Sarbanes-Oxley Act Section 404 and Filing Status

This thesis focuses on Sarbanes-Oxley Act, which is a United States federal law that sets new or expanded requirements for all U.S. public companies. Section 404 aims to ensure that all public companies have effective internal controls. Section 404 is not applied uniformly across different filers. This thesis focuses on the analyses of small companies as these companies, i.e. non-accelerated filers, got exemption from Section 404 (b), which requires a publicly-held company’s auditor to attest to, and report on, management’s assessment of its internal controls. Because this exemption allows non-accelerated filers to avoid significant compliance cost, the purpose of the thesis is to provide a trend analysis of changing proportion of non-accelerated filers from 2002-2016 to see if companies have taken advantage of the exemption only applicable to non-accelerated filers. I find that there was a larger proportion of non-accelerated filers right before the effective date of the exemption rule for Section 404 (b) in 2010. In addition, I find that the proportion of companies changing from accelerated to non-accelerated filers is the largest right before 2010. These results are consistent with the notion that non-accelerated filers have the incentive to avoid the huge cost to comply with Section 404 (b)

A New Perspective on the Development of Cholesterol- Lowering Products

Cardiovascular disease (CVD) is the principal cause of death worldwide, representing nearly 30% of the annual global mortality and 10% of global health burden. The current status of CVD is now on international scale; which can be considered as the commonest chronic illness in both developed and developing countries, causing the most deaths and the greatest impact on morbidity. In 2006, CVD was the leading cause of death for Canadians, representing 30% of all deaths. A total number of 1.3 million Canadians are diagnosed having heart disease accounting for 5% among those above 12 years and 23% at 75 years and older. The increased rate of obesity and diabetes combined with further aging of the population will likely lead to an increase in the number of people with CVD in the future. This will compromise the health of Canadians, put a strain on the health care system, and have a significant economic impact on Canada. Similarly, over the past five decades the prevalence of CVD has steadily increased in economically developing countries. These countries will account for 76% of an estimated 25 million death due to CVD in 2020. On an international basis, by 2020 CVD will reach nearly epidemic proportions and become the cause of more deaths, disability and economic loss than any others group of diseases. The number of fatalities by CVD projected to increase to over 20 million a year by 2020 and over 24 million a year by 2030. Apparently, understanding the aetiology of CVD and accordingly develop preventive and therapeutic approaches to address this health threat continues to be critically important in the next decades although significant achievements have been made in the past decades.Peer reviewed: YesNRC publication: Ye

BSD2000 Deep Hyperthermia Combined with Chemotherapy of PT regimen in Patients with Non-small Cell Lung Cancer

Background and objective The aim of this study is to determine the short-term efficacy, toxicity and the rate of life-quality improvement of BSD2000 deep hyperthermia combined with chemotherapy of PT regimen in patients with non-small cell lung cancer (NSCLC) by comparation with PT regimen alone. Methods Sixty patients with NSCLC were randomly divided into the treatment group and control group, with 30 each. The treatment group was treated with chemotherapy (paclitaxel:135mg/m2 ivdirp 3 h qd d1+cisplatin: 20 mg/m2 ivdirp qd d1-5) in combination with BSD2000 deep hyperthermia, and hyperthermia was positioned precisely and maintained for 60 min (2 times a cycle: d1, 4 after the end of chemotherapy within two hours). The control group was treated with chemotherapy alone. Treatment response in both groups were evaluated as well as side-effects after 3 cycles. By observing the results, comparing response rate, toxic side effects and quality of life improvement rate in two groups. Results The efficiency and the rate of life-quality improvement in the treatment group were 63.33%, 76.67% respectively, and 36.67%, 40.00% in the control group respectively. There were significant differences between two groups (P < 0.05). The main side-effects were myelosuppression and gastrointestinal reactions, no significant difference between two groups (P > 0.05). Conclusion BSD2000 deep hyperthermia combined with chemotherapy in patients with NSCLC can significantly increase the efficacy, response rate and quality of life improvement and without increasing sideeffects compared to chemotherapy alone

Gamified Live-streaming: Is Avatar Better than Human Being?

Live-streaming has emerged as a popular direct selling channel to foster synchronous interaction between streamers and consumers, with the avatar streamer largely underexplored. Using the data from a fashion retailer, we adopt the Generalized Synthetic Control (GSC) method to examine the effect of gamified and human live-streaming on product sales and return rate. We find that (1) the gamified live-streaming reduces product sales and the return rate simultaneously; (2) human live-streaming boosts product sales but increases the return rate, and (3) the dual-type live-streaming can increase product sales and decrease return rates. Furthermore, we proposed that the reason for the differentiated effects between gamified and human live-streaming could be driven by the impulse-buying behavior of viewers only in human live-streaming. Our findings contribute to the growing literature on the business value of AI technology and gamification in live-streaming and shed light on practical decisions made by online retailers

Space-Invariant Projection in Streaming Network Embedding

Newly arriving nodes in dynamics networks would gradually make the node embedding space drifted and the retraining of node embedding and downstream models indispensable. An exact threshold size of these new nodes, below which the node embedding space will be predicatively maintained, however, is rarely considered in either theory or experiment. From the view of matrix perturbation theory, a threshold of the maximum number of new nodes that keep the node embedding space approximately equivalent is analytically provided and empirically validated. It is therefore theoretically guaranteed that as the size of newly arriving nodes is below this threshold, embeddings of these new nodes can be quickly derived from embeddings of original nodes. A generation framework, Space-Invariant Projection (SIP), is accordingly proposed to enables arbitrary static MF-based embedding schemes to embed new nodes in dynamics networks fast. The time complexity of SIP is linear with the network size. By combining SIP with four state-of-the-art MF-based schemes, we show that SIP exhibits not only wide adaptability but also strong empirical performance in terms of efficiency and efficacy on the node classification task in three real datasets

Spatial+:A new cross-validation method to evaluate geospatial machine learning models

Random cross-validation (CV) is often used to evaluate geospatial machine learning models, particularly when a limited amount of sample data are available, and collecting an extra test set is unfeasible. However, the prediction locations can be substantially different from the available sample, leading to over-optimistic evaluation results. This has fostered the development of spatial CV methods. Yet these methods only focus on spatial autocorrelation and cannot sufficiently guarantee that the validation subset is a good proxy of the test set with significant differences. In this paper, we propose the spatial+ cross-validation (SP-CV) method. This method, which considers both the geographic and feature spaces, is composed of two stages. The first stage addresses spatial autocorrelation issues by using agglomerative hierarchical clustering to divide the available sample into blocks. The second stage deals with multiple sources of differences. It uses cluster ensembles to split the blocks into training and validation folds based on the locations of the sample data and the values of the covariates and target variable. The proposed method is compared against random and block CV methods in a series of experiments with Amazon basin above ground biomass and California houseprice datasets. Our results show that SP-CV provided the smallest error differences with respect to the reference error. This means that SP-CV produced more representative splits and led to more reliable model evaluations. It suggests that a reliable model evaluation requires to consider both the geographic and the feature spaces in a comprehensive manner.</p