Microstructural wettability of oil and gas condensate zones of the Karachaganak field

The Karachaganak field is represented by gas condensate and oil zones, a convenient object for studying changes in microstructural wettability during the transition from one zone to another. Microstructural wettability was characterized by a hydrophobization coefficient, Ѳн, which determines the proportion of the pore surface area occupied by adsorbed hydrocarbons. It was found that Ѳн of the samples of the gas and gas condensate zones is the same (on average 0.140), the oil zone - on average 0.250. Analysis of the IR spectra of extracted hydrocarbons showed that the microstructural wettability of the oil zone contains more aromatic, aliphatic, oxidized and sulfur-containing structures and fewer branched structures than in the gas condensate zone. The microstructural wettability of carbonate reservoirs depends on the hydrocarbon composition of the adsorbed oil. Keywords: microstructural wettability; hydrophobic coefficient; hydrocarbons; spectral coefficients.

The reliability evaluation of the water surface remote monitoring satellite equipment

The work is devoted to the reliability and effectiveness of the water surface remote monitoring space system. Analysis of existing monitoring methods has shown a high potential for using nanosatellites to solve the problem. As an object of study, 3U CubeSat was chosen with a payload placed on board in the form of a hyperspectral camera. To assess the reliability of the system, a mathematical model on the failure rates of subsystems is proposed. The structural and parametric reliability of the object was investigated in the Windchill Risk and Reliability software complex, taking into account the cyclogram of work and the specifics of the degradation process of individual components due to the influence of an aggressive space environment. To assess the effectiveness of the nanosatellite application, the dependence of the accuracy of determining water pollution on the operability of the photodetector was analyzed, since the photodetector is the central link in the transmission of optical information. The effective measurement of brightness spectral coefficients over the entire service life of nanosatellite is also obtained. Minimum required number of operable pixels was identified for monitoring the water surface with sufficient accuracy and reliability.

The PAU survey: measurement of narrow-band galaxy properties with approximate bayesian computation

Narrow-band imaging surveys allow the study of the spectral characteristics of galaxies without the need of performing their spectroscopic follow-up. In this work, we forward-model the Physics of the Accelerating Universe Survey (PAUS) narrow-band data. The aim is to improve the constraints on the spectral coefficients used to create the galaxy spectral energy distributions (SED) of the galaxy population model in Tortorelli et al. 2020. In that work, the model parameters were inferred from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) data using Approximate Bayesian Computation (ABC). This led to stringent constraints on the B-band galaxy luminosity function parameters, but left the spectral coefficients only broadly constrained. To address that, we perform an ABC inference using CFHTLS and PAUS data. This is the first time our approach combining forward-modelling and ABC is applied simultaneously to multiple datasets. We test the results of the ABC inference by comparing the narrow-band magnitudes of the observed and simulated galaxies using Principal Component Analysis, finding a very good agreement. Furthermore, we prove the scientific potential of the constrained galaxy population model to provide realistic stellar population properties by measuring them with the SED fitting code CIGALE. We use CFHTLS broad-band and PAUS narrow-band photometry for a flux-limited (i < 22.5) sample of galaxies up to redshift z ∼ 0.8. We find that properties like stellar masses, star-formation rates, mass-weighted stellar ages and metallicities are in agreement within errors between observations and simulations. Overall, this work shows the ability of our galaxy population model to correctly forward-model a complex dataset such as PAUS and the ability to reproduce the diversity of galaxy properties at the redshift range spanned by CFHTLS and PAUS.

Sampling theorem of satellite gravimetry from the perspective of the Bender configuration

&lt;p&gt;Satellites in different orbital configurations acquire gravity signals differently. Thus, a difference in admissible spectral coefficients can be expected when the orbital geometry changes. A simple illustration of this phenomenon is seen in the Bender configuration of two GRACE-like satellite pairs - polar and inclined. In the Bender configuration, the polar pair covers the entire globe. In contrast, the inclined pair does not cover the higher latitudes leaving a local discontinuity around the poles in the acquired signal (better known as the &lt;em&gt;Polar Gap problem&lt;/em&gt;). Similarly, due to its north-south orientation, the polar pair can capture the features that are predominantly oriented in the east-west direction. Trying to understand better the relationship between satellite geometry and signal acquisition led us to take our first steps in the direction of a unified sampling theory in satellite gravimetry. To this end, we employed the concepts behind the rotation of spherical harmonic coefficients built upon Inclination functions to express the geopotential functionals. Our work utilizes the Lomb-Scargle Periodogram based approach to ascertain limiting frequencies from the systemic quasi-regular sampling net formed on the satellite torus contrary to interpolation and FFT based techniques used in earlier such research endeavors. Through our work, we aim at improving our understanding of how the transformation of the geopotential occurs from the global to the spectral domain. We hope that this will help design future satellite missions with geometries best suited for their objective based on the precise determination of essential spectral coefficients.&lt;/p&gt;

Deriving accurate molecular indicators of protein synthesis through Raman-based sparse classification

Raman spectroscopy has the ability to retrieve molecular information from live biological samples non-invasively through optical means. Coupled with machine learning, it is possible to use the large amount of information contained in a Raman spectrum to create models that can predict the state of new samples based on statistical analysis from previous measurements. Furthermore, in case of linear models, the separation coefficients can be used to interpret which bands are contributing to the discrimination between experimental conditions, which correspond here to single-cell measurements of macrophages under in vitro immune stimulation. We here evaluate a typical linear method using discriminant analysis and PCA, and compare it to regularized logistic regression (Lasso). We find that the use of PCA is not beneficial to the classification performance. Furthermore, the Lasso approach yields sparse separation vectors, since it suppresses spectral coefficients which do not improve classification, making interpretation easier. To further evaluate the approach, we apply the Lasso technique to a well-defined case where protein synthesis is inhibited, and show that the separating features are consistent with RNA accumulation and protein levels depletion. Surprisingly, when Raman features are selected purely in terms of their classification power (Lasso), the selected coefficients are contained in side bands, while typical strong Raman peaks are not present in the discrimination vector. We propose that this occurs because large Raman bands are representative of a wide variety of cellular molecules and are therefore less suited for accurate classification.

Rapid computation of effective conductivity of 2D composites by equivalent circuit and spectral methods

<abstract><p>This work presents models for homogenizing or finding the effective transport or mechanical properties of microscale composites formed from highly contrasting phases described on a grid. The methods developed here are intended for engineering applications where speed and geometrical flexibility are a premium. A canonical case that is mathematically challenging and yet can be applied to many realistic materials is a 4-phase 2-dimensional periodic checkerboard or tiling. While analytic solutions for calculating effective properties exist for some cases, versatile methods are needed to handle anisotropic and non-square grids. A reinterpretation and extension of an existing analytic solution that utilizes equivalent circuits is developed. The resulting closed-form expressions for effective conductivity are shown to be accurate within a few percent or better for multiple cases of interest. Secondly a versatile and efficient spectral method is presented as a solution to the 4-phase primitive cell with a variety of external boundaries. The spectral method expresses the solution to effective conductivity in terms of analytically derived eigenfunctions and numerically determined spectral coefficients. The method is validated by comparing to known solutions and can allow extensions to cases with no current analytic solution.</p></abstract>

Estimation of ASR Parameterization for Interactive System

In this study, the authors explore the integration of speaker-independent automatic Amazigh speech recognition technology into interactive applications to extract data remotely from a distance database. Based on the combined interactive voice response (IVR) and automatic speech recognition (ASR) technologies, the authors built an interactive speech system to allow users to interact with the interactive system through voice commands. The hidden Markov models (HMMs), Gaussian mixture models (GMMs), and Mel frequency spectral coefficients (MFCCs) are used to develop a speech system based on the ten first Amazigh digits and six Amazigh words. The best-obtained performance is 89.64% by using 3 HMMs and 16 GMMs.

Obtaining humic materials by mechanochemical reduction

In the study, a mechanochemical modification of peat was carried out, leading to an increase in the number of functional groups and an increase in the solubility of humic complexes with metals. It was shown that the mechanical activation of peat in the presence of Zn significantly increased the yield of extractable fulvic acids. An analysis of the spectral coefficients showed that humic and fulvic acids extracted from peat were characterized by an increased content of alcohol hydroxyl groups, C–O carbohydrate moieties, and carboxyl groups.

Development of lightweight polymeric-composite superstructure for a fast boat

This paper discusses the design of lightweight polymeric-composite superstructure for a fast boat (displacement up to 1 t) with solar panels powering its propulsion motor. The superstructure is made up by composite beams with sufficient dynamic stiffness and strength to withstand operational loads. External load was defined as spectral, inertial, transmitted as accelerations or displacements from hull to the superstructure via bearing joints. The material was GFRP with foam filler. The simulation is performed as per finite-element method in linear spatial beam formulation, solving the problems of natural vibrations and maximum dangerous response to spectral kinematic effect as a superposition of modes weighed by spectral coefficients. The study presents calculation for the initial superstructure design and its variations, to analyse sensitivity of dynamic and spectral responses to design changes. The study implements a new technique of generating a model of composite binary profile on single mesh. The results of this analysis, further verified on a more detailed idealization and supplemented by a calculation of inertial disturbances due to pitching and rolling, will yield the methodology for more computer-efficient design of lightweight superstructures for small boats made of polymeric composite materials.