Abstract
Sharing and managing knowledge is an essential resource for the success of a Scrum team. However, widely known knowledge management practices typically address only team level collaboration, which cannot be easily scaled to multiple Scrum teams. In this sense, this paper conducts an empirical study at one of the largest information technology companies operating in Portugal, in which multiple Scrum teams working locally and in remote offices develops e-commerce solutions for the African market. Several facilitators’ knowledge practices are identified and discussed, and also the main challenges and difficulties of scaling Scrum practices for large-scale teams are addressed. © 2019, © 2019 Fernando Almeida, Emanuel Miranda and João Falcão.

Abstract
Switched reluctance machines (SRM) are simple, robust and fault tolerant machines, usually operating under strong nonlinear characteristics. Hence, modeling this machine is a most demanding task. While magnetic saturation is often addressed, hysteresis effect is usually disregarded. In order to include this phenomenon, an SRM drive simulation model was built, where magnetization characteristics are generated through the Jiles-Atherton (J-A) hysteresis model. SRM losses estimation is a challenging task, which demands continuous research efforts. This paper intends to investigate hysteresis impact on SRM copper losses. Due to the machine features, skin and proximity effects are considered. Different steady-state operation scenarios are simulated and compared.

Abstract
The usage of unmanned vehicles for professional, recreational and healthy purposes has increased and is a huge signal of their advantages. Among other benefits, they reduce or even cancel the need of having human lives aboard, which means that there is no risk of injuries in dangerous tasks. Although, most of the time the users are near the vehicles, which cannot be possible nor proper for personal or security reasons. Therefore, it is proposed a software solution to allow users to control and monitor unmanned vehicles remotely in real-time just as if they were in the vehicles’ place. Then it follows an implementation to control and monitor remotely-piloted cars of different types. This solution has been applied to a real-case scenario for testing purposes and it has been concluded that the software architecture proposed can be generically applied to different kinds of vehicles with transparency to the users that are able to control, from everywhere and with their own personal devices, whatever vehicles they want. © Springer Nature Switzerland AG 2019.

Abstract
There is a growing need for fast and accurate methods for testing developmental neurotoxicity across several chemical exposure sources. Current approaches, such as in vivo animal studies, and assays of animal and human primary cell cultures, suffer from challenges related to time, cost, and applicability to human physiology. Prior work has demonstrated success employing machine learning to predict developmental neurotoxicity using gene expression data collected from human 3D tissue models exposed to various compounds. The 3D model is biologically similar to developing neural structures, but its complexity necessitates extensive expertise and effort to employ. By instead focusing solely on constructing an assay of developmental neurotoxicity, we propose that a simpler 2D tissue model may prove sufficient. We thus compare the accuracy of predictive models trained on data from a 2D tissue model with those trained on data from a 3D tissue model, and find the 2D model to be substantially more accurate. Furthermore, we find the 2D model to be more robust under stringent gene set selection, whereas the 3D model suffers substantial accuracy degradation. While both approaches have advantages and disadvantages, we propose that our described 2D approach could be a valuable tool for decision makers when prioritizing neurotoxicity screening. © 2019 IEEE.

Abstract
In the context of continuous-time control systems, we address the problem of guaranteeing that the constraints imposed along the trajectory are in fact satisfied for all times. The problem is relevant and non-trivial in situations in which a continuous-time internal representation of the system is used with a digital device, such as in sampled-data model-based control, in an optimal control solver, or in sampled-data model predictive control. In this letter, we establish a condition that when verified on a finite set of time instants (using limited computational power) can guarantee that the trajectory constraints are satisfied on an uncountable set of times. The case of constrained optimal control problems is further explored here. We develop an algorithm for the numerical solution of constrained nonlinear optimal control problems that combines a guaranteed constraint satisfaction strategy with an adaptive mesh refinement strategy. © 2017 IEEE.

Abstract
The Power Normal (PN) family of distributions is obtained by inverting the Box-Cox (BC) transformation over a truncated normal (TN) (or for some cases normal) random variable. In this paper we explore the PN distribution. We give a formula for the ordinary moments and considering the bivariate PN (BPN) distribution we calculate the marginal and conditional probability density functions (pdf). We prove that they are not univariate PN distributed. We also calculate the correlation curve and we fit a power law model.