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This study aims to improve the accuracy of hyperspectral estimation of soil organic matter content.

Based on the uncertainty in spectral estimation, 76 soil samples collected in Zhangqiu District, Jinan City, Shandong Province, were studied in this paper. First, the spectral transformation of the spectral data after denoising was carried out by means of 11 transformation methods such as reciprocal and square, and the estimation factor was selected according to the principle of maximum correlation. Secondly, the grey weighted distance was used to calculate the grey relational degree between the samples to be estimated and the known patterns, and the local linear regression estimation model of soil organic matter content was established by using the pattern samples closest to the samples to be identified. Thirdly, the models were optimized by gradually increasing the number of modeling samples and adjusting the decision coefficient, and a comprehensive index was constructed to determine the optimal predicted value. Finally, the determination coefficient and average relative error are used to evaluate the validity of the model.

The results show that the maximum correlation coefficient of the seven estimated factors selected is 0.82; the estimation results of 14 test samples are of high accuracy, among which the determination coefficient R² = 0.924, and the average relative error is 6.608%.

Studies have shown that it is feasible and effective to estimate the content of soil organic matter by using grey correlation local linear regression model.

The paper succeeds in realizing both the soil organic matter hyperspectral grey relation estimating pattern based on the grey relational theory and the estimating pattern by using the local linear regression.

In order to reflect the essential characteristics of interval grey number and study the ranking method of interval grey number as a whole, this paper aims to establish a ranking method of interval grey number.

First, based on the generalised greyness of interval grey number, the definitions of referenced grey number and proximity degree are given. Second, based on the greyness distance of interval grey number, the proximity degree model is constructed and its properties are analysed. Finally, some examples are given to illustrate the effectiveness of the proximity degree model.

The results show that the index of proximity degree can better reflect the degree that the interval grey number is relatively close to the referenced grey number in different cases. The proximity degree model used to compare interval grey numbers is an extension of the model used to compare real numbers. The examples show that the proximity degree model of interval grey number proposed in this paper is feasible and effective.

The research studies show that the proximity degree model can be used for the ranking of interval grey numbers or real numbers and also for the ranking of numbers where interval grey numbers coexist with real numbers. In addition, the proximity degree model provides a theoretical basis for the establishment of grey comprehensive evaluation model.

The paper succeeds in putting forward the conceptions of referenced grey number and proximity degree based on the generalised greyness of interval grey number and constructing the proximity degree model for the ranking of interval grey number.

The purpose of this paper is to improve the properties of metal nanoparticles which are easy to agglomerate and hard to disperse evenly, thus limiting the application of metal nanoparticles in grease. A novel technology was proposed for modifying metal oxide to improve the dispersibility of nanoparticles.

SA-TiO2 nanoparticles were synthesized using an in-situ esterification method followed by surface modification with stearic acid. The microstructure of the nanoparticles was characterized by scanning electron microscope, transmission electron microscope and Fourier transform infrared spectroscopy and their thermal stability was evaluated by thermogravimetric analyzer. The tribological properties of the SA-TiO2 nanoparticles as additives in lithium grease were evaluated with a four-ball tester and TE77 reciprocating friction tester. The worn surfaces of the steel balls were investigated by EDS and XPS.

The prepared nanoparticles can be well dispersed in the lithium grease and possess much better tribological properties compared to traditional nanoparticles. The results indicated that the excellent tribological performance of SA-TiO2 was attributed to the chemical reaction film composing of Fe2O3, iron oxide and other organic compounds.

This paper provides a method to prevent the agglomeration of nano-TiO2 by surface modification with stearic acid. And the prepared nanoparticles can effectively improve the tribology performance of lithium grease.

In the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and knowledge systems. This article aims to fill this gap.

Based on the attraction-selection-attrition (ASA) framework, this paper constructs a simulation model to study the coevolution of these two systems under different levels of membership fluidity.

By analyzing the evolution of these systems with the vector autoregression (VAR) method, we find that social and knowledge systems become more orderly as the coevolution progresses. Furthermore, in communities with low membership fluidity, the microlevel of the social system (i.e. users) drives the coevolution, whereas in communities with high membership fluidity, the microlevel of the knowledge system (i.e. users' views) drives the coevolution.

This paper extends the application of the ASA framework and enriches the literature on membership fluidity of online communities and the literature on driving factors for coevolution of the social and knowledge systems in OKCs. On a practical level, our work suggests that community administrators should adopt different strategies for different membership fluidity to efficiently promote the coevolution of the social and knowledge systems in OKCs.

The purpose of this paper is to present a novel high-precision relative navigation method for tight formation-keeping based on thrust on-line identification.

Considering that thrust acceleration cannot be measured directly, an on-line identification method of thrust acceleration is explored via the estimated acceleration of major space perturbation and the inter-satellite relative states obtained from space-borne acceleration sensors; then, an effective identification model is designed to reconstruct thrust acceleration. Based on the identified thrust acceleration, relative orbit dynamics for tight formation-keeping is established. Further, using global positioning system (GPS) measurement information, a modified extended Kalman filter (EKF) is suggested to obtain the inter-satellite relative position and relative velocity.

Compared with the normal EKF and the adaptive robust EKF, the proposed modified EKF has better estimation accuracy in radial and along-track directions because of accurate compensation of thrust acceleration. Meanwhile, high-precision relative navigation results depend on high-precision acceleration sensors. Finally, simulation studies on a chief-deputy formation flying control system are performed to verify the effectiveness and superiority of the proposed relative navigation algorithm.

This paper provides a reference in solving the problem of high-precision relative navigation in tight formation-keeping application.

This paper proposes a novel on-line identification method for thrust acceleration and shows that thrust identification-based modified EKF is more efficient in relative navigation for tight formation-keeping.

Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political centralization”). Given the significant level of IPO underpricing in China, we examine the impact of local political uncertainty (measured by prefecture-level city official turnover rate) on IPO underpricing.

Using 2,259 IPOs of A-share listed companies from 2001 to 2019, we employ a structural equation model (SEM) to examine the channel (voluntarily lower the issuance price vs aftermarket trading) through which political uncertainty affects IPO underpricing. We check the robustness of the results using bootstrap tests, adopting alternative proxies for political uncertainty and IPO underpricing and employing subsample analysis.

Local official turnover-induced political uncertainty increases IPO underpricing by IPO firms voluntarily reducing the issuance price rather than by affecting investor sentiment in aftermarket trading. These relations are stronger in firms with pre-IPO political connections. The effect of political uncertainty on IPO underpricing is also contingent upon the industry and the growth phase of an IPO firm, more pronounced in politically sensitive industries and firms listed on the growth enterprise market board.

Local government officials in China usually have a short tenure and Chinese firms witness significantly severe IPO underpricing. By introducing the SEM model in studying China IPO underpricing, this study identifies the channel through which local government official turnover to political uncertainty on IPO underpricing.

Unhealthy diets are associated with an increased risk of non-communicable diseases and present a significant public health challenge. When developing effective interventions and policies, consideration must be given to the unique social culture in which food choice is embedded. Health vulnerabilities to poor nutrition exist throughout life but may be influenced by socio-cultural factors such as age. This study aimed to assess the attitudes of older or younger Chinese consumers towards healthy eating and explore the factors influencing their food choices.

Semi-structured interviews were conducted in Wuhan, China, with 20 consumers aged 18–25 (Group A) and 20 consumers aged 65 and over (Group B).

Thematic analysis revealed that the two groups had positive attitudes towards healthy eating, although Group A participants were more knowledgeable. Time pressure, food prices and social networks differentially influence healthy eating practices across age groups.

Given China’s economic and cultural context, healthy eating interventions should consider the individual characteristics and food preferences of the different age groups. This approach can optimize targeted healthy eating interventions, and media communications related to healthy eating.

Through the scientific and reasonable evaluation of the site selection of the emergency material reserve, the optimal site selection scheme is found, which provides reference for the future emergency decision-making research.

In this paper, we have chosen three primary indicators and twelve secondary indicators to construct an assessment framework for the determination of suitable locations for storing emergency material reserves. By mean of the improved entropy weight-order relationship weight determination method, the evaluation model of kullback leibler-technique for order preference by similarity to an ideal solution (KL-TOPSIS) emergency material reserve location based on relative entropy is established. On this basis, 10 regional storage sites in Beijing are selected for evaluation.

The results show that the evaluation model of the location of emergency material reserve not only respects the objective knowledge, but also considers the subjective information of the experts, which makes the ranking result of the location of the emergency material reserve more accurate and reliable.

Firstly, the modification factor is added to the calculation formula of traditional entropy weight method to complete the improvement of entropy weight method. Secondly, the order relation analysis method is used to assign subjective weights to the indicators. The principle of minimum information entropy is introduced to determine the comprehensive weight of the index. Finally, KL distance and TOPSIS method are combined to determine the relative entropy and proximity degree of alternative solutions and positive and negative ideal solutions, and the scientific and effective of the method is proved by case study.

It is necessary to implement green supply chains. But green development needs to be gradual and coexist with ordinary products in the market. This paper aims to study the green and ordinary product pricing and green decision-making under chain-to-chain competition.

This paper considers consumers' multiple preferences and takes two competitive supply chains with asymmetric channels as the research object. Through the construction of the game models involving different competitive situations, this paper studies the pricing, green decision-making and the supply chains' profits, and discusses the impact of consumer green preference, channel preference, green investment and competition on the decision-making and performance. Finally, this paper further studies the impact of the decision structure on the environmental and economic benefits of supply chains.

The results show that consumer green preference has an incentive effect on the green supply chain and also provides an opportunity for the regular supply chain to increase revenue. Specifically, consumers' preference for green online channels improves the product greenness, but its impact on the green retailer and regular supply chain depends on the green investment cost. Moreover, competition not only fosters product sustainability, but also improves supply chain performance. This paper also points out that the decentralization of the regular supply chain is conducive to the environmental attributes of the green product, while the environment-friendly structure of the green supply chain is different under different conditions. In addition, the profit of a supply chain under centralized decision is not always higher than that under decentralized decision.

The novelty of this paper is that it investigates the pricing of two heterogeneous alternative products and green decision-making for the green product under the competition between two supply chains with asymmetric channels, in which the green supply chain adopts dual channels and the regular supply chain adopts a single retail channel.

Learning from safety accidents and sharing safety knowledge has become an important part of accident prevention and improving construction safety management. Considering the difficulty of reusing unstructured data in the construction industry, the knowledge in it is difficult to be used directly for safety analysis. The purpose of this paper is to explore the construction of construction safety knowledge representation model and safety accident graph through deep learning methods, extract construction safety knowledge entities through BERT-BiLSTM-CRF model and propose a data management model of data–knowledge–services.

The ontology model of knowledge representation of construction safety accidents is constructed by integrating entity relation and logic evolution. Then, the database of safety incidents in the architecture, engineering and construction (AEC) industry is established based on the collected construction safety incident reports and related dispute cases. The construction method of construction safety accident knowledge graph is studied, and the precision of BERT-BiLSTM-CRF algorithm in information extraction is verified through comparative experiments. Finally, a safety accident report is used as an example to construct the AEC domain construction safety accident knowledge graph (AEC-KG), which provides visual query knowledge service and verifies the operability of knowledge management.

The experimental results show that the combined BERT-BiLSTM-CRF algorithm has a precision of 84.52%, a recall of 92.35%, and an F1 value of 88.26% in named entity recognition from the AEC domain database. The construction safety knowledge representation model and safety incident knowledge graph realize knowledge visualization.

The proposed framework provides a new knowledge management approach to improve the safety management of practitioners and also enriches the application scenarios of knowledge graph. On the one hand, it innovatively proposes a data application method and knowledge management method of safety accident report that integrates entity relationship and matter evolution logic. On the other hand, the legal adjudication dimension is innovatively added to the knowledge graph in the construction safety field as the basis for the postincident disposal measures of safety accidents, which provides reference for safety managers' decision-making in all aspects.