Abstract
Proper management of supply chains is fundamental in the overall system performance of forest based activities. Usually, efficient, management techniques a decision support, software, which needs to be able to generate fast and effective outputs from the set of possibilities. In order to do this, it is necessary to provide accurate models representative of the dynamic interactions of systems. Due to forest-based supply chains' nature, event-based models are more suited to describe their behaviours. This work proposes the modelling and simulation of a forest based supply chain, in particular the biomass supply chain, through the SimPy framework. This Python based tool allows the modelling of discrete-event, systems using operations such as events, processes Mid resources. The developed model was used to access the impact of changes in the daily working plan in three situations. First, as a control case, the deterministic behaviour was simulated. As a second approach, a machine delay was introduced and its implications in the plan accomplishment were analysed. Finally, to better address real operating conditions, stochastic; behaviours of processing and driving times were simulated. The obtained results validate the SirriPy simulation environment as a framework for modelling supply chains in general and for the biomass problem in particular.

Abstract
Wind power is the source of electrical energy that has grown more over the last years, with annual rate in installed capacity around 20%. Therefore, it is important to optimize the production efficiency of wind farms. In a wind farm, the electrical energy is collected at a central substation from different wind turbines placed nearby. This paper addresses the optimal design of the cable network interconnecting the turbines to the substation aiming to minimize not only the infrastructure cost but also the cost of the energy losses in the cables. Although this problem is non-linear, different integer linear programming models are proposed considering the wind farm technical constraints. The models are applied to three real cases Portuguese wind farms. The computational results show that the proposed models are able to compute the optimal solutions for all cases.

Abstract
The increase of distributed energy resources, mainly based on renewable sources, requires new solutions that are able to deal with this type of resources' particular characteristics (namely, the renewable energy sources intermittent nature). The smart grid concept is increasing its consensus as the most suitable solution to facilitate the small players' participation in electric power negotiations while improving energy efficiency. The opportunity for players' participation in multiple energy negotiation environments (smart grid negotiation in addition to the already implemented market types, such as day-ahead spot markets, balancing markets, intraday negotiations, bilateral contracts, forward and futures negotiations, and among other) requires players to take suitable decisions on whether to, and how to participate in each market type. This paper proposes a portfolio optimization methodology, which provides the best investment profile for a market player, considering different market opportunities. The amount of power that each supported player should negotiate in each available market type in order to maximize its profits, considers the prices that are expected to be achieved in each market, in different contexts. The price forecasts are performed using artificial neural networks, providing a specific database with the expected prices in the different market types, at each time. This database is then used as input by an evolutionary particle swarm optimization process, which originates the most advantage participation portfolio for the market player. The proposed approach is tested and validated with simulations performed in multiagent simulator of competitive electricity markets, using real electricity markets data from the Iberian operator-MIBEL.

Abstract
Coverage path planning (CPP) is a fundamental agricultural field task required for autonomous navigation systems. It is also important for resource management, increasingly demanding in terms of reducing costs and environmental polluting agents as well as increasing productivity. Additional problems arise when this task involves irregular agricultural terrains where the crop follows non-uniform configurations and extends over steep rocky slopes. For mountain vineyards, finding the optimal path to cover a restricted set of terraces, some of them with dead ends and with other constraints due to terrain morphology, is a great challenge. The problem involves other variables to be taken into account such as speed, direction and orientation of the vehicle, fuel consumption and tank capacities for chemical products. This article presents a decision graph-based approach, to solve a Rural Postman Coverage like problem using A* and Dijkstra algorithms simultaneously to find the optimal sequence of terraces that defines a selected partial coverage area of the vineyard. The decision structure is supported by a graph that contains all the information of the Digital Terrain Model (DTM) of the vineyard. In this first approach, optimality considers distance, cost and time requirements. The optimal solution was represented in a graphical user OpenGL application developed to support the path planning yprocess. Based on the results, it was possible to prove the applicability of this approach for any vineyards which extend like routes. Near optimal solutions based on other specific criteria could also be considered for future work.

Abstract

Abstract
The aim of this study was to evaluate the effect of essential oils (EOs) of plants naturally occurring in northern Portugal on the spoilage of fresh Maronesa beef burgers stored at 2 and 8 degrees C under different packaging conditions. EOs were obtained from dried leaves of laurel (Laurus Nobilis L.) and rosemary (Rosmarinus officinallis L.) by hydro-distillation using a Clevenger-type apparatus. Analysis of volatile composition of essential oils of rosemary and laurel was achieved by Gas Chromatography-Mass Spectrometry (GC-MS) and Gas Chromatography-Thermal Conductivity Detection (GC-TCD) resulting in the detection of 95.8% and 89.4% of its compounds, respectively. Fresh beef (semitendinosus and semimembranosus) of DOP-Maronesa breed (males; n = 4) were obtained from local market and transported to the laboratory. Samples were stored at 2 and 8 degrees C in two different conditions: aerobiosis (A) and vacuum (V) and analyzed at 0,1, 2, 3, 5, 7,10,14, 21 and 28 days for Lactic acid bacteria (LAB), Enterobacteriaceae, Pseudomonas spp., Fungi, Total mesophilic (TM) and psychrotrophic (TP), color (L*a*b*) and pH. Laurel was the most effective EO keeping pH from increasing. Coordinates L* and a* were higher on samples containing laurel EO for both A and V packaging. Laurel also showed better effect in reducing microbiologic counts in samples packed in A at both 2 and 8 degrees C and packed in V at 8 degrees C. Rosemary was effective in reducing microbial counts on all V samples stored at 2 degrees C. This study allows to conclude that Laurel EO has significant effect in shelf-life, maintaining fresh beef color.