Diagnostic problems in two-dimensional shear wave elastography of the liver

Two-dimensional shear wave elastography(2D-SWE)is used in the clinical setting for observation of the liver.Unfortunately,a wide spectrum of artifactual images are frequently encountered in 2D-SWE,the precise mechanisms of which remain incompletely understood.This review was designed to present many of the artifactual images seen in 2D-SWE of the liver and to analyze them by computer simulation models that support clinical observations.Our computer simulations yielded the following suggestions:(1)When performing 2D-SWE in patients with chronic hepatic disease,especially liver cirrhosis,it is recommended to measure shear wave values through the least irregular hepatic surface;(2)The most useful 2D-SWE in patients with focal lesion will detect lesions that are poorly visible on B-mode ultrasound and will differentiate true tumors from pseudo-tumors(e.g.,irregular fatty change);and(3)Measurement of shear wave values in the area posterior to a focal lesion must be avoided.

Crop Yield Prediction Using Machine Learning Approaches on a Wide Spectrum

The exponential growth of population in developing countries likeIndia should focus on innovative technologies in the Agricultural processto meet the future crisis. One of the vital tasks is the crop yield predictionat its early stage;because it forms one of the most challenging tasks inprecision agriculture as it demands a deep understanding of the growth patternwith the highly nonlinear parameters. Environmental parameters like rainfall,temperature, humidity, and management practices like fertilizers, pesticides,irrigation are very dynamic in approach and vary from field to field. In theproposed work, the data were collected from paddy fields of 28 districts in widespectrum of Tamilnadu over a period of 18 years. The Statistical model MultiLinear Regression was used as a benchmark for crop yield prediction, whichyielded an accuracy of 82% owing to its wide ranging input data. Therefore,machine learning models are developed to obtain improved accuracy, namelyBack Propagation Neural Network (BPNN), Support Vector Machine, andGeneral Regression Neural Networks with the given data set. Results showthat GRNN has greater accuracy of 97% (R2 = 0.97) with a normalizedmean square error (NMSE) of 0.03. Hence GRNN can be used for crop yieldprediction in diversified geographical fields.

Dynamic Behavior and Deformation Analysis of the Fish Cage System Using Mass-Spring Model

Fish cage systems are influenced by various oceanic conditions, and the movements and deformation of the system by the external forces can affect the safety of the system itself, as well as the species of fish being cultivated. Structural durability of the system against environmental factors has been major concern for the marine aquaculture system. In this research, a mathematical model and a simulation method were presented for analyzing the performance of the large-scale fish cage system influenced by current and waves. The cage system consisted of netting, mooring ropes, floats, sinkers and floating collar. All the elements were modeled by use of the mass-spring model. The structures were divided into finite elements and mass points were placed at the mid-point of each element, and mass points were connected by springs without mass. Each mass point was applied to external and internal forces, and total force was calculated in every integration step. The computation method was applied to the dynamic simulation of the actual fish cage systems rigged with synthetic fiber and copper wire simultaneously influenced by current and waves. Here, we also tried to find a relevant ratio between buoyancy and sinking force of the fish cages. The simulation results provide improved understanding of the behavior of the structure and valuable information concerning optimum ratio of the buoyancy to sinking force according to current speeds.

Simulation of secondary nucleation of polymer crystallization via a model of microscopic kinetics

We present simulations of the mechanism of secondary nucleation of polymer crystallization,based on a new model accounting for the microscopic kinetics of attaching and detaching.As the key feature of the model,we introduced multibody-interaction parameters that establish correlations between the attaching and detaching rate constants and the resulting thickness and width of the crystalline lamella.Using MATLAB and Monte Carlo method,we followed the evolution of the secondary nuclei as a function of various multibody-interaction parameters.We identified three different growth progressions of the crystal：（i） Widening,（ii） thickening and（iii） simultaneously thickening and widening of lamellar crystals,controlled by the corresponding kinetic parameters.