A Simple Egg Membrane Patch Bridging Method for Repairing Tympanic Membrane Perforation

Objective:To analyze the clinical effect of a simple egg membrane patch bridging method in repairing tympanic membrane perforation.Methods:A total of 93 tympanic membrane perforation patients admitted to the hospital between September 2022 and October 2023 were selected and divided into two groups according to the random number table method.The control group implemented the conventional treatment(n=46 cases),and the patch group adopted the simple egg membrane patch bridging method(n=47 cases).The healing rate of the tympanic membrane,the air-bone gap,the air conduction hearing threshold,the dry ear rate,and the incidence of complications in both groups were compared before and after treatment.Results:The healing rate of the tympanic membrane in the patch group was significantly higher than that of the control group(95.75%vs.76.09%),with P<0.05;there was no difference in the air-bone gap and air conduction hearing threshold levels between the two groups before treatment(P>0.05),and the hearing indexes of the patch group were significantly lower than those of the control group 3 months after treatment(P<0.05);the dry ear rate in the patch group was significantly higher than that of the control group after treatment(85.11%vs.67.39%),and the total incidence of complications was also significantly lower than that of the control group(6.38%vs.21.74%),with P<0.05.Conclusion:The simple egg membrane patch bridging method is effective in repairing tympanic membrane perforation,which can effectively improve patients’hearing levels and reduce the occurrence of post-treatment complications.Thus,it is worth popularizing and applying in the clinic.

Computation of Ship Hydrodynamic Interaction Forces in Restricted Waters using Potential Theory

A computer code based on the double-body potential flow model and the classic source panel method has been developed to study various problems of hydrodynamic interaction between ships and other objects with solid boundaries including the seabed. A peculiarity of the proposed implementation is the application of the so-called ＂moving-patch＂ method for simulating steady boundaries of large extensions. The method is based on an assumption that at any moment just the part of the boundary （＂moving patch＂） which lies close to the interacting ship is significant for the near-field interaction. For a specific case of the fiat bottom, comparative computations were performed to determine optimal dimensions of the patch and of the constituting panels based on the trade-off between acceptable accuracy and reasonable efficiency. The method was applied to estimate the sway force on a ship hull moving obliquely across a dredged channel. The method was validated for a case of ship-to-ship interaction when tank data were available. This study also contains a description of a newly developed spline approximation algorithm necessary for creating consistent discretizations of ship hulls with various degrees of refinement.

Fast image super-resolution algorithm based on multi-resolution dictionary learning and sparse representation

Sparse representation has attracted extensive attention and performed well on image super-resolution（SR） in the last decade. However, many current image SR methods face the contradiction of detail recovery and artifact suppression. We propose a multi-resolution dictionary learning（MRDL） model to solve this contradiction, and give a fast single image SR method based on the MRDL model. To obtain the MRDL model, we first extract multi-scale patches by using our proposed adaptive patch partition method（APPM）. The APPM divides images into patches of different sizes according to their detail richness. Then, the multiresolution dictionary pairs, which contain structural primitives of various resolutions, can be trained from these multi-scale patches.Owing to the MRDL strategy, our SR algorithm not only recovers details well, with less jag and noise, but also significantly improves the computational efficiency. Experimental results validate that our algorithm performs better than other SR methods in evaluation metrics and visual perception.