Abstract
In today's competing business market, companies are constantly challenged to dynamically adapt to customer expectations by diminishing the time response that goes from the beginning of the business opportunity to the satisfaction of the customer need. Simultaneously, there is increased recognition of the advantages that companies obtain in focusing on their core business and seeking other competencies through partnerships with other partners by forming collaborative networks. These new collaborative organizational structures require a new set of methods and tools to support the management of manufacturing processes across the entire supply chain. The present paper addresses the collaborative production planning problem in networks of non-hierarchical, decentralized, and independent companies. By proposing a collaborative planning intelligent framework composed of a web-based set of methods, tools, and technologies, the present study intends to provide network stakeholders with the necessary means to responsively and efficiently address each one of the market business opportunities. Through this new holistic framework, the managers of the networked companies can address the challenges posed during collaborative network formation and supply chain production planning.

Abstract
In Portugal, almonds are a very important crop, due to their nutritional properties. In the northeastern part of the country, the almond sector has endured over time, with strong cultural traditions and key economic significance. In these areas, several cultivars are used. In effect, the presence of various almond cultivars implies differentiated management in irrigation, disease control, pruning system, and harvest planning. Therefore, cultivar classification is essential over large agricultural areas. Over the last decades, remote-sensing data have led to important breakthroughs in the classification of different cultivars for several crops. Nonetheless, for almonds, studies are incipient. Thus, this study aims to fill this knowledge gap and explore the classification of almond cultivars in an almond orchard. High-resolution multispectral data were acquired by an unmanned aerial vehicle (UAV). Vegetation indices (VIs) and tree structural parameters were, subsequently, estimated. To obtain an accurate cultivar identification, four machine learning classifiers, such as K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied and optimized through the fine-tuning process. The accuracy of machine learning classifiers was analysed. SVM and RF performed best with OAs of 76% and 74% using VIs and spectral bands (GREEN, GRVI, GN, REN, ClRE). Adding the canopy height model (CHM) improved performance, with RF and XGBoost having OAs of 88% and 84%. kNN performed worst with an OA of 73% using only VIs and spectral bands, 80% with VIs, spectral bands and CHM, and 93% with VIs, CHM, and tree crown area (TCA). The best performance was achieved by RF and XGBoost with OAs of 99% using VIs, CHM, and TCA. These results demonstrate the importance of the feature selection process. Moreover, this study reveals the feasibility of remote-sensing data and machine learning classifiers in the classification of almond cultivars.

Abstract
The increasing integration of Distribution Energy Resources (DER) in the distribution system has brought the necessity of a change in grid management and also for better coordination between the Transmission System Operator (TSO) and the Distribution System Operator (DSO). This work proposes a reactive power management model to be used by DSOs, in which reactive power flexibility from DER, and also from On-Load Tap Changer (OLTC) transformers and capacitor banks are used to handle voltage problems that may arise in both transmission and distribution grids due to the uncertain production of Renewable Energy Sources (RES). Besides, it is proposed that the DSO may provide a service to the TSO, in which the latter requests a reactive power setpoint from the first one, in the TSO-DSO boundary. Adaptive robust optimization on an Alternating Current Optimal Power Flow (AC-OPF) is modelled, ensuring that the DSO receives a feasible solution and is able to manage congestion and voltage problems. The proposed model is compared with its stochastic equivalent to assess its strengths and drawbacks. To test and validate the proposed models, a 37-bus Medium Voltage (MV) distribution grid with high RES penetration is used. An important conclusion is that, though the robust model presents a safer solution than the stochastic model, the operator must be aware of the trade-off between the desired level of robustness and the expected operating cost.

Abstract
Objectives:Angiotensin-converting enzymes' (ACEs) relationship with blood pressure (BP) during childhood has not been clearly established. We aimed to compare ACE and ACE2 activities between BMI groups in a sample of prepubertal children, and to characterize the association between these enzymes' activities and BP.Methods:Cross-sectional study of 313 children aged 8-9 years old, included in the birth cohort Generation XXI (Portugal). Anthropometric measurements and 24-h ambulatory BP monitoring were performed. ACE and ACE2 activities were quantified by fluorometric methods.Results:Overweight/obese children demonstrated significantly higher ACE and ACE2 activities, when compared to their normal weight counterparts [median (P25-P75), ACE: 39.48 (30.52-48.97) vs. 42.90 (35.62-47.18) vs. 43.38 (33.49-49.89) mU/ml, P for trend = 0.009; ACE2: 10.41 (7.58-15.47) vs. 21.56 (13.34-29.09) vs. 29.00 (22.91-34.32) pM/min per ml, P for trend < 0.001, in normal weight, overweight and obese children, respectively]. In girls, night-time systolic BP (SBP) and diastolic BP (DBP) increased across tertiles of ACE activity (P < 0.001 and P = 0.002, respectively). ACE2 activity was associated with higher night-time SBP and DBP in overweight/obese girls (P = 0.037 and P = 0.048, respectively) and night-time DBP in the BMI z-score girl adjusted model (P = 0.018). Median ACE2 levels were significantly higher among nondipper girls (16.7 vs. 11.6 pM/min per ml, P = 0.009).Conclusions:Our work shows that obesity is associated with activation of the renin-angiotensin-aldosterone system, with significant increase of ACE and ACE2 activities already in childhood. Also, we report sex differences in the association of ACE and ACE2 activities with BP.

Abstract
At a time when tourists are increasingly looking to live memorable tourist experiences, gastronomy, in general, and traditional products can be valuable contributions to achieving this purpose. The main objective of this investigation is the survey and analysis of tourist developments registered in the municipality of Viseu, which include in their meals some traditional certified product, produced in the Dão Region. Another of the research objectives concerns the analysis of the communication that is made between tourist enterprises and tourists, with regard to traditional products. In order to achieve the defined objectives, a qualitative methodology was used, using a survey technique, applied to all tourist accommodations registered in the municipality of Viseu. Thirty-one accommodations were identified in the municipality of Viseu and 7 certified products in the Dão region. Most enterprises use at least one of the certified products in the meals they serve to tourists, the most common being Queijo Serra da Estrela PDO and Dão wine PDO. As for communication, this is mainly done through the accommodation staff, who during the service talk about the product and its characteristics. © 2023, Universidade de Aveiro. All rights reserved.

Abstract
Recently, deep learning has proven to be a successful technique especially in medical image analysis. This paper aims to highlight the importance of deep learning architectures in lung disease diagnosis using CXR images. Related articles were identified through searches of electronic resources, including IEEE, Springer, Elsevier, PubMed, Nature and, Hindawi digital library. The inclusion of articles was based on high-performance artificial intelligence models, developed for the classification of possible findings in CXR images published from 2018 to 2023.After the quality assessment of papers, 129 articles were included according to PRISMA guidelines. Papers were studied by types of lung disease, data source, algorithm type, and outcome metrics. Three main categories of computer-aided lung disease detection were covered: traditional machine learning, deep learning-based methods, and combination of aforementioned methods for all lung diseases.The results showed that various pre-trained networks including ResNet, VGG, and DenseNet, are the most frequently used CNN architectures and would result in a notable increase in sensitivity and accuracy. Recent research suggests that utilizing a combination of deep networks with a robust machine learning classifier can outperform deep learning approaches that rely solely on fully connected neural networks as their classifier. Finally, the limitations of the existing literature and potential future research opportunities in possible findings in CXR images using deep learning architectures are discussed in this systematic review.