Abstract
Port wine plays a crucial role in the Douro region in Portugal, providing significant economic support and international recognition. The efficient and sustainable management of the wine sector is of utmost importance, which includes the verification of abandoned vineyard plots in the region, covering an area of approximately 250,000 hectares. The manual analysis of aerial images for this purpose is a laborious and resource-intensive task. However, several artificial intelligence (AI) methods are available to assist in this process. This paper presents the development of AI models, specifically deep learning models, for the automatic detection of abandoned vineyards using aerial images. A private image database was expanded, containing a larger collection of images with both abandoned and non-abandoned vineyards. Multiple AI algorithms, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), were explored for classification. The results, particularly with the ViTs approach, achieved high accuracy and demonstrated the effectiveness of automatic detection, with the ViT models achieving an accuracy of 99.37% and an F1-score of 98.92%. The proposed AI models provide valuable tools for monitoring and decision-making related to vineyard abandonment. © 2024 The Author(s). Published by Elsevier B.V.

Abstract
This special section features extended versions of papers originally published in the 2022 IEEE Vehicle Power and Propulsion Conference (VPPC22), hosted by the University of California, Merced, USA. This was the first time that the VPPC took place in California, USA. It was a timely visit. California recently announced that only zero-emission vehicles (ZEVs) will be allowed to be sold in the state by 2035. Other states and countries will surely follow. The VPPC, as one of the pioneer forums dedicated to electric mobility, is in a privileged position to create and disseminate knowledge that will help our communities transition toward sustainable transportation, improving air quality and reducing greenhouse emissions.

Abstract
This research idea paper proposes leveraging Large Language Models (LLMs) to enhance the productivity of Dafny developers. Although the use of verification-aware languages, such as Dafny, has increased considerably in the last decade, these are still not widely adopted. Often the cost of using such languages is too high, due to the level of expertise required from the developers and challenges that they often face when trying to prove a program correct. Even though Dafny automates a lot of the verification process, sometimes there are steps that are too complex for Dafny to perform on its own. One such case is that of missing lemmas, i.e. Dafny is unable to prove a result without being given further help in the form of a theorem that can assist it in the proof of the step. In this paper, we describe preliminary work on using LLMs to assist developers by generating suggestions for relevant lemmas that Dafny is unable to discover and use. Moreover, for the lemmas that cannot be proved automatically, we attempt to provide accompanying calculational proofs. We also discuss ideas for future work by describing a research agenda on using LLMs to increase the adoption of verification-aware languages in general, by increasing developers productivity and by reducing the level of expertise required for crafting formal specifications and proving program properties.

Abstract
PurposeTo develop, implement and assess the results of psychoeducation to improve the QoL of parents with CHD newborns.MethodsParticipants were parents of inpatient newborns with the diagnosis of non-syndromic CHD. We conducted a parallel RCT with an allocation ratio of 1:1 (intervention vs. control), considering the newborns, using mixed methods research. The intervention group received psychoeducation (Parental Psychoeducation in CHD [PPeCHD]) and the usual routines, and the control group received just the regular practices. The allocation concealment was assured. PI was involved in enrolling participants, developing and implementing the intervention, data collection and data analysis. We followed the Consolidated Standards of Reporting Trials (CONSORT) guidelines.ResultsParents of eight newborns were allocated to the intervention group (n = 15 parents) and eight to the control group (n = 13 parents). It was performed as an intention-to-treat (ITT) analysis. In M2 (4 weeks), the intervention group presented better QoL levels in the physical, psychological, and environmental domains of World Health Organization Quality of Life instrument (WHOQOL-Bref). In M3 (16 weeks), scores in physical and psychological domains maintained a statistically significant difference between the groups.ConclusionsThe PPeCHD, the psychoeducational intervention we developed, positively impacted parental QoL. These results support the initial hypothesis. This study is a fundamental milestone in this research field, adding new essential information to the literature.

Abstract

Abstract
This paper presents BATS-PT, the manual translation of the lexicographic portion of the Bigger Analogy Test Set (BATS) to European Portuguese. BATS-PT covers ten types of lexico-semantic analogies and can be used for assessing word embeddings and language models. Following this, the dataset is showcased while assessing two pretrained language models for Portuguese, BERTimbau and Albertina, in two tasks: analogy solving and relation completion, both in zero- and few-shot mask-prediction approaches. Experiments reveal different performance across relations and, in both tasks, the best overall performance was achieved with BERTimbau, in a five-shot scenario. We further discuss the limitations of the reported experiments and directions towards future improvements in these tasks. © 2024 The Association for Computational Linguistics.