Mineralogy of supports and selected pigments in mediaeval churches of Slovenia

FEDER MAT2002-0254

Multivariate Statistical Analysis of Phyllite Samples Based on Chemical (XRF) and Mineralogical Data by XRD

It is presented the results obtained of a multivariate statistical analysis concerning the chemical and phase composition, as a characterization purpose, carried out with 52 rock phyllite samples selected from the provinces of Almería and Granada (SE Spain). Chemical analysis was performed by X-ray fluorescence (XRF). Crystalline phase analysis was performed by X-ray powder diffraction (XRD) and the mineralogical composition was then deduced. Quantification of weight loss (100° and 1000°C) was carried out by thermal analysis. The aims of this investigation were to analyze and compare the chemical and mineralogical composition of all these samples and to find similarities and differences between them to allow a classification. Several correlations between results of the characterization techniques have been also investigated. All the data have been processed using the multivariate statistical analysis method. The XRF macroelements (10) and microelements (39) data generate one macrogroup with two new subgroups (1 and 2), and an isolated sample. In subgroup 1 of macroelements, a positive correlation was found between XRF results and geographic location characterized by lower MgO content, which is associated to its geological origins. When multivariate statistical analysis is applied to results obtained by XRD, two groups appear: the first one with a sample with zero percentage of iron oxide and the second one with the rest of the samples, which is classified in two groups. A correlation is observed between the alkaline content (XRF) and illite (XRD), CaO and MgO with dolomite and indirectly between the weight loss after heating at 1000°C and the contents of phase minerals that lose structural water (illite + chlorite) or carbon dioxide (dolomite). The present investigation has interest and implications for geochemistry and analytical chemistry concerning earth rocks and silicate raw material

Cherenkov telescope array extragalactic survey discovery potential and the impact of axion-like particles and secondary gamma rays

The Cherenkov Telescope Array (CTA) is about to enter construction phase and one of its main key science projects is to perform an unbiased survey in search of extragalactic sources. We make use of both the latest blazar gamma--ray luminosity function and spectral energy distribution to derive the expected number of detectable sources for both the planned Northern and Southern arrays of the CTA observatory. We find that a shallow, wide survey of about 0.5 hour per field of view would lead to the highest number of blazar detections. Furthermore, we investigate the effect of axion-like particles and secondary gamma rays from propagating cosmic rays on the source count distribution, since these processes predict different spectral shape from standard extragalactic background light attenuation. We can generally expect more distant objects in the secondary gamma-ray scenario, while axion-like particles do not significantly alter the expected distribution. Yet, we find that, these results strongly depend on the assumed magnetic field strength during the propagation. We also provide source count predictions for the High Altitude Water Cherenkov observatory (HAWC), the Large High Altitude Air Shower Observatory (LHAASO) and a novel proposal of a hybrid detector.Comment: 16 pages, 4 figures, ApJ 2017 in pres

Modeling electricity prices: international evidence

This paper analyses the evolution of electricity prices in deregulated markets. We present a general model that simultaneously takes into account the possibility of several factors: seasonality, mean reversion, GARCH behaviour and time-dependent jumps. The model is applied to equilibrium spot prices of electricity markets from Argentina, Australia (Victoria), New Zealand (Hayward), NordPool (Scandinavia), Spain and U.S. (PJM) using daily data. Six different nested models were estimated to compare the relative importance of each factor and their interactions. We obtained that electricity prices are mean-reverting with strong volatility (GARCH) and jumps of time-dependent intensity even after adjusting for seasonality. We also provide a detailed unit root analysis of electricity prices against mean reversion, in the presence of jumps and GARCH errors, and propose a new powerful procedure based on bootstrap techniques

Dependence of exchange anisotropy and coercivity on the Fe–oxide structure in oxygen-passivated Fe nanoparticles

Ultrafine Fe particles have been prepared by the inert gas condensation method and subsequently oxygen passivated. The as-obtained particles consist in an Fe core surrounded by an amorphous Fe-oxide surface layer. The antiferromagnetic character of the Fe-oxide surface induces an exchange anisotropy in the ferromagnetic Fe core when the system is field cooled. Samples have been heat treated in vacuum at different temperatures. Structural changes of the Fe–O layer have been monitored by x-ray diffraction and transmission electron microscopy. Magnetic properties as coercivity, hysteresis loop shift, and evolution of magnetization with temperature have been analyzed for different oxide crystallization stages. A decrease of the exchange anisotropy strength is reported as the structural disorder of the surface oxide layer is decreased with thermal treatment

Axion searches with Fermi and IACTs

Axion Like Particles (ALPs), postulated to solve the strong-CP problem, are predicted to couple with photons in the presence of magnetic fields, which may lead to a significant change in the observed spectra of gamma-ray sources such as AGNs. Here we simultaneously consider in the same framework both the photon/axion mixing that takes place in the gamma-ray source and that one expected to occur in the intergalactic magnetic fields. We show that photon/axion mixing could explain recent puzzles regarding the observed spectra of distant gamma-ray sources as well as the recently published lower limit to the EBL intensity. We finally summarize the different signatures expected and discuss the best strategy to search for ALPs with the Fermi satellite and current Cherenkov telescopes like CANGAROO, HESS, MAGIC and VERITAS.Comment: 4 pages, 4 figures. To appear in the proceedings of the "2nd Roma International Conference on Astroparticle Physics", Villa Mondragone, Rome, Italy, May 13-15 200

Unexpected Event Prediction in Wire Electrical Discharge Machining Using Deep Learning Techniques

Theoretical models of manufacturing processes provide a valuable insight into physical phenomena but their application to practical industrial situations is sometimes difficult. In the context of Industry 4.0, artificial intelligence techniques can provide efficient solutions to actual manufacturing problems when big data are available. Within the field of artificial intelligence, the use of deep learning is growing exponentially in solving many problems related to information and communication technologies (ICTs) but it still remains scarce or even rare in the field of manufacturing. In this work, deep learning is used to efficiently predict unexpected events in wire electrical discharge machining (WEDM), an advanced machining process largely used for aerospace components. The occurrence of an unexpected event, namely the change of thickness of the machined part, can be effectively predicted by recognizing hidden patterns from process signals. Based on WEDM experiments, different deep learning architectures were tested. By using a combination of a convolutional layer with gated recurrent units, thickness variation in the machined component could be predicted in 97.4% of cases, at least 2 mm in advance, which is extremely fast, acting before the process has degraded. New possibilities of deep learning for high-performance machine tools must be examined in the near future.The authors gratefully acknowledge the funding support received from the Spanish Ministry of Economy and Competitiveness and the FEDER operation program for funding the project "Scientific models and machine-tool advanced sensing techniques for efficient machining of precision components of Low Pressure Turbines" (DPI2017-82239-P) and UPV/EHU (UFI 11/29). The authors would also like to thank Euskampus and ONA-EDM for their support in this project