In the era of social media, the social media and smartphone popularity has enhanced exponentially. By the electronic media, fake news has rising quick with new information which are hugely untrustworthy. The search en...
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Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from ineff...
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Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen
Pursuing nonradiating sources and radiationless motion for accelerated charged particles has captivated physicists for generations. Nonradiating sources represent intricate current-charge configurations that do not em...
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Pursuing nonradiating sources and radiationless motion for accelerated charged particles has captivated physicists for generations. Nonradiating sources represent intricate current-charge configurations that do not emit radiation beyond their source domain. This study investigates a single nonradiating source comprising a low-index dielectric disk excited by a split-ring resonator. Employing analytical and numerical methods, we demonstrate that this configuration supports an anapole state, exhibiting minimal or no radiation, effectively representing a nonradiating source. The radiation suppression is accomplished through the destructive interference of electric dipoles excited on the metallic and dielectric components of the proposed prototype. Transforming the design into a cost-effective device capable of suppressing radiation, we achieve excellent numerical and experimental agreement, affirming the formation of the anapole state using the lowest-order multipoles. Moreover, the devised anapole device is remarkably compact, constructed from a low-index dielectric, and employs readily available components. As a versatile platform, the proposed device can spearhead anapole research for diverse applications, including sensing, wireless charging, radio frequency identification tags, and other nonlinear applications.
Since the late 20th century, engineering Education Research (EER) has been expanding globally as a field, although its identity varies across institutions and countries around the world. This diversity in how EER is e...
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In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Or...
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In early December 2019,the city of Wuhan,China,reported an outbreak of coronavirus disease(COVID-19),caused by a novel severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).On January 30,2020,the World Health Organization(WHO)declared the outbreak a global pandemic *** the face of the COVID-19 pandemic,the most important step has been the effective diagnosis and monitoring of infected *** COVID-19 using Machine Learning(ML)technologies can help the health care unit through assistive diagnostic suggestions,which can reduce the health unit's burden to a certain *** paper investigates the possibilities of ML techniques in identifying/detecting COVID-19 patients including both conventional and exploring from chest X-ray images the effect of viral *** approach includes preprocessing,feature extraction,and ***,the features are extracted using the Histogram of Oriented(HOG)and Local Binary Pattern(LBP)feature ***,for the extracted features classification,six ML models of Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)is *** results show that the diagnostic accuracy of random forest classifier(RFC)on extracted HOG plusLBP features is as high as 94%followed by SVM at 93%.The sensitivity of the K-nearest neighbour model has reached an accuracy of 88%.Overall,the predicted approach has shown higher classification accuracy and effective diagnostic *** is a highly useful tool for clinical practitioners and radiologists to help them in diagnosing and tracking the cases of COVID-19.
Traditional Non-homogeneous Poisson process (NHPP) software reliability growth models (SRGM) enable quantitative assessment of software systems based on failure data collected during testing. However,traditional model...
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Now a days detection of various anomalies finds all the unexpected events in the computing information over the internet are defined as anomalous events or inconsistency events. While detecting the various inconsisten...
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A voltage security region(VSR)is a powerful tool for monitoring the voltage security in bulk power grids with high penetration of *** can prevent cascading failures in wind power integration areas caused by serious ov...
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A voltage security region(VSR)is a powerful tool for monitoring the voltage security in bulk power grids with high penetration of *** can prevent cascading failures in wind power integration areas caused by serious over or low voltage *** bottlenecks of a VSR for practical applications are computational efficiency and *** bridge these gaps,a general optimization model for tracking a voltage security region boundary(VSRB)in bulk power grids is developed in this paper in accordance with the topological characteristics of the ***,the initial VSRB point on the VSRB is examined with the traditional OPF by using the base case parameters as initial ***,the rest of the VSRB points on the VSRB are tracked one after another,with the proposed optimization model,by using the parameters of the tracked VSRB point as the initial value to explore its adjacent VSRB *** proposed approach can significantly improve the computational efficiency of the VSRB tracking over the existing algorithms,and case studies,in the WECC 9-bus and the Polish 2736-bus test systems,demonstrate the high accuracy and efficiency of the proposed approach on exploring the VSRB.
Research exploring how to support decision-making has often used machine learning to automate or assist human decisions. We take an alternative approach for improving decision-making, using machine learning to help st...
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