This study focuses on the improvement strategy of information technology application ability of science education teachers and students under the background of informatization. Firstly, the current status of informatization of science education and the importance of the information technology application ability of teacher training students are analyzed. Subsequently, the promotion strategies were discussed, including curriculum design and implementation, teacher training and development, provision of practice environment and conditions, and construction of evaluation mechanisms. These strategies are expected to systematically improve the information technology application ability of teacher training students and provide effective support for the development of science education. However, these strategies also need to be tried and refined in practice to adapt to the development needs of information technology and science education.

This study examines the impact of education quality and innovative activities on economic growth in Shanghai through international trade and fixed asset formation. The study examines how higher education quality and innovation activities drive regional economic growth, with a focus on the mediating effects of international trade and fixed asset formation in Shanghai. The study adopts a quantitative approach utilizing panel data from 31 provinces in China covering the period from 1999 to 2022. The study incorporates variables such as education quality, innovation capacity, and GDP per capita, as well as control variables like labor, capital, and infrastructure. The methodology involves multiple regression models and robustness tests to verify the relationships between and effects of education quality and innovation with regard to economic growth. This study analyzes the direct and indirect effects of university R&D expenditure and innovation on economic growth using a regression model, based on data from 2014 to 2022 in relation to Shanghai. The model introduces variables such as international trade, capital formation, and urbanization to analyze the relationship between higher education quality and economic growth.

The purpose of this study is to explore new financial product’s impact on the behaviour of individual investors. To analyze investors’ risk and return expectations, this article investigates trading volumes before and after the introduction of financial product innovation. An event research technique was used to gather data from the National Stock Exchange. Data was analyzed using descriptive statistics and the Sharpe ratio approach, which were provided by different investors. The research results highlight that individual investors’ overreaction behaviour is brought out by financial product innovation. Furthermore, the study’s results imply that rising trading volumes are not entirely explained by updated risk-adjusted returns and that new financial products lead to excessive trading by investors and lowering returns. Higher trading volumes are not explained by better risk-adjusted returns. Young investors often respond irrationally to information offered by financial advisors, resulting in short-term gains at the expense of long-term gains. The study demonstrates that the development of innovative financial products does not always result in investors’ long-term prosperity. Worse outcomes and excessive trading could follow from it. The paper concludes by providing various real-world implications that the benefits and drawbacks of innovative financial products should be spelled out in detail by financial institutions and representatives. his research contributes to the implementation of individual investors’ overreaction behaviour that is brought out by financial product innovation. It highlights that higher trading volumes are not explained by better risk-adjusted returns.

Since the Industrial Revolution, there has been an evolution in the paradigms under which the industrial worker is perceived and dealt with. These paradigms can be briefly listed in the order of their evolutionary stage as: the food-gatherer, the economic man, the social man, the resourceful man, and the enterprising man. Each of them is a combination of two basic paradigms in different proportions, namely, the outsider paradigm and the partnership paradigm. Obviously, the paradigmatic perspectives of management about their workers will have a significant influence on how they treat their workers, which may become especially conspicuous during recessions and other kinds of hard times. It was in this context that we designed a study to understand the human resource strategies of companies during a period of recession. Data for this study was collected through the content analysis of 46 published cases, wherein we developed the ratings of two sets of variables, namely: the external and internal environments of the company and the strategic actions taken by the respective managements. A surprising finding of the study is that the correlations between the environmental factors and the strategy factors were small and non-significant; moreover, the correlations involving the external environment were smaller than those involving the internal environment. Hence, it may be inferred that strategic actions are influenced primarily by the paradigmatic perspectives of management rather than environmental factors. In order to identify the different types of paradigmatic perspectives, we have further carried out a cluster analysis to develop a taxonomy of paradigms. The results showed that there are five sub-paradigms, which are: (1) Pacifiers, constituting 35% of the sample; (2) Modifiers, constituting 22%; (3) Molders, constituting 17%; (4) Enhancers, constituting 15%; and (5) Exploiters, constituting 11%. The limitations of the study and the implications of the findings are discussed in the concluding part.

Machine analysis of detection of the face is an active research topic in Human-Computer Interaction today. Most of the existing studies show that discovering the portion and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. To overcome these problems, we present a method based on Extended Semi-Local Binary Patterns. For each frame, an aggregation of the pixel values over a neighborhood is considered and a local binary pattern is obtained. From these a binary code is obtained for each pixel and then histogram features is computed. Adaboost algorithm is used to learn and classify these discriminative features with the help of exemplar face and non-face signature of the images for detecting the location of face region in the frame. This Extended Semi Local Binary Pattern is sturdy to variations in illumination and noisy images. The developed methods are deployed on the real time YouTube video face databases and found to exhibit significant performance improvement owing to the novel features when compared to the existing techniques.