Conformal and non Conformal Dilaton Gravity

The quantum dynamics of the gravitational field non-minimally coupled to an (also dynamical) scalar field is studied in the {\em broken phase}. For a particular value of the coupling the system is classically conformal, and can actually be understood as the group averaging of Einstein-Hilbert's action under conformal transformations. Conformal invariance implies a simple Ward identity asserting that the trace of the equation of motion for the graviton is the equation of motion of the scalar field. We perform an explicit one-loop computation to show that the DeWitt effective action is not UV divergent {\em on shell} and to find that the Weyl symmetry Ward identity is preserved {\em on shell} at that level. We also discuss the fate of this Ward identity at the two-loop level --under the assumption that the two-loop UV divergent part of the effective action can be retrieved from the Goroff-Sagnotti counterterm-- and show that its preservation in the renormalized theory requires the introduction of counterterms which exhibit a logarithmic dependence on the dilaton field.Comment: LateX, 50 pages. Several points clarified; references added. New section on Weyl invariant renormalisation adde

The Innocent Can Still Be Found Guilty

Wrongful convictions target specific groups of people within society in the U.S. The criminal justice and court systems are heavily influenced by the racial biases that surround their integral processes when it comes to convicting citizens of their accused crimes. African American men are heavily targeted when it comes to being convicted of a violent crime that they did not commit, when compared to that of white males. These racial biases can be viewed through careful observation of prior research and shows how these biases have been ingrained within the training police officers undergo. It is also evident that these biases are seemingly present in the minds of victims of violent crimes. As racial stereotypes obscure their sense of judgment when it comes to identifying the perpetrator of the crime from a group of people in a lineup. A lineup is a method of identification that is used in order to help victims identify the offender from a group of people who have been arrested and match a similar description. Statistics on the groups of people that are wrongfully convicted are analyzed, as well as cases that involve the use of different kinds of evidence which have led to wrongful convictions. The systems in place that are meant to fairly convict offenders of their crimes are heavily flawed and outdated as statistics clearly outline the margins of error included within every wrongful conviction that is made. These findings may also suggest which racial group is targeted the most when it comes to being wrongfully convicted as a result of errors created from these flawed systems. Changes in policies such as making it mandatory for investigators to film interrogation, could reduce the rate of wrongful convictions. However, the enforcement of policy changes can be ignored by those in power, in order to reap the underlying benefits that come with a wrongful conviction. Key Words: wrongful convictions, evidence, racial biases, eyewitness, policy, exonerate, racial stereotypes&nbsp

Modelling of the intensified esterification using ozone-rich microbubbles for biodiesel production

The aim of this thesis is to model the intensified esterification in order to improve the pretreatment stage of biodiesel production, where the free fatty acids found in vegetable oils are converted to fatty acid methyl esters. The intensified esterification considers the use of a microbubble reactive distillation as an alternative to the acid pretreatment. The proposed set of reactions based on a free-radical mechanism would favour the process towards completion achieving a yield higher than 90%. This is achieved due to the respective water stripping and removal, leading to a higher efficiency of the process and avoiding inhibition caused by products. Both the 0-D irreversible and reversible model are built in order to portray the relevance of the reverse reaction, since it is known that esterification is a reversible reaction of second order. The rate constants obtained in these models are fed into the 2-D model, where the reaction kinetics, mass and heat transfer and surface reactions in the gas-liquid interface are studied. Some of the results obtained in the 2-D model for the reversible esterification are described below. A higher FAME concentration is obtained due to the free-radical direct injection into microbubbles with plasma and the water removal (Le Chatelier’s push and pull). An enhanced reaction kinetics is found with shorter residence times. An increase in temperature would mean an increase in both forward and reverse rate constants, favouring the forward rate constant (Esterification is endothermic). Decreasing the bubble size results in an increase of the FAME production due to the enhanced gas-liquid ratio at the interface and the increased vaporisation and stripping of water. Increasing the concentration of the O· radical results in an increase in the FAME concentration in the liquid domain. A higher bubble temperature results in a higher water concentration inside the bubble, leading to a higher reaction rate and water stripping. These findings are used in order to propose an esterification reversible model using J. platyphylla, which accounts shorter residence times lower than 1x10-4 s, in other words (τres<1x10-4 s), when the maximum water concentration in the bubble is reached before it reaches the chemical equilibrium

Deep Spatiotemporal Model for COVID-19 Forecasting

COVID-19 has caused millions of infections and deaths over the last 2 years. Machine learning models have been proposed as an alternative to conventional epidemiologic models in an effort to optimize short- and medium-term forecasts that will help health authorities to optimize the use of policies and resources to tackle the spread of the SARS-CoV-2 virus. Although previous machine learning models based on time pattern analysis for COVID-19 sensed data have shown promising results, the spread of the virus has both spatial and temporal components. This manuscript proposes a new deep learning model that combines a time pattern extraction based on the use of a Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN) over a preceding spatial analysis based on a Convolutional Neural Network (CNN) applied to a sequence of COVID-19 incidence images. The model has been validated with data from the 286 health primary care centers in the Comunidad de Madrid (Madrid region, Spain). The results show improved scores in terms of both root mean square error (RMSE) and explained variance (EV) when compared with previous models that have mainly focused on the temporal patterns and dependencies.This work is part of the agreement between the Community of Madrid and the Universidad Carlos III de Madrid for the funding of research projects on SARS-CoV-2 and COVID-19 disease, project name “Multi-source and multi-method prediction to support COVID-19 policy decision making”, which was supported with REACT-EU funds from the European regional development fund “a way of making Europe”. This work was supported in part by the projects “ANALISIS EN TIEMPO REAL DE SENSORES SOCIALES Y ESTIMACION DE RECURSOS PARA TRANSPORTE MULTIMODAL BASADA EN APRENDIZAJE PROFUNDO” MaGIST-RALES, funded by the Spanish Agencia Estatal de Investigación (AEI, doi: 10.13039/501100011033) under grant PID2019- 105221RB-C44/AEI/10.13039/501100011033 and “FLATCITY-APP: Aplicación móvil para FlatCity” funded by the Spanish Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU/PRTR” under grant PDC2021-121239-C33

Enantiomeric Ratio Changes of Terpenes in Essential Oils from Hybrid Eucalyptus grandis × E. tereticornis and its Pure Species

Some Eucalyptus species produce oils with biological activities and the effect of their interspecifc hybridization on the enantiomeric composition of terpenes has not been reported. The enantiomeric excesses of monoterpenes in the essential oil of Eucalyptus grandis × E. tereticornis and its parental taxa were determined by gas chromatography-mass spectrometry (GC-MS), and to resolve coelutions problems by preparative high performance liquid chromatography (HPLC)and GC-MS with two columns in series. The essential oil composition of the hybrid presented qualitative and quantitative differences with the composition of its parental taxa. Great differences were found for the enantiomeric ratio in monoterpene alcohols among the three essential oils. Our results suggest that the enantiomeric analysis can be a reliable method for the study of how theinterspecifc hybridization can module the enantiomeric chemical profle in Eucalyptus essential oils. These results suggest the use of interspecifc hybridization to improve or expand the source of bioactive compounds.Fil: Naspi, Cecilia Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Investigación y Desarrollo Estratégico para la Defensa. Ministerio de Defensa. Unidad de Investigación y Desarrollo Estratégico para la Defensa; ArgentinaFil: Alvarez Costa, Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Investigación y Desarrollo Estratégico para la Defensa. Ministerio de Defensa. Unidad de Investigación y Desarrollo Estratégico para la Defensa; ArgentinaFil: Lucia, Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Investigación y Desarrollo Estratégico para la Defensa. Ministerio de Defensa. Unidad de Investigación y Desarrollo Estratégico para la Defensa; ArgentinaFil: Gonzalez Audino, Paola Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Investigación y Desarrollo Estratégico para la Defensa. Ministerio de Defensa. Unidad de Investigación y Desarrollo Estratégico para la Defensa; ArgentinaFil: Masuh, Hector Mario. Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Investigación y Desarrollo Estratégico para la Defensa. Ministerio de Defensa. Unidad de Investigación y Desarrollo Estratégico para la Defensa; Argentin

Cartesian Difference Categories

Cartesian differential categories are categories equipped with a differential combinator which axiomatizes the directional derivative. Important models of Cartesian differential categories include classical differential calculus of smooth functions and categorical models of the differential $\lambda$-calculus. However, Cartesian differential categories cannot account for other interesting notions of differentiation of a more discrete nature such as the calculus of finite differences. On the other hand, change action models have been shown to capture these examples as well as more ``exotic'' examples of differentiation. But change action models are very general and do not share the nice properties of Cartesian differential categories. In this paper, we introduce Cartesian difference categories as a bridge between Cartesian differential categories and change action models. We show that every Cartesian differential category is a Cartesian difference category, and how certain well-behaved change action models are Cartesian difference categories. In particular, Cartesian difference categories model both the differential calculus of smooth functions and the calculus of finite differences. Furthermore, every Cartesian difference category comes equipped with a tangent bundle monad whose Kleisli category is again a Cartesian difference category.Comment: This paper is an extended version of a conference paper [arXiv:2002.01091] in Foundations of Software Science and Computation Structures: 23rd International Conference (FOSSACS 2020). This paper has been submitted to a special issue of Logical Methods in Computer Science (LMCS) devoted to FOSSACS 2020 paper