Abstract
This paper describes a mobile application, developed in an educational context, by the students of the Degree in Computer Engineering of the Instituto Politécnico de Bragança, allowing them to develop skills, based on real-world community problems solving, promoting by this way its engagement, and, at the same time, provide a solution to an effective need of the local community. The developed application has as goal to support the Human Veterinary Resources Management in a Low Density Population Context. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
The conventional approach for Federated Learning (FL) is to train a global model by averaging local models trained on local data sets. However, given the limited computing resources at the mobile-edge nodes, unreliable models may be received from the Edge Nodes (ENs), which can lead to a significant performance degradation in the FL. Thus, this paper proposes a reliable and age sensitive FL framework that captures the dynamic nature of the local data and computing resources at each participating EN. Specifically, we formulate two optimization problems to select the optimal subset of ENs that can upload their local models in each round of the global model training, given a limited learning cost budget. The first problem aims at selecting the most reliable ENs that should cooperate to complete the FL process, while considering stationary data distributions at different ENs. The second problem aims at minimizing the average age of information experienced by each EN while selecting the most reliable ENs, given fast changing data distributions. Efficient solutions are proposed for the two problems with a worst-case linear complexity. Our Results, leveraging a real-world dataset, depict the efficiency of our solutions in obtaining a better performance compared to conventional FL approach.

Abstract
The design of novel molecular structures with tunable photophysical properties is an important research field for many applications including optoelectronics, sensing and bioimaging. Porphyrin and rhodamine/rosamine derivatives are among the most studied and relevant chemosensors and imaging probes due to their attractive photophysical properties, such as high absorption coefficients and long emission wavelengths. In this work, we present the synthesis and the structural characterization of a new porphyrin-rosamine conjugate H2P3 and its related triarylmethane precursors H2P1 and H2P2. The photophysical properties of H2P1, H2P2 and H2P3, and their ability to chelate iron(III) and copper(II) ions, were evaluated by absorption and emission spectroscopy. The formation of copper(II) complexes was confirmed by electron paramagnetic resonance (EPR), which also allowed the detection of an intense and stable radical signal for the free-base H2P3. Further studies involving the addition of the 2,2,6,6-tetramethylpiperidine spin trap to derivatives H2P1, H2P2 and H2P3, showed that only H2P3 gives rise to an EPR detectable signal due to a strong generation of singlet oxygen.

Abstract
This study presents a dataset containing three layers of data that are useful for body position classification and all uses related to it. The PoPu dataset contains simultaneously collected data from two different sensor sheets-one placed over and one placed under a mattress; furthermore, a segmentation data layer was added where different body parts are identified using the pressure data from the sensors over the mattress. The data included were gathered from 60 healthy volunteers distributed among the different gathered characteristics: namely sex, weight, and height. This dataset can be used for position classification, assessing the viability of sensors placed under a mattress, and in applications regarding bedded or lying people or sleep related disorders. Dataset The dataset is available on GitHub: https://github.com/rdionisio1403/PoPu/. Dataset License The dataset is available under Creative Commons (CC0).

Abstract
The grape moth is a common pest that affects grapevines by consuming both fruit and foliage, rendering grapes deformed and unsellable. Integrated pest management for the grape moth heavily relies on pheromone traps, which serve a crucial function by identifying and tracking adult moth populations. This information is then used to determine the most appropriate time and method for implementing other control techniques. This study aims to find the best method for detecting small insects. We evaluate the following recent YOLO models: v5, v6, v7, and v8 for detecting and counting grape moths in insect traps. The best performance was achieved by YOLOv8, with an average precision of 92.4% and a counting error of 8.1%.

Abstract
The digital transformation of manufacturing activities is expected to bring large societal benefits in terms of productivity and sustainability. However, uptake of digital technologies is slower than desirable. As a result, governments are taking action to try to overcome some of the barriers to adoption. However, the mechanisms through which government may act are quite diverse. In this paper, we compare the national strategies across the 27 countries members of the European Union. We map each country's initiative to 14 barriers to the adoption of digital technologies in manufacturing observed in the literature. We observe that most institutional efforts focus on providing funding, developing new regulatory frameworks related to data privacy and security, and creating human capital. Some known barriers to adoption observed at the firm level, such as the lack of off-the-shelf solutions, or the need for retrofitting old equipment, are largely overlooked. We do not find any relationship between the number of initiatives proposed by each country, and the country's existing level of digitalization. We conclude by proposing several policy recommendations, as well as directions for future research.