Abstract
This study aims at characterizing the spatial and temporal dynamics of spatio-temporal data sets, characterized by high resolution in the temporal dimension which are becoming the norm rather than the exception in many application areas, namely environmental modelling. In particular, air pollution data, such as NO2 concentration levels, often incorporate also multiple recurring patterns in time imposed by social habits, anthropogenic activities and meteorological conditions. A two-stage modelling approach is proposed which combined with a block bootstrap procedure correctly assesses uncertainty in parameters estimates and produces reliable confidence regions for the space-time phenomenon under study. The methodology provides a model that is satisfactory in terms of goodness of fit, interpretability, parsimony, prediction and forecasting capability and computational costs. The proposed framework is potentially useful for scenario drawing in many areas, including assessment of environmental impact and environmental policies, and in a myriad applications to other research fields.

Abstract
Virtual Reality (VR) is again in the spotlight. However, interactions and modeling operations are still major hurdles to its complete success. To make VR Interaction viable, many have proposed mid-air techniques because of their naturalness and resemblance to physical world operations. Still, natural mid-air metaphors for Constructive Solid Geometry (CSG) are still elusive. This is unfortunate, because CSG is a powerful enabler for more complex modeling tasks, allowing to create complex objects from simple ones via Boolean operations. Moreover, Head-Mounted Displays occlude the real self, and make it difficult for users to be aware of their relationship to the virtual environment. In this paper we propose two new techniques to achieve Boolean operations between two objects in VR. One is based on direct-manipulation via gestures while the other uses menus. We conducted a preliminary evaluation of these techniques. Due to tracking limitations, results allowed no significant conclusions to be drawn. To account for self-representation, we compared full-body avatar against an iconic cursor depiction of users' hands. In this matter, the simplified hands-only representation improved efficiency in CSG modelling tasks.

Abstract
In the dynamic scenarios faced nowadays, when handling non stationary data streams it is of utmost importance to perform change detection tests. In this work, we propose the Intrinsic Page Hinkley Test (iPHT), which enhances the Page Hinkley Test (PHT) eliminating the user-defined parameter (the allowed magnitude of change of the data that are not considered real distribution change of the data stream) by using the second order intrinsic mode function (IMF) which is a data dependent value reflecting the intrinsic data variation. In such way, the PHT change detection method is expected to be more robust and require less tunes. Furthermore, we extend the proposed iPHT to a blockwise approach. Computing the IMF over sliding windows, which is shown to be more responsive to changes and suitable for online settings. The iPHT is evaluated using artificial and real data, outperforming the PHT. © Springer International Publishing AG 2017.

Abstract
This paper provides a new approach in decision making process for shunt capacitor placement in distribution networks. The main core of the evaluation process is a multi-objective framework to allocate the capacitor banks. The power loss and the total harmonic distortion (THD) are the objective functions of the system under study in a long-term planning horizon. In order to select the executive plan introduced by using a multi-objective model, transient switching overvoltages have been considered. As the size and location of shunt capacitors may result in unacceptable overvoltages, the proposed technical decision making framework can be applied to avoid corresponding damages. In this paper, an iterative conventional power flow technique is introduced. This technique can be applied to evaluate THD for distribution networks as well as other power flow based objectives, such as power losses calculation and voltage stability assessment. The presented framework is a two stage one where at the first stage, a non-dominated sorting genetic algorithm (NSGA-II) augmented with a local search technique is used in order to solve the addressed multi-objective optimization problem. Then, at the second stage, a decision making support technique is applied to determine the best solution from the obtained Pareto front. In order to evaluate the effectiveness of the proposed method, two benchmarks are addressed in this paper. The first test system is a 9-bus distribution network and the second one is an 85-bus large scale distribution network. The simulation results show that the presented method is satisfactory and consistent with the expectation.

Abstract
The executive summary is a fundamental element of a business plan, being essential to present it in a concise, practical and objective manner. This article presents an innovative approach of building an executive summary that facilitates its creation and maintenance process. For this purpose, a canvas model framework consisting of five building blocks is proposed, respectively: product presentation, value proposition, marketing analysis, strategic positioning, and financial analysis. This framework was adopted and validated in the context of an entrepreneurship classroom. The findings indicate that three benefits stand out, namely the offer of a more focus tool, clear and concise and fast to build. The perception of these benefits is greater for students with previous experience as entrepreneurs, being independent to the origin course of students and their professional experience.

Abstract
Fusion splicing technique was explored for the fabrication of two sensing structures based on hollow microsphere Fabry-Perot cavity. The first sensor proposed was fabricated with a hollow microsphere tip, working as a probe sensor. This structure was studied for lateral load pressure, yielding a 1.56 +/- 0.01 nm/N sensitivity. The second sensing structure relied on an in-line hollow microsphere, which allowed the detection of lateral load, with a sensitivity of 2.62 +/- 0.02 nm/N. Furthermore, the proposed structure enabled strain sensing, with a sensitivity of 4.66 +/- 0.03 pm/mu epsilon. The two sensing structures were subjected to temperature, presenting low thermal cross-sensitivity.