Abstract
The integration of renewable energy sources (RESs) cannot be postponed given the several techno-economic and environmental factors. However, the intermittent nature of RESs brings several challenges because such sources introduce significant operational variability and uncertainty in the system. Framed in this context a new stochastic mathematical planning model is proposed considering the placement and sizing of energy storage systems and reactive power sources that altogether enable high penetration of RESs. The model employs a linearized AC model, balancing well accuracy with computational burden. The model is tested on a standard distribution network system. Numerical results show that the simultaneous integration of energy storage systems and reactive power sources largely leads to a substantially increased penetration of RESs in distribution systems. Furthermore, dramatic reductions in losses, system costs and emissions are observed. Results analysis also reveal the positive contribution of such a planning on the overall voltage stability in the system.

Abstract
Burden reduction is a key issue in modern public administrations' and businesses' agendas. Compliance with mandatory regulations can have a direct impact on a country's economic performance, growth, and development. Research in this area, contributes to a better understanding of the implications and context of administrative burden, and increases the efficiency of the strategies adopted to reduce it. The goal of this study is to undertake a review of the current state of the art on Administrative Burden Reduction (ABR), in order to gain a deeper insight about the subject, identify current gaps, and better plan for future research. A total of 122 papers were identified as relevant, out of a pool of 742 papers retrieved from the current literature. The relevant papers were analyzed across four dimensions: methodology, type and focus, and targeted stakeholders. Three key gaps were identified and discussed in relation to: citizen orientated services and burden reduction; empirical research and post-initiative re-evaluation; and, the role of stakeholders, interest groups and end-users in driving ABR. Lastly a conceptual framework model and next steps are proposed.

Abstract
The container loading problem (CLP) is a real-world driven, combinatorial optimisation problem that addresses the maximisation of space usage in cargo transport units. The research conducted on this problem failed to fulfill the real needs of the transportation industry, owing to the inadequate representation of practical-relevant constraints. The dynamic stability of cargo is one of the most important practical constraints. It has been addressed in the literature in an over-simplified way which does not translate to real-world stability. This paper proposes a physics simulation tool based on a physics engine, which can be used to translate real-world stability into the CLP. To validate the tool, a set of benchmark tests is proposed and the results obtained with the physics simulation tool are compared to the state-of-the-art simulation engineering software Abaqus Unified FEA. Analytical calculations have been also conducted, and it was also possible to conclude that the tool proposed is a valid alternative.

Abstract
Multi-master replication in a distributed system setting allows each node holding a replica to update and query the local replica, and disseminate updates to other nodes. Obtaining high availability typically entails allowing replicas to diverge and requires a background mechanism for re-establishing consistency. Conflict-free Replicated Data Types (CRDTs) extend standard sequential data-Types with appropriate merge functions, and often can be composed together to create more complex ones. In this work we add a generic CRDT composition approach that explores the single-writer principle. By carefully controlling which part of the composition can be updated by each replica, we can derive efficient designs that cover new usecases. After introducing the new construction we exemplify some uses, including how to emulate a simple Doodle functionality for selecting a common meeting schedule among different participants.

Abstract
Cryptocurrency and blockchain technologies are recently gaining wide adoption since the introduction of Bitcoin, being distributed, authority-free, and secure. Proof of Work (PoW) is at the heart of blockchain's security, asset generation, and maintenance. Although simple and secure, a hash-based PoW like Bitcoin's puzzle is often referred to as "useless", and the used intensive computations are considered "waste" of energy. A myriad of Proof of "something" alternatives have been proposed to mitigate energy consumption; however, they either introduced new security threats and limitations, or the "work" remained far from being really "useful". In this work, we introduce Proof of eXercise (PoX): a sustainable alternative to PoW where an eXercise is a real world matrix-based scientific computation problem. We provide a novel study of the properties of Bitcoin's PoW, the challenges of a more "rational" solution as PoX, and we suggest a comprehensive approach for PoX.

Abstract
Meta-heuristic search methods are used to find near optimal global solutions for difficult optimization problems. These meta-heuristic processes usually require some kind of knowledge to overcome the local optimum locations. One way to achieve diversification is to start the search procedure from a solution already obtained through another method. Since this solution is already validated the algorithm will converge easily to a greater global solution. In this work, several well-known meta-heuristics are used to solve the problem of electricity markets participation portfolio optimization. Their search performance is compared to the performance of a proposed hybrid method (ad-hoc heuristic to generate the initial solution, which is combined with the search method). The addressed problem is the portfolio optimization for energy markets participation, where there are different markets where it is possible to negotiate. In this way the result will be the optimal allocation of electricity in the different markets in order to obtain the maximum return quantified through the objective function.