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The TC4 (Ti-6Al-4V) alloy has problems such as low material hardness, poor wear resistance, and abnormal sensitivity to adhesive wear and fretting wear. In this study, we used graphene-reinforced Ti/BN composite coatings prepared on the surface of the TC4 alloy by argon arc cladding technology. We explored the optimal content of graphene to improve its hardness and wear resistance. The physical phases and microstructures of the coatings were analyzed using an X-ray diffractometer, metallurgical microscope, and scanning electron microscope. Microhardness and wear properties of the cladding coating were measured by a Vickers hardness tester and a universal friction and wear tester. The incorporation of graphene resulted in a transformation of the reinforcing phase in the coating from TiN to Ti(N, C). The C element in the molten pool was substituted with the N element in an unending solid solution, resulting in the formation of Ti(N, C) through intermittent nucleation. As the amount of graphene in the molten pool increases, the concentration of carbon (C) also increases. This leads to the continuous growth of Ti(N, C) particles, resulting in a coarser coating structure and a decrease in coating performance. When the graphene content is 5 wt.%, the microstructure refinement of the coating is the most obvious, the microhardness is 900 HV0.2, which is 3 times higher than that of the matrix, and the wear rate is 4.9 × 10−5 mm3/(N·m), which is 4.9 times higher than that of the matrix. The wear mechanism of the coating is primarily abrasive wear with some slight adhesive wear.

Complex data pose unique challenges for data processing [...]

This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Classical methods are labour-intensive, and, hence, expensive in terms of healthcare resources. Recent research supplements classical methods with automated mammogram analysis. The basic FCM method relies upon the Euclidean distance, which is not optimal for measuring non-spherical structures. To address these limitations, we review the implementation of a Mahalanobis-distance-based FCM (FCM-M). The three objectives of the paper are: (1) review FCM, FCM-M, and three centroid initialization algorithms in the literature, (2) illustrate the effectiveness of these algorithms in image segmentation, and (3) develop a Python package with the optimized algorithms to upload onto GitHub. Image analysis of the algorithms shows that using one of the three centroid initialization algorithms enhances the performance of FCM. FCM-M produced higher clustering accuracy and outlined the tumour structure better than basic FCM.

The dilemma of aging and lack of successors must be understood to improve the scale efficiency and competitiveness of China’s agriculture. This paper uses a survey of 1347 farmers in Liaoning, Jiangsu, and Jiangxi and applies tobit and probit models to explore the impact of intergenerational inheritance on farmland management in terms of the current scale and willingness for expansion. The results show that (1) an increased probability of intergenerational inheritance in agriculture can significantly increase the scale of farmland operations, with a greater effect on the current scale than on the willingness to expand; (2) the scale upgrading effect of agricultural intergenerational inheritance is greater in regions with frequent nonagricultural activities and in families with middle or low-scale farmland operations; and (3) the promotion effect on the current scale is greater for elderly farmers 60 years old or above than for farmers who are 40–59 years old, while the promotion effect on the willingness for expansion exists only for the latter. Therefore, policies should attract young, skilled laborers to return to their hometowns for agricultural employment and entrepreneurship and support farmland transfer and scale operation in regions with frequent nonagricultural activities or a lower scale of agricultural operations.

In this paper, we focus on the homogeneity test that evaluates whether two multivariate samples come from the same distribution. This problem arises naturally in various applications, and there are many methods available in the literature. Based on data depth, several tests have been proposed for this problem but they may not be very powerful. In light of the recent development of data depth as an important measure in quality assurance, we propose two new test statistics for the multivariate two-sample homogeneity test. The proposed test statistics have the same χ2(1) asymptotic null distribution. The generalization of the proposed tests into the multivariate multisample situation is discussed as well. Simulations studies demonstrate the superior performance of the proposed tests. The test procedure is illustrated through two real data examples.

In recent years, the Poisson lognormal mixed model has been frequently used in modeling count data because it can accommodate both the over-dispersion of the data and the existence of within-subject correlation. Since the likelihood function of this model is expressed in terms of an intractable integral, estimating the parameters and obtaining inference for the parameters are challenging problems. Some approximation procedures have been proposed in the literature; however, they are computationally intensive. Moreover, the existing studies of approximate parameter inference using the Gaussian variational approximation method are usually restricted to models with only one predictor. In this paper, we consider the Poisson lognormal mixed model with more than one predictor. By extending the Gaussian variational approximation method, we derive explicit forms for the estimators of the parameters and examine their properties, including the asymptotic distributions of the estimators of the parameters. Accurate inference for the parameters is also obtained. A real-life example demonstrates the applicability of the proposed method, and simulation studies illustrate the accuracy of the proposed method.

A change point is a location or time at which observations or data obey two different models: before and after. In real problems, we may know some prior information about the location of the change point, say at the right or left tail of the sequence. How does one incorporate the prior information into the current cumulative sum (CUSUM) statistics? We propose a new class of weighted CUSUM statistics with three different types of quadratic weights accounting for different prior positions of the change points. One interpretation of the weights is the mean duration in a random walk. Under the normal model with known variance, the exact distributions of these statistics are explicitly expressed in terms of eigenvalues. Theoretical results about the explicit difference of the distributions are valuable. The expansions of asymptotic distributions are compared with the expansion of the limit distributions of the Cramér-von Mises statistic and the Anderson and Darling statistic. We provide some extensions from independent normal responses to more interesting models, such as graphical models, the mixture of normals, Poisson, and weakly dependent models. Simulations suggest that the proposed test statistics have better power than the graph-based statistics. We illustrate their application to a detection problem with video data.

Disputes over farmland constitute an important challenge for tenure security, economic growth and social stability. Land titling is a theoretically promising policy instrument that can enhance tenure security and reduce the occurrence of farmland disputes in the developing world. However, the impact of land titling on the occurrence of disputes has been found to be highly conditional. Empirical evidence on this issue has been surprisingly limited and has often lacked the consideration of a specific context. In this study, whether land titling affects the incidence of farmland disputes in the context of China is investigated, focusing particularly on the interaction between land titling and social capital and household capability. Both the probit and CLL models are applied, using household data entailing a total of 3693 samples located in rural China. The results show that (1) land titling in China reduces the incidence of farmland disputes, and that (2) the analysis based on interaction terms indicates that households who are disadvantaged in social capital and household capability are more likely to experience fewer disputes. Implications for the government and organizations are as follows: (1) the titling programme should seriously consider the current tenure arrangement. Confirmation of current tenure is recommended over the establishment of a new system to avoid possible institutional shopping and overlapping claims; and (2) land titling, if well implemented, is recommended to redress local power asymmetry and to help vulnerable groups defend their property.

To reduce the amount of computation in traditional model predictive current control, to improve the flexibility in choosing the direction and amplitude in the voltage vector synthesis of a dual three-phase motor by two degrees of freedom, and to reduce the output torque ripple and current ripple, this paper proposes a dual second-order model predictive control algorithm based on current loop operation optimization. Compared with the conventional speed loop using the PI control algorithm and the traditional MPC control algorithm, the proposed algorithm adopts the second-order MPC control mode in the speed loop, which decreases the speed regulation time and increases motor immunity. Meanwhile, the second-order MPC control mode is adopted in the current loop, and the traditional iterative calculation method is improved to calculate the direction and amplitude of the output voltage vector through the analytic function, which increases the flexibility of the output voltage vector. Additionally, a four-vector SVPWM is employed to modulate the voltage vector to reduce the current amplitude in the harmonic subspace. The simulation results indicate that the algorithm proposed in this paper can significantly reduce the torque ripple and the current ripple as well as increase the transient performance of the motor.

To limit the high-frequency chattering originating from the conventional sliding mode observer in a PMSM sensorless control system of vacuum pumps and reduce the steady-state error caused by the phase lag of the coordinate system on the estimated speed, a speed estimation algorithm was proposed based on the super-twisting sliding mode observer. Then, the proposed algorithm was applied to observe an electromotive force. To this end, the Lyapunov function was established to analyze the stability of the current dynamic error equation. In this regard, the estimated phase lag of the rotating coordinate system was considered to study the influence of the speed estimation error. Moreover, a speed estimation algorithm considering the d-axis estimation of back electromotive force is proposed. The performed analyses show that the speed estimation algorithm based on a super-twisting sliding mode observer has promising estimation accuracy. It is concluded that the proposed algorithm can be effectively applied to suppress high-frequency chattering in the system, reduce the influence of phase lag in the estimation coordinate system on the speed estimation error, remove the low-pass filter and phase compensation module, and improve the accuracy of speed estimation.

Medical data are often missing during epidemiological surveys and clinical trials. In this paper, we propose the MCMCINLA estimation method to account for missing data. We introduce a new latent class into the spatial lag model (SLM) and use a conditional autoregressive specification (CAR) spatial model-based approach to impute missing values, making the model fit into the integrated nested Laplace approximation (INLA) framework. Combining the advantages of both the Markov chain Monte Carlo (MCMC) and INLA frameworks, the MCMCINLA algorithm is used to implement imputation of the missing data and fit the model to derive estimates of the parameters from the posterior margins. Finally, the economic data and the hemorrhagic fever with renal syndrome (HFRS) disease data of mainland China from 2016–2018 are used as examples to explore the development of public health in China in the post-epidemic era. The results show that compared with expectation maximization (EM) and full information maximum likelihood estimation (FIML), the predicted values of the missing data obtained using our method are closer to the true values, and the spatial distribution of HFRS in China can be inferred from the imputation results with a southern-heavy and northern-light distribution. It can provide some references for the development of public health in China in the post-epidemic era.