Production technology for low-carbon,low-sulfur boron steel

A technology for slag formation in the ladle–furnace unit is considered; the slag is based on the CaO–SiO₂–MgO–Al₂O₃–B₂O₃ system. This technology permits both microalloying of the steel with boron (reduced from the oxide phase) and desulfurization of the steel. The resulting boron content in the steel is 0.001–0.008%; the sulfur content in low-alloy steel and pipe steel is low (0.004–0.010%); and the consumption of manganese ferroalloys is reduced to 0.5 kg/t for 08кп steel and 1.4 kg/t for 09Г2C steel. In addition, the proposed technology increases the strength of the rolled steel, without loss in its plasticity; and reduces the environmental impact thanks to the replacement of fluorspar by colemanite.     

Lecture capture with real‐time rearrangement of visual elements: impact on student performance

The primary goal of this study is to create and test a lecture‐capture system that can rearrange visual elements while recording is still taking place, in such a way that student performance can be positively influenced. The system we have devised is capable of integrating and rearranging multimedia sources, including learning content, the instructor and students' images, into lecture videos that are embedded in a website for students to review after school. The present study employed a two‐group experimental design, with 153 participants (145 females and 8 males) making up an experimental group in which lecture courses were recorded using the new lecture‐capture system, and 149 participants (140 females and 9 males) forming a control group whose lectures were recorded by traditional means. All participants were in the freshman college and studying Introduction to Computer and Information Science in one of six classes, and were randomly assigned to one of the two groups. The participants' midterm examination and final examination scores were collected as indicators of their academic performance, with their mathematics entrance scores used as a pre‐test. The findings obtained from analysis of covariance (ANCOVA) suggest that appropriate rearrangement of visual elements in lecture videos can significantly impact students' learning performance.     

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.     

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.