论红色旅游与区域经济协调发展

红色旅游在现阶段意义重大,备受关注。文章在红色旅游与区域经济协调发展现状的基础上,分析了红色旅游与区域经济协调发展存在的“软”、“硬”障碍,提出了加大投资、改进基础设施、加强区域合作、搞好与区域绿色旅游、扶贫旅游的多产品结合,做好区域整体营销策划,打造好红色旅游品牌的建议措施,为最终实现与区域经济协调发展的目标提供可靠的途径。     

天然甲醛消除剂的研究

鉴于室内空气甲醛污染的危害，根据化学消醛剂的原理从中草药植物中找出了能消除甲醛的有效成分并制成甲醛消除剂，对其进行性能检测发现，该甲醛消除剂可快速、有效地消除室内空气中的甲醛。     

整流器件的谐波抑制仿真

电力有源滤波器能够解决电力系统中非线性负载引起的谐波污染问题。阐述了有源滤波器的基本原理,建立了Matlab/Simulink仿真模型并且进行了详细的仿真分析。仿真实验结果表明,有源滤波器具备很好的补偿特性。     

The effect of COVID-19 on consumers’ channel shopping behaviors: A segmentation study

COVID-19, combined with firms' multichannel adjustments, has accelerated changes in consumer shopping behaviors. This paper explores these issues through a segmentation study to examine consumers’ pre- and during-COVID-19 channel shopping behavior. Using survey data from 485 U.S. participants, Latent Profile Analysis identified six segments that vary in three dimensions: in-store vs. online behavior pre-COVID-19, in-store vs. online behavior during-COVID-19, and the change in channel shopping behavior from pre- to during-COVID-19. Motivation (utilitarian and hedonic), opportunity (time and physical), and ability play significant roles in explaining the findings, while psychographic and demographic variables play lesser roles. Further analysis revealed that these segments have different channel switching intentions when the focal retailer closed its physical store. These findings add greater understanding and support to the assumption that consumers more frequently shopped online than in-store during- than pre-COVID-19. In sum, this research addresses how changes in the shopping environment may affect consumer segments and their behavior.     

联合谐波小波与递归神经网络的股市时间序列预测

为了提高神经网络对股市时间序列的预测精度，首先利用谐波小波对股票市场数据进行多尺度分解，将其分解为不同尺度且具有平移不变特征的谐波小波分量；然后根据股市时间序列的特点，构建递归神经网络模型进行短期预测，以不同尺度的谐波小波分量为输入数据，对股市数据进行多尺度预测；最后对不同尺度的预测结果进行谐波小波重构，得到最终的股市预测数据。对我国股票市场进行了实验分析，结果表明：股市时间序列经谐波小波分解后，股市数据中不同投资时间水平的价格波动可以被较好的分离，有效地提高了股票市场数据的预测精度。