Abstract
The focus of this paper is on optimal control problems with mixed state and control inequality constraints. We identify a class of problems which can be associated with an auxiliary problem with both regular mixed constraints and pure state constraints. For that class of problems we derive a new set of necessary conditions of optimality. © 2007 EUCA.

Abstract
Due to the Web expansion, the prediction of online news popularity is becoming a trendy research topic. In this paper, we propose a novel and proactive Intelligent Decision Support System (IDSS) that analyzes articles prior to their publication. Using a broad set of extracted features (e.g., keywords, digital media content, earlier popularity of news referenced in the article) the IDSS first predicts if an article will become popular. Then, it optimizes a subset of the articles features that can more easily be changed by authors, searching for an enhancement of the predicted popularity probability. Using a large and recently collected dataset, with 39,000 articles from the Mashable website, we performed a robust rolling windows evaluation of five state of the art models. The best result was provided by a Random Forest with a discrimination power of 73%. Moreover, several stochastic hill climbing local searches were explored. When optimizing 1000 articles, the best optimization method obtained a mean gain improvement of 15 percentage points in terms of the estimated popularity probability. These results attest the proposed IDSS as a valuable tool for online news authors.

Abstract
In this paper it is clearly demonstrated that InSAR techniques may be particularly useful as a hot spot indicator of relative structures deformation over large areas, making it possible to develop interferometric based methodologies for SHM. Different case studies from structural health monitoring of buildings, bridges and highways and dams in Slovakia, Czech Republic, Hong Kong and Portugal processed within the scope of "RemotWatch - Alert and Monitoring System for Physical Structures" project using non-linear and other SHM-optimized algorithms of SARPROZ software, are reported. For the future investigation it is expected, that due to the faster product delivery of new missions (e.g. SENTINEL-1), it will be possible to deliver new workflows suitable for near-real time analysis aimed to better understanding of the deformation characteristics of the structures in urban and extra urban areas, important for structure stability and risk management applications.

Abstract
We investigated the reductive intramolecular cyclization of bromopropargyl ethers derivatives, catalyzed by electrogenerated (1,4,8,11-tetramethyl-1,4,8,11-tetraaza-cyclotetradecane)nickel(I), [Ni(tmc)]+ as the catalysts in N,N,N-trimethyl-N-(2- hydroxyethyl)ammonium bis(trifluoromethylsulfonyl)imide,[N1112(OH)][NTf2] and 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, [C2mim][NTf2] by cyclic voltammetry and controlled-potential electrolysis. The results show that the reaction leads to the formation of the expected cyclic compounds, which are important intermediates in the synthesis of natural products with possible biological activities. © The Electrochemical Society.

Abstract
Identifying the best machine learning algorithm for a given problem continues to be an active area of research. In this paper we present a new method which exploits both meta-level information acquired in past experiments and active testing, an algorithm selection strategy. Active testing attempts to iteratively identify an algorithm whose performance will most likely exceed the performance of previously tried algorithms. The novel method described in this paper uses tests on smaller data sample to rank the most promising candidates, thus optimizing the schedule of experiments to be carried out. The experimental results show that this approach leads to considerably faster algorithm selection.

Abstract
The three-phase state estimation algorithms developed for distribution systems (DS) are based on traditional approaches, requiring components modeling and the complete knowledge of grid parameters. These algorithms are capable of dealing with the particular characteristics of DS but cannot be used in cases where grid topology and parameters are unknown, which is the most common situation in existing low voltage grids. This paper presents a novel three-phase state estimator for DS that enables the explicit estimation of voltage magnitudes and phase angles in all phases, neutral, and ground wires even when grid topology and parameters are unknown. The proposed approach is based on the use of auto-associative neural networks, the autoencoders (AE), which only require an historical database and few quasi-real-time measurements to perform an effective state estimation. Two test cases were used to evaluate the algorithm's performance: a low and a medium voltage grid. Results show that the algorithm provides accurate results even without information about grid topology and parameters. Several tests were performed to evaluate the best AE configuration. It was found that training an AE for each network feeder leads generally to better results than having a single AE for the entire system. The same happened when different AE were trained for each network phase in comparison with a single AE for the three phases.