An Empirical Research on Factors Influencing Purchase Intention of Mobile Shopping

基于沉浸体验理论，本文结合感知价值理 论、创新扩散理论来构建智能手机用户的购 买意愿影响因素模型。本研究旨在理解沉浸 体验对中国的移动购物意愿的影响。应用李 克特5度量表，本文对430位调查对象进行实 证研究来研究此问题。通过运用SPSS 22.0和 AMOS 21.0统计分析软件，对影响因素进行 编码和描述性分析，最终建立结构性方程模 型。结果显示，沉浸体验对移动购物的购买 意愿有显著的正向影响。同时，消费者创新 理论通过正向影响沉浸体验来间接影响消费 者的购物意愿。最后，本研究也表明感知价 值对移动购物有显著的影响。 Based on the theory of Flow experience, this thesis combines the theory of perceived value with the theory of customer innovation, and constructs the influencing factor model of Smartphone users’ purchase intention. The paper aims to understand the impact of Flow experience in the intention mobile shopping in China. To address this, an empirical study with 430 subjects was carried out. It collected the data by questionnaire with 5-point Likert scale, and used SPSS.22.0 and AMOS.21 to encode and analysis the data by the method of factor analysis, descriptive statistics and structural equation modeling. The results indicate that flow experience has a significant positive effect on the purchase intention of mobile shopping, and customer innovation has a direct effect on flow experience which in turn purchase intention. Furthermore, this study also reveals that perceived value influence mobile shopping deepl

HFGD: High-level Feature Guided Decoder for Semantic Segmentation

Existing pyramid-based upsamplers (e.g. SemanticFPN), although efficient, usually produce less accurate results compared to dilation-based models when using the same backbone. This is partially caused by the contaminated high-level features since they are fused and fine-tuned with noisy low-level features on limited data. To address this issue, we propose to use powerful pretrained high-level features as guidance (HFG) when learning to upsample the fine-grained low-level features. Specifically, the class tokens are trained along with only the high-level features from the backbone. These class tokens are reused by the upsampler for classification, guiding the upsampler features to more discriminative backbone features. One key design of the HFG is to protect the high-level features from being contaminated with proper stop-gradient operations so that the backbone does not update according to the gradient from the upsampler. To push the upper limit of HFG, we introduce an context augmentation encoder (CAE) that can efficiently and effectively operates on low-resolution high-level feature, resulting in improved representation and thus better guidance. We evaluate the proposed method on three benchmarks: Pascal Context, COCOStuff164k, and Cityscapes. Our method achieves state-of-the-art results among methods that do not use extra training data, demonstrating its effectiveness and generalization ability. The complete code will be releasedComment: Revised version, refactored presentation and added more experiment

A Quantitative Study of the Division Cycle of Caulobacter crescentus Stalked Cells

Progression of a cell through the division cycle is tightly controlled at different steps to ensure the integrity of genome replication and partitioning to daughter cells. From published experimental evidence, we propose a molecular mechanism for control of the cell division cycle in Caulobacter crescentus. The mechanism, which is based on the synthesis and degradation of three “master regulator” proteins (CtrA, GcrA, and DnaA), is converted into a quantitative model, in order to study the temporal dynamics of these and other cell cycle proteins. The model accounts for important details of the physiology, biochemistry, and genetics of cell cycle control in stalked C. crescentus cell. It reproduces protein time courses in wild-type cells, mimics correctly the phenotypes of many mutant strains, and predicts the phenotypes of currently uncharacterized mutants. Since many of the proteins involved in regulating the cell cycle of C. crescentus are conserved among many genera of α-proteobacteria, the proposed mechanism may be applicable to other species of importance in agriculture and medicine

Synthesis of graphene oxide–methacrylic acid–sodium allyl sulfonate copolymer and its tanning properties

AbstractGraphite oxide nanosheets (GONs) and the copolymer of GONs with methacrylic acid (MAA) and sodium allyl sulfonate (SAS) (poly(GON–MAA–SAS)) were prepared. The GONs in poly(GON–MAA–SAS) are smaller and uniformly dispersed, allowing them to penetrate into collagen fibers of leather and produce better tanning effects than current nano-tanning agents. Tanning effects due to chemical bonding and nanoeffects are elucidated by measuring the shrinkage temperature (Ts) of wet and dry leather. The results indicate that poly(GON–MAA–SAS) could be used alone as a tanning agent to provide excellent mechanical properties, especially good elasticity and softness, although the Ts is slightly lower than that of chrome-tanned leather. Poly(GON–MAA–SAS) in combination with a chrome tanning agent could allow the dosage of the latter to be halved. These results indicate the potential for new nano-tanning agents to reduce the pollution caused by tanning agents