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Numerical simulations of the fracture process are challenging, and the discrete element (DE) method is an effective means to model fracture problems. The DE model comprises the DE connective model and DE contact model, where the former is used for the representation of isotropic solids before cracks initiate, while the latter is employed to represent particulate materials after cracks propagate. In this paper, a DE particle-based cohesive crack model is developed to model the mixed-mode fracture process of brittle materials, aiming to simulate the material transition from a solid phase to a particulate phase. Because of the particle characteristics of the DE connective model, the cohesive crack model is constructed at inter-particle bonds in the connective stage of the model at a microscale. A potential formulation is adopted by the cohesive zone method, and a linear softening relation is employed by the traction-separation law upon fracture initiation. This particle-based cohesive crack model bridges the microscopic gap between the connective model and the contact model and, thus, is suitable to describe the material separation process from solids to particulates. The proposed model is validated by a number of standard fracture tests, and numerical results are found to be in good agreement with the analytical solutions. A notched concrete beam subjected to an impact loading is modeled, and the impact force obtained from the numerical modeling agrees better with the experimental result than that obtained from the finite element method.The importance of the immune system for cardiac repair following myocardial infarction is undeniable; however, the complex nature of immune cell behavior has limited the ability to develop effective therapeutics. This limitation highlights the need for a better understanding of the function of each immune cell population during the inflammatory and resolution phases of cardiac repair. The development of reliable therapies is further complicated by aging, which is associated with a decline in cell and organ function and the onset of cardiovascular and immunological diseases. Aging of the immune system has important consequences on heart function as both chronic cardiac inflammation and an impaired immune response to cardiac injury are observed in older individuals. Several studies have suggested that rejuvenating the aged immune system may be a valid therapeutic candidate to prevent or treat heart disease. Here, we review the basic patterns of immune cell behavior after myocardial infarction and discuss the autonomous and nonautonomous manners of hematopoietic stem cell and immune cell aging. Lastly, we identify prospective therapies that may rejuvenate the aged immune system to improve heart function such as anti-inflammatory and senolytic therapies, bone marrow transplant, niche remodeling and regulation of immune cell differentiation.Analysis of physiological signals, electroencephalography more specifically, is considered a very promising technique to obtain objective measures for mental workload evaluation, however, it requires a complex apparatus to record, and thus, with poor usability in monitoring in-vehicle drivers' mental workload. This study proposes a methodology of constructing a novel mutual information-based feature set from the fusion of electroencephalography and vehicular signals acquired through a real driving experiment and deployed in evaluating drivers' mental workload. Mutual information of electroencephalography and vehicular signals were used as the prime factor for the fusion of features. In order to assess the reliability of the developed feature set mental workload score prediction, classification and event classification tasks were performed using different machine learning models. Moreover, features extracted from electroencephalography were used to compare the performance. In the prediction of mental workload score, expert-defined scores were used as the target values. For classification tasks, true labels were set from contextual information of the experiment. An extensive evaluation of every prediction tasks was carried out using different validation methods. Cytidine 5′-triphosphate mouse In predicting the mental workload score from the proposed feature set lowest mean absolute error was 0.09 and for classifying mental workload highest accuracy was 94%. According to the outcome of the study, it can be stated that the novel mutual information based features developed through the proposed approach can be employed to classify and monitor in-vehicle drivers' mental workload.The purpose of this study was to translate the Pregnancy Physical Activity Questionnaire, a semi-quantitative tool that asks participants about time spent on 32 activities, into Korean and verify its validity and reliability. In total, 363 pregnant women under prenatal care at an obstetrics and gynecology hospital and a postpartum care facility in Gyeonggi-do completed the Korean version of the Pregnancy Physical Activity Questionnaire. The questionnaire's content validity, construct validity, concurrent validity, and reliability were verified. After verifying the validity of the contents of the Pregnancy Physical Activity Questionnaire, all the questions were included in the Korean version. For construct validity, we divided the participants into primipara and multipara groups based on their delivery history. On comparison of the two groups' physical activity based on the responses to the Pregnancy Physical Activity Questionnaire, there was a statistically significant difference in the total activity (t = -4.56, p less then 0.001) and the total activity (light activity or more) (t = -5.80, p less then 0.001). The correlation between the Pregnancy Physical Activity Questionnaire and the Global Physical Activity Questionnaire was tested to establish concurrent validity, and a significant correlation was found between all items except for vigorous physical activity. The Guttmann reliability coefficient by the odd-even method was 84. The Korean version of the Pregnancy Physical Activity Questionnaire is a suitable tool to measure the physical activity of pregnant women and can be used in clinical practice.