Space maintainer ‘Y model’ as a preventive orthodontic treatment for paediatric patients: a case report

Background: Caries is one of the most common oral diseases that occur among children. Caries and dental trauma in children may cause early tooth loss, also known as premature loss, and result in occlusion abnormalities caused by the dental arch narrowing. A space maintainer is a preventive orthodontic appliance designed to maintain a narrow arch to prevent premature loss. Purpose: This study aims to describe the treatment of a case of space management in a patient with premature loss by using the space maintainer ‘Y model’. Case: An eight-year-old boy was accompanied by his mother, complaining that the lower posterior right tooth had been extracted. The mother was worried that the new tooth would have an overlapping growth. Case Management: The diagnosis was mandibular primary molar loss. The study cast was analysed based on Moyers 2.62 cm, Huckaba 2.24 mm, and curve determination 2.40 mm. The mandibular removable space maintainer treatment was performed on the patient and was followed by nine control visits every week. The outcome was a successful treatment from the use of the space maintainer ‘Y model’. Conclusion: The space maintainer treatment with the Y model in the paediatric patient showed a good result, evidenced by the tube opening of 1.2 mm, showing that the appliance followed lateral jaw growth.

FepiM: A Novel Inverse Piecewise Method to Determine Isothermal Flow Curves for Hot Working

In forming simulations, flow curves are cardinal inputs to predict features, such as forming forces and material flow. The laboratory-scale experiments to determine them, like compression or tensile tests, are affected by deformation heating, restricting direct flow curve determination. In principle, the current analytical and inverse methods determine flow curves from these tests, but while the analytical methods assume a simplified temperature profile, the inverse methods require a closed-form flow curve equation, which mostly cannot capture complex material behavior like multiple recrystallization cycles. Therefore, the inverse piecewise flow curve determination method “FepiM” previously developed and published by the current authors is extended by introducing a two-step procedure to obtain isothermal flow curves at elevated temperatures and different strain rates. Thereby, the flow curve is represented as tabular data instead of an equation to reproduce complex flow curve shapes while also compensating the effect of inhomogeneous temperature profiles on the flow stress. First, a flow curve at the highest temperature is determined. In the second step, using this first flow curve as a reference, the flow curves at lower temperatures are obtained via interpolation. Flow curves from conventional compression tests for aluminum and copper in the temperature range of 20–500 °C are predicted, and it is shown that these flow curves can reproduce the experimental forces with a maximum deviation of less than 1%. Therefore, the proposed new piecewise method accurately predicts isothermal flow curves for compression tests, and the method could be further extended to highly inhomogeneous methods in the future.

Evolution Model for Epidemic Diseases Based on the Kaplan-Meier Curve Determination

We show a simple model of the dynamics of a viral process based, on the determination of the Kaplan-Meier curve P of the virus. Together with the function of the newly infected individuals I, this model allows us to predict the evolution of the resulting epidemic process in terms of the number E of the death patients plus individuals who have overcome the disease. Our model has as a starting point the representation of E as the convolution of I and P. It allows introducing information about latent patients—patients who have already been cured but are still potentially infectious, and re-infected individuals. We also provide three methods for the estimation of P using real data, all of them based on the minimization of the quadratic error: the exact solution using the associated Lagrangian function and Karush-Kuhn-Tucker conditions, a Monte Carlo computational scheme acting on the total set of local minima, and a genetic algorithm for the approximation of the global minima. Although the calculation of the exact solutions of all the linear systems provided by the use of the Lagrangian naturally gives the best optimization result, the huge number of such systems that appear when the time variable increases makes it necessary to use numerical methods. We have chosen the genetic algorithms. Indeed, we show that the results obtained in this way provide good solutions for the model.