This study contributes a number of innovative and interesting empirical findings with a view of four audit committee characteristics to predict overall value creation efficiency and capital employed efficiency using v...This study contributes a number of innovative and interesting empirical findings with a view of four audit committee characteristics to predict overall value creation efficiency and capital employed efficiency using value added intellectual coefficient （VAICTM） method. Using purposive sampling, 34 property, real estate, and building construction firms listed on Indonesia Stock Exchange in 2011 were selected. Empirical findings could not provide a significant relationship between audit committee characteristics and the overall value creation efficiency as well as capital employed efficiency. This implies that currently, the number of members, number of meetings, number of independent commissioners, and accounting or finance expertise in audit committee cannot be expected as drivers of business value creation in Indonesian context, more specifically for property, real estate, and building construction industry. The overall lack of significant relationships may potentially result from limited human capacity, lack of financial expertise, and inadequate knowledge about the role of audit committee to add value to the business.展开更多
This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by cons...This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.展开更多
文摘This study contributes a number of innovative and interesting empirical findings with a view of four audit committee characteristics to predict overall value creation efficiency and capital employed efficiency using value added intellectual coefficient （VAICTM） method. Using purposive sampling, 34 property, real estate, and building construction firms listed on Indonesia Stock Exchange in 2011 were selected. Empirical findings could not provide a significant relationship between audit committee characteristics and the overall value creation efficiency as well as capital employed efficiency. This implies that currently, the number of members, number of meetings, number of independent commissioners, and accounting or finance expertise in audit committee cannot be expected as drivers of business value creation in Indonesian context, more specifically for property, real estate, and building construction industry. The overall lack of significant relationships may potentially result from limited human capacity, lack of financial expertise, and inadequate knowledge about the role of audit committee to add value to the business.
文摘This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.
