Abstract
Decentralization of power systems is creating a need for tools which can provide fast and accurate optimal power flow (OPF) solutions, without being dependent on the availability of all system information and/or uncertain variables. In this study, a hybrid probabilistic algorithm is proposed to accurately and efficiently predict ideal generation levels of individual generators to minimize the total system cost (as per AC-OPF), while having no information on the grid structure and with limited information on system variables. The proposed hybrid algorithm combines the use of correlation analysis, k-means clusters, and kernel density estimation (KDE), to predict ideal generation levels of each generator based only on historical datasets of local information (i.e. adjacent load centers). By simulating the AC-OPF problem on the IEEE 9-bus test system, a historical dataset of 1000 samples is synthetically generated and randomized local information is given as input for each agent. Quasi-deterministic Monte-Carlo simulations with 100000 samples were used for validation. In the most uncertain operating conditions, the proposed algorithm was capable of predicting the ideal generation level of the most expensive generator with a 1.65% error, while being three times faster than a Neural Network (NN), taking only 0.39 seconds to run on a standard laptop computer.

Abstract
In the last few decades, there has been a strong trend towards integrating renewable-based distributed generation systems into the power grid, and advanced management strategies have been developed in order to provide a reliable, resilient, economic, and sustainable operation. Moreover, demand response (DR) programs, by taking the advantage of flexible loads' energy reduction capabilities, have presented as a promising solution considering reliability issues. Therefore, the impacts of combined system architecture with on-site photovoltaic (PV) generation units and residential demand reduction strategies were taken into consideration on distribution system reliability indices in this study. The load model of this study was created by using load data of the distribution feeder provided by Bosphorus Electric Distribution Corporation (BEDAS). Additionally, the reliability parameters of the feeder components were determined based on these provided data. The calculated load point and feeder side indicators were analyzed comprehensively from technical and economic perspectives. In order to validate the effectiveness of the proposed structure, four case studies were carried out in both DigSILENT PowerFactory and MATLAB environments.

Abstract
The current context of organizations has meant that their business models have been continually evolving, creating opportunities for technological innovations. Business strategies become a significant challenge, mainly if organizations are small or medium-sized, with all the constraints typically associated with them. The most used digital transformation frameworks are centered on large organizations and those that until now have been proposed for application in smaller organizations present some questions. In this article, a new methodology proposal, SimpliSMEEA, is presented and its application to a specific company is described and evaluated. © Atas da Conferencia da Associacao Portuguesa de Sistemas de Informacao 2020.

Abstract
Educational activities where students do not stay at a fixed, predetermined location, has been growing in research output. The pedagogies and processes for these environments where students move are typically less documented than those of traditional classrooms. We have surveyed the areas of problems addressed by the literature on the field, and the areas of its contributions, to build a panorama of the research foci for managing learning in environments where students move. This was done by means of an exploratory literature review, followed by thematic analysis and triangulation. We established that research focuses on problems and contributions dealing with the design, creation, and deployment of these learning activities, and to a lesser degree, on problems and contributions dealing with the potential of use of technology. However, there is a lack of research focusing on other areas relevant to more widespread implementation across the educational system, namely structural, logistic, and assessment problems and contributions. This points towards a need to develop research in these areas, in order to contribute to more grounded and successful efforts to implement learning activities in environments where students move.

Abstract
The main focus of an Intelligent environment, as with other applications of Artificial Intelligence, is generally on the provision of good decisions towards the management of the environment or the support of human decision-making processes. The quality of the system is often measured in terms of accuracy or other performance metrics, calculated on labeled data. Other equally important aspects are usually disregarded, such as the ability to produce an intelligible explanation for the user of the environment. That is, asides from proposing an action, prediction, or decision, the system should also propose an explanation that would allow the user to understand the rationale behind the output. This is becoming increasingly important in a time in which algorithms gain increasing importance in our lives and start to take decisions that significantly impact them. So much so that the EU recently regulated on the issue of a “right to explanation”. In this paper we propose a Human-centric intelligent environment that takes into consideration the domain of the problem and the mental model of the Human expert, to provide intelligible explanations that can improve the efficiency and quality of the decision-making processes. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract