Modeling Tracer Flow Characteristics in Different Types of Pores: Visualization and Mathematical Modeling

Structure of porous media and fluid distribution in rocks can significantly affect the transport characteristics during the process of microscale tracer flow.To clarify the effect of micro heterogeneity on aqueous tracer transport,this paper demonstrates microscopic experiments at pore level and proposes an improved mathematical model for tracer transport.The visualization results show a faster tracer movement into movable water than it into bound water,and quicker occupancy in flowing pores than in storage pores caused by the difference of tracer velocity.Moreover,the proposed mathematical model includes the effects of bound water and flowing porosity by applying interstitial flow velocity expression.The new model also distinguishes flowing and storage pores,accounting for different tracer transport mechanisms(dispersion,diffusion and adsorption)in different types of pores.The resulting analytical solution better matches with tracer production data than the standard model.The residual sum of squares(RSS)from the new model is 0.0005,which is 100 times smaller than the RSS from the standard model.The sensitivity analysis indicates that the dispersion coefficient and flowing porosity shows a negative correlation with the tracer breakthrough time and the increasing slope,whereas the superficial velocity and bound water saturation show a positive correlation.

New Asymmetric Model for Predicting Ternary Thermodynamic Properties

The prediction of the thermodynamic properties of ternary systems from the properties of their sub-binary systems is of great importance to phase diagram calculations. In the present study, a new asymmetric model which has more clear physical significance has been developed for evaluating the ternary thermodynamic properties from its three binary components. The model is considered to be rigorous in the case where the pseudobinary systems of fixed X2/X3 are regular are regular solution. The application of new model to the prediction of ternary enthalpies of mixing for Bi-Ga-Sn, Au-Ag-Sn and NaCl-KCl-CaCl2 systems shows that the calculated results by new model are closer to experimental data than those by Toop's model.

Dose prediction of lopinavir/ritonavir for 2019-novel coronavirus (2019-nCoV) infection based on mathematic modeling

Wuhan novel coronavirus or 2019-novel coronavirus(2019-nCoV)infection is a rapidly emerging respiratory viral disease[1].2019-nCoV infection is characterized as febrile illness with possible severe lung complication[1].The disease was firstly reported in China in December 2019 and then spread to many countries(such as Thailand,Japan and Singapore)[2,3].As a new disease,there is a limited knowledge of treatment for the infection.Lu recently proposed that some drug might be useful in treatment of 2019-nCoV infection[3].

Mathematical Model of the Spread of the Coronavirus Disease 2019 (COVID-19) in Burkina Faso

In this paper, we develop a mathematical model of the COVID-19 pandemic in Burkina Faso. We use real data from Burkina Faso National Health Commission against COVID-19 to predict the dynamic of the disease and also the cumulative number of reported cases. We use public policies in model in order to reduce the contact rate, this allows to show how the reduction of the daily report of infectious cases goes, so we would like to draw the attention of decision makers for a rapid treatment of reported cases.

A Method for Constructing Mathematical Modeling of the Spread of a New Crown Pneumonia Epidemic Based on the Effect of Temperature

To better predict the spread of the COVID-19 outbreak, mathematical modeling and analysis of the spread of the COVID-19 outbreak is proposed based on data analysis and infectious disease theory. Firstly, the mathematical model indicators of the spread of the new coronavirus pneumonia epidemic are determined by combining the theory of infectious diseases, the basic assumptions of the spread model of the new coronavirus pneumonia epidemic are given based on the theory of data analysis model, the spread rate of the new coronavirus pneumonia epidemic is calculated by combining the results of the assumptions, and the spread rate of the epidemic is inverted to push back into the assumptions to complete the construction of the mathematical modeling of the diffusion. Relevant data at different times were collected and imported into the model to obtain the spread data of the new coronavirus pneumonia epidemic, and the results were analyzed and reflected. The model considers the disease spread rate as the dependent variable of temperature, and analyzes and verifies the spread of outbreaks over time under real temperature changes. Comparison with real results shows that the model developed in this paper is more in line with the real disease spreading situation under specific circumstances. It is hoped that the accurate prediction of the epidemic spread can provide relevant help for the effective containment of the epidemic spread.

Effects of Estrogen Contamination on Human Cells: Modeling and Prediction Based on Michaelis-Menten Kinetics

In this paper, we propose a novel prevention strategy to alert citizens when water is contaminated by estro-gen. Epidemiological studies have shown that chronic exposure to high blood level of estrogen is associated with the development of breast cancer. The preventive strategy proposed in this paper is based on the predic-tion of estrogen effects on human living cells. Based on first principle insights, we develop in this work, a mathematical model for this prediction purpose. Dynamic measurements of cell proliferation response to es-trogen stimulation were continuously monitored by a real-time cell electronic sensor (RT-CES) and used in order to estimate the parameters of the model developed.

Proposal and evaluation of a new norm index-based QSAR model to predict pEC50 and pCC50 activities of HEPT derivatives

The search and development of anti-HIV drugs is currently one of the most urgent tasks of pharmacological studies. In this work, a quantitative structure-activity relationship （QSAR） model based on some new norm indexes, was obtained to a series of more than 150 HEPT derivatives （1-[（2-hydroxyethoxy）methyl]-6-（phenylthio）thymine） to find their pEC50 （the required effective concentration to achieve 50% protection of MT-4 cells against the cytopathic effect of virus） and pCC50 （the required cytotoxic concentration to reduce visibility of 50% mock infected cell） activities. The model efficiencies were then validated using the leave-one-out cross validation （LOO-CV） and y- randomization test. Results indicated that this new model was efficient and could provide satisfactory results for prediction of pECso and pCC50 with the higher R2 train and the higher Rt2est. By using the leverage approach, the applicability domain of this model was further investigated and no response outlier was detected for HEFT derivatives involved in this work. Comparison results with reference methods demonstrated that this new method could result in significant improvements for predicting pEC50 and pCC50 of anti-HIV HEPT derivatives. Moreover, results shown in this present study suggested that these two absolutely different activities pECso and pCC50 of anti-HIV HEPT derivatives could be predicted well with a totally similar QSAR model, which indicated that this model mizht have the potential to be further utilized for other biological activities of HEFT derivatives.

Statistical Time Series Forecasting Models for Pandemic Prediction

COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.

Mathematical Morphology-Based Artificial Technique for Renewable Power Application

This paper suggests a combined novel control strategy for DFIG based wind power systems(WPS)under both nonlinear and unbalanced load conditions.The combined control approach is designed by coordinating the machine side converter(MSC)and the load side converter(LSC)control approaches.The proposed MSC control approach is designed by using a model predictive control(MPC)approach to generate appropriate real and reactive power.The MSC controller selects an appropriate rotor voltage vector by using a minimized optimization cost function for the converter operation.It shows its superiority by eliminating the requirement of transformation,switching table,and the PWM techniques.The proposed MSC reduces the cost,complexity,and computational burden of the WPS.On the other hand,the LSC control approach is designed by using a mathematical morphological technique(MMT)for appropriate DC component extraction.Due to the appropriate DC-component extraction,the WPS can compensate the harmonics during both steady and dynamic states.Further,the LSC controller also provides active power filter operation even under the shutdown of WPS condition.To verify the applicability of coordinated control operation,the WPS-based microgrid system is tested under various test conditions.The proposed WPS is designed by using a MATLAB/Simulink software.

Residual subsidence time series model in mountain area caused by underground mining based on GNSS online monitoring

The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm,which seriously threatens the safety of people's production and life in the mining area.Therefore,it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining.Compared with traditional level survey and InSAR(Interferometric Synthetic Aperture Radar)technology,GNSS(Global Navigation Satellite System)online monitoring technology has the advantages of long-term monitoring,high precision and more flexible monitoring methods.The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation,which can not only describe the residual subsidence process from the mechanism,but also predict the residual subsidence.Therefore,based on GNSS online monitoring technology,combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics,this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area.Through the example,the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence.The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground,and the model prediction effect is good,which provides a new method for the prediction of residual subsidence in mountain mining.

Mathematical modeling and the transmission dynamics in predicting the Covid-19-What next in combating the pandemic

Mathematical predictions in combating the epidemics are yet to reach its perfection.The rapid spread,the ways,and the procedures involved in containment of a pandemic demand the earliest understanding in finding solutions in line with the habitual,physiological,biological,and environmental aspects of life with better computerised mathematical modeling and predictions.Epidemiology models are key tools in public health management programs despite having a high level of uncertainty in each one of these models.This paper describes the outcome and the challenges of SIR,SEIR,SEIRU,SIRD,SLIAR,ARIMA,SIDARTHE,etc models used in prediction of spread,peak,and reduction of Covid-19 cases.

Dynamic matrix predictive control for a hydraulic looper system in hot strip mills

Controlling the looper height and strip tension is important in hot strip mills because these variables affect both the strip quality and strip threading. Many researchers have proposed and applied a variety of control schemes for this problem, but the increasingly strict market demand for strip quality requires further improvements. This work describes a dynamic matrix predictive control(DMC) strategy that realizes the optimal control of a hydraulic looper multivariable system. Simulation experiments for a traditional controller and the proposed DMC controller were conducted using MATLAB/Simulink software. The simulation results show that both controllers acquire good control effects with model matching. However, when the model is mismatched, the traditional controller produces an overshoot of 32.4% and a rising time of up to 2120.2 ms, which is unacceptable in a hydraulic looper system. The DMC controller restricts the overshoot to less than 0.08%, and the rising time is less than 48.6 ms in all cases.

Development of predictive models for egg freshness and shelf-life under different storage temperatures

The objective of the present study was to develop models for egg freshness and shelf-life predictions for the selected evaluation indicators including egg weight,Flaugh unit(HU),and albumen height.Experiments were carried out at different storage temperatures for a total period of 29-32 d.All data were collected and fitted in to Arrhenius equation for egg freshness,while the HU data were applied to a probability model for shelf-life prediction.The results showed that egg weight,albumen height,and HU decreased significantly,while albumen pH increased with the extension of storage time.The higher the storage temperature,the faster the egg quality decreased.In addition,the bias factor,accuracy factor,and the standard error of prediction were selected to verify the developed quality models.Maximum rescaled R-square statistic,the Hosmer-Lemeshow goodness-of-fit statistic,and the receiver operating characteristic curve were used to evaluate the goodness-of-fit of the developed probability model for the shelf-life of eggs,which indicated that the presented predictive models can be used to assess egg freshness and predict shelf-life during different storage temperatures.

PREDICTION OF OCEANIC DATA

The Kalman filter is used to predict the velocity of littoral current, the wave direction, the sea depth and the wave steepness. In this paper the Kazumasa model has been modified to deal with two cases: 1) For the positions a bit far from the shore, the interaction between the velocity of littoral current as well as the wave direction and the sea depth as well as the wave steepness must be considered. 2) For the positions very close to the shore, three new parameters describing the asymmetry wave are introduced to deal with wave breaking. The results from the modified model are compared with observed data, and the comparison indicates that the modified model is better and capable of giving more accurate results.

Pandemic Analysis and Prediction of COVID-19 Using Gaussian Doubling Times

COVID-19 has become a pandemic,with cases all over the world,with widespread disruption in some countries,such as Italy,US,India,South Korea,and Japan.Early and reliable detection of COVID-19 is mandatory to control the spread of infection.Moreover,prediction of COVID-19 spread in near future is also crucial to better plan for the disease control.For this purpose,we proposed a robust framework for the analysis,prediction,and detection of COVID-19.We make reliable estimates on key pandemic parameters and make predictions on the point of inflection and possible washout time for various countries around the world.The estimates,analysis and predictions are based on the data gathered fromJohns Hopkins Center during the time span of April 21 to June 27,2020.We use the normal distribution for simple and quick predictions of the coronavirus pandemic model and estimate the parameters of Gaussian curves using the least square parameter curve fitting for several countries in different continents.The predictions rely on the possible outcomes of Gaussian time evolution with the central limit theorem of statistics the predictions to be well justified.The parameters of Gaussian distribution,i.e.,maximumtime and width,are determined through a statisticalχ^(2)-fit for the purpose of doubling times after April 21,2020.For COVID-19 detection,we proposed a novel method based on the Histogram of Oriented Gradients(HOG)and CNN in multi-class classification scenario i.e.,Normal,COVID-19,viral pneumonia etc.Experimental results show the effectiveness of our framework for reliable prediction and detection of COVID-19.

Intelligent prediction of RBC demand in trauma patients using decision tree methods

Background:The vital signs of trauma patients are complex and changeable,and the prediction of blood transfusion demand mainly depends on doctors'experience and trauma scoring system;therefore,it cannot be accurately predicted.In this study,a machine learning decision tree algorithm[classification and regression tree(CRT)and eXtreme gradient boosting(XGBoost)]was proposed for the demand prediction of traumatic blood transfusion to provide technical support for doctors.Methods:A total of 1371 trauma patients who were diverted to the Emergency Department of the First Medical Center of Chinese PLA General Hospital from January 2014 to January 2018 were collected from an emergency trauma database.The vital signs,laboratory examination parameters and blood transfusion volume were used as variables,and the non-invasive parameters and all(non-invasive+invasive)parameters were used to construct an intelligent prediction model for red blood cell(RBC)demand by logistic regression(LR),CRT and XGBoost.The prediction accuracy of the model was compared with the area under curve(AUC).Results:For non-invasive parameters,the LR method was the best,with an AUC of 0.72[95%confidence interval(CI)0.657–0.775],which was higher than the CRT(AUC 0.69,95%CI 0.633–0.751)and the XGBoost(AUC 0.71,95%CI 0.654–0.756)(P<0.05).The trauma location and shock index are important prediction parameters.For all the prediction parameters,XGBoost was the best,with an AUC of 0.94(95%CI 0.893–0.981),which was higher than the LR(AUC 0.80,95%CI 0.744–0.850)and the CRT(AUC 0.82,95%CI 0.779–0.853)(P<0.05).Haematocrit(Hct)is an important prediction parameter.Conclusions:The classification performance of the intelligent prediction model of red blood cell transfusion in trauma patients constructed by the decision tree algorithm is not inferior to that of the traditional LR method.It can be used as a technical support to assist doctors to make rapid and accurate blood transfusion decisions in emergency rescue environment,so as to improve the success rate of patient treatment.

Mathematical model to predict COVID-19 mortality rate

Objective:Covid-19 is a highly contagious viral infection that has recently become a pandemic.Since the beginning of the pandemic,the disease has affected millions of people and taken many people's lives.The purpose of this paper is to predict and compare the number of cases and mortality rate due to Covid-19 every quarter in 2020 and 2021 in three countries:Iran,the United States,and South Korea.Materials and methods:The data of this study include the mortality rate of different countries of the world due to Covid-19,which has been approved by the World Health Organization(WHO).In this paper,to develop the mathematical model for mortality rate prediction,the data of the countries of Iran,the United States,and South Korea during the last two years from March 1,2020,to March 1,2022,have been used.In addition,the mortality trend was modeled using the MATLAB software toolbox version 2022b.During modeling,six methods including Fourier,Interpolant,Gaussian,Polynomial,Sum of Sine,and Smoothing Spline were implemented.Root Mean square error(RMSE)and final prediction error were used to evaluate the performance of these proposed methods.Results:As a result of the analysis,it was shown that the Smoothing Spline model with the lowest error rate was capable of accurately evaluating and predicting Covid-19 incidence and mortality rate.Using RMSE,a prediction of the Covid-19 mortality rate for three countries is 3.76498×10^(-5).The values of R-Square and Adj R-sq were 1 in all the experiments,which indicates the full compliance of the prediction model.Conclusion:Using the proposed method,the incidence rate and mortality rate can be properly assessed and compared with each other in three countries.This provides a better view of the progression of the coronavirus outbreak in spring,summer,autumn,and winter.By using the proposed method,governments will be able to prevent disease and alert people to follow health guidelines more closely,thereby reducing infection numbers and mortality rates.

An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores

Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.

ESP工艺下DP600热轧双相钢铁素体相变模型

为了建立ESP工艺条件下DP600热轧双相钢的铁素体相变动力学数学模型,采用动态相变膨胀仪对实验钢分别进行等温相变和连续冷却相变实验.基于实测的铁素体相变孕育期和铁素体体积分数,在变形温度以上结合经典形核理论计算铁素体相变孕育期,变形温度以下通过实验数据拟合△GV计算铁素体相变孕育期.考虑冷却速度的影响对可加性法则进行修正并基于此计算了连续冷却条件下的铁素体相变开始温度和体积分数.结果表明:修正后的相变模型计算的铁素体相变开始温度和体积分数与实测值吻合良好,可用于预测ESP工艺下DP600钢的铁素体相变行为.

基于长短时记忆网络的恒温水浴锅温度模型预测

【目的】由于恒温水浴锅温度系统存在强非线性及大滞后性,本研究提出一种基于长短时记忆网络的恒温水浴锅温度模型预测方法。【方法】首先,对采集到的数据进行标准化处理,寻找长短时记忆网络的最优结构及超参数,用来拟合出最佳的数据映射特征,并构建恒温水浴锅温度的动态数学模型。其次,通过模型对未来一段时间内的温度趋势进行预测。最后,使用本研究提出的方法与最小二乘法所预测的结果进行对比分析。【结果】本研究所提方法构建的模型的拟合度达到了98.2%,预测结果的MSE及MAE比最小二乘法模型分别降低了4.616、0.823。【结论】本研究所提方法具有更高的预测精度,对提高恒温水浴锅的生产效率及控制精度具有重要意义。