JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)

Network security acquisition of evidence reasoning and retrieval data

CHEN Shu-zhen, LI Shou-wei, SHI Kai-quan

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 1-9. doi:10.6040/j.issn.1671-9352.0.2022.493

Abstract ( 421 )

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Using the dynamic mathematical model, the concepts of inner-data, outer-data and inner & outer-data are given firstly; then, the intelligent acquisition theorems of evidence reasoning and retrieval data and the theorems of network data space and data evidence relationship are given; then, the network security acquisition algorithm of retrieval data is given; finally, the application of these concepts and theoretical results are also given.

Dynamic behaviors analysis of COVID-19 model with environmental virus effects

LI Xiao-wei, LI Gui-hua

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 10-15. doi:10.6040/j.issn.1671-9352.0.2021.460

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To investigate the dynamics of COVID-19 infectious disease with environmental virus effects, we establish an SEIARc model and analyse its dynamic behaviors. Firstly, the next generation matrix method is used to calculate the basic reproduction number R^*₀. When R^*₀ <1, the disease-free equilibrium exists and is locally asymptotically stable. Furthermore, by using of Metzler matrix and other related theories, we prove that the globally asymptotic stability of the disease-free equilibrium. When R^*₀>1, the system always has a unique endemic equilibrium, and the conditions for the locally asymptotic stability of the equilibrium of endemic diseases are given. Finally, by numerical simulation, the results of the theoretical analysis and the globally asymptotic stability of the endemic equilibrium is verified. It is shown that the transmission of COVID-19 disease can be effectively controlled by reducing the source of the environmental virus or cutting off the transmission routes.

Stability and Hopf bifurcation of diffusive predator-prey system with Holling-Ⅲ type functional response

HUO Lin-jie, ZHANG Cun-hua

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 16-24. doi:10.6040/j.issn.1671-9352.0.2021.788

Abstract ( 466 )

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This paper considers a predator-prey reaction-diffusion system with Holling-Ⅲ type functional response function. By analyzing the distribution in the complex plane of the roots of the eigenvalue problem of the linearized system at the constant positive equilibrium solution, the stability and Hopf bifurcation of the constant positive equilibrium solution are analyzed.

Consistency of wavelet estimator for regression model with mixed noise

HUANG Qin-mei, KOU Jun-ke

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 25-30. doi:10.6040/j.issn.1671-9352.0.2021.798

Abstract ( 265 )

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For the problem of regression estimations with mixed noise, a linear wavelet estimator is constructed by wavelet method. The L^p(1≤p<∞) consistency of wavelet estimator is proved without any smooth conditions of the regression function.

Functions and values of carbon sink in nature protected areas of Shandong under the background of “Double Carbon”

CUI Xiao-wei, DING Cui-hua, WANG Ren-qing, WANG Fu-hai

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 31-39. doi:10.6040/j.issn.1671-9352.0.2022.197

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We systematically analyzed the carbon sink function and differences of different ecosystems in the nature protected areas in sink is the largest one(1.20×10⁶ t/a). Then from the large to the total carbon sink of nature protected area is 2.99×10⁶ t/a, which the forest ecosystem carbon small is following by farmland ecosystems(9.09×10⁵ t/a), off shore ecosystems(5.22×10⁵ t/a), coastal wetland ecosystem(2.27×10⁵ t/a), inland wetland ecosystem(1.28×10⁵ t/a), grassland ecosystems(0.04×10⁴ t/a). The total value of carbon sink is 135 million yuan and the average value is 7.61×10³ yuan/hm². To realize the carbon sink value, we exploded the path based on the perspective of ecological compensation, and constructed a framework that combined the carbon trading and ecological compensation of nature protected areas.

A similarity case matching method combining segment encoding and affine-mechanism

LAI Hua, ZHANG Heng-tao, XIAN Yan-tuan, HUANG Yu-xin

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 40-47. doi:10.6040/j.issn.1671-9352.1.2021.048

Abstract ( 360 )

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Similarity case matching(SCM)task is to judge whether the cases described in two judgment documents are similar. SCM is usually regarded as the text matching problem of judgment documents and has important applications in the judicial trial. Existing deep learning models mostly encode long texts of cases into a single vector, and it is difficult for the model to learn the subtle differences between the cases from long texts. Considering that the content of each part of the case text is relatively fixed, this paper proposes to split the long case text into multiple pieces and encode them separately to obtain the subtle features of different parts. At the same time, learnable affine-transformation is used to improve the similarity scoring module, so that the model learn more subtle differences, which further improves the performance of case matching. The experimental results on the CAIL2019-SCM data set show that compared to another model, the accuracy of the method proposed in this paper have increased by 1.89%.

Question generation method based on conditional variational autoencoder

LIU Dong, HONG Yu, SU Yu-lan, ZHANG Min

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 48-58. doi:10.6040/j.issn.1671-9352.2.2021.035

Abstract ( 355 )

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The conditional variational autoencoder, as an auxiliary module, is introduced into the encoding and decoding process of the pre-trained language model. It improves the robustness of the model through data augmentation(potential semantic expansion), and establishes a high-dimensional distribution connection between declarative sentences and interrogative sentences, which implements one-to-many question generation by sampling from the distribution. The results show that the fusion of conditional variational autoencoder can not only generate diverse questions, but also help to improve the performance of the question generation model. On the two answer-known question generation datasets Split1 and Split2, which are based on the SQuAD dataset, the BLEU-4 score is improved to 20.75% and 21.61%, respectively.

Density-sensitive spectral clustering based on three-way decision

FAN Jia-chen, WANG Ping-xin, YANG Xi-bei

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 59-66. doi:10.6040/j.issn.1671-9352.0.2021.671

Abstract ( 315 )

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This paper integrates density sensitive spectral clustering with three-way decision and presents a density sensitive spectral clustering algorithm based on three-way decision. In the proposed algorithm, the upper bound of each cluster is obtained by introducing tolerance parameters in the process of density-sensitive spectral clustering, and the core is separated from the upper bound by the perturbation analysis algorithm. The difference between the upper bound and the core region is regarded as the fringe region of the specific cluster. The clustering result uses the core region and the fringe region to represent each cluster, which can more comprehensively display the data structure. Compared with the experimental results of the traditional hard clustering algorithm on the UCI data set, the proposed algorithm is effective in improving the value of AS and ACC and reducing the value of DBI by using the core region to calculate the evaluation indicators of clustering, which indicates that the proposed algorithm can be used to solve the problem of clustering uncertain objects.

AdaBoost algorithm based on model decision tree

LIANG Yun, MEN Chang-qian, WANG Wen-jian

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 67-75. doi:10.6040/j.issn.1671-9352.2.2021.139

Abstract ( 386 )

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The AdaBoost algorithm is an ensemble algorithm that combines multiple base learners through reasonable strategies to generate a strong learner. Its performance depends on the accuracy and diversity of the base learners. However, the poor classification accuracy of weak learners often leads to poor performance of the final strong classifier. Therefore, in order to further improve the classification accuracy of the algorithm, this paper proposes an MDTAda model, which first uses Gini index to iteratively construct an incomplete decision tree. Then add a simple classifier to the non-pure pseudo-leaf nodes of the decision tree to generate MDT(model decision tree), use MDT as the base classifier of AdaBoost algorithm, and weighted average to generate a strong classifier. Experiments on standard data sets show that compared with the traditional AdaBoost algorithm, the algorithm proposed in this paper has better generalization performance and better margin distribution, and requires fewer iterations to achieve the same accuracy as AdaBoost algorithm.

Closed-loop supply chain network equilibrium considering government subsidy under outsourcing remanufacturing

LIU Xue-qi, ZHOU Yan

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 76-88. doi:10.6040/j.issn.1671-9352.0.2021.231

Abstract ( 277 )

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In order to study the impacts of the government subsidy mode and subsidy level, a closed-loop supply chain network equilibrium model considering government subsidy under outsourcing remanufacturing was established based on non-cooperative game theory. It analyzes the importance of government subsidies, the choice of government subsidy mode and subsidy level, and provides a scientific evidence for the government to formulate the subsidy strategy. The results show that the governments subsidy strategy will stimulate the consumption, promote the realization of outsourcing remanufacturing, and achieve the purpose of protecting the environment and saving resources. When the recovery rate of the product is low, the government should implement the recovery subsidy and increase the subsidy level, so as to realize the triple goals of resources, environment and outsourcing remanufacturing. When the recovery rate of the product is high, the government should implement the remanufacturing subsidy from the perspective of resources and environment, and it should implement the recovery subsidy to control and reduce the subsidy level from the perspective of outsourcing remanufacturing.

Wholesale, agency or hybrid? sales mode choices of hybrid platform in competition with self-run platform

ZHOU Chi, WANG Yi-xin, YU Jing

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 89-100. doi:10.6040/j.issn.1671-9352.0.2021.144

Abstract ( 364 )

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Aiming at the problem of the optimal sales model selection and strategy of cooperation between composite E-commerce platform and manufacturers under the condition of self-operated platform competition, three sales modes is wholesale, agency and hybrid are considered to establish Stackelberg game model of multi-channel pricing decision-making. Then analyzing the influence of potential market size, channel flexibility and unit commission on the optimal strategies of all parties in E- supply chain, and the optimal sales model of composite platform is obtained. The results show that an increase in the potential market scale and channel flexibility will increase the hybrid platforms profit, and the hybrid platform should set a moderate unit commission to maximize the profit. When the channel flexibility is low, the hybrid platform should choose the agency sales mode if the unit commission is moderate. The hybrid platform should choose the wholesale sales mode if the unit commission is sufficiently low or high. When the channel flexibility is sufficiently high and the unit commission is not sufficiently low, the hybrid sales mode is the optimal choice for the hybrid platform. In addition, the paper further discusses win-win conditions for the hybrid platform and manufacturer in the sales mode choices.

Portfolio research based on temporal network and random matrix theory

ZHAO Xia, ZHU Yi-pin, YANG Ya-jie, XU Lan-tao

JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE). 2023, 58(1): 101-110. doi:10.6040/j.issn.1671-9352.0.2021.564

Abstract ( 201 )

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Considering the complex linear and non-linear dynamic relationship and evolutional information among assets, temporal networks are constructed based on Pearson correlation coefficient, Kendall rank correlation coefficient and Tail correlation coefficient, and further deal with their noise reductions through random matrix, employ conditional centrality of temporal network as selection standard of optimal asset portfolio. Nine network models for asset screening are constructed from three aspects of dependency relationship, matrix noise reduction and central measure, and the mean-variance optimal strategy is further calculated based on SSE 180 indices. The in-sample and out-of-sample performances of investment strategies under different models are compared through indicators such as portfolio turnover and Omega ratio. The results show that the portfolio selected by the model with Kendall and Tail correlation coefficients has lower transaction costs. The application of random matrix theory can significantly improve investment returns and the introduction of temporal conditional centrality is helpful to screen out better portfolios.

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