Improving the practical skills of Science, Technology, Engineering and Mathematics (STEM) students at a historically black college and university (HBCU) was done by implementing a transformative teaching model. The model was implemented on undergraduate students of different educational levels in the Electrical Engineering (EE) Department at HBCU. The model was also extended to carefully chosen high and middle schools. These middle and high school students serve as a pipeline to the university, with a particular emphasis on fostering growth within the EE Department. The model aligns well with the core mission of the EE Department, aiming to enhance the theoretical knowledge and practical skills of students, ensuring that they are qualified to work in industry or to pursue graduate studies. The implemented model prepares students for outstanding STEM careers. It also increases enrolment, student retention, and the number of underrepresented minority graduates in a technology-based workforce.

With the in-depth development and widespread application of educational informatization, digital education has also become one of the important features of educational modernization. Designing and completing a visual teaching system based on Web technology is of great significance for promoting further reform and development of teaching, especially for achieving remote education, which has great application value. Based on visual teaching needs analysis and B/S architecture, effective system development is achieved through Access database. According to the specific needs of teaching functions, the system can be divided into multiple modules, and the management and login of teaching resources for users can also be smoothly achieved. This has important research value for achieving the goal of remote visualization of teaching.

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.

As the global ecological and environmental problems become more and more serious, the concept of green finance and sustainable development has been advocated by more and more domestic and foreign experts, scholars and investors, and the Environmental Responsibility, Social Responsibility, and Corporate Governance (ESG) rating has gradually become a hotspot of attention. ESG is a kind of investment concept and a comprehensive assessment criterion of corporate performance for systematic evaluation of enterprises, and it has become an important indicator of the ability of measuring the sustainable development of enterprises. It has become an important indicator of corporate sustainable development capability. In this paper, we investigate the relationship between ESG ratings and cumulative abnormal returns of listed companies’ stocks under the impact of sudden risk events. The outbreak of the New Crown epidemic as an exogenous risk event provides an opportunity for this paper. This paper examines the role of firms’ ESG ratings and the three sub-dimensions of ratings on the cumulative abnormal returns of listed firms’ stocks during the New Crown Epidemic outbreak and verifies the role of ESG ratings on firms in times of crisis. The final regression results prove that under the impact of sudden exogenous risk events, listed firms’ ESG ratings have a positive effect on the cumulative abnormal stock returns during the event window. Finally, this paper provides recommendations to help firms and investors prevent and mitigate risks.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.