Journal of Systems Science and Information最新文献

Aiming at the lack of classification and good standard corpus in the task of joint entity and relationship extraction in the current Chinese academic field, this paper builds a dataset in management science that can be used for joint entity and relationship extraction, and establishes a deep learning model to extract entity and relationship information from scientific texts. With the definition of entity and relation classification, we build a Chinese scientific text corpus dataset based on the abstract texts of projects funded by the National Natural Science Foundation of China (NSFC) in 2018-2019. By combining the word2vec features with the clue word feature which is a kind of special style in scientific documents, we establish a joint entity relationship extraction model based on the BiLSTM-CNN-CRF model for scientific information extraction. The dataset we constructed contains 13060 entities (not duplicated) and 9728 entity relation labels. In terms of entity prediction effect, the accuracy rate of the constructed model reaches 69.15%, the recall rate reaches 61.03%, and the F1 value reaches 64.83%. In terms of relationship prediction effect, the accuracy rate is higher than that of entity prediction, which reflects the effectiveness of the input mixed features and the integration of local features with CNN layer in the model.

In this paper, we discuss the development process of local government special bonds, and the role channels of local government special debt investment in driving China's economic growth. Based on the specific decomposition of Xinjiang local government special bond investment, this paper uses the non-competitive input-output model for the first time to analyze the net pulling effect of Xinjiang local government special bond investment on Xinjiang's GDP and employment in 2020. Two measure calibers are set in this paper based on whether the financing costs are considered or not; in addition, we set up four scenarios based on two conditions: Whether to consider retained fun and whether to consider using special-purpose bond investment to leverage social capital. The results show that: 1) when financing costs are not considered, the RMB77.4 billion local government special-purpose bonds can push the GDP of Xinjiang to grow by RMB42.27 billion, RMB35.12 billion, RMB77.548 billion and RMB69.34 billion respectively under the four scenarios; 2) when financing costs are not considered, the number of jobs driven by the RMB77.4 billion local government special-purpose bonds was respectively 372, 300, 324, 500, 718, 500 and 601, 300 in the four scenarios; 3) when financing costs are considered, the RMB77.4 billion local government special-purpose bonds can push the GDP of Xinjiang to grow by RMB71.876 billion and RMB64.268 billion under scenario 3) and scenario 4).

The presence and effective implementation of the intellectual potential of construction enterprises is important for creating unique competitive advantages as a response to challenges caused by globalization, the era of the knowledge economy, as well as the development of communication and information technologies. The purpose of the study is to provide a comprehensive understanding of the factors that contribute to the development of intellectual capital in construction enterprises; development of an algorithm for thorough response by the enterprise to the action of determinants. The following methods were used: Analysis and synthesis, induction and deduction when studying the variety of determinants of the development of intellectual capital; grouping in the process of classifying determinants; statistical methods in the process of researching the reporting of construction enterprises; systematic approach in the process of forming an algorithm of actions aimed at ensuring effective management of the intellectual capital of construction enterprises; abstraction to generalize research results and outline significant trends in changing indicators that reflect the development of individual components of intellectual capital. In the paper, the determinants of the development of intellectual capital are grouped according to the classification features important for Ukrainian construction enterprises. The indicators, the monitoring of which is necessary in the process of implementing a conscious response to the action of the determinants of the development of the intellectual capital of construction enterprises, are studied on the example of Ukrainian construction enterprises.

Industrial relocation and ecological subsidy are viewed as effective ways to overcome transboundary industrial pollution. In this paper, we study the transboundary industrial pollution control problem in the Beijing-Tianjin-Hebei region in the context of industrial relocation. Firstly, we construct an economic model of pollution control with relevant variables such as environmental tax and environmental damage coefficient. Secondly, we solve the economic contributions by using the proportional split-off solution. Finally, we compare the optimal relocation quantity and welfare functions in both cooperative and non-cooperative cases. Our research finds that: 1) The optimal strategy is closely related to utility coefficient, environmental loss coefficient and incentive intensity coefficient. 2) The welfare function and the optimal relocation quantity in the cooperative case are significantly greater than those in the non-cooperative case. Based on the analysis, some suggestions are provided for transboundary industrial pollution management.

The space-temporal evolution of economic inequality is examined with Markov chain test method, and the dynamic interrelationships among environmental quality, energy consumption, and economic inequality in China from the province-level are tested by focusing on accounting for structural shifts in causal linkages in this paper. We first employ the Toda-Yamamoto causality framework and then augment it with a Fourier approximation which captures structural changes as a smooth process. The empirical findings show that taking into account smooth structural shifts is important for the causal linkages between economic inequality and energy consumption, and also between environmental quality and energy consumption. The causality analysis with structural changes provides a causal linkage between economic inequality and energy consumption in 26 out 30 provinces and a causal linkage between environmental quality and energy consumption in 7 out 30 provinces, while the quantities are 22 out 30 and 5 out 30 respectively when not accounting for structural shifts. These findings are consistent with the fact that provincial economics in China have experienced structural changes in economy-environment-energy sectors. We also conduct additional analyses which point out that regional and cyclical dependency matter for the causal relationships, and the method of HP filtering can not effectively solve the problem of smooth shifts in economy-environment-energy causality.

In this paper, stochastic global optimization algorithms, specifically, genetic algorithm and simulated annealing are used for the problem of calibrating the dynamic option pricing model under stochastic volatility to market prices by adopting a hybrid programming approach. The performance of this dynamic option pricing model under the obtained optimal parameters is also discussed. To enhance the model throughput and reduce latency, a heterogeneous hybrid programming approach on GPU was adopted which emphasized a data-parallel implementation of the dynamic option pricing model on a GPU-based system. Kernel offloading to the GPU of the compute-intensive segments of the pricing algorithms was done in OpenCL. The GPU approach was found to significantly reduce latency by an optimum of 541 times faster than a parallel implementation approach on the CPU, reducing the computation time from 46.24 minutes to 5.12 seconds.

The bond market is an important market for investment and financing in China's economic sectors, and also an important part of the monetary policy framework. The internal transmission of bond market is an important part of market interest rate transmission, which iscritical to the effectiveness of monetary policy. However, few scholars have studied the characteristics of interest rate transmission in China. An in-depth study of the interest rate transmission mechanism and its dynamic evolution between different bond markets is conducive to clarify the pulse of transmission within Chinese bond market and to further unblock the transmission mechanism of monetary policy. From the perspective of system theory and based on the analysis method of Granger causality complex network, this paper finds that the interest rate transmission among various varieties in China's bond market is relatively significant. Treasury bonds and CDB bonds are the two core bond varieties of interest rate transmission in the bond market. Simultaneously, this study concludes that the medium and long-term interest rate played a dominant role in the transmission of market interest rate during the easing phase of monetary policy, while the short-term interest rate played a dominant role in the transmission of market interest rate during the tightening phase of monetary policy. This paper also gives enlightenment and suggestions.

A critical problem plaguing regulators in promoting pharmaceutical innovation is to design and select efficient incentive policies. In this study, we develop a stylized model comprising a regulator and two representative drug producers to evaluate the effects of three incentive policies: Innovation subsides, inclusion new drugs in the health insurance plan, and the combination of the above two policies (also called hybrid policy). Our analysis shows that innovation subsidies and inclusion of new drugs in the health insurance plan can both promote pharmaceutical innovation, but their incentive effects vary in different policy objectives. Specifically, if the regulator aims to improve patient welfare, he should incorporate new drugs into the health insurance plan to expand the accessibility of new drug when the copayment level is low. However, if the regulator aims to improve social welfare, he should choose innovation subsidies when the copayment level is high, and the hybrid policy when the copayment level is low. In particular, with a sufficiently low copayment level, the hybrid policy allows the new drug producer, patients and the regulator to achieve Pareto improvement due to a lower regulator's innovation subsidy expenditure, higher profits of the new drug producer and consumer surplus.

To explore the factors and paths that influence the willingness to use sharing new energy autonomous vehicles (SNEAVs), this paper incorporates the unified theory of acceptance and use of technology (UTAUT2) as the basic frameworks, with gender serving as a moderating variable. Seven psychological latent variables, including performance expectancy, social influence, hedonic motivation, price sensitivity, perceived risk, trust in technology, and innovativeness, are considered to examine their effects on behavioral intention. Quantitative data (n = 1082) was collected via an online questionnaire in Beijing. The ordered logit model was used to preliminarily demonstrate the significant impact of gender, with further parameter fitting confirming the good fit of the psychological latent variable model. Path analysis results reveals that gender influences the willingness to use SNEAVs in multiple aspects. Specially, females are more significantly influenced by hedonic motivation, whereas males prioritize performance expectation. Furthermore, price sensitivity positively has a positive impact on male behavioral intention, but a negative effect on female behavioral intention. Additionally, trust in technology plays a more important role for women compared to men. These findings are crucial in promoting the development of SNEAVs.