Development of 3D top coal caving angle model for fully mechanized extra-thick coal seam mining

During high-intensity,fully mechanized mining of extra-thick coal seam,the top coal would cave to a certain 3D form.Based on the data collected during drilling,a 3D model of top coal caving surface space was established to determine the relationship between the location of the stope roof and the caving surface,enabling the mathematical computation of the top caving angle(φ).The drilling method was employed to measure the top caving angle on two extra-thick fully mechanized coal caving faces under the conditions of three geological structures,namely,no geological structure,igneous rock structure,and fault structure.The results show that the value of top caving angle could be accurately estimated on-site with the 9-parameter 3D top coal caving surface model built with the drilling method.This method is a novel on-site measurement that can be easily applied.Our findings reveal that the characteristics of the coal-rock in the two mining faces are different;yet their caving angles follow the ruleφ_(igneous rock structure)<φ_(no geological structure)<φ_(fault structure).Finally,through the data fitting with two indexes(the top coal uniaxial compressive strength and the top caving angle),it is found that the relationship between the two indexes satisfies an exponential decay function.

Modelling Epidemiological Data Using Box-Jenkins Procedure

In this paper, the Box-Jenkins modelling procedure is used to determine an ARIMA model and go further to forecasting. We consider data of Malaria cases from Ministry of Health (Kabwe District)-Zambia for the period, 2009 to 2013 for age 1 to under 5 years. The model-building process involves three steps: tentative identification of a model from the ARIMA class, estimation of parameters in the identified model, and diagnostic checks. Results show that an appropriate model is simply an ARIMA (1, 0, 0) due to the fact that, the ACF has an exponential decay and the PACF has a spike at lag 1 which is an indication of the said model. The forecasted Malaria cases for January and February, 2014 are 220 and 265, respectively.

Short-term forecast in the early stage of the COVID-19 outbreak in Italy. Application of a weighted and cumulative average daily growth rate to an exponential decay model

To estimate the size of the novel coronavirus(COVID-19)outbreak in the early stage in Italy,this paper introduces the cumulated and weighted average daily growth rate(WR)to evaluate an epidemic curve.On the basis of an exponential decay model(EDM),we provide estimations of the WR in four-time intervals from February 27 to April 07,2020.By calibrating the parameters of the EDM to the reported data in Hubei Province of China,we also attempt to forecast the evolution of the outbreak.We compare the EDM applied to WR and the Gompertz model,which is based on exponential decay and is often used to estimate cumulative events.Specifically,we assess the performance of each model to short-term forecast of the epidemic,and to predict the final epidemic size.Based on the official counts for confirmed cases,the model applied to data from February 27 until the 17th of March estimate that the cumulative number of infected in Italy could reach 131,280(with a credibility interval 71,415-263,501)by April 25(credibility interval April 12 to May 3).With the data available until the 24st of March the peak date should be reached on May 3(April 23 to May 23)with 197,179 cumulative infections expected(130,033e315,269);with data available until the 31st of March the peak should be reached on May 4(April 25 to May 18)with 202,210 cumulative infections expected(155.235 e270,737);with data available until the 07st of April the peak should be reached on May 3(April 26 toMay 11)with 191,586(160,861-232,023)cumulative infections expected.Based on the average mean absolute percentage error(MAPE),cumulated infections forecasts provided by the EDM applied to WR performed better across all scenarios than the Gompertz model.An exponential decay model applied to the cumulated and weighted average daily growth rate appears to be useful in estimating the number of cases and peak of the COVID-19 outbreak in Italy and the model was more reliable in the exponential growth phase.

Calibration of Soil Electromagnetic Conductivity in Inverted Salinity Profiles with an Integration Method

Various calibration methods have been propounded to determine profiles of apparent bulk soil electrical conductivity (ECa) and soil electrical conductivity of a saturated soil paste extract (ECe) or a 1:5 soil water extract (EC1:5) using an electromagnetic induction instrument (EM38). The modeled coefficients, one of the successful and classical methods hitherto, were chosen to calibrate the EM38 measurements of the inverted salinity profiles of characteristic coastal saline soils at selected sites of Xincao Farm, Jiangsu Province, China. However, this method required three parameters for each depth layer. An integration approach, based on an exponential decay profile model, was proposed and the model was fitted to all the calibration sites. The obtained model can then be used to predict EC1:5 at a certain depth from electromagnetic measurements made using the EM38 device positioned in horizontal and vertical positions at the soil surface. This exponential decay model predicted the EC1:5 well according to the results of a one-way analysis of variance, and the further comparison indicated that the modeled coefficients appeared to be slightly superior to, but not statistically different from, this exponential decay model. Nevertheless, this exponential decay model was more significant and practical because it depended on less empirical parameters and could be used to perform point predictions of EC1:5 continuously with depth.

Reliable Analytic Strategy to Correlate the Morphological and Cytological Parameters on Lupinus termis L, against Fusarium oxysporum Infection

Many analytic strategies have emerged to estimate plant responses to Fusarium wilt. The demand for fast and reliable method （diagnosis, prediction） to determine isolate strength accurately is not established yet. Early determination of pathogen strength helps in plant medication. The aim of this study was to develop a faster strategy and method for early determination of fungal isolates strength in correlation to plant response. Till now, the scientists have no consensus on the most correlated parameters that could express wilt precisely. In this study, 30 isolates of Fusarium oxysporum isolated from Lupinus termis L. were used to provide an explicit image about the real strength of Fusarium isolates and its impact on the plant. Wilting percentage ranged from 26.67% to 93.33% of the infected plants depending on isolate virulence. Some of cellular, morphological and physical measurements were conducted on 8 out of 30 isolates, including root （length, fresh weight （FW） and dry weight （DW））, nodules （water content （WC）, FW, DW）, stem （height, WC, FW, DW）, total leaves/plant （WC, FW, DW） and the fourth leaf （WC, FW, DW, leaf area, epidermal cell area, epidermal cell number, succulence）. Hierarchical clustering was used to determine the variance between the isolates. Detrended correspondence analysis （DCA） and canonical correspondence analysis （CCA） were used to determine the most important growth parameters that could express wilting accurately. The CCA results showed that most of the measured parameters on the fourth leaf, except for leaf epidermal cell number, were highly and positively correlated to wilt. That makes these specific parameters valuable and sensitive for any changes in isolates strength. Accordingly, a mathematical model was created to be helpful in the quick determination of isolate strength and precise medication.