Study on the Improvement of College Teachers' Informationized Teaching Ability

The 13th Five-Year Plan clearly proposes to enhance teachers’ ability of informationized teaching, so as to make informationized teaching a routine mode. In 2020,in order to prevent and control COVID-19, colleges have launched online teaching activities, which is necessary to use information technology in teaching and also a test of teachers’ ability of informationized teaching. In this context,teachers should change their teaching ideology and improve their ability of informatizationized teaching in an all-round way. Firstly, this paper analyzes the current situation of university teachers’ informationized teaching. Secondly, it analyzes the significance of improving the informationized teaching ability of university teachers. Finally, it analyzes the promotion strategies of university teachers’ ability of informatization teaching

ACCOUNTING AND FINANCIAL STATEMENTS AUTO ANALYSIS SYSTEM

This project was motivated by the need to revolutionize the generation of financial statements and financial analysis process thus speeding up business decision making. The research questions were: 1) How can machine learning increase the speed of financial statement preparation and automate financial statements analysis? 2) How can businesses balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias? 3) Can the Java J2EE framework provide a reliable running environment for machine learning? The findings were: 1) Machine learning can significantly increase the accuracy and speed of financial analysis. Using machine learning algorithms, financial data can be processed and analyzed in real-time, allowing for quicker and more precise financial analysis. Machine learning models can identify patterns and trends in financial data that may not be easily detectable by humans, leading to more accurate financial statements and analysis. Additionally, machine learning can automate repetitive tasks in the financial analysis process, saving time and resources for businesses. 2) Businesses need to carefully balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias. While machine learning can offer significant advantages in terms of accuracy and speed, it also requires handling sensitive financial data. Therefore, it is crucial for businesses to implement robust data security measures to protect against potential data breaches and ensure compliance with privacy regulations. Additionally, businesses need to be mindful of potential biases in machine learning algorithms, as biased algorithms can result in biased financial analysis. Regular audits and monitoring of machine learning models should be conducted to address and mitigate any potential biases. 3) The Java J2EE framework can provide a reliable running environment for machine learning. Java J2EE (Java 2 Platform, Enterprise Edition) is a widely used and mature framework for developing enterprise applications, including machine learning applications. It offers scalability, reliability, and security features that are essential for running machine learning algorithms in a production environment. Java J2EE provides robust support for distributed computing, allowing for efficient processing of large financial datasets. Furthermore, it offers a wide range of libraries and tools for implementing machine learning algorithms, making it a viable choice for running machine learning applications in the financial industry. The conclusions were: 1) Machine learning has the potential to significantly increase the accuracy and speed of financial analysis, thereby revolutionizing the generation of financial statements and the financial analysis process. Various machine learning algorithms, such as decision trees, random forests, and deep learning algorithms, can be utilized to identify patterns, trends, and hidden risks in financial data, leading to more informed and efficient business decision making. 2) Businesses need to carefully balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias. While machine learning can offer significant advantages in terms of accuracy and speed, there are ethical considerations that need to be addressed, such as ensuring data privacy, implementing effective data security measures, and mitigating biases in machine learning algorithms used in financial analysis. Businesses should adopt a responsible approach to machine learning implementation, considering the potential risks and benefits. 3) The Java J2EE framework can provide a reliable running environment for machine learning applications, but further research is needed to evaluate the performance and scalability of machine learning models in this framework. Identifying potential optimizations for running machine learning applications at scale in the Java J2EE framework can lead to more efficient and effective implementation of machine learning in financial analysis and decision-making processes. Further research in this area can contribute to the development of robust and scalable machine learning applications for financial analysis in the business domain. Areas for further study include: 1) Exploring different machine learning algorithms and techniques to further improve the accuracy and speed of financial analysis. 2) Conducting research on the impact of machine learning on financial decision making and business performance. 3) Investigating methods for addressing and mitigating biases in machine learning algorithms used in financial analysis. 4) Evaluating the effectiveness of different data security measures in protecting sensitive financial data in machine learning applications. 5) Studying the performance and scalability of machine learning models in the Java J2EE framework and identifying potential optimizations for running machine learning applications at scale

{6,6′-Dimeth­oxy-2,2′-[6-bromo­pyridine-2,3-diylbis(nitrilo­methyl­idyne)]­diphenol­ato}­copper(II) methanol solvate

In the title compound, [Cu(C21H16BrN3O4)]·CH3OH, the CuII ion is coordinated by two N [Cu—N = 1.814 (3) and 1.917 (3) Å] and two O [Cu—O = 1.805 (3) and 1.893 (3) Å] atoms from the tetra­dentate Schiff base ligand in a distorted square-planar geometry. In the crystal structure, the approximately planar Cu complex mol­ecules are paired into centrosymmetric dimers with short inter­molecular Cu⋯N distances of 3.162 (3) Å. Weak O---H...O hydrogen bonds may help to stabilize the structure

Relationship between Real Earnings Management with Cost of Debt in Chinese Listed High-Tech Enterprises: The Perspective of Corporate Income Tax Incentives

To encourage corporate investment in innovation or R&D and foster innovative firms, the government of China established standards for the certification of high-tech enterprises in 2008. The business entities that fulfill these standards are entitled to tax deductions. One of the criteria is the ratio of R&D expenses to sales exceeding a specific percentage (which depends on the annual revenue) in the preceding 3 years. Moreover, this study examines data from the CSMAR database for the period 2008-2019 and includes data from 8,233 listed high-tech enterprises. The results show that if the proportion of pre-managed R&D expenses to pre-managed sales that are less than 6% (or 5%), 4%, or 3% in the past three years of firms with different sales range in the current year and managed earnings through sales or R&D expenses to fulfill the standards required for the certification positively influenced the costs of debt (non-significant)

Relationship between Fuel Price Volatility with Earnings Management in African Airlines: The Perspective of Real Activities Earnings Management

This study examines data from the COMPUSTAT database, and annual reports for the period from 2002Q1 to 2018O4 in African Airlines. The results show that fuel price volatility positively influenced real earnings management such as cash flow from operations and discretionary expenditures. In addition, fuel price volatility also positively to real earnings management such as product costs (but this variable is non-significant)