The obstacle problem for nonlinear elliptic equations with variable growth and L1-data

The aim of this paper is twofold: to prove, for L1-data, the existence and uniqueness of an entropy solution to the obstacle problem for nonlinear elliptic equations with variable growth, and to show some convergence and stability properties of the corresponding coincidence set. The latter follow from extending the Lewy–Stampacchia inequalities to the general framework of L

The nonlinear N-membranes evolution problem

The parabolic N-membranes problem for the p-Laplacian and the complete order constraint on the components of the solution is studied in what concerns the approximation, the regularity and the stability of the variational solutions. We extend to the evolutionary case the characterization of the Lagrange multipliers associated with the ordering constraint in terms of the characteristic functions of the coincidence sets. We give continuous dependence results, and study the asymptotic behavior as $t \to \infty$ of the solution and the coincidence sets, showing that they converge to their stationary counterparts.Comment: 16 page

CMSA algorithm for solving the prioritized pairwise test data generation problem in software product lines.

In Software Product Lines, it may be difficult or even impossible to test all the products of the family because of the large number of valid feature combinations that may exist (Ferrer et al. in: Squillero, Sim (eds) EvoApps 2017, LNCS 10200, Springer, The Netherlands, pp 3–19, 2017). Thus, we want to find a minimal subset of the product family that allows us to test all these possible combinations (pairwise). Furthermore, when testing a single product is a great effort, it is desirable to first test products composed of a set of priority features. This problem is called Prioritized Pairwise Test Data Generation Problem. State-of-the-art algorithms based on Integer Linear Programming for this problem are faster enough for small and medium instances. However, there exists some real instances that are too large to be computed with these algorithms in a reasonable time because of the exponential growth of the number of candidate solutions. Also, these heuristics not always lead us to the best solutions. In this work we propose a new approach based on a hybrid metaheuristic algorithm called Construct, Merge, Solve & Adapt. We compare this matheuristic with four algorithms: a Hybrid algorithm based on Integer Linear Programming, a Hybrid algorithm based on Integer Nonlinear Programming, the Parallel Prioritized Genetic Solver, and a greedy algorithm called prioritized-ICPL. The analysis reveals that CMSA is statistically significantly better in terms of quality of solutions in most of the instances and for most levels of weighted coverage, although it requires more execution time.This research has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and the European Regional Development Fund (FEDER) under contract TIN2017-88213-R (6city project), the University of Málaga, Consejerıa de Economıa y Conocimiento de la Junta de Andalucıa and FEDER under contract UMA18-FEDERJA-003 (PRECOG project), the Ministry of Science, Innovation and Universities and FEDER under contract RTC-2017-6714-5 (ECOIoT project), the H2020 European Project Tailor (H2020-ICT-2019-3), the Spanish SBSE Research Network (RED2018-102472-T), and the University of Málaga under contract PPIT-UMA-B1-2017/07 (EXHAURO Project). J. Ferrer thanks University of Mállaga for his postdoc fellowship

Effect of agro-climatic conditions on near infrared spectra of extra virgin olive oils

Authentication of extra virgin olive oil requires fast and cost-effective analytical procedures, such as near infrared spectroscopy. Multivariate analysis and chemometrics have been successfully applied in several papers to gather qualitative and quantitative information of extra virgin olive oils from near infrared spectra. Moreover, there are many examples in the literature analysing the effect of agro-climatic conditions on food content, in general, and in olive oil components, in particular. But the majority of these studies considered a factor, a non-numerical variable, containing this meteorological information. The present work uses all the agro-climatic data with the aim of highlighting the linear relationships between them and the near infrared spectra. The study begins with a graphical motivation, continues with a bivariate analysis and, finally, applies redundancy analysis to extend and confirm the previous conclusions.Peer Reviewe

Estimating Software Testing Complexity

Context: Complexity measures provide us some information about software artifacts. A measure of the difficulty of testing a piece of code could be very useful to take control about the test phase. Objective: The aim in this paper is the definition of a new measure of the difficulty for a computer to gen erate test cases, we call it Branch Coverage Expectation (BCE). We also analyze the most common com plexity measures and the most important features of a program. With this analysis we are trying to discover whether there exists a relationship between them and the code coverage of an automatically generated test suite. Method: The definition of this measure is based on a Markov model of the program. This model is used not only to compute the BCE, but also to provide an estimation of the number of test cases needed to reach a given coverage level in the program. In order to check our proposal, we perform a theoretical val idation and we carry out an empirical validation study using 2600 test programs. Results: The results show that the previously existing measures are not so useful to estimate the difficulty of testing a program, because they are not highly correlated with the code coverage. Our proposed mea sure is much more correlated with the code coverage than the existing complexity measures. Conclusion: The high correlation of our measure with the code coverage suggests that the BCE measure is a very promising way of measuring the difficulty to automatically test a program. Our proposed measure is useful for predicting the behavior of an automatic test case generator.This work has been partially funded by the Spanish Ministry of Science and Innovation and FEDER under contract TIN2011-28194 (the roadME project

Biochemical regulation of arginine biosynthesis in plants

Arginine plays a relevant role in plant metabolism due to its importance as building block of proteins but also as precursor of multiple secondary metabolites, polyamines and nitric oxide. Importantly, arginine frequently plays an essential role as a major nitrogen storage form in seeds and other vegetative tissues and its mobilization provides an efficient flux of nitrogen for different physiological processes [1][2][3]. Despite its importance, the biochemical regulation and kinetics of the enzymes involved in arginine biosynthesis remains poorly characterized in plants. In this work, we provide new knowledge about the biochemical regulation of the three enzymes involved in the last steps of the arginine pathway: ornithine transcarbamoylase (OTC), argininosuccinate synthetase (ASSY), and argininosuccinate lyase (ASL). Our results indicate that these enzymes are regulated by the concentration of different amino acids and metabolites, including arginine, suggesting that feedback regulatory loops could play and important role in the homeostasis of this amino acid. Besides, these regulatory mechanisms seem to have been subjected to a progressive refinement during the evolution of land plants, pointing towards a coevolution with the higher requirements of arginine in seed plants.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech