Abstract
Unpredictable faults always reduce the stability and reliability of the electrical system. The increasing use of renewable energy sources (RES) in recent decades has exacerbated power system problems. Micro grids (MG) participation in Ancillary Services (AS) market is a suitable solution for the optimal performance of power systems in these conditions. MGs can also maximize their profits by participating in the AS market. In this paper, the optimal stochastic bidding strategy in joint energy and AS (regulation up and regulation down, spinning reserve and non-spinning reserve) market is modeled. Uncertainties of wind speed and solar radiation are modeled using Weibull and Beta probability distribution functions (PDFs) and probability of call AS is computed for all available AS. Therefore, the risk of the bidding strategy is controlled using conditional value at risk (CVaR). ERCOT market simulation has been carried out in order to determine the participation of each generator in all of the mentioned markets for different prices of energy and also to present the bidding curve, based on real-world data.

Abstract
The importance of agriculture and the production of fruits and vegetables has stood out mainly over the past few years, especially for the benefits for our health. In 2021, in the international year of fruit and vegetables, it is important to encourage innovation and evolution in this area, with the needs surrounding the different processes of the different cultures. This paper compares the performance between two datasets for robotics fruit harvesting using four deep learning object detection models: YOLOv4, SSD ResNet 50, SSD Inception v2, SSD MobileNet v2. This work aims to benchmark the Open Images Dataset v6 (OIDv6) against an acquired dataset inside a tomatoes greenhouse for tomato detection in agricultural environments, using a test dataset with acquired non augmented images. The results highlight the benefit of using self-acquired datasets for the detection of tomatoes because the state-of-the-art datasets, as OIDv6, lack some relevant characteristics of the fruits in the agricultural environment, as the shape and the color. Detections in greenhouses environments differ greatly from the data inside the OIDv6, which has fewer annotations per image and the tomato is generally riped (reddish). Standing out in the use of our tomato dataset, YOLOv4 stood out with a precision of 91%. The tomato dataset was augmented and is publicly available (See https://rdm.inesctec.pt/ and https://rdm.inesctec.pt/dataset/ii-2021-001).

Abstract
Cardiac auscultation is the key screening procedure to detect and identify cardiovascular diseases (CVDs). One of many steps to automatically detect CVDs using auscultation, concerns the detection and delimitation of the heart sound boundaries, a process known as segmentation. Whether to include or not a segmentation step in the signal classification pipeline is nowadays a topic of discussion. Up to our knowledge, the outcome of a segmentation algorithm has been used almost exclusively to align the different signal segments according to the heartbeat. In this paper, the need for a heartbeat alignment step is tested and evaluated over different machine learning algorithms, including deep learning solutions. From the different classifiers tested, Gate Recurrent Unit (GRU) Network and Convolutional Neural Network (CNN) algorithms are shown to be the most robust. Namely, these algorithms can detect the presence of heart murmurs even without a heartbeat alignment step. Furthermore, Support Vector Machine (SVM) and Random Forest (RF) algorithms require an explicit segmentation step to effectively detect heart sounds and murmurs, the overall performance is expected drop approximately 5% on both cases.

Abstract
Purpose: The study purpose was to analyse the excellence and operational efficiency of Portuguese industrial companies through the measurement of lean practices implementation. Additionally, it intended to propose a new model to assess the lean production system. Design/methodology/approach: The research presents an in-depth lean literature review that served as a basis for the creation of a questionnaire. The survey was addressed to Portuguese industrial companies to obtain data about their lean implementation success. Collected data were analysed, resorting descriptive and exploratory statistics. A principal component analysis was applied to reduce the amount of data and define a new model that characterizes the lean practices adoption level. Some Chi-square tests were used to assess the independence of some variables. Findings: The results indicate that a significant percentage of organizations use lean practices within their activity. Concerning the lean implementation maturity, a plus side revealed by the study concerns the adoption of the teamwork, internal information shared principles, increase of process capability to produce conforming products, and reduction in set up times. On the other hand, responsibilities decentralization, more employees acting as team leaders, implementation of employees' suggestions, and interconnection with suppliers, are some of the principles that need to be given greater attention by the Portuguese industrial organizations. The key contribution consists of a new model for lean determinants based on three dimensions: work method, productive process elements, and work efficiency. Originality/value: The present research provides a new Lean determinants model that allows understanding the importance of the operational efficiency provided by a mature lean production system, being the key to the competitiveness in the global business market.

Abstract
This research aims to contribute to bridging the gap between the connection of logistics and regional development. Firstly, based on the available literature, the contribution of logistics to socioeconomic development was analyzed, and having in mind the importance of Regional Development for economic and social development, this research brings to the light the importance of logistics activities to regional social development, and framework to assess these connections is proposed. Then a framework comprising a set of indicators to evaluate logistics performance was proposed. As a main result, a framework for the assessment of regional logistics performance is proposed together with several logistics performance indicators to assess the impact of logistics on regional development.

Abstract
Aims. We aim to demonstrate that the presence and mass of an exoplanet can now be effectively derived from the astrometry of another exoplanet. Methods. We combined previous astrometry of beta Pictoris b with a new set of observations from the GRAVITY interferometer. The orbital motion of beta Pictoris b is fit using Markov chain Monte Carlo simulations in Jacobi coordinates. The inner planet, beta Pictoris c, was also reobserved at a separation of 96 mas, confirming the previous orbital estimations. Results. From the astrometry of planet b only, we can (i) detect the presence of beta Pictoris c and (ii) constrain its mass to 10.04(-3.10)(+4.53) M-Jup . If one adds the astrometry of beta Pictoris c, the mass is narrowed down to 9.15(-1.06)(+1.08) M-Jup. The inclusion of radial velocity measurements does not affect the orbital parameters significantly, but it does slightly decrease the mass estimate to 8.89(-0.75)(+0.75) M-Jup.With a semimajor axis of 2.68 +/- 0.02 au, a period of 1221 +/- 15 days, and an eccentricity of 0.32 +/- 0.02, the orbital parameters of beta Pictoris c are now constrained as precisely as those of beta Pictoris b. The orbital configuration is compatible with a high-order mean-motion resonance (7:1). The impact of the resonance on the planets' dynamics would then be negligible with respect to the secular perturbations, which might have played an important role in the eccentricity excitation of the outer planet.