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High-throughput phenotype monitoring systems for field crops can not only accelerate the breeding process but also provide important data support for precision agricultural monitoring. Traditional phenotype monitoring methods for field crops relying on artificial sampling and measurement have some disadvantages including low efficiency, strong subjectivity, and single characteristics. To solve these problems, the rapid monitoring, acquisition, and analysis of phenotyping information of field crops have become the focus of current research. The research explores the systematic framing of phenotype monitoring systems for field crops. Focusing on four aspects, namely phenotyping sensors, mobile platforms, control systems, and phenotyping data preprocessing algorithms, the application of the sensor technology, structural design technology of mobile carriers, intelligent control technology, and data processing algorithms to phenotype monitoring systems was assessed. The research status of multi-scale phenotype monitoring products was summarized, and the merits and demerits of various phenotype monitoring systems for field crops in application were discussed. In the meantime, development trends related to phenotype monitoring systems for field crops in aspects including sensor integration, platform optimization, standard unification, and algorithm improvement were proposed. [ABSTRACT FROM AUTHOR]

Escherichia coli (E. coli) is an opportunistic pathogen that can cause clinical mastitis in dairy cows worldwide. Mastitis produces severe symptoms in dairy cows, such as udder inflammation, the production of harmful substances, reduced milk production, and altered milk quality. Intramammary injections of rifaximin have a beneficial effect on dairy cow mastitis, especially for mastitis caused by E. coli. However, we do not know whether the currently accepted clinical administration scheme is reasonable. Therefore, the purpose of this experiment was to evaluate the clinical dosing regimen for curing mastitis induced by E. coli. In this study, the pharmacokinetics of four single dose groups (50, 100, 200, and 400 µg/gland) were studied in CD-1 lactating mice, and the main pharmacokinetic parameters were obtained by non-compartment and two-compartment model of Phoenix 8.1 software. A total of 5,000 colony-forming units (CFU) of E. coli ATCC25922 were injected into the mammary glands of mice under anatomic microscope guidance. After 12 h of growth in vivo, the mouse mastitis model was successfully developed. In pharmacodynamics experiment, 12 different dosing regimens (doses ranged from 25 to 800 µg/gland and two dosing intervals of 12 and 24 h) were used to study the therapeutic potential of rifaximin for mastitis. The PK/PD model was established by integrating pharmacokinetics and pharmacodynamics using the inhibitory sigmoid Emax model. The optimal antibacterial effect was 2log10CFU/gland reduction of bacterial colony counts in vivo, when the magnitude of AUC24/MIC exceeded 57.80 h. A total of 57.80 h of AUC24/MIC was defined as a target value in the Monte Carlo simulation. The clinically recommended dosage regimen of 100 mg/gland every 12 h in a day achieved a 91.08% cure rate for the treatment of bovine mastitis caused by E. coli infection. [ABSTRACT FROM AUTHOR]

This study aims to prove the intercepting effectiveness of the new active interception net. According to the timing sequence of intercepting the incoming missile body by the new interception net, the external ballistic equations of the incoming missile body and the interception net are established, respectively, and then the mathematical probability model conforming to the physical characteristics of the interception process is constructed. The process of intercepting the incoming missile by intercepting net is simulated 1000 times by Monte Carlo method on the basis of considering the random parameters such as route shortcut, dive angle, and incoming velocity of the incoming missile. The simulation results under different intercept conditions, including the optimal intercept angle, the power of the payload warhead, and the probability of intercept damage, are analyzed. Through analysis, it is found that with the cooperation of multiple interception nets, the probability of interception and damage to the incoming target can be significantly improved. When the velocity of the incoming missile body is up to 900 m/s, the probability of damage is over 65%. The research content theoretically verifies the effectiveness of ultra-short-range interception of the new active interception net. In order to further verify the reliability of interception, a new interception net was carried out to verify the interception of the incoming missile body. Through the analysis of the test results, it is found that the new interception net can intercept the incoming missile body effectively after launch, which well verifies the Monte Carlo simulation results and further proves the intercepting effectiveness of the new interception net. [ABSTRACT FROM AUTHOR]

This paper considers a constant‐stress accelerated dependent competing risks model under Type‐II censoring. The dependent structure between competing risks is modeled by a Marshall‐Olkin bivariate exponential distribution, and the accelerated model is described by the power rule model. The point and interval estimation of the model parameters and the reliability function under the normal usage condition at mission time are obtained by using the maximum likelihood estimation method and the bootstrap sampling technique. Moreover, the pivotal quantities based estimation are adopted to estimate the model parameters and the generalized confidence intervals. As a comparison, we also consider the Bayes estimation and the highest posterior density credible intervals for the model parameters based on conjugate priors and importance sampling method, respectively. To illustrate the proposed methodology, a Monte Carlo simulation is used to study the performances of different estimation methods. Finally, a dataset is analyzed for illustrative purpose and a comparison with the original results is also given. [ABSTRACT FROM AUTHOR]

In this paper, a new censoring scheme named by adaptive progressively interval censoring scheme is introduced. The competing risks data come from Marshall–Olkin extended Chen distribution under the new censoring scheme with random removals. We obtain the maximum likelihood estimators of the unknown parameters and the reliability function by using the EM algorithm based on the failure data. In addition, the bootstrap percentile confidence intervals and bootstrap-t confidence intervals of the unknown parameters are obtained. To test the equality of the competing risks model, the likelihood ratio tests are performed. Then, Monte Carlo simulation is conducted to evaluate the performance of the estimators under different sample sizes and removal schemes. Finally, a real data set is analyzed for illustration purpose. [ABSTRACT FROM AUTHOR]

In this paper, we study the estimation for the reliability of a multicomponent system, named N - M -cold-standby redundancy system, based on progressive Type-II censoring sample. In the system, there are N subsystems consisting of M statistically independent distributed strength components, and only one of these subsystems works under the impact of stresses at a time and the others remain as standbys. Whenever the working subsystem fails, one from the standbys takes its place. The system fails when the entire subsystems fail. It is supposed that the underlying distributions of random strength and stress both belong to the generalized half-logistic distribution with different shape parameter. The reliability of the system is estimated by using both classical and Bayesian statistical inference. Uniformly minimum variance unbiased estimator and maximum likelihood estimator for the reliability of the system are derived. Under squared error loss function, the exact expression of the Bayes estimator for the reliability of the system is developed by using the Gauss hypergeometric function. The asymptotic confidence interval and corresponding coverage probabilities are derived based on both the Fisher and the observed information matrices. The approximate highest probability density credible interval is constructed by using Monte Carlo method. Monte Carlo simulations are performed to compare the performances of the proposed reliability estimators. A real data set is also analyzed for an illustration of the findings. [ABSTRACT FROM AUTHOR]

Highlights • The stress–strength reliability is analyzed by the truncated PHR distribution. • The pivotal quantity estimation is deduced for stress–strength reliability. • Boot-p technique is used to obtain the confidence interval of stress–strength reliability. • The modified generalized confidence interval is adopted to estimate the stress–strength reliability. • Accuracy of the proposed methodology is tested by simulation and real datasets. Abstract This paper considers the reliability inference for the truncated proportional hazard rate stress–strength model based on progressively Type-II censoring scheme. When the stress and strength variables follow the truncated proportional hazard rate distributions, the maximum likelihood estimation and the pivotal quantity estimation of stress–strength reliability are derived. Based on the percentile bootstrap sampling technique, the 95% confidence interval of stress–strength reliability is obtained, as well as the related coverage percentage. Moreover, based on the Fisher Z transformation and the modified generalized pivotal quantity, the 95% modified generalized confidence interval for the stress–strength reliability is obtained. The performance of the proposed method is evaluated by the Monte Carlo simulation. The numerical results show that the pivotal quantity estimators performs better than the maximum likelihood estimators. At last, two real datasets are analyzed by the proposed methodology for illustrative purpose. The results of real example analysis show that our model can be applied to the practical problem, the truncated proportional hazard rate distribution can fit the failure data better than other distributions, and the algorithms in this paper are suitable to handle the small sample data. [ABSTRACT FROM AUTHOR]

In reliability analysis of the stress–strength models, the stress and strength variables are typically assumed as independent. However, such an assumption may be unrealistic in some applications. It is a meaningful issue to estimate the reliability of the stress–strength model for dependent stress and strength variables. In this paper, we estimate the reliability of multicomponent stress–strength model by assuming the dependent Weibull stress variables and exponential strength variables based on Gumbel copula under Type-I progressively hybrid censoring scheme. The estimators of the unknown parameters and reliability are obtained by using the maximum likelihood estimation method. Also, the asymptotic confidence intervals and Bootstrap percentile confidence intervals of the unknown parameters and reliability of stress–strength model are derived. Monte Carlo simulations are used to evaluate the performance of the maximum likelihood estimators, asymptotic confidence intervals and Bootstrap percentile confidence intervals. Finally, real data are analyzed to demonstrate the practicability of the stress–strength model in this article. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the statistical inference of constant-stress accelerated dependent competing risks model under Type-II hybrid censoring schemes. The dependency structure is modeled by a Marshall–Olkin bivariate Weibull distribution. Both the shape and the scale parameters in the model are assumed to be dependent on the stress levels through a log-linear relationship. The maximum likelihood estimates (MLEs) of the model parameters are derived. Confidence intervals (CIs) of the model parameters are constructed based on the asymptotic normality of MLEs and the bias-corrected percentile bootstrap method. Bayes estimates with the squared error loss function and the highest posterior density (HPD) credible intervals (CIs) are obtained by using an importance sampling method. In addition, the estimates for the accelerated coefficients and the reliability under normal use stress level at mission time are derived. A Monte Carlo simulation study is used to evaluate the performance of the proposed statistical inference methods. A real data example is used to illustrate the methodologies proposed in this paper and to compare the proposed model with the copula model. [ABSTRACT FROM AUTHOR]