Sequential sampling strategy for the modeling of parameterized microwave and RF components

Accurate modeling of parameterized microwave and RF components often requires a large number of full-wave electromagnetic simulations. In order to reduce the overall simulation cost, a sequential sampling algorithm is proposed that selects a sparse set of data samples which characterize the overall response of the system. The resulting data samples can be fed into existing modeling techniques. The effectiveness of the approach is illustrated by a parameterized H-shaped microwave antenna

Norepinephrine and dopamine increase motility, biofilm formation and virulence of Vibrio harveyi

Vibrio harveyi is one of the major pathogens of aquatic organisms, affecting both vertebrates and invertebrates, and causes important losses in the aquaculture industry. In order to develop novel methods to control disease caused by this pathogen, we need to obtain a better understanding of pathogenicity mechanisms. Sensing of catecholamines increases both growth and production of virulence-related factors in pathogens of terrestrial animals and humans. However, at this moment, knowledge on the impact of catecholamines on the virulence of pathogens of aquatic organisms is lacking. In the present study, we report that in V harveyi, norepinephrine (NE) and dopamine (Dopa) increased growth in serum-supplemented medium, siderophore production, swimming motility, and expression of genes involved in flagellar motility, biofilm formation, and exopolysaccharide production. Consistent with this, pretreatment of V harveyi with catecholamines prior to inoculation into the rearing water resulted in significantly decreased survival of gnotobiotic brine shrimp larvae, when compared to larvae challenged with untreated V harveyi. Further, NE-induced effects could be neutralized by alpha-adrenergic antagonists or by the bacterial catecholamine receptor antagonist LED209, but not by beta-adrenergic or dopaminergic antagonists. Dopa-induced effects could be neutralized by dopaminergic antagonists or LED209, but not by adrenergic antagonists. Together, our results indicate that catecholamine sensing increases the success of transmission of V harveyi and that interfering with catecholamine sensing might be an interesting strategy to control vibriosis in aquaculture. We hypothesize that upon tissue and/or hemocyte damage during infection, pathogens come into contact with elevated catecholamine levels, and that this stimulates the expression of virulence factors that are required to colonize a new host

Granular crystals in palm oil based shortening/margarine : a review

Palm oil based shortenings and margarines are important products within the lipid industry. However, a widespread quality deterioration issue is often reported regarding their long-term storage: the appearance of granular crystals or grains that are regarded as unwanted because of the deflecting visual appearance and the negative mouthfeel during consumption. In this review, the role of fat blends composition, crystallization process and storage conditions in the formation and growth of these unwanted granular crystals will be discussed and summarized. In addition, some potential approaches in preventing the formation of granular crystals in palm oil based shortening and margarine are also introduced

SD Prism: Visualizing the Standard Deviation as the Size of a Prism Using R/RStudio

SD Prism is a graphical R package used for visualizing the standard deviation of a data set. Given a raw data set, the standard deviation (SD) is defined as the square-root of twice the mean square of all pairwise half deviations between any two sample observations. This interpretation leads to a geometric visualization of the sample SD, and a more elementary explanation as to why the denominator in the sample variance is one less than the sample size. In this article we will explain step by step how to understand it mathematically and how the package implements the methodology to visualize the SD

SD Prisms 2D & 3D: Visualizing the Standard Deviation

SD Prism is a graphical R package used for visualizing the standard deviation of a data set. Given a raw data set, the standard deviation (SD) is defined as the square root of the sample variance. Sarkar and Rashid (2017) interpret the sample SD as the square root of twice the mean square of all pairwise half deviations between any two sample observations. This interpretation leads to a geometric visualization of the sample SD and a more elementary explanation as to why the denominator in the sample variance is one less than the sample size. In this article, we will explain step by step how to understand it mathematically and how the package implements the methodology to visualize the SD