BackgroundAcephalic spermatozoa is an extremely rare type of teratozoospermia that is associated with male infertility. Several genes have been reported to be relevant to acephalic spermatozoa. Thus, more genetic pathogenesis needs to be explored.MethodsWhole‐exome sequencing was performed in a patient with acephalic spermatozoa. Then Sanger sequencing was used for validation in the patient and his family. The patient's spermatozoa sample was observed by papanicolaou staining and transmission electron microscopy. Western blot and immunofluorescence were performed to detect the level and localization of related proteins.ResultsA novel homozygous frameshift insertion mutation c.545dupT;p.Ala183Serfs*10 in exon 8 of TSGA10 (NM_001349012.1) was identified. Our results showed misarranged mitochondrial sheath and abnormal flagellum in the patient's spermatozoa. TSGA10 failed to be detected in the patient's spermatozoa. However, the expression of SUN5 and PMFBP1 remained unaffected.ConclusionThese results suggest that the novel homozygous frameshift insertion mutation of TSGA10 is a cause of acephalic spermatozoa.

In this study, a random forest machine‐learning method is introduced on the basis of the analysis of measured constant amplitude stress fatigue data. This method aims to predict rubber fatigue life under constant amplitude stress. Strain mean value, strain amplitude, and strain ratio are used as independent variables, and the prediction model of rubber fatigue life under constant amplitude stress is established. A nonlinear cumulative fatigue damage model is proposed to calculate rubber fatigue life under the variable amplitude stress. Results show that the random forest method has high precision and generalization capability for rubber fatigue life prediction under constant amplitude stress and the nonlinear cumulative fatigue damage model could be employed to calculate the fatigue life of rubber under variable amplitude stress with enough accuracy according to the constant amplitude stress fatigue life data. This research can provide a reference for rubber fatigue life prediction. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48519.

Reliability engineering plays an important role in the design, manufacture, maintenance, and replacement of industrial products. Over the last few decades, accelerated degradation testing (ADT) has been largely utilized to shorten test durations, reduce the samples needed, and provide sufficient degradation data to ensure the effective reliability assessment of the concerned products. Meanwhile, performance degradation modeling has been recognized as an essential approach to help researchers and producers understand the health conditions of the deteriorating systems. However, the diversity in reliability tests, degradation models, and statistical analysis techniques has increased the difficulty in selecting appropriate reliability assessment methods in specific scenarios. Besides, there are no systematic reviews focused on modeling and analysis of performance degradation data. Therefore, this paper aims to (1) present ADT fundamentals, including the basic theory, ADT methods, accelerated stress variables, type of acceleration models, as well as ADT optimization, (2) comprehensively review current states and future challenges in degradation modeling, (3) discuss the problem of model mis-specification and compare different approaches for parameter estimation, (4) highlight future opportunities and possible directions deserving further research.

Given the small sample size, nonlinearity, and large dispersion of the measured data of fatigue performance for vibration isolation rubbers, the fatigue life prediction model for vibration isolation rubber materials was established using a support vector machine (SVM). A modified gravity search algorithm (MGSA) is proposed to optimize the parameters of the SVM. Using environmental temperature, the Rockwell hardness of the rubber compound, and the engineering strain peak as the input variables, the model was trained based on the experimental fatigue data of vibration isolation rubber materials. For comparison, the standard genetic algorithm, the standard particle swarm algorithm, and the standard simulated annealing algorithm are also implemented. Moreover, a back propagation neural network regression model is applied to the life prediction, with the conclusion that the prediction accuracy and the efficiency of MGSA are better than those of extant methods. This work can provide reference for further fatigue life prediction and structural improvement of rubber parts.

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With a focus on predicting the degradation of rubber performance in the natural environment and evaluating its reliability, the distribution law of accelerated aging life is analyzed through the accelerated aging test of hot oxygen. A Weibull distribution model is then established to verify the consistency of the accelerated aging mechanism. Through the constant stress accelerated aging test data, the aging characteristics of the rubber under alternating thermal stress load in the natural environment are inferred. The concept of thermal stress amplitude coefficient is proposed, and its numerical value is calculated by the combination of numerical simulation and single-peak curve fitting. Under the principle of time-temperature equivalence, the segmentation time equivalent method and the overall temperature equivalent method are employed to calculate the rubber performance degradation curve under natural environment aging conditions. Finally, according to the Weibull distribution, the aging reliability of the rubber over time is simulated. This research can provide a reference for the aging reliability evaluation of products under alternating stress.INDEX TERMS Rubber, natural aging, alternating stress, thermal stress amplitude coefficient, Weibull distribution, reliability.

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Due to the lack of natural driving databases containing heterogeneous traffic in the existing heterogeneous car-following modeling research, there is an urgent need for the support of a large amount of measured trajectory data for modeling. To this end, four different car-following modes of heterogeneous traffic under the influence of different vehicle types are extracted from the HighD data set, with which the statistical characteristics of the following car speed, speed difference, gap, time headway and acceleration in each mode are studied separately. Moreover, the correlation analysis of two parameters in speed-gap and speed difference-gap is carried out. On this basis, the intelligent driver model (IDM) and the full velocity difference (FVD) model are, respectively, employed to model the car-following characteristics in each mode. The results show that the existence of the truck in the following vehicle pair makes the following vehicle tend to maintain a larger gap and a smaller following speed, that is, larger time headway and gap. With the increase of trucks’ ratio, the capacity of traffic decreases. The research can lay the foundation for more accurate mixed traffic flow modeling of heterogeneous human driving vehicles, and even subsequent research on heterogeneous traffic characteristics under a mixture of human driving vehicles and autonomous vehicles.