Wavelength calibration of the JWST-MIRI medium resolution spectrometer

We present the wavelength and spectral resolution characterisation of the Integral Field Unit (IFU) Medium Resolution Spectrometer for the Mid-InfraRed Instrument (MIRI), to fly onboard the James Webb Space Telescope in 2014. We use data collected using the Verification Model of the instrument and develop an empirical method to calibrate properties such as wavelength range and resolving power in a portion of the spectrometer's full spectral range (5-28 microns). We test our results against optical models to verify the system requirements and combine them with a study of the fringing pattern in the instrument's detector to provide a more accurate calibration. We show that MIRI's IFU spectrometer will be able to produce spectra with a resolving power above R=2800 in the wavelength range 6.46-7.70 microns, and that the unresolved spectral lines are well fitted by a Gaussian profile.Comment: 12 pages, submitted to SPIE Proceedings vol. 7731, Space Telescopes and Instrumentation 2010: Optical, Infrared, and Millimeter Wav

Cracking Control in Mezzanine Floor Slabs using Rice Husk Ash and Polypropylene Fibers

The continuous population increase in recent years requires a greater number of households to be built quickly, with good materials and produced under quality standards that guarantee their manufacturing process. The prefabricated concrete, produced and supplied by concrete plants, is poured into the different structural elements, the mezzanine slabs being the most careful surfaces in the appearance of fissures; because being horizontal and having larger dimensions, the dimensional changes in the concrete appear more frequently due to the rapid loss of water from the surface of the concrete before setting; which generates superior stresses to the resistant capacity of the concrete at early ages, which affect the durability and reduce the resistance of the structures, causing greater economic expenses in maintenance and repairs. In the present investigation, 5%, 10% and 15% of rice husk ash was used as a replacement for cement and 900g/m3 of polypropylene fiber; The results indicate that as the percentage of rice husk ash increases, there is a reduction in the slump and the crack fissures, and that the resistance to compression and flexion decreases, with respect to the concrete pattern

Very short-term load forecaster based on a neural network technique for smart grid control

Electrical load forecasting plays a crucial role in the proper scheduling and operation of power systems. To ensure the stability of the electrical network, it is necessary to balance energy generation and demand. Hence, different very short-term load forecast technologies are being designed to improve the efficiency of current control strategies. This paper proposes a new forecaster based on artificial intelligence, specifically on a recurrent neural network topology, trained with a Levenberg–Marquardt learning algorithm. Moreover, a sensitivity analysis was performed for determining the optimal input vector, structure and the optimal database length. In this case, the developed tool provides information about the energy demand for the next 15 min. The accuracy of the forecaster was validated by analysing the typical error metrics of sample days from the training and validation databases. The deviation between actual and predicted demand was lower than 0.5% in 97% of the days analysed during the validation phase. Moreover, while the root mean square error was 0.07 MW, the mean absolute error was 0.05 MW. The results suggest that the forecaster’s accuracy is considered sufficient for installation in smart grids or other power systems and for predicting future energy demand at the chosen sites

The Neo-Ricardian Theory of Trade and its Critical Evaluation

The fundamental aim of this essay is a comprehensive evaluation of the Neo-Ricardian theory of international trade, with a view to grasping its relevance and explanatory power for an understanding of the international exchange process in the context of the capitalist mode of production

Isolation and characterization of polymorphic microsatellite markers for peacock wrasse (Symphodus tinca)

Eight polymorphic microsatellite loci were isolated and characterized for the peacock wrasse (Symphodus tinca), a labrid fish inhabiting the Mediterranean and Black seas. Characterization of 35 individuals from the western Mediterranean indicated a relatively high allelic diversity (mean = 12.4, range 9-17), and observed heterozygosity ranging from 0.65 to 0.91. We found no evidence of linkage disequilibrium between pairs of loci. Two loci showed significant departure from Hardy-Weinberg equilibrium. These polymorphic markers can be useful in most basic population genetic applications. © 2006 The Authors.Peer Reviewe

Chromo-fluorogenic probes for beta-galactosidase detection

[EN] beta-Galactosidase (beta-Gal) is a widely used enzyme as a reporter gene in the field of molecular biology which hydrolyzes the beta-galactosides into monosaccharides. beta-Gal is an essential enzyme in humans and its deficiency or its overexpression results in several rare diseases. Cellular senescence is probably one of the most relevant physiological disorders that involve beta-Gal enzyme. In this review, we assess the progress made to date in the design of molecular-based probes for the detection of beta-Gal both in vitro and in vivo. Most of the reported molecular probes for the detection of beta-Gal consist of a galactopyranoside residue attached to a signalling unit through glycosidic bonds. The beta-Gal-induced hydrolysis of the glycosidic bonds released the signalling unit with remarkable changes in color and/or emission. Additional examples based on other approaches are also described. The wide applicability of these probes for the rapid and in situ detection of de-regulation beta-Gal-related diseases has boosted the research in this fertile fieldR.M laboratory members received the financial support from the Spanish Government (project RTI2018-100910-B-C41) and the Generalitat Valenciana (project PROMETEO 2018/024). B.L-T. received support from the Spanish Ministry of Economy for their PhD grants (FPU15/02707). J. F.-B received fellowship (CD19/00038)Lozano-Torres, B.; Blandez, JF.; Sancenón Galarza, F.; Martínez-Máñez, R. (2021). Chromo-fluorogenic probes for beta-galactosidase detection. Analytical and Bioanalytical Chemistry. 413(9):2361-2388. https://doi.org/10.1007/s00216-020-03111-8S236123884139Fernandes P. Enzymes in food processing: a condensed overview on strategies for better biocatalysts. Enzyme Res. 2010;2010:86253–73.Likidlilid A, Patchanans N, Peerapatdit T, Sriratanasathavorn C. 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Glycosyltransferase and glycosidase activities in ovarian cancer

The AGN Luminosity Fraction in Merging Galaxies

Galaxy mergers are key events in galaxy evolution, often causing massive starbursts and fueling active galactic nuclei (AGN). In these highly dynamic systems, it is not yet precisely known how much starbursts and AGN respectively contribute to the total luminosity, at what interaction stages they occur, and how long they persist. Here we estimate the fraction of the bolometric infrared (IR) luminosity that can be attributed to AGN by measuring and modeling the full ultraviolet to far-infrared spectral energy distributions (SEDs) in up to 33 broad bands for 24 merging galaxies with the Code for Investigating Galaxy Emission. In addition to a sample of 12 confirmed AGN in late-stage mergers, found in the $Infrared$ $Array$ $Satellite$ Revised Bright Galaxy Sample or Faint Source Catalog, our sample includes a comparison sample of 12 galaxy mergers from the $Spitzer$ Interacting Galaxies Survey, mostly early-stage. We perform identical SED modeling of simulated mergers to validate our methods, and we supplement the SED data with mid-IR spectra of diagnostic lines obtained with $Spitzer$ InfraRed Spectrograph. The estimated AGN contributions to the IR luminosities vary from system to system from 0% up to 91% but are significantly greater in the later-stage, more luminous mergers, consistent with what is known about galaxy evolution and AGN triggering.Comment: 26 pages, 10 figure

Acelerador Financiero y Ciclos Económicos en Colombia: Un Ejercicio Exploratorio

A partir del debate sobre la respuesta de política a fluctuaciones en los precios de los activos, en el presente documento se estudia la importancia de estos precios en el contexto de una economía emergente como la colombiana, caracterizada por un grado variable de represión financiera y vulnerabilidad a choques externos, en particular de flujos de capital y términos de intercambio. El trabajo encuentra evidencia sobre el funcionamiento del acelerador financiero y su relación con dichos choques en el período 1970 - 2006. A partir de allí se identifican tres ciclos de precios de los activos que coinciden con ciclos del PIB. Los resultados subrayan la incidencia de choques externos y del diseño macroeconómico de la política monetaria en la dinámica del acelerador financiero. Esto corrobora la importancia de variables como los precios de los activos y el crédito para el diseño de una política monetaria contra-cíclica.Acelerador financiero, ciclo económico, burbujas de precios de activos. Classification JEL: E32; E44; E58.

Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control

In recent years, the photovoltaic generation installed capacity has been steadily growing thanks to its inexhaustible and non-polluting characteristics. However, solar generators are strongly dependent on intermittent weather parameters, increasing power systems' uncertainty level. Forecasting models have arisen as a feasible solution to decreasing photovoltaic generators' uncertainty level, as they can produce accurate predictions. Traditionally, the vast majority of research studies have focused on the develop- ment of accurate prediction point forecasters. However, in recent years some researchers have suggested the concept of prediction interval forecasting, where not only an accurate prediction point but also the confidence level of a given prediction are computed to provide further information. This paper develops a new model for predicting photovoltaic generators' output power confidence interval 10 min ahead, based on deep learning, mathematical probability density functions and meteorological parameters. The model's accuracy has been validated with a real data series collected from Spanish meteorological sta- tions. In addition, two error metrics, prediction interval coverage percentage and Skill score, are computed at a 95% confidence level to examine the model's accuracy. The prediction interval coverage percentage values are greater than the chosen confidence level, which means, as stated in the literature, the proposed model is well-founded