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This study aims to introduce an innovative approach that uses a decoder with multiple layers to accurately identify Chinese nested entities across various nesting depths. To address potential human intervention, an advanced optimization algorithm is used to fine-tune the decoder based on the depth of nested entities present in the data set. With this approach, this study achieves remarkable performance in recognizing Chinese nested entities.

This study provides a framework for Chinese nested named entity recognition (NER) based on sequence labeling methods. Similar to existing approaches, the framework uses an advanced pre-training model as the backbone to extract semantic features from the text. Then a decoder comprising multiple conditional random field (CRF) algorithms is used to learn the associations between granularity labels. To minimize the need for manual intervention, the Jaya algorithm is used to optimize the number of CRF layers. Experimental results validate the effectiveness of the proposed approach, demonstrating its superior performance on both Chinese nested NER and flat NER tasks.

The experimental findings illustrate that the proposed methodology can achieve a remarkable 4.32% advancement in nested NER performance on the People’s Daily corpus compared to existing models.

This study explores a Chinese NER methodology based on the sequence labeling ideology for recognizing sophisticated Chinese nested entities with remarkable accuracy.

The purpose of this paper is to overcome the deficiency of the current GM(1,N) such as low‐prediction precision, extend the scope of GM(1,N) and provide an effective grey dynamic model GM(1,N) for the relationship of cost and variability.

The relationship between two factors of variety and the cost of manufacturing system is studied on the basis of the variety reduction program theory. Based on the Grey system and the gradient algorithm, a Grey dynamic model GM(1,N) is proposed between cost and variety by optimizing the coefficient and background value of the model which is used to check validity for the relation of plasm‐yarn machine product and variety.

The proposed Grey dynamic prediction model GM(1,N) for the relationship of cost and variability has high precision and easy‐to‐use.

A Grey model GM(1,N) for prediction is proposed.

The proposed model should also have potential for multifactor system prediction in engineering.

The deficiency of the current GM(1,N) is overcome, the scope of GM(1,N) is extended and the proposed Grey dynamic model GM(1,N) has high‐prediction precision.

The purpose of this paper is to propose a pressure-, temperature- and acceleration-sensitive structure-integrated inductor-capacitor (LC) resonant ceramic sensor to fulfill the measurement of multi-parameters, such as the measurement of pressure, temperature and acceleration, simultaneously in automotive, aerospace and aeronautics industries.

The ceramic-based multi-parameter sensor was composed of three LC tanks, which have their resonant frequencies sensitive to pressure, temperature and acceleration separately. Two aspects from the specific sensitive structure design to the multiple signals reading technology are considered in designing the multi-parameter ceramic sensor. Theoretical analysis and ANSYS simulation are used in designing the sensitive structure, and MATLAB simulation and experiment are conducted to verify the feasibility of non-coverage of multi-readout signals.

It is found that if the parameters of sensitive structure and layout of the LC tanks integrated into the sensor are proper, the implementation of a multi-parameter sensor could be feasible.

The ceramic sensor proposed in the paper can measure pressure, temperature and acceleration simultaneously in harsh environments.

The paper creatively proposes a pressure-, temperature- and acceleration-sensitive structure-integrated LC resonant ceramic sensor for harsh environments and verifies the feasibility of the sensor from sensitive structure design to multiple-signal reading.

The purpose of this paper is to analyze and access the extent that emotion contagion (EC) affects the passenger aggressive behavior, and then reveal key factors triggering the passenger mass violence event (MVE) under an irregular flight situation.

This paper presents the group behaviors simulation framework based on multi-agent. The EC model is put forward to explore the EC effect among passengers, integrating the influence of intervention measures, power difference and intimacy. Moreover, the authors propose the aggressive behavior decision-making model tailed to the passenger MVE by combining the improved social force model and adaptive neuro-fuzzy inference system.

The experiment results show that the method proposed in this paper can simulate the realistic evolution process of passenger MVE .The EC effect among passengers is the catalyst for the quick spreading of MVE. The intervention measures, such as providing food and endorsing service, can effectively prevent from the MVE situation deterioration.

The research of this paper has important practical significance for the evolution analysis of passenger MVE and provides the basis for airport safety management.

The purpose of this paper is to answer the question that what the best shape of fuzzy sets is in fuzzy systems for function approximation which is essential in many applications of fuzzy systems.

The uniform approximation rates indicate the approximating capabilities of fuzzy systems for function approximation. By Fourier analysis, the uniform approximation rates are estimated for the fuzzy systems with various shapes of if‐part fuzzy sets in the case of single‐input and single‐output. Based on the approximation rates, the relationships between the approximating capabilities and the shapes of fuzzy sets are developed and compared.

The since functions as the input membership functions in fuzzy systems are proved to have the almost best approximation property in a particular class of membership functions.

From the viewpoint of function approximation, the input membership functions are not necessarily positive in fuzzy systems.

For engineers, the sinc‐shaped membership function is a good choice to improve their fuzzy systems in real applications.

The uniform approximation rates of fuzzy systems for function approximation are estimated. Mathematically, the relationships between the approximating capabilities and the shapes of fuzzy sets are analyzed for fuzzy systems.

The purpose of this paper is to investigate the statistical correlation properties of the Shanghai Interbank Offered Rate (SHIBOR) interbank lending market.

The authors apply methods of correlation analysis, random matrix theory (RMT) and minimum spanning tree (MST) to investigate the correlation properties of Chinese interbank lending market and analyze how the SHIBOR panel banks behave in different market periods.

First, the largest eigenvalue λ ₁ is the index to describe the market mode of the whole market when all banks behavior collectively and λ ₁/N is a good estimator of the average correlation <C> of the correlation matrix. Second, notably, the authors find the “market mode” is weakened in two crises periods of 2008 stock market crash and 2009 Global Financial Crisis. This is significantly different from other market where the “market mode” is normally strengthened in crises periods. Third, the authors subtract the contribution of λ ₁, the second and third eigenvalue, λ ₂ and λ ₃, will fall outside of the predicted interval. And both λ ₂ and λ ₃ are getting times larger in the crises periods than in “Non-Crisis” period. Fourth, and in the MST analysis, the authors find again that the average distances of the MST are the times larger in crises periods than in “Non-Crisis” period and the second largest eigenvalue is a good estimator of the average distance of the MST.

According to the best knowledge, this paper is the first work on the study of the statistical properties of an interbank lending market using quotation level data of panel banks, which allows us to analyze the properties of the interest rate formation and how all panel banks behavior in different periods. This work is also the first study on the SHIBOR market using econophysics methods of correlation analysis, RMT and MST.

This study aims to investigate the association of FinTech innovation (FinTechINN) and firm performance (FP) by considering the role of knowledge assets (KA) as a causal mechanism underlying the FinTechINN – FP association.

In this study, the authors consider panel data of 1,049 Chinese A-listed firm and construct a structural model for corporate FinTech innovation, knowledge assets and firm performance while considering endogeneity issues in analyses over the period of 2014–2022. The modified value added intellectual capital (VAIC) and research and development (R&D) expenses are used as a proxy measure for knowledge assets, considering governance and corporate performance measures.

According to the findings of this study FinTech innovation (FinTechINN) has a positive significant effect on firm performance. Particularly; the findings disclose that FinTech innovations has a link with knowledge assets, FinTech innovations indirectly affects firm performance, and the association between FinTech innovation and firm performance is partially mediated by knowledge assets (MVAIC and R&D expenses).

Rooted in the dynamic capability and resource-based view, this study pioneers an empirical exploration of the association of FinTech innovation with firm performance. Moreover, it introduces the novel dimension of knowledge assets (on firm-level), acting as a mediating factor with in this relationship.

The purpose of this study is to investigate the main effect of top executive smog risk perception on green innovation and to examine the mediating role of corporate social responsibility (CSR) in the positive relationship between smog risk perception and green innovation along with the moderating role of smog knowledge.

A theoretical framework is developed based on the upper echelons theory to argue that top executive smog risk perception can be closely related to green innovation. Hierarchical analysis is conducted using a sample of eight firms in China.

Hypothesis testing indicates that physical health risk perception and mental health risk perception positively affect green innovation, and that these effects are positively mediated by CSR. In addition, smog knowledge moderates the relationship between physical health risk perception and green innovation.

The findings extend current studies on green innovation by highlighting the role of top executives’ perceptions beyond studying top executives’ attributes. The findings suggest that top executives should actively respond to smog pollution and fulfill CSR.

Previous studies have suggested that top executives’ demographic characteristics are the determining factors of green innovation. This empirical paper fills a gap in the literature by exploring the impact of top executive smog risk perception on green innovation within the framework of the upper echelons theory.

In this paper, six forms of non-linear Taylor rule have been applied to compare the fitting and prediction of response function of monetary policy of China, in an attempt to figure out a form of non-linear Taylor rule that accords with Chinese practices. The paper aims to discuss this issue.

In this paper, the authors will conduct in-sample fitting and out-of-sample prediction on the response function of monetary policy of China by introducing the factor of exchange rate and by applying forward-looking, backward-looking and within-quarters non-linear Taylor rule with data from the first quarter of 1994 to the second quarter of 2011, with a view to provide reference for formulation and implementation of monetary policies of China.

By analyzing the experimental data, the authors find that first, after introducing the factor of exchange rate, both the implementation effect and prediction ability of the monetary policies improve. Exchange rate has a relatively greater influence on the effect of the monetary policies during low inflation period. Introduction of exchange rate can improve the prediction accuracy of our monetary policies significantly. Second, as the implementation effect of monetary policy under different macro-background varies greatly, the situation should be correctly appraised when formulating and implementing monetary policies. According to the empirical results, the monetary policies have obvious non-linear characteristics, and transit smoothly with the change of inflation rate. On the two sides of inflation rate of 2.174 percent, there is an asymmetry response.

Surely, the conclusions are reached on the basis of quarterly data and one-step prediction method. It is no doubt that use of frequency mixing data including quarterly and monthly data will provide more sample information for studying relevant issues. And the use of multiple-step prediction method may cause a dynamic change of prediction indicators of models, which will help choose more appropriate prediction models. That is what the authors will study next.

First, by introducing exchange rate, this paper will extend non-linear Taylor rules and test its applicability and fitting effect in China. Second, figure out a non-linear Taylor rule that conforms to Chinese practices with data. In this paper, multiple forms of non-linear Taylor rules and actual macro date will be adopted for fitting and finding out a non-linear Taylor rule that fits Chinese practices. Third, empirical basis will be provided for further perfecting monetary policies prediction models. As there are few studies in connection with the prediction accuracy of non-linear Taylor rules so far, this paper will compare and study the prediction accuracy of non-linear Taylor rules by utilizing multiple advanced prediction techniques, so as to offer a beneficial thinking for predicting and formulating monetary policies by the central bank.

Economic policy uncertainty has increased around the world since the financial crisis of 2007–2008. While scholars have devoted a lot of time and energy to investigating the impact of economic policy uncertainty (EPU) on firm innovation, they have not reached consistent research conclusions. This study aimed to clarify the above research differences by exploring the impact of EPU on firms' relative exploitative innovation emphasis, so as to provide a more comprehensive and granular understanding of the relationship between EPU and firm innovation.

This study obtained 17,165 firm-year data points from 3,107 listed companies in China. It analyzed the above data with a fixed effects model. In addition, this study used an instrumental variables method to solve potential endogeneity problems.

Based on real options theory and contingency theory, the authors proposed and found that EPU has a significant positive effect on relative exploitative innovation emphasis. In addition, the authors proposed and found that this effect is more pronounced in industries with high technological uncertainty, low competitive intensity, and low state monopolization.

This study is the first to explore why firms prefer exploitative innovation over exploratory innovation from the perspective of EPU. In doing so, this study expands and enriches the EPU literature and the innovation literature. Furthermore, by introducing the moderating role of industry environment, this study deepens the authors' understanding of how complex interactions between industry and institutional environments work together to shape firm strategic choices, and especially firm innovation. Finally, the conclusions of this study have important practical implications for shareholders to take measures to balance exploitative innovation and exploratory innovation to achieve better development.