bluuebunny/crossref_metadata_2025_split huggingface dataset:
Increasing the photoluminescence intensity of silicon nitride by forming K and N radioactive centres
Authors: Murzalinov D O , Shaikenova A A , Umirzakov A G , Fedosimova A I , Baitimbetova B A , Dmitriyeva Y A , Rakymetov B A 
Published: January 2022

Abstract
Creating a light emitter to transfer an electrical signal by optical way has a great importance in development of optoelectronics. The silicon nitride films studied by photoluminescence techniques, and determined luminescence is associated with presence of an extended zone of tail states. Defects play the main role in radiative recombination for structures annealed at 600 °C and 1100 °C. Photoluminescence (Pl) intensity of obtained films by plasma enhanced chemical vapor deposition is increased after annealing at 600 °C which are related to increased concentration of defects as a result of broken Si–H and N–H bonds. Due to the formation of N-centers through the breaking of N–H bonds, annealing at 1100 °C led to sharp decrease in the luminescence intensity 5 and 3 times for SiN1.1 and SiN1.5 samples respectively. Replacement of Si-Si bonds by Si-N enhance Eg with increasing stoichiometric parameter, which leads to blue shift edge of photoluminescence maximum. Carbon implantation of silicon nitride films with extra Si obtained by Plasma Enhanced Chemical Vapor deposition at 1x1014 cm‒2, 2x1015 cm‒2, and 1x1016 cm‒2 fluencies, in combination with prolonged annealing at 1100 °C temperature leads to the formation of additional K-centers.
Structure of tantalum coatings on NiTi substrate deposited by magnetron sputtering
Authors: Dikov A S , Larionov A S , Zhakanbayev E A , Dikova L A , Sanssyzbayeva A Zh , Akaev S O 
Published: January 2022

Abstract
The paper provides the results of studies of the structural-phase state of tantalum coatings prepared by magnetron deposition. The coatings were deposited on substrates made of titanium nickelide with a shape memory. The NiTi temperature during coating deposition did not exceed 100 °C. The structural-phase state of Ta was determined by X-ray diffraction at different stages of coating formation. It has been shown that at the initial stage of deposition, two-phase coatings (α- and β-Ta) are formed. The synthesis of the coating from Ta leads to the growth of interplanar distance of the B2 austenite phase in the crystallographic direction (100). The growth of interplanar spacing is caused by formation of microstresses during interaction with incident tantalum ions. The lattice parameters of the B19 ‘phase, responsible for appearance of the shape memory effect, do not change during deposition of the tantalum coating.
Determination of thermophysical properties of prototypes of tin-lithium alloy by differential scanning calorimetry
Authors: Bochkov V , Ponkratov Yu , Nikitenkov N , Baklanova Yu , Gordienko Yu , Tulubayev Ye , Samarkhanov K , Karambayeva I 
Published: January 2022

Abstract
This paper presents a description of research works to determine the thermophysical properties of a tin-lithium alloy with a different percentage of lithium and tin atoms in the alloy. The method of differential scanning calorimetry (DSC) was used for the studies, by which the thermophysical properties of the alloy (temperature of phase transition and enthalpy) were determined. The work was carried out at the TiGrA experimental complex. Studies to determine the enthalpy and temperature of phase transition of prototypes of tin-lithium alloy were carried out in the temperature range from 150°C to 500°C at a heating rate of 10°C/min. The experiments were carried out with a pristine sample of tin (reference) and prototypes of a tin-lithium alloy, the percentage of lithium in which was 20, 25 and 27 at. %. As a result of the work performed, the melting point of the prototypes was determined, which was 224°C and 218°C. The values of the specific heat of fusion (enthalpy) of the investigated alloys were determined, which amounted to 76.5 J/g, 80.7 J/g and 86.3 J/g, respectively.
Development Of Scenarios For Controlling The Fuel Campaign Of The Ivg.1M Reactor With Leu-Fuel
Authors: Zhanbolatov O. M. , Vurim A. D. , Surayev A. S. , Irkimbekov R. A. 
Published: January 2022

Abstract
The paper provides calculation of various campaign scenarios of the IVG.1M Research Reactor with LEU-fuel. The schemes of replacement and transfer of the fuel are suggested. Several options are considered that include a complete and partial replacement of spent nuclear fuel with fresh one, change in the reactor reactivity margin during campaign is calculated.
Non-radioactive scandium oxide receiving out of uranium ISR solutions
Authors: Kylyshkanov M K , Yaroshenko N N , Khlebnikova I A 
Published: January 2022

Abstract
The task of the research was to study and determine an effective method for the preparation of non-radioactive scandium compounds from uranium In-Situ Recovery (ISR) solutions. The widespread use of scandium is restrained by the high price due to its small production volumes, low content in the raw materials (scandium is a scattered element and does not form its own deposits), as well as the complexity of technological schemes for its extraction. Scandium receiving out of uranium reverses ISR solutions technological scheme was experimental tested, including sorption on MTS 9580 (Purolite’s production) ion exchanger with recurrent ballast impurities desorption and receiving concentrate that contains scandium. New radiation cleaning technological sequencing based on different solubility of radioactive elements and scandium in carbonate solutions, that accompanied by insoluble macro components complex formation, that contains in deactivated scandium concentrate and allows to get scandium oxide with desired component maintenance more than 94 % and less than 0,3 kilobecquerels/kg specific activity level was developed. The developed technology is based on the ability to form soluble carbonate complexes of scandium and radioactive elements, while the main macro components of the concentrate - ferrum, aluminum, calcium, silicon and others under the conditions of carbonation of the concentrate are inert or form insoluble compounds. Optimal radioactive impurity removing from concentrate conditions and scandium leaching from deactivated residue of scandium and macro impurity were studied and identified in laboratory conditions and during pilot tests.
Research on obtaining gadolinium oxide from waste technologies for processing of uranium-gadolinium containing materials
Authors: Kylyshkanov M K , Yaroshenko N N , Gusakova G V , Dorn A A , Gofman A A , Varyvdin Y V 
Published: January 2022

Abstract
One of the activities of the Uranium production of JSC “UMP” is the processing of hard-to-open uranium-gadolinium-containing scraps. When processing materials of this type, after their dissolution, the gadolinium fluoride precipitation operation is carried out with the subsequent extraction purification of the obtained uranyl nitrate solutions.
At the deposition stage, almost all the gadolinium contained in the scraps is transferred to the GdF3 precipitate and sent to the tailings dump as part of the solid waste.
In order to determine the possibility of obtaining gadolinium oxide from waste processing of uran-gadolinium containing materials, exploratory studies were initiated.
In the course of the work, various methods of obtaining gadolinium oxide were tested. A number of experiments were carried out to refine the modes of obtaining gadolinium oxide by the method of two-stage precipitation of oxalate. A technological scheme was developed, according to which a finished product was obtained, suitable for further use in the technology of obtaining uranium-gadolinium tablets of UMP JSC.
The scheme consists of the following main operations: dissolution of gadolinium fluoride in a solution of aluminum nitrate, precipitation of gadolinium oxalate, washing of gadolinium oxalate in the first stage of precipitation with a solution of nitric acid, conversion of oxalate to gadolinium hydroxide, dissolution of hydroxide in a solution of nitric acid, re-precipitation of gadolinium oxalate, calcination to gadolinium oxide.
Route measurements of natural surface radiation background in the Almaty region
Authors: Bigeldiyeva M T , Dyachkov V V , Zherebchevsky V I , Zaripova Yu A , Yushkov A V 
Published: January 2022

Abstract
Measurements of the spatial distribution of radon isotopes were carried out from April 2021 to August 2021 in the foothills of the Trans-Ili Alatau of the Tien Shan in the Almaty region at various heights above sea level: from 500 to 2500 meters. They were carried out using electronic radiometric equipment: beta-dosimeter “RKS-01B-SOLO”; gamma dosimeter “RKS-01G-SOLO”; radiometer of radon and its daughter decay products “RAMON- 02” in the field. As a result, preliminary assessment schemes were built for route measurements of the 222Rn radon isotope, beta and gamma radiation fields from natural daughter products of decay of radon isotopes and radionuclides located in the surface atmospheric layer.
Application of the medical linear accelerator ELEKTA AXESSE in the study of sorption properties of impurities and absorption coefficients of medium and heavy chemical elements
Authors: Zaripova Yu A , Gladkikh T M , Bigeldiyeva M T , Dyachkov V V , Yushkov A V 
Published: January 2022

Abstract
In this article, the authors propose a new technique for measuring linear attenuation coefficients on the medical linear accelerator Elekta Axesse. Linear attenuation coefficients were obtained for four samples at different concentrations of substances at a gamma-ray energy of 6 MeV. A unified ionization chamber was used as a detector to register the transmitted gamma-ray beam through the samples under study. Linear absorption coefficients were obtained for elements B, C, O, S, Fe, Ba taking into account their concentration, as well as taking into account the different mass inclusion of paraffin in the samples under study, which is acyclic hydrocarbons CnH2n+2. The measurement results showed that taking into account certain components in impurities leads to relatively small, but quite noticeable differences in the determination of the total absorption coefficients. This is especially important to take into account for determining the concentration of light elements in samples. To determine the content of medium and heavy chemical elements, taking into account the content of light elements can be neglected. The use of a 6 MeV gamma-ray beam made it possible to reduce the errors in determining the absorption coefficients, since their dependence on energy in the region of applicable gamma-ray energies is not so great in comparison with the low-energy region, in which the shell effects for heavy elements will introduce significant contribution.
High-sensitive colour indicator of absorbed dose of epithermal neutrons radiation
Authors: Kulabdullaev G A , Kim A A , Djuraeva G T , Nebesniy A F , Abdullaeva G A , Yuldashev D O 
Published: January 2022

Abstract
In our study, the high-sensitivity colour indicator of the absorbed dose of radiation of epithermal neutrons with energy 0 to 10 keV for dosimetry of low-energy neutrons was developed. We had been developed an indicator on the basis of the dye solution of arsenazo III and gadopentetic acid, allowing precisely define of absorbed dose in the range 2 to 103 Gy. The properties of arsenazo III as metallic indicator, which changes colour after binding of free ions of metals, were used. Colour of the indicator solution before irradiation and after it is stable enough in time at storage in the dark, at artificial illumination or at scattered sunlight. The developed indicator, consisting of a solution of arsenazo III and gadopentetic acid, allows estimating the absorbed dose of epithermal neutron irradiation with good accuracy and reduces the error of measurement related to changing colour of dye under the influence of other factors (light, temperature etc.) Dosimeter is tissue-equivalent and possesses a high-sensitivity neutron radiation due to the content of gadolinium in solution, which has great neutron capture cross-section. The developed dosimeter persists spectrophotometric characteristics after irradiaion within few weeks that allows to use it for measurement of the absorbed dose, both in real time mode and with the delayed measurement within few weeks.
Synthesis of CH3NH3PbI3 hybrid perovskite crystals for optoelectronic devices
Authors: Dyussembekova S , Kinev V , Smirnova A , Gladyshev P 
Published: January 2022

Abstract
This article is deals with the synthesis of hybrid organic-inorganic perovskite crystals. We present a brief review of the literature devoted to the synthesis of perovskite crystals. We have analyzed methods for the synthesis of perovskite crystals to select optimal conditions. Bulk perovskite crystals of CH3NH3PbI3 were synthesized. The resulting crystals were analyzed by X-ray phase analysis, which confirms the formation of tetragonal perovskite crystals and can be used to obtain thin films.
Quality control challenges post covid-19 crisis: an integrated IoT and IoP approach
Authors: Sharma Harshit , Sumathi G 
Published: January 2022

Abstract
The Covid -19 is arguably the biggest pandemic in history and there are a lot of challenges that must be dealt with. One of the biggest challenges post Covid-19 is to tackle quality control challenges. This research paper discusses some of these challenges and solutions using an integrated internet of things (IoT) and internet of protocols (IoP) based approach and further showing its implementation in the industry world and hence, proving to be a solution for damage assessment. With the help of IoT- enabled quality control system, six-sigma rule is also analysed. Post Covid crisis, it is important for every institution to gain back customer trust so quality of materials should be maintained and IoT enables us to do the same. The unification of industrial IoT (IIoT) and industry 4.0 is also discussed as it leads us to understand that this unification is the next evolution of smart manufacturing and digital technologies. This methodology can lead us to accelerated innovation in applications for overcoming the eventual challenges post Covid in the near future. Also, small-scale/large-scale companies making use of the above research methodology can adhere to six-sigma criterion.
Performance analysis of cost effective multi-hop Time Sensitive Network for IEEE 802.1Qbv and IEEE 802.1Qbu standards
Authors: Hagargund Asha G , kulkarni Muralidhar , Satheesh Hariram S 
Published: January 2022

Abstract
Time-Sensitive Networking (TSN) is an emerging technology, which enables advancements in applications like industrial automation, automatic vehicle-to-vehicle communication, etc. which hosts various time-critical applications, ensuring bounded latency. The novel idea of this paper is to present OMNET++ simulation-based complex multi-hop TSN network using the native VLAN concept to bring out a cost-effective model for inter-TSN and Intra-TSN domains. This paper investigates the performance of hybrid IEEE standards, ie.IEEE 802.1Qbu and IEEE 802.1Qbv standards. The simulation results show that the combination of these standards, when effectively scheduled in switches will reduce the latency by 3.3 µseconds in time-critical applications. Further, it is observed that in Best effort traffic, frame loss is also very less in the range of 2-5 frames out of 1385 frames. These results certainly will be of great value in more complex TSN deployments.
Forecasting variance of NiftyIT index with RNN and DNN
Authors: Karthik C R , Raghunandan  , Ashwath Rao B , Subba Reddy N V 
Published: January 2022

Abstract
A time series is an order of observations engaged serially in time. The prime objective of time series analysis is to build mathematical models that provide reasonable descriptions from training data. The goal of time series analysis is to forecast the forthcoming values of a series based on the history of the same series. Forecasting of stock markets is a thought-provoking problem because of the number of possible variables as well as volatile noise that may contribute to the prices of the stock. However, the capability to analyze stock market leanings could be vital to investors, traders and researchers, hence has been of continued interest. Plentiful arithmetical and machine learning practices have been discovered for stock analysis and forecasting/prediction. In this paper, we perform a comparative study on two very capable artificial neural network models i) Deep Neural Network (DNN) and ii) Long Short-Term Memory (LSTM) a type of recurrent neural network (RNN) in predicting the daily variance of NIFTYIT in BSE (Bombay Stock Exchange) and NSE (National Stock Exchange) markets. DNN was chosen due to its capability to handle complex data with substantial performance and better generalization without being saturated. LSTM model was decided, as it contains intermediary memory which can hold the historic patterns and occurrence of the next prediction depends on the values that preceded it. With both networks, measures were taken to reduce overfitting. Daily predictions of the NIFTYIT index were made to test the generalizability of the models. Both networks performed well at making daily predictions, and both generalized admirably to make daily predictions of the NiftyIT data. The LSTM-RNN outpaced the DNN in terms of forecasting and thus, grips more potential for making longer-term estimates.
Classification of plant seedlings using deep convolutional neural network architectures
Authors: Makanapura Namratha , Sujatha C , Patil Prakash R , Desai Padmashree 
Published: January 2022

Abstract
Weed management has a vital role in applications of agriculture domain. One of the key tasks is to identify the weeds after few days of plant germination which helps the farmers to perform early-stage weed management to reduce the contrary impacts on crop growth. Thus, we aim to classify the seedlings of crop and weed species. In this work, we propose a plant seedlings classification using the benchmark plant seedlings dataset. The dataset contains the images of 12 different species where three belongs to plant species and the other nine belongs to weed species. We implement the classification framework using three different deep convolutional neural network architectures, namely ResNet50V2, MobileNetV2 and EfficientNetB0. We train the models using transfer learning and compare the performance of each model on a test dataset of 833 images. We compare the three models and demonstrate that the EfficientNetB0 performs better with an average F1-Score of 96.26% and an accuracy of 96.52%.
Temporal face feature progression with cycle GAN
Authors: Ashish Roy , Prasad B G 
Published: January 2022

Abstract
The aging process creates significant changes in the appearances of people’s faces. When compared to other causes of variation in face imaging, aging-related variation has specific distinct properties. Facial Aging variations, for example, is unique for each person; it occurs gradually and is significantly influenced by other characteristics including health, gender, and life-style. As a result, the proposed effort will use Generative Adversarial Networks to address these critical concerns (GANs). Generative Adversarial Networks (GAN’s) is made up of a generator and a discriminator network. The generator model generates images that a discriminator model analyses to determine if they are real or fake. This paper provides a Temporal Face Feature Progressive framework with Cycle GAN, which maintains the initial appearance and identity in the elderly aspect of their facial structure. To address aging concerns, our goal is to transform an initial age category image into a targeted age with age progression. We show that our temporal face features progressive cycle GAN learns and transfers facial traits from the source group to the targeted group by training various images. The IMDB-WIKI Face dataset has been used to obtain the results for the same.
Medical diagnosis of COVID-19 using blood tests and machine learning
Authors: Chadaga Krishnaraj , Prabhu Srikanth , Vivekananda Bhat K , Umakanth Shashikiran , Sampathila Niranjana 
Published: January 2022

Abstract
Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2), colloquially known as Coronavirus surfaced in late 2019 and is an extremely dangerous disease. RT-PCR (Reverse transcription Polymerase Chain Reaction) tests are extensively used in COVID-19 diagnosis. However, they are prone to a lot of false negatives and erroneous results. Hence, alternate methods are being researched and discovered for the detection of this infectious disease. We diagnose and forecast COVID-19 with the help of routine blood tests and Artificial Intelligence in this paper. The COVID-19 patient dataset was obtained from Israelita Albert Einstein Hospital, Brazil. Logistic regression, random forest, k nearest neighbours and Xgboost were the classifiers used for prediction. Since the dataset was extremely unbalanced, a technique called SMOTE was used to perform oversampling. Random forest obtained optimal results with an accuracy of 92%. The most important parameters according to the study were leukocytes, eosinophils, platelets and monocytes. This preliminary COVID-19 detection can be utilised in conjunction with RT-PCR testing to improve sensitivity, as well as in further pandemic outbreaks.
Drone to Obstacle Distance Estimation Using YOLO V3 Network and Mathematical Principles
Authors: Aswini N , Uma S V , Akhilesh V 
Published: January 2022

Abstract
Now a days, drones are very commonly used in various real time applications. Moving towards autonomy, these drones rely on obstacle detection sensors and various collision avoidance algorithms programmed into it. Development of fully autonomous drones provide the fundamental benefits of being able to operate in hazardous environments without a human pilot. Among the various sensors, monocular cameras provide a rich source of information and are one of the main sensing mechanisms in low flying drones. These drones can be used for rescue and search operations, traffic monitoring, infrastructure, and pipeline inspection, and in construction sites. In this paper, we propose an onboard obstacle detection model using deep learning techniques, combined with a mathematical approach to calculate the distance between the detected obstacle and the drone. This when implemented does not need any additional sensor or Global Positioning Systems (GPS) other than the vision sensor.
Area and Energy Efficient QCA Based Compact Serial Concatenated Convolutional Code Encoder
Authors: Premananda B.S. , Dhanush T.N. , Parashar Vaishnavi S. 
Published: January 2022

Abstract
Quantum-dot Cellular Automata (QCA) is a transistor-less technology known for its low power consumption and higher clock rate. Serial Concatenated Convolutional Coding (SCCC) encoder is a class of forward error correction. This paper picturizes the implementation of the outer encoder as a (7, 4, 1) Bose Chaudhary Hocquenghem encoder that serves the purpose of burst error correction, a pseudo-random inter-leaver used for permuting of systematic code words and finally the inner encoder which is used for the correction of random errors in QCA. Two different architectures of the SCCC encoder have been proposed and discussed in this study. In the proposed two architectures, the first based on external clock signals whereas the second based on internal clock generation. The sub-blocks outer encoder, pseudo-random inter-leaver and inner encoder of the SCCC encoder are optimized, implemented and simulated using QCADesigner and then integrated to design a compact SCCC encoder. The energy dissipation is computed using QCADesigner-E. The proposed SCCC encoder reduced the total area by 46% and energy dissipation by 50% when compared to the reference SCCC encoder. The proposed encoders are more efficient in terms of cell count, energy dissipation and area occupancy respectively.