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This paper aims to optimize the weighing control system and compensate weighing error for weighing control system of coal mine paste-filling weighing control system.

The process of the paste-filling weighing control system is analyzed and the mathematical model of the paste-filling material weight is established. Then, the back-propagation (BP) neural network is used to optimize the control system and compensate the weighing error.

Without the BP neural network, the weighing error of the paste-filling control system is more than 3 per cent, whereas after optimization with the BP neural network, the weighing error is less than 1 per cent. With the simulation results, it is seen that the weighing error of the paste-filling control system decreases and the accuracy of the weighing control system improves and optimizes.

The method can be further used to improve the control precision of the coal mine paste-filling system.

This paper aims to study a modular method for designing a paste filling station (PFS) for a coal mine (CM) to reduce the PFS’s input cost and achieve reutilization.

Modular design criteria for the PFS are proposed and a modular division (MDiv) model and an evaluation method are established. The correlation-strength matrix of the PFS parts expressed in the form of rough numbers is transformed into a fuzzy equivalent matrix. The theory of rough sets and the fuzzy clustering method are introduced for PFS MDiv. The evaluating method is established for the PFS MDiv scheme based on the principle of fuzzy comprehensive evaluation.

Taking a particular CM PFS as an example, the above method is used to modularize the PFS, and the optimal division of the PFS is finally determined via the above evaluation system. Applying this method solves the problem of high cost investment in the initial stage of PFS construction.

The theory of rough sets and the fuzzy clustering method are introduced for PFS MDiv. An evaluating method is established for the PFS MDiv scheme based on the principle of fuzzy comprehensive evaluation, thereby providing new ideas for PFS transformation and reutilization.

The purpose of this study is to propose extended potentials and investigate the applicability of extended Finnis–Sinclair (FS) potential to Cr with the unit cell structure of body-centered cubic (BCC Cr).

The parameters of each potential are determined by fitting the elastic constants, cohesive energy and mono-vacancy formation energy. Furthermore, the ability of the extended FS potential to describe the crystal defect properties is tested. Finally, the applicability of reproducing the thermal properties of Cr is discussed.

The internal relationship between physical properties and potential function is revealed. The mathematical relationship between physical properties and potential function is derived in detail. The extended FS potential performs well in reproducing physical properties of BCC Cr, such as elastic constants, cohesive energy, surface energy and the properties of vacancy et al. Moreover, good agreement is obtained with the experimental data for predicting the melting point, specific heat and coefficient of thermal expansion.

In this study, new extended potentials are proposed. The extended FS potential is able to reproduce the physical and thermal properties of BCC Cr. Therefore, the new extended potential can be used to describe the crystal defect properties of BCC Cr.

This research aims to explore how consumers' local brand choices differ between air-polluted days and clean days, and why the difference occurs.

Two studies were conducted. Study 1 used the longitudinal consumption data of various yogurt brands and daily air quality indexes in 2014 and 2015. Study 2 conducted three rounds of surveys on a clean day, a general air-polluted day and a seriously air-polluted day.

The findings indicate that consumers show less tendency of attribution and compensatory consumption during air-polluted days, which in turn decrease their willingness to choose local brands.

Implications are provided for future research and marketing practice, especially for local companies that rely heavily on local consumers, and retailers in heavy air-polluted areas.

This paper is the first to illustrate the influence of air pollution on consumers' local brand choices, and it extends current understanding on air pollution and consumer choices by discovering psychological process underneath to explain the effect.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

The purpose of this paper is to make a theoretical comprehensive efficiency evaluation of a nonlinear analysis method based on the Woodbury formula from the efficiency of the solution of linear equations in each incremental step and the selected iterative algorithms.

First, this study employs the time complexity theory to quantitatively compare the efficiency of the Woodbury formula and the LDLT factorization method which is a commonly used method to solve linear equations. Moreover, the performance of iterative algorithms also significantly effects the efficiency of the analysis. Thus, the three-point method with a convergence order of eight is employed to solve the equilibrium equations of the nonlinear analysis method based on the Woodbury formula, aiming to improve the iterative performance of the Newton–Raphson (N–R) method.

First, the result shows that the asymptotic time complexity of the Woodbury formula is much lower than that of the LDLT factorization method when the number of inelastic degrees of freedom (IDOFs) is much less than that of DOFs, indicating that the Woodbury formula is more efficient for local nonlinear problems. Moreover, the time complexity comparison of the N–R method and the three-point method indicates that the three-point method is more efficient than the N–R method for local nonlinear problems with large-scale structures or a larger ratio of IDOFs number to the DOFs number.

This study theoretically evaluates the efficiency of nonlinear analysis method based on the Woodbury formula, and quantitatively shows the application condition of the comparative methods. The comparison result provides a theoretical basis for the selection of algorithms for different nonlinear problems.

Studies on cross-culture marketing often focus on either localization or globalization strategies. Based on data from pre-communist China (1912–1949), product hybridization – defined as a process or strategy that generates symbols, designs, behaviors and cultural identities that blend local and global elements – emerges as a popular intermediate strategy worthy of further inquiry. After examining the mechanisms and processes underlying this strategy, a schema for classifying product hybridization strategies is developed and illustrated. The paper aims to discuss these issues.

Critical historical research method is applied to historical data and historical “traces” from pre-communist China’s corporate documents, memoirs, posters, advertisements, newspapers and secondhand sources.

Strategic interactions between domestic and foreign companies in pre-communist China fostered products and a city (Shanghai) containing Chinese and non-Chinese elements. Informed by historical traces and data from pre-communist China (1912-1949), a 2 × 2 classification schema relating company type (i.e. foreign or domestic) to values spectrum endpoint (i.e. domestic vs foreign) was formulated. This schema reflects the value of communication, negotiation and cultural (inter)penetration that accompanies cross-culture product flows.

Cross-culture marketing strategies meant to help companies satisfy diverse marketplace interests can induce a mélange of product design elements. Because product hybridization reflects reciprocity between domestic and foreign companies that embodies multiple interests and contrasting interpretations of product meanings, researchers should examine globalization and localization synergistically.

Strategies adopted by domestic and foreign companies in pre-communist China (1912–1949) can help contemporary companies design effective cross-culture marketing strategies in a global marketplace infused with competing meanings and interests.

Examining historical strategies adopted in pre-communist China (1912–1949) can inform contemporary marketers’ intuitions. Understanding product hybridization in global marketplaces can improve marketing efficiency.

This study aims to investigate the potential of pumpkin seed oil (PSO) and zinc to attenuate oxidative stress and neuroinflammation caused by chronic mild stress (CMS) in the cerebral cortex of male rats.

The rats were submitted to stress for six weeks and then the behavior of the rats was tested by forced swimming test (FST) and novel cage test. The treated groups were given venlafaxine (20 mg/kg), pumpkin seed oil (40 mg/kg) and zinc (4 mg/kg). The cortex homogenate was used for the detection of the oxidative stress parameters, the concentration of neurotransmitters, tumor necrosis factor-α (TNF-α) and interleukin 1β (IL-1β), Na+/K+-ATPase activity, and the expression of histamine N-methyltransferase (Hnmt) and tyrosine hydroxylase (Th).

CMS causes a significant increase in immobility time in the FST and a significant decrease in the number of rearing in the novel cage test. CMS group showed a significant increase in alanine aminotransferase (ALT) activity, levels of cortisol, TNF-α, IL-1β, nitric oxide and malondialdehyde. CMS caused a significant decrease in the concentrations of serotonin, GABA, norepinephrine, and the activities of glutathione peroxidase, catalase, superoxide dismutase and Na⁺/K⁺-ATPase. CMS caused a marked reduction in the expression of Hnmt and Th in the cortex. PSO and zinc attenuated the Na⁺/K⁺-ATPase activity, oxidative parameters and neuroinflammation induced by the CMS, and this was reflected by the elevation of the concentration of neurotransmitters and reduction of cortisol and ALT, in addition to the behavior normalization. PSO and zinc attenuated the CMS by improving the antioxidant milieu and anti-inflammatory status of the cerebral cortex.

There are no studies on the effect of pumpkin seed oil on depression