Direct observation of particle kinematics in biaxial shearing test

Biaxial shearing tests on dual-sized, 2d particle assemblies are conducted at several confining pressures. The effect of particle angularity, an important mesoscale shape descriptor, is investigated at the macro and micro levels. Macroscopically, it is observed that assemblies composed of angular particles exhibit higher strengths and dilations. The difference observed in bulk behavior due to particle angularity can be explained reasonably by considering particle-level mechanisms. A novel 2D image analysis technique is employed to estimate particle kinematics. Particle rotation results to be a key mechanism strongly influenced by particle shape determining the overall granular behavior. Unlike circular particles, angular ones are more resistant to rotations due to stronger interlocking and consequently exhibit higher strengths.

The Use of Image Analysis and Sensory Analysis for the Evaluation of Cultivar Differentiation of Freeze-Dried and Lacto-Fermented Beetroot (Beta vulgaris L.)

The aim of the research was to compare the possibility of distinguishing the cultivars of processed beetroots using image analysis technique and sensory evaluation. The differentiation of processed samples was tested for freeze-dried beetroot ‘Czerwona Kula’ and ‘Cylindra’, lacto-fermented beetroot ‘Czerwona Kula’ and ‘Cylindra’, freeze-dried lacto-fermented beetroot ‘Czerwona Kula’ and ‘Cylindra’. The textures from the images of quarters of root slices, as well as sensory attributes evaluated by expert sensory assessors, were determined. The differences in the means of selected textures from color spaces Lab, RGB and XYZ for different cultivars of raw and processed beetroots were observed. The raw beetroots ‘Czerwona Kula’ and ‘Cylindra’ were discriminated with the accuracy of up to 94.5% for models built based on selected texture from color space RGB. In the case of processed beetroots ‘Czerwona Kula’ and ‘Cylindra’, the accuracy reached 96% (color space Lab) for freeze-dried beetroots, 99% (color space Lab) for lacto-fermented beetroots, 98.5% (color space Lab) for freeze-dried lacto-fermented beetroots. In the case of sensory attributes, no statistically significant differences were observed between the beetroot samples.

Detection and Prediction Analysis of Featured Dataset of Viral Diseases

Viral Diseases occurs due to pathogenic microorganisms such as bacteria, viruses, parasites and fungi. The disease spreads directly or indirectly from one person to another. Animal diseases are animal diseases that cause disease when they affect humans. Viral diseases are diseases caused by living organisms such as bacteria, viruses, fungi and parasites. Many creatures live in our bodies. Usually these are harmless and convenient. This research illustrates a review of the latest mobile health applications based on Internet of Things that are used for viral disease management. With quick progressions in remote and web advancements, various applications dependent on Internet of Things have been proposed for the executives of viral disease. The vast majority of these applications center around tolerant checking and innovation based basic leadership. This investigation breaks down the working and fundamental design of these most recent application onviral examine the significant issues and difficulties looked by them. The fundamental goal of this article is to explore the analysts in planning propelled applications for viral disease the executives. Furthermore, exploration examine and proposed answers for rising security worries from a multipoint of view to distinguish the dangers and alleviations. Initially, this gives an assessment of protection issues and worries in AI frameworks because of asset limitations. Second, the proposed work is also investigating on image-based prediction using image analysis technique and AI.

Hydration Kinetics for Alkaline Activation of Slag from Color Variation Data

In this study, we explore a new method based on color variation data to derive the kinetics of the entire process of the hydration of alkali-activated slag (AAS). Using this image analysis technique, we can monitor the induction period that cannot be observed using conventional microcalorimetry techniques. Color variation was recorded across a sequence of 9999 images, which were processed via MATLAB software package. Further, an average pixel value (APV) was determined to represent the color in each image. Reaction parameters, such as color variation velocity v(t), reaction speed ε(t), and hydration degree α(t), that govern the entire hydration process were determined. On the basis of the reaction parameters and a Krstulovic–Dabic kinetic model, integral and differential equations were derived to simulate the three basic processes of AAS hydration. Equations describing the reaction kinetics of AAS with solutions of three different concentrations of NaOH were extracted using this method.