Abstract
The uncertainty associated with the increasingly wind power penetration in power systems must be considered when performing the traditional day-ahead scheduling of conventional thermal units. This uncertainty can be represented through a set of representative wind power scenarios that take into account the time-dependency between forecasting errors. To create robust Unit Commitment ( UC) schedules, it is widely seen that all possible wind power scenarios must be used. However, using all realizations of wind power might be a poor approach and important savings in computational effort can be achieved if only the most representative subset is used. In this paper, the new hybrid metaheuristic DEEPSO and clustering techniques are used in the traditional stochastic formulation of the UC problem to investigate the robustness of the UC schedules with increasing number of wind power scenarios. For this purpose, expected values for operational costs, wind spill, and load curtailment for the UC solutions are compared for a didactic 10 generator test system. The obtained results shown that it is possible to reduce the computation burden of the stochastic UC by using a small set of representative wind power scenarios previously selected from a high number of scenarios covering the entire probability distribution function of the forecasting uncertainty.

Abstract
In cloud environments, resources should be acquired and released automatically and quickly at runtime. Therefore, ensuring the desired QoS is a great challenge for the cloud service provider. Moreover, it increases when we have large amount of data to be manipulated in this environment. Considering that, performance is an important requirement for most customers when they migrate their applications to the cloud. In this paper, we propose a model for measuring a Service Response Time estimated for different request types on large databases available in a cloud environment. This work allows the cloud service provider and its customers establish an appropriate SLA relative to performance expected of services available in the cloud. Finally, the model was evaluated in Amazon EC2 cloud infrastructure and the TPC-DS like benchmark was used for generating a database of structured data, considering that some cloud computing platforms support SQL queries directly or indirectly. This makes the proposed solution relevant for these kind of problems.

Abstract
The use of keyboard and mouse combinations to navigate 3D environments of virtual worlds requires the coordination of both hands in order for the 2D degrees of motion of the mouse to transform into the variety of motions available in a 3D space. Such coordination may pose a challenge to people with motor disabilities. 3D controllers known as "3D mice" are presented by manufacturers as significant interface alternatives. To establish the feasibility of such claims, the authors conducted a usability test of two 3D mice marketed by 3Dconnexion, in parallel with a keyboard + mouse test. The 10 participants had motor disabilities due to medullary lesions on vertebrae C5-D11 and performed 13 different tasks in the Second Life virtual world: 5 participants used 3D mice and 5 used keyboard + mouse. The main conclusion is that 2-3 of the 5 most challenging tasks in the keyboard + mouse combination become less challenging using 3D mice. Participants' feedback was more positive regarding 3D mice, but with significant differences between mice. Contrary to initial expectations of the authors, the least stable mouse, Space Navigator, produced the best feedback.

Abstract
Many efforts are being devoted towards achieving optimal planning and operation of DER (Distributed Energy Resources). However, during the planning process, not all relevant thermal constraints of the distribution network are considered; some works claim that they must be taken into account, while others follow the single-node approach. This paper assesses the effects of the distribution network thermal constraints in DER planning, using a deterministic linear programming problem to find the optimal DER planning and operation. Three case studies with different network topologies under several DER implementation scenarios are analyzed. A DC load flow is used to estimate the required network reinforcements to accommodate optimal DER investments, if any. Reinforcement costs are then calculated to assess the net benefit compared to limiting DER investments and operation, according to the network thermal limits. Results suggest that there is no significant economic advantage in limiting DER investments and line flows, compared to reinforcing the low voltage network to allow the larger flows that result from an unconstrained network problem.

Abstract
Value Stream Mapping (VSM) is a very popular tool in lean environments to represent production flows, mapping value stream of a product or family of products, and helps to identify some types of waste. Although very popular, this tool has some limitations as already described in many publications, especially in terms of restrictions in showing most types of waste as well as in its inability to represent various production routes. The purpose of this study is to introduce the waste identification diagram (WID), a new tool to represent production units with its different forms of waste, which overcomes some VSM limitations. The originality of WID comes from the use of its symbols' dimensions to convey, in a visual and immediate way, relevant information about a production unit. In this paper, WID is applied on a production unit of a lift manufacturer, for testing its performance and comparing it to VSM. The main findings are that WID is in general more effective than VSM in terms of representation of complex production units and in terms of identification of more forms of waste. WID must however overcome some of its limitations such as the lack of information-flow representation and the links to suppliers and clients.

Abstract
The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of opinion mining. We design and implement the POPmine system which is able to collect texts from web-based conventional media (news items in mainstream media sites) and social media (blogs and Twitter) and to process those texts, recognizing topics and political actors, analyzing relevant linguistic units, and generating indicators of both frequency of mention and polarity (positivity/negativity) of mentions to political actors across sources, types of sources, and across time.