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Graduated compression shaping pants (GCSPs) are shapewears sharing the same action mechanisms as medical compression stockings (MCSs), setting four stages of pressure on lower limbs that gradually decreasing from the ankle to the thigh root. They are claimed to be able to not only shaping bodies but also promoting blood circulation in legs. However, there are few studies on whether GCSPs perform the advertised functions and how effective GCSPs could be. The purpose of this paper is to explore and evaluate the pressure distribution and body-shaping effectivity of GCSPs.

The authors first select two graduated compression shaping pants (GCSPs-A, GCSPs-B) and a pair of professional shaping pants as the Controls. Then objective pressure test and 3D body scanning test are conducted. Finally, the pressure distribution and body-shaping effectivity are demonstrated by ORIGIN and MATLAB, compared with controls.

GCSPs-A perform significant body-shaping effectivity at the calf, thigh and thigh root, which are less effective than the Controls. The body-shaping effectivity of GCSPs-B is predicted weaker than GCSPs-A at the calf and thigh, while better at the thigh root. Both GCSPs-A and GCSPs-B show gradual pressure, which could be classified into Class I or II of MCSs. Comprehensively, GCSPs-A are superior than GCSPs-B.

In this paper, authors evaluate the pressure distribution and body-shaping effectivity of GCSPs, which could provide guidance for enterprises to further optimize and produce GCSPs, performing better functions that meet consumers' needs better.

In order to study granularity, this paper aims to discuss how to construct granules from the view of panweighted field of pansystem.

By changing the panweights of panweighted field – subdivision of panweights, increase/decrease of panweights and reallocating panweights, to construct proper granules is the approach taken.

This paper provides a new method of studying granularity. If the weights of panweighted field are subdivided, then the granularity diminishes; if the panweights of panweighted field are increased, then the corresponding granularity diminished. Contrarily, the decrease of panweights of panweighted field results in the corresponding granularity increased; by reallocating panweights, use different method to construct different granules, such as compatible class, neighbor operator, compatible core, s‐s core and so on.

How to reallocate panweights is the main limitation.

A very useful advice for studying granularity.

This paper combines the granularity with panweighted field of pansystem, and studies granularity from the view of panweighted field of pansystems.

Discovering the research topics and trends from a large quantity of library electronic references is essential for scientific research. Current research of this kind mainly depends on human justification. The purpose of this paper is to demonstrate how to identify research topics and evolution in trends from library electronic references efficiently and effectively by employing automatic text analysis algorithms.

The authors used the latent Dirichlet allocation (LDA), a probabilistic generative topic model to extract the latent topic from the large quantity of research abstracts. Then, the authors conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics.

First, this paper discovers 32 significant research topics from the abstracts of 3,737 articles published in the six top accounting journals during the period of 1992-2014. Second, based on the document-topic distributions generated by LDA, the authors identified seven hot topics and six cold topics from the 32 topics.

The topics discovered by LDA are highly consistent with the topics identified by human experts, indicating the validity and effectiveness of the methodology. Therefore, this paper provides novel knowledge to the accounting literature and demonstrates a methodology and process for topic discovery with lower cost and higher efficiency than the current methods.

In novelty detection, the autoencoder based image reconstruction strategy is one of the mainstream solutions. The basic idea is that once the autoencoder is trained on normal data, it has a low reconstruction error on normal data. However, when faced with complex natural images, the conventional pixel-level reconstruction becomes poor and does not show the promising results. This paper aims to provide a new method for improving the performance of novelty detection based autoencoder.

To solve the problem that conventional pixel-level reconstruction cannot effectively extract the global semantic information of the image, a novel model with the combination of attention mechanism and self-supervised learning method is proposed. First, an auxiliary task, reconstruct rotated image, is set to enable the network to learn global semantic feature information. Then, the channel attention mechanism is introduced to perform adaptive feature refinement on the intermediate feature map to optimize the correspondingly passed feature map.

Experimental results on three public data sets show that the proposed method has potential performance for novelty detection.

This study explores the ability of self-supervised learning methods and attention mechanism to extract features on a single class of images. In this way, the performance of novelty detection can be improved.

Developing general closed-form solutions for six-degrees-of-freedom (DOF) serial robots is a significant challenge. This paper thus aims to present a general solution for six-DOF robots based on the product of exponentials model, which adapts to a class of robots satisfying the Pieper criterion with two parallel or intersecting axes among its first three axes.

The proposed solution can be represented as uniform expressions by using geometrical properties and a modified Paden–Kahan sub-problem, which mainly adopts the screw theory.

A simulation and experiments validated the correctness and effectiveness of the proposed method (general resolution for six-DOF robots based on the product of exponentials model).

The Rodrigues rotation formula is additionally used to turn the complex problem into a solvable trigonometric function and uniformly express six solutions using two formulas.

Due to high efficiency and low carbon of natural gas, the consumption of natural gas is increasing rapidly, and the prediction of natural gas consumption has become the focus. The purpose of this paper is to employ a prediction technique by combining grey prediction model and trigonometric residual modification for predicting average per capita natural gas consumption of households in China.

The GM(1,1) model is utilised to obtain the tendency term, then the generalised trigonometric model is used to catch the periodic phenomenon from the residual data of GM(1,1) model for improving predicting accuracy.

The case verified the view of Xie and Liu: “When the value of a is less, DGM model and GM(1,1) model can substitute each other.” The combination of the GM(1,1) and the trigonometric residual modification technique can observably improve the predicting accuracy of average per capita natural gas consumption of households in China. The mean absolute percentage errors of GM(1,1) model, DGM(1,1), unbiased grey forecasting model, and TGM model in ex post testing stage (from 2013 to 2015) are 32.5510, 33.5985, 36.9980, and 5.2996 per cent, respectively. The TGM model is suitable for the prediction of average per capita natural gas consumption of households in China.

According to the historical data of average per capita natural gas consumption of households in China, the authors construct GM(1,1) model, DGM(1,1) model, unbiased grey forecasting model, and GM(1,1) model with trigonometric residual modification. The accuracy of TGM is the best. TGM helps to improve the accuracy of GM(1,1).

This paper gives a successful practical application of grey model GM(1,1) with the trigonometric residual modification, where the cyclic variations exist in the residual series. The case demonstrates the effectiveness of trigonometric grey prediction model, which is helpful to understand the modeling mechanism of trigonometric grey prediction model.

This paper aims to study the interplay between a risk-averse national brand manufacturer's (NBM) selling mode decision and a risk-neutral e-platform's private brand (PB) introduction decision.

A game theory model is used to solve selling mode decision, that is whether transform the selling mode from the wholesale mode to the marketplace mode, and PB introduction decision, that is, whether introduce the PB.

The results show that for the NBM, under certain condition, the NBM's selling mode decision is not affected by the e-platform's PB introduction decision. High revenue-sharing rate is conducive only when the difference in consumer preference between the PB and the national brand (NB) is small. The NBM's risk aversion will improve the applicability of the marketplace mode. For the e-platform, high PB preference of consumers and risk-averse behavior of the NBM is not conducive to PB introduction. For the supply chain, scenarios that the NB monopolizes the market under the wholesale mode and PB introduction under the marketplace mode should be prevented. PB introduction under the wholesale mode will become the only equilibrium with the increase of risk aversion of the NBM. Finally, the authors extend the scenario that consumers prefer the PB and the e-platform is risk-averse enterprise and find that PB introduction under the wholesale mode is detrimental to the NBM but beneficial to the supply chain. The impact of consumers' PB preference on the e-platform's PB introduction is opposite to the basic model. The impact of the e-platform's risk aversion on game equilibrium is opposite to that of the NBM's risk aversion.

This paper is first to study selling mode decision and PB introduction decision when considering enterprises' risk-averse attitude.

Given the significance of innovation in enabling firms to maintain a long-term competitive edge and secure excess profits, this paper aims to investigate whether and how stakeholders’ attention to innovation (SATI) influences corporate innovation.

This paper introduces a novel variable, SATI, which is achieved by segmenting stakeholders’ attention into two categories: attention to innovation and attention to other facets, using textual analysis methods. Subsequently, this paper empirically examines the influence of SATI on corporate innovation.

This paper finds that SATI positively affects corporate innovation input, and the result remains true after addressing possible endogeneity issues using instrumental variable regression. Furthermore, the positive effect of SATI on corporate innovation is stronger in firms facing greater financing constraints, thus verifying the financing constraints hypothesis. The positive effect is also stronger in firms with lower risk-taking levels, thus confirming the innovation failure tolerance hypothesis. Further analysis suggests that SATI increases both corporate innovation output and efficiency, thus ruling out the catering hypothesis.

This paper highlights the importance of SATI in driving corporate innovation. It enriches the literature on the repercussions of stakeholders’ attention and determinants of corporate innovation. In addition, it provides practical suggestions for further implementing China’s national innovation-driven development strategy.

Because of the inconvenience and inflexibility of the laser controller, the applied range of optogenetics is limited. This paper aims to present the design of a portable remote-controlled laser controller system, including the remote-controlled system and the laser stimulator.

The remote-controlled system is handheld, which can wirelessly adjust the power and the emitting frequency of the laser by utilizing the ZigBee module.

The laser stimulator can be mounted on the animal as it is light weight (35 g) and small in size (40 × 40 × 20 mm), and its power and frequency can be appropriately adjusted by changing the current amplitude and duty radio. In the end, the experiments verify the reliability and effectiveness of the laser controller.

In virtue of the modular design of the driven circuit and the reasonable layout, the whole system has the advantages of small volume, convenient control and high stability, which provide the convenience for the development of portable optogenetics animal robot experiment and has broad market prospects.

The purpose of this paper is to design and implement a novel type of PCI eXtension for Instruments (PXI) bus‐based airborne data transfer equipment (DTE) test system.

First, the basic principle of PXI bus is introduced in detail. Then, the hardware and software are developed for the PXI bus‐based airborne DTE test system. Based on the description of the basic conceptions of rough set theory, a novel hybrid approach for fault diagnosis in PXI bus‐based airborne DTE test system is proposed, which is based on rough set theory, genetic algorithm and neural network. Combining with rough set theory, genetic algorithm is used to compute the reductions of the decision table. Subsequently, the condition attributes of decision table are regarded as the input nodes of neural network and the decision attributes are regarded as the output nodes of neural network correspondingly.

The exact application results are also presented to verify the feasibility and effectiveness of the developed PXI bus‐based airborne DTE test system, and the test results can also be saved automatically. The exact application results show that the various faults within the PXI bus‐based airborne DTE test system can be located on board level, and the newly developed airborne DTE test system is also easy to be extended and upgraded.

The proposed hybrid rough set theory, genetic algorithm and neural network approach could reduce the number of attributes in the decision table, simplify the structure of neural network and improve the ability of generality. The airborne DTE test system is also capable of different unit under test (UUT), which can be selected by the definite operators at the start of the test, to ensure that failures and problems are handled automatically and without intervention. This newly developed PXI bus‐based airborne DTE test system can be located on board level, and it is also very easy to be extended and upgraded. Practical implementations show that hidden errors can be effectively detected by the developed PXI bus‐based airborne DTE test system. The proposed methodology can help improve the general performance of the airborne DTE test system, and the faults can be checked with minimum time and effort. This system can enhance the army combat capability efficiently.

This paper develops a novel type of PXI bus‐based airborne DTE test system. In particular, a hybrid approach for fault diagnosis in PXI bus‐based airborne DTE test system is proposed, which is based on rough set theory, genetic algorithm and neural network. This approach provides an effective way to diagnosis the faults of the airborne DTE test system.