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This paper aims to investigate a novel shape from focus (SFF) algorithm based on maximum correntropy Kalman filtering (SFF-MCKF) for solving the problem that traditional SFF methods are weak in de-noising and spatial continuity.

To be specific, it was first assumed that the predicted depth of next pixel is equal to the depth of the current pixel according to spatial continuity. Besides, the observing data are derived from the estimation of traditional SFF and the corresponding covariance of noise is adaptively calculated by the entropy along the optical axis. Finally, to enhance robustness, we systematically conduct MCKF iteratively in four transfer directions that are 0°, 90°, 45° and −45°, respectively.

The experimental results indicate that the robustness of SFF-MCKF facing noises as well as the spatial continuity is better than that of the existing representative ones.

As the proposed method is aimed at precision objects, high demand is for experimental device. Unstable device unregister the source images, which is unfavorable for observing data.

SFF-MCKF can be applied to many precision object measurements without significant drifts, such as the surface reconstruction of metal objects and electronic components. Besides, the computation cost is low, and SFF-MCKF has a wide range of real-time industrial applications.

The stability maintenance system has played an essential role in maintaining social stability although it also has brought about social problems worthy of attention. Admittedly compensation-based stability maintenance policy can address the appeals of citizens whose rights are infringed and the dissolving effect in the provision of compensation can save the cost of stability maintenance but such stability maintenance system lacks equilibrium.

The establishment of a strict assessment system for stability maintenance performance can encourage the stability maintenance authorities to eliminate the “fuse effect” as much as possible and ensure the effective implementation of the stability maintenance system. However, the rigorous stability maintenance performance assessment also provides the possibility for profit-driven petitions.

Due to the continuous accumulation of social dissatisfaction and the lack of stability maintenance equilibrium in the implementation of the compensation-based stability maintenance policy, public governance will fall into a stability maintenance paradox of “greater instability resulting from stability maintenance”.

The provision of sufficient means for the people to protect their interest by implementing measures such as strengthening the rule of law mechanisms is the key to achieve long-term social stability.

The purpose of this paper is to design an algorithm to predict RNA secondary structure, compared with other relevant algorithm, its time complexity and space complexity are reduced.

The dynamic programming algorithm need more time and space; it is very difficult to predict the RNA secondary structure which have more 1,000 bases. The nested RNA secondary structure algorithms cannot predict the RNA secondary structure containing pseudoknots, so the fast algorithm is needed to predict the RNA secondary structure containing pseudoknots urgently. Based on the greedy principle, a model is designed to solve the problem.

A greedy algorithm is presented to predict RNA secondary structure.

The problem for predicting RNA secondary structure including pseudoknots is NP‐complete.

The paper presents a valuable and useful method for predicting the RNA secondary structure.

The new algorithm needs O(n³) time and O(n) space; the experimental results indicate that the algorithm has good accuracy and sensitivity.

This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks detection of multicrystalline solar cell surface based on machine vision is fast, economical, intelligent and easier for on-line detection. However, the generalization capability of feature extraction scheme adopted by existed methods is limited, which has become an obstacle for further improving the detection accuracy.

A novel micro-cracks detection method based on self-learning features and low-rank matrix recovery is proposed in this paper. First, the input image is preprocessed to suppress the noises and remove the busbars and fingers. Second, a self-learning feature extraction scheme in which the feature extraction templates are changed along with the input image is introduced. Third, the low-rank matrix recovery is applied to the decomposition of self-learning feature matrix for obtaining the preliminary detection result. Fourth, the preliminary detection result is optimized by incorporating the superpixel segmentation. Finally, the optimized result is further fine-tuned by morphological postprocessing.

Comprehensive evaluations are implemented on a data set which includes 120 testing images and corresponding human-annotated ground truth. Specifically, subjective evaluations show that the shape of detected micro-cracks is similar to the ground truth, and objective evaluations demonstrate that the proposed method has a high detection accuracy.

First, a self-learning feature extraction method which has good generalization capability is proposed. Second, the low-rank matrix recovery is combined with superpixel segmentation for locating the defective regions.

When using a machine vision inspection system for rail surface defect detection, many complex factors such as illumination changes, reflection inequality, shadows, stains and rust might inevitably deform the scanned rail surface image. This paper aims to reduce the influence of these factors, a pipeline of image processing algorithms for robust defect detection is developed.

First, a new inverse Perona-Malik (P-M) diffusion model is presented for image enhancement, which takes the reciprocal of gradient as feature to adjust the diffusion coefficients, and a distinct nearest-neighbor difference scheme is introduced to select proper defect boundaries during discretized implementation. As a result, the defect regions are sufficiently smoothened, whereas the faultless background remains unchanged. Then, by subtracting the diffused image from the original image, the defect features will be highlighted in the difference image. Subsequently, an adaptive threshold binarization, followed by an attribute opening like filter, can easily eliminate the noisy interferences and find out the desired defects.

Using data from our developed inspection apparatus, the experiments show that the proposed method can attain a detection and measurement precisions as high as 93.6 and 85.9 per cent, respectively, while the recovery accuracy remains 93 per cent. Additionally, the proposed method is computationally efficient and can perform robustly even under complex environments.

A pipeline of algorithms for rail surface detection is proposed. Particularly, an inverse P-M diffusion model with a distinct discretization scheme is introduced to enhance the defect boundaries and suppress noises. The performance of the proposed method has been verified with real images from our own developed system.

The objective of this study is to investigate the impact of longitudinal forces on extreme-long heavy-haul trains, providing new insights and methods for their design and operation, thereby enhancing safety, operational efficiency and track system design.

A longitudinal dynamics simulation model of the super long heavy haul train was established and verified by the braking test data of 30,000 t heavy-haul combination train on the long and steep down grade of Daqing Line. The simulation model was used to analyze the influence of factors on the longitudinal force of super long heavy haul train.

Under normal conditions, the formation length of extreme-long heavy-haul combined train has a small effect on the maximum longitudinal coupler force under full service braking and emergency braking on the straight line. The slope difference of the long and steep down grade has a great impact on the maximum longitudinal coupler force of the extreme-long heavy-haul trains. Under the condition that the longitudinal force does not exceed the safety limit of 2,250 kN under full service braking at the speed of 60 km/h the maximum allowable slope difference of long and steep down grade for 40,000 t super long heavy-haul combined trains is 13‰, and that of 100,000 t is only 5‰.

The results will provide important theoretical basis and practical guidance for further improving the transportation efficiency and safety of extreme-long heavy-haul trains.

This paper sets out to discuss white‐collar crime and fraud in China and to draw implications for Western businesses wishing to enter the Chinese market.

The paper provides examples of recent frauds committed in China and also provides a brief review of the literature. It traces the development of China's economy, describing China's modern‐day economy, and also looks at cultural issues and differences between Western and Chinese attitudes.

Western businesses planning to enter the Chinese market need to do their due diligence before committing resources. What works in Western society may not work in another culture, like China's. However, gaining an understanding of the culture and general way of life will go a long way towards laying the groundwork for success.

Additional focus should be placed on examining how cultural differences affect judgments and behaviors.

From a practical standpoint, the paper provides insights into possible areas of disagreement between employees from different cultures that can result from their divergent opinions about individuals' responsibility for fraudulent acts.

The Sports Live Streaming Platforms (SLSPs) have taken centre stage in broadcasting sporting events. This study adopts the value creation sphere (VCS) model and the service dominant logic (SDL) to unpack the value co-creation process on SLSPs.

A case study with one of the most representative SLSPs in China, involving the netnographic approach and in-depth interviews, was conducted.

This study redefines the value co-creation spheres in the context of SLSPs and identifies four actors who contribute to viewers' value perceptions. The findings show that viewers' values can be co-created individually and collectively with other actors in both the customer sphere and the joint sphere.

This study extends the theoretical boundary of value co-creation into the context of SLSPs. The study findings help SLSPs managers and decision makers understand the value co-creation process to gain competitive advantages and enhance the sustainability of their services.

Nowadays, star athletes are global brand personalities. The increased popularity of the professional sport has contributed to elevating exceptional athletes to international star status. This empirical study aims to assess the impact of athlete performance and brand social value on product involvement with the mediation effect of celebrity athlete endorsement.

A quantitative survey was conducted with 399 Chinese participants. PLS-SEM is adopted to examine the associated paths and the mediating effect of celebrity endorsement.

The results demonstrate the significant impact of athlete performance and brand social value on product involvement. Celebrity endorsement partially mediates the effects of athlete performance and brand social value on product involvement.

This study extends understanding of celebrity athlete endorsement and provides insight into the strategic implications for Chinese social media-based marketing initiatives in the context of the recent Olympic Game in Tokyo 2021.