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This paper aims to propose a fast machine compression scheme, which can solve the problem of low-bandwidth transmission for underwater images.

This fast machine compression scheme mainly consists of three stages. Firstly, raw images are fed into the image pre-processing module, which is specially designed for underwater color images. Secondly, a divide-and-conquer (D&C) image compression framework is developed to divide the problem of image compression into a manageable size. And extreme learning machine (ELM) is introduced to substitute for principal component analysis (PCA), which is a traditional transform-based lossy compression algorithm. The execution time of ELM is very short, thus the authors can compress the images at a much faster speed. Finally, underwater color images can be recovered from the compressed images.

Experiment results show that the proposed scheme can not only compress the images at a much faster speed but also maintain the acceptable perceptual quality of reconstructed images.

This paper proposes a fast machine compression scheme, which combines the traditional PCA compression algorithm with the ELM algorithm. Moreover, a pre-processing module and a D&C image compression framework are specially designed for underwater images.

This paper aims to address the issue of model discontinuity typically encountered in traditional Denavit-Hartenberg (DH) models. To achieve this, we propose the use of a local Product of Exponentials (POE) approach. Additionally, a modified calibration model is presented which takes into account both kinematic errors and high-order joint-dependent kinematic errors. Both kinematic errors and high-order joint-dependent kinematic errors are analyzed to modify the model.

Robot positioning accuracy is critically important in high-speed and heavy-load manufacturing applications. One essential problem encountered in calibration of series robot is that the traditional methods only consider fitting kinematic errors, while ignoring joint-dependent kinematic errors.

Laguerre polynomials are chosen to fitting kinematic errors and high-order joint-dependent kinematic errors which can avoid the Runge phenomenon of curve fitting to a great extent. Levenberg–Marquard algorithm, which is insensitive to overparameterization and can effectively deal with redundant parameters, is used to quickly calibrate the modified model. Experiments on an EFFORT ER50 robot are implemented to validate the efficiency of the proposed method; compared with the Chebyshev polynomial calibration methods, the positioning accuracy is improved from 0.2301 to 0.2224 mm.

The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed calibration methods on an industrial serial robot.

As a key basic component used in machining, high-speed steel (HSS) tools often prone to wear and failure during machining. Therefore, the purpose of this study is to adopt a suitable approach to improve the stability of the cutting force, the service life and the wear resistance.

Laser shock processing (LSP) was used to process the tool rake face and the tribological test was performed with ball-on-disk wear tester.

Experimental results show that cutting force of the LSP-treated tool is lower than untreated tool under the same cutting conditions. Wear rate of the tool nose treated by LSP decreases obviously and the tool life increases by 40 per cent.

HSS is often used in the manufacture of complex cutting tools. The main value of this article is to improve the tool surface wear resistance, thereby extending the service life of cutter. This paper is valuable not only in theory but also with reference value in engineering practice.

Drawing on the stakeholder theory, this study aims to empirically analyse the impact of platform enterprises’ corporate social responsibility (CSR) announcements on corporate stock market value. This study also estimates the moderating effect of stakeholder orientation and responsibility categories of CSR announcements, the platform enterprise type and the degree of CSR disclosure.

The event study method is used to analyse the change in stock market value of 191 CSR announcements from 137 Chinese platform enterprises. In addition, a case analysis is presented for two platform enterprises with the best practices to validate and complement study findings.

CSR announcements improve platform enterprises’ stock market value. Specifically, CSR announcements responding to platform enterprises’ external stakeholders, and CSR announcements with economic responsibility, have obvious positive impacts on stock market value. Furthermore, the maker platform’s CSR announcement has a more positive impact on stock market value than the exchange platform.

To the best of the authors’ knowledge, this study is the first attempt to identify the link between platform enterprises’ CSR announcements and stock market performance by empirical evidence, and it contributes to new knowledge of operating and evaluating platform enterprises’ CSR.

The paper aims to explore the correlation between the agglomeration of regional innovation elements and the attraction of talent.

This paper uses the factor analysis method to measure the innovation elements index (IEI). The proportion of the regional resident population and registered population is used to measure the attractiveness of talents. The PVAR model is used to analyze the interaction between innovation element agglomeration and talent attraction.

(1) According to the annual increase rate of IEI, the order is eastern region > central region > western region. (2) Panel vector autoregressive (PVAR) research shows that the agglomeration of innovation factors has a short-term thrust on the attraction of regional talents. (3) The agglomeration of innovative elements is the Granger cause of talent attraction; talent attraction is not the Granger reason for the agglomeration of innovative elements. (4) Pulse analysis and variance decomposition show that the agglomeration of innovative elements has a one-way positive effect on talent attraction.

This study takes China’s provincial panel data as a sample without considering the differences between cities. There may be significant differences in innovation factor agglomeration and talent attraction in different cities.

The findings of this study provide valuable insights into innovation ecosystem practices. Policymakers should pay close attention to promoting the agglomeration of innovation factors by optimizing the innovation ecosystem in order to increase the attractiveness of talents.

(1) This study uses the proportion of regional resident population and household registration population to measure the attractiveness of talents, which is more realistic. (2) This paper is one of the few that examines the relationship between innovation factor agglomeration and talent attraction.

As a relationship‐oriented culture, customer‐firm relationship plays an important role in consumer decision making process in China. Moreover, there are significant regional differences of Chinese consumers in terms of relationship proneness and its impact on relationship marketing outcomes. The purpose of this paper is to examine the differences of relationship proneness and its effect on relational satisfaction, relationship commitment and benefits between consumers from north and south of mainland China.

Based on previous research and relationship marketing theories, a series of hypotheses were developed comparing the two populations on relationship‐related variables. Data were collected from two cities of China using survey method.

Regional differences exist between the two groups of Chinese consumers in relationship proneness, commitment, trust, and relationship benefits perceptions. Gender differences also exist. Relationship proneness interacts with region resulting in moderating effects on relationship marketing outcomes.

This research has treated two groups of Chinese consumers as cohorts and individual differences are not taken into consideration. The sample includes only graduate students whose attitudes toward relationship marketing may deviate from average consumers of the country as they have higher education levels but lower income levels.

The findings of the research provide managerial implications for marketing segmentation in China. International and domestic marketers should consider using different marketing strategies on consumers from different regions of China.

The paper confirms the regional differences of Chinese consumers in relationship proneness, trust, relationship benefits and commitment. The paper contributes to the relationship marketing literature by relating consumer relationship proneness to relationship benefits, and confirming the moderating effect of relationship proneness on relationship benefits and relationship commitment.

The purpose of this paper is to examine the construction of national heritage through the interpretation of sites and events, with a particular focus on hot interpretation at difficult heritage sites. 

This paper examines the processes of difficult heritage interpretation at the Memorial Hall of the Nanjing Massacre over the past 30 years, and examines the resulting political implications.

Aligning with contemporary national social and political agendas, heritage interpretation at the Memorial Hall actively serves as an authorised educational tool. Despite the hot interpretation techniques used to stimulate the emotional impact of visitor experiences, this particular traumatic past has been utilised in nation building practices that legitimise specific histories and form a national image on an international stage.

Heritage interpretation of difficult history will benefit from open dialogue and assessment of the past from multiple perspectives. This requires all stakeholders to work together to develop interpretation strategies that acknowledge and prioritise the needs of post-conflict societies. Without this form of open dialogue and reflection, the official claims of heritage interpretation achieving reconciliation between conflicted peoples remain superficial. 

This study offers a novel contribution to the discussion of heritage interpretation. The results shed light on the cultural processes surrounding state interpretation of traumatic pasts for specific political uses. The study suggests ways in which heritage sectors and authorities can achieve social goals, such as public education, reconciliation and peacebuilding, through such processes of heritage interpretation.

Community question answering (CQA) platforms play a significant role in knowledge dissemination and information retrieval. Expert recommendation can assist users by helping them find valuable answers efficiently. Existing works mainly use content and user behavioural features for expert recommendation, and fail to effectively leverage the correlation across multi-dimensional features.

To address the above issue, this work proposes a multi-dimensional feature fusion-based method for expert recommendation, aiming to integrate features of question–answerer pairs from three dimensions, including network features, content features and user behaviour features. Specifically, network features are extracted by first learning user and tag representations using network representation learning methods and then calculating questioner–answerer similarities and answerer–tag similarities. Secondly, content features are extracted from textual contents of questions and answerer generated contents using text representation models. Thirdly, user behaviour features are extracted from user actions observed in CQA platforms, such as following and likes. Finally, given a question–answerer pair, the three dimensional features are fused and used to predict the probability of the candidate expert answering the given question.

The proposed method is evaluated on a data set collected from a publicly available CQA platform. Results show that the proposed method is effective compared with baseline methods. Ablation study shows that network features is the most important dimensional features among all three dimensional features.

This work identifies three dimensional features for expert recommendation in CQA platforms and conducts a comprehensive investigation into the importance of features for the performance of expert recommendation. The results suggest that network features are the most important features among three-dimensional features, which indicates that the performance of expert recommendation in CQA platforms is likely to get improved by further mining network features using advanced techniques, such as graph neural networks. One broader implication is that it is always important to include multi-dimensional features for expert recommendation and conduct systematic investigation to identify the most important features for finding directions for improvement.

This work proposes three-dimensional features given that existing works mostly focus on one or two-dimensional features and demonstrate the effectiveness of the newly proposed features.

This paper aims to study vehicle-type strategies for the manufacturer's car sharing by accounting for consumers' behavior and the subsidy.

The authors develop a game model, in which a monopoly manufacturer that can produce gasoline vehicles (GVs) or energy vehicles (EVs) not only sells vehicles in the sales market, but also rents them out in the sharing market by the self-built platform. The manufacturer strategically chooses which type of vehicles based on consumers' behavior and whether the government provides the EVs’ subsidy.

When consumers' low-carbon awareness is relatively high or the marginal cost is low, the manufacturer chooses EVs. The manufacturer chooses GVs when the low-carbon awareness and the marginal cost are low. Only when the low-carbon awareness and the subsidy are not too low, the manufacturer who originally chose GVs launches EVs. When the low-carbon awareness is high, the excessive subsidy discourages the manufacturer from entering the sharing market. If the government provides the subsidy, the manufacturer launches high-end EVs. Otherwise, the manufacturer launches low-end EVs. Moreover, the subsidy increases consumer surplus and social welfare since the high subsidy makes EVs’ sharing market demand be negative.

This study enriches the literature on vehicle-type strategies for the manufacturer's car sharing, owns a practical significance to guide the manufacturer's operation management in the car sharing market and provides advice on whether the government should provide EVs’ subsidy.

Because of the multiple design elements and complicated relationship among design elements of complex products design, it is tough for designers to systematically and dynamically express and manage the complex products design process.

To solve these problems, a supernetwork model of complex products design is constructed and analyzed in this paper. First, the design elements (customer demands, design agents, product structures, design tasks and design resources) are identified and analyzed, then the sub-network of design elements are built. Based on this, a supernetwork model of complex products design is constructed with the analysis of the relationship among sub-networks. Second, some typical and physical characteristics (robustness, vulnerability, degree and betweenness) of the supernetwork were calculated to analyze the performance of supernetwork and the features of complex product design process.

The design process of a wind turbine is studied as a case to illustrate the approach in this paper. The supernetwork can provide more information about collaborative design process of wind turbine than traditional models. Moreover, it can help managers and designers to manage the collaborative design process and improve collaborative design efficiency of wind turbine.

The authors find a new method (complex network or supernetwork) to describe and analyze complex mechanical product design.