A single shallow learning algorithm cannot fit the characteristics of high-voltage reactors well. Aiming at the above problems, this paper uses the error back propagation neural network and the particle swarm algorithm optimized by adaptive inertia weight to optimize the combined prediction model B for data training, verification and testing are carried out to achieve the purpose of effectively reducing the manufacturing cost of high-voltage reactors. Through experimental verification, the maximum error between the predicted value and the true value is 2.9%, and the minimum error is 0.05%. This provides certain technical support and inspiration for future devices such as optimizing high-voltage reactors.

OBJECTIVE This study aims to quantitatively characterize the passive kinematics of the healthy, soft tissue-intact equine stifle to establish an objective foundation for providing insights into the etiology of stifle disorders and developing a possible surgical treatment for stifle degenerative disease. ANIMALS 5 whole-horse specimens. PROCEDURES Reflective markers with intracortical bone pins and a motion capture system were used to investigate the stifle joint kinematics. Kinematics of 5 whole-horse specimens euthanized within 2 hours were calculated for internal/external rotation, adduction/abduction, and cranial/caudal translation of the medial and lateral femoral condyles and estimated joint contact centroids as functions of joint extension angle. RESULTS From 41.7° to 121.6° (mean ± SD, range of motion: 107.5° ± 7.2°) of joint extension, 13° ± 3.7° of tibial external rotation and 6° ± 2.7° of adduction were observed. The lateral femoral condyle demonstrated significantly greater cranial translation than the medial during extension (23.7 mm ± 9.3 mm vs. 14.3 mm ± 7.0 mm, P = .01). No significant difference was found between the cranial/caudal translation of estimated joint contact centroids in the medial and lateral compartment (13.3 mm ± 7.7 mm vs. 16.4 mm ± 5.8 mm, P = .16). CLINICAL RELEVANCE The findings share similarities with kinematics for human knees and sheep and dog stifles, suggesting it may be possible to translate what has been learned in human arthroplasty to treatment for equine stifles.

No abstract

The use of magnetic resonance imaging (MRI) has led to increased clinical and research applications using 3D segmentation and reconstructed volumetric data in musculoskeletal imaging. Lesions of the deep digital flexor tendon (DDFT) are a common pathology in horses undergoing MRI. Three-dimensional MRI reconstruction performed for volumetric tendon analysis in horses has not previously been documented. The aim of this proof-of-concept study was to evaluate the 3D segmentation of horses undergoing repeated MRI at several time points and to perform an analysis of the segmented DDFTs across time. MRI DICOM files were acquired from six horses undergoing repeated MRI examination of the foot for DDFT injury. Once segmented, volumetric tendon surface tessellation language (STL) files were created. Thickness and volumetric data were acquired for each tendon in addition to a tendon comparison across timepoints within each horse. Pearson correlation coefficients were calculated for comparison of MRI reports to computer analysis. There was a significant and positive correlation between MRI and medial record reports of clinical improvement or deterioration and computer analysis (r = 0.56, p = 0.01). The lower end range limit for tendon thickness varied between 0.16 and 1.74 mm. The upper end range limit for DDFT thickness varied between 4.6 and 23.6 mm. During tendon part comparison, changes in DDFT were reported between −3.0 and + 14.3 mm. Changes in DDFT size were non-uniform and demonstrated fluctuations throughout the tendon. The study was successful in establishing the volumetric appearance and thickness of the DDFT as it courses in the foot and tracking this over time. We encountered difficulties in accurate segmentation of the distal insertion of the DDFT as it blends with the distal phalanx. The data demonstrated that the DDFT can be segmented and volumetric studies based on size and shape can be performed using an in silico approach.