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The purpose of this paper is to investigate the wear resistance behavior of the striated tool for cross wedge rolling (CWR).

A mechanical-thermal coupled, temperature-dependent FE wear model was developed to explore the wear behaviors for striated CWR tools. To verify the proposed FE model, a newly developed measuring device was also developed to measure wear on the tool ridge. To find the impact order of the parameters of striate unit, orthogonal experiment was carried out.

The experimental and numerical results both indicate that the wear resistance of striated tool is better than that of smooth tool. Minimum tool ridge wear can be achieved by choosing proper tool contact temperature with striated units on crossed ridge. The order of the striation geometrical factors’ impact on ridge wear is striation width > striation interval > striation length.

Because of the specified tool, the research results may lack generalizability. Therefore, researchers are encouraged to test the proposed propositions further.

It is shown that the wear resistance of striated CWR tool is better than that of smooth tool. The information may help CWR manufactures to design and produce tools with less wear.

This study aims to present an automated guided logistics robot mainly designed for pallet transportation. Logistics robot is compactly designed. It could pick up the pallet precisely and transport the pallet up to 1,000 kg automatically in the warehouse. It could move freely in all directions without turning the chassis. It could work without any additional infrastructure based on laser navigation system proposed in this work.

Logistics robot should be able to move underneath and lift up the pallet accurately. Logistics robot mainly consists of two sub-robots, like two forks of the forklift. Each sub-robot has front and rear driving units. A new compact driving unit is compactly designed as a key component to ensure access to the narrow free entry of the pallet. Besides synchronous motions in all directions, the two sub-robots should also perform synchronous lifting up and laying down the pallet. Logistics robot uses a front laser to detect obstacles and locate itself using on-board navigation system. A rear laser is used to recognize and guide the sub-robots to pick up the pallet precisely within ± 5mm/1o in x-/yaw direction. Path planning algorithm under different constraints is proposed for logistics robot to obey the traffic rules of pallet logistics.

Compared with the traditional forklift vehicles, logistics robot has the advantages of more compact structure and higher expandability. It can realize the omnidirectional movement flexibly without turning the chassis and take zero-radius turn by controlling compact driving units synchronously. Logistics robot can move collision-free into any pallet that has not been precisely placed. It can plan the paths for returning to charge station and charge automatically. So it can work uninterruptedly for 7 × 24 h. Path planning algorithm proposed can avoid traffic congestion and improve the passability of the narrow roads to improve logistics efficiencies. Logistics robot is quite suitable for the standardized logistics factory with small working space.

This is a new innovation for pallet transportation vehicle to improve logistics automation.

This paper aims to examine how using lattice Boltzmann method (LBM) aids the study of the isothermal‐gas flow with slight rarefaction in long microtubes.

A revised axisymmetric lattice Boltzmann model is proposed to simulate the flow in microtubes. The wall boundary condition combining the bounce‐back and specular‐reflection schemes is used to capture the slip velocity on the wall. Appropriate relation between the Knudsen number and relax‐time constant is defined.

The computed‐slip velocity, average velocity and non‐linear pressure distribution along the microtube are in excellent agreement with analytical solution of the weakly compressible Navier‐Stokes equations. The calculated‐friction factors are also consistent with available experimental data. For simulations of slip flow in microtube, LBM is more accurate and efficient than DSMC method.

The laminar flow in circular microtube is assumed to be axisymmetric. The present LBM is only applied to the simulation of slip flows (0.01 < Kn₀<0.1) in microtube.

Lattice‐BGK method is a very useful tool to investigate the micro slip flows.

A revised axisymmetric D2Q9 lattice Boltzmann model is proposed to simulate the slip flow in axisymmetric microtubes.

The aim of the present study is to evaluate the accuracy and efficiency of Lattice‐BGK (LBGK) method application in simulation of the 3D flow through complex geometry. On the other hand, the steady flows through vascular tube with Reynolds number 10‐150 and different constriction spacing ratios are simulated.

The numerical method is based on the LGBK method with an incompressible D3Q19 model. To treat the curved boundary, the “bounce back” scheme combined with spatial interpolation of second order is applied.

The highly axisymmetric property in the direct 3D tube flow simulation is observed. Solutions obtained from LBGK method are quite consistent with that of finite volume method (FVM). The overall order of accuracy of these LBGK solutions is about 1.89. The LBGK incompressible D3Q19 model with the curved boundary treatment can handle the problems of 3D steady flow through complex geometry.

Investigating the flow in constricted vascular tubes with different stenose shape and higher Reynolds number is left for future work.

Lattice BGK method is the very useful tool to investigate the steady vascular flow.

Applying LBGK method with incompressible D3Q19 model to simulate the steady flow through complex geometry. The accuracy and efficiency of the present LBGK solver are examined.

The purpose of this paper is to apply the lattice Boltzmann method (LBM) to simulate mixed flow, which combines natural convection for temperature difference and forced convection for lid driven, in a two‐dimensional rectangular cavity over a wide range of aspect ratios (A), Rayleigh numbers (Ra) and Reynolds numbers (Re).

The LBM is applied to simulate the mixed flow. A multi‐relaxation technique was used successfully. A scale order analysis helped the understanding and predicting the overall heat transfer.

In the considered lid driven cavity, the Richardson number emerges as a measure of relative importance of natural and forced convection modes on the heat transfer. An expression of the overall heat transfer depending on the cavity slender (A) is deduced. The validity of the obtained expression was checked in mixed convection under the condition of low Richardson number (Ri) and the limitation condition was deduced.

This paper has implications for cooling system optimization and LBM technique development.

This paper presents a new cooling configuration, avoiding critical situation where the opposing effect induce weak heat transfer; and a stable and fast LBM approach allowing complex geometry treatment.

Based on the person-environment (P-E) fit theory, this study aims to explore the effect of customer mistreatment on the reduced service performance of hospitality employees mediated by person-job (P-J) fit perceptions and moderated by job crafting.

The authors tested this study’s hypotheses with a nine-day diary study involving 83 service employees located in Lanzhou, China; a total of 548 daily surveys were completed. The authors used multilevel structural equation modeling to analyze the data.

Employees who experienced daily customer mistreatment suffered diminished P-J fit perceptions, leading to lower levels of service performance the next day. In addition, job crafting significantly buffered the impact of customer mistreatment on P-J fit perceptions and the indirect impact of customer mistreatment on service performance through P-J fit perceptions.

Given the damaging effect that customer mistreatment has on service performance, where employees’ P-J fit perceptions are impaired, hotel managers should implement service competence improvement training programs and managerial preventions to reduce the possibility of customer mistreatment behavior. The moderating role of job crafting behavior suggests that managers should offer supportive practices (i.e. job autonomy) to encourage job crafting behaviors among employees.

This study reveals that individuals’ P-J fit perceptions can explain the damaging impacts of customer mistreatment on service performance, a finding that contributes valuable information to the literature on customer mistreatment and P-E fit. Second, this study also tests the impact of individuals’ job crafting behaviors in terms of mitigating the negative effect of customer mistreatment. Finally, this study’s findings broaden the scope of predictors of P-J fit perceptions by revealing that customer mistreatment can pose a threat to hospitality employees’ P-J fit perceptions.

To propose a new investment-income valuation model by real options approach (ROA) for old community renewal (OCR) projects, which could help the government attract private capital's participation.

The new model is proposed by identifying the types of options private capital has in the OCR project, selecting the option model most suitable for private capital investment decisions, improving the valuation model through the triangular fuzzy numbers to take into account the uncertainty and flexibility, and demonstrating the feasibility of the calculation model through an actual OCR project case.

The new model can valuate OCR projects more accurately based on considering uncertainty and flexibility, compared with conventional methods that often underestimate the value of OCR projects.

The investment-income of OCR projects shall be re-valuated from the lens of real options, which could help reveal more real benefits beyond the capital growth of OCR projects, enable the government to attract private capital's investment in OCR, and alleviate government fiscal pressure.

The proposed OCR-oriented investment-income valuation model systematically analyzes the applicability of real option value (ROV) to OCR projects, innovatively integrates the ROV and the net present value (NPV) as expanded net present value (ENPV), and accurately evaluate real benefits in comparison with existing models. Furthermore, the newly proposed model holds the potential to be transferred to various social welfare projects as a tool to attract private capital's participation.

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 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.

The purpose of this paper is to improve control performance and safety of a real two-wheeled inverted pendulum (TWIP) robot by dealing with model uncertainty and motion restriction simultaneously, which can be extended to other TWIP robotic systems.

The inequality of lumped model uncertainty boundary is derived from original TWIP dynamics. Several motion restriction conditions are derived considering zero dynamics, centripedal force, ground friction condition, posture stability, control torque limitation and so on. Sliding-mode control (SMC) and model predictive control (MPC) are separately adopted to design controllers for longitudinal and rotational motion, while taking model uncertainty into account. The reference value of the moving velocity and acceleration, delivered to the designed controller, should be restricted in a specified range, limited by motion restrictions, to keep safe.

The cancelation of model uncertainty commonly existing in real system can improve control performance. The motion commands play an important role in maintaining safety and reliability of TWIP, which can be ensured by the proposed motion restriction to avoid potential movement failure, such as slipping, lateral tipping over because of turning and large fluctuation of body.

An inequation of lumped model uncertainty boundary incorporating comprehensive errors and uncertainties of system is derived and elaborately calculated to determine the switching coefficients of SMC. The motion restrictions for TWIP robot moving in 3D are derived and used to impose constraints on reference trajectory to avoid possible instability or failure of movement.