A Study of Influencing Traditional Travel Agencies’ Decision Making to Introducing B2C E-commerce

Before the emerging of Internet, traditional travel agencies had been an industry that adopted information technology greatly, and common for each travel agency to have Computerized Reservation System to connect with airlines and hotels through dedicated lines. This kind of inter-organizational information system has been adopted for years; while viewing the travel industry as a whole, the changes on value chain and competition and cooperation relation are caused by the commercial behavior of Internet, E-commerce (EC). Therefore, the objectives of this study are: to understand the influence of top managements’ characteristics on introducing B2C EC, to understand the influence of travel agencies’ attributes on introducing B2C EC, and to understand the decision-making factors travel agencies considered while introducing B2C EC. To the data analysis, we adopt scale reliability analysis, scale validity analysis, descriptive statistics, chi-square test, independent sample t-test, Pearson’s correlation analysis, factor analysis and discriminated analysis, etc. According to the analysis results, this study found that “education level”, “job position”, “consuming experience in EC websites”, and “viewpoint on EC prospective” of the top management’s characteristics would directly influence the introduction of e-commerce and have positive correlation with the decision significantly; the travel agency’s attributes of “type of travel agency”, “company’s age”, “number of employees” and “participating in the promotion and assistance measure” would directly influence whether to introduce e-commerce or not, and the decision is positively correlated with significance of EC introduction. Decision factors are divided into six dimensions, “Product Competence”, “Relationship of Investment Capital”, “E-business’ Level”, “Consumers Demand”, “E-commerce Environment”, and “Management Knowledge Mode”. Results show that the dimension “E-business’ Level” affects the most and dimension “Relationship of Investment Capital” affects next the decision to introducing EC

Potentiometric titration curves of aluminium salt solutions and its species conversion in the hydrolysis-polymerization course

A new concept of critical point is expounded by analysing the potentiometric titration curves of aluminium salt solutions under the moderate slow rate of base injection. The critical point is defined as the characteristic spot of the Al3+ salt solutions potentiometric titration curve, which is related to the experiment conditions. In addition, the changes of critical points reflect the influence of experiment conditions on the course of the hydrolysis-polymerization and the conversion of hydroxyl polynuclear aluminum species. According to the OH/Al mole ratio, the Al species can be divided into four regions quantitatively by three characteristic points on the titration curves: Part I, Al3+/Ala region, consist chiefly of Al3+ and mononuclear Al; Part II, the small/middle polynuclear Al region, including Al2-Al12; Part III, the large-size polynuclear aluminum region, consistent with predominantly Al13-Al54 and a little sol/gel Al(OH)3; Part IV, the dissolving region of sol/gel Alc, only Al(OH)3 (aq or am) and Al(OH)4- species, which set up a base to study on the hydrolysis-polymerization of Al3+. At the same time, significant effects of total aluminum concentration, temperature, halide ion, silicate radical, and organic acid radical on the titration curves and its critical points were observed. Given the three critical points which demarcating the aluminum forms, we carry out a through investigation into the fundamental regulations of these factors’ influence, and offer a fresh train of thought to study the hydrolysis-polymerization of Al3+ in soil solutions. KEY WORDS: Potentiometric titration, Hydroxyl polynuclear aluminum species, Hydrolysis-polymerization, Critical point, Factors affecting titration curves  Bull. Chem. Soc. Ethiop. 2008, 22(2), 155-164

The androgen receptor plays different roles in macrophage-induced proliferation in prostate stromal cells between transitional and peripheral zones of benign prostatic hypertrophy

Macrophages play a critical role in the process of excessive stromal proliferation of benign prostatic hyperplasia (BPH). In our previous study, we used a BPH mouse model to elucidate a potential mechanism whereby macrophage infiltration promotes stromal cell proliferation in the prostate via the androgen receptor (AR)/inflammatory cytokine CCL3-dependent pathway. In our present study, we used the co-culture system of human macrophages and various prostatic zone stromal cells to further demonstrate that infiltrating macrophages promote prostatic stromal cell proliferation through stromal AR-dependent pathways, and we show that the stroma of TZ and PZ respond to macrophages differently because of differences in stromal AR signaling; this could possibly be one of the key pathways for stromal expansion during BPH development and progression. We hypothesize that AR and different downstream inflammatory mediators between TZ and PZ could serve as potential targets for the future design of therapeutic agents for BPH and our results provide significant insights into the search for targeted therapeutic approaches to battle BPH

Mutual Balancing in State-Object Components for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions from seen states and objects. The disparity between the manually labeled semantic information and its actual visual features causes a significant imbalance of visual deviation in the distribution of various object classes and state classes, which is ignored by existing methods. To ameliorate these issues, we consider the CZSL task as an unbalanced multi-label classification task and propose a novel method called MUtual balancing in STate-object components (MUST) for CZSL, which provides a balancing inductive bias for the model. In particular, we split the classification of the composition classes into two consecutive processes to analyze the entanglement of the two components to get additional knowledge in advance, which reflects the degree of visual deviation between the two components. We use the knowledge gained to modify the model's training process in order to generate more distinct class borders for classes with significant visual deviations. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art on MIT-States, UT-Zappos, and C-GQA when combined with the basic CZSL frameworks, and it can improve various CZSL frameworks. Our codes are available on https://anonymous.4open.science/r/MUST_CGE/

Pre-training Graph Transformer with Multimodal Side Information for Recommendation

Side information of items, e.g., images and text description, has shown to be effective in contributing to accurate recommendations. Inspired by the recent success of pre-training models on natural language and images, we propose a pre-training strategy to learn item representations by considering both item side information and their relationships. We relate items by common user activities, e.g., co-purchase, and construct a homogeneous item graph. This graph provides a unified view of item relations and their associated side information in multimodality. We develop a novel sampling algorithm named MCNSampling to select contextual neighbors for each item. The proposed Pre-trained Multimodal Graph Transformer (PMGT) learns item representations with two objectives: 1) graph structure reconstruction, and 2) masked node feature reconstruction. Experimental results on real datasets demonstrate that the proposed PMGT model effectively exploits the multimodality side information to achieve better accuracies in downstream tasks including item recommendation, item classification, and click-through ratio prediction. We also report a case study of testing the proposed PMGT model in an online setting with 600 thousand users