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The sources of magnetic sensors errors are numerous, such as currents around, soft magnetic and hard magnetic materials and so on. The traditional methods mainly use explicit error models, and it is difficult to include all interference factors. This paper aims to present an implicit error model and studies its high-precision training method.

A multi-level extreme learning machine based on reverse tuning (MR-ELM) is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. To ensure the real-time performance of the algorithm, the network structure is fixed to two ELM levels, and the maximum number of levels and neurons will not be continuously increased. The parameters of MR-ELM are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time can still be guaranteed.

The results show that the training time of the MR-ELM is 19.65 s, which is about four times that of the fixed extreme learning algorithm, but training accuracy and generalization performance of the error model are better. The heading error is reduced from the pre-compensation ±2.5° to ±0.125°, and the root mean square error is 0.055°, which is about 0.46 times that of the fixed extreme learning algorithm.

MR-ELM is presented to compensate for magnetic compass measurement errors by increasing the depth of the network. In this case, the multi-level ELM network parameters are further modified by reverse tuning to ensure network accuracy. Because the parameters of the network have been basically determined by least squares, the number of iterations is far less than that in the traditional BP neural network, and the real-time training can still be guaranteed. The revised manuscript improved the ELM algorithm itself (referred to as MR-ELM) and bring new ideas to the peers in the magnetic compass error compensation field.

The three-axis magnetic sensors are mostly calibrated by scalar method such as ellipsoid fitting and so on, but these methods cannot completely determine the 12 parameters of the error model. A two-stage calibration method based on particle swarm optimization (TSC-PSO) is proposed, which makes full use of the amplitude invariance and direction invariance of Earth’s magnetic field vector.

The TSC-PSO designs two-stage fitness function. Stage 1: design a fitness function of the particle swarm by the amplitude invariance of the Earth’s magnetic field to obtain a preliminary error matrix G and the bias error B. Stage 2: further design the fitness function of the particle swarm by the invariance of the Earth’s magnetic field to obtain a rotation matrix R, thereby determining the error matrix uniquely.

The proposed TSC-PSO can completely determine 12 unknown parameters in error model and further decrease the maximum fluctuation error of the Earth’s magnetic field amplitude and the absolute error of heading.

The proposed TSC-PSO provides an effective solution for three-axis magnetic sensor error compensation, which can greatly reduce the price of magnetic sensors and be used in the fields of Earth’s magnetic survey, drilling and Earth’s magnetic integrated navigation.

The proposed TSC-PSO has significantly improved the magnetic field amplitude and heading accuracy and does not require additional heading reference. In addition, the method is insensitive to noise and initialization conditions, has good robustness and can converge to a global optimum.

This paper assesses how discretionary accruals (DAs) affect corporate cash savings policies and the motivation behind this cash saving behavior and, also whether the linkage between DAs and cash saving affect the market-perceived cash value.

We construct the measure of DAs using the previous five-year average information to investigate the association of DAs with the change in cash. Moreover, the Faulkender and Wang (2006) methodology is utilized to examine the market-perceived cash value in DAs.

The key finding is that firms with high DAs save significantly more cash. A one standard deviation increase in DAs saves cash by 12.59%. Furthermore, the value of cash is low for these firms. The effect is stronger in firms with poor governance but not present in financially constrained firms.

The empirical evidence highlights DAs have negative effect on market-perceived cash value, which underscores the insight that managers manage earnings opportunistically using DAs.

Taken together, we provide more evidence on the literature of accruals in earnings manipulation.

The purpose of this paper is to study the extensive method of grey target based on multi‐stage linguistic label, which is a hypothesis of incomplete information of the weight of stage and decision maker.

For the incomplete weight information case, the weight model for the stage and decision maker is put forward based on the requirement of maximum difference among the alternatives. Also, the weight of the decision maker is estimated by the grey relationship method and the Euclid distance method.

As a result, the method of multiple stage grey target decision making based on linguistic label is suggested. In the incomplete information case, the weight model is suggested and the aggregation is put forward. The suggested method is clear and simple, which can be used in multi‐criteria decision making fields.

This paper offers a very useful result for multi‐attribute decision making.

This paper succeeds in studying the extensive method of grey target based on multi‐stage linguistic label, which is a hypothesis of incomplete information of the weight of stage and the decision maker.

Prior studies have demonstrated the important role of coopetition in firms’ innovation. Based on the paradox perspective, this study aims to focus on technology transfer, the pre-innovation stage, to provide a supplementary understanding of the complementarity and contradictoriness of paradoxical coopetition, with the formal and informal governance mechanisms which are suitable with this understanding in coopetition.

This study conducted an original, multisource survey of 280 Chinese manufacturing firms. Hypotheses were tested through multiple regressions.

Coopetition has a positive impact on technology transfer between firms. Along with the increasing specificity of assets invested ex ante as a kind of formal governance mechanism, the relationship between coopetition and technology transfer becomes stronger. Meanwhile, inter-firm justice as an informal governance mechanism in the technology transfer process can be positively affected by coopetition between partners.

The study adds to the business-to-business coopetition literature on how to properly treat and use coopetition in technology transfer. Using the paradox perspective in the Chinese context, the findings emphasize the positive role of coopetition in the inter-firm technological exchange process, enriching the understanding of the complementary and contradictory features of paradoxical coopetition. To govern coopetitive relationships, the firms should also implement two fundamental governance mechanisms, that is, specialty asset and inter-firm justice.

From the atom economy and environmentally friendly point of views, the development of clean and green approaches using ionic liquids (ILs) as recyclable catalysts has attracted increasing attention. The purpose of this study is to investigate the effect of task-specific ILs content on the one-pot three-component Biginelli reaction.

A series of halogen-free quaternary ammonium ILs functionalized with –SO₃H group were prepared and characterized by ¹H nuclear magnetic resonance (NMR), ¹³C NMR and electrospray ionization mass spectrometry. The ILs were used as catalysts for Biginelli reaction among aromatic aldehydes, urea or thiourea and β-dicarbonyl compounds. Anions and cations of ILs were varied to observe their effects on and contributions to the catalysts. The influencing factors, such as the amount of catalyst, solvent, reaction time and reaction temperature, were investigated.

The effect and contribution of cations of ILs were observed. Results showed that 3-(N, N-dimethylhexadecylammonium) propanesulfonic acid toluene sulfate ([DHPA][Tos]) showed comparable catalytic activity. Good adaptability to the reaction substrate and maximum product yield was observed when [DHPA][Tos] was used as catalyst. It was found that Biginelli reaction catalyzed by 10 mol% [DHPA][Tos] for 3 h under solvent-free conditions at 80 °C gave the best yield of 94%. Post-processing steps were simple, and the catalyst could be reused easily.

This paper demonstrates that ILs containing a long carbon chain and a bulky Tos anion efficiently promoted the reaction, in which the long carbon chains facilitate mass transfer in the reaction system.

This study constructs a three-stage evaluation model for interdisciplinary organizations to solve their ranking problems effectively.

A three-stage interdisciplinary organization evaluation model abounds the key problems of “who will evaluate the projects?” and “how to evaluate the projects?”. In the first stage, the authors build a consensus maximization model to identify the selected experts based on the interval grey number because of the uncertainty in assessment. In the second stage, considering the reliability of the experts, the authors calculate the reliability of the experts based on historical data. Meanwhile, considering the gradual changes of the experts, the dynamic weighting method is obtained based on the clustering method. In the third stage, considering decision-makers regret psychological behavior, the authors construct a cross-organizational performance evaluation model based on consensus expectations.

First, for selecting the experts responsible for assessing interdisciplinary organizations, the consensus-reaching method can effectively avoid cognitive bias. Second, during the assessment, the authors obtained more reasonable results by considering the psychological changes of experts based on regret theory. Third, based on the results, the cross-organization of colleges focused on the achievements of talent training, cross effects, and system construction.

Our study could help organizations establish a suitable assessment mechanism and promote interdisciplinary development.

First, considering the importance of selecting the experts, the authors use the consensus-reaching process for expert selection. This method could guarantee most experts' preferences. Then, the authors propose a two-stage dynamic weighting method, including a pre-determined and adjusted process. The dynamic method can better perform the preferences of experts. Third, the authors studied the assessment in interdiscipline. In addition, based on the framework and considering the features of the interdiscipline, the authors use the grey number to perform the uncertain preferences of the experts using regret theory.