The More, The Better? A Case History Of Audit Committee Regulations

An understanding of changing auditing regulatory environment is vital in preparing students for the challenges in the accounting profession. The revised requirements for audit committees are one of the significant changes after the Sarbanes-Oxley Act of 2002. Presenting a case history of regulatory changes for audit committees, this study requires students to critically analyze information and to conduct research on auditing topics. Meanwhile, integrating further discussion on corporate governance into auditing class can enrich students learning experience by stimulating critical thinking

EVALUATION OF THE ACCURACY OF GAIT EVENTS DETECTED USING THREE DIFFERENT METHODS DURING RUNNING

Different methods have been proposed for determining initial contact and toe off with inertial measurement unit (IMU) attached to different anatomical positions. However, the accuracy has not yet been compared. This study aimed to evaluate the accuracy of three commonly used methods (S-, M-, and L-method) in detecting gait events at two running speed conditions (slow and fast). Obvious differences of detected initial contact and toe off and estimated stance duration among the three detection methods using IMU were found at the two running speed conditions. It was shown that initial contact detected using the S-method and toe off detected by the M-method were the best estimates of the gait events. Combined use of both methods is recommended for determining stance duration during overground running

Metabolic responses of alfalfa (Medicago Sativa L.) leaves to low and high temperature induced stresses

To explore adaptation mechanism of alfalfa to cold and heat stresses, status of sucrose metabolism and relative water content (RWC) in leaves under low and high temperature treatments were studied. Seedlings (35 day old) were transferred to chambers for treatments. First group was subjected to 5°C as low temperature (LT) stress, second group at 33°C as high temperature (HT) stress and third group at 25°C as the control (CK). Results indicated that, both stresses led to an increase in degree and duration of genes expression and corresponding enzymes activities of sucrose phosphate synthase (SPS) and sucrose synthase (SS), but LT showed a more significant effect. As a result, lower starch content and higher contents of soluble reducing sugar, fructose and sucrose were observed under LT stress. Moreover, LT stress lowered malondialdehyde (MDA) content and electrolyte leakage in leaves, thus achieving a higher RWC. It was suggested that, relatively higher RWC in leaves by LT stress resulted from lowered water potential, and transpiration can explain the reason that alfalfa plants are cold-tolerant but heat-sensitive.Key words: Alfalfa, temperature stress, sucrose, sucrose phosphate synthase (SPS), relative water content

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

Stock market plays an important role in the economic development. Due to the complex volatility of the stock market, the research and prediction on the change of the stock price, can avoid the risk for the investors. The traditional time series model ARIMA can not describe the nonlinearity, and can not achieve satisfactory results in the stock prediction. As neural networks are with strong nonlinear generalization ability, this paper proposes an attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price. The model constructed in this paper integrates the time series model, the Convolutional Neural Networks with Attention mechanism, the Long Short-Term Memory network, and XGBoost regressor in a non-linear relationship, and improves the prediction accuracy. The model can fully mine the historical information of the stock market in multiple periods. The stock data is first preprocessed through ARIMA. Then, the deep learning architecture formed in pretraining-finetuning framework is adopted. The pre-training model is the Attention-based CNN-LSTM model based on sequence-to-sequence framework. The model first uses convolution to extract the deep features of the original stock data, and then uses the Long Short-Term Memory networks to mine the long-term time series features. Finally, the XGBoost model is adopted for fine-tuning. The results show that the hybrid model is more effective and the prediction accuracy is relatively high, which can help investors or institutions to make decisions and achieve the purpose of expanding return and avoiding risk. Source code is available at https://github.com/zshicode/Attention-CLX-stock-prediction.Comment: arXiv admin note: text overlap with arXiv:2202.1380

Direct fiber vector eigenmode multiplexing transmission seeded by integrated optical vortex emitters

Spatial modes have received substantial attention over the last decades and are used in optical communication applications. In fiber-optic communications, the employed linearly polarized modes and phase vortex modes carrying orbital angular momentum can be synthesized by fiber vector eigenmodes. To improve the transmission capacity and miniaturize the communication system, straightforward fiber vector eigenmode multiplexing and generation of fiber-eigenmode-like polarization vortices (vector vortex modes) using photonic integrated devices are of substantial interest. Here, we propose and demonstrate direct fiber vector eigenmode multiplexing transmission seeded by integrated optical vortex emitters. By exploiting vector vortex modes (radially and azimuthally polarized beams) generated from silicon microring resonators etched with angular gratings, we report data-carrying fiber vector eigenmode multiplexing transmission through a 2-km large-core fiber, showing low-level mode crosstalk and favorable link performance. These demonstrations may open up added capacity scaling opportunities by directly accessing multiple vector eigenmodes in the fiber and provide compact solutions to replace bulky diffractive optical elements for generating various optical vector beams