Exaggerated placental site in a cesarean scar: Misdiagnosed as gestational trophoblastic neoplasia: A case report.

The present study reports a rare case of an exaggerated placental site (EPS) in a caesarean scar that was misdiagnosed as gestational trophoblastic neoplasia (GTN) by imaging, resulting in unnecessary surgical treatment. A 38-year-old woman underwent hysteroscopic resection of a cesarean scar pregnancy (CSP). The patient's serum ß-human chorionic gonadotropin (ß-hCG) level was elevated (76,196 mIU/ml) at the 24-day postoperative follow-up visit. On postoperative day 51, the patient experienced vaginal bleeding for three days and ß-hCG levels were 2,799 mIU/ml. Ultrasonography and MRI revealed a heterogeneous mass and hypervascularity. The patient was diagnosed with a GTN in a cesarean scar and treated with methotrexate (MTX). ß-hCG levels decreased after 3 MTX doses, but the mass did not change in size and was still hypervascular on imaging. Total hysterectomy was performed due to the serious side effects of chemotherapy and the lack of desire to preserve fertility. The histological findings supported the diagnosis of an EPS reaction. The present case is unique because of the rare intrauterine mass and possibility of retained trophoblastic changes causing EPS. EPS differs from GTN both clinically and pathologically and should be considered a possible diagnosis in any woman who has irregular bleeding following CSP resection.

Research on the Fault Diagnosis Method of an Internal Gear Pump Based on a Convolutional Auto-Encoder and PSO-LSSVM.

The raw signals produced by internal gear pumps are susceptible to noises brought on by mechanical vibrations and the surrounding environment, and the sample count collected during the various operating periods is not distributed evenly. Accurately diagnosing faults in internal gear pumps is significantly complicated by these factors. In light of these issues, accelerated life testing was performed in order to collect signals from an internal gear pump during various operating periods. Based on the architecture of a convolutional auto-encoder network, preprocessing of the signals in the various operating periods was performed to suppress noise and enhance operating period-representing features. Thereafter, variational mode decomposition was utilized to decompose the preprocessed signal into multiple intrinsic mode functions, and the multi-scale permutation entropy value was extracted for each intrinsic mode function to form a feature set. The feature set was subsequently divided into a training set and a test set, with the training set being trained to utilize a particle swarm optimization-least squares support vector machine network. For pattern recognition, the test set samples were fed into the trained model. The results demonstrated a 99.2% diagnostic accuracy. Compared to other methods of fault diagnosis, the proposed method is more effective and accurate.

End-to-End Continuous/Discontinuous Feature Fusion Method with Attention for Rolling Bearing Fault Diagnosis.

Mechanical equipment failure may cause massive economic and even life loss. Therefore, the diagnosis of the failures of machine parts in time is crucial. The rolling bearings are one of the most valuable parts, which have attracted the focus of fault diagnosis. Many successful rolling bearing fault diagnoses have been made based on machine learning and deep learning. However, most diagnosis methods still rely on complex signal processing and artificial features, bringing many costs to the deployment and migration of diagnostic models. This paper proposes an end-to-end continuous/discontinuous feature fusion method for rolling bearing fault diagnosis (C/D-FUSA). This method comprises long short-term memory (LSTM), convolutional neural networks (CNN) and attention mechanism, which automatically extracts the continuous and discontinuous features from vibration signals for fault diagnosis. We also propose a contextual-dependent attention module for the LSTM layers. We compare the method with the other simpler deep learning methods and state-of-the-art methods in rolling bearing fault data sets with different sample rates. The results show that our method is more accurate than the other methods with real-time inference. It is also easy to be deployed and trained in a new environment.

Numerical simulation and experimental evaluation of flow ripple characteristics of Truninger pump.

Compared with involute internal gear pumps and gerotor pumps, lower flow ripple is the main advantage of Truninger pumps. Understanding the flow ripple mechanism and characteristics is of great significance to guide the design and manufacture of this type of pump. In this paper, the theoretical flow ripple and flow ripple rate expressions of the pump are derived based on the vector ray method, and the effects of variations of the design parameters of the pump on the theoretical flow ripple characteristics are studied. A three-dimensional numerical simulation model was established in Simerics-MP+ that accounted for the fluid properties and cavitation. All the geometric features, including unloading grooves, the oil distribution areas, the shapes of the suction and delivery passageways, and the axial and radial leakage gaps, were considered to achieve the highest accuracy in the prediction of flow ripple. Finally, a flow ripple test platform was built based on the secondary source method. The validity and accuracy of the model were verified by test results. The flow ripple characteristics under different working conditions were compared and analyzed. The following conclusions were obtained: (1) The smaller module, the larger addendum coefficient and the half angle of the tooth profile in the design process, the lower the pump speed during operation is beneficial to reduce the vibration and noise of this pump; (2) Flow ripple is the comprehensive result of the oil characteristics, internal leakage, and geometric characteristics through the comparisons of theoretical, simulation and experimental results; (3) The flow ripple amplitude and the ripple rate increased with the increase in the outlet pressure and the influence of the pump speed variations on the flow ripple characteristics is less than that of outlet pressure variations. The conclusions obtained in this paper will help designers understand the flow ripple mechanism, achieve low-noise pump designs, and optimize Truninger pumps.

Palladium (III) Fluoride Bulk and PdF3/Ga2O3/PdF3 Magnetic Tunnel Junction: Multiple Spin-Gapless Semiconducting, Perfect Spin Filtering, and High Tunnel Magnetoresistance.

Spin-gapless semiconductors (SGSs) with Dirac-like band crossings may exhibit massless fermions and dissipationless transport properties. In this study, by applying the density functional theory, novel multiple linear-type spin-gapless semiconducting band structures were found in a synthesized R 3 - c -type bulk PdF3 compound, which has potential applications in ultra-fast and ultra-low power spintronic devices. The effects of spin-orbit coupling and on-site Coulomb interaction were determined for the bulk material in this study. To explore the potential applications in spintronic devices, we also performed first-principles combined with the non-equilibrium Green's function for the PdF3/Ga2O3/PdF3 magnetic tunnel junction (MTJ). The results suggested that this MTJ exhibits perfect spin filtering and high tunnel magnetoresistance (~5.04 × 107).