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This paper presents a comprehensive pallet-picking approach for forklift robots, comprising a pallet identification and localization algorithm (PILA) to detect and locate the pallet and a vehicle alignment algorithm (VAA) to align the vehicle fork arms with the targeted pallet.

Opposing vision-based methods or point cloud data strategies, we utilize a low-cost RGB-D camera, and thus PILA exploits both RGB and depth data to quickly and precisely recognize and localize the pallet. The developed method guarantees a high identification rate from RGB images and more precise 3D localization information than a depth camera. Additionally, a deep neural network (DNN) method is applied to detect and locate the pallet in the RGB images. Specifically, the point cloud data is correlated with the labeled region of interest (RoI) in the RGB images, and the pallet's front-face plane is extracted from the point cloud. Furthermore, PILA introduces a universal geometrical rule to identify the pallet's center as a “T-shape” without depending on specific pallet types. Finally, VAA is proposed to implement the vehicle approaching and pallet picking operations as a “proof-of-concept” to test PILA’s performance.

Experimentally, the orientation angle and centric location of the two kinds of pallets are investigated without any artificial marking. The results show that the pallet could be located with a three-dimensional localization accuracy of 1 cm and an angle resolution of 0.4 degrees at a distance of 3 m with the vehicle control algorithm.

PILA’s performance is limited by the current depth camera’s range (< = 3 m), and this is expected to be improved by using a better depth measurement device in the future.

The results demonstrate that the pallets can be located with an accuracy of 1cm along the x, y, and z directions and affording an angular resolution of 0.4 degrees at a distance of 3m in 700ms.

Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.

This paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .

The experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.

Since air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.

Given the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.

The self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

Restaurants’ outdoor signage plays an irreplaceable role in attracting potential diners, as it conveys important functional and symbolic meanings of the businesses. The purpose of this study is to investigate the effect of typographic design elements of outdoor signage on consumers’ perceptions of authenticity. This study also tests the linkage between authenticity and willingness to dine, as well as the moderating effect of frequency of dining in ethnic restaurants on the relationship.

Using a 2 (simplified vs traditional Chinese characters) × 2 (calligraphy vs computer font) × 2 (vertical vs horizontal text flow) between-subject design, the authors did two experiments with 786 Chinese diners. Restaurant authenticity and willingness to dine are dependent variables, and openness to ethnic cuisine is the control variable.

Display characters and text flow significantly affect restaurant authenticity. Furthermore, the results of this study demonstrate that display characters interact with typeface to influence restaurant authenticity. Consumers’ perceived authenticity significantly increases their willingness to dine. The frequency of dining in ethnic restaurants moderates the relationship between restaurant authenticity and willingness to dine.

Ethnic restaurateurs should pay attention to the outdoor signage design, as it affects potential consumers’ authenticity perceptions. Specifically, in Mainland China, traditional Chinese characters and vertical text direction increase potential consumers’ authenticity perceptions.

This study extends the semiotic theory and applies the cue–judgment–behavior model in the hospitality literature. This study also provides new understanding of authenticity by identifying the influence of typographic design on authenticity, which confirms the semiotic theory that certain semiotic cues affect consumers’ judgments.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

For Internet celebrities, self-disclosure (SD) is a crucial step in building relationships with their followers who perceive this communication as para-social interaction (PSI), which facilitates socialization among followers. Normative commitment (NC) is critical for creating bonds among community members that are strengthened through socialization. However, research on the predictive relationships among SD, PSI and NC has been insufficient. This paper aims to investigate the effects of two facets of Internet celebrity SDs (i.e. private life and opinion) and two facets of PSI (i.e. companionship and following) on NC. The mediating role of PSI on the effects of SD on NC was also analyzed.

People who follow at least one Internet celebrity on a social networking site were recruited to participate in this study, and 494 valid questionnaires were collected for examination. The collected data were analyzed using structural equation modeling (SEM).

The results revealed that both private-life and opinion SDs have positive effects on companionship and following PSI, which consequently influence NC. A mediation test revealed that companionship and following PSI mediate the effects of private-life and opinion SD on NC. This study's findings also revealed that NC is influenced more by following PSI than it is by companionship PSI. Furthermore, opinion SD was determined to be the more influential factor in following PSI, whereas private-life SD was the more influential factor in companionship PSI.

This paper is useful for understanding the influence mechanism of the SD of Internet celebrities on PSI and NC.

The study aims to understand how cultural route heritage is conceptualized and managed in China by systematically reviewing the research literature on Chinese cultural route heritage (CRH). The study intends to inspire further discussion on the theoretical and practical development of cultural routes since the development is still at a liminal stage in China.

A total of 253 research articles related to Chinese cultural rote heritage from major Chinese and English research databases China National Knowledge Infrastructure (CNKI), Web of Science (WOS) and Scopus have been comprehensively identified and reviewed for the purpose of the study.

Four major themes of research on Chinese CRH have been identified: conceptual evaluation, list of the routes and characteristics of the routes, conservation and utilization. The results revealed that China has very rich resources in CRH, many of which were formed a long time ago, which exist across vast geographic regions and have assumed multiple functions and undergone dynamic reciprocal exchanges among diverse cultures and ethnicities.

The paper summarizes some major obstacles faced by CRH in China and proposes a strategic model to address the need for a more sustainable development of CRH in the Chinese context.

The paper offers a comprehensive overview of CRH in China and discusses practical issues in management and development of heritage great in size, number and complexity.

This paper empirically examines the impact of corporate governance reforms on the financial performance of Indian state-owned enterprises (SOEs) for the period 2003–2011.

The findings indicate that the various corporate governance reforms collectively exhibited a statistically significant positive impact on performance when a difference in difference estimation process is used. However, the performance of SOEs is less than that of publicly listed companies, which is consistent with prior research. When the SOEs are compared with a matched pairing of publicly listed companies of similar size and same industry, their performance was comparable and in many instances superior. This is indicative of the regulatory constraints on competitors and preferential access to resources and markets given to the SOEs. As SOEs move towards a more mixed ownership model with more of them listed on the stock exchange and greater public ownership of shares the corporate governance issues will increase in importance.

The controlled sell down of shares in SOEs presents a need for continuing governance reforms and ongoing research to track progress.

The most striking observation from the study is that changes that were introduced as a corporate governance reform, such greater professionalism in boards, did not gain traction and enhance performance, rather the process of director selection and the concentrated bureaucratic and political interference stymied what was asserted to be conceptually sound reforms.

Nation-states, researchers, and organizations realized the pivotal role of social entrepreneurship in economic development in the recent two decades. The body of knowledge in this domain witnessed consistent research growth but it still lacks a critical review to guide future researchers. This chapter contributes to the body of knowledge by presenting the first bibliometric analysis (using software such as HistCite, VOSviewer, and Biblioshiny). The relevant articles were searched in the Web of Science (WOS) database to ensure the inclusion of high-quality research. Among 910 initially retrieved publications 86 most relevant publications were included in the final dataset. The analysis included various bibliometrics and visualization maps such as the publication trends, most influential articles and journals, most productive countries and organizations, co-citation analysis, and thematic clustering of the research. The findings showed an overall consistent growth in research publications during the last 16 years (2005–2020) with Social entrepreneurship and enterprise: international and innovation perspectives' as the most influential article, Entrepreneurship and Regional Development as the most influential journal, and the United States and the United Kingdom as the most productive countries. The keyword co-occurrence analysis exposed five thematic clusters within the selected dataset indicating a relatively less focus on economic development aspects. Thus, future research should explore how social entrepreneurship, while solving social, cultural, environmental, and community issues, contributes to economic development. The research implications and limitations are discussed in the end.

Using cognitive appraisal theory, this study aims to propose and test an integrated framework – comprising robotic service quality, robotic service authenticity, customer existential authenticity and customer revisit intention – on diners with experience using robotic technology in restaurants. The moderating role of robotic appearance is in the hypothesised relationship.

Data was gathered through a Web-based survey delivered to 428 diners who had experience using robotic services in restaurants in China. The hypotheses were analysed using a structural equation model and multi-group analysis was used to analyse the moderating effect.

The findings indicate that functional service quality positively influences robotic service authenticity and existential authenticity. However, technical service quality only affects existential authenticity, which leads to revisit intention. Robotic appearance moderated the relationship between functional service quality and service authenticity.

Restaurateurs should enhance robotic service authenticity, existential authenticity and revisit intention by improving robotic technical service collaborating with robot manufacturers and operators.

Focusing on cognitive appraisal theory, the findings serve as a starting point for investigating robotic service quality and authenticity in robotic service settings theoretically and empirically.