Modelling of Soil Profile Pollutant Yield on Slaughterhouse Wastes Deposited Land

Aim: The study aimed at modeling the concentration of pollutants along soil profile using finite element method. Study Design: Data was generated from the laboratory on the concentrations of selected heavy metals at varying depths of land discharged slaughterhouses. This was used to estimate the level of nutrient build-up in the soil within these environs, hence, used to verify and validate the finite element analysis. The model upon validation was used to predict the rate of pollutant build-up in the soil within the slaughterhouses discharge areas. Methodology: A total of twelve composite samples were collected from three different land discharged locations. The three composite samples each were collected from the sampling locations at a depth of 0 to 10cm, 10 to 20cm, 20 to 30cm and 30 to 40cm. Four composite samples each were collected for analysis from the three sampling locations on specified sampling dates. The samples were then placed in sterile polythene bags and transported to the laboratory for processing. The laboratory results obtained for heavy metals were used for the generated model verification and validation, hence predictions for pollutants accumulation was done on a time step. Results: Model verification showed a good fit of a nonlinear polynomial curve for both the measured and predicted values with R² values of 0.9978 to 0.9985 for zinc and 0.9978 to 0.9984 for lead at a selected time step of 15years. It was observed however, that there was an increasing tendency to uniformity of concentration as the time step increased; this was due to parameters build-up with time in the soil. Conclusion: Finite element results revealed a high build-up in the concentration of pollutants (Zinc and Lead) in the land discharged slaughterhouses.

On Digital Twins, Mirrors, and Virtualizations: Frameworks for Model Verification and Validation

A powerful new idea in the computational representation of structures is that of the digital twin. The concept of the digital twin emerged and developed over the last decade, and has been identified by many industries as a highly desired technology. The current situation is that individual companies often have their own definitions of a digital twin, and no clear consensus has emerged. In particular, there is no current mathematical formulation of a digital twin. A companion paper to the current one will attempt to present the essential components of the desired formulation. One of those components is identified as a rigorous representation theory of models; most importantly, governing how they are verified and validated, and how validation information can be transferred between models. Unlike its companion, which does not attempt detailed specification of any twin components, this paper will attempt to outline a rigorous representation theory of models, based on the introduction of two new concepts: mirrors and virtualizations. The paper is not intended as a passive wish list; it is intended as a rallying call. The new theory will require the active participation of researchers across a number of domains including: pure and applied mathematics, physics, computer science, and engineering. The paper outlines the main objects of the theory and gives examples of the sort of theorems and hypotheses that might be proved in the new framework.

Using the CVP Traffic Detection Model at Road-Section Applies to Traffic Information Collection and Monitor - the Case Study

This paper proposes a using Cellular-Based Vehicle Probe (CVP) at road-section (RS) method to detect and setup a model for traffic flow information (info) collection and monitor. There are multiple traffic collection devices including CVP, ETC-Based Vehicle Probe (EVP), Vehicle Detector (VD), and CCTV as traffic resources to serve as road condition info for predicting the traffic jam problem, monitor and control. The main project has been applied at Tai # 2 Ghee-Jing roadway connects to Wan-Li section as a trial field on fiscal year of 2017-2018. This paper proposes a man-flow turning into traffic-flow with Long-Short Time Memory (LTSM) from recurrent neural network (RNN) model. We also provide a model verification and validation methodology with RNN for cross verification of system performance.

Is Precipitation a Good Metric for Model Performance?

Precipitation has often been used to gauge the performances of numerical weather and climate models, sometimes together with other variables such as temperature, humidity, geopotential, and clouds. Precipitation, however, is singular in that it can present a high spatial variability and probably the sharpest gradients among all meteorological fields. Moreover, its quantitative measurement is plagued with difficulties, and there are even notable differences among different reference datasets. Several additional issues sometimes lead to questions about its usefulness in model validation. This essay discusses the use of precipitation for model verification and validation and the crucial role of highly precise and reliable satellite estimates, such as those from NASA’s Global Precipitation Mission Core Observatory.