Fuzzy recognition of missile borne multi-line array infrared detection based on size calculating

In order to improve the infrared detection and discrimination ability of the smart munition to the dynamic armor target under the complex background,the multi-line array infrared detection system is established based on the combination of the single unit infrared detector.The surface dimension features of ground armored targets are identified by size calculating solution algorithm.The signal response value and the value of size calculating are identified by the method of fuzzy recognition to make the fuzzy classification judgment for armored target.According to the characteristics of the target signal,a custom threshold de-noising function is proposed to solve the problem of signal preprocessing.The multi-line array infrared detection can complete the scanning detection in a large area in a short time with the characteristics of smart munition in the steady-state scanning stage.The method solves the disadvantages of wide scanning interval and low detection probability of single unit infrared detection.By reducing the scanning interval,the number of random rendezvous in the infrared feature area of the upper surface is increased,the accuracy of the size calculating is guaranteed.The experiments results show that in the fuzzy recognition method,the size calculating is introduced as the feature operator,which can improve the recognition ability of the ground armor target with different shape size.

Consumer Panel Size in Sensory Cosmetic Product Evaluation: A Pilot Study from a Statistical Point of View

Subjective evaluations are nowadays applied more commonly in cosmetic product assessment. They are used in quality control, product development steps and efficacy studies for claim support. Several studies have been published to determine the adequate number of panelists, but recommendations and guidelines dealing with this topic are rare in the cosmetic sector. The aim of the present pilot study was to recommend a suitable study plan and define the adequate consumer panel size for cosmetic consumer assessment. A questionnaire-based product evaluation study, with three different cosmetic products, was organized as a consumer test using a seven-point scale. As a last step, a specific statistical calculation was performed to define the minimum sample size. It showed that the minimum sample size, besides the obvious statistical parameters of standard deviation and confidence interval, also depends on age and gender of the panelists and product assessment item. Utilizing a CI of 95% a minimum of 60 panelists seems to be sufficient for home-use-test (HUT) with a given seven-point scale. A minimum of 101 panelists are shown to be sufficient utilizing a CI of 99%.

First-Principles Calculations of the Quantum Size Effects on the Stability and Reactivity of Ultrathin Ru (0001) Films

We carry out first-principles calculations of Ru（0001） films up to 30 monolayers （MLs） to study the quantum size effect （Q, SE） of Ru films for two cases： the freestanding Ru films and Ru films on Pt（111） substrates. Our studies show that the properties of these films （surface energy, work-function, charge density decay length in a vacuum and chemical reactivity） exhibit pronounced oscillatory behavior as a function of the film thickness, with an oscillation period of about four MLs for both cases due to the relationship of the match between the Fermi wave vector and the film thickness. Due to the localization of d-electron of Ru films, these quantum oscillations almost disappear when the thickness of the film is more than -20 ML for the free standing Ru films, while for the Ru films on Pt substrates the oscillations disappear quickly when the thickness of the film is beyond -13 ML. Our results reveal that the stability and reactivity of the Ru films could be tailored through Q, SE and the Ru bilayer grown on Pt substrates observed in the experiment is also related to the effect.

Microbiome data analysis with applications to pre-clinical studies using QIIME2: Statistical considerations

Diversity analysis and taxonomic profiles can be generated from marker-gene sequence data with the help of many available computational tools.The Quantitative Insights into Microbial Ecology Version 2(QIIME2)has been widely used for 16S rRNA data analysis.While many articles have demonstrated the use of QIIME2 with suitable datasets,the application to preclinical data has rarely been talked about.The issues involved in the pre-clinical data include the low-quality score and small sample size that should be addressed properly during analysis.In addition,there are few articles that discuss the detailed statistical methods behind those alpha and beta diversity significance tests that researchers are eager to find.Running the program without knowing the logic behind it is extremely risky.In this article,we first provide a guideline for analyzing 16S rRNA data using QIIME2.Then we will talk about issues in pre-clinical data,and how they could impact the outcome.Finally,we provide brief explanations of statistical methods such as group significance tests and sample size calculation.