The Ready-Made Garments (RMG) industry is a major contributor to the economy of Bangladesh, accounting for over 80% of the country’s total exports. In recent years, the industry has faced challenges due to changing g...The Ready-Made Garments (RMG) industry is a major contributor to the economy of Bangladesh, accounting for over 80% of the country’s total exports. In recent years, the industry has faced challenges due to changing global fashion trends and increasing competition from other manufacturing countries. One of the latest trends in the fashion industry is micro seasonal fashion, which has significant impacts on the RMG industry of Bangladesh. The purpose of this study is to examine the impact of micro seasonal fashion on the RMG industry of Bangladesh. It will examine the changes in buyer and consumer behavior and demand patterns, the implications for manufacturers and suppliers, and the strategies adopted by industry players to adapt to this trend. The emergence of micro seasonal fashion has disorderly the traditional seasonal cycle of the fashion industry. Consumers are no longer satisfied with the two-season model of spring/summer and fall/winter, but instead demand new styles and trends every few weeks. This trend has created opportunities for the RMG industry of Bangladesh to cater to the fast-changing demands of consumers. Micro Seasonal Fashion also poses significant challenges for the industry. Manufacturers and suppliers must be able to produce and deliver garments quickly and efficiently, while ensuring high quality and sustainability standards. This requires investments in technology, supply chain management, and training of workers. To address these challenges, industry players in Bangladesh have adopted several strategies, such as diversifying product offerings, investing in technology and innovation, and enhancing sustainability practices. These strategies have helped the industry to remain competitive and meet the demands of fast-changing consumer preferences. The industry must be able to adapt quickly to the changing demands of consumers and invest in technology and sustainability practices to remain competitive. With the right strategies and investments, the RMG industry of Bangladesh can control the opportunities presented by micro seasonal fashion and continue to be a major player in the global fashion industry.展开更多
In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern....In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.展开更多
Background In the context of China's aging population,meeting consumer demand is an essential way for nursing homes to fulfill social responsibilities and improve competitive advantages.However,since little is kno...Background In the context of China's aging population,meeting consumer demand is an essential way for nursing homes to fulfill social responsibilities and improve competitive advantages.However,since little is known about the elderly's service level and price choices for nursing home care,this study aims to explore the non-disabled elderly's nursing home admission intention,service level,and price choices.Methods A cross-sectional survey of 402 non-disabled respondents was conducted in three different income level cities of Zhejiang Province,in July and August 2018.Multinomial logistic regression and multiple linear regression were used to identify the determinants of admission intention,service level choice,and price choice.Results Education,residence,and number of children were significantly associated with nursing home admission intention.Compared to those with no intention,the elderly with higher income and household wealth were less likely to have conditional intentions,and those living with the family were less likely to have unconditional intentions.Compared to medium-level services,the elderly with higher monthly income(relative risk ratio[RRR]3.07,95%confidence interval[CI]:1.801 to 5.233),household wealth(RRR 5.451,95%CI:2.249 to 13.216),and age(RRR 1.528,95%CI:1.004 to 2.326)were more likely to prefer high-level services,while older adults with higher monthly income(RRR 0.516,95%CI:0.344 to 0.774),and those with pensions(RRR 0.267,95%CI:0.076 to 0.931)were less likely to prefer low-level services.The elderly's price preference increased by 398 CNY as monthly income increased by 1000 CNY,and by 270 CNY as the housing number increased by one.Having pensions increased price preference(468 CNY),whereas having health insurance decreased price preference(–690 CNY).Conclusion The elderly's intention of nursing home admission was primarily affected by sociodemographic factors,while price and service level choices were primarily affected by financial factors.Nursing homes should use the market segmentation method to provide precision nursing home care for different groups of non-disabled elderly.展开更多
文摘The Ready-Made Garments (RMG) industry is a major contributor to the economy of Bangladesh, accounting for over 80% of the country’s total exports. In recent years, the industry has faced challenges due to changing global fashion trends and increasing competition from other manufacturing countries. One of the latest trends in the fashion industry is micro seasonal fashion, which has significant impacts on the RMG industry of Bangladesh. The purpose of this study is to examine the impact of micro seasonal fashion on the RMG industry of Bangladesh. It will examine the changes in buyer and consumer behavior and demand patterns, the implications for manufacturers and suppliers, and the strategies adopted by industry players to adapt to this trend. The emergence of micro seasonal fashion has disorderly the traditional seasonal cycle of the fashion industry. Consumers are no longer satisfied with the two-season model of spring/summer and fall/winter, but instead demand new styles and trends every few weeks. This trend has created opportunities for the RMG industry of Bangladesh to cater to the fast-changing demands of consumers. Micro Seasonal Fashion also poses significant challenges for the industry. Manufacturers and suppliers must be able to produce and deliver garments quickly and efficiently, while ensuring high quality and sustainability standards. This requires investments in technology, supply chain management, and training of workers. To address these challenges, industry players in Bangladesh have adopted several strategies, such as diversifying product offerings, investing in technology and innovation, and enhancing sustainability practices. These strategies have helped the industry to remain competitive and meet the demands of fast-changing consumer preferences. The industry must be able to adapt quickly to the changing demands of consumers and invest in technology and sustainability practices to remain competitive. With the right strategies and investments, the RMG industry of Bangladesh can control the opportunities presented by micro seasonal fashion and continue to be a major player in the global fashion industry.
基金The authors acknowledge the support from the Ministry of Education and the Deanship of Scientific Research,Najran University,Saudi Arabia,under code number NU/-/SERC/10/616.
文摘In the Smart Grid(SG)residential environment,consumers change their power consumption routine according to the price and incentives announced by the utility,which causes the prices to deviate from the initial pattern.Thereby,electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability.Due to the massive amount of data,big data analytics for forecasting becomes a hot topic in the SG domain.In this paper,the changing and non-linearity of consumer consumption pattern complex data is taken as input.To minimize the computational cost and complexity of the data,the average of the feature engineering approaches includes:Recursive Feature Eliminator(RFE),Extreme Gradient Boosting(XGboost),Random Forest(RF),and are upgraded to extract the most relevant and significant features.To this end,we have proposed the DensetNet-121 network and Support Vector Machine(SVM)ensemble with Aquila Optimizer(AO)to ensure adaptability and handle the complexity of data in the classification.Further,the AO method helps to tune the parameters of DensNet(121 layers)and SVM,which achieves less training loss,computational time,minimized overfitting problems and more training/test accuracy.Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes.Our proposed method has achieved a minimal value of the Mean Average Percentage Error(MAPE)rate i.e.,8%by DenseNet-AO and 6%by SVM-AO and the maximum accurateness rate of 92%and 95%,respectively.
基金This work was supported by the China Ministry of Education Project of Humanities and Social Sciences(Grant No.18YJC630100)the Project of Philosophy and Social Science of Hangzhou City of China(Grant No.2018JD50).
文摘Background In the context of China's aging population,meeting consumer demand is an essential way for nursing homes to fulfill social responsibilities and improve competitive advantages.However,since little is known about the elderly's service level and price choices for nursing home care,this study aims to explore the non-disabled elderly's nursing home admission intention,service level,and price choices.Methods A cross-sectional survey of 402 non-disabled respondents was conducted in three different income level cities of Zhejiang Province,in July and August 2018.Multinomial logistic regression and multiple linear regression were used to identify the determinants of admission intention,service level choice,and price choice.Results Education,residence,and number of children were significantly associated with nursing home admission intention.Compared to those with no intention,the elderly with higher income and household wealth were less likely to have conditional intentions,and those living with the family were less likely to have unconditional intentions.Compared to medium-level services,the elderly with higher monthly income(relative risk ratio[RRR]3.07,95%confidence interval[CI]:1.801 to 5.233),household wealth(RRR 5.451,95%CI:2.249 to 13.216),and age(RRR 1.528,95%CI:1.004 to 2.326)were more likely to prefer high-level services,while older adults with higher monthly income(RRR 0.516,95%CI:0.344 to 0.774),and those with pensions(RRR 0.267,95%CI:0.076 to 0.931)were less likely to prefer low-level services.The elderly's price preference increased by 398 CNY as monthly income increased by 1000 CNY,and by 270 CNY as the housing number increased by one.Having pensions increased price preference(468 CNY),whereas having health insurance decreased price preference(–690 CNY).Conclusion The elderly's intention of nursing home admission was primarily affected by sociodemographic factors,while price and service level choices were primarily affected by financial factors.Nursing homes should use the market segmentation method to provide precision nursing home care for different groups of non-disabled elderly.
