bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neutron diffraction study of the Ti1-xMoxC alloy
Authors: Khidirov I , Rakhmanov S J , Parpiev A S 
Published: January 2022

Abstract
In this work, the crystal structure and (RMSD) root-mean-square displacement of atoms in new cubic refractory interstitial alloys Ti0.74Mo0.26C and Ti0.70Mo0.30C were determined by neutron diffraction. These alloys are obtained by powder metallurgy by sintering fine powders of cubic titanium carbides TiC and molybdenum MoC. The values of the RMSD of atoms in these alloys, obtained by the methods of least squares and full-profile analysis from the neutron diffraction data, turned out to be identical. They turned out to be much larger than in the stoichiometric TiC alloy, which has a similar cubic structure and close geometric parameters of atoms with the Ti0.74Mo0.26C and Ti0.70Mo0.30C alloys. These alloys were explained by the occurrence of large static distortions in the lattice of the Ti0.70Mo0.30C alloy due to the differences in the masses and atomic radii of the titanium and molybdenum elements.
Theoretical aspects of direct conversion of radio-chemical energy in electric by radiation-stimulated SiC*/Si heterostructure
Authors: Gurskaya A V , Chepurnov V I , Dolgopolov M V , Puzyrnaya G V , Petenko I A 
Published: January 2022

Abstract
The authors consider their own CVD technology for the SiC growing on a Si substrate in order to create a beta converter. Since the beta converter contains a heavy C-14 atom, the finished beta converter works as an ”inner sun”, and the structure has specific mark * in the name: SiC*/Si. Authors focus on the problems of the theoretical description of: 1) the growth of the SiC*/Si film (with C-14 atoms inside) and the position of the p-n junction in the doping process; 2) method of a placement radioisotopes into a semiconductor material; 3) physical properties of radioisotopes; 4) defects formation; 5) generation of secondary electrons in the region of the p-n junction.
Integration of the differential evolution algorithm and the Monte Carlo code to create a spectrometric detector model
Authors: Prozorova I V 
Published: January 2022

Abstract
A standard procedure for characterizing the high-purity germanium detector (HPGe), manufactured by Canberra Industries Inc [1], is performed directly by the company using patented methods. However, the procedure is usually expensive and must be repeated because the characteristics of the HPGe crystal change over time. In this work, the principles of a technique are developed for use in obtaining and optimizing the detector characteristics based on a cost-effective procedure in a standard research laboratory. The technique requires that the detector geometric parameters are determined with maximum accuracy by the Monte Carlo method [2] in parallel with the optimization based on evolutionary algorithms. The development of this approach facilitates modeling of the HPGe detector as a standardized procedure. The results will be also beneficial in the development of gamma spectrometers and/or their calibrations before routine measurements.
Determination of the content of rare metals in spent anion-exchange resin SIM202 by methods of neutron-activation and mass-spectrometric analysis
Authors: Sadikov I I , Kh Yarmatov B , Usmanov T M 
Published: January 2022

Abstract
The work is devoted to the development of methods for determining the elemental and isotopic composition of spent ion-exchange resin, industrial waste and environmental objects using an inductively coupled plasma mass spectrometer and analyzing specific samples to determine the content of noble and rare metals in technological materials, industrial waste and natural objects with application of the developed techniques. This article determines the elemental composition of the spent ion-exchange resin SIM202 with the Inductively coupled plasma mass spectrometry (ICP-MS) method and also shows the comparison of the results with neutron activation analysis (NAA). The distribution coefficient of elements in a chromatographic column in ion-exchange resins TAO and SIM202 is given.
The current state of uranium legacy sites remediation issues in Uzbekistan
Authors: Kuldjanov BK 
Published: January 2022

Abstract
The uranium production legacy sites in Central Asia are a source of high-level radiological risks for the population and the environment. This fact is recognized by the international community, which is taking measures to remediate the sites. A number of international and national projects have been implemented in the Republic of Uzbekistan, but there is no single source that covers their main results. The article provides a brief overview of the main international initiatives, the results obtained and the lessons learned. The list of necessary measures for the nearest future to recover the environment and ensure normal conditions for the local population is given. The obtained material can be used for planning and implementation remediation of other uranium legacy sites in Central Asia, as well as similar objects around the world.
Technologies for studying functional neural networks of the human brain based on data of nuclear functional magnetic tomography
Authors: Enyagina I M , Poyda A A , Orlov V A , Kozlov S O , Polyakov A N , Ushakov V L , Sharaev M G 
Published: January 2022

Abstract
Nuclear functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the functional activity of the human brain. In particular, this method is used in medicine to obtain information about the state of the functional networks of the patient’s brain. However, the process of processing and analysis of experimental fMRI data is complex and requires the selection of the correct technique, depending on the specific task. Practice has shown that different processing methods can give slightly different results for the same set of fMRI data. There are a number of alternative specialized software packages for processing and analysis, but the methodology still needs improvement and development. We are working in this direction: we analyze the effectiveness of existing methods; we develop our own methods; we create software services for processing and analysis of fMRI data on the basis of the distributed modular platform “Digital Laboratory”, with the involvement of the supercomputer NRC “Kurchatov Institute”. For research we use experimental fMRI data obtained on the scanner Siemens Verio Magnetom 3T at the Kurchatov Institute. One of our tasks within the framework of this project is to improve the technology for studying large-scale functional areas of the cerebral cortex at rest. To build a hierarchical model of interaction of large-scale neural networks, a verified binding of functional areas to anatomy is required. Today, there are a number of generally accepted atlases of the functional areas of the human cerebral cortex, which, nevertheless, are constantly being finalized and refined. This article presents the results of our study of the Glasser atlas for the consistency of voxels within one region and the connectivity metrics of voxel dynamics.
Multistate Active Combined Power and Message/Data Rate Adaptive Decentralized Congestion Control Mechanisms for Vehicular Ad Hoc Networks
Authors: Deeksha M , Patil Ashish , Kulkarni Muralidhar , V. Shet N. Shekar , Muthuchidambaranathan P. 
Published: January 2022

Abstract
Vehicular ad hoc networks (VANETs) have emerged in time to reduce on-road fatalities and provide efficient information exchange for entertainment-related applications to users in a well-organized manner. VANETs are the most instrumental elements in the Internet of Things (IoT). The objective lies in connecting every vehicle to every other vehicle to improve the user’s quality of life. This aim of continuous connectivity and information exchange leads to the generation of more information in the medium, which could congest the medium to a larger extent. Decentralized congestion control (DCC) techniques are specified to reduce medium congestion and provide various safety applications. This article presents two DCC mechanisms that adapt message rate and data rate combined with transmit power control mechanism. These mechanisms are developed under multi-state active design proposed by the standard. The proposed methods deliver better performance over other mechanisms in terms of power, channel load, and channel utilization using real-time-based scenarios by simulation in SUMO.
Real Time Face Mask Detection using Google Cloud ML and Flutter
Authors: Kummar Sohit , Mohanty Asutosh , Jyotsna  , Chakraborty Sudeshna 
Published: January 2022

Abstract
Coronavirus (Covid-19) pandemic has impacted the whole world and has forced health emergencies internationally. The contact of this pandemic has been fallen over almost all the development sectors. A lot of precautionary measures have been taken to control the Covid-19 spread, where wearing a face mask is an essential precaution. Wearing a face mask correctly has been essential in controlling the Covid-19 transmission. Moreover, this research aims to detect the face mask with fine-grained wearing states: face with the correct mask and face without mask. Our work has two challenging tasks due to two main reasons firstly the presence of augmented data set available in the online market and the training of large datasets. This paper represents a mobile application for face mask detection. The fully automated Machine Learning Cloud service known as Google Cloud ML API is used for training the model in TensorFlow file format. This paper highlights the efficiency of the ML model. Additionally, this paper examines the advancement of the cloud technology used for machine learning over the traditional coding methods.
Performance analysis of texture characterization techniques for lung nodule classification
Authors: Devdatt Kawathekar Ishan , Shaju Areeckal Anu 
Published: January 2022

Abstract
Lung cancer ranks very high on a global index for cancer-related casualties. With early detection of lung cancer, the rate of survival increases to 80-90%. The standard method for diagnosing lung cancer from Computed Tomography (CT) scans is by manual annotation and detection of the cancerous regions, which is a tedious task for radiologists. This paper proposes a machine learning approach for multi-class classification of the lung nodules into solid, semi-solid, and Ground Glass Object texture classes. We employ feature extraction techniques, such as gray-level co-occurrence matrix, Gabor filters, and local binary pattern, and validate the performance on the LNDb dataset. The best performing classifier displays an accuracy of 94% and an F1-score of 0.92. The proposed approach was compared with related work using the same dataset. The results are promising, and the proposed method can be used to diagnose lung cancer accurately.
Design and Implementation of Carbon Nano-tube based Full Adder at 32nm Technology for High Speed and Power Efficient Arithmetic Applications
Authors: Ahmed Khan Imran 
Published: January 2022

Abstract
Due to physical, material, technological, power-thermal and economical difficulties, scaling of CMOS transistors will stop very soon. Due to efficiency of power and speed compared to CMOS transistors, Carbon Nano-tube transistors are best suitable element to design logic circuits. So, CNTFETS have been utilized in designing of proposed full adder (FA) and 4-bit ripple carry adder (RCA) in this paper. Proposed FA and RCA have been compared to rival designs on bases of power, speed and power-delay-product (PDP). FA and RCA circuits have been analysed with the variation of temperature from 0°C to 100°C while the variation of supply voltages is from 0.7V to 1.3V. For all temperatures and all supply voltages, proposed FA and proposed RCA have the least power consumption, shortest delay and lowest PDP. SPICE has been utilized for simulating FAs and RCAs in 32 nm process node. Even though the fabrication is complicated than CMOS counterparts but simulation results confirm usefulness of proposed FA and RCA for high speed and power efficient arithmetic applications.
Machine Learning Approaches in Stock Price Prediction: A Systematic Review
Authors: Soni Payal , Tewari Yogya , Krishnan Deepa 
Published: January 2022

AbstractPrediction of stock prices is one of the most researched topics and gathers interest from academia and the industry alike. With the emergence of Artificial Intelligence, various algorithms have been employed in order to predict the equity market movement. The combined application of statistics and machine learning algorithms have been designed either for predicting the opening price of the stock the very next day or understanding the long term market in the future. This paper explores the different techniques that are used in the prediction of share prices from traditional machine learning and deep learning methods to neural networks and graph-based approaches. It draws a detailed analysis of the techniques employed in predicting the stock prices as well as explores the challenges entailed along with the future scope of work in the domain.
Measurement of ionization loss of 50 GeV protons in silicon with smoothly tunable up to 1 cm thickness using a single flat detector
Authors: Nazhmudinov R.M. , Shchagin A.V. , Kubankin A.S. , Afonin A.G. , Britvich G.I. , Durum A.A. , Kostin M.Y. , Maisheev V.A. , Pitalev V.I. , Chesnokov Y.A. , Yanovich A.A. 
Published: January 2022

Abstract
Research of the ionization loss of 50 GeV protons, the path of which in the depleted layer of the silicon detector was smoothly regulated in the range from 0.3 to 10 mm, is presented. In the experiment, we used a flat silicon detector with a fixed thickness of the depleted layer of 300 μm. The smooth regulation of the path was realized due to the variation of the angle between the surface of the detector and the incident proton beam. The comparison of experimental data and theoretical calculations of the ionization loss demonstrates agreement in all range of thicknesses. Results of the research can be used in order to control the angle between the surface of the detector and the incident beam of relativistic particles. Besides, the results can be used in the analysis of data from astrophysical silicon detectors of charged particles if high-energy particles crossed flat detectors at arbitrary angle.
Stronger Arctic amplification from ozone-depleting substances than from carbon dioxide
Authors: Liang Yu-Chiao , Polvani Lorenzo M , Previdi Michael , Smith Karen L , England Mark R , Chiodo Gabriel 
Published: January 2022

Abstract
Arctic amplification (AA)—the greater warming of the Arctic near-surface temperature relative to its global mean value—is a prominent feature of the climate response to increasing greenhouse gases. Recent work has revealed the importance of ozone-depleting substances (ODS) in contributing to Arctic warming and sea-ice loss. Here, using ensembles of climate model integrations, we expand on that work and directly contrast Arctic warming from ODS to that from carbon dioxide (CO2), over the 1955–2005 period when ODS loading peaked. We find that the Arctic warming and sea-ice loss from ODS are slightly more than half (52%–59%) those from CO2. We further show that the strength of AA for ODS is 1.44 times larger than that for CO2, and that this mainly stems from more positive Planck, albedo, lapse-rate, and cloud feedbacks. Our results suggest that AA would be considerably stronger than presently observed had the Montreal Protocol not been signed.
Wear behavior of hydroxyapatite reinforced polymer composite for biomedical applications
Authors: Srinivasan D , Balasundaram R , Ravichandran M , Sakthi balan G , Vijay S , Vishnu S 
Published: January 2022

Abstract
In medical fields, hydroxyapatite (HA) is used for restoring the human bones and teeth. Nowadays, newer bio composites are being developed to replace the broken or removed teeth in human using hydroxyapatite. The original teeth may be removed from human, due to various reasons such as tooth decay, periodontal disease and gum diseases. Due to the bioactivity property of HA, it helps the tooth to grow. In this work, tribological properties of HA powder reinforced composite are investigated. Three most important factors that affect the properties of the composites were chosen and varied during wear testing of the composites. The HA powder weight %, applied load (P) and sliding distance (SD) were varied on the basis of Taguchi’s experimental design. Analysis of variance (ANOVA) and (Signal to Noise) SN ratio analysis was used to study the influence of each factor on the specific wear rate (SWR). It was concluded that the HA wt% highly influences the SWR of the composite during pin on disc testing.
Improving element uniformity and mechanical properties of Al–Mg–Si alloy fabricated by twin roll casting with superheat melt treatment
Authors: Wei Bowen , Jiang Tao , Li Shiju , Zhang Youyun , Xu Guangming , Li Yong , Wang Zhaodong 
Published: January 2022

Abstract
The effect of superheat melt treatment(SMT) on microstructure and properties of Al–Mg–Si alloy fabricated by twin roll casting(TRC) was analyzed using optical microscope, scanning electron microscopy, emission electron probe micro analyzer and transmission electron microscopy. SMT increased the subcooling degree of melt during TRC. The solidification microstructure with high dendrite density and small dendrite spacing was obtained. The second phase was evenly distributed between dendrites and the solute concentration gradient was decreased. Intergranular solute aggregation caused by Reynolds’ dilatancy in TRC slab was effectively suppressed. The homogeneous solute distribution of TRC slab with SMT can be realized by short-time homogenization heat treatment. The size of insoluble particles was greatly reduced. The complete decomposition of non-equilibrium eutectic phase increased the solute concentration in α-Al, which promoted the precipitation of precipitates during aging heat treatment. The tensile strength and yield strength of T6 slab were improved, while the uniform elongation are almost not decreased. The strength and uniform of slab in T4P state were both improved. The obtained results can help further shorten the production cycle of TRC slabs and improve mechanical properties.
An electromagnetic wave attenuation superposition structure for thin-layer plasma
Authors: ZHANG Wenyuan , XU Haojun , PEI Binbin , WEI Xiaolong , FENG Pei , ZHANG Lin 
Published: January 2022

Abstract
This work proposes a new plasma super-phase gradient metasurfaces (PS-PGMs) structure, owing to the limitations of the thin-layer plasma for electromagnetic wave attenuation. Based on the cross-shaped surface unit configuration, we have designed the X-band absorbing structure through the dispersion control method. By setting up the Drude dispersion model in the computer simulation technology, the designed phase gradient metasurfaces structure is superposed over the plasma, and the PS-PGMs structure is constructed. The electromagnetic scattering characteristics of the new structure have been simulated, and the reflectance measurement has been carried out to verify the absorbing effect. The results demonstrate that the attenuation effect of the new structure is superior to that of the pure plasma structure, which invokes an improved attenuation effect from the thin layer plasma, thus enhancing the feasibility of applying the plasma stealth technology to the local stealth of the strong scattering part of a combat aircraft.
Healthcare for older people in lower and middle income countries
Authors: Tan Maw Pin 
Published: January 2022

Abstract
Two-thirds of the world’s population aged 60 years and over will reside in lower and middle income countries (LMIC) by 2050. Many LMICs are experiencing rapid population ageing at a faster rate than in Western Europe and North America, but may not have the resources to respond to the World Health Organization’s call to action. As population ageing is a global issue, effective and sustainable global solutions are much needed. Proposed strategies include stemming the outflow of trained healthcare workers to high-income nations where migrants from LMICs often contribute to the work force caring for older people. Public education, preventive measures and innovative approaches to training are additional proposed solutions. Higher income countries have a responsibility to contribute towards the development of healthcare services for older people in LMICs.
Metalworking Fluid Exposure and Stroke Mortality Among US Autoworkers
Authors: Elser Holly , Chen Kevin T , Arteaga Daniel , Reimer Richard , Picciotto Sally , Costello Sadie , Eisen Ellen A 
Published: January 2022

Abstract
Although air pollution is an important risk factor for stroke, few studies have considered the impact of workplace exposure to particulate matter (PM). We examined implications of exposure to PM composed of metalworking fluids (MWFs) for stroke mortality in the United Autoworkers–General Motors cohort. Cox proportional hazards models with age as the timescale were used to estimate the association of cumulative straight, soluble, and synthetic MWF exposure with stroke mortality, controlling for sex, race, plant, calendar year, and hire year. Among 38,553 autoworkers followed during 1941–1995, we identified 114 ischemic stroke deaths and 113 hemorrhagic stroke deaths. Overall stroke mortality risk was increased among workers in the middle exposure category for straight MWF (hazard ratio (HR) = 1.31, 95% confidence interval (CI): 0.87, 1.98) and workers in the highest exposure category for synthetic MWF (HR = 1.94, 95% CI: 1.13, 3.16) compared with workers who had no direct exposure. Ischemic stroke mortality risk was increased among workers in the highest exposure categories for straight MWF (HR = 1.45, 95% CI: 0.83, 2.52) and synthetic MWF (HR = 2.39, 95% CI: 1.39, 4.50). We observed no clear relationship between MWF exposure and hemorrhagic stroke mortality. Our results support a potentially important role for occupational PM exposures in stroke mortality and indicate the need for further studies of PM exposure and stroke in varied occupational settings.
Pesticide Residue Intake From Fruit and Vegetable Consumption and Risk of Glioma
Authors: Cote David J , Bever Alaina M , Chiu Yu-Han , Sandoval-Insausti Helena , Smith-Warner Stephanie A , Chavarro Jorge E , Stampfer Meir J 
Published: January 2022

Abstract
We aimed to determine whether intake of pesticide residues from fruits and vegetables was associated with glioma. Within 3 prospective cohorts from 1998–2016—the Nurses’ Health Study (NHS), Nurses’ Health Study II (NHSII), and Health Professionals Follow-up Study—we computed multivariable-adjusted hazard ratios (MVHRs) and 95% confidence intervals (CI) for glioma by quintiles of intake of low- and high-pesticide-residue fruits and vegetables using Cox proportional hazards regression. Fruits and vegetables were categorized as high or low residue using a validated method based on pesticide surveillance data. We confirmed 275 glioma cases across 2,745,862 person-years. A significant association was observed between intake of high-residue fruits and vegetables and glioma in NHS (MVHR = 2.99, 95% CI: 1.38, 6.44 comparing highest with lowest quintile, P for trend = 0.02). This was not identified in NHSII (MVHR = 0.52, 95% CI: 0.19, 1.45, P for trend = 0.20) or Health Professionals Follow-up Study (MVHR = 1.01, 95% CI: 0.42, 2.45, P for trend = 0.39). No significant associations were observed by intake of low-residue fruits and vegetables; overall intake was not significantly associated with glioma in any cohort. We found no evidence for an inverse relationship of fruit and vegetable intake with glioma. Although limited in power, this study suggests a possible association between fruit-and-vegetable pesticide residue intake and risk of glioma that merits further study.