Photovoltaic Power Supply Unit for the Basic Stations of Cellular Companies

The data on the use of solar photovoltaic plants (PVPs) for providing a reliable and guaranteed power supply to telecommunication systems and cellular communication systems in the conditions prevalent in Uzbekistan are given. The research-based structures developed by OOO MIR SOLAR and the selection of PVP elements ensuring their reliable operation are described. The main influencing factors are discussed, and the use of effective combinations of different types of panels (from monocrystalline and polycrystalline silicon) and a specially developed controller are considered.     

Modeling the Motion of a Saw Ginning Machine

The dynamic characteristics of a saw gin as a subsystem with lumped and distributed parameters are considered in the paper. On the basis of investigation of the machine assembly, graphs are drawn that allow establishing the maximum values of the angle of relative rotation and the angle of rotation of the saw cylinder shaft under torsion.     

Phase-Field Modeling of Nugget Zone for a AZ31-Mg-Alloy Friction Stir Weld

This work presents simulation of microstructure evolution in the nugget zone (NZ) of a AZ31-Mg-alloy friction stir weld. The process parameters (tool geometrical characteristics, rotational speed, travel speed, applied load) have been correlated with the resulting microstructural features in the NZ of the weld (grain size and population) with the aid of the MICRESS software, which provides the ability to simulate both nucleation and grain growth during dynamic recrystallization phenomena evolving in the NZ during the weld thermal cycle. The input parameters of the developed model include the tool geometry, the welding conditions as well as the recrystallization energy, the grain boundary mobility and specific material properties. NZ microstructure obtained by simulation shows good agreement with experimental measurements for both grain population and size.     

Methyltransferase Contingencies in the Pathway of Everninomicin D Antibiotics and Analogues

Everninomicins are orthoester oligosaccharide antibiotics with potent activity against multidrug-resistant bacterial pathogens. Everninomicins act by disrupting ribosomal assembly in a distinct region in comparison to clinically prescribed drugs. We employed microporous intergeneric conjugation with Escherichia coli to manipulate Micromonospora for targeted gene-replacement studies of multiple putative methyltransferases across the octasaccharide scaffold of everninomicin effecting the A₁, C, F, and H rings. Analyses of gene-replacement and genetic complementation mutants established the mutability of the everninomicin scaffold through the generation of 12 previously unreported analogues and, together with previous results, permitted assignment of the ten methyltransferases required for everninomicin biosynthesis. The in vitro activity of A₁- and H-ring-modifying methyltransferases demonstrated the ability to catalyze late-stage modification of the scaffold on an A₁-ring phenol and H-ring C-4’ hydroxy moiety. Together these results establish the potential of the everninomicin scaffold for modification through mutagenesis and in vitro modification of advanced biosynthetic intermediates.     

Retinal Vessel Extraction Framework Using Modified Adaboost Extreme Learning Machine

An explicit extraction of the retinal vessel is a standout amongst the most significant errands in the field of medical imaging to analyze both the ophthalmological infections, for example, Glaucoma, Diabetic Retinopathy (DR), Retinopathy of Prematurity (ROP), Age-Related Macular Degeneration (AMD) as well as non retinal sickness such as stroke, hypertension and cardiovascular diseases. The state of the retinal vasculature is a significant indicative element in the field of ophthalmology. Retinal vessel extraction in fundus imaging is a difficult task because of varying size vessels, moderately low distinction, and presence of pathologies such as hemorrhages, microaneurysms etc. Manual vessel extraction is a challenging task due to the complicated nature of the retinal vessel structure, which also needs strong skill set and training. In this paper, a supervised technique for blood vessel extraction in retinal images using Modified Adaboost Extreme Learning Machine (MAD-ELM) is proposed. Firstly, the fundus image preprocessing is done for contrast enhancement and in-homogeneity correction. Then, a set of core features is extracted, and the best features are selected using “minimal Redundancy-maximum Relevance (mRmR).” Later, using MAD-ELM method vessels and non vessels are classified. DRIVE and DR-HAGIS datasets are used for the evaluation of the proposed method. The algorithm’s performance is assessed based on accuracy, sensitivity and specificity. The proposed technique attains accuracy of 0.9619 on the DRIVE database and 0.9519 on DR-HAGIS database, which contains pathological images. Our results show that, in addition to healthy retinal images, the proposed method performs well in extracting blood vessels from pathological images and is therefore comparable with state of the art methods.