of the yield and ensures a high-quality end product. The most common way of monitoring the grapevine is through the leaves (preventive way) since the leaves first manifest biophysical lesions. However, this does not exclude the possibility of biophysical lesions manifesting in the grape berries. Thus, this work presents three pre-trained YOLO models (YOLOv5x6, YOLOv7-E6E, and YOLOR-CSP-X) to detect and classify grape bunches as healthy or damaged by the number of berries with biophysical lesions. Two datasets were created and made publicly available with original images and manual annotations to identify the complexity between detection (bunches) and classification (healthy or damaged) tasks. The datasets use the same 10,010 images with different classes. The Grapevine Bunch Detection Dataset uses the Bunch class, and The Grapevine Bunch Condition Detection Dataset uses the OptimalBunch and DamagedBunch classes. Regarding the three models trained for grape bunches detection, they obtained promising results, highlighting YOLOv7 with 77% of mAP and 94% of the F1-score. In the case of the task of detection and identification of the state of grape bunches, the three models obtained similar results, with YOLOv5 achieving the best ones with an mAP of 72% and an F1-score of 92%.

Abstract
Narratives are present in many forms of human expression and can be understood as a fundamental way of communication between people. Computational understanding of the underlying story of a narrative, however, may be a rather complex task for both linguists and computational linguistics. Such task can be approached using natural language processing techniques to automatically extract narratives from texts. In this paper, we present an in depth survey of narrative extraction from text, providing a establishing a basis/framework for the study roadmap to the study of this area as a whole as a means to consolidate a view on this line of research. We aim to fulfill the current gap by identifying important research efforts at the crossroad between linguists and computer scientists. In particular, we highlight the importance and complexity of the annotation process, as a crucial step for the training stage. Next, we detail methods and approaches regarding the identification and extraction of narrative components, their linkage and understanding of likely inherent relationships, before detailing formal narrative representation structures as an intermediate step for visualization and data exploration purposes. We then move into the narrative evaluation task aspects, and conclude this survey by highlighting important open issues under the domain of narratives extraction from texts that are yet to be explored.

Abstract
A significant concern that is widely discussed in the field of Higher Education is declining student participation. In several institutions, attendance is optional, allowing students to attend lectures at their convenience. This study proposes the integration of Artificial Intelligence and Gamification to improve student engagement and attendance rates. The initiative combines advanced technological strategies with conventional educational methodologies to enhance the lecture experience. The initiative is significant as formal lectures often witness dwindling student interest and frequent absenteeism, undermining the educational process and student's future career prospects. This combination has the potential to revolutionise Higher Education by providing a more interactive and engaging learning experience. While gamification has positively impacted learning in various contexts, integration with Artificial Intelligence is a game-changer, paving the way for a modernised educational experience. This innovative exploration of the AI-gamification blend sets the stage for future research and the implementation of updated academic strategies, ultimately addressing student engagement and attendance. This position paper presents the bases and foundations for understanding the problem of student attendance and engagement and the role of AI and gamification in Higher Education in alleviating it.;Uma inquietação relevante e extensamente discutida no domínio do Ensino Superior é a diminuição da participação dos estudantes. Em diversas instituições, a assiduidade é facultativa, permitindo aos estudantes a frequência às aulas segundo a sua disponibilidade. Este estudo propõe a integração da Inteligência Artificial e da Gamification como meios para melhorar o envolvimento e as taxas de assiduidade dos estudantes. A iniciativa em causa articula estratégias tecnologicamente avançadas com metodologias educativas convencionais no intuito de enriquecer a experiência de ensino. Tal iniciativa assume importância dada à constante diminuição do interesse dos estudantes e a assiduidade irregular nas aulas formais, fatores que afetam negativamente o processo de ensino e as perspetivas de carreira dos estudantes. Esta combinação ostenta o potencial de revolucionar o Ensino Superior, proporcionando uma experiência de aprendizagem mais interativa e envolvente. Embora a Gamification já tenha impactado positivamente o processo de aprendizagem em diversos contextos, a sua integração com a Inteligência Artificial surge como um elemento transformador, abrindo caminho para uma experiência educacional modernizada. Esta investigação inovadora que combina a IA e Gamification prepara as bases para investigações futuras e a implementação de estratégias académicas aprimoradas, concentrando-se principalmente no envolvimento e na assiduidade dos estudantes. Este artigo de posicionamento apresenta as bases e os fundamentos necessários para a compreensão do problema da frequência e envolvimento dos estudantes no Ensino Superior, assim como o papel da IA e da Gamification na sua mitigação.