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This paper aims to describe a novel 3D finite element mesh modification method for motors with the skewed rotor in detail, the method having been developed by solving the Laplace equation.

Using the mesh of the skewed squirrel‐cage induction motor created by the novel method, the torque, the bar‐current, the electrical losses and so on are analyzed by the 3D finite element method.

It was found that the torque ripple, the bar‐current ripple and the losses of the motor with the skewed rotor are smaller than those of the motor with the no‐skewed rotor. In addition, the validity of the analysis is clarified by comparing the calculated and the measured results.

The usefulness of the method is clarified by the 3D finite element analysis of a skewed squirrel‐cage induction motor.

This study aims to expand the reliability and special functions of lightweight materials for high-end equipment and green manufacturing, so that it is the first such research to carry out nano-composite technology of nickel-coated carbon nanotubes (Ni-CNTs)-based titanium-zirconium chemical conversion on aluminum alloy substrate.

Corrosion behavior of various coatings was investigated using dropping corrosion test, linear polarization and electrochemical impedance spectroscopy. The results showed that the corrosion resistance of the nano-composite conversion coatings was significantly improved to compare with the conventional titanium-zirconium conversion coating. The morphology and microdomain characteristics of the nano-composite conversion coatings were characterized by SEM/eds/EPMA, which indicated that the CNT or Ni-CNTs addition promoting the integrity coverage of coatings in a short time.

Surface morphology of titanium-zirconium (Ti-Zr)/Ni-CNT specimens exhibited smooth, compact and little pores. The nano-composite conversion coatings are mainly composed of Al, O, C and Ti elements and contain a small amount of F and Zr elements, which illuminated that CNT or Ni-CNT addition could co-deposit with aluminum and titanium metal oxides.

The study of corrosion resistance of nano-composite conversion coatings and the micro-zone film-formation characteristics would be provided theoretical support for the development of basic research on surface treatment of aluminum alloys.

The expected benefits of newly developed transportation infrastructures are the saving of travel time and further promoted transport economics. There is a need for a methodology of travel time estimation with acceptable robustness and practicability. Macroscopic fundamental diagram (MFD) represents the overall traffic performance at a network level by linking average flow, speed and density. MFD can be used to estimate network state and to describe various traffic management strategies. This study aims to describe the effect of new infrastructure development on the network performance using the MFD framework.

The scenarios of Islamabad Road network before and after the infrastructure construction were simulated, in which the floating car data set (FCD) for multiple modes was extracted. MFD has been formed for the whole region and partitioned region, which was divided on the basis of infrastructural changes. Moreover, this study has been extended to calculate travel time for multiple modes using the MFD results and the Bureau of Public Roads (BPR) function at a neighborhood level.

MFD results for the whole network showed that the speed of traffic improves after the construction of new infrastructure. The travel time estimates using MFD results were dependent on the speed estimates, whereas the estimates obtained using the BPR function were found to be dependent on the traffic volume variation during different intervals of the day. By using the FCD for multiple modes, travel time estimates for multiple modes were obtained. The BPR function method was found valid for estimating travel time of traffic stream only.

This paper innovatively investigates the change in network performance for pre-construction and post-construction scenarios using the MFD framework. In practice, the approach presented can be used by transportation agencies to evaluate the effect of different traffic management strategies and infrastructural changes.

To maintain the bottom line of food import risk in China, this paper proposes a novel risk state evaluation model based on bottom-line thinking after analyzing the decision-making ideas embedded in the bottom-line thinking method.

First, the order relation analysis method (G1 method) and Laplacian score (LS) are applied to calculate the constant weights of indexes. Then, the worst-case scenario of food import risk can be estimated to strive for the best result, so the penalty state variable weight function is introduced to obtain variable weights of indexes. Finally, the study measures the risk state of China's food import from the overall situation using the set pair analysis (SPA) method and identifies the key factors affecting food import risk.

The risk states of food supply in eight countries are in the state of average potential and partial back potential as a whole. The results indicate that China's food import risks are at medium and upper-medium risk levels in most years, fluctuating slightly from 2010 to 2020. In addition, some factors are diagnosed as the primary control objects for holding the bottom line of food import risk in China, including food output level, food export capacity, bilateral relationship and political risk.

This paper proposes a novel risk state evaluation model following bottom-line thinking for food import risk in China. Besides, SPA is first applied to the risk evaluation of food import, expanding the application field of the SPA method.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.