A new smart smudge attack using CNN

International Journal of Information Security - This paper deals with a well-known problem in the area of the smudge attacks: when a user draws a pattern to unlock the pattern lock on a smartphone...     

Comparison of yield and metabolites according to the types of hilling materials utilized during Glehnia littoralis sprout vegetable cultivation

Food Science and Biotechnology - Various hilling materials (rice hulls, pine sawdust, and perlite) were compared to produce sprout vegetables using beach silvertop (Glehnia littoralis Fr. Schm. ex...     

Capsiate Intake with Exercise Training Additively Reduces Fat Deposition in Mice on a High-Fat Diet,but Not without Exercise Training

While exercise training (ET) is an efficient strategy to manage obesity, it is recommended with a dietary plan to maximize the antiobesity functions owing to a compensational increase in energy intake. Capsiate is a notable bioactive compound for managing obesity owing to its capacity to increase energy expenditure. We aimed to examine whether the antiobesity effects of ET can be further enhanced by capsiate intake (CI) and determine its effects on resting energy expenditure and metabolic molecules. Mice were randomly divided into four groups (n = 8 per group) and fed high-fat diet. Mild-intensity treadmill ET was conducted five times/week; capsiate (10 mg/kg) was orally administered daily. After 8 weeks, resting metabolic rate and metabolic molecules were analyzed. ET with CI additively reduced the abdominal fat rate by 18% and solely upregulated beta-3-adrenoceptors in adipose tissue (p = 0.013) but did not affect the metabolic molecules in skeletal muscles. Surprisingly, CI without ET significantly increased the abdominal fat rate (p = 0.001) and reduced energy expenditure by 9%. Therefore, capsiate could be a candidate compound for maximizing the antiobesity effects of ET by upregulating beta-3-adrenoceptors in adipose tissue, but CI without ET may not be beneficial in managing obesity.     

Role of additives in fabrication of soy-based rigid polyurethane foam for structural and thermal insulation applications

Environmental concerns continue to pose the challenge to replace petroleum-based products with renewable ones completely or at least partially while maintaining comparable properties. Herein, rigid polyurethane (PU) foams were prepared using soy-based polyol for structural and thermal insulation applications. Cell size, density, thermal resistivity, and compression force deflection (CFD) values were evaluated and compared with that of petroleum-based PU foam Baydur 683. The roles of different additives, that is, catalyst, blowing agent, surfactants, and different functionalities of polyol on the properties of fabricated foam were also investigated. For this study, dibutyltin dilaurate was employed as catalyst and water as environment friendly blowing agent. Their competitive effect on density and cell size of the PU foams were evaluated. Five different silicone-based surfactants were employed to study the effect of surface tension on cell size of foam. It was also found that 5 g of surfactant per 100 g of polyol produced a foam with minimum surface tension and highest thermal resistivity (R value: 26.11 m²·K/W). However, CFD values were compromised for higher surfactant loading. Additionally, blending of 5 g of higher functionality soy-based polyol improved the CFD values to 328.19 kPa, which was comparable to that of petroleum-based foam Baydur 683.     

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.