UNDERSTANDING COLLABORATIVE STICKINESS INTENTION IN SOCIAL NETWORK SITES FROM THE PERSPECTIVE OF KNOWLEDGE SHARING

This study aims to investigate users’ knowledge sharing intention and collaborative stickiness intention towards social network sites (SNS). SNS offer an opportunity for users to interact and form relationships, while knowledge is accrued by integrating user’s information, experience, and practice. However, there have been few systematic studies that ask why people use SNS to share knowledge. We adopt social capital theory, social identity theory, as well as use and gratification theory to explore the determinants of members’ knowledge sharing intention in SNS. The survey was conducted on two education VCs of facebook, while most members were teachers and educators. Data analysis was carried out to validate our research model, and SmartPLS were used to analyze users’ collaborative stickiness intention. The result shows that social capital and social identity have impact on teacher’s knowledge sharing intention, in turn, influence on collaborative stickiness intention toward on SNS. Our findings not only help researchers interpret why members sharing their knowledge in VC, but also assist practitioners in developing better SNS strategy

Innovative Approaches: Leveraging Neuroscience Technologies for Understanding of Consumer Behavior in E-Commerce

The surge in interest surrounding the application of advanced technology in consumer behavior analysis within the e-commerce domain has grown significantly in recent years. Traditional market research methods, constrained by limitations in capturing accurate consumer responses, have paved the way for these sophisticated technologies to provide deeper insights into the intricacies of consumer behavior and decision-making processes. This comprehensive review navigates through various techniques utilized for scrutinizing consumer behavior, delving into the capabilities and limitations of each technology. EEG emerges as a powerful tool capable of measuring brain activity, shedding light on cognitive and emotional responses to marketing stimuli. The review further explores the potential applications of these technologies in the e-commerce landscape. Examples include assessing website design effectiveness using EEG. This review underscores the advantages of deploying advanced technologies in analyzing consumer behavior in e-commerce, showcasing their potential to enhance marketing strategies and user experiences. This article is particularly pertinent to applied science readers interested in the practical implementation of cutting-edge technologies in consumer behavior analysis

UNDERSTANDING COLLABORATIVE STICKINESS INTENTION IN SOCIAL NETWORK SITES FROM THE PERSPECTIVE OF KNOWLEDGE SHARING

Abstract This study aims to investigate users' knowledge sharing intention and collaborative stickiness intention towards social network sites (SN

Crystallization of Adenylylsulfate Reductase from Desulfovibrio gigas: A Strategy Based on Controlled Protein Oligomerization

Adenylylsulfate reductase (adenosine 5′-phosphosulfate reductase, APS reductase or APSR, E.C.1.8.99.2) catalyzes the conversion of APS to sulfite in dissimilatory sulfate reduction. APSR was isolated and purified directly from massive anaerobically grown Desulfovibrio gigas, a strict anaerobe, for structure and function investigation. Oligomerization of APSR to form dimers–α_2β_2, tetramers–α_4β_4, hexamers–α_6β_6, and larger oligomers was observed during purification of the protein. Dynamic light scattering and ultracentrifugation revealed that the addition of adenosine monophosphate (AMP) or adenosine 5′-phosphosulfate (APS) disrupts the oligomerization, indicating that AMP or APS binding to the APSR dissociates the inactive hexamers into functional dimers. Treatment of APSR with β-mercaptoethanol decreased the enzyme size from a hexamer to a dimer, probably by disrupting the disulfide Cys156—Cys162 toward the C-terminus of the β-subunit. Alignment of the APSR sequences from D. gigas and A. fulgidus revealed the largest differences in this region of the β-subunit, with the D. gigas APSR containing 16 additional amino acids with the Cys156—Cys162 disulfide. Studies in a pH gradient showed that the diameter of the APSR decreased progressively with acidic pH. To crystallize the APSR for structure determination, we optimized conditions to generate a homogeneous and stable form of APSR by combining dynamic light scattering, ultracentrifugation, and electron paramagnetic resonance methods to analyze the various oligomeric states of the enzyme in varied environments

Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

[[abstract]]In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.[[incitationindex]]SCI[[booktype]]紙

Counting Crowds in Bad Weather

Crowd counting has recently attracted significant attention in the field of computer vision due to its wide applications to image understanding. Numerous methods have been proposed and achieved state-of-the-art performance for real-world tasks. However, existing approaches do not perform well under adverse weather such as haze, rain, and snow since the visual appearances of crowds in such scenes are drastically different from those images in clear weather of typical datasets. In this paper, we propose a method for robust crowd counting in adverse weather scenarios. Instead of using a two-stage approach that involves image restoration and crowd counting modules, our model learns effective features and adaptive queries to account for large appearance variations. With these weather queries, the proposed model can learn the weather information according to the degradation of the input image and optimize with the crowd counting module simultaneously. Experimental results show that the proposed algorithm is effective in counting crowds under different weather types on benchmark datasets. The source code and trained models will be made available to the public.Comment: including supplemental materia

Isolation and characterization of stromal progenitor cells from ascites of patients with epithelial ovarian adenocarcinoma

<p>Abstract</p> <p>Background</p> <p>At least one-third of epithelial ovarian cancers are associated with the development of ascites containing heterogeneous cell populations, including tumor cells, inflammatory cells, and stromal elements. The components of ascites and their effects on the tumor cell microenvironment remain poorly understood. This study aimed to isolate and characterize stromal progenitor cells from the ascites of patients with epithelial ovarian adenocarcinoma (EOA).</p> <p>Methods</p> <p>Seventeen ascitic fluid samples and 7 fresh tissue samples were collected from 16 patients with EOA. The ascites samples were then cultured in vitro in varying conditions. Flow cytometry and immunocytochemistry were used to isolate and characterize 2 cell populations with different morphologies (epithelial type and mesenchymal type) deriving from the ascites samples. The in vitro cell culture model was established using conditional culture medium.</p> <p>Results</p> <p>The doubling times of the epithelial type and mesenchymal type cells were 36 h and 48 h, respectively, indicating faster growth of the epithelial type cells compared to the mesenchymal type cells. Cultured in vitro, these ascitic cells displayed the potential for self-renewal and long-term proliferation, and expressed the typical cancer stem/progenitor cell markers CD44^high, CD24^low, and AC133⁺. These cells also demonstrated high BMP-2, BMP4, TGF-β, Rex-1, and AC133 early gene expression, and expressed EGFR, integrin α₂β₁, CD146, and Flt-4, which are highly associated with tumorigenesis and metastasis. The epithelial type cells demonstrated higher cytokeratin 18 and E-cadherin expression than the mesenchymal type cells. The mesenchymal type cells, in contrast, demonstrated higher AC133, CD73, CD105, CD117, EGFR, integrin α₂β₁, and CD146 surface marker expression than the epithelial type cells.</p> <p>Conclusion</p> <p>The established culture system provides an in vitro model for the selection of drugs that target cancer-associated stromal progenitor cells, and for the development of ovarian cancer treatments.</p