The rapid transformation of e-platforms and the growth of e-commerce in China provide consumers with the most advanced channels and creative options for buying and selling through online podium. Online shopping is categorically going to be the world's future shopping mode. Besides, The Belt and Road Initiative (BRI) of China aims to place the world on a new trajectory of higher growth and human development through the connectivity of infrastructure, increased trade, and investment. Cross-border online shopping has become one of the forthcoming prime business models through the implication of the Belt & Road Initiative. By analyzing the representative group of consumers from Belt & Road countries, this study is to explore possible guiding constraints about online shopping decisions that may assist to understand behavioral traits to cross-border online shopping. In the expansion of online shopping, consumers, particularly young people, and students play the most important role. It is, therefore, imperative for companies to investigate and consider the factors that affect the decision of students and young people to get online shopping. This paper has attempted to explore various factors that will affect the online shopping decision of consumers particularly from Belt & Road countries. During this analysis, a qualitative study approach was adopted and a sample of 105 higher education students from the Belt & Road Initiative (BRI) countries studying at Capital Normal University was drawn up. The study will ascertain the ‘Online Shopping Behaviors’ of typical consumers from BRI (Belt & Road) countries to explore a wider direction for cross-border online shopping decisions. It is discovered that online shopping makes life easier, and it is not so far that online shopping can infinitely overtake conventional physical shopping in the whole world.

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In this paper, we propose a novel speech enhancement method based on dual-tree complex wavelet transforms (DTCWT) and nonnegative matrix factorization (NMF) that exploits the subband smooth ratio mask (ssRM) through a joint learning process. The discrete wavelet packet transform (DWPT) suffers the absence of shift invariance, due to downsampling after the filtering process, resulting in a reconstructed signal with significant noise. The redundant stationary wavelet transform (SWT) can solve this shift invariance problem. In this respect, we use efficient DTCWT with a shift invariance property and limited redundancy and calculate the ratio masks (RMs) between the clean training speech and noisy speech (i.e., training noise mixed with clean speech). We also compute RMs between the noise and noisy speech and then learn both RMs with their corresponding clean training clean speech and noise. The auto-regressive moving average (ARMA) filtering process is applied before NMF in previously generated matrices for smooth decomposition. An ssRM is proposed to exploit the advantage of the joint use of the standard ratio mask (sRM) and square root ratio mask (srRM). In short, the DTCWT produces a set of subband signals employing the time-domain signal. Subsequently, the framing scheme is applied to each subband signal to form matrices and calculates the RMs before concatenation with the previously generated matrices. The ARMA filter is implemented in the nonnegative matrix, which is formed by considering the absolute value. Through ssRM, speech components are detected using NMF in each newly formed matrix. Finally, the enhanced speech signal is obtained via the inverse DTCWT (IDTCWT). The performances are evaluated by considering an IEEE corpus, the GRID audio-visual corpus, and different types of noises. The proposed approach significantly improves objective speech quality and intelligibility and outperforms the conventional STFT-NMF, DWPT-NMF, and DNN-IRM methods.

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