Lecture capture with real‐time rearrangement of visual elements: impact on student performance

The primary goal of this study is to create and test a lecture‐capture system that can rearrange visual elements while recording is still taking place, in such a way that student performance can be positively influenced. The system we have devised is capable of integrating and rearranging multimedia sources, including learning content, the instructor and students' images, into lecture videos that are embedded in a website for students to review after school. The present study employed a two‐group experimental design, with 153 participants (145 females and 8 males) making up an experimental group in which lecture courses were recorded using the new lecture‐capture system, and 149 participants (140 females and 9 males) forming a control group whose lectures were recorded by traditional means. All participants were in the freshman college and studying Introduction to Computer and Information Science in one of six classes, and were randomly assigned to one of the two groups. The participants' midterm examination and final examination scores were collected as indicators of their academic performance, with their mathematics entrance scores used as a pre‐test. The findings obtained from analysis of covariance (ANCOVA) suggest that appropriate rearrangement of visual elements in lecture videos can significantly impact students' learning performance.     

Correlation between instrumental texture and colour quality attributes with sensory analysis of selected cheeses as affected by fat contents

This study evaluated several physical and sensory parameters of different types of cheese available in the Polish market. The measurements of textural properties were conducted in an Instron universal testing machine, while the colour properties of cheeses were measured using a Minolta chromameter. The chemical composition was determined by means of the near‐infrared spectroscopy (NIRs). Moreover, a trained sensory panel was invited to assess the cheese texture‐related properties. Generally, cheeses with reduced fat content were characterised by higher hardness, adhesiveness, cohesiveness and elasticity. Texture‐related parameters of cheese with canola oil were comparable to that of most of full‐fat cheeses. The correlation analysis between physical and sensory attributes related to cheese textural properties indicated the potential applications of TPA, shear and penetration tests (r = 0.766, r = 0.75 and r = 0.765, respectively) for the evaluation of sensory properties related to the hardness. Meanwhile, the elasticity of cheese obtained from sensory evaluation was strongly correlated with the elasticity determined from the shear test (r = 0.722) and moderately correlated with the elasticity from penetration test (r = 0.588), indicating a need to refine the method of penetration test. In addition, cheeses exhibited higher meltability during convection heating at 230 °C than microwave heating. The values of meltability for cheese with reduced fat content were lower than those of full‐fat cheese.     

The Spatial LASSO With Applications to Unmixing Hyperspectral Biomedical Images

Hyperspectral imaging (HSI) is a spectroscopic method that uses densely sampled measurements along the electromagnetic spectrum to identify the unique molecular composition of an object. Traditionally HSI has been associated with remote sensing-type applications, but recently has found increased use in biomedicine, from investigations at the cellular to the tissue level. One of the main challenges in the analysis of HSI is estimating the proportions, also called abundance fractions of each of the molecular signatures. While there is great promise for HSI in the area of biomedicine, large variability in the measurements and artifacts related to the instrumentation has slow adoption into more widespread practice. In this article, we propose a novel regularization and variable selection method called the spatial LASSO (SPLASSO). The SPLASSO incorporates spatial information via a graph Laplacian-based penalty to help improve the model estimation process for multivariate response data. We show the strong performance of this approach on a benchmark HSI dataset with considerable improvement in predictive accuracy over the standard LASSO. Supplementary materials for this article are available online.     

Phase-Structural and Electrochemical Properties of La2MgNi9 Alloys

Powder Metallurgy and Metal Ceramics - Multiphase La 2 MgNi 9 alloys are synthesized by induction melting. The crystalline structures of all phases in the alloys are determined with X-ray...