Modular stem in total hip arthroplasty for patients with trochanter valgus deformity: surgical technique and case series.

BACKGROUND: Trochanter valgus deformity (TVD) is a rare condition of total hip arthroplasty (THA). Femoral osteotomy could be required in correcting the deformity to implant femoral stem in severe TVD. In this study, we described one unpublished technique of reverse sleeve of S-ROM to get through the complex situation. This study aimed to summarize and evaluate its technical challenges, safety and effectiveness. METHODS: From January 2006 to December 2014, we enrolled patients whose sleeves were implanted towards the great trochanter in THA with TVD. Their demographics, perioperative and postoperative information were recorded. To explore its indication, we measured and analyzed the ratio of greater trochanter/lesser trochanter (G/L ratio) and trochanter valgus angle (TVA). RESULTS: Twelve patients (1 male and 11 female, average age 42.30 ± 10.23) had mean follow-up of 6 years. Among them, only two patients had intraoperative femoral fracture. The survivorship of femoral prosthesis was 100%. The Harris hip score (HHS) increased from preoperative 34.31 ± 14.43 to postoperative 84.12 ± 11.33. All patients\u27 G/L ratio were larger than 1.50. CONCLUSIONS: The reverse sleeve of S-ROM was a reliable method for the patients with severe TVD, which brought satisfying clinical outcomes in mid-term follow-up

Preventing Over-Smoothing for Hypergraph Neural Networks

In recent years, hypergraph learning has attracted great attention due to its capacity in representing complex and high-order relationships. However, current neural network approaches designed for hypergraphs are mostly shallow, thus limiting their ability to extract information from high-order neighbors. In this paper, we show both theoretically and empirically, that the performance of hypergraph neural networks does not improve as the number of layers increases, which is known as the over-smoothing problem. To tackle this issue, we develop a new deep hypergraph convolutional network called Deep-HGCN, which can maintain the heterogeneity of node representation in deep layers. Specifically, we prove that a $k$-layer Deep-HGCN simulates a polynomial filter of order $k$ with arbitrary coefficients, which can relieve the problem of over-smoothing. Experimental results on various datasets demonstrate the superior performance of the proposed model comparing to the state-of-the-art hypergraph learning approaches

Single image super resolution based on multi-scale structure and non-local smoothing

In this paper, we propose a hybrid super-resolution method by combining global and local dictionary training in the sparse domain. In order to present and differentiate the feature mapping in different scales, a global dictionary set is trained in multiple structure scales, and a non-linear function is used to choose the appropriate dictionary to initially reconstruct the HR image. In addition, we introduce the Gaussian blur to the LR images to eliminate a widely used but inappropriate assumption that the low resolution (LR) images are generated by bicubic interpolation from high-resolution (HR) images. In order to deal with Gaussian blur, a local dictionary is generated and iteratively updated by K-means principal component analysis (K-PCA) and gradient decent (GD) to model the blur effect during the down-sampling. Compared with the state-of-the-art SR algorithms, the experimental results reveal that the proposed method can produce sharper boundaries and suppress undesired artifacts with the present of Gaussian blur. It implies that our method could be more effect in real applications and that the HR-LR mapping relation is more complicated than bicubic interpolation

Output prediction of alpha-type Stirling engines using gradient boosted regression trees and corresponding heat recovery system optimization based on improved NSGA-II

Climate change is becoming a pressing global concern, and the search for new energy and energy recovery technologies is becoming a worldwide research imperative. The broad adaptability of the Stirling engine to a wide variety of heat sources makes it a promising technology for industrial waste heat recovery and solar thermal generation. The operation of the Stirling engine involves a multi-physical coupled process of heat transfer and mechanics as well as non-linear losses due to mechanical friction and gas charge leaking. Therefore, accurate prediction of Stirling engine power output through theoretical analysis is complex and costly. Emerging machine learning algorithms like Gradient Boosted Regression Trees (GBRT) can offer new approaches to solve this problem. The GBRT model consists of multiple decision trees that branch by exhausting thresholds for all features under study to find the best split structure for data regression, and the principle of GBRT gives it the natural advantage of finding a wide range of distinguishing features and combinations, and a powerful generalization capability. A GBRT forecasting model is thus constructed to model the output power of Alpha-type Stirling engines. Test data from the General Motors 4L23 Stirling Engine are applied as the training and test set. Results from the random test set accounting for 25% of the total samples indicate that the GBRT model has a prediction accuracy of 96.23%. Furthermore, a regional microgrid containing Stirling engines, photovoltaic panels and batteries for industrial waste heat recovery is constructed and an evaluation system for energy supply performance is also established. Finally, based on the proposed power output model, multi-objective optimization based on improved NSGA-II is implemented, providing guidance for industrial application of Stirling engines

Use-wear experimental studies for differentiating flint tools processing bamboo from wood

Bamboo is widespread in south China and is one of the major organic resources in daily use through history due to its similar potential use value as wood. Due to the unfavourable preservation conditions and taphonomic alteration, the rare discovery of well-preserved organic remains from Palaeolithic sites means there is a lack of direct studies on the technology and behaviour of early prehistoric humans. Use-wear analysis has been proved as a reliable method to detect evidence left by working wood and bamboo on stone artefacts. This study aims to provide an experimental reference of use-wear features and patterns to identify and interpret the exploration of bamboo and wood resources in prehistory. In this experiment, 12 flint flakes were selected for processing bamboo stems and pine branches with working motions of whittling, sawing, and chopping. The results show that the use-wear features, including edge scarring, edge rounding, and polish, of bamboo-working and wood-working are distinctive. Edge scarring is closely related to the working motion, and moderate bright to very bright polish is a significant feature associated with bamboo-working. It is possible to distinguish wear traces caused by bamboo-working from those by wood-processing through a combination of low-power and high-power techniques under a 3D digital microscope

Origin and tuning of the magnetocaloric effect for the magnetic refrigerant MnFe(P1-xGex)

Neutron diffraction and magnetization measurements of the magneto refrigerant Mn1+yFe1-yP1-xGex reveal that the ferromagnetic and paramagnetic phases correspond to two very distinct crystal structures, with the magnetic entropy change as a function of magnetic field or temperature being directly controlled by the phase fraction of this first-order transition. By tuning the physical properties of this system we have achieved a maximum magnetic entropy change exceeding 74 J/Kg K for both increasing and decreasing field, more than twice the value of the previous record.Comment: 6 Figures. One tabl

A Macroscopic Perspective on Lithic Technology and Human Behavior during Pleistocene in Zhejiang Province, Southeastern China

Paleolithic archeological remains were not reported from Zhejiang until 2002. Up to now, over 70 Paleolithic sites and/or localities have been recovered through a series of surveys mainly in the north part of Zhejiang. An overview of the Paleolithic record and archeological sequence in this region during the Early to Late Pleistocene are present from a macroscopic perspective in this article, as well as the brief introduction of lithic technology and human adaptation in south China. In general, the lithic assemblages in Zhejiang represent the features of Pebble Industry in south China and show a trend of reduction on the size of stone artifacts since the Late Paleolithic. It is presumed that prehistoric humankind has shown the behavioral strategies as followed: a) exploited local raw material; b) the utilization of core and the degree of proficiency in knapping have been improved gradually; c) the retouching focused on the areas of edges; and d) preferred to use sharp edges of tools