Collective magnetization dynamics in ferromagnetic (Ga,Mn)As mediated by photo-excited carriers

We present a study of photo-excited magnetization dynamics in ferromagnetic (Ga,Mn)As films observed by time-resolved magneto-optical measurements. The magnetization precession triggered by linearly polarized optical pulses in the absence of an external field shows a strong dependence on photon frequency when the photo-excitation energy approaches the band-edge of (Ga,Mn)As. This can be understood in terms of magnetic anisotropy modulation by both laser heating of the sample and by hole-induced non-thermal paths. Our findings provide a means for identifying the transition of laser-triggered magnetization dynamics from thermal to non-thermal mechanisms, a result that is of importance for ultrafast optical spin manipulation in ferromagnetic materials via non-thermal paths.Comment: 11 pages, 9 figure

T-spline based unifying registration procedure for free-form surface workpieces in intelligent CMM

With the development of the modern manufacturing industry, the free-form surface is widely used in various fields, and the automatic detection of a free-form surface is an important function of future intelligent three-coordinate measuring machines (CMMs). To improve the intelligence of CMMs, a new visual system is designed based on the characteristics of CMMs. A unified model of the free-form surface is proposed based on T-splines. A discretization method of the T-spline surface formula model is proposed. Under this discretization, the position and orientation of the workpiece would be recognized by point cloud registration. A high accuracy evaluation method is proposed between the measured point cloud and the T-spline surface formula. The experimental results demonstrate that the proposed method has the potential to realize the automatic detection of different free-form surfaces and improve the intelligence of CMMs

Surface fitting for quasi scattered data from coordinate measuring systems

Non-uniform rational B-spline (NURBS) surface fitting from data points is wildly used in the fields of computer aided design (CAD), medical imaging, cultural relic representation and object-shape detection. Usually, the measured data acquired from coordinate measuring systems is neither gridded nor completely scattered. The distribution of this kind of data is scattered in physical space, but the data points are stored in a way consistent with the order of measurement, so it is named quasi scattered data in this paper. Therefore they can be organized into rows easily but the number of points in each row is random. In order to overcome the difficulty of surface fitting from this kind of data, a new method based on resampling is proposed. It consists of three major steps: (1) NURBS curve fitting for each row, (2) resampling on the fitted curve and (3) surface fitting from the resampled data. Iterative projection optimization scheme is applied in the first and third step to yield advisable parameterization and reduce the time cost of projection. A resampling approach based on parameters, local peaks and contour curvature is proposed to overcome the problems of nodes redundancy and high time consumption in the fitting of this kind of scattered data. Numerical experiments are conducted with both simulation and practical data, and the results show that the proposed method is fast, effective and robust. What’s more, by analyzing the fitting results acquired form data with different degrees of scatterness it can be demonstrated that the error introduced by resampling is negligible and therefore it is feasible

Measuring information integration model for CAD/CMM

A CAD/CMM workpiece modeling system based on IGES file is proposed. The modeling system is implemented by using a method of labelling the tolerance items of 3D workpiece. The concept-feature face is used in the method. Firstly the CAD data of workpiece are extracted and recognized automatically. Then a workpiece model is generated, which is the integration of pure 3D geometry form with its corresponding inspection items. The principle of workpiece modeling is also presented. At last, the experiment results are shown and correctness of the model is certified

Fatigue Reliability Assessment of Orthotropic Bridge Decks under Stochastic Truck Loading

A steady traffic growth has posed a threat to the fatigue safety of existing bridges. Uncertainties in traffic flows add to the challenge of an accurate fatigue safety assessment. This article utilizes a stochastic traffic load model to evaluate the fatigue reliability of orthotropic steel bridge decks. The traffic load model is simulated by site-specific weigh-in-motion measurements. A response surface method is presented to solve the time-consuming problem caused by hotspot stress simulations in the finite element model. Applications of the stochastic traffic load model for probabilistic modeling and fatigue reliability assessment are demonstrated in the case study of a steel box-girder bridge. Numerical results indicate that the growth rate of the gross vehicle weight leads to a rapid decrease of the fatigue reliability in comparison to the traffic volume growth. Even though the traffic volume growth is rapid, the control of overloaded trucks in comparison to the traffic volume is an effective way to ensure the fatigue safety of the steel bridges.National Basic Research Program of ChinaNational Science Foundation of ChinaKey Research Program in Civil Engineering from Changsha University of Science and Technolog

The Connecting Method for the Spiral Blades of Concrete Mixer Truck

For the spiral blade of the concrete mixing tank of concrete mixer truck, in order to satisfy the stirring and discharging performance, the installation angle and helix angle of the blade should be assigned according to their segmentation function. Due to the different helix angles and the installation tilt angles, a junction gap of blade is formed at the joint, resulting in the severe silting of discharge or the uneven agitation. We put forward a more reasonable solution by using the MFG (method of fitting gradient) to solve the gap connection of spiral blades. The MFG that can reduce the discharging residual rate of the mixing material has been verified by experiments and applied to actual mass production. We also make the coupling simulation of Multi-Physics Field based on simulation software 17-STARCCM+® to visually verify the scientificity of design and study the complex stresses distribution inside the actual mixing tank. Finally, we provide an up-to-date reference for the design of spiral logarithmic blade, solving the problem of the gap at the traditional spiral-blade connection

High order entropy stable schemes for the quasi-one-dimensional shallow water and compressible Euler equations

High order schemes are known to be unstable in the presence of shock discontinuities or under-resolved solution features for nonlinear conservation laws. Entropy stable schemes address this instability by ensuring that physically relevant solutions satisfy a semi-discrete entropy inequality independently of discretization parameters. This work extends high order entropy stable schemes to the quasi-1D shallow water equations and the quasi-1D compressible Euler equations, which model one-dimensional flows through channels or nozzles with varying width. We introduce new non-symmetric entropy conservative finite volume fluxes for both sets of quasi-1D equations, as well as a generalization of the entropy conservation condition to non-symmetric fluxes. When combined with an entropy stable interface flux, the resulting schemes are high order accurate, conservative, and semi-discretely entropy stable. For the quasi-1D shallow water equations, the resulting schemes are also well-balanced

DiGAN breakthrough: advancing diabetic data analysis with innovative GAN-based imbalance correction techniques

In the rapidly evolving field of medical diagnostics, the challenge of imbalanced datasets, particularly in diabetes classification, calls for innovative solutions. The study introduces DiGAN, a groundbreaking approach that leverages the power of Generative Adversarial Networks (GAN) to revolutionize diabetes data analysis. Marking a significant departure from traditional methods, DiGAN applies GANs, typically seen in image processing, to the realm of diabetes data. This novel application is complemented by integrating the unsupervised Laplacian Score for sophisticated feature selection. The pioneering approach not only surpasses the limitations of existing techniques but also sets a new benchmark in classification accuracy with a 90% weighted F1-score, achieving a remarkable improvement of over 20% compared to conventional methods. Additionally, DiGAN demonstrates superior performance over popular SMOTE-based methods in handling extremely imbalanced datasets. This research, focusing on the integrated use of Laplacian Score, GAN, and Random Forest, stands at the forefront of diabetic classification, offering a uniquely effective and innovative solution to the long-standing data imbalance issue in medical diagnostics

Comprehensive analysis of REST corepressors (RCORs) in pan-cancer

REST corepressors (RCORs) are the core component of the LSD1/CoREST/HDACs transcriptional repressor complex, which have been revealed differently expressed in various cancers, but the therapeutic and prognostic mechanisms in cancer are still poorly understood. In this study, we analyzed expression, prognostic value, molecular subtypes, genetic alteration, immunotherapy response and drug sensitivity of RCORs in pan-cancer. Clinical correlation, stemness index, immune infiltration and regulatory networks of RCORs in hepatocellular carcinoma (HCC) were detected through TCGA and GSCA database. In-vitro experiments were conducted to explore the role of RCOR1 in HCC cells. The expression of RCORs varied among different cancers, and have prognostic values in several cancers. Cancer subtypes were categorized according to the expression of RCORs with clinical information. RCORs were significantly correlated with immunotherapy response, MSI, drug sensitivity and genetic alteration in pan-cancer. In HCC, RCORs were considered as potential predictor of stemness and also had association with immune infiltration. The ceRNA-TF-kinase regulatory networks of RCORs were constructed. Besides, RCOR1 acts as an oncogene in HCC and promotes the proliferation of HCC cells by inhibiting cell cycle arrest and cell apoptosis. Taken together, our study revealed the potential molecular mechanisms of RCORs in pan-cancer, offering a benchmark for disease-related research