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During pregnancy, traumas can threaten maternal and fetal health. Various trauma effects on a pregnant uterus are little investigated. In the present study, a finite element model of a uterus along with a fetus, placenta, amniotic fluid, and two most effective ligament sets is developed. This model allows numerical evaluation of various loading on a pregnant uterus. The model geometry is developed based on CT-scan data and validated using anthropometric data.Applying Ogden hyper-elastic theory, material properties of uterine wall and placenta are developed. After simulating the "rigid-bar" abdominal loading, the impact force and abdominal penetration are investigated. Findings are compared with the experimental abdominal response corridor, previously developed for a nonpregnant abdomen. "Response corridor" denotes a bounded envelope in response space, within which the system responses usually lie. Results show that at low abdominal penetrations (less than 45 mm), the pregnant abdomen response is highly compatible with the nonpregnant case. While, at large penetrations, the pregnant abdomen demonstrates stiffer behavior. The reason must be the existence of a fetus in the model. This reveals that the existing response corridors would not be reliable to be extended for a pregnant abdomen. Hence, response corridor development for a pregnant abdomen is a crucial task. In this study, a new fixed-back rigid-bar loading response corridor is proposed for a pregnant abdomen using the loadpenetration behavior of the developed model. This model and response corridor can help to study the pregnant uterus response to environmental loading and investigate the injury risk to the uterus and fetus. K E Y W O R D S abdominal response corridor, biomechanical model, finite element method, Ogden hyper-elastic material, pregnant uterus, rigid-bar loading

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Promoting safe tires with low external rolling noise increases the environmental efficiency of road transport. Although tire builders have been striving to reduce emitted noise, the issue’s sophisticated nature has made it difficult. This article aims to make the problem straightforward, relying on recent significant improvements in statistical science. In this regard, the prediction ability of new methods in this field, including support vector machine, relevance vector machine, and convolutional neural network, along with the new architecture of the neural network is compared. Tire noise is measured under the coast-by condition. Two training strategies are proposed: extracting features from a tread pattern image and directly importing an image to the model. The relevance vector method, which is trained using the first strategy, has provided the most accurate results with an error of 0.62 dB(A) in predicting the total noise level. This precise model is used instead of experimentation to analyze the sensitivity of tire noise to its parameters using a small central composite design. The parametric study reveals striking tips for reducing noise, especially in terms of interactions between parameters that have not previously been shown. Finally, a novel two-stage approach for reducing noise by tread pattern optimization is proposed, inspired by two regression models derived from statistical investigation and variance analysis. Changes in tread pattern specifications of two case studies and their randomization have resulted in a reduction of 3.2 dB(A) for a high-noise tire and 0.4 dB(A) decrement for a quieter tire.