RESUMEN
This study aimed to introduce FD-2, a newly developed anticervical stenosis device for patients with cervical cancer undergoing radical trachelectomy. Using ethylene-vinyl acetate copolymers, we developed FD-2 to prevent uterine cervical stenosis after radical trachelectomy. The tensile test and extractables and leachables testing were performed to evaluate FD-2's safety as a medical device. FD-2 was indwelled in three patients with cervical cancer during radical trachelectomy and its utility was preliminarily evaluated. FD-2 consists of a head (fish-born-like structure), neck (connecting bridges), and body (tubular structure); the head is identical to FD-1, an intrauterine contraceptive device. FD-2 passed the tensile test and extractables and leachables testing. The average time required for the application or removal of FD-2 in cervical cancer patients was less than 10 s. The median duration of FD-2 indwelling was 8 weeks. No complications, including abdominal pain, pelvic infections, or hemorrhages, associated with FD-2 indwelling were reported. At the 3-12-month follow-up after the radical trachelectomy, no patients developed cervical stenosis or experienced dysmenorrhea. In conclusion, we developed FD-2, a novel device that can be used for preventing cervical stenosis after radical trachelectomy for uterine cervical cancer.
RESUMEN
We present a construction method for reduced-order models (ROMs) to explore alternatives to numerical simulations. The proposed method can efficiently construct ROMs for non-linear problems with contact and impact behaviors by using tensor decomposition for factorizing multidimensional data and Akima-spline interpolation without tuning any parameters. First, we construct learning tensor data of nodal displacements or accelerations using finite element analysis with some representative parameter sets. Second, the data are decomposed into a set of mode matrices and one small core tensor using Tucker decomposition. Third, Akima-spline interpolation is applied to the mode matrices to predict values within the data range. Finally, the time history responses with new parameter sets are generated by multiplying the expanded mode matrices and small core tensor. The performance of the proposed method is studied by constructing ROMs for airbag impact simulations based on limited learning data. The proposed ROMs can accurately predict airbag deployment behavior even for new parameter sets using the Akima-spline interpolation scheme. Furthermore, an extremely high data compression ratio (more than 1000) and efficient predictions of the response surfaces and Pareto frontier (2000 times faster than that of full finite element analyses using all parameter sets) can be realized.
RESUMEN
A computational method is proposed in order to predict mechanical properties of discontinuous fiber composites (DFCs) based on computational homogenization with statistically similar representative volume elements (SSRVEs). The SSRVEs are obtained by reducing the complexity of real microstructures based on statistical measures. Specifically, they are constructed by minimizing an objective function defined in terms of differences between the power spectral density of target microstructures and that of the SSRVEs. In this paper, an extended construction method is proposed based on the reformulation of the objective function by integer design variables. The proposed method is applied to the representation of a real material, namely glass fiber reinforced nylon 6. The results show that the mechanical properties computed by numerical material tests using the SSRVEs agree with experimental results. Therefore, it is found that the nonlinear mechanical properties of the DFC can be suitably predicted by the proposed method without any special calibration to experiments performed on the composites.
