Analysis of the chemical evolution of the Galactic disk via dynamical simulations of the open cluster system

For several decades now, open clusters have been used to study the structure and chemical evolution of the disk of our Galaxy. Due to the fact that their ages and metallicities can be determined with relatively good precision, and since they can be observed even at great distances, they are excellent tracers of the variations in the abundance of heavy chemical elements with age and position in the Galactic disk. In the present work we analyze the star formation history and the chemical evolution of the disk of the Galaxy using numerical simulations of the dynamical evolution of the system of open clusters in the Milky Way. Starting from hypotheses on the history of cluster formation and the chemical enrichment of the disk, we model the present properties of the Galactic open cluster system. The comparison of these models with the observations allows us to examine the validity of the assumed hypotheses and to improve our knowledge about the initial conditions of the chemical evolution of the Galactic disk

Evaluation of cardiovascular risk in adults with type 1 diabetes: Poor concordance between the 2019 ESC risk classification and 10-year cardiovascular risk prediction according to the Steno Type 1 Risk Engine

Background: Patients with type 1 diabetes (T1D) have higher mortality risk compared to the general population; this is largely due to increased rates of cardiovascular disease (CVD). As accurate CVD risk stratification is essential for an appropriate preventive strategy, we aimed to evaluate the concordance between 2019 European Society of Cardiology (ESC) CVD risk classification and the 10-year CVD risk prediction according to the Steno Type 1 Risk Engine (ST1RE) in adults with T1D. Methods: A cohort of 575 adults with T1D (272F/303M, mean age 36 ± 12 years) were studied. Patients were stratified in different CVD risk categories according to ESC criteria and the 10-year CVD risk prediction was estimated with ST1RE within each category. Results: Men had higher BMI, WC, SBP than women, while no difference was found in HbA1c levels between genders. According to the ESC classification, 92.5% of patients aged 20 years) alone identified few patients (< 30%) among those aged ≥35 years, who were at very high risk according to ESC, in whom this condition was confirmed by ST1RE; conversely, the coexistence of two or more of these criteria identified about half of the patients at high/very high risk also according to this predicting algorithm. When only patients aged ≥ 50 years were considered, there was greater concordance between ESC classification and ST1RE prediction, since as many as 78% of those at high/very high risk according to ESC were confirmed as such also by ST1RE. Conclusions: Using ESC criteria, a large proportion (45%) of T1D patients without CVD are classified at very high CVD risk; however, among them, none of those < 35 years and only 12% of those ≥ 35 years could be confirmed at very high CVD risk by the ST1RE predicting algorithm. More studies are needed to characterize the clinical and metabolic features of T1D patients that identify those at very high CVD risk, in whom a very aggressive cardioprotective treatment would be justified

Flame quenching by the wall-fundamental characteristics

Knowledge of flame-wall interaction allowed us to understand the phenomena of near wall combustion and flame extinction. The study of near wall flame propagation is important because it is related to engineering applications, such as possible misfiring in internal combustion engines, optimization of combustion, and reduction of unburned hydrocarbons in the combustion products. In the present work different characteristics of the quenching distance were measured in square narrow quenching channels. The channel widths were changed from 2.5mm to 15mm, their length being 30cm. Propane/air mixture was employed in experiments. Direct visualization has been used to observe flame behaviour under quenching conditions. Numerical simulation revealed structure of limit flames during their propagation in quenching channels. It was found satisfactory agreement between numerical calculations and experiments. In conclusions it was confirmed that flame quenching depends on the relation between heat release rate to heat loss rate. Dead space appeared to be larger for rich mixtures in comparison with the lean ones. Flame curvature reached maximum value for stoichiometry and decreased for leaner or richer mixtures

Fluid inclusions constraints on P-T conditions during accretionary complex formation: the case of the Robertson Bay Terrane (north Victoria Land, Antartica).

This paper describes the fluids circulating in the extensive thrust-related quartz-bearing vein systems developed within the low-grade turbidite-dominated Early Paleozoic Robertson Bay Terrane of north Victoria Land, Antarctica. It provides a well-preserved example of fossil accretionary complex, developed during the Paleozoic subdution-related accretionary process at the paleo-Pacifica margin of Gondwana. Fluid inclusions are analysed in quartz crystals hosted within distinct generations of veins, which are interpreted to record the incremental deformation history during shortening and accretionary complex formation. Our data provides clues to the tectono-thermal history associated with orogenic complex formation, also providing inferences on the fluid storage and recycling through time

A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks

In recent years neuroevolution has become a dynamic and rapidly growing research field. Interest in this discipline is motivated by the need to create ad-hoc networks, the topology and parameters of which are optimized, according to the particular problem at hand. Although neuroevolution-based techniques can contribute fundamentally to improving the performance of artificial neural networks (ANNs), they present a drawback, related to the massive amount of computational resources needed. This paper proposes a novel population-based framework, aimed at finding the optimal set of synaptic weights for ANNs. The proposed method partitions the weights of a given network and, using an optimization heuristic, trains one layer at each step while “freezing” the remaining weights. In the experimental study, particle swarm optimization (PSO) was used as the underlying optimizer within the framework and its performance was compared against the standard training (i.e., training that considers the whole set of weights) of the network with PSO and the backward propagation of the errors (backpropagation). Results show that the subsequent training of sub-spaces reduces training time, achieves better generalizability, and leads to the exhibition of smaller variance in the architectural aspects of the network