Abstract
In this article we discuss three different notions of linearity: syntactical, operational and denotational. We briefly define each notion of linearity, pointing out some of the main results in the area, and describe applications of linear languages and type systems.

Abstract
During the last years research and development on legged robots has grown steadily. Leggedsystems present major advantages when compared with “traditional” vehicles, allowinglocomotion in terrain inaccessible to vehicles with wheels and tracks. However, its energy consumption still lag being these vehicles, existing several aspects that need to be improvedand optimized.One of them regards the parameters values that these machines should adopt to minimize the energy consumption. Due to the large number of parameters involved in this optimization process one way to achieve meaningful results is using evolutionary strategies. Genetic Algorithms are away to “imitate nature”replicating the process that nature designed forthe generation and evolutionof species. The objective of this paper is to present a genetic algorithm, running over a simulationapplication oflegged robots, which allows the optimization of several parameters of a quadrupedrobot model, for distinct locomotion gaits. © Springer Science+Business Media Dordrecht 2014.

Abstract
Contemporary manufacturing scheduling has still limitations in real-world environments where disturbances on working conditions could occur over time. Therefore, human intervention is required to maintain real-time adaptation and optimization and efficiently adapt to the inherent dynamic of markets. This paper addresses the problem of incorporating rush orders into the current schedule of a manufacturing shop floor organization. A set of experiments were performed in order to evaluate the applicability of supervised classification algorithms in the attempt to predict the best integration mechanism when receiving a new order in a dynamic scheduling problem.

Abstract
Smart grid is a recently growing area of research including optimum and reliable operation of bulk power grid from production to end-user premises. Demand side activities like demand response (DR) for enabling consumer participation are also vital points for a smarter operation of the electric power grid. For DR activities in end-user level regulated by energy management systems, a dynamic price variation determined by optimum operating strategies should be provided aiming to shift peak demand periods to off-peak periods of energy usage. In this regard, an optimum generation scheduling based price making strategy is evaluated in this paper together with the analysis of the impacts of dynamic pricing on demand patterns with case studies. Thus, the importance of considering DR based demand pattern changes on price making strategy is presented for day-ahead energy market structure.

Abstract
Logistics network design is a major strategic issue due to its impact on the efficiency and responsiveness of the supply chain. This paper focuses on strategic and tactical design of steel supply chain (SSC) networks. Ever-increasing demand for steel products enforces the steel producers to expand their production and storage capacities. The main purpose of the paper includes preparing a countrywide production, inventory, distribution, and capacity expansion plan to design an SSC network. The SSC networks consist of iron ore mines as suppliers, raw steel producer companies as producers, and downstream steel companies as customers. Demand is assumed stochastic with normal distribution and known at the beginning of planning horizon. To achieve the service level of interest, a potential production capacity along with two kinds of safety stocks including emergency and shared safety stocks are suggested by the authors. A mixed integer nonlinear programming (MINLP) model and a mixed integer linear programming (MILP) model are presented to design dynamic multi-commodity SSC networks. To evaluate the performance of the MILP model, a real case of SSC network design is solved. Furthermore, solving two proposed models by using a commercial solver for a set of numerical test cases shows that the MILP model outperforms MINLP in medium- and large-scale problems in terms of computational time. Finally, the complexity of the linear model is investigated by relaxing some major assumptions.

Abstract
With the restructuring of the electricity sector in recent years, and the increased variability and uncertainty associated with electricity market prices, it has become necessary to develop forecasting tools with enhanced capabilities to support the decisions of market players in a competitive environment. Hence, this paper proposes a new hybrid evolutionary-adaptive methodology for electricity prices forecasting in the short-term, i.e., between 24 and 168 h ahead, successfully combining mutual information, wavelet transform, evolutionary particle swarm optimization, and the adaptive neuro-fuzzy inference system. In order to determine the accuracy, competence and proficiency of the proposed methodology, results from real-world case studies using real data are presented, together with a thorough comparison considering the results obtained with previously reported forecasting tools. Not only is the accuracy an important factor, but also the computational burden is relevant in a comparative study. The results show that it is possible to reduce the uncertainty associated with electricity market prices prediction without using any exogenous data, just the historical values, thus requiring just a few seconds of computation time.