Aggregate quasi rents and auditor independence : evidence from audit firm mergers in China

Using a sample of audit firm mergers in China\u27s audit market, this paper provides evidence on the way auditor independence can be improved following audit firm mergers as a result of a change in the aggregate quasi rents that are exposed to risk (i.e., the quasi rents at stake). This setting allows us to examine the relationship between auditor independence and the aggregate quasi rents at stake directly after controlling for the confounding effects of auditor competence, audit firm brand name, and the self-selection problem that may exist in previous studies. We hypothesize that auditors become more independent in the post-merger period only if the mergers increase the aggregate quasi rents at stake. Proxying audit quality by the frequency of modified audit opinions (MAOs) and using a \u27\u27difference-in-differences\u27\u27 research design, we conduct separate tests for two types of mergers under the institutional arrangements in China: one with an increase in the aggregate quasi rents at stake and the other with little change in these rents. Consistent with our hypothesis, we observe an improvement in auditor independence, but only for mergers that increase auditors\u27 aggregate quasi rents at stake. Moreover, the post-merger increase in the propensity for MAOs in this type of merger is positively associated with the magnitude of the change in the aggregate quasi rents at stake. Our empirical findings support the theory that auditor independence is a positive function of the aggregate quasi rents at stake

Stronger microbial nutrient limitations in subsoil along the precipitation gradient of agroecosystem: insights from soil enzyme activity and stoichiometry

IntroductionSoil extracellular enzymes are central in terrestrial ecosystem responses to climate change, and their research can be crucial for assessing microbial nutrient demand. However, the effects of climate-induced precipitation patterns on soil microbial nutrient demand in different soil profiles of agroecosystems are rarely studied.MethodsHere, we present how the precipitation gradient affects soil enzymes related to carbon (C), nitrogen (N) and phosphorus (P) cycling and identified microbial nutrient limitation determinants at five depth intervals (0–10, 10–20, 20–30, 30–40, and 40–50 cm) in seven agroecosystems.Results and DiscussionWe found that N- and P-acquiring enzymes have a tendency to decrease or increase, respectively, but C-acquiring enzymes did not change along the precipitation gradient throughout soil profiles. Soil pH and moisture were the most important factors affecting the enzyme activity in 0–50 cm. Our results also revealed a crucial soil boundary (at 20 cm) that differentiated responses of microbial nutrient limitation to precipitation changes. In the topsoil (0–20 cm), the stoichiometry of soil nutrients did not vary with precipitation. Microbial P limitation was exacerbated with increased precipitation, which was controlled by soil pH and moisture in the topsoil. In contrast, in the subsoil (20–50 cm), soil nutrient stoichiometry decreased with increasing precipitation, and microbial C and P limitation displayed a positive correlation with precipitation. Furthermore, microbial P limitation tended to be stronger in the subsoil than in the topsoil along the precipitation gradient. Microbial C and P limitation was regulated by the soil nutrients and their stoichiometry in the subsoil. Our study is an essential step in soil enzyme activity and stoichiometry response to precipitation in agroecosystems and provides novel insights into understanding microbial nutrient limitation mechanisms in soil profiles along the precipitation gradient

Physiology and Pathology of Multidrug-Resistant Bacteria: Antibodies- and Vaccines-Based Pathogen-Specific Targeting

Multidrug-resistant bacteria (MDR) are increasing rapidly and posing a global threat to mankind. Alternative strategies other than antibiotics have to be explored urgently. In this chapter, we review the current status of nonantibiotics strategies including antibody-based therapy and vaccine development for targeting Gram-positive strains (methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus faecium) and MDR Gram-negative strains (Acinetobacter baumannii and Pseudomonas aeruginosa). Biologics-based clinical progress against these bacterial infections is updated

Physiology and Pathology of Multidrug-Resistant Bacteria: Phage-Related Therapy

Multidrug-resistant bacteria (MDR) are spreading rapidly across the world that outpace development of new antibiotics. Options other than antibiotics treatment are urgently needed. In this chapter, we review the current status of nonantibiotics-based strategies including phage therapy and phage-derived protein therapy for targeting Gram-positive strains (methicillin-resistant Staphylococcus aureus and vancomycin-resistant Enterococcus faecium) and MDR Gram-negative strains (Acinetobacter baumannii and Pseudomonas aeruginosa)

Extremal properties of the first eigenvalue and the fundamental gap of a sub-elliptic operator

We consider the problems of extreming the first eigenvalue and the fundamental gap of a sub-elliptic operator with Dirichlet boundary condition, when the potential $V$ is subjected to a $p$-norm constraint. The existence results for weak solutions, compact embedding theorem and spectral theory for sub-elliptic equation are given. Moreover, we provide the specific characteristics of the corresponding optimal potential function

Optimal Actuator Location of the Norm Optimal Controls for Degenerate Parabolic Equations

This paper focuses on investigating the optimal actuator location for achieving minimum norm controls in the context of approximate controllability for degenerate parabolic equations. We propose a formulation of the optimization problem that encompasses both the actuator location and its associated minimum norm control. Specifically, we transform the problem into a two-person zero-sum game problem, resulting in the development of four equivalent formulations. Finally, we establish the crucial result that the solution to the relaxed optimization problem serves as an optimal actuator location for the classical problem

Null controllability of two kinds of coupled parabolic systems with switching control

The focus of this paper is on the null controllability of two kinds of coupled systems including both degenerate and non-degenerate equations with switching control. We first establish the observability inequality for measurable subsets in time for such coupled system, and then by the HUM method to obtain the null controllability. Next, we investigate the null controllability of such coupled system for segmented time intervals. Notably, these results are obtained through spectral inequalities rather than using the method of Carleman estimates. Such coupled systems with switching control, to the best of our knowledge, are among the first to discuss

Structural and Biochemical Studies of Proteins Involved in Polyamine Transport from Pseudomonas Aeruginosa PAO1 and mRNA Decay From Saccharomyces Cerevisiae

Ph.DDOCTOR OF PHILOSOPH

Three Essays in Finance : Machine Learning and Finance Applications

The thesis contains three chapters. All chapters are centered around the themes of deep learning/machine learaning and finance applications. The first chapter improve out of sample forecasting ability using an artificial neural network. Compared with traditional methods, deep learning I exploited two settings, respectively, memoryless and memory models. Memory models consistently outperform previous research and achieve both out of sample statistical and economic significance. I visualize the decision-making process of memory models and show that the model does not only pay attention to the current state during the forecasting process. The second chapter extends on first chapter. By adding attention mechanism, I can investigate how the decision-making process of model is related to variations of economic conditions. I deploy dual attentive LSTM model to predict equity premiums and then extract the attention weights after the training process. The attention weights are regressed on economic condition variables. I find that the weights are either negatively or positively related to economic conditions and represent a cyclical pattern. In the third chapter, I use machine learning to re-evaluate the classic, but controversial corporate governance problem------board structure and firm performance. I first show how GMM results are sensitive to various lag lengths selection. Based on the selected lag instruments using three alternative algorithms, I re-evaluate the effects of board structure on firm performance using GMM and find significant effects of board structure on firm performance under some cases