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The authors develop a framework to build an early warning mechanism in detecting financial deterioration of Chinese companies. Many studies in the financial distress and bankruptcy prediction literature rarely do they examine the impact of pre-processing financial indicators on the prediction performance. The purpose of this paper is to address this shortcoming.

The proposed framework is evaluated by using both original and discretized data, and a least absolute shrinkage and selection operator (LASSO) selection technique for choosing an appropriate subset of financial ratios for improved predictive performance. The financial ratios are then analyzed by five different data mining techniques. Managerial insights, using data from Chinese companies, are revealed by the methodology employed.

The prediction accuracy increases after we discretized the continuous variables of financial ratios. A better prediction performance can be achieved by including fewer, but relatively more significant variables. Random forest has the highest overall performance following closely by SVM and neural network.

The contribution of this study is fourfold. First, the authors add to the literature on defaults by showing variable discretization to be an essential pre-processing step to improve the prediction performance for classification problems. Second, the authors demonstrate that machine learning approaches can achieve better performance than traditional statistical methods in classification tasks. Third, the authors provide the evidence for the adoption of C5.0 over other methods because rules generated with C5.0 provide managerial insights for managers. Finally, the authors demonstrate the effectiveness of the LASSO technique for identifying the most important financial ratios from each category, enabling one to build better predictive models.

The purpose of this paper is to draw on the theory of servant leadership to examine the influence of chief executive officer (CEO) servant leadership on firm performance in the hospitality industry. It examined the mediating role of the service climate and the moderating role of competitive intensity in the relationship between CEO servant leadership and firm performance.

Multiple regression was used to analyze multi-wave, multi-source data from 92 hotels in China. A moderated path analysis was used to test the moderating effects.

CEO servant leadership positively influenced firm performance via the service climate in the hospitality industry. Competitive intensity strengthened the direct effect of the service climate on firm performance, and the indirect effect of CEO servant leadership on firm performance via service climate.

The findings offer managerial insights into CEO succession, service management and human resource management.

This is one of the first studies to investigate how and when CEO servant leadership might shape firm outcomes in the hospitality industry. Theoretically, the findings enrich our understanding of how CEO leadership might shape firm-level outcomes.

Ensemble models that combine multiple base classifiers have been widely used to improve prediction performance in credit risk evaluation. However, an arbitrary selection of base classifiers is problematic. The purpose of this paper is to develop a framework for selecting base classifiers to improve the overall classification performance of an ensemble model.

In this study, selecting base classifiers is treated as a feature selection problem, where the output from a base classifier can be considered a feature. The proposed correlation-based classifier selection using the maximum information coefficient (MIC-CCS), a correlation-based classifier selection under the maximum information coefficient method, selects the features (classifiers) using nonlinear optimization programming, which seeks to optimize the relationship between the accuracy and diversity of base classifiers, based on MIC.

The empirical results show that ensemble models perform better than stand-alone ones, whereas the ensemble model based on MIC-CCS outperforms the ensemble models with unselected base classifiers and other ensemble models based on traditional forward and backward selection methods. Additionally, the classification performance of the ensemble model in which correlation is measured with MIC is better than that measured with the Pearson correlation coefficient.

The study provides an alternate solution to effectively select base classifiers that are significantly different, so that they can provide complementary information and, as these selected classifiers have good predictive capabilities, the classification performance of the ensemble model is improved.

This paper introduces MIC to the correlation-based selection process to better capture nonlinear and nonfunctional relationships in a complex credit data structure and construct a novel nonlinear programming model for base classifiers selection that has not been used in other studies.

This study aims to learn the dynamic radar cross-section (RCS) of a deflection air brake.

The aircraft model with delta wing, V-shaped tail and blended wing body is designed, and high-precision unstructured grid technology is used to deal with the surface of air brake and fuselage. The calculation method based on multiple tracking and dynamic scattering is presented to calculate RCS.

The fuselage has a low scattering level, and the opening air brake will bring obvious dynamic RCS effects to itself and the whole machine. The average indicator of air brake RCS can be lower than –0.6 dBm² under the tail azimuth, while that of forward and lateral direction is lower. The mean RCS of fuselage is obviously higher than that of air brake, while the deflected air brake and its cabin can still provide strong scattering sources at some azimuths. When the air brake is opening, the change amplitude of the aircraft forward RCS can exceed 19.81 dBm².

This research has practical significance for the dynamic electromagnetic scattering analysis and stealth design of the air brake.

The calculation method for aircraft RCS considering air brake dynamic deflection has been established.

This study aims to examine newcomers experiencing maladjustment due to cognitive diversity, specifically, how maladjustment affects their turnover intentions; the mediating role of reported workplace bullying; and the buffering effect of perceived inclusive practices in the hospitality sector.

The authors collected time-lagged data from 403 respondents and analyzed the data through hierarchical regression analyses using statistical package for the social sciences (SPSS) 25.0.

Role ambiguity, low self-efficacy and social exclusion could each lead to newcomers’ reported workplace bullying (NRB). Perceived inclusive practices buffered the impacts of role ambiguity and social exclusion. NRB negatively mediated the relationships between role ambiguity and NRB; and social exclusion and NRB.

Hospitality practitioners should specify work procedures to minimize role ambiguities and record service processes to correct mistakes, reward veterans who help newcomers improve self-efficacy, invite newcomers to develop inclusive practices and review employee comments on third-party platforms to understand factors responsible for turnover intention.

This study contextualized cognitive diversity into newcomers’ maladjustment-bullying-turnover model in China’s hospitality industry. It highlighted the buffering effect of perceived inclusive practices in the relationships between maladjustment and reported bullying and turnover intentions among newcomers and confirmed the important role of self-efficacy in addressing adverse work events.

This paper aims to discuss the electromagnetic scattering characteristics of the afterbody model with two drag plates.

The plane shape of the drag plate model is designed as a rectangle. High-precision unstructured grid technology is used to treat the target surface. A calculation method based on multiple tracking and dynamic scattering module is presented to calculate the radar cross section (RCS).

The results show that under the given observation conditions, the RCS and surface scattering characteristics of a single drag plate change with the increase of the opening angle, which makes the forward RCS of the afterbody model change more than 8.43 dBm2. The opening of two resistance plates at different fixed angles has little effect on the peak value and position of the RCS of the afterbody model. The dynamic deflection of the two drag plates can bring 16.78 dBm2 fluctuations to the forward RCS of the afterbody model, and more than 25.59 dBm2 fluctuations to the side RCS.

The installation positions of the drag plate on the aircraft are various, so the method in this paper can provide reference and support for RCS analysis of the speed brake at other positions.

The presented calculation method is of engineering value to analyze the electromagnetic scattering characteristics of the drag plate.

This study aims to study the radar cross-section (RCS) of an intermeshing rotor with blade pitch.

The variation of rotor blade pitch is designed into three modes: fixed mode, linear mode and smooth mode. The dynamic process of two crossed rotors is simulated, where the instantaneous RCS is calculated by physical optics and physical theory of diffraction.

Increasing the pitch angle in the fixed mode can reduce the average RCS of rotor at the given head azimuth. The RCS curve of helicopter in linear mode and smooth mode will have a large peak in the side direction at the given moment. Although the blade pitch in smooth mode is generally larger than that in fixed mode, the smooth mode is conducive to reducing the peak and mean value of helicopter RCS at the given heading azimuth.

The calculation method for analyzing RCS of intermeshing rotor with variable blade pitch is established.

The purpose of this paper is to identify a subset of key financial ratios and factors that provide the best discriminating power to distinguish between creditworthy companies (CWCs) and less creditworthy companies (LCWCs) in the USA with the proposed method.

A proposed framework of Bisection Method Based on Tabu Search + Support Vector Machines (BMTS + SVM) is used to select subset of financial ratios from a pool of candidate ratios. The selected ratios and their corresponding financial factors are considered as the key financial ratios and factors that provide the best discriminating power to distinguish between CWCs and LCWCs. The authors collected financial data for the US companies and then identify the key financial ratios and factors which the selected key financial ratios belong.

It is found that the four selected financial ratios from the proposed method and eight financial ratios which are used by Standard & Poor for their credit-rating system can be attributed to the same four financial factors, namely, cash flow factor, profitability factor, solvency factor and leverage factor. This result lends support that the proposed method can be applied to identify key financial factors to differentiate CWCs and LCWCs.

This study provides a tool for managers in financial institutions to gain better understanding about the credit risk of their applicants by focusing on a parsimonious model with fewer ratios in the key financial factors. In addition, companies that attempt to borrow money from financial institutions can also use these key financial ratios and factors as reference to attain clearer vision on what are the most important factors for being considered a creditworthy company and thus develop specific strategies to improve their financial performance.

Based on data analytic techniques, this paper identifies key financial ratios and factors for examining the creditworthiness of US companies with the proposed framework using BMTS + SVM method.

This paper aims to improve the general circuit of driving and protection based on insulated gate bipolar transistor (IGBT) in dielectric barrier discharge power supply by designing a novel half-bridge inverter circuit with discrete components.

With one SG3524 chip, the structure based on discrete components is used to design the IGBT drive circuit. The driving waveform is isolated and sent out by photo-coupler 6N137. The protection circuit is realized by Hall sensor directly detecting the main circuit current, supplemented by a few components, including diodes, resistors, capacitors and triodes. It improves the reliability of the protection circuit.

In the driving circuit, the phase difference of signals from two channels are 180°. Moreover, when the duty cycle is set at 40%, it can ensure sufficient pulse width modulation response time. In the protection circuit, when over-current occurs, an intermittent output signal is automatically sent out. Furthermore, the over-current response time can be controlled independently. The peak voltage can be adjusted continuously from 0 to 30 kV with its frequency from 8 to 25 kHz and the power output up to 150 W.

The novel circuit of driving and protection makes not only its structure simpler and easier to be realized but also key parameters, such as frequency, the duty cycle and the driving voltage, continuously adjustable. Moreover, the power supply is suitable for other discharges such as corona discharge and jet discharge.

The purpose of this paper is to reduce the acoustic noise of helicopter ducted tail rotor.

To predict the noise accurately, a thin-body boundary element method (BEM)/Ffowcs Williams–Hawkings method is developed in this paper. It is a hybrid method combining the BEM with computational aeroacoustics and can be used efficiently to predict the propagation of sound wave in the duct.

Compared with the experimental results, the proposed method of acoustic noise is rather desirable.

Then several geometry parameters are modified to investigate the noise reduction of ducted tail rotor by using the numerical prediction method.

The aerodynamic and acoustic performance of different modification tasks is discussed. These results demonstrate the validity of design parameters modification of ducted tail rotor in acoustic noise reduction.