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Food safety risk brought by environmental pollution seriously threatens human health and affects national economic and social development. In particular, heavy metal pollution and nutrient deficiency have caused regional diseases. Thus, the purpose of this paper is to present a risk early warning method of food safety considering environmental and nutritional factors.

A novel risk early warning modelling method based on the long short-term memory (LSTM) neural network integrating sum product based analytic hierarchy process (AHP-SP) is proposed. The data fuzzification method is adopted to overcome the uncertainty of food safety detection data and the processed data are viewed as the input of the LSTM. The AHP-SP method is used to fuse the risk of detection data and the obtained risk values are viewed as the expected output of the LSTM. Finally, the proposed method is applied on one group of sterilized milk data from a food detection agency in China.

The experimental results show that compared with the back propagation and the radial basis function neural networks, the proposed method has higher accuracy in predicting the development trend of food safety risk. Moreover, the causal factors of the risk can be figured out through the predicted results.

The proposed modelling method can achieve accurate prediction and early warning of food safety risk, and provide decision-making basis for the relevant departments to formulate targeted risk prevention and control measures, thereby avoiding food safety incidents caused by environmental pollution or nutritional deficiency.

This study aims to accurately evaluate the influence of various error intervals on the performance of the wiper.

The wiper structural system is decomposed into classical four-link planar for kinematics analysis, and it was modeled respectively by using interval method, universal grey number theory and enumeration approach depending on the nature of uncertainty.

The universal grey number theory is a viable methodology for the accurate analysis of uncertain structural system.

(1) The model of uncertain wiper structural system is established. (2) Universal grey number theory and new parameters are adopted to analyze the presence of uncertain wiper structural system. (3) Comparative analysis of response quantities is obtained by interval method, universal grey number theory and enumeration method.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Underpinned by the ability–motivation–opportunity framework, this paper aims to establish a framework of employee creativity antecedents in the hospitality and tourism industries and meta-analytically examine the magnitude of effect sizes as well as the moderating effects of cultural factors.

A meta-analysis using data from 82 independent studies was conducted to explore the hypothesized relationships and verify how they were contingent on uncertainty avoidance and long-term orientation.

The results supported the majority of hypotheses about the relationships between antecedents and creativity. Furthermore, they showed that the effects of intrinsic motivation, positive affect and climate for innovation on creativity in the hospitality and tourism industries were significantly larger than those reported in previous meta-analyses. It also showed that uncertainty avoidance and long-term orientation could buffer or strengthen some associations.

This study generates some essential managerial suggestions for organizations in need of innovation. Managers can learn from the results so as to effectively promote the ability, motivation and opportunity for creativity and merge cultural elements with innovation strategy when they operate globally.

This study provides a theory-based explanation for how employee creativity can be activated. To the best of the authors’ knowledge, this study is a first attempt to meta-analytically test the underlying determinants of employee creativity in the hospitality and tourism industries. Additionally, the search for boundary conditions of the proposed relationships is likely to reconcile existing conflicts and inspire future studies.

This paper aims to elucidate the systemic processes underlying the enhanced information-control measures taken by the Chinese Communist Party (CCP) under the leadership of President Xi Jinping. The tightening of state information control has stimulated increasingly sophisticated methods of disseminating information on the part of professional and citizen journalists. Drawing on social systems theory as articulated by Niklas Luhmann and others, the authors frame the CCP’s enhanced information-control efforts as a response to the increasing systemic complexity of Chinese journalism, which is part of a self-reproducing, self-regulating (autopoietic) global journalism system. The authors use both subtle and overt protests over Chinese censorship as evidence for the journalism system’s increasing complexity and autonomy. The authors observe that levels of complexity ratchet up as the CCP and Chinese journalism counter each other’s moves. Finally, the authors suggest that the increasing complexity of the CCP’s information-control apparatus may be unsustainable.

The authors ground their argument in Luhmannian social systems theory.

The CCP's effort to control journalism leads to increased internal complexity in the form of huge bureaucracies that themselves must be overseen in an almost endless proliferation of surveillance.

This paper contributes to theoretical work in post-humanism.

To the authors’ knowledge, no studies have examined the tension between CCP censors and Chinese journalism from a Luhmannian systems theory perspective.

The purpose of this paper is to provide theoretical basis and data support for researchers to choose appropriate international partners, provide a basis for Chinese research funding agencies, such as National Natural Science Foundation of China (NSFC) to formulate international research collaboration (IRC) strategies and policies and provide recommendations for the improvement of the internationalization level of China's basic scientific research.

Based on existing research, this study took output of “Major International (Regional) Joint Research Project” (MIJRP) funded by NSFC and participated by Chinese scholars in the meantime as the analysis object, proposed hypotheses and constructed the indicators of IRC and research output (RO). In addition, the mathematical statistics was used to compare the RO of China's IRC and nonIRC, and the statistical analysis model was used to measure the influence on RO of collaboration country's research capacity, research collaboration between China and US, scope of international research collaboration and reprint author country.

The RO of China's IRC is higher than that of nonIRC; research capacity of collaboration country has no inevitable effect on the RO of China's IRC; the RO of China's IRC participated by Americans is higher than that without American scholars; expanding the scope of China's IRC to some degree can increase RO; the RO of China's IRC led by foreigners is higher than that led by Chinese. In particular, China–US IRC and foreign scholars acting as the reprint author are two major factors for the RO of China's IRC.

Most of the traditional research on IRC are based on the co-author papers, and this study tried to analyze the characteristics and regularities on IRC from a new view of international collaboration projects, which can be a supplement to the traditional international collaboration research on co-author papers.

The purpose of this paper is to investigate how supplier concentration influences a buyer firm's R&D intensity. This study proposes a mediation and moderation model to test this relationship in the Chinese household appliance industry. Specifically, this study tests the mediation effect of operational slack on the relationship between supplier concentration and R&D intensity and the moderation effect of financial constraints on this relationship.

Drawing upon real options theory and resource dependence theory, the proposed relationships are tested with the Chinese household appliance market using financial data from listed companies over a ten-year span from 2012 to 2021. Fixed effects (within-group) panel regression models are used to test the hypotheses. In addition, the authors use the bias-corrected bootstrap method to test the mediation effect.

The authors find that supplier concentration negatively affects a buyer firm's R&D intensity and that internal operational slack mediates this relationship. Interestingly, financial constraints from the external financing organization weaken the negative relationship between the buyer firm's supplier concentration and R&D intensity.

Based on the argument of real options theory and resource dependence theory, this study provides novel insights into the issue of how concentration on several major suppliers may reduce buyer firms' R&D intensity. First, this study introduces operational slack as a form of internal uncertainty that mediates the supplier concentration–R&D intensity relationship. Second, this study suggests that the effect of supplier concentration on R&D intensity is contingent upon firms' financial constraints from external financial organizations, disclosing a synergetic interactive effect of supplier concentration and financial constraints on firms' R&D activities. Third, this study is conducted in the unique institutional context of China, providing meaningful insights into the relationship between supplier concentration and R&D intensity.

This study aims to investigate the factors and mechanism which influence the doctor’s social and economic benefits from the perspective of social capital.

This paper mainly investigates the factors and mechanism influencing the doctor's social and economic benefits from the perspective of social capital and then constructs a doctor's social capital model and discusses the effects of doctor's social capital on their economic and social benefits; what is more, this paper also considers the moderating effect of patients’ group behavior.

The results show that the doctor's social capital has a positive and significant effect on doctor's economic benefits, while it has a negative and significant effect on doctors' social benefits. Patients’ group behavior plays an important moderating role; in particular, the number of online patients of doctors can effectively strengthen follow-up patients’ positive perception of the doctor capital, while the number of offline patients has a negative effect on doctors’ economic benefits, but it can reduce its negative impact on doctors’ social benefits by establishing trust between patient and physician.

This paper enriches the relevant research of social capital theory in the medical field and broadens the research about online health care. For platforms, they should give more attention to doctors and their income issues, which is of great significance for their healthy and sustainable development.

The purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.

This method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.

The algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.

This paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.