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Shadows, the common phenomena in most outdoor scenes, bring many problems in practical image processing. Shadow detection and removal, especial in uncalibrated outdoor image, is still a difficult problem. The purpose of this paper is to detect and to remove shadows in single outdoor image based on retinex theory.

The shadow extraction algorithm originates from a simple idea that the human‐vision‐based retinex has the natural ability to enhance the shadow regions of an image no matter it is penumbrae or umbrae. Shadows are detected by comparing the retinex‐enhanced images with original images in the paper. The shadow removal algorithm in the paper deals with the shadow regions and non‐shadow regions in the images separately using the retinex enhancement algorithm. Through adding smooth light forcibly to shadow edges and introducing shadow edge masks, the authors reduce the effects of shadow edges in shadow removal processing.

Some real single outdoor images with the umbra regions and those with penumbra regions are both experimented in the paper. Experimental results validate the feasibility of the approach.

The approach proposed here does not use any special prior knowledge and assumptions. The feasibility of this method is testified for detecting and removing both penumbrae and umbrae in the outdoor images.

Active contour can describe target's silhouette accurately and has been widely used in image segmentation and target tracking. Its main drawback is huge computation that is still not well resolved. The purpose of this paper is to optimize the evolving path of active contour, to reduce the computation cost and to make the evolution effectively.

The contour‐evolution process is separated into two steps: global translation and local deformation. The contour global translation and local deformation are realized by average and normal gradient flow of the evolving contour curve, respectively.

When a contour is far away from the object to be segmented or tracked, the effective way of contour evolution is that it moves to the object without deformation first and then it deforms into the shape of the object when it moves on the object.

The method presented in this paper can optimize the curve evolving path effectively without complicated calculation, such as rebuilding a new inner product, and its computation cost is largely reduced.

This paper aims to study the vehicle routing problem with dynamic customers considering dual service (including home delivery [HD] and customer pickup [CP]) in the last mile delivery in which three decisions have to be made: determine routes that lie along the HD points and CP facilities; optimize routes in real time, which mode is better between simultaneous dual service (SDS, HD points and CP facilities are served simultaneously by the same vehicle); and respective dual service (RDS, HD points and CP facilities are served by different vehicles)?

This paper establishes a mixed integer linear programing model for the dynamic vehicle routing problem considering simultaneous dual services (DVRP-SDS). To increase the practical usefulness and solve large instances, the authors designed a two-phase matheuristic including construction-improvement heuristics to solve the deterministic model and dynamic programing to adjust routes to dynamic customers.

The computational experiments show that the CP facilities offer greater flexibility for adjusting routes to dynamic customers and that the SDS delivery system outperforms the RDS delivery system in terms of cost and number of vehicles used.

The results provide managerial insights for express enterprises from the perspective of operation research to make decisions.

This paper is among the first papers to study the DVRP-SDS. Moreover, this paper guides the managers to select better delivery mode in the last mile delivery.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

The paper investigates firms' optimal pricing policies and green strategies in a dynamic green supply chain with consideration of different retail service strategies. The purpose of the paper is to address the following research questions: (1) What are the optimal pricing policies and green strategies of the dynamic decentralized supply chain with the competitive or supportive retail service? (2) How does the dynamic consumer's perception of green product affect these equilibrium solutions?

The paper establishes the dynamic game models and then derives a firm's instantaneous and steady-state feedback equilibrium solutions in three scenarios as follows: (1) the integrated supply chain; (2) the decentralized supply chain with competitive retail service and (3) the decentralized supply chain with supportive retail service. Finally, we conduct numerical analyses to compare the firm's instantaneous and steady-state equilibrium solutions and profit in the three scenarios.

The theoretical and numerical analysis results suggest that the supportive retail service is less inefficient than the competitive retail service in the decentralized supply chain and that the types of retail service have no influence on the green strategy. Moreover, a firm's myopia leads to lowering the greenness degree, retail service level and severe price competition, resulting in economic losses. Consumers’ initial perception of greenness degree determines whether the retailer should adopt the skimming pricing strategy or penetration pricing strategy. Furthermore, only when consumers’ perception of greenness degree is higher than a threshold, will the manufacturer produce green product with positive greenness degree.

This is one of few studies on the effect of different types of retail service on horizontal competition in green supply chain. The extension of the static study by adopting differential game approaches provides researchers with a deeper understanding of the application of retail service in green supply chain.

The purpose of this paper is to examine the business-government nexus in Hong Kong as a special case, thereby contributing to a comparative examination of various significant variables affecting the study of political and economic developments in East Asia.

The paper considers the privileged position afforded to the major business groups in Hong Kong’s political system as part of the pro-Beijing United Front. It adopts a historical perspective, relying on existing literature including media reports. As the author is both an academic and a long-term pro-democracy activist, his actual experiences and extensive interviews with various important participants in Hong Kong’s business and political communities offer valuable perspectives.

In the past, the business-government nexus was perceived to be a positive factor that contributed to the territory’s economic growth. In the past decade or so, though, increasing dissatisfaction with the Hong Kong government’s performance and the widening gap between the rich and the poor have led to the emergence of a legitimacy deficit on the part of the government, resulting in rising demands for democracy which have not been met. Divisions on these issues within the local business community have posed a severe challenge to the continuation of the business-government nexus.

The author’s actual experiences and his interviews with other participants in Hong Kong’s politics provide observations of significant value. The Occupy Central campaign, which took off in the territory in 2014, demands serious consideration in terms of its political development and a key element for consideration is the response of the business-government nexus to it.

This paper aims to address the problem of uncertain product quality in digital light processing (DLP) three-dimensional (3D) printing, a scheme is proposed to qualitatively estimate whether a layer is printed with the qualified quality or not cured .

A thermochromic pigment whose color fades at 45°C is prepared as the indicator and it is mixed with the resin. A visual surveillance framework is proposed to monitor the visual variation in a period of the entire curing process. The exposure region is divided into 30 × 30 sub-regions; gray-level variation curves (curing curves) in all sub-regions are classified as normal or abnormal and a corresponding printing control strategy is designed to improve the percentage of qualified printed objects.

The temperature variation caused by the releasing reaction heat on the exposure surface is consistent in different regions under the homogenized light intensity. The temperature in depth begins to rise at different times. The temperature in the regions near the light source rises earlier, and that far from the light source rises later. Thus, the color of resin mixed with the thermochromic pigment fades gradually over a period of the entire solidification process. The color variation in the regions with defects of bubbles, insufficient material filling, etc., is much different from that in the normal curing regions.

A temperature-sensitive organic chromatic chemical pigment is prepared to present the visual variation over a period of the entire curing process. A novel 3D printing scheme with visual surveillance is proposed to monitor the layer-wise curing quality and to timely stop the possible unqualified printing resulted from bubbles, insufficient material filling, etc.

The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.

A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.

The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.

The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.

Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

The purpose of this paper is to examine the post-enactment status of China’s Labour Contract Law and Labour Dispute Mediation and Arbitration Law, focusing on the dramatic rise in remuneration litigation amidst much criticism of weak or ineffective implementation of these laws.

This paper deploys both quantitative and qualitative analysis methods to investigate the features of remuneration litigation. Remuneration judgments by Beijing People’s Courts from 1 January 2014 to 31 December 2017 provide the primary empirical data. The intrinsic features of remuneration disputes are investigated to delineate subcategories of claims. Several judges were also interviewed to further explore the nature of remuneration disputes.

Four types of remuneration claims were identified: regular wage, minimum wage, overtime and others (including subsidies and welfare). Examination of these four types, especially how they are processed until concluded by court adjudication, provides a fuller picture of the post-enactment status of these laws and yields objective and rational findings. To explain the continuing steady rise in the volume of remuneration claims, as more workers have knowledge of their rights and access to the courts, this study identifies an increase in the number of factually complicated cases (e.g. overtime claims) and abmiguity in the relevant law, leaving some remuneration disputes difficult, if not impossible, to adjudicate. Conversely, the study also finds significant positive trends following these laws’ enactment, particularly a reduction in straightforward cases, such as disputes concerning non-payment of wages/minimum wages, on which the law is clear. It is evidently imperative to improve the clarity of the current laws through further legislation, as the most appropriate next step in China’s juridification process of developing its own rule of Labour Law.

This study is purposely limited to examining remuneration litigation in Beijing’s courts from 2014 to 2017, which is representative of the national trend of dramatically rising remuneration disputes, and thus provides valuable insights. Future studies should cover a wider geographic territory and other categories of labour disputes to provide an even more comprehensive picture of the challenges and potential solutions.

By understanding the driving factors of rising labour remuneration disputes, the legislature, workers and employers can act accordingly to curb labour conflicts. The growing complexity and technicality of remuneration litigation indicates that the pressing need of labour juridification is to deploy a subtle, comprehensive method to improve legal clarity and judicial professionalism.

This study uniquely divides the types of remuneration litigation in Beijing, adopting methods and yielding findings absent from the prior literature. Both the progress and challenges in China’s rule of Labour Law process are reflected in this work, together with public policy and theoretical implications for further study.

To build adaptive learning systems for a better learning experience, designers need to identify users’ behaviour patterns and provide adaptive learning materials accordingly. This study involved a quasi-experiment and also this paper aims to investigate the accuracy of eye-tracking technology in identifying visualisers and verbalisers and the contributing factors to diverse levels of accuracy, which lays the foundation for the establishment of adaptive learning systems.

The authors prepared eight documents with different image-text combinations with the intention of triggering participants’ natural reading habits. By analysing the eye-movement data, this author categorised the 22 participants as visualisers or verbalisers. The results were compared for accuracy measure with participants’ self-reports in response to the index of learning style questionnaire.

The results showed that visualisers and verbalisers presented significantly different eye-movement patterns, which was confirmed by the fixation data from the Tobii eye-tracker with the detection accuracy ranged from 38% to 77%. Various factors contributed to a range of levels of accuracy, including highlighted elements, learning context, complex background, low relevance of images and texts, learner differences, awareness of experimental settings, self-conception and prior knowledge.

To the authors’ knowledge, this is the first paper investigating the feasibility of eye-tracking technology to identify visualisers and verbalisers for the development of adaptive learning systems.