In this paper, we describe the Graphics Processing Unit (GPU) implementation of our City-LES code on detailed large eddy simulations, including the multi-physical phenomena on fluid dynamics, heat absorption and refle...
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In this paper, we present a parallel hybrid algorithm for solving global optimization problems that is based on the coupling of a stochastic global (Simultaneous-Perturbation Stochastic Approximation, Simulated Anneal...
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The method of fundamental solutions (MFS) is first proposed in 1964 by Kupradze and theoretical basis of this method is constructed at the end of 1980's. As a meshless method, no domain meshing is required for MFS...
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In this work, ab initio spin-polarised Density Functional Theory (DFT) calculations are performed to study the interaction of a Ti atom with a NaAlH4(001) surface. We confirm that an interstitially located Ti atom in ...
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A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,w...
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A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.
Mild traumatic brain injury (mTBI), or concussion, is one of the most common forms of injury sustained throughout Operations Iraqi Freedom and Enduring Freedom. Diagnosis is difficult for many of the symptoms are very...
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Numerical model and finite element analysis of an independent wire rope core (IWRC) which is bent over a sheave is investigated in this paper. 3-D solid model of the IWRC is constructed by using mesh generation techni...
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ISBN:
(纸本)9781618040305
Numerical model and finite element analysis of an independent wire rope core (IWRC) which is bent over a sheave is investigated in this paper. 3-D solid model of the IWRC is constructed by using mesh generation techniques first. Then, problem of wire rope bending over sheave is considered. Finite element model is constructed and analyzed for a wire rope bent over sheave problem here. Two different lengths of IWRCs are analyzed for bending problem, a 9mm and a 300mm lengths respectively. Force distribution within an IWRC is presented by obtained wire-by-wire based numerical results. This analysis gives insight of the wire rope behavior while it is bent over sheave. Using the proposed analysis technique, stress distributions within an IWRC can be obtained for each wire composing the whole geometry.
Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanical (QM) models. However, for different...
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Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find...
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Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things(IoT),sensor technologies,cloud computing,and ***,the latest advances of Artificial Intelligence(AI)tools find helpful for decision-making in innovative healthcare to diagnose several *** Cancer(OC)is a kind of cancer that affects women’s ovaries,and it is tedious to identify OC at the primary stages with a high mortality *** OC data produced by the Internet of Medical Things(IoMT)devices can be utilized to differentiate *** this aspect,this paper introduces a new quantum black widow optimization with a machine learningenabled decision support system(QBWO-MLDSS)for smart *** primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and ***,the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the *** addition,the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature ***,symbiotic organisms search(SOS)with extreme learning machine(ELM)model is applied as a classifier for the detection and classification of ELM model,thereby improving the overall classification *** design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s *** experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset,and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.
The interdisciplinary master's degree program in computationalscience and engineering (CSE) at North Carolina A&T State University, Greensboro, NC is now more than 3 years old, and provides graduate education...
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The interdisciplinary master's degree program in computationalscience and engineering (CSE) at North Carolina A&T State University, Greensboro, NC is now more than 3 years old, and provides graduate education in several computational areas and the associated primary field disciplines. The CSE program since its inception has presently graduated more than 12 students who are currently placed in several major industries. Our CSE graduate program offers an interdisciplinary curriculum combining computational core areas along with various domain areas. The students enrolled in the program begin with diversified backgrounds (prior undergraduate studies in various fields such as engineering, physical sciences, life sciences, mathematics, business, etc), and are required to take four core courses relevant to CSE for their graduation in the areas of applied probability and statistics, comprehensive numerical analysis, Data structures and parallel programming, computational and scientific visualization irrespective of their prior background. The preparation level for the diversified group of students in these courses depends on their undergraduate major. This poses significant challenges to graduate faculty teaching these courses and mentoring these students with diversified backgrounds. Our experiences and observations with the course content and structure, teaching methods, evaluation and student performances in these courses with diversified graduate students and their mentoring for the past 3 years are presented. The performances of the students in these core courses are correlated to their background and analyzed. Our experiences indicate students with a lesser preparation level seem to get geared quickly via peer guidance from the stronger students. In several cases, the performances were not related to an individual's prior background, but on their motivation and willingness to strive and succeed. The experiences indicate that the students can benefit well with additi
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