Bayesian estimation of the Gaussian mixture GARCH model

Bayesian inference and prediction for a generalized autoregressive conditional heteroskedastic (GARCH) model where the innovations are assumed to follow a mixture of two Gaussian distributions is performed. The mixture GARCH model can capture the patterns usually exhibited by many financial time series such as volatility clustering, large kurtosis and extreme observations. A Griddy–Gibbs sampler implementation is proposed for parameter estimation and volatility prediction. Bayesian prediction of the Value at Risk is also addressed providing point estimates and predictive intervals. The method is illustrated using the Swiss Market Index

Using network science to analyze football passing networks: dynamics, space, time and the multilayer nature of the game

From the diversity of applications of Network Science, in this Opinion Paper we are concerned about its potential to analyze one of the most extended group sports: Football (soccer in U.S. terminology). As we will see, Network Science allows addressing different aspects of the team organization and performance not captured by classical analyses based on the performance of individual players. The reason behind relies on the complex nature of the game, which, paraphrasing the foundational paradigm of complexity sciences "can not be analyzed by looking at its components (i.e., players) individually but, on the contrary, considering the system as a whole" or, in the classical words of after-match interviews "it's not just me, it's the team".Comment: 7 pages, 1 figur

Influence of boron content on the fracture toughness and fatigue crack propagation kinetics of bainitic steels

The relatively good combination of high strength and ductility makes bainitic steels a candidate to replace many other steels in industrial applications. However, in service, ductility and strength are not up to standard requirements. In many industrial components, toughness and fatigue performance are also very relevant. In the present study, bainitic steels with varying content of boron were fabricated, with the aim of analyzing the fracture toughness and changes in the fatigue life. The results show that a relatively small change in the boron content can cause a notable variation in the fracture toughness of bainitic steels. The maximum value obtained in fracture toughness was for the steel with the highest boron content. It was observed that the amount of interlath martensite constituents decreases in steels with the addition of boron, leading to the promotion of the presence of void coalescence and a remarkable rise in the toughness of bainitic steels. An increase on the fatigue life of the bainitic steels with an increase in the boron content was also observed, through analysis by means of Paris’ law. A comprehensive micrographic study was carried out in order to examine the mechanics of fatigue crack growth in the bainitic steels, revealing small longitudinal cracks in bainitic steels that lack boron. These cracks tend to disappear in bainitic steels that contain boron. To elucidate this behavior, micrographs of the surfaces generated by the crack growth process were taken, showing that several nano-cracks appeared between the bainite laths. It is finally argued that this high-energy consumption process of nano-crack nucleation and growth is the reason for the improved toughness and fatigue life observed in bainitic steels.Peer ReviewedPostprint (author's final draft

Modulating the water behavior, microstructure, and viscoelasticity of plasma-derived hydrogels by adding silica nanoparticles with tailored chemical and colloidal properties

The viscoelastic properties of hydrogels depend on the tridimensional polymeric structure and the behavior of the liquid confined in their pores. The objective here is to modulate these characteristics in plasma-derived hydrogels by the addition of glycidoxypropyl-silica nanoparticles. These nanoparticles exhibited a hydrodynamic average size between 105.4 − 151.0 nm and surface coverage with (3-Glycidoxypropyl) trimethoxysilane of 0–96 %. The reinforced hydrogels are porous networks with spherical nanoparticles homogeneously distributed into their walls. The silanol groups of silica increase four-fold humidity retention compared with the native hydrogel. This correlates with bound water &gt; 45 % on these reinforced hydrogels, in contrast with 75 % of free water on the native one (calculated from DSC in frozen hydrogels). The humidity stability can be also achieved in the hydrogel prepared with nanoparticles exhibiting 96 % organic coverage. Furthermore, this organic content promotes the microstructure chemical crosslinking, resulting in 3.9 and 1.6 higher Young's modulus compared with native and silica-reinforced hydrogels, respectively. The presence of glycidoxypropyl-silica nanoparticles in reinforced hydrogels modulated its viscoelasticity behavior, decreasing stress relaxation, which was explained using the generalized Maxwell-Wiechert model. In conclusion, novel organic-inorganic hybrid hydrogels based on plasma-derived ones and glycidoxypropyl-silica nanoparticles were developed. These nanoparticles are versatile and allow the production of hydrogels with improved viscoelastic behavior that also exhibits high water retention and morphological stability.</p