Abstract
Nanotechnology is a newand very promising area of research which will allow several new applications to be created in different fields, such as, biological, medical, environmental, military, agricultural, industrial and consumer goods. This paper focuses specifically on nanocommunications, which will allow interconnected devices, at the nano-scale, to achieve collaborative tasks, greatly changing the paradigm in the fields described. Molecular communication is a new communication paradigm which allows nanomachines to exchange information using molecules as carrier. This is the most promising nanocommunication method within nanonetworks, since it can use bio-inspired techniques, inherit from studied biological systems, which makes the connection of biologic and man-made systems a easier process. At this point, the biggest challenges in these type of nanocommunication are to establish feasible and reliable techniques that will allow information to be encoded, and mechanisms that ensure a molecular communication between different nodes. This paper focus on creating concepts and techniques to tackle these challenges, and establishing new foundations on which future work can be developed. The created concepts and techniques are then applied in an envisioned medical application, which is based on a molecular nanonetwork deployed inside the Human body. The goal of this medical application is to automatously monitor endocrine diseases using the benefits of nanonetworks, which in turn connects with the internet, thus creating a Internet of NanoThings system. The concepts and techniques developed are evaluated by performing several simulations and comparing with other researches, and the results and discussions are presented on the later sections of this paper.

Abstract
This article is a case study, based on Participant Observation, which focuses on a digital platform which manages assets. This platform is for sale in different countries, including Portugal and Mozambique. As national cultures and institutions differ from country to country and as this is all the more the case when different continents are at stake, we herein discuss the adaptation of products and services to the cultures and institutions of each environment, for the optimization of sales and product positioning. The data of the case study comes mainly from the experience of one of the authors, who lived in Mozambique for close to two years, linked to the selling there of the digital platform. A review of the existing literature was also performed. The strategic notion of positioning is central to the success of organizations and should differ from context to context, where necessary. Namely, different economic and education conditions lead to distinct behaviours, which have a repercussion on the posture to be adopted for the selling of products. © 2017 AISTI.

Abstract

Abstract
Large-scale integration of variable Renewable Energy Sources (RES) brings significant challenges to grid operation that require new approaches and tools for distribution system management, particularly concerning voltage control. Therefore, an innovative approach for voltage control at the MV level is presented. It is based on a preventive day-ahead analysis that uses data from load/RES forecasting tools to establish a plan for operation of the different Distributed Energy Resources (DER) for the next day. The approach is formulated as a multi-temporal Optimal Power Flow (OPF) solved by a meta-heuristic, used to tackle complex multi-dimensional problems. The tuning of the meta-heuristic parameters was performed to ensure the robustness of the proposed approach and enhance the performance of the algorithm. It was tested through simulation in a large scale test network with good results.

Abstract
The strong growth that is felt at the level of photovoltaic (PV) power generation craves for more sophisticated and accurate forecasting methods that could be able to support its proper integration into the energy distribution network. Through the combination of the vector autoregression model (VAR) with the least absolute shrinkage and selection operator (LASSO) framework, a set of sparse VAR structures can be obtained in order to capture the dynamic of the underlying system. The robust and efficient alternating direction method of multipliers (ADMM), well known for its great ability dealing with high-dimensional data (scalability and fast convergence), is applied to fit the resulting LASSO-VAR variants. This spatial-temporal forecasting methodology has been tested, using 1-hour and 15-minutes resolution, for 44 microgeneration units time-series located in a city in Portugal. A comparison with the conventional autoregressive (AR) model is performed leading to an improvement up to 11%.

Abstract
Vast increase of renewable energy resources' (RER) share in total electricity production have led to evolving studies regarding different aspects of renewables integration. Other than their effects on network, the electricity markets are also affected by uncertain behavior of RERs in the market place. Hence, new approaches for market clearing are investigated. One of the possible solutions is the deployment of stochastic market clearing. However, the adaption of new market models should consider different market characteristics. As a result, this paper assesses the adaption of stochastic market in a hydro-penetrated system. The co-optimized energy and reserve schedule in the day-ahead time frame is derived using the mixed integer linear programming (MILP). The model is tested with Portuguese electricity market data as a real-case of hydro-penetrated system.