局部施肥对植株生长及根系形态的影响

本文论述了局部施肥对植株地上部与地下部的影响。局部施肥促进了施肥区根系的发达 ,第一侧根与第二侧根较多 ,根的吸收表面积增多 ,根鲜重增加 ,对养分的吸收能力增强 ,但整个根系与地上部的干鲜重下降。     

Study of the growth of YBa2Cu3O7−x films on a Al2O3 single crystal with a CeO2 buffer sublayer

Superconducting YBa₂Cu₃O_7−x films were prepared by magnetron sputtering on Al₂O₃ single crystals with a CeO₂ sublayer. Scattering of moderate-energy ions and x-ray diffraction were used to show that the films exhibit good single-crystal properties over the entire thickness up to 2.6 μm. The hypothesis is advanced that the indentations formed by the growth of films above “extraneous” phase grains may act as defect sinks. Pis’ma Zh. Tekh. Fiz. 24, 91–95 (January 12, 1998)     

Evolving Least Squares Support Vector Machines for Stock Market Trend Mining

In this paper, an evolving least squares support vector machine (LSSVM) learning paradigm with a mixed kernel is proposed to explore stock market trends. In the proposed learning paradigm, a genetic algorithm (GA), one of the most popular evolutionary algorithms (EAs), is first used to select input features for LSSVM learning, i.e., evolution of input features. Then, another GA is used for parameters optimization of LSSVM, i.e., evolution of algorithmic parameters. Finally, the evolving LSSVM learning paradigm with best feature subset, optimal parameters, and a mixed kernel is used to predict stock market movement direction in terms of historical data series. For illustration and evaluation purposes, three important stock indices, S&P 500 Index, Dow Jones Industrial Average (DJIA) Index, and New York Stock Exchange (NYSE) Index, are used as testing targets. Experimental results obtained reveal that the proposed evolving LSSVM can produce some forecasting models that are easier to be interpreted by using a small number of predictive features and are more efficient than other parameter optimization methods. Furthermore, the produced forecasting model can significantly outperform other forecasting models listed in this paper in terms of the hit ratio. These findings imply that the proposed evolving LSSVM learning paradigm can be used as a promising approach to stock market tendency exploration.     

Credit scoring using support vector machines with direct search for parameters selection

Support vector machines (SVM) is an effective tool for building good credit scoring models. However, the performance of the model depends on its parameters’ setting. In this study, we use direct search method to optimize the SVM-based credit scoring model and compare it with other three parameters optimization methods, such as grid search, method based on design of experiment (DOE) and genetic algorithm (GA). Two real-world credit datasets are selected to demonstrate the effectiveness and feasibility of the method. The results show that the direct search method can find the effective model with high classification accuracy and good robustness and keep less dependency on the initial search space or point setting.     

Applications, techniques, and microfluidic interfacing for nanoscale biosensing

Biosensors based on nanotechnology are rapidly developing and are becoming widespread in the biomedical field and analytical chemistry. For these nanobiosensors to reach their potential, they must be integrated with appropriate packaging techniques, which are usually based on nano/microfluidics. In this review we provide a summary of the latest developments in nanobiosensors with a focus on label-based (fluorescence and nanoparticle) and label-free methods (surface plasmon resonance, micro/nanocantilever, nanowires, and nanopores). An overview on how these sensors interface with nano/microfluidics is then presented and the latest papers in the area summarized.     

A two-step surface-based 3D deep learning pipeline for segmentation of intracranial aneurysms

Computational Visual Media - The exact shape of intracranial aneurysms is critical in medical diagnosis and surgical planning. While voxel-based deep learning frameworks have been proposed for this...     

Dynamical systems identification using Gaussian process models with incorporated local models

Gaussian process (GP) models form an emerging methodology for modelling nonlinear dynamic systems which tries to overcome certain limitations inherent to traditional methods such as e.g. neural networks (ANN) or local model networks (LMN).The GP model seems promising for three reasons. First, less training parameters are needed to parameterize the model. Second, the variance of the model's output depending on data positioning is obtained. Third, prior knowledge, e.g. in the form of linear local models can be included into the model. In this paper the focus is on GP with incorporated local models as the approach which could replace local models network.Much of the effort up to now has been spent on the development of the methodology of the GP model with included local models, while no application and practical validation has yet been carried out. The aim of this paper is therefore twofold. The first aim is to present the methodology of the GP model identification with emphasis on the inclusion of the prior knowledge in the form of linear local models. The second aim is to demonstrate practically the use of the method on two higher order dynamical systems, one based on simulation and one based on measurement data.