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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.

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 magnetometer measurement update plays a key role in correcting yaw estimation in fusion algorithms, and hence, the yaw estimation is vulnerable to magnetic disturbances. The purpose of this study is to improve the ability of the fusion algorithm to deal with magnetic disturbances.

In this paper, an adaptive measurement equation based on vehicle status is derived, which can constrain the yaw estimation from drifting when vehicle is running straight. Using this new measurement, a Kalman filter-based fusion algorithm is constructed, and its performance is evaluated experimentally.

The experiments results demonstrate that the new measurement update works as an effective supplement to the magnetometer measurement update in the present of magnetic disturbances, and the proposed fusion algorithm has better yaw estimation accuracy than the conventional algorithm.

The paper proposes a new adaptive measurement equation for yaw estimation based on vehicle status. And, using this measurement, the fusion algorithm can not only reduce the weight of disturbed sensor measurement but also utilize the character of vehicle running to deal with magnetic disturbances. This strategy can also be used in other orientation estimation fields.

Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.

This paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.

The research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.

Unlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

Financial services industry is increasingly showing interest in automated financial advisors, or robo-advisors, with the aim of democratizing access to financial advice and stimulating investment behavior among populations that were previously less active and less served. However, the extent to which consumers trust this technology influences the adoption of rob-advisors. The resemblance to a human, or anthropomorphism, can provide a sense of social presence and increase trust.

In this paper, we conduct an experiment (N = 223) to test the effect of anthropomorphism (low vs medium vs high) and gender (male vs female) of the robo-advisor on social presence. This perception, in turn, enables consumers to evaluate personality characteristics of the robo-advisor, such as competence, warmth, and persuasiveness, all of which are related to trust in the robo-advisor. We separately conduct an experimental study (N = 206) testing the effect of gender neutrality on consumer responses to robo-advisory anthropomorphism.

Our results show that consumers prefer human-alike robo-advisors over machinelike or humanoid robo-advisors. This preference is only observed for male robo-advisors and is explained by perceived competence and perceived persuasiveness. Furthermore, highlighting gender neutrality undermines the positive effect of robo-advisor anthropomorphism on trust.

We contribute to the body of knowledge on robo-advisor design by showing the effect of robot’s anthropomorphism and gender on consumer perceptions and trust. Consequently, we offer insightful recommendations to promote the adoption of robo-advisory services in the financial sector.

The perseverative cognition framework suggests that observing ostracism has negative implications for observers due to affective rumination and that a proactive personality might make observers more vulnerable to this effect.

Data from 49 team leaders and 218 team members were obtained through a three-wave survey in China. Path analysis was used to examine the theoretical model.

The results indicate that observing ostracism increased turnover intention and reduced task performance and that these relationships were mediated by affective rumination. Furthermore, these effects were stronger for observers with high proactive personality.

Workplace ostracism harms employees; however, its effects on observers remain underexplored. This paper extends research on the effects of ostracism by revealing that ostracism is not only harmful to the well-being of its victims but also adversely affects the work-related attitudes and behaviors of observers, especially those with proactive personality.

Organizations should be aware of the harmful effects of workplace ostracism on observers, and take actions to inhibit workplace ostracism as well as reduce the negatives impacts.

The results reveal the cognitive mechanism of affective rumination, in which observing workplace ostracism affects observers' behaviors and attitudes, highlighting the importance of observing effect of workplace ostracism.

The purpose of this paper is to study the antirust, tribological performance and anti-wear (AW) mechanism of the of soybean lecithin (SL) as a kind of multifunctional lubricant additive.

As a kind of multifunctional lubricant additive, the antirust performance of SL was tested according to ASTM D 665, and meanwhile, its tribological performances were also evaluated by Optimol SRV-I oscillating reciprocating friction and wear tester and four ball tester. The worn steel surfaces were investigated by scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS).

The results showed that the SL exhibited excellent antirust properties in different base stock, and could effectively improve the AW and extreme pressure (EP) performances. The results of SEM and XPS indicated that a protective film was formed between steel-steel friction pair during the tribological test.

This paper first investigated the antirust properties and the tribological mechanism of the SL as a kind of multifunctional lubricant additive, which can be very useful and will promote the application of SL in lubricant industry.

The economic reforms which turned the centrally planned economy to a market economy have profoundly changed the tripartite relationship between the state, work unit, and citizen in urban China and brought significant changes to the institutional care provision for young children. The aim of this paper is to investigate the changes to the institutional care since 1980, with particular emphasis on the most recent years from mid‐1990s, and explore how the institutional care has changed over the recent decades without a clear institutional basis.

The analysis draws on second‐hand materials from published literature, a range of longitudinal national and local statistics and policy documents, and also on first‐hand information which was collected in Beijing from in‐depth interviews with key informants and case studies of different kinds of kindergartens.

The paper finds that the previous work‐unit based public care system has changed to a much more complicated care mix in which the roles of the state, employer, community, market and the informal sector of the family in terms of provision and funding have all changed significantly.

The findings of this paper may help to inform appropriate policy responses in Chinese child care provision. The study suggests that formal care provision should be expanded towards universal access regardless of people's income and employment status in China.

The paper questions and complicates the “state withdrawal” representation of social welfare change and argues that it is not “the state” but “the work unit and community organization” retreat from public care provision. It also argues that the change in the role of the state has been multifaceted, and not a simple one‐directional movement of marketization in which the state retreated from welfare provision in entirety.

Efficiency of a commercial bank affects both its competitiveness and the role it plays in the process of economic development. Although great efforts have been exerted in developing the various aspects of banking efficiency, there seems to be a lack of research on examining the impact of the bank efficiency from the employee wage perspective. The mechanism of how employee wage affects commercial bank efficiency and the relationship between the two were analyzed in this paper. Based on the growing body of research on efficiency in banking, the aim of this paper is to examine if competitiveness of employee wages at any commercial bank has any impact on the bank efficiency score.

The method used was quantitative analysis, which was based on comparing the evaluated efficiencies of the banks with employee wages published in the bank reports. The empirical data in this paper were based on 16 Chinese listed commercial banks from 2004 to 2012. The per capita wage of commercial banks was selected as the wage indicator, and the efficiency value obtained by the slack-based measure (SBM) model was selected as the efficiency indicator. According to the calculated data, the Tobit regression model was built to analyze the relationship between employee wage and commercial bank efficiency.

The research results show that employee wage is the key variable that influences the efficiency of Chinese commercial banks, and the inverted U-shaped relationship between employee wage and commercial banks efficiency shows up.

The wage structure data of the composition of basic pay and bonus were not available at the time of conducting the research. Per capita wages were used instead to reflect the employee wage levels of Chinese banks.

This study can provide some help for the banking industry by analyzing the wage levels from the perspective of efficiency and also further enriches the theoretical system of the relationship between employee wage and bank efficiency.

Asia's traditional experiences with care provision differ considerably from those of the West given the prevalent family‐based social norms about care and policies in this region. In recent decades, Asia has experienced profound social and demographic transformations and is thus faced with significant challenges around care. However, care in Asian countries is a relatively less studied topic. There is an urgent need for a comparative study on recent policy and practice changes in care for the elderly and young children in different regions of Asia. The purpose of this special issue is to examine complicated boundary shift in care provision and financing between the state, market, community and family in East, Southeast and South Asia and to explore the implications of these changes in care policies and practices for social stratification by class and gender in Asia.

The introduction to this special issue gives an overview of the social and demographic transformations and new strains on care in Asia as a background and introduces the framework of welfare mix employed in this special issue, especially the concepts of social care and the welfare/care diamond.

The introduction summarizes the variations in regard to the governance and provision of care between different Asian countries and compares the differences in the state involvement between Asia and Europe.

The authors also discuss some of their contributions to methodological approaches and analytical frameworks in studying care and the implications of the current research for future studies.