Abstract
In Europe, the Common Agricultural Policy (CAP) encouraged the specialisation of agriculture and forestry systems by supporting schemes that promoted productivity, despite the socio-ecological changes' detrimental effects on ecosystem services and biodiversity. In the case of mountain viticulture of southern Europe, the adoption of intensive management techniques triggered noticeable changes in farming systems, namely the removal of traditional stonewalls and semi-natural vegetation, partially compensated by eco schemes and agri-environment-climate measures. By combining fieldwork information with spatio-temporal modelling techniques, a novel hybrid framework is explained and implemented to predict the population trends of a critically en-dangered bird species in Portugal, the Black Wheatear (Oenanthe leucura), to the individual and/or combined effects of the removal of traditional stonewall terraced vineyards and the implementation of cover crops. The results obtained demonstrate the relevance of stonewall terraced vineyards (and the negative effects of their removal) for the conservation of Black Wheatear, namely during the breeding season when holes and crevices are used for nesting. Conversely, and in accordance with our simulations, the increase in the area occupied by vineyards with cover crops seems particularly detrimental for the species, by decreasing the quality of the feeding grounds. As cover crops, and possibly other eco schemes and agri-environment-climate measures, might not be the panacea for halting biodiversity loss in mountain viticulture, adaptation of measures to species' ecological requirements is urgent for a successful EU biodiversity strategy for 2030.

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Abstract
Glaucoma is one of the leading causes of irreversible blindness in the world. Early diagnosis and treatment increase the chances of preserving vision. However, despite advances in techniques for the functional and structural assessment of the retina, specialists still encounter many challenges, in part due to the different presentations of the standard optic nerve head (ONH) in the population, the lack of explicit references that define the limits of glaucomatous optic neuropathy (GON), specialist experience, and the quality of patients' responses to some ancillary exams. Computer vision uses deep learning (DL) methodologies, successfully applied to assist in the diagnosis and progression of GON, with the potential to provide objective references for classification, avoiding possible biases in experts' decisions. To this end, studies have used color fundus photographs (CFPs), functional exams such as visual field (VF), and structural exams such as optical coherence tomography (OCT). However, it is still necessary to know the minimum limits of detection of GON characteristics performed through these methodologies. This study analyzes the use of deep learning (DL) methodologies in the various stages of glaucoma screening compared to the clinic to reduce the costs of GON assessment and the work carried out by specialists, to improve the speed of diagnosis, and to homogenize opinions. It concludes that the DL methodologies used in automated glaucoma screening can bring more robust results closer to reality.

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The gastrointestinal tract is part of the digestive system, fundamental to digestion. Digestive problems can be symptoms of chronic illnesses like cancer and should be treated seriously. Endoscopic exams in the tract make detecting these diseases in their initial stages possible, enabling an effective treatment. Modern endoscopy has evolved into the Wireless Capsule Endoscopy procedure, where patients ingest a capsule with a camera. This type of exam usually exports videos up to 8 hours in length. Support systems for specialists to detect and diagnose pathologies in this type of exam are desired. This work uses a rarely used dataset, the ERS dataset, containing 121.399 labelled images, to evaluate three models from the EfficientNet family of architectures for the binary classification of Endoscopic images. The models were evaluated in a 5-fold cross-validation process. In the experiments, the best results were achieved by EfficientNetB0, achieving average accuracy and F1-Score of, respectively, 77.29% and 84.67%. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

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