Abstract
Industrial consumers have traditionally provided flexibility for power systems through various Demand Response (DR) programs in different regions of the world, but below their real potential. Utilizing DR in industries will reduce the need for more expensive alternative forms of flexibility like storage or backup plants. In current increasingly flexible electricity market, it is an excellent chance for the industrial sites to carry out the most of energy management with DR, especially for those plants that are already equipped with the required facilities for DR. This paper aims at providing a comprehensive review of applications of DR in the industrial sector. On this basis, this survey firstly presents the contribution of ancillary services and their potential in industries and then introduces different types of industries with higher potential for DR programs. Finally, the main barriers that hinder the widespread utilization of these programs in industries are presented and categorized.

Abstract
This paper describes a 3D object perception and perceptual learning system developed for a complex artificial cognitive agent working in a restaurant scenario. This system, developed within the scope of the European project RACE, integrates detection, tracking, learning and recognition of tabletop objects. Interaction capabilities were also developed to enable a human user to take the role of instructor and teach new object categories. Thus, the system learns in an incremental and open-ended way from user-mediated experiences. Based on the analysis of memory requirements for storing both semantic and perceptual data, a dual memory approach, comprising a semantic memory and a perceptual memory, was adopted. The perceptual memory is the central data structure of the described perception and learning system. The goal of this paper is twofold: on one hand, we provide a thorough description of the developed system, starting with motivations, cognitive considerations and architecture design, then providing details on the developed modules, and finally presenting a detailed evaluation of the system; on the other hand, we emphasize the crucial importance of the Point Cloud Library (PCL) for developing such system.(1)

Abstract

Abstract
This paper investigates the impact of internationalization and diversification strategies on the financial performance of construction industry companies. The results obtained can guide the design of strategies to pursue company growth and achieve competitive advantage. The evaluation of companies' performance is based on the use of the Data Envelopment Analysis technique to aggregate financial indicators using optimized weights. The impact of internationalization and diversification on company performance is explored using truncated regression, controlling for the effect of contextual factors such as company age, size and time. Data Envelopment Analysis and truncated regression were complemented with bootstrapping to ensure the robustness of the results obtained. The activity of Portuguese and Spanish contractors in the period 2002 to 2011 is used as case study. The empirical results show that internationalization has a positive impact on financial performance, although this effect is only statistically significant for Spanish contractors. Diversification has a nonlinear relationship with performance, benefiting companies with either a small number of core activities or companies with a broad scope of activities.

Abstract
OPTICON currently supports a Joint Research Activity (JRA) dedicated to providing easy to use image reconstruction algorithms for optical/IR interferometric data. This JRA aims to provide state-of-the-art image reconstruction methods with a common interface and comprehensive documentation to the community. These tools will provide the capability to compare the results of using different settings and algorithms in a consistent and unified way. The JRA is also providing tutorials and sample datasets to introduce the principles of image reconstruction and illustrate how to use the software products. We describe the design of the imaging tools, in particular the interface between the graphical user interface and the image reconstruction algorithms, and summarise the current status of their implementation.

Abstract
This paper presents a new solution approach to solve the resource-constrained project scheduling problem in the presence of three types of logical constraints. Apart from the traditional AND constraints with minimal time-lags, these precedences are extended to OR constraints and bidirectional (BI) relations. These logical constraints extend the set of relations between pairs of activities and make the RCPSP definition somewhat different from the traditional RCPSP research topics in literature. It is known that the RCPSP with AND constraints, and hence its extension to OR and BI constraints, is NP-hard. The new algorithm consists of a set of network transformation rules that removes the OR and BI logical constraints to transform them into AND constraints and hereby extends the set of activities to maintain the original logic. A satisfiability (SAT) solver is used to guarantee the original precedence logic and is embedded in a metaheuristic search to resource feasible schedules that respect both the limited renewable resource availability as well as the precedence logic. Computational results on two well-known datasets from literature show that the algorithm can compete with the multi-mode algorithms from literature when no logical constraints are taken into account. When the logical constraints are taken into account, the algorithm can report major reductions in the project makespan for most of the instances within a reasonable time.