Abstract
The use of several distributed generators as well as the energy storage system in a local microgrid require an energy management system to maximize system efficiency, by managing generation and loads. The main purpose of this work is to find the optimal set-points of distributed generators and storage devices of a microgrid, minimizing simultaneously the energy costs and the greenhouse gas emissions. A multi-objective approach called Pareto-search Algorithm based on direct multi-search is proposed to ensure optimal management of the microgrid. According to the non-dominated resulting points, several scenarios are proposed and compared. The effectiveness of the algorithm is validated, giving a compromised choice between two criteria: energy cost and GHG emissions.

Abstract
In this paper, we consider a variant of adaptive robust combinatorial optimization problems where the decision maker can prepare K solutions and choose the best among them upon knowledge of the true data realizations. We suppose that the uncertainty may affect the objective and the constraints through functions that are not necessarily linear. We propose a new exact algorithm for solving these problems when the feasible set of the nominal optimization problem does not contain too many good solutions. Our algorithm enumerates these good solutions, generates dynamically a set of scenarios from the uncertainty set, and assigns the solutions to the generated scenarios using a vertex p-center formulation, solved by a binary search algorithm. Our numerical results on adaptive shortest path and knapsack with conflicts problems show that our algorithm compares favorably with the methods proposed in the literature. We additionally propose a heuristic extension of our method to handle problems where it is prohibitive to enumerate all good solutions. This heuristic is shown to provide good solutions within a reasonable solution time limit on the adaptive knapsack with conflicts problem. Finally, we illustrate how our approach handles nonlinear functions on an all-or-nothing subset problem taken from the literature.

Abstract
It has been known for a while that the problem of multi-source domain adaptation can be regarded as a single source domain adaptation task where the source domain corresponds to a mixture of the original source domains. Nonetheless, how to adjust the mixture distribution weights remains an open question. Moreover, most existing work on this topic focuses only on minimizing the error on the source domains and achieving domain-invariant representations, which is insufficient to ensure low error on the target domain. In this work, we present a novel framework that addresses both problems and beats the current state of the art by using a mildly optimistic objective function and consistency regularization on the target samples.

Abstract

Abstract
Defining an appropriate cybersecurity incident response model is a critical challenge that all companies face on a daily basis.However, there is not always an adequate answer. This is due to the lack of predictive models based on data (evidence). There is a significant investment in research to identify the main factors that can cause such incidents, always trying to have the most appropriate response and, consequently, enhancing response capacity and success. At the same time, several different methodologies assess the risk management and maturity level of organizations.There is, however, a gap in determining an organization's degree of proactive responsiveness to successfully adopt cybersecurity and an even more significant gap in assessing it from a risk management perspective. This paper proposes a model to evaluate this capacity, a model that intends to evaluate the methodological aspects of an organization and indicates the apparent gaps that can negatively impact the future of the organization in the management of cybersecurity incidents and presents a model that intends to be proactive.

Abstract
The magnitude of the subprime crisis effects caused recessions in several economies, giving rise to the global financial crisis. The scale of this major shock and the different recovery profiles of European economies motivated this paper. The main objective is to look for evidence of contagion between the North American financial market (S&P500) and the financial markets of Portugal (PSI20), Spain (IBEX35), Greece (ATHEX) and Italy (FTSEMIB), in the South of Europe, and the financial markets of Sweden (OMXS30), Denmark (OMX2C0), Finland (OMXH25) and Norway (OsloOBX), in the North of Europe. Considering the period from January 1, 2003 to December 31, 2013, the ARMA-GARCH models were estimated to remove the autoregressive and conditional heteroscedastic effects from the time series of the daily returns. Then, the copula models were used to estimate the dependence relationships between the European stock indexes and the North American stock index, from the pre -crisis subperiod to the crisis subperiod. The results indicate financial contagion of the subprime crisis for all analyzed European countries. The North European markets intensified the relations of financial integration (both in negative and positive shocks) with the North American market, apart from the Danish against the Portuguese. In addition to the contribution made by the joint application of the ARMA-GARCH models, the findings are useful to identify channels of financial contagion between markets and to warn about the effects of possible new crisis, which will require different levels of adaptation by the companies' financial managers and intervention by the authorities.