Search results

The purpose of this study is to evaluate the tannase-assisted extraction of tea stem pigment from waste tea stem, after which the stability of the purified pigment was determined and analyzed.

The extracting process was optimized using the response surface methodology (RSM) approach. Material-liquid ratio, temperature and time were chosen as variables and the absorbance as a response. The stability of the tea stem pigment at the different conditions was tested and analyzed.

The optimized extraction technology was as follows: material-liquid ratio 1:20 g/ml, temperature 50°C and time 60 min. The stability test results showed that tea stem pigment was sensitive to oxidants, but the reducing agents did not affect it. The tea stem pigment was unstable under strong acid and strong alkali and was most stable at pH 6. The light stability was poor. Tea stem pigment would form flocculent precipitation under the action of Fe²⁺ or Fe³⁺ and be relatively stable in Cu²⁺ and Na²⁺ solutions. The tea stem pigment was relatively stable at 60°C and below.

No comprehensive and systematic study reports have been conducted on the extraction of pigment from discarded tea stem, and researchers have not used statistical analysis to optimize the process of tannase-assisted tea stem pigment extraction using RSM. Additionally, there is a lack of special reports on the systematic study of the stability of pigment extracted from tea stem.

This study aims to clarify the effect of team effort allocation between knowledge exploration and exploitation on the generation of extremely good or poor innovations. The influence of previous collaborative experience among team members on the effect of team effort allocation is also investigated to understand the relationship between team members’ collaboration networks and knowledge learning.

This study uses data of all patents granted by the US Patent and Trademark Office between 1984 and 2010. The inventors involved in a patent are regarded as members of the focal team. Logistic regression is used to analyze the data.

Allocating greater effort to exploration than to exploitation is beneficial to achieving breakthrough innovations despite the risk of generating particularly poor innovations. This benefit increases with collaborative experience among team members. Placing an equal emphasis on knowledge exploration and exploitation is not particularly effective in achieving breakthrough innovations; it is, however, the best strategy for avoiding particularly poor innovations.

This research not only provides valuable insights for research on innovation and knowledge management by studying the team effort allocation strategy used to achieve breakthroughs and avoid particularly poor innovations but also represents an advancement in bridging two streams of research – knowledge learning and social networks – by highlighting the influence of the team members’ collaborative networks on the effect of team effort allocation between knowledge exploration and exploitation.

The purpose of this paper is to design a simple and accurate a-posteriori Lagrangian-based error estimator is developed for the class of backward differentiation formula (BDF) algorithms with variable time step size, and the adaptive time-stepping in BDF algorithms is demonstrated for efficient time-dependent simulations in fluid flow and heat transfer.

The Lagrange interpolation polynomial is used to predict the time derivative, and then the accurate primary result is obtained by the Gauss integral, which is applied to evaluate the local error. Not only the generalized formula of the proposed error estimator is presented but also the specific expression for the widely applied BDF1/2/3 is illustrated. Two essential executable MATLAB functions to implement the proposed error estimator are appended for practical applications. Then, the adaptive time-stepping is demonstrated based on the newly proposed error estimator for BDF algorithms.

The validation tests show that the newly proposed error estimator is accurate such that the effectivity index is always close to unity for both linear and nonlinear problems, and it avoids under/overestimation of the exact local error. The applications for fluid dynamics and coupled fluid flow and heat transfer problems depict the advantage of adaptive time-stepping based on the proposed error estimator for time-dependent simulations.

In contrast to existing error estimators for BDF algorithms, the present work is more accurate for the local error estimation, and it can be readily extended to practical applications in engineering with a few changes to existing codes, contributing to efficient time-dependent simulations in fluid flow and heat transfer.

The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.

The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.

The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.

The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.

The purpose of this paper is to report the use of decision system analysis (DSA) mapping the streams of communications (i.e. interactions), thoughts, actions and decisions involved for advertising agencies as executives in these firms gain client approval, and design creative, promotional and media strategies.

This study uses DSA. This little‐used technique requires protocol analysis, interviews and observation before transcription of the organizational decision processes into flow charts.

The research first identifies four models, describing four specific decision types, then derives a general model from them. Executives from four agencies not in the original sample later confirms the models. The models generally confirm the existing knowledge base, with a few minor exceptions.

This qualitative technique suffers the common malady of the researchers losing objectivity because of their immersion in the case‐companies. Wherever possible the research employs quantitative techniques to verify observational judgments.

The “thick description” and the summary charts of the advertising agency decision processes have the potential to aid agency decision makers to better structure their decision processes.

The findings themselves are of significance to those involved in the advertising industry, and there is an element of originality in the classification and the thick descriptions of advertising agency decision systems. The resurrection of DSA and the demonstration that the technique is viable and valuable is also a contribution of the study.