Modelling of browning kinetics of bread crust during baking

Abstract A mathematical model was set up to predict browning kinetics of bread crust during baking. A bread dough was placed into a cylindrical steel mould and baked in a pilot forced-convection oven at 200 and 250°C. The sample surface temperature was measured using both a type J thermocouple and an infrared thermometer. Surface browning (ΔE) of bread crust during baking was measured by a tristimulus colorimeter. The kinetic model for bread crust browning was obtained by instant heating of dried crumb on contact with a refractory plate at 140, 150, 165, 185, 210, 235 and 250°C. At all temperatures ΔE tended asymptotically to ΔE∞ = 52, which corresponded to the burnt sample. The colour difference varies as a function of first-order kinetics. The rate constant k depends on temperature according to the Arrhenius equation (ko = 42,000 s−1; Ea = 64,151 J/mol). Kinetics was validated under dynamic temperature conditions: the experimental results were compared with those obtained from a mathematical model for heat and mass transfer during baking connected to the kinetic model for browning

Searching for dominant high-level features for music information retrieval

Music Information Retrieval systems are often based on the analysis of a large number of low-level audio features. When dealing with problems of musical genre description and visualization, however, it would be desirable to work with a very limited number of highly informative and discriminant macro-descriptors. In this paper we focus on a specific class of training-based descriptors, which are obtained as the loglikelihood of a Gaussian Mixture Model trained with short musical excerpts that selectively exhibit a certain semantic homogeneity. As these descriptors are critically dependent on the training sets, we approach the problem of how to automatically generate suitable training sets and optimize the associated macro-features in terms of discriminant power and informative impact. We then show the application of a set of three identified macro-features to genre visualization, tracking and classification

Strain partitioning in host rock controls LREE release from allanite-(Ce) in subduction zones

Combined microstructural, mineral chemical, X-ray maps, and X-ray single-crystal diffraction analyses are used to reveal the rheological behaviour of individual grains of magmatic allanite relicts hosted in variably deformed metagranitoids at Lago della Vecchia (inner part of the Sesia-Lanzo Zone, Western Alps, Europe), which experienced high pressure and low temperature metamorphism during the Alpine subduction. X-ray single crystal diffraction shows that none of the allanite crystals, irrespective of the strain state of the host rock, record any evidence of plastic deformation (i.e., intracrystalline deformation), as indicated by the shape of the Bragg diffraction spots, the atomic site positions, and their displacement around the centre of gravity. On the contrary, strong plastic deformation affected matrix minerals, such as quartz, white mica, and feldspar of the hosting rocks, during the development of the Alpine eclogitic- and blueschist-facies metamorphism. Despite the strain-free atomic structures of allanite, different patterns of chemical zoning, as a function of strain accumulated in the rock matrix, are observed. Since allanite occurs in magmatic and metamorphic rocks and it is stable at high pressure and low temperature conditions, we infer that allanite could behave as one of the main carriers of light-rare-earth-elements into the mantle wedge during subduction of continental crust. In particular, the release of light-rare-earth-elements from allanite, under high pressure conditions in subduction zones, is facilitated by high strain accumulated in the host rock

Integrating the landscape epidemiology and genetics of RNA viruses: rabies in domestic dogs as a model

Landscape epidemiology and landscape genetics combine advances in molecular techniques, spatial analyses and epidemiological models to generate a more real-world understanding of infectious disease dynamics and provide powerful new tools for the study of RNA viruses. Using dog rabies as a model we have identified how key questions regarding viral spread and persistence can be addressed using a combination of these techniques. In contrast to wildlife rabies, investigations into the landscape epidemiology of domestic dog rabies requires more detailed assessment of the role of humans in disease spread, including the incorporation of anthropogenic landscape features, human movements and socio-cultural factors into spatial models. In particular, identifying and quantifying the influence of anthropogenic features on pathogen spread and measuring the permeability of dispersal barriers are important considerations for planning control strategies, and may differ according to cultural, social and geographical variation across countries or continents. Challenges for dog rabies research include the development of metapopulation models and transmission networks using genetic information to uncover potential source/sink dynamics and identify the main routes of viral dissemination. Information generated from a landscape genetics approach will facilitate spatially strategic control programmes that accommodate for heterogeneities in the landscape and therefore utilise resources in the most cost-effective way. This can include the efficient placement of vaccine barriers, surveillance points and adaptive management for large-scale control programmes

Functionalized lactic acid macromonomers polycondensation

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Gate stability of GaN-Based HEMTs with P-Type Gate

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Structural analysis of a subduction-related contact in southern Sesia-Lanzo Zone (Austroalpine Domain, Italian Western Alps)

A new foliation trajectory map at 1:10000 scale, represented here with an interpretative structural map, is derived from an original field analysis at 1:5000 scale in the southern Sesia-Lanzo Zone (SLZ). It shows the relative chronology of overprinting foliations, characterised by the mineral assemblages that mark superposed fabrics in each rock type. This map and the associated cross-sections, which synthesise the 3D structural outline of the tectonic contact between the Eclogitic Micaschists Complex (EMC), the Rocca Canavese Thrust Sheets and the Lanzo Ultramafic Complex, allow the correlation of the structural and metamorphic imprints that developed in these crustal and mantle complexes during Alpine subduction. Furthermore, the map and cross-sections allow the immediate perception of the metamorphic environments in which the structural imprints developed in each complex successively under eclogite, blueschist and greenschist facies conditions. The represented structural and metamorphic evolution of the southern end of the SLZ (internal Western Alps) has been inferred based on multiscale structural analysis. The dominant fabrics at the regional scale are two superposed mylonitic foliations that developed under blueschist and greenschist facies conditions, respectively. Metamorphic assemblages underlying the successive fabrics in the different metamorphic complexes allow us to identify contrasting metamorphic evolutions indicating that the tectonic contacts between the EMC, the Rocca Canavese Thrust Sheets and the Lanzo Ultramafic Complex developed under blueschist facies conditions and were successively reactivated during the greenschist facies retrogression

Variational Autoencoders for chord sequence generation conditioned on Western harmonic music complexity

In recent years, the adoption of deep learning techniques has allowed to obtain major breakthroughs in the automatic music generation research field, sparking a renewed interest in generative music. A great deal of work has focused on the possibility of conditioning the generation process in order to be able to create music according to human-understandable parameters. In this paper, we propose a technique for generating chord progressions conditioned on harmonic complexity, as grounded in the Western music theory. More specifically, we consider a pre-existing dataset annotated with the related complexity values and we train two variations of Variational Autoencoders (VAE), namely a Conditional-VAE (CVAE) and a Regressor-based VAE (RVAE), in order to condition the latent space depending on the complexity. Through a listening test, we analyze the effectiveness of the proposed techniques