Abstract
Concentrating solar power (CSP) plants with thermal energy storage (TES) are emerging renewable technologies with the advantage that TES decreases the uncertainty in the generation of CSP plants. This study introduces a stochastic mixed integer linear programming model, where the objective function is the maximization of the expected profit that can be obtained by selling the energy generated by the CSP plant in the day-ahead electricity market. The proposed model considers three main blocks of constraints, namely, renewable generator constraints, TES constraints, and electricity market constraints. The last category of constraints considers the penalties incurred due to positive or negative imbalances in the balancing market. A case study using data from the Spanish electricity market is introduced, described and analyzed in terms of trading of the CSP plant generation. The conclusions highlight the influence of TES capacity on the energy trading profile, the expected profits and the volatility (risk) in the trading decisions.

Abstract
In this paper, a new system identification algorithm is proposed for linear and time invariant systems with multiple input and single output. The system is described by a state-space model in the canonical observable form and represented by a Luenberger observer with a known state matrix. Thence, the identification problem is reduced to the estimation of the system input matrix and the observer gain which can be performed by a simple Least Square Estimator. The quality of the estimator depends on the observer state matrix. In the proposed algorithm, this matrix is found by an iterative process where, in each iteration, a state matrix called curiosity is generated. A weight depending on the value of the Least Square Cost is associated to each curiosity. The optimal state matrix is the barycenter of the curiosities. This iterative process is a free derivative optimization algorithm with its roots in non-iterative barycenter methods previously introduced to solve adaptive control and system identification problems. Although the Barycenter iterative version was recently proposed as an optimization method, here it will be implemented in an identification algorithm for the first time.

Abstract
Autonomous Underwater Vehicles (AUV) are growing in importance for their many underwater applications, due to their characteristics and functionalities. Making use of a imaging sonar, it is possible to acquire the AUV's distance to existing obstacles. Then, through an implementation of a feature detection algorithm and an estimator, it is possible to interpolate the vehicle's relative position. This paper proposes a localization method for structured environments employing a mechanical scanning sonar feeding an extended Kalman filter. Some tests were then run in two different water tanks in order to verify the effectiveness of the solutions. These tests were performed in two different phases. For the first one, all the readings were taken with the vehicle steady and immobile. The second phase was executed with the vehicle in motion. The results are presented and compared against ground-truth measurements.

Abstract
Recent advances in high-throughput technologies have given rise to collecting large amounts of multidimensional heterogeneous data that provide diverse information on the same biological samples. Integrative analysis of such multisource datasets may reveal new biological insights into complex biological mechanisms and therefore remains an important research field in systems biology. Most of the modern integrative clustering approaches rely on independent analysis of each dataset and consensus clustering, probabilistic or statistical modeling, while flexible distance-based integrative clustering techniques are sparsely covered. We propose two distance-based integrative clustering frameworks based on bi-level and bi-objective extensions of the p-median problem. A hybrid branch-and-cut method is developed to find global optimal solutions to the bi-level p-median model. As to the bi-objective problem, an epsilon-constraint algorithm is proposed to generate an approximation to the Pareto optimal set. Every solution found by any of the frameworks corresponds to an integrative clustering. We present an application of our approaches to integrative analysis of NCI-60 human tumor cell lines characterized by gene expression and drug activity profiles. We demonstrate that the proposed mathematical optimization-based approaches outperform some state-of-the-art and traditional distance-based integrative and non-integrative clustering techniques.

Abstract
Image dehazing tries to solve an undesired loss of visibility in outdoor images due to the presence of fog. Recently, machine-learning techniques have shown great dehazing ability. However, in order to be trained, they require training sets with pairs of foggy images and their clean counterparts, or a depth-map. In this paper, we propose to learn the appearance of fog from weakly-labeled data. Specifically, we only require a single label per-image stating if it contains fog or not. Based on the Multiple-Instance Learning framework, we propose a model that can learn from image-level labels to predict if an image contains haze reasoning at a local level. Fog detection performance of the proposed method compares favorably with two popular techniques, and the attention maps generated by the model demonstrate that it effectively learns to disregard sky regions as indicative of the presence of fog, a common pitfall of current image dehazing techniques.

Abstract
A single synchronous controller (SSC) technique is proposed in this paper for control of interfaced converters under high penetration of renewable energy resources (RER) into the power grid. The proposed SSC is based on a new dynamic model concerning to the power grid stability (PGS) and modeled based on all features of a synchronous generator (SG), which can properly improve the performance of the power grid in those scenarios in which a large-scale penetration of RERs is considered. Different transfer functions are achieved to assess the high performance of the proposed control technique. Simulation results are presented to demonstrate the superiority of the proposed SSC in the control of the power electronic-based synchronous generator under high penetration of RERs into the power grid.