The impact of audit quality, analysts' forecasts and new accounting pronouncements on bond pricing and investment : an empirical analysis

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Value relevance of discretionary R&D capitalization.

this study examines the value-relevance of capitalized R&D in a discretionary R&D expensing and capitalization environment. The study posits that the R&D capitalized during a fiscal year is value relevant. Additionally, its value relevance is enhanced when investors perceive that the firm is capitalizing R&D for efficiency-based reasons rather than for opportunistic reasons.Master of Accountanc

Auditor Independence and the Cost of Capital Before and After Sarbanes-Oxley: The Case of Newly Issued Public Debt

An important aim of the Sarbanes-Oxley Act (SOX) was to reduce the cost of capital by enhancing auditor independence. However, prior literature has argued that SOX has been ineffective in meeting this objective. We contribute to this debate by first providing evidence suggesting that auditor independence has increased following SOX. Though we posit an inverse relationship between auditor independence and cost of capital, it is an open question whether this relationship has become stronger or weaker following SOX. An examination of this relationship reveals that auditor independence is more strongly related to bond rating and bond yield premium in the post-SOX period relative to the period before SOX. This evidence suggests greater price sensitivity of corporate debt to the level of auditor independence following SOX. We also show that controlling for the effect of auditor independence and other factors, cost of debt decreased following SOX.

Production of Biologically Activated Carbon from Orange Peel and Landfill Leachate Subsequent Treatment Technology

In order to improve adsorption of macromolecular contaminants and promote the growth of microorganisms, active carbon for biological wastewater treatment or follow-up processing requires abundant mesopore and good biophile ability. In this experiment, biophile mesopore active carbon is produced in one-step activation with orange peel as raw material, and zinc chloride as activator, and the adsorption characteristics of orange peel active carbon is studied by static adsorption method. BET specific surface area and pore volume reached 1477 m2/g and 2.090 m3/g, respectively. The surface functional groups were examined by Fourier transform infrared spectroscopy (FT-IR). The surface of the as-prepared activated carbon contained hydroxyl group, carbonyl group, and methoxy group. The analysis based on X-ray diffraction spectrogram (XRD) and three-dimensional fluorescence spectrum indicated that the as-prepared activated carbon, with smaller microcrystalline diameter and microcrystalline thickness and enhanced reactivity, exhibited enhanced adsorption performance. This research has a deep influence in effectively controlling water pollution, improving area water quality, easing orange peel waste pollution, and promoting coordinated development among society, economy, and environment

An Intelligent Detection System for Surface Shape Error of Shaft Workpieces Based on Multi-Sensor Combination

As the main components of mechanical products and important transmission components of mechanical motion, shaft workpieces (SW) need to undergo high-speed motion while also withstanding high torque motion, which has high processing requirements. At the same time, the processing quality of the workpieces determines the success of the entire processing process, and the quality-inspection methods and the accuracy of the technology directly affect the evaluation of the product. This paper designs an intelligent detection system for the surface shape error (SSE) of SW that combines multiple sensors. Based on the principle of sensor use and specific experimental status, the overall scheme of the detection system is designed, followed by research on the spatial positioning algorithm and surface measurement algorithm of the workpiece to be tested. We then compensate and correct the errors with the algorithm. The effectiveness of the system is verified by measuring the surface size of the workpiece. Finally, the radial circular runout error is taken as an example to verify the detection system. The results show that the measurement error is less than 5%, and the accuracy of the system is high

A Novel Reference-Based and Gradient-Guided Deep Learning Model for Daily Precipitation Downscaling

The spatial resolution of precipitation predicted by general circulation models is too coarse to meet current research and operational needs. Downscaling is one way to provide finer resolution data at local scales. The single-image super-resolution method in the computer vision field has made great strides lately and has been applied in various fields. In this article, we propose a novel reference-based and gradient-guided deep learning model (RBGGM) to downscale daily precipitation considering the discontinuity of precipitation and ill-posed nature of downscaling. Global Precipitation Measurement Mission (GPM) precipitation data, variables in ERA5 re-analysis data, and topographic data are selected to perform the downscaling, and a residual dense attention block is constructed to extract features of them. By exploring the discontinuous feature of precipitation, we introduce gradient feature to reconstruct precipitation distribution. We also extract the feature of high-resolution monthly precipitation as a reference feature to resolve the ill-posed nature of downscaling. Extensive experimental results on benchmark data sets demonstrate that our proposed model performs better than other baseline methods. Furthermore, we construct a daily precipitation downscaling data set based on GPM precipitation data, ERA5 re-analysis data and topographic data