Chinese independent auditing standards (CIAS) have been promulgated since 1995. This paper contextualizes the various Chinese attempts at setting auditing standards, especially the making of the CIAS, by identifying factors that motivated these efforts. It finds that, prior to the CIAS, some auditing standards and procedures were issued on a voluntary basis in order to educate auditors and auditees, to improve audit quality, and to help auditors survive the competition between various consultancy firms. However, these standards failed to achieve their objectives and could not prevent auditors from being involved in a number of well-publicized major financial scandals. Amid crises of public confidence, the CPA Law was stipulated and the Chinese regulators began to formulate the CIAS to strengthen the legal system that regulates the audit profession and to harmonize Sino-foreign auditing. The paper also illuminates some major features of the Chinese audit market, such as the lack of audit independence, the shortage of well-qualified auditors, an environment of extensive corruption, and the existence of many misconceptions about the audit. These conditions will significantly hinder the further development of the audit profession in China. In particular, they will severely impair the effectiveness of the CIAS and recently developed accounting standards. Consequently, investments in Chinese companies will still involve considerable risks despite the existence of the CIAS.
Understanding and predicting dynamic change of algae population in freshwater reservoirs is particularly important, as algae-releasing cyanotoxins are carcinogens that would affect the health of public. However, the high complex nonlinearity of water variables and their interactions makes it difficult to model the growth of algae species. Recently, support vector machine (SVM) was reported to have advantages of only requiring a small amount of samples, high degree of prediction accuracy, and long prediction period to solve the nonlinear problems. In this study, the SVM-based prediction and forecast models for phytoplankton abundance in Macau Storage Reservoir (MSR) are proposed, in which the water parameters of pH, SiO2, alkalinity, bicarbonate(HCO3 -), dissolved oxygen (DO), total nitrogen (TN), UV254, turbidity, conductivity, nitrate, total nitrogen (TN), orthophosphate(PO4 3−), total phosphorus (TP), suspended solid (SS) and total organic carbon (TOC) selected from the correlation analysis of the 23 monthly water variables were included, with 8-year (2001–2008) data for training and the most recent 3 years (2009–2011) for testing. The modeling results showed that the prediction and forecast powers were estimated as approximately 0.76 and 0.86, respectively, showing that the SVM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir.
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