Abstract
Distributed Smart Systems (DSS) should operate and restore discontinued service to consumers. In order to the system gain theses ability it is necessary to replace the manual switches for remotely controlled switches, improving the system restoration capability having in view the Smart Grids implementation. This paper aims to develop a new model, determining the minimal set of switches to replace in order to automate the system, along with a senility analysis on the position of the new switches, whether it should be placed in the same place as the manual switch or in a new location. The optimization of the system is made considering the renewable energy sources (RES) integration in the grid and energy storage systems (ESS), simultaneously, in order to improve the system reliability. The computational tool is tested using the IEEE 119 Bus test system, where different types of loads are considered, residential, commercial and industrial.

Abstract
Over time, the electricity price and energy consumption are increasingly growing their weight as prime foundations of the electrical sector, with their analysis and forecasts being targeted as key elements for the stable maintenance of electricity markets. The advent of smart grids is escalating the importance of forecasting because of the expected ubiquitous monitoring and growing complexity of a data-rich ever-changing milieu. So, the increasing data volatility will require forecasting tools able to rapidly readjust to a dynamic environment. The Generalized Regression Neural Network (GRNN) approach is a solution that has recently re-emerged, emphasizing good performance, fast run-times and ease of parameterization. The merging of this model with more conventional methods allows us to obtain more sturdy solutions with shortened training times, when compared to conventional Artificial Neural Networks (ANN). Overall, the performance of the GRNN, although slightly inferior to that of the ANN, is suitable, but linked to much lower training times. Ultimately, the GRNN would be a proper solution to blend with the latest smart grids features, which may require much reduced forecasting training times.

Abstract
Multi-stage manufacturing, typical in important industrial sectors, is inherently a complex process. The application of the zero defect manufacturing (ZDM) philosophy, together with recent technological advances in cyber-physical systems (CPS), presents significant challenges and opportunities for the implementation of new methodologies towards the continuous system improvement. This paper introduces the main principles of a multi-agent CPS aiming the application of ZDM in multi-stage production systems, which is being developed under the EU H2020 GOOD MAN project. In particular, this paper describes the MAS architecture that allows the distributed data collection and the balancing of the data analysis for monitoring and adaptation among cloud and edge layers, to enable the earlier detection of process and product variability, and the generation of new optimized knowledge by correlating the aggregated data.

Abstract
The integration of IoT infrastructures across production systems, together with the extensive digitalisation of industrial processes, are drastically impacting manufacturing value chains and the business models built on the top of them. By exploiting these capabilities companies are evolving the nature of their businesses shifting value proposition towards models relying on product servitization and share, instead of ownership. In this paper, we describe the semantic data-model developed to support a digital platform fostering the reintroduction in the loop and optimization of unused industrial capacity. Such data-model aims to establish the main propositions of the semantic representation that constitutes the essential nature of the ecosystem to depict their interactions, the flow of resources and exchange of production services. The inference reasoning on the semantic representation of the ecosystem allows to make emerge nontrivial and previously unknown opportunities. This will apply not only to the matching of demand and supply of manufacturing services, but to possible and unpredictable relations. For instance, a particular kind of waste being produced at an ecosystem node can be linked to the requirements for an input material needed in a new product being developed on the platform, or new technologies can be suggested to enhance processes under improvement. The overall architecture and individual ontologies are presented and their usefulness is motivated via the application to use cases.

Abstract
Estuaries are transitional zones between marine and freshwater environments and are ideal systems to study the influence of environmental gradients on microbial biodiversity and activity. In this study, we investigated the effect of a salinity gradient on the structure of prokaryotic communities from intertidal sediments of the Douro estuary, and on the nitrification process. Four locations were chosen with distinct salinities and characterized for a range of environmental parameters including measurements of potential nitrification rates. The structure of prokaryotic communities and ammonia-oxidizing bacteria and archaea were described and identified using the 16S rRNA gene. Potential nitrification rates ranged from 1.3 to 7.4 mu mol cm(-2) h(-1), with the highest rate at mesohaline sites; however, the relative abundance of nitrifying taxa was higher at locations with higher salinity. Ammonia-oxidizing bacteria could not be detected in oligohaline sites, in contrast to ammonia-oxidizing archaea, which showed a ubiquitous distribution. Nitrite-oxidizing bacteria were more abundant than ammonia-oxidizing groups across meso-oligohaline sites, showing increased relative abundance at less saline sites. One operational taxonomic unit closely related to Nitrospira moscoviensis showed a positive correlation with potential nitrification rates, suggesting a possible association of N. moscoviensis with ammonia-oxidizing organisms in a natural ecosystem. Such results point out the need to re-assess the relative roles of different nitrifying groups in the nitrification process.

Abstract
In this paper, the gas dynamics within the pipelines is written as a wave repetitive process, and modified in a way that the dynamics is driven by the boundary conditions. We study controllability of the system through boundary control and every agent, as well as observability of the system being steered by initial and boundary data. Next, we obtain sufficient criteria for the existence and uniqueness of boundary equilibrium controls. From the point of view of some applications, e.g. in high pressure gas pipeline management, it seems to make sense to consider boundary data controls. The same problem is then extended to its infinite counterpart since it may run infinitely and, in this case, we become interested in studying its stabilisation.