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Ab-initio Study of Structural and Electronic Properties of Perovskite Nanocrystals of the CsSn[Br1−xIx]3 Family
Get PDFIn this study, by means of quantum-chemical calculations within the framework of density functional theory, we considered a number of structural and electronic properties of nanocrystals of the CsSn[Br1−xIx]3 (systems CsSnBr3, CsSnBr2I, CsSnBrI2 and CsSnI3) and discussed the effect of iodine concentration on the geometry and electronic properties of these materials. The exchange correlation effects of electrons were taken into account by the LDA, GGA and the modified Becke-Jones exchange correlation potential (mBJ). The results obtained in the framework of the DFT-mBJ and the Wien2k packages are in good agreement with the data from experimental measurements and open up the possibility of accurately predicting a number of fundamental properties of perovskite-like complex structures and the development of new materials. Doi: 10.28991/HIJ-2022-03-02-03 Full Text: PD
The Performance of a Cross-flow Turbine as a Function of Flowrates and Guide Vane Angles
Get PDFThis study looked at the effects of flow rates and guide vane angles on the performance of a cross flow turbine, which can be used to generate energy and hydraulic power with low head and low flow rates of water. A fluid dynamic analysis was performed on the cross-flow turbine with the aid of finite element techniques. The simulation was solved after validating the convergence of the provided model and its boundary conditions, with the outputs being the velocity profiles of the flow in the rotor and the pressure distribution on the rotor surface during its rotations. Experimental evaluation of the cross-flow turbine guide vane positions at a flow rate of 0.8, 0.6, and 0.5 m3/s was conducted, and it was discovered that a maximum turbine speed of 482 rpm and a generator speed of 1920 rpm were produced at the rotor shaft at a flow rate of 0.8 m3/s with a head of 25 m, and this data was validated by the results produced from the simulation. Doi: 10.28991/HIJ-2022-03-01-06 Full Text: PD
Social Network Analysis of Cryptocurrency using Business Intelligence Dashboard
Get PDFThere are currently more than 10.000 cryptocurrencies available to buy from the online market, with a vast range of prices for each coin it sells. The fluctuation of each coin is affected by any social events or by several important companies or people behind it. The aim of this research is to compare three cryptocurrencies, which are Bitcoin, Ethereum, and Binance Coin, using Social Network Analysis (SNA) by visualizing them using Business Intelligence (BI Dashboard). This study uses the SNA parameters of degree, diameter, modularity, centrality, and path length for each network and its actors and their actual market price by crawling(data collecting process) from Twitter as one of the social media platforms. From the research conducted, the popularity of cryptocurrencies is affected by their market price and the activeness of their actors on social media. These results are important because they could help in the decision-making to buy cryptocurrencies with high popularity on social media because they tend to retain their value over time and could benefit from price spikes from influential people. Doi: 10.28991/HIJ-2022-03-02-09 Full Text: PD
DeepImageTranslator V2: Analysis of Multimodal Medical Images using Semantic Segmentation Maps Generated through Deep Learning
Get PDFIntroduction: Analysis of multimodal medical images often requires the selection of one or many anatomical regions of interest (ROIs) for extraction of useful statistics. This task can prove laborious when a manual approach is used. We have previously developed a user-friendly software tool for image-to-image translation using deep learning. Therefore, we present herein an update to the DeepImageTranslator V2 software with the addition of a tool for multimodal medical image segmentation analysis (hereby referred to as the MMMISA). Methods: The MMMISA was implemented using the Tkinter library; backend computations were implemented using the Pydicom, Numpy, and OpenCV libraries. We tested our software using 4188 slices from whole-body axial 2-deoxy-2-[18F]-fluoroglucose-position emission tomography/ computed tomography scans ([¹â¸F]-FDG-PET/CT) of 10 patients from the American College of Radiology Imaging Network-Head and Neck Squamous Cell Carcinoma (ACRIN-HNSCC) database. Using the deep learning software DeepImageTranslator, a model was trained with 36 randomly selected CT slices and manually labelled semantic segmentation maps. Utilizing the trained model, all the CT scans of the 10 HNSCC patients were segmented with high accuracy. Segmentation maps generated using the deep convolutional network were then used to measure organ specific [¹â¸F]-FDG uptake. We also compared measurements performed using the MMMISA and those made with manually selected ROIs. Results: The MMMISA is a tool that allows user to select ROIs based on deep learning-generated segmentation maps and to compute accurate statistics for these ROIs based on coregistered multimodal images. We found that organ-specific [¹â¸F]-FDG uptake measured using multiple manually selected ROIs is concordant with whole-tissue measurements made with segmentation maps using the MMMISA tool. Doi: 10.28991/HIJ-2022-03-03-07 Full Text: PD
Eye Tracking Algorithm Based on Multi Model Kalman Filter
Get PDFOne of the most important pieces of Human Machine Interface (HMI) equipment is an eye tracking system that is used for many different applications. This paper aims to present an algorithm in order to improve the efficiency of eye tracking in the image by means of a multi-model Kalman filter. In the classical Kalman filter, one model is used for estimation of the object, but in the multi-model Kalman filter, several models are used for estimating the object. The important features of the multiple-model Kalman filter are improving the efficiency and reducing its estimating errors relative to the classical Kalman filter. The proposed algorithm consists of two parts. The first step is recognizing the initial position of the eye, and Support Vector Machine (SVM) has been used in this part. In the second part, the position of the eye is predicted in the next frame by using a multi-model Kalman filter, which applies constant speed and acceleration models based on the normal human eye. Doi: 10.28991/HIJ-2022-03-01-02 Full Text: PD
CFD Study of Behavior of Transition Flow in Distinct Tubes of Miscellaneous Tape Insertions
Get PDFApplication of transition flow can be found in several processes and systems. It has been revealed through findings from various researchers that the values of Reynolds numbers at which transition flow occurs vary. In the current work, investigations were numerically conducted by Fluent on transition of water flow in three assorted plain tubes fitted with miscellaneous tape insertions. They are plain tube with crossed-axes-circle-cut tape insert (C-C tube), plain tube with crossed-axes-triangle-cut tape insert (C-T tube), and plain tube with crossed-axes-ellipse-cut tape insert (C-E tube). The focus of the work is to explore the influence of the tape insertion on commencement and finish of transition flow in the tubes with respect to the Reynolds number of the flow. The Reynolds number (Re) taken into account for the transition flow is 2,150≤Re≤4,650, and the variation of Shear-Stress Transport κ-ω model that deals with transition flow was utilized. The results showed that transition flow starts at Re=2,300 and finishes at Re=4,400 in C-T tube, starts at Re=2,780 and finishes at Re=4,610 in C-C tube, but starts at Re=2,550 and finishes at Re=4,500 in C-E tube. The Nusselt number in C-T tube is 19.3% to 45.6% higher than that in C-C tube, but the Nusselt number in C-T tube is 3.6% to 28.3% more than that in C-E tube. The friction factor in C-T tube is 2.15% to 4.56% higher than that in C-C tube; the friction factor in C-T tube is 0.83% to 3.33% more than that in C-E tube. These results indicate that for the case of the tubes considered in this work, the C-T tube, which is the first one in which transition flow commences and ends, has the highest Nusselt number, but C-C tube, in which transition flow commences and finishes last, has the least Nusselt number. Interestingly, the same phenomenon applies to the friction factor. Doi: 10.28991/HIJ-2022-03-02-02 Full Text: PD
Sensor Technology for Opening New Pathways in Diagnosis and Therapeutics of Breast, Lung, Colorectal and Prostate Cancer
Get PDFThis study analyzes the interaction between sensor research and technology and different types of cancer (breast, lung, colorectal, and prostate) with the goal of detecting new directions for improving diagnosis and therapeutics in medicine. This study develops an approach to computational scientometrics based on data from the Web of Science from the 1991 to 2021 period. The results of this analysis show the vital role of biosensors and electrochemical biosensors applied in breast cancer, lung cancer, and prostate cancer research. Instead, scientific research of optical sensors is developing main technological trajectories in breast, prostate, and colorectal cancer for improving diagnostics. Finally, oxygen sensor research has a main technological development in breast and lung cancer for new applications in breath analysis directed to treatment processes. Preliminary results presented here clearly illustrate the evolutionary paths of sensor research and technologies that have great potential for developing incremental and radical innovations in cancer diagnosis and therapies. These conclusions are, of course, tentative. There is a need for much more detailed research based on other aspects and factors for detecting stable technological trajectories that can foster the technology transfer of new sensor in cancer research for improving diagnosis and therapeutics, reducing, whenever possible, world-wide mortality of cancer in society.JEL Classification: I10, O30, O31, O32; O33. Doi: 10.28991/HIJ-2022-03-03-010 Full Text: PD
Socioeconomic impacts of Households' Vulnerability during COVID-19 Pandemic in South Africa: Application of Tobit and Probit Models
Get PDFCoronavirus is a public health issue with socioeconomic and livelihood dimensions. The World Health Organization declared the current novel coronavirus disease (COVID-19) epidemic a public health emergency of international concern on January 30, 2020, and a global pandemic on March 11, 2020. The South African government has implemented different strategies, ranging from total lockdown in certain locations and provision of palliatives in some provinces across the country. This study, therefore, investigated the correlates of vulnerability and responsiveness to the adverse impacts of COVID-19 in South Africa. The study utilized primary data collected among 477 respondents. Descriptive statistical tools, Tobit and Probit regression models, were used to analyze the data. The study found different levels of vulnerability (low, medium, and high) and responsiveness among households, including stocking up of food items, remote working, reliance on palliatives, and social grant provision, among others. Some of the correlates of responsiveness to the COVID-19 pandemic include being employed, the type of community, and the income of respondents. The study, therefore, recommends increased investments in welfare programmes (safety nets, palliative measures and economic stimulus packages) as well as capacity building of households through education to reduce vulnerability. Doi: 10.28991/HIJ-2022-03-04-02 Full Text: PD
The Use of a Convolutional Neural Network in Detecting Soldering Faults from a Printed Circuit Board Assembly
Get PDFAutomatic Optical Inspection (AOI) is any method of detecting defects during a Printed Circuit Board (PCB) manufacturing process. Early AOI methods were based on classic image processing algorithms using a reference PCB. The traditional methods require very complex and inflexible preprocessing stages. With recent advances in the field of deep learning, especially Convolutional Neural Networks (CNN), automating various computer vision tasks has been established. Limited research has been carried out in the past on using CNN for AOI. The present systems are inflexible and require a lot of preprocessing steps or a complex illumination system to improve the accuracy. This paper studies the effectiveness of using CNN to detect soldering bridge faults in a PCB assembly. The paper presents a method for designing an optimized CNN architecture to detect soldering faults in a PCBA. The proposed CNN architecture is compared with the state-of-the-art object detection architecture, namely YOLO, with respect to detection accuracy, processing time, and memory requirement. The results of our experiments show that the proposed CNN architecture has a 3.0% better average precision, has 50% less number of parameters and infers in half the time as YOLO. The experimental results prove the effectiveness of using CNN in AOI by using images of a PCB assembly without any reference image, any complex preprocessing stage, or a complex illumination system. Doi: 10.28991/HIJ-2022-03-01-01 Full Text: PD
Analytical Investigation of Higher Education Quality Improvement by Using Six Sigma Approach
Get PDFFor over two decades in India, the technical industry's unique selling proposition (USP), with its wide infrastructure of technical institutes, has been capable of supplying best-in-class engineers. But recently, this claim does not hold water. According to the All India Council for Technical Education (AICTE), about 2.6 lakh mechanical engineers graduate every year in India. But the real count of industry ready mechanical engineers is approximately 7%. Hence, there is a need to assess the quality of engineering education in India to reduce the flaws in higher education. The purpose of the paper is to identify the various defects associated with technical education and eliminate those defects using various quality tools. This research is based on the six sigma technique, which is used to assess the quality criteria proposed by the National Board of Accreditation India (NBA). The proposed model is then applied to a typical tier II Indian engineering college located in south India. Six Sigma has two main methodologies: DMAIC and DFSS. The DMAIC (Define, Measure, Analyze, Improve, and Control) methodology is implemented for existing systems, whereas DFSS (Design for Six Sigma) is for assuring quality in new products. In this project, the conclusion is driven by the DMAIC methodology. Various statistical and non-statistical tools are employed in this research. The tools used are CTS-CTQ, SIPOC, Pareto chart, normal process capability analysis, one-way ANOVA, Ishikawa diagram, FMEA, RCBD, and SPC chart. All the statistical processes are done using Minitab analytical software. From the results, it is identified that the factors that have a risk priority number (RPN) greater than 300 need improvement, such as versatility in program curriculum, laboratories and workshops, and credibility among universities. Six Sigma can be achieved by developing proper strategies for mitigating these defects. Doi: 10.28991/HIJ-2022-03-02-07 Full Text: PD