The numerical study of pressure and temperature effects on mechanical properties of baghdadite-based nanostructure: molecular dynamics simulation.

Bioceramics have been commonly implemented to replace and restore hard tissues such as teeth and bones in recent years. Among different bioceramics, Baghdadite (BAG) has high bioactivity due to its ability to form apatite and stimulate cell proliferation. So, this structure is used widely for medical applications to treat bone-based diseases. Physically, we expect changes in temperature and pressure to affect the Baghdadite-based nanostructure's mechanical behaviour. So, in this computational study, we report the pressure/temperature effect on Baghdadite matrix with nanoscale size by using Molecular Dynamics (MD) approach. To this end, physical values like the total energy, temperature, final strength (FS), stress-strain curve, potential energy, and Young's modulus (YM) are reported. Simulation results indicated the mechanical properties of Baghdadite (BAG) nanostructure weakened by temperature and pressure increase. Numerically, the FS and YM of the defined structure reach 131.40 MPa/159.43 MPa, and 115.15 MPa/139.72 MPa with temperature/pressure increasing. Therefore, the increase in initial pressure and temperature leads to a decrease in the mechanical properties of nanostructures. These results indicate the importance of the initial condition in the Baghdadite-based nanostructures' mechanical behaviour that must be considered in clinical applications.

Implementing PSO-ELM Model to Approximate Trolox Equivalent Antioxidant Capacity as One of the Most Important Biological Properties of Food.

In this paper, the Trolox equivalent antioxidant capacity (TEAC) is estimated through a robust machine-learning algorithm known as the Particle Swarm Optimization-based Extreme Learning Machine (PSO-ELM) model. For this purpose, a large dataset from previously published reports was gathered. Various analyses were performed to evaluate the proposed model. The results of the statistical analysis showed that this model can predict the actual values with high accuracy, so that the calculated R 2 and RMSE values were equal to 0.973 and 3.56, respectively. Sensitivity analysis was also performed on the effective input parameters. The leverage technique was also performed to check the accuracy of real data, and the results showed that the majority of data are reliable. This simple yet accurate model can be very powerful in predicting the Trolox equivalent antioxidant capacity values and can be a good alternative to laboratory data.