Determination of formation dip and strike from transient LWD electromagnetic measurements

An original method has been developed for estimating formation dip and strike from transient induction LWD data, based on focusing in the time domain. The focusing consists in decomposing the measured signals into a time series and diagonalizing the matrix of focused magnetic field components. We have implemented the method and comprehensively tested it in horizontally-layered media used for LWD data inversion to solve geosteering problems and evaluate the formation resistivity. Estimates of the angles contribute to reliable geosteering when choosing a direction of drilling, as well as when inverting data for a complex earth model. A significant reduction in the resource intensity of inversion and model equivalence is achieved by reducing the number of determined parameters.

FunOrder 2.0 – a fully automated method for the identification of co-evolved genes

Coevolution is an important biological process that shapes interacting species or even proteins – may it be physically interacting proteins or consecutive enzymes in a metabolic pathway. The detection of co-evolved proteins will contribute to a better understanding of biological systems. Previously, we developed a semi-automated method, termed FunOrder, for the detection of co-evolved genes from an input gene or protein set. We demonstrated the usability and applicability of FunOrder by identifying essential genes in biosynthetic gene clusters from different ascomycetes. A major drawback of this original method was the need for a manual assessment, which may create a user bias and prevents a high-throughput application. Here we present a fully automated version of this method termed FunOrder 2.0. To fully automatize the method, we used several mathematical indices to determine the optimal number of clusters in the FunOrder output, and a subsequent k-means clustering based on the first three principal components of a principal component analysis of the FunOrder output. Further, we replaced the BLAST with the DIAMOND tool, which enhanced speed and allows the future integration of larger proteome databases. The introduced changes slightly decreased the sensitivity of this method, which is outweighed by enhanced overall speed and specificity. Additionally, the changes lay the foundation for future high-throughput applications of FunOrder 2.0 in different phyla to solve different biological problems.

Synthesis, Hydrophilicity and Micellization of Coil–Brush Polystyrene-b-(polyglycidol-g-polyglycidol) Copolymer—Comparison with Linear Polystyrene-b-Polyglycidol

In this paper, an original method of synthesis of coil–brush amphiphilic polystyrene-b-(polyglycidol-g-polyglycidol) (PS-b-(PGL-g-PGL)) block copolymers was developed. The hypothesis that their hydrophilicity and micellization can be controlled by polyglycidol blocks architecture was verified. The research enabled comparison of behavior in water of PS-b-PGL copolymers and block–brush copolymers PS-b-(PGL-g-PGL) with similar composition. The coil–brush copolymers were composed of PS-b-PGL linear core with average DPn of polystyrene 29 and 13 of polyglycidol blocks. The DPn of polyglycidol side blocks of coil–b–brush copolymers were 2, 7, and 11, respectively. The copolymers were characterized by 1H and 13C NMR, GPC, and FTIR methods. The hydrophilicity of films from the linear and coil–brush copolymers was determined by water contact angle measurements in static conditions. The behavior of coil–brush copolymers in water and their critical micellization concentration (CMC) were determined by UV-VIS using 1,6-diphenylhexa-1,3,5-trien (DPH) as marker and by DLS. The CMC values for brush copolymers were much higher than for linear species with similar PGL content. The results of the copolymer film wettability and the copolymer self-assembly studies were related to fraction of hydrophilic polyglycidol. The CMC for both types of polymers increased exponentially with increasing content of polyglycidol.

Assessment of the Sustainability of Concrete by Ensuring Performance during Structure Service Life

The article presents an original method to assess the sustainability of concrete. The method uses three parameters, namely, performance, lifetime and environmental impact, to calculate a sustainability index. The originality and simplicity of the proposed method is shown when finding the sustainability index, where the first two factors (service life and performance) are kept constant. This approach is possible within the context of the new proposals that specify the durability of structural concrete in EN 1992 and EN 206. It allows the classification of concrete according to its performance, through environmental action resistance classes (ERC). For this purpose, specific experimental methods were used in order to determine the performance of concrete exposed to carbonation. The concretes were prepared with two cement types with additions (CEM II/A-S and CEM II/A-M (S-LL)). Based on the carbonation resistance classes (first parameter—performance) and exposure classes, the thickness of the concrete cover layer was determined to ensure a certain service lifetime (second parameter—service lifetime). Lastly, the global warming potential was calculated for each composition, allowing the users of the method to select the compositions with the lowest environmental impact.

Influence of the measurement configuration on the results of Raman microspectroscopy of human hair

Increasing interest in spectroscopic studies of human hair raises the question about the accuracy of measurement of their spectra and requires optimisation of experimental facilities. An original method of obtaining transverse hair sections without using a microtome and chemical influence is proposed. The results obtained by confocal Raman microspectroscopy of human hair differently oriented with respect to the optical axis of the measuring setup are compared. It is shown that, in addition to expected changes in the spectra measured at different distances from the hair periphery in the direction to its centre, the spectra measured in the case of hair excitation perpendicular and parallel to its axis are also considerably different.

Evaluation of alcohol consumption motivation: psychometric properties of the Alcohol Consumption Motivation Inventory by V.Yu. Zavyalov

The alcohol consumption motivation inventory (ACM) was developed by V.Yu. Zavyalov. Now it is a widespread psychometric tool in research and clinical practice for the evaluation of alcohol consumption motivation in Russian-speaking patients with alcohol use disorders. The aim of the study is to analyze the psychometric properties of the ACM inventory. Results show that the factor structure of the ACM inventory significantly differs from the one stated in the original. The results of the statistical analysis allowed three significant clusters. Cluster A was composed of the scales of the conditioned triad—traditional, submissive, and pseudo-cultural motives. This cluster also partially included hedonistic motives and self-harm motives. Cluster B was composed of scales of personal and pathological triads—withdrawal, ataractic and hyperactivational motives. Hedonistic motives and addictive motives were also partially included. Cluster B included self-harm motives and hangover (addictive) motives. According to the data obtained, the ACM inventory cannot measure an alcohol consumption motivation in the way of the original method. Future work on the modification of the ACM inventory should be based on the results obtained recently in neurophysiological and clinicalpsychopharmacological studies in the field of motivation for alcohol consumption, and includes the revision of the items of the questionnaire, a statistically substantiated gradation of levels for assessing the effectiveness of the motives for alcohol use, and the validation of the version of the ACM questionnaire in women sample.

Specific immunotherapy in the complex treatment of ovarian cancer

The article is devoted to the search for new solutions in the field of treatment of malignant ovarian tumors and, in particular, new approaches for overcoming immune resistance in a complex method of therapy. The experience of treating this pathology with an original method of postoperative immunotherapy using autologous immunomodulators, which, due to its simplicity and relatively low cost, should be widely used in gynecological oncology, is considered.

Robust Lane Detection and Tracking Algorithm for Steering Assist Systems

Modern vehicles rely on a multitude of sensors and cameras to both understand the environment around them and assist the driver in different situations. Lane detection is an overall process as it can be used in safety systems such as the lane departure warning system (LDWS). Lane detection may be used in steering assist systems, especially useful at night in the absence of light sources. Although developing such a system can be done simply by using global positioning system (GPS) maps, it is dependent on an internet connection or GPS signal, elements that may be absent in some locations. Because of this, such systems should also rely on computer vision algorithms. In this paper, we improve upon an existing lane detection method, by changing two distinct features, which in turn leads to better optimization and false lane marker rejection. We propose using a probabilistic Hough transform, instead of a regular one, as well as using a parallelogram region of interest (ROI), instead of a trapezoidal one. By using these two methods we obtain an increase in overall runtime of approximately 30%, as well as an increase in accuracy of up to 3%, compared to the original method.

Gearbox compound fault diagnosis based on a combined MSGMD-MOMEDA method

Weak fault detection is a complex and challenging task when two or more faults (compound fault) with discordant severity occur in different parts of a gearbox. The weak fault features are prone to be submerged by the severe fault features and strong background noise, which easily lead to a missed diagnosis. To solve this problem, a novel diagnosis method combining muti-symplectic geometry mode decomposition and multipoint optimal minimum entropy deconvolution adjusted (MSGMD-MOMEDA) is proposed for gearbox compound fault in this paper. Specifically, different fault components are separated by the improved symplectic geometry mode decomposition (SGMD), namely, multi-SGMD (MSGMD) method. The weak fault features are enhanced by the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA). In the process of research, a new scheme of selecting key parameters of MOMEDA is proposed, which is a key step in applying MOMEDA. Compared with SGMD, the proposed MSGMD has two main improvements, including suppressing mode mixing and preventing the generation of the pseudo components. Compared with the original method of selecting parameters based on multipoint kurtosis, the proposed MOMEDA parameters selecting scheme has more merits of high accuracy and precision. The analysis results of two cases of simulation and experiment signal reveal that the MSGMD-MOMEDA method can accurately diagnose the gearbox compound fault even under strong background noise.