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This study aims to investigate the impact of the development of high-speed rail (HSR) network on earnings management, especially on the trade-off between the usage of accruals-based earnings management (AM) and real earnings management (RM) techniques, and consequently, examines the extent to which the HSR network–earnings management nexus is moderated by governance and religion factors.

Using a sample of Chinese A-listed firms over an 11-year period, this study uses regression techniques as the baseline methodology while controlling for industry and year-fixed effects. The authors also use endogeneity tests (including instrumental variable method, Generalized Methods of Moments estimation and difference-in-difference) and different robustness checks.

The key findings are threefold. First, the HSR network development reduces AM. This suggests that the presence of HSR network is effective in reducing information asymmetry. Second, the use of RM technique increases with the HSR network development. This indicates that managers do not seem to engage in less earnings management with the HSR network development but instead appear to switch from the easy-to-detect AM to the more costly RM approach. Finally, the HSR network and earnings management nexus is moderated by governance and religion factors.

This study provides new evidence on the trade-off between AM and RM by managers and pioneers in examining the impacts of governance and religion factors on the relationship between the HSR network and the trade-off of earnings management techniques.

Vehicles estimation can be used in evaluating traffic conditions and facilitating traffic control, which is an important task in intelligent transportation system. The paper aims to propose a vehicle-counting method based on the analysis of surveillance videos.

The paper proposes a novel two-step method using low-rank representation (LRR) detection and locality-constrained linear coding (LLC) classification to count the number of vehicles in traffic video sequences automatically. The proposed method is based on an offline training to understand an LLC-based classifier with extracted features for vehicle and pedestrian classification, followed by an online counting algorithm to count the number of vehicles detected from the image sequence.

The proposed method allows delivery estimation (counting the number of vehicles at each frame only) and total number estimation of vehicles shown in the scene. The paper compares the proposed method with other similar methods on three public data sets. The experimental results show that the proposed method is competitive and effective in terms of computational speed and evaluation accuracy.

The proposed method does not consider illumination. Hence, the results might be unsatisfactory under low-lighting condition. Therefore, researchers are encouraged to add a term that controls the illumination changes into the energy function of vehicle detection in future work.

The paper bridges the gap between LRR detection and vehicle counting by taking advantage of existing LLC classification algorithm to distinguish different moving objects.

The purpose of this paper is to focus on the design and development of computer-aided fabric defect detection and classification employing advanced learning algorithm.

To make a fast and effective classification of fabric defect, the authors have considered a characteristic of texture, namely its colour. A deep convolutional neural network is formed to learn from the training phase of various defect data sets. In the testing phase, the authors have utilised a learning feature for defect classification.

The improvement in the defect classification accuracy has been achieved by employing deep learning algorithm. The authors have tested the defect classification accuracy on six different fabric materials and have obtained an average accuracy of 96.55 per cent with 96.4 per cent sensitivity and 0.94 success rate.

The authors had evaluated the method by using 20 different data sets collected from different raw fabrics. Also, the authors have tested the algorithm in standard data set provided by Ministry of Textile. In the testing task, the authors have obtained an average accuracy of 94.85 per cent, with six defects being successfully recognised by the proposed algorithm.

The quantitative value of performance index shows the effectiveness of developed classification algorithm. Moreover, the computational time for different fabric processing was presented to verify the computational range of proposed algorithm with the conventional fabric processing techniques. Hence, this proposed computer vision-based fabric defects detection system is used for an accurate defect detection and computer-aided analysis system.