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This study aims to investigate the effects of social capital (defined as networks) on the performance of Chinese private enterprises.

The paper employ an econometric model to test relationships between social capital and firm performance, using a sample of private enterprises in a survey in China in 2000.

The analysis suggests two findings. First, memberships in various organizations do not appear to be significant in affecting the performance of Chinese private enterprises. Second, short‐time investment in, and the flow of, social capital are significant determinants of enterprise performance.

The study examines only the effects of the short‐time investment in social capital. Further research is suggested to examine the effects of long‐term investments in social capital, the relationship between short‐time investment and long‐time investment in social capital, and the network as a whole, including informal networks.

The paper uses the survey data in China to test Krishna's hypothesis which suggests that short‐term investments can play a positive role in the financial performance of firms.

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.

As an important connecting component, the reliability of aluminium alloy welded joints influences the whole structural effectiveness and stability of equipment. The purpose of this paper is to propose a novel reliability estimation approach to the welded joints based on time-transformed Wiener process with automatic image measurement of crack growth. The crack length information of the welded joints is incorporated into reliability analysis to reflect the product time-varying characteristics.

The proposed approach is superior to other crack growth estimations in that it innovatively introduce a non-contact and flexible photogrammetry technique.First, on-line crack growth images of aluminium alloy welded joints are acquired by the designed monitor system. Second, crack length is calculated with image measurement, then the crack growth data during the manufacturing process is prepared. Finally, a time-transformed Wiener process is used to modeling the degradation, and reliability estimation is carried out with Wiener model. The approach has been validated on five 7075-T7351 welded joint samples.

The method has a twofold task: first, the extraction of crack length growth data by a sequence of image processing. The main step is to model the crack skeleton with crack skeleton tree, and remove it edges to calculate the length of crack; second, the prediction of crack growth and reliability estimation.

The limitation of proposed method should not be ignored. The pixel/mm scale should be calibrated in advance that means once we have built the monitor system, the relative position of the CCD camera and the surveyed crack cannot change anymore. It has reduced the flexibility. To improve this, we can obtain binocular vision in crack image measurement. The 3-D measurements could solve calibration problem and provide more information, such as the depth and the orientation of crack to research. Therefore, future work can be centered on the improvement of monitor system and measurement precision.

In the paper a novel method to estimate reliability of crack growth from welded joint based on image measurement has been presented. This method could be widely applied in different filed of manufacturing systems, reliability engineering and structural analysis.

This paper aims to investigate the effectiveness of Language Enhancement Program (LEP) activities in enhancing the oral communication skills of English as a Foreign Language (EFL) students at King Khalid University.

A mixed-method study design was used. Simple random sampling techniques were adopted to recruit the study participants. For the survey, a total of 58 male students were recruited and for interviews, 20 male students were selected. The 58 participants were randomly divided into experimental and control groups. The traditional method was adopted to teach the control group, while LEP activities were conducted to teach the experimental group.

A post-test was conducted to examine the oral communication skills of the participants of the experimental group. The scores of both groups were analyzed using a t-test value at a significant level of 0.05. The content analysis method was adopted to assess the enhancement of the oral communication of the students enrolled in the LEP. The questionnaires and interview results showed that the LEP program has a central focus on improving students' oral communication skills.

It is limited to the EFL students at King Khalid University.

LEP is a good program and can be implemented in Saudi Universities.

Students can interact with one another through the LEP activities that promote their English proficiency as well as their personality characteristics.

The paper spells out the role of LEP activities in improving students' oral communication in English and students' opinions about LEP activities in enhancing their English language in different types of communicative contexts. Further, it suggests some pedagogical implications for overcoming the difficulties faced by EFL students in various communicative contexts.

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.