Black hole solutions of gravity theories with nonminimal coupling between matter and curvature

We study black hole solutions in an extension of General Relativity (GR) with an explicit non-minimal coupling between matter and curvature. General black hole solutions satisfying the known energy conditions are derived including the ones with anti-de Sitter background. These solutions differ from those of GR just by a coupling function dependent rescaling of the mass and charge of the black hole and by a "dressing" of the cosmological constant. The existence of black hole solutions of the nonminimally coupled theory as well as the conditions for a suitable weak field limit are considered as a constraint on the coupling function responsible for the nonminimal coupling between matter and curvature. The "dressing" of the cosmological constant is then used to address the cosmological constant problem.Comment: 24 pages, no figure

Qualitative properties of solutions to elliptic singular problems

We investigate the singular boundary value problem Δu+u−γ=0 in D, u=0 on ∂D, where γ>0. For γ>1, we find the estimate |u(x)−b0δ2/(γ+1)(x)| <βδ(γ−1)/(γ+1)(x), where b0 depends on γ only, δ(x) denotes the distance from x to ∂D and is β suitable constant. For γ>0, we prove that the function u(1+γ)/2 is concave whenever D is convex. A similar result is well known for the equation Δu+up=0, with 0≤p≤1. For p=0, p=1 and γ≥1 we prove convexity sharpness results

Achieving Quality through Software Maintenance and Evolution: on the role of Agile Methodologies and Open Source Software

Agile methodologies, open source software development, and emerging new technologies are at the base of disruptive changes in software engineering. Being effort estimation pivotal for effective project management in the agile context, in the first part of the thesis we contribute to improve effort estimation by devising a real-time story point classifier, designed with the collaboration of an industrial partner and by exploiting publicly available data on open source projects. We demonstrate that, after an initial training on at least 300 issue reports, the classifier estimates a new issue in less than 15 seconds with a mean magnitude of relative error between 0.16 and 0.61. In addition, issue type, summary, description, and related components prove to be project-dependent features pivotal for story point estimation. Since story points are the most popular effort estimation metric in the agile context, in the second study presented in the thesis we investigate the role of agile methodologies in software maintenance and evolution, and prove its undoubted influence on the refactoring research field over the last 15 years. In the later part of the thesis, we focus on recent technologies to understand their impact on software engineering. We start by proposing a specialized blockchain-oriented software engineering, on the basis of the peculiar challenges the blockchain sector must confront with and statistical data retrieved from a corpus of open source blockchain-oriented software repositories, identified relying upon the 2016 Moody’s Blockchain Report. We advocate the need for new professional roles, enhanced security and reliability, novel modeling languages, and specialized metrics, along with new research directions focusing on collaboration among large teams, testing, and specialized tools for the creation of smart contracts. Along with the blockchain, in the later part of this work we also study the growing mobile sector. More specifically, we focus on the relationships between software defects and the use of the underlying system API, proving that our findings are aligned with those in the literature, namely, that the applications which are more connected to API classes are also more defect-prone. Finally, in the last work presented in the dissertation, we conducted a statistical analysis of 20 open source object-oriented systems, 10 written in the highly popular language Java and 10 in the rising language Python. We leveraged two statistical distribution functions–the log-normal and the double Pareto distributions–to provide good fits, both in Java and Python, for three metrics, namely, the NOLM, NOM, and NOS metrics. The study, among other findings, revealed that the variability of the number of methods used in Python classes is lower than in Java classes, and that Java classes, on average, feature fewer lines of code than Python classes

Behavioral and neurochemical assessment of the role of ERK pathway in the psychopharmacological effects of ethanol, caffeine and of their interaction

The intracellular signaling cascades constitute the means by which addictive substances induce the remodeling of the circuits involved in motivated behaviors which underlie the learning processes and the development of memories at the basis of the progression of addiction. A molecule protagonist in such signaling cascades is the Extracellular signal-Regulated Kinase (ERK), a member of the mitogen-activated protein kinase, that constitutes an important biochemical factor common to many cellular functions. The abundant expression of ERK in brain areas of the addiction circuits emphasizes the relevance of these kinases in modulating behavioral functions mediated by these circuits. Thus, the general aim of the present doctoral thesis was to study the role of ERK in terms of protein kinase expression and behavioral responses induced by ethanol, caffeine and their association. The first and second chapters examined the involvement of protein kinases ERK in different aspects of ethanol-induced place conditioning. Specifically, in the first chapter we used the MEK inhibitor SL327 to study how the blockade of this cascade could affect the acquisition and expression of Conditioned Place Preference (CPP) and Conditioned Place Aversion (CPA) elicited by ethanol. The second chapter explored the results of the pharmacological relationship between caffeine and ethanol in ethanol-elicited CPP and CPA. In this chapter we also investigated the expression of pERK as a result of 1) the acute administration of both substances and 2) the presentation of stimuli positively (CPP) or negatively (CPA) conditioned to ethanol. The last chapter examines the pharmacological relationship between caffeine and ethanol through the analysis of horizontal and vertical locomotion and evaluated whether there was reciprocal cross-sensitization with respect to these effects. We also examined the phosphorylation of DARPP-32(Thr75), another factor of intracellular signaling cascade, and of ERK in the nucleus accumbens. The findings of these studies revealed that MEK/ERK pathway is differentially involved in distinct phases of associative learning behavior expressed in the CPP and CPA elicited by ethanol. Moreover, our data disclose that ERK activation takes place differentially in distinct brain regions depending on the motivational significance of the conditioned stimulus. Furthermore, we demonstrated that caffeine significantly impaires ethanol-elicited place conditioning (both CPP and CPA) and prevents ethanol-induced pERK expression in several brain areas with different activation patterns depending on the brain area examined. Our observations also revealed that caffeine and ethanol affect horizontal and vertical locomotion in different manner and without undergoing cross-sensitization. Finally, caffeine prevents ethanol-elicited pERK expression in the nucleus accumbens whereas there were no effects on pDARPP-32(Thr75). Taken together the results of the present thesis offer new insights into the complexity of the involvement of ERK cascade in the acquisition and expression of associative learning and provide new information about the antagonistic interaction between caffeine and ethanol expressed in place conditioning and locomotor activation

Role of modelling on state and parameter estimation

In process industry, plants are generally operated at conditions that differ from the designed ones mainly due to disturbances. Disturbances can enter the system in form of fluctuations in feed flow, temperature and composition, or fluctuation of the utilities quality. These events cause a deterioration of the plant performance that cannot be quantified and online compensated by means of controllers unless online measurements of the quality targets (e.g. concentration, conversion, etc) are available. However the problem of online monitoring cannot be always solved in practice by means of hardware analysers because of unreliable and delayed measurements. An alternative approach is based on estimators that infer the variables of interests by means of secondary measurements and a often nonlinear model of the process. This type of realization of observers can include the online estimation of model parameters for a more accurate alignment of the model with the process behaviour. This work addresses the role of the estimation model on estimation performance. Recent studies [1, 2] pointed out that for a defined set of plant measurements the choice of the estimation model and the innovated states play a key role on the performance of the estimator regardless the algorithm employed. Even if in the cited studies some features of the estimation model (such as level of detail, computational complexity) have been taken into account, the effect on the estimation performance of model manipulations such as variables and parameters scaling [3] and transformation have not been investigated yet. For this reason the role of different realizations of the same estimation model needs to be further investigated

Second-order boundary estimates for solutions to singular elliptic equations in borderline cases

Let \Omega \subsetR^N be a bounded smooth domain. We investigate the effect of the mean curvature of the boundary \partial \Omega on the behaviour of the solution to the homogeneous Dirichlet boundary value problem for the equation \Delta u + f(u) = 0. Under appropriate growth conditions on f(t) as t approaches zero, we find asymptotic expansions up to the second order of the solution in terms of the distance from x to the boundary \partial \Omega

Problems for elliptic singular equations with a quadratic gradient term

AbstractWe investigate the homogeneous Dirichlet problem for a class of second-order elliptic partial differential equations with a quadratic gradient term and singular data. In particular, we study the asymptotic behaviour of the solution near the boundary under suitable assumptions on the growth of the coefficients of the equation

On the model-based monitoring of industrial batch crystallizers

Crystallization is an important separation process to obtain high value-added chemicals in crystalline form from liquid solution in pharmaceutical, food and fine chemical industries. As most of the particulate processes, the quality of the solid product is determined by its particle size distribution (PSD). The achievement of the desired quality targets of the fine crystalline products relies on an efficient online process monitoring for separation supervision and control. However, hardware analyzers able to online measure the PSD and the solute concentration are rarely available, due to their costs \cite{Multi}. These unmeasured process variables can be estimated by state estimators that combine information from the process model and secondary measurements. The problem of designing state observers for online monitoring the PSD evolution has been mostly addressed under the assumption that some PSD measurements were available (see \cite{Mesb} and literature therein), which is not likely in practice. This work proposes a methodology to asses the feasibility of using common measurements (e.g. temperature and liquid fraction) for estimation purposes based on local observability \cite{Herm} and detectability \cite{AlFer} arguments. The results are supported using a data-derived technique, with data generated by a simulation model of the industrial crystallizer. Based on the results of the observability analysis, the structure of a state estimator is proposed