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The COVID‐19 pandemic has affected thousands of people around the world. In this study, we used artificial neural network (ANN) models to forecast the COVID‐19 outbreak for policymakers based on 1st January to 31st October 2021 of positive cases in India. In the confirmed cases of COVID‐19 in India, it's critical to use an estimating model with a high degree of accuracy to get a clear understanding of the situation. Two explicit mathematical prediction models were used in this work to anticipate the COVID‐19 epidemic in India. A Boltzmann Function‐based model and Beesham's prediction model are among these methods and also estimated using the advanced ANN‐BP models. The COVID‐19 information was partitioned into two sections: training and testing. The former was utilized for training the ANN‐BP models, and the latter was used to test them. The information examination uncovers critical day‐by‐day affirmed case changes, yet additionally unmistakable scopes of absolute affirmed cases revealed across the time span considered. The ANN‐BP model that takes into consideration the preceding 14‐days outperforms the others based on the archived results. In forecasting the COVID‐19 pandemic, this comparison provides the maximum incubation period, in India. Mean square error, and mean absolute percent error have been treated as the forecast model performs more accurately and gets good results. In view of the findings, the ANN‐BP model that considers the past 14‐days for the forecast is proposed to predict everyday affirmed cases, especially in India that have encountered the main pinnacle of the COVID‐19 outbreak. This work has not just demonstrated the relevance of the ANN‐BP techniques for the expectation of the COVID‐19 outbreak yet additionally showed that considering the incubation time of COVID‐19 in forecast models might produce more accurate assessments. [ABSTRACT FROM AUTHOR]

We aimed to propose a reliable and accurate model using non-destructive measurements of leaf length (L) and/or width (W) for estimating leaf area (LA) of three genotypes of purging nut (Jatropha curcas L.), i.e. Astrea and Barranca, both Colombian and a Mexican genotype. For model construction, ~500 healthy leaves were collected from at least 20 healthy plants naturally grown at each locations, and encompassed the full spectrum of measurable leaf sizes. Equations with L and/or W were generated; however, the best fit were made when product between L and W (LW). To validate these models, independent data set of 450 leaves were used. Thus, we developed a single model (Yi = β0 * LWβ1) with high precision and accuracy, random dispersion pattern of residuals and unbiased. In spite of this, a single equation, joining all the studied genotypes, was developed [LA = 0.822 * (LW)0.983; standard errors: β0 = 0.020, β1 = 0.004; R2a = 0.976], which greatly simplifies the process of measuring the leaf area of the genotypes, a fact that should be highlighted.

We aimed to propose a reliable and accurate model using non-destructive measurements of leaf length (L) and/or width (W) for estimating leaf area (LA) of Surinam cherry (Eugenia uniflora L.). For model construction, 560 leaves were randomly sampled from different levels of the tree canopies and encompassed the full spectrum of measurable leaf sizes. Power models better fit E. uniflora leaf area than linear models; but, among of then, the best fit were made when product of the L and W (LW) were used. To validate these models, independent data set of 156 leaves were used. Thus, we developed a single power model (Yi = β0 xβ1) [LA = 0.685 (LW)0.989; standard errors: β0 = 0.014, β1 = 0.005; R2 a = 0.997] with high precision and accuracy, random dispersal pattern of residuals and unbiased. A simpler linear model [LA = 0.094 + (LW * 0.655); standard errors: β0 = 0.025, β 1 = 0.001; R2 a = 0.998] also described here to estimate leaf area of E. uniflora, which are as good as the first. The simplicity of the latter model may be relevant in field studies, as it does not demand high precision or expensive instruments.

Grassland biomass is a key biophysical metrics to characterize grassland growth and a sensitive indicator of environmental change and ecological functioning. The quantification and timely information on aboveground biomass (AGB) of grassland are crucial for the sustainable use and management of grassland resources. In this study, AGB for main grassland types in the North Tibet was analyzed using field measurements and climatic controls of variations of aboveground biomass were explored, and the general estimate models for grassland AGB were further developed based on the relationships between MODIS vegetation indices (VIs) and the field measurements. The results indicated that alpine swamp meadow has the highest AGB of 356.8 g/m² on average, followed by temperate steppe (64.5 g/m²) and alpine meadow (61.6 g/m²), and AGB of alpine steppe is lowest at 48.9 g/m². Precipitation is main climate factor impacting variations of grassland AGB. The exponential relationships existed between grassland AGB and MODIS VIs and exponential models based on MODIS NDVI were found to be optimum for monitoring and estimating grassland AGB. Study also showed that AGB in the North Tibet spatially decreases from southeast to northwest, which is above 100 g/m² in the southeast and is below 20 g/m² in the northwest. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Archives of Mining Sciences is the property of Polish Academy of Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The COVID-19 pandemic has affected thousands of people around the world. In this study, we used artificial neural network (ANN) models to forecast the COVID-19 outbreak for policymakers based on 1st January to 31st October 2021 of positive cases in India. In the confirmed cases of COVID-19 in India, it's critical to use an estimating model with a high degree of accuracy to get a clear understanding of the situation. Two explicit mathematical prediction models were used in this work to anticipate the COVID-19 epidemic in India. A Boltzmann Function-based model and Beesham's prediction model are among these methods and also estimated using the advanced ANN-BP models. The COVID-19 information was partitioned into two sections: training and testing. The former was utilized for training the ANN-BP models, and the latter was used to test them. The information examination uncovers critical day-by-day affirmed case changes, yet additionally unmistakable scopes of absolute affirmed cases revealed across the time span considered. The ANN-BP model that takes into consideration the preceding 14-days outperforms the others based on the archived results. In forecasting the COVID-19 pandemic, this comparison provides the maximum incubation period, in India. Mean square error, and mean absolute percent error have been treated as the forecast model performs more accurately and gets good results. In view of the findings, the ANN-BP model that considers the past 14-days for the forecast is proposed to predict everyday affirmed cases, especially in India that have encountered the main pinnacle of the COVID-19 outbreak. This work has not just demonstrated the relevance of the ANN-BP techniques for the expectation of the COVID-19 outbreak yet additionally showed that considering the incubation time of COVID-19 in forecast models might produce more accurate assessments., (© 2022 John Wiley & Sons Ltd.)

Soil plays an important role in coastal wetland ecosystems. The estimation of soil organic matter (SOM), total nitrogen (TN), and total carbon (TC) was investigated at the topsoil (0–20 cm) in the coastal wetlands of Dafeng Elk National Nature Reserve in Yancheng, Jiangsu province (China) using hyperspectral remote sensing data. The sensitive bands corresponding to SOM, TN, and TC content were retrieved based on the correlation coefficient after Savitzky–Golay (S–G) filtering and four differential transformations of the first derivative (R′), first derivative of reciprocal (1/R)′, second derivative of reciprocal (1/R)″, and first derivative of logarithm (lgR)′ by spectral reflectance (R) as R′, (1/R)′, (1/R)″, (lgR)′ of soil samples. The estimation models of SOM, TN, and TC by support vector machine (SVM) and back propagation (BP) neural network were applied. The results indicated that the effective bands can be identified by S–G filtering, differential transformation, and the correlation coefficient methods based on the original spectra of soil samples. The estimation accuracy of SVM is better than that of the BP neural network for SOM, TN, and TC in the Yancheng coastal wetland. The estimation model of SOM by SVM based on (1/R)′ spectra had the highest accuracy, with the determination coefficients (R2) and root mean square error (RMSE) of 0.93 and 0.23, respectively. However, the estimation models of TN and TC by using the (1/R)″ differential transformations of spectra were also high, with determination coefficients R2 of 0.88 and 0.85, RMSE of 0.17 and 0.26, respectively. The results also show that it is possible to estimate the nutrient contents of topsoil from hyperspectral data in sustainable coastal wetlands. [ABSTRACT FROM AUTHOR]

Aboveground biomass is an important indicator of grassland community. Direct harvesting has a great damage to the fragile ecosystem. In order to estimate the aboveground biomass of Trifolium repens community more effectively without destroying herbaceous community, various handmade measuring plates were used to study the community height and aboveground biomass of T. repens community in Duzhai Village, Dazhai Town, Yangling Zone, Shaanxi Province. Measuring plates used for trials were made of 17 kinds of specifications of aluminum-plastic and polymer plates. The plate was laid flat on the community crown, the height of plant community from ground level to the plate was measured and recorded, and measurements were repeated 20 times for each specification of plate. Then the plants of whole areal part below the measuring plate were cut to ground level from the corresponding quadrat, and the biomass was obtained after oven-drying and weighing. Finally, the modeling and correlation analysis were made, and the best estimate model was verified. The results showed that multiple regression equations were established with aboveground biomass of corresponding area as dependent variable, and community height below the measuring plate as indepen-dent variable. The independent variable was positively correlated with the dependent variable, and the determination coefficients, R 2 , ranged from 0.37 to 0.76. Compared with the polymer plates, the aluminum-plastic plates performed better on coefficients of variation of biomass, determination coefficients of regression equations, R 2 , and degree of accuracy. The best measuring plate was circular aluminum-plastic plate with diameter of 35 cm, and its best regression model was y=1.6460x-3.3462, where the R 2 was 0.76 and the prediction accuracy was 92.1%.