Abstract
CRDTs are distributed data types that make eventual consistency of a distributed object possible and non ad-hoc. Specifically, state-based CRDTs ensure convergence through disseminating the entire state, that may be large, and merging it to other replicas; whereas operation-based CRDTs disseminate operations (i.e., small states) assuming an exactly-once reliable dissemination layer. We introduce Delta State Conflict-Free Replicated Datatypes (d-CRDT) that can achieve the best of both worlds: small messages with an incremental nature, disseminated over unreliable communication channels. This is achieved by defining d-mutators to return a delta-state, typically with a much smaller size than the full state, that is joined to both: local and remote states. We introduce the d-CRDT framework, and we explain it through establishing a correspondence to current state-based CRDTs. In addition, we present an anti-entropy algorithm that ensures causal consistency, and two d-CRDT specifications of well-known replicated datatypes.

Abstract
A new weighted rank correlation coefficient r(W2) has been introduced in Pinto da Costa (2011), following the coefficient r(W) introduced in Pinto Da Costa and Soares (2005); Soares et al. (2001); Pinto Da Costa et al. (2001). We give the expression of r(W2) in the case of ties and also present some simulations to study the behaviour of the coefficient.

Abstract
The increase of renewable based generation as alternative power source brings an added uncertainty to power systems. The intermittent nature of renewable resources, such as wind speed and solar intensity, requires the use of adequate forecast methodologies to support the management and integration of this type of energy resources. This paper proposes a clustering methodology to group historic data according to the data correlation and relevance for different contexts of use. Using the clustering process as a data filter only the most adequate data is used for the training process of forecasting methodologies. Artificial Neural Networks and Support Vector Machines are used to test and compare the quality of forecasts when using the proposed methodology to select the training data. Data from the Brazilian city of Florianopolis, Santa Catarina, has been used, including solar irradiance components and other meteorological variables, e.g. temperature, wind speed and humidity. Experimental findings show that using the proposed method to filter data used for training ANN and SVM achieved promising results, outperforming the approaches without clustering.

Abstract
The classical multi-level capacitated lot-sizing problem formulation is often not suitable to correctly capture resource requirements and precedence relations. Depending on lead time assumptions, either the model provides infeasible production plans or plans with costly needless inventory. We tackle this issue by explicitly modeling these two aspects and the synchronization of batches of products in the multi-level lot-sizing and scheduling formulation. Two models are presented; one considering batch production and the other one allowing lot-streaming. Comparisons with traditional models demonstrate the capability of the new approach in delivering more realistic results. The generated production plans are always feasible and cost savings of 30-40 percent compared to classical models are observed.

Abstract
We define a framework for the analysis of access control policies that aims at easing the specification and verification tasks for security administrators. We consider policies in the category-based access control model, which has been shown to subsume many of the most well known access control models (e.g., MAC, DAC, RBAC). Using a graphical representation of category-based policies, we show how answers to usual administrator queries can be automatically computed, and properties of access control policies can be checked. We show applications in the context of emergency situations, where our framework can be used to analyse the interaction between access control and emergency management.

Abstract
This paper discusses some problems related with the current Brazilian electricity market, brings out some dilemmas that should be examined in order to implement a more market oriented approach, and proposes a new market design to overcome these issues. The proposed market design is based on the concept of energy right accounts as virtual reservoirs and aims at enhancing the flexibility to enable market participants to comply with their contracts, while still ensuring the efficient use of the energy resources and maintaining the current security supply level. In addition, in order to simulate the behavior of the market participants in this new framework, an Agent-Based Model - ABM where agents use reinforcement Q-Learning is developed for the study case. The results show that this new market design is suitable to be applied to hydrothermal systems having a large share of hydros.