Abstract
Transfer learning focuses on building better predictive models by exploiting knowledge gained in previous related tasks, being able to soften the traditional supervised learning assumption of having identical train-test distributions. Most efforts on transfer learning consider revisiting the data from the source tasks or rely on transferring knowledge for specific models. In this paper, a general framework is proposed for transferring knowledge by including a regularization factor based on the structural model similarity between related tasks. The proposed approach is instantiated to different models for regression, classification, ranking and recommender systems, obtaining competitive results in all of them. Also, we explore high-level concepts in transfer learning like sparse transfer, partially observable transfer and cross-model transfer.

Abstract
This paper estimates the profit of the joint operation of a wind farm and a li-ion battery energy storage system (BESS) in the Iberian electricity market (MIBEL). The day-ahead and first intraday energy markets, and the tertiary regulation market are considered to optimize the joint operation of both assets. A rolling window combined with a non-linear optimization model are used to design the operation strategy. The BESS lifetime (as a function of the depth of discharge) is considered in the optimization problem, and different BESS capacities and initial state of charge values are tested to determine their approximate optimum values. The model and strategy designed were tested using real data from the MIBEL market and predicted data from Sotavento wind farm. The resulting incomes were compared to the BESS investments costs to determine, for a given capacity, when the project becomes viable. © 2019 IEEE.

Abstract
This work discusses the solution of a Large-scale global optimization problem named Security Constrained Optimal Power Flow (SCOPF) using a method based on Cross Entropy (CE) and Evolutionary Particle Swarm Optimization (EPSO). The obtained solution is compared to the Entropy Enhanced Covariance Matrix Adaptation Evolution Strategy (EE-CMAES) and Shrinking Net Algorithm (SNA). Experiments show the approach reaches competitive results.

Abstract
The paper presents a multi-year, multi-objective framework for integrating Renewable Energy Sources (RESs) into the high voltage transmission network of Iran's National Power Grid (INPG). The objective functions in this study are the total cost, including the investment cost and operating cost for the planning horizon, and the system reliability. The first objective function is stated from the economic point of view, while the second objective function is considered as a security index in the expansion planning issue. The main purpose of this paper is to increase the RES penetration into the generation mix of INPG. Since the mentioned 230 to 400-kV INPG is a large-scale power system, the problem formulation is investigated in a mixed-integer programming, and then, the developed multi-objective problem has been solved using the augmented epsilon-constraint optimization method. In order to select the executive plan for installation, the fuzzy satisfying decision-making procedure is adopted in this study. © 2018 John Wiley & Sons, Ltd.

Abstract
The increase of variable renewable energy generation has brought several new challenges to power and energy systems. Solutions based on storage systems and consumption flexibility are being proposed to balance the variability from generation sources that depend directly on environmental conditions. The widespread use of electric vehicles is seen as a resource that includes both distributed storage capabilities and the potential for consumption (charging) flexibility. However, to take advantage of the full potential of electric vehicles' flexibility, it is essential that proper incentives are provided and that the management is performed with the variation of generation. This paper presents a research study on the impact of the variation of the electricity prices on the behavior of electric vehicle's users. This study compared the benefits when using the variable and fixed charging prices. The variable prices are determined based on the calculation of distribution locational marginal pricing, which are recalculated and adapted continuously accordingly to the users' trips and behavior. A travel simulation tool was developed for simulating real environments taking into account the behavior of real users. Results show that variable-rate of electricity prices demonstrate to be more advantageous to the users, enabling them to reduce charging costs while contributing to the required flexibility for the system.

Abstract
Design-for-X (DfX) approaches continues to prove their importance to support design management in increased complexity products and towards sustainable development. Permanent increasing of market competitiveness lead the companies to narrow budgets and increase the application of Lean practices among their departments. Lean Design for eXcellence (LeanDfX) methodology was developed to cross Lean Thinking and Design-for-X project support, assessing multiple domains such as optimization, manufacturing, assembly, maintenance, eco-design, modularity or adaptability. This approach brings a systematic applicability for design engineers and technical managers, assessing the effectiveness and efficiency of a given product design. A LeanDIX index metric, ranging between 0-100%, and original scorecard were created for consistent decision support for comparison of different design concepts or design versions of products, integrating different 'X' domains. In this work, the LeanDIX methodology results are presented for a real industrial case study related to a robot gripper design of a palletizing production cell. (C) 2019 The Authors. Published by Elsevier B.V.