Live houses emerged in China during the 1990s as the country’s rock music scene began to flourish.Today,live houses can be found in almost every major city in China and have become an essential part of the country’s...Live houses emerged in China during the 1990s as the country’s rock music scene began to flourish.Today,live houses can be found in almost every major city in China and have become an essential part of the country’s music culture.The growth of live houses in China has been driven by changing consumer tastes,the expansion of the music industry,and a desire for new and innovative forms of entertainment.These venues offer a unique and intimate setting for music lovers to experience live performances,fostering a sense of community and connection between artists and audiences.The cross-cultural influence of live houses in China has been substantial,with international musicians and audiences increasingly drawn to the country’s vibrant music scene.Chinese live houses have hosted a diverse range of international artists,providing opportunities for cross-cultural exchange and collaboration.As a result,live houses have become a hub for promoting Chinese culture and soft power,enhancing global cultural diversity,and increasing the visibility of Chinese artists on the international stage.展开更多
The purpose of this analysis is to delve into the application of color psychology in the logo design of Korean cosmetic brands,using Etude House as an example for an exhaustive analysis.By examining the history of the...The purpose of this analysis is to delve into the application of color psychology in the logo design of Korean cosmetic brands,using Etude House as an example for an exhaustive analysis.By examining the history of the Etude House brand,the evolution of the logo design,and the changes in color choices,we analyze the traditional concepts of color symbolism in Korean culture and the culture of color in contemporary society in order to reveal the important role of color in cosmetic brand image.Through an in-depth analysis of the use of color in Etude House’s brand identity,we further analyze the impact of color on consumer emotions and purchasing behavior,as well as the potential impact of brand identity changes on market performance.Finally,the conclusions of the analysis summarize the practical application of color psychology in Etude House’s brand logo design,suggest recommendations for other Korean cosmetic brands to draw upon in their logo design,and discuss future directions.展开更多
The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)...The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.展开更多
Split sleeve cold expansion(SSCX)can efiectively enhance fatigue life of holes by improving the field of residual stress.Numerical simulations were conducted to investigate the parameter influence mechanism and obtain...Split sleeve cold expansion(SSCX)can efiectively enhance fatigue life of holes by improving the field of residual stress.Numerical simulations were conducted to investigate the parameter influence mechanism and obtain higher compressive residual stress(CRS).Expansion method,degree of cold expansion(DCE),friction coefficient between laminations and depth-diameter ratio were analyzed.For Ti-Al stacked joint holes,two expansion methods are proposed,namely aluminum alloy first followed titanium alloy(Al first)and titanium alloy first followed aluminum alloy(Ti first).The results show that expansion method and DCE have significant efiects on the field of circumferential residual stress,and the friction has a negligible influence.A higher value of CRS and a wider layer of plastic deformation are induced with Ti first.Optimal DCE of TiAl stacked structure is 5.2%-5.6%.As the depth-diameter ratio is in the range of 0.5-1.25,a positive linear correlation between the maximum compressive residual stress(CRS_(max))and depth-diameter ratio is shown.展开更多
Generating sufficient strains on metal surfaces are highly challenging owing to that most metals can deform plastically to relax the strains on the surfaces.In this work,we developed a facile but highly efficient stac...Generating sufficient strains on metal surfaces are highly challenging owing to that most metals can deform plastically to relax the strains on the surfaces.In this work,we developed a facile but highly efficient stacked deposition strategy to in situ activation and reconstruction of NiO/NiOOH on Ni matrix,following with the migration of Fe ions to NiOOH.The Fe sites on the Ni/NiO/NiOOH facilitate the formation of the stable*OH oxygenated intermediates,and the Ni matrix in the catalyst provides the catalyst excellent stability.The oxygen evolution reaction(OER)performance of the stacked NiFe-5 with compressive strain displays the strengthened binding to oxygenated intermediates and superior OER activity,the ultralow overpotentials of 162 versus reversible hydrogen electrode at 10 mA cm^(-2).On the other hand,the Ni-5 without the incorporation of Fe has shown an outstanding hydrogen evolution reaction(HER)activity,affording an overpotential of 47 mV at 10 mA cm^(-2).The NiFe-5‖Ni-5 enables the overall water splitting at a voltage of 1.508 V to achieve 20 mA cm^(-2) with remarkable durability.The stacked deposition strategy improves binding strength of Ni-based catalysts to oxygenated intermediates via generating compressive strain,causing high catalytic activities on OER and HER.展开更多
Cardiac disease is a chronic condition that impairs the heart’s functionality.It includes conditions such as coronary artery disease,heart failure,arrhythmias,and valvular heart disease.These conditions can lead to s...Cardiac disease is a chronic condition that impairs the heart’s functionality.It includes conditions such as coronary artery disease,heart failure,arrhythmias,and valvular heart disease.These conditions can lead to serious complications and even be life-threatening if not detected and managed in time.Researchers have utilized Machine Learning(ML)and Deep Learning(DL)to identify heart abnormalities swiftly and consistently.Various approaches have been applied to predict and treat heart disease utilizing ML and DL.This paper proposes a Machine and Deep Learning-based Stacked Model(MDLSM)to predict heart disease accurately.ML approaches such as eXtreme Gradient Boosting(XGB),Random Forest(RF),Naive Bayes(NB),Decision Tree(DT),and KNearest Neighbor(KNN),along with two DL models:Deep Neural Network(DNN)and Fine Tuned Deep Neural Network(FT-DNN)are used to detect heart disease.These models rely on electronic medical data that increases the likelihood of correctly identifying and diagnosing heart disease.Well-known evaluation measures(i.e.,accuracy,precision,recall,F1-score,confusion matrix,and area under the Receiver Operating Characteristic(ROC)curve)are employed to check the efficacy of the proposed approach.Results reveal that the MDLSM achieves 94.14%prediction accuracy,which is 8.30%better than the results from the baseline experiments recommending our proposed approach for identifying and diagnosing heart disease.展开更多
文摘Live houses emerged in China during the 1990s as the country’s rock music scene began to flourish.Today,live houses can be found in almost every major city in China and have become an essential part of the country’s music culture.The growth of live houses in China has been driven by changing consumer tastes,the expansion of the music industry,and a desire for new and innovative forms of entertainment.These venues offer a unique and intimate setting for music lovers to experience live performances,fostering a sense of community and connection between artists and audiences.The cross-cultural influence of live houses in China has been substantial,with international musicians and audiences increasingly drawn to the country’s vibrant music scene.Chinese live houses have hosted a diverse range of international artists,providing opportunities for cross-cultural exchange and collaboration.As a result,live houses have become a hub for promoting Chinese culture and soft power,enhancing global cultural diversity,and increasing the visibility of Chinese artists on the international stage.
文摘The purpose of this analysis is to delve into the application of color psychology in the logo design of Korean cosmetic brands,using Etude House as an example for an exhaustive analysis.By examining the history of the Etude House brand,the evolution of the logo design,and the changes in color choices,we analyze the traditional concepts of color symbolism in Korean culture and the culture of color in contemporary society in order to reveal the important role of color in cosmetic brand image.Through an in-depth analysis of the use of color in Etude House’s brand identity,we further analyze the impact of color on consumer emotions and purchasing behavior,as well as the potential impact of brand identity changes on market performance.Finally,the conclusions of the analysis summarize the practical application of color psychology in Etude House’s brand logo design,suggest recommendations for other Korean cosmetic brands to draw upon in their logo design,and discuss future directions.
基金supported by financial support from Universiti Sains Malaysia(USM)under FRGS Grant Number FRGS/1/2020/TK03/USM/02/1the School of Computer Sciences USM for their support.
文摘The detection of brain disease is an essential issue in medical and research areas.Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging(MRI)images.These techniques involve training neural networks on large datasets of MRI images,allowing the networks to learn patterns and features indicative of different brain diseases.However,several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques.This paper implements a Feature Enhanced Stacked Auto Encoder(FESAE)model to detect brain diseases.The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy.Therefore,the standard Stack Auto Encoder(SAE)is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy froman image.The proposed model consists of four stages.First,pre-processing is performed to remove noise,and the greyscale image is converted to Red,Green,and Blue(RGB)to enhance feature details for discriminative feature extraction.Second,feature Extraction is performed to extract significant features for classification using DiscreteWavelet Transform(DWT)and Channelization.Third,classification is performed to classify MRI images into four major classes:Normal,Tumor,Brain Stroke,and Alzheimer’s.Finally,the FESAE model outperforms the state-of-theart,machine learning,and deep learning methods such as Artificial Neural Network(ANN),SAE,Random Forest(RF),and Logistic Regression(LR)by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images.The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.
基金Funded by National Natural Science Foundation of China(No.51175257)。
文摘Split sleeve cold expansion(SSCX)can efiectively enhance fatigue life of holes by improving the field of residual stress.Numerical simulations were conducted to investigate the parameter influence mechanism and obtain higher compressive residual stress(CRS).Expansion method,degree of cold expansion(DCE),friction coefficient between laminations and depth-diameter ratio were analyzed.For Ti-Al stacked joint holes,two expansion methods are proposed,namely aluminum alloy first followed titanium alloy(Al first)and titanium alloy first followed aluminum alloy(Ti first).The results show that expansion method and DCE have significant efiects on the field of circumferential residual stress,and the friction has a negligible influence.A higher value of CRS and a wider layer of plastic deformation are induced with Ti first.Optimal DCE of TiAl stacked structure is 5.2%-5.6%.As the depth-diameter ratio is in the range of 0.5-1.25,a positive linear correlation between the maximum compressive residual stress(CRS_(max))and depth-diameter ratio is shown.
基金supported by the National Natural Science Foundations of China(21965024,22269016,51721002)the Inner Mongolia funding(2020JQ01,21300-5223601)the funding of Inner Mongolia University(10000-21311201/137,213005223601/003,21300-5223707)。
文摘Generating sufficient strains on metal surfaces are highly challenging owing to that most metals can deform plastically to relax the strains on the surfaces.In this work,we developed a facile but highly efficient stacked deposition strategy to in situ activation and reconstruction of NiO/NiOOH on Ni matrix,following with the migration of Fe ions to NiOOH.The Fe sites on the Ni/NiO/NiOOH facilitate the formation of the stable*OH oxygenated intermediates,and the Ni matrix in the catalyst provides the catalyst excellent stability.The oxygen evolution reaction(OER)performance of the stacked NiFe-5 with compressive strain displays the strengthened binding to oxygenated intermediates and superior OER activity,the ultralow overpotentials of 162 versus reversible hydrogen electrode at 10 mA cm^(-2).On the other hand,the Ni-5 without the incorporation of Fe has shown an outstanding hydrogen evolution reaction(HER)activity,affording an overpotential of 47 mV at 10 mA cm^(-2).The NiFe-5‖Ni-5 enables the overall water splitting at a voltage of 1.508 V to achieve 20 mA cm^(-2) with remarkable durability.The stacked deposition strategy improves binding strength of Ni-based catalysts to oxygenated intermediates via generating compressive strain,causing high catalytic activities on OER and HER.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work through Project Number 223202.
文摘Cardiac disease is a chronic condition that impairs the heart’s functionality.It includes conditions such as coronary artery disease,heart failure,arrhythmias,and valvular heart disease.These conditions can lead to serious complications and even be life-threatening if not detected and managed in time.Researchers have utilized Machine Learning(ML)and Deep Learning(DL)to identify heart abnormalities swiftly and consistently.Various approaches have been applied to predict and treat heart disease utilizing ML and DL.This paper proposes a Machine and Deep Learning-based Stacked Model(MDLSM)to predict heart disease accurately.ML approaches such as eXtreme Gradient Boosting(XGB),Random Forest(RF),Naive Bayes(NB),Decision Tree(DT),and KNearest Neighbor(KNN),along with two DL models:Deep Neural Network(DNN)and Fine Tuned Deep Neural Network(FT-DNN)are used to detect heart disease.These models rely on electronic medical data that increases the likelihood of correctly identifying and diagnosing heart disease.Well-known evaluation measures(i.e.,accuracy,precision,recall,F1-score,confusion matrix,and area under the Receiver Operating Characteristic(ROC)curve)are employed to check the efficacy of the proposed approach.Results reveal that the MDLSM achieves 94.14%prediction accuracy,which is 8.30%better than the results from the baseline experiments recommending our proposed approach for identifying and diagnosing heart disease.
