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This paper studies stress distribution in a single-walled carbon nanotube (SWCNT) under internal pressure with various chirality. Strain gradient theory is used to capture the size-dependent behavior of the SWCNT. Minimum total potential energy principle is successfully applied to derive the governing differential equation and its associated boundary conditions. Due to complexity of the governing differential equation and boundary conditions, numerical scheme is used to solve the problem. Comparing the results based on strain gradient theory and that of classical elasticity shows a major difference between these two methods. However, a close examination of the results indicates that both theories predict the same trend for variations in the radial displacement along the SWCNT radius. Numerical results also indicate that the proposed model can lead into the classical elasticity model, provided the material length scale parameters are taken to be zero. Additionally, for plane strain condition, the radial displacements predicted by strain gradient theory are lower than those predicted by classical elasticity theory. Moreover, numerical results show that in a SWCNT, the non-dimensional radial and circumferential stresses along the wall thickness of the SWCNT increase as the radius is increased. The opposite behavior is true for non-dimensional high-order stresses.

This study presents an algorithm to optimally adjust the input parameters of the wirecut to align its output with the customer’s expectations. For this, AHP and QFD are used to identify and prioritize customer needs in the form of a desirability function. Then, using the Taguchi method, variance analysis, and regression, a fitness function is prepared and optimized by the multi-objective genetic algorithm. Through a case study, the proposed method is validated in terms of flexibility, simplicity, speed, cost-effectiveness, and updateability. Also, customer satisfaction is calculated for two groups of 45 people, with and without using the proposed method. The growth of the customer satisfaction index (CSAT) from 57.6 to 70.3, and the customer satisfaction score from 30.2 to 54.2, show the positive performance of the method. This converts regular customers into loyal ones. It also makes them encourages others to use the mentioned services and widen the customer network. It is clearly seen in the growth of the net promoter score from 6.67 to 31.11. All in all, it can be said that this algorithm helps the survival, profitability, and expansion of an industrial organization.

COVID-19 pandemic caused an increasing demand for online academic classes, which led to the demand for effective online exams with regards to limitations on time and resources. Consequently, holding online exams with sufficient reliability and effectiveness became one of the most critical and challenging subjects in higher education. Therefore, it is essential to have a preventive algorithm to allocate time and financial resources effectively. In the present study, a fair test with sufficient validity is first defined, and then by analogy with an engineering product, the design process is implemented on it. For this purpose, a hybrid method based on FMEA, which is a preventive method to identify potential failure modes and prioritize their risk, is employed. The method's output is provided to the QFD algorithm as the needs of product customers. Then, the proposed solutions to prevent failures are weighted and prioritized as the product's technical features. Some modifications are made to the classic form of FMEA in the proposed method to eliminate its deficiencies and contradictions. Therefore, our proposed algorithm is a precautionary approach that works to prevent breakdowns instead of fixing them following their occurrence. This issue is very effective in increasing the efficiency of activities in times of crisis. Eventually, a prioritized list of preventive actions is provided, allowing us to choose from available solutions in the circumstances with limited time and budgetary, where we cannot take all possible actions.