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Under the background of economic and technological globalization, all countries will pay attention to science and technology innovation policies. At this time, the era of innovation policy plays an important role. In order to thoroughly implement the spirit of the 19th National Congress of the Communist Party of China, earnestly innovate development concepts and strengthen the key supporting role of scientific and technological innovation for social development.

The research will make an in-depth study on the policy of scientific and technological innovation, take the change trend of the number of laws and regulations and the analysis of the basic content as the entry point, and classify it from five different aspects, such as the policy of scientific and technological system and mechanism, the policy of financial guidance, the policy of technological innovation of enterprises, the policy of scientific and technological talents, and the policy of intellectual property.

Through the comparison of the development history of science and technology policies at home and abroad, the development history of science and technology innovation policies under the special national conditions of China is obtained. Combining with the relevant basic theories of science and technology innovation system, designing from the publishing department, main content, and implementation subject, constructing a synergistic science and technology innovation system of “government, industry, university and research,” and forming an “integration of supply and demand” technology innovation framework will guide the development of China's science and technology innovation in the future.

The authors construct the policy framework of science and technology collaborative innovation based on the concept of science and technology innovation policies. The framework has realistic significance for its future development.

This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.

Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.

The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.

The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

The purpose of this paper is to address the opposing views of the relationship between directors’ and officers’ liability insurance (D&O insurance) and stock price crash risk in a major Asian emerging stock market.

This paper finds an endogenous relationship between D&O insurance and stock price crash risk. Hence, the two-stage least squares regression analysis is used to address the endogeneity issue when the relationship is examined. Moreover, this paper further controls the quality of other corporate governance mechanisms to investigate whether D&O insurance still has an effect on stock price crash risk.

The effect of D&O insurance coverage is significantly negatively related to firm-specific stock price crash risk in Taiwan. More importantly, even when the quality of other corporate governance mechanisms is controlled, the negative relationship between D&O insurance coverage and firm-specific stock price crash risk remains significant. The evidence supports that D&O insurance serves as an effective external monitoring mechanism, strengthens corporate governance, and thus reduces stock price crash risk.

Emerging Asian markets suffer a dearth of research on the relationship of D&O insurance coverage and the firm-specific stock price crash risk. Investigating the relationship in Taiwan, the present study fills the research void. The findings show that D&O insurance plays an important role in reducing stock price crash risk of Taiwanese firms even when other corporate governance mechanisms are in place.

This paper investigates the extent to which the COVID-19 pandemic has accelerated service innovation in the restaurant sector. It explores the use of digital technologies (DT) as a safety-empowerment and resilient strategy in the food-service industry during the pandemic. It also investigates the impact of DT on customers' risk perception (CRP) and customers' intention to go to restaurants (CIR) in Italy.

Based on the theory of planned behaviour and perceived risk theory, this study investigates a sample of customers residing in Italy. Multiple regression and mediation analyses are conducted to test the research hypotheses, adapting the logic model developed. Using the bootstrapping technique, this study also explores whether the pandemic has moderated the relationship among several variables adapted from the literature. Robustness tests are also performed to corroborate the analysis.

The pandemic has accelerated the food-service industry's digital transformation, forcing restauranteurs to implement DT to survive. Findings show that DT support restauranteurs in implementing innovative services that reduce interactions and empower cleanliness among workers and customers, reducing CRP and preserving CIR. Thus, managing risk perception is helping the restaurant sector to recover.

Practical implications are presented for policymakers to catalyse the digital transformation in small- and medium-sized restaurants. The results may also be beneficial for entrepreneurs who can implement innovative service practices in order to reduce interaction and empower cleanliness levels. Moreover, academics can use these results to conduct similar research in other geographical contexts.

The present research represents the first study investigating the relationship between the use of digital technologies and the intentions of customers to go out for dinner during the ongoing pandemic in Italy.

The purpose of this study is to examine the moderating role of the characteristics of the chief executive officer (CEO) on the association between CEO power and corporate social responsibility (CSR) performance.

This paper conducts multiple regression analyses to empirically test the proposed hypotheses based on a sample of US-based publicly held companies. The sample period extends from 2000 to 2018. Firm-level CSR ratings are obtained from the Kinder, Lydenberg and Domini (KLD) database (currently known as MSCI ESG STATS). Financial data and CEO data are retrieved from Compustat and ExecuComp databases, respectively. Additional test and robustness analysis are performed.

This paper shows that firms with more powerful CEOs are less likely to engage in CSR activities. The negative association between CEO power and CSR is found to be exacerbated by CEOs who are younger, more competent and overconfident; however, this negative association is mitigated by CEOs who are female. This paper also finds that gender plays a more important role among CEO characteristics. Collectively, the findings highlight the potential opportunities to better understand the role of various CEO characteristics that jointly affect CSR.

First, this is the first study providing a comprehensive empirical analysis of how various CEO characteristics jointly affect CSR. Prior studies that focus on standalone CEO characteristics offer an incomplete picture of the relation between a single CEO characteristic and a firm's CSR performance. The current study thus extends the research field by examining the association between seemingly unrelated CEO characteristics and CSR performance. The results also highlight that gender is the critical factor moderating the relationship between CEO power and CSR performance when it is compared with CEO age, ability and overconfidence. Second, the authors add to the literature on employee selection by showing that female CEOs mitigate the negative effect of managerial power on CSR performance. Although the currently available empirical research in management control systems focuses on ex-post analyses of moral hazard mitigation for incumbent employees, both the economics and management literature acknowledge ex ante evidence suggesting that employee selection is even more important. Our findings may provide insight into the selection of CEOs.

The purpose of this work is to bridge FL and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. and blockchain technology through designing a blockchain-based smart agent system architecture and applying in FL. FL is an emerging collaborative machine learning technique that trains a model across multiple devices or servers holding private data samples without exchanging their data. The locally trained results are aggregated by a centralized server in a privacy-preserving way. However, there is an assumption where the centralized server is trustworthy, which is impractical. Fortunately, blockchain technology has opened a new era of data exchange among trustless strangers because of its decentralized architecture and cryptography-supported techniques.

In this study, the author proposes a novel design of a smart agent inspired by the smart contract concept. Specifically, based on the proposed smart agent, a fully decentralized, privacy-preserving and fair deep learning blockchain-FL framework is designed, where the agent network is consistent with the blockchain network and each smart agent is a participant in the FL task. During the whole training process, both the data and the model are not at the risk of leakage.

A demonstration of the proposed architecture is designed to train a neural network. Finally, the implementation of the proposed architecture is conducted in the Ethereum development, showing the effectiveness and applicability of the design.

The author aims to investigate the feasibility and practicality of linking the three areas together, namely, multi-agent system, FL and blockchain. A blockchain-FL framework, which is based on a smart agent system, has been proposed. The author has made several contributions to the state-of-the-art. First of all, a concrete design of a smart agent model is proposed, inspired by the smart contract concept in blockchain. The smart agent is autonomous and is able to disseminate, verify the information and execute the supported protocols. Based on the proposed smart agent model, a new architecture composed by these agents is formed, which is a blockchain network. Then, a fully decentralized, privacy-preserving and smart agent blockchain-FL framework has been proposed, where a smart agent acts as both a peer in a blockchain network and a participant in a FL task at the same time. Finally, a demonstration to train an artificial neural network is implemented to prove the effectiveness of the proposed framework.

This study investigates the impact of uncertainty on the mean-variance relationship. We find that the stock market's expected excess return is positively related to the market's conditional variances and implied variance during low uncertainty periods but unrelated or negatively related to conditional variances and implied variance during high uncertainty periods. Our empirical evidence is consistent with investors' attitudes toward uncertainty and risk, firms' fundamentals and leverage effects varying with uncertainty. Additionally, we discover that the negative relationship between returns and contemporaneous innovations of conditional variance and the positive relationship between returns and contemporaneous innovations of implied variance are significant during low uncertainty periods. Furthermore, our results are robust to changing the base assets to mimic the uncertainty factor and removing the effect of investor sentiment.

Mining user-concerned actionable and interpretable hot topics will help management departments fully grasp the latest events and make timely decisions. Existing topic models primarily integrate word embedding and matrix decomposition, which only generates keyword-based hot topics with weak interpretability, making it difficult to meet the specific needs of users. Mining phrase-based hot topics with syntactic dependency structure have been proven to model structure information effectively. A key challenge lies in the effective integration of the above information into the hot topic mining process.

This paper proposes the nonnegative matrix factorization (NMF)-based hot topic mining method, semantics syntax-assisted hot topic model (SSAHM), which combines semantic association and syntactic dependency structure. First, a semantic–syntactic component association matrix is constructed. Then, the matrix is used as a constraint condition to be incorporated into the block coordinate descent (BCD)-based matrix decomposition process. Finally, a hot topic information-driven phrase extraction algorithm is applied to describe hot topics.

The efficacy of the developed model is demonstrated on two real-world datasets, and the effects of dependency structure information on different topics are compared. The qualitative examples further explain the application of the method in real scenarios.

Most prior research focuses on keyword-based hot topics. Thus, the literature is advanced by mining phrase-based hot topics with syntactic dependency structure, which can effectively analyze the semantics. The development of syntactic dependency structure considering the combination of word order and part-of-speech (POS) is a step forward as word order, and POS are only separately utilized in the prior literature. Ignoring this synergy may miss important information, such as grammatical structure coherence and logical relations between syntactic components.