Teachers attending to student reasoning: Do beliefs matter?

We present the results of a quasi-experimental study of pre-service elementary teachers' learning to recognize students' mathematical reasoning from classroom videos. Researchers examined the nature of participants’ beliefs regarding mathematics education. We found that pre-service elementary teachers whose beliefs were consistent with NCTM Process Standards (NCTM, 2000), or that transitioned in the direction of consistency with the Standards, regarding the teaching and learning of mathematics, were more successful in recognizing students' reasoning than those whose beliefs were generally inconsistent. Predictive Analytics and Generalized Linear Regression modeling were used to quantify the magnitude of experimental pre-service teachers’ reasoning growth and combined pre/post study assessment reasoning success in contrast to that of the comparison groups. The resulting model explained nearly 90% of the variability in success on the reasoning assessment, showing that beliefs do indeed matter for recognition of reasoning.     

Some theoretical aspects of elastic wave modeling with a recently developed spectral element method

A spectral element method has been recently developed for solving elastodynamic problems. The numerical solutions are obtained by using the weak formulation of the elastodynamic equation for heterogeneous media, based on the Galerkin approach applied to a partition, in small subdomains, of the original physical domain. In this work, some mathematical aspects of the method and the associated algorithm implementation are systematically investigated. Two kinds of orthogonal basis functions, constructed with Legendre and Chebyshev polynomials, and their related Gauss-Lobatto collocation points are introduced. The related integration formulas are obtained. The standard error estimations and expansion convergence are discussed. An element-by-element pre-conditioned conjugate gradient linear solver in the space domain and a staggered predictor/multi-corrector algorithm in the time integration are used for strong heterogeneous elastic media. As a consequence, neither the global matrices nor the effective force vector is assembled. When analytical formulas are used for the element quadrature, there is even no need for forming element matrix in order to further save memory without losing much in computational efficiency. The element-by-element algorithm uses an optimal tensor product scheme which makes this method much more efficient than finite-element methods from the point of view of both memory storage and computational time requirements. This work is divided into two parts. The first part mainly focuses on theoretical studies with a simple numerical result for the Che-byshev spectral element, and the second part, mainly with the Legendre spectral element, will give the algorithm implementation, numerical accuracy and efficiency analyses, and then the detailed modeling example comparisons of the proposed spectral element method with a pseudo-spectral method, which will be seen in another work by Lin, Wang and Zhang.     

基于高光谱成像技术的番茄叶片灰霉病早期检测研究

提出了独立软模式法(SIMCA)的番茄叶片灰霉病特征波段图像的提取,并通过多元线性回归法(MLR)提取波段融合图像,通过最小距离法获取番茄灰霉病患病信息的技术路线。利用680～740 nm波段的方差图像和建模能力参数提取的特征波段,并作为输入变量进行MLR分析,在0.5准确率阈值下,准确率均大于99%,说明特征波段可以实现番茄叶片灰霉病的检测,并利用MLR回归系数提取波段融合图像,通过最小距离法获取番茄灰霉病患病信息,结果表明所提出的方法具有很好的预测能力,为番茄灰霉病的早期检测提供了一种新方法,且大大降低了高光谱图像的数据处理时间。     

真空条件下LiCl溶液蒸发量随浓度变化规律研究

除湿溶液再生是除湿空调系统的核心过程,为了提高除湿溶液再生效率,以蒸发量作为评价标准,在不同的真空度下对不同浓度LiCl溶液进行再生。对不同再生条件下,溶液的再生情况进行数学模拟及实验研究,并将模拟结果与实验结果进行对比分析,结果表明,实验结果同模拟结果具有相同的变化趋势。在6 kPa和8 kPa的压强下,LiCl溶液的蒸发量随浓度的增大先上升后下降,而在1 kPa的压强下,LiCl溶液的蒸发量随浓度的增大呈现出先下降再升高接着又下降的趋势。     

Disentangling stochastic signals superposed on short localized oscillations

We introduce a procedure for separating periodic oscillations superposed on a stochastic signal. The procedure combines empirical mode decomposition (EMD) of a signal with tools of data analysis based on stochastic differential equations, namely nonlinear Langevin equations. Taking the set of modes retrieved from the EMD of the signal, our procedure is able to separate them into two groups, one composing the periodic signal and another composing the stochastic signal. The framework is robust for a broad family of localized oscillations, in the range of large frequencies. In particular, we show that, in this context, the EMD method outperforms a low-pass filter and is robust for a wide interval of different frequency ranges and amplitudes of the periodic oscillation, as well as for a broad family of different non-linear Langevin processes.     

A survey on HHL algorithm: From theory to application in quantum machine learning

The Harrow-Hassidim-Lloyd (HHL) algorithm is a method to solve the quantum linear system of equations that may be found at the core of various scientific applications and quantum machine learning models including the linear regression, support vector machines and recommender systems etc. After reviewing the necessary background on elementary quantum algorithms, we provide detailed account of how HHL is exploited in different quantum machine learning (QML) models, and how it provides the desired quantum speedup in all these models. At the end, we briefly discuss some of the remaining challenges ahead for HHL-based QML models and related methods.     

Modeling the drying of ultrasound and glucose pretreated sweet potatoes: The impact on phytochemical and functional groups

The influence of ultrasonic frequency (20 kHz) and glucose pretreatments either alone or in combination on the drying of sweet potato slices (3 mm) using a hot-air dryer at 60 °C was tested to study the kinetics modeling, phytochemicals, antioxidant activities, and functional and textural changes of the final dried product. The results indicated that total phenolic content and total flavonoid content were significantly higher in glucose-pretreated samples while antioxidant activities were higher in ultrasound- and glucose-pretreated samples. For vitamin C, much degradation occurred in the glucose-pretreated samples when compared with the other pretreated samples apart from the control. Enzymatic browning made a minor contribution to the ultrasound/glucose-pretreated samples, while no significant differences were noted in the glucose-pretreated samples. A modified Henderson and Pabis (MHP) model, followed by the two-term and Hii models, fitted best among the 15 selected mathematical models. Fourier Transform Infrared Spectroscopy (FTIR) analysis revealed the presence of glucose, phenols, and flavonols in all samples. Microstructural analysis confirmed the hardness (N) in the final glucose-pretreated samples due to glucose layers and less cell damage.     

高精度半导体激光打标机F-θ镜头设计

为保证半导体激光打标机F-θ镜头的扫描质量,实现系统像高与扫描角的线性变化,需对F-θ镜头给予一定的畸变量,并使其满足等晕条件。分析F-θ镜头工作原理及像差要求,根据1 064 nm半导体激光打标机的光源成像要求选择合适的玻璃材料,合理分配每片透镜的光焦度,以保证等晕成像;根据F-θ镜头线性成像要求,计算系统总畸变量为1.6%,系统总畸变量为系统的实际桶形畸变与相对畸变量之和;在光学系统优化设计时,引入这两项优化参数,优化过程中观察系统成像变化情况。设计结果表明:系统MTF曲线接近衍射极限,F-θ镜头相对畸变小于0.36%,各视场均方根半径均小于艾里斑直径,并且整个系统70%的能量集中在直径为16 μm的圆内,系统总畸变量为1.58%,满足设计指标要求。     

Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.     

Applying Text Analytics for Studying Research Trends in Dependability

The dependability of systems and networks has been the target of research for many years now. In the 1970s, what is now known as the top conference on dependability—The IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)—emerged gathering international researchers and sparking the interest of the scientific community. Although it started in niche systems, nowadays dependability is viewed as highly important in most computer systems. The goal of this work is to analyze the research published in the proceedings of well-established dependability conferences (i.e., DSN, International Symposium on Software Reliability Engineering (ISSRE), International Symposium on Reliable Distributed Systems (SRDS), European Dependable Computing Conference (EDCC), Latin-American Symposium on Dependable Computing (LADC), Pacific Rim International Symposium on Dependable Computing (PRDC)), while using Natural Language Processing (NLP) and namely the Latent Dirichlet Allocation (LDA) algorithm to identify active, collapsing, ephemeral, and new lines of research in the dependability field. Results show a strong emphasis on terms, like ‘security’, despite the general focus of the conferences in dependability and new trends that are related with ’machine learning’ and ‘blockchain’. We used the PRDC conference as a use case, which showed similarity with the overall set of conferences, although we also found specific terms, like ‘cyber-physical’, being popular at PRDC and not in the overall dataset.