Abstract
This paper tackles the efficient Power-Flow solution of ill-conditioned cases. In that sense, those methods based on the Continuous Newton's philosophy look very promising, however, these methodologies still present some issues mainly related with the computational efficiency or the robustness properties. In order to overcome these drawbacks, we suggest several modifications about the standard structure of the Continuous Newton's method. Thus, the standard Continuous Newton's paradigm is firstly modified with a frozen Jacobian scheme for reducing its computational burden; secondly, it is extended for being used with High-order Newton-like method for achieving higher convergence rate and, finally, a regularization scheme is introduced for improving its robustness features. On the basis of the suggested improvements, a Power-Flow solution paradigm is developed. As example, a novel Power-Flow solver based on the introduced solution framework and the 4th order Runge-Kutta formula is developed. The novel technique is validated in several realistic large-scale ill-conditioned systems. Results show that the suggested modifications allow to overcome the drawbacks presented by those methodologies based on the Continuous Newton's method. On the light of the results obtained it can be also claimed, that the developed solution paradigm constitutes a promising framework for developing robust and efficient Power-Flow solution techniques.

Abstract
Computer-aided design (CAD) and 3D modeling are similar, but they have different functionalities and applications. CAD is a fundamental tool to create object models, design parts, and create 2D schematics from 3D designed objects that can later be used in manufacturing. Meanwhile, 3D modeling is mostly used in entertainment, to create meshes for animation and games. When there is the necessity of using real-life object models in game engines, a conversion process is required to go from CAD to 3D meshes. Converting from the continuous domain of CAD to the discrete domain of 3D models represents a trade-off between processing cost and visual accuracy, in order to obtain the best user experience. This work explores different methods for the creation of meshes and the reduction of the number of polygons used to represent them. Based on these concepts, an interactive application was created to allow the users to control how the model looks in the game engine, in a simple way, while also optimizing and simplifying the mapping of textures for the generated meshes. This application (CADto3D) generates accurate 3D models based on CAD surfaces while giving the user more control over the final result than other current solutions.

Abstract
Energy communities and self-consumption are considered as major drivers to achieve the decarbonization of the power sector, contributing to further increase the penetration of renewables, and empowering end consumers by turning them more active and flexible regarding electricity consumption. Considering the relevance of this topic and given that the corresponding legislation is recent, this paper summarizes the European Regulation on self-consumption and renewable energy communities and describes, analyses and compares its transposition to the Portuguese and Spanish national regulations. The main regulatory barriers for renewable energy communities' rollout are identified and regulatory improvements are proposed to overcome them.

Abstract
This work presents a taxonomic proposition that integrates six views of business process (BP), allowing the identification of different BP perspectives as well as their elements used in the BP modelling activity. A literature review (LR) was conducted to identify the theoretical elements of the BP construct. Based on the LR findings, a taxonomic proposition of BP is presented. Finally, interviews were conducted with practitioners to validate it. The taxonomy contributes to the systematisation of knowledge around the theoretical construct BP and offers the practitioner a broad spectrum of points of view for the analysis of a given BP.

Abstract
Background: Pelvic floor muscle training can cure or alleviate stress urinary incontinence. This study aimed to evaluate maximum voluntary contractions of the pelvic floor muscle in sportswomen and verify the association with leg stiffness and muscle power, both maximal and submaximal. Methods: The sample consisted of 41 sportswomen between 18 and 42 years of age. Pelvic floor muscle strength was measured by the manometer. The sportswomen were instructed to perform 3 maximum voluntary contractions of the perineum, held for 3 seconds. Maximal and submaximal leg stiffness and muscle power were measured with a force platform, in two conditions: 1st condition was the sub-maximal, double leg hop test, which was performed allowing sportswomen to self-select their preferred frequency and 2nd condition was the maximal double leg hop test, which was performed asking athletes to maximize hop height and minimize contact time on the top of the force platform for 6 consecutive hops. Findings: Maximal and submaximal leg stiffness values increase with increasing age, weight, height, and body mass index, showing positive and significant (p <0.05) or close correlations. There are strong positive correlations between maximal and submaximal leg stiffness (r = 0.759) and between maximal and submaximal muscle power. Maximum voluntary contractions values decrease with increasing leg stiffness: the correlation is significant with maximal leg stiffness. Interpretation: Maximum voluntary contractions values decrease with increasing leg stiffness and increase with increasing muscle power values. If the training program aims to increase muscle power, it may also increase maximum voluntary contractions.

Abstract
Through technological advents from Industry 4.0 and the Internet of Things, as well as new Big Data solutions, predictive maintenance begins to play a strategic role in the increasing operational performance of any industrial facility. Equipment failures can be very costly and have catastrophic consequences. In its basic concept, Predictive maintenance allows minimizing equipment faults or service disruptions, presenting promising cost savings. This paper presents a data-driven approach, based on multiple-instance learning, to predict malfunctions in End-of-Line Testing Systems through the extraction of operational logs, which, while not designed to predict failures, contains valid information regarding their operational mode over time. For the case study performed, a real-life dataset was used containing thousands of log messages, collected in a real automotive industry environment. The insights gained from mining this type of data will be shared in this paper, highlighting the main challenges and benefits, as well as good recommendations, and best practices for the appropriate usage of machine learning techniques and analytics tools that can be implemented in similar industrial environments. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.