Control of root rot disease of sugar beet using certain antioxidants and fungicides

This study was carried out to investigate the effect of five chemical inducers i.e. salicylic acid, ascorbic acid, catechol, citric acid and potassium silicate and six fungicides i.e. Actamyl70%, Chlorothalonil 50%, Evito 48%, Shenzy 34%, Pyrus 40% and Fentobein 32.5% in order to control Rhizoctonia solani and Macrophomina phaseolina which infect sugar beet roots. The antioxidants, catechol and salicylic acid achieved the best disease control at all rates of application followed by citric acid and potassium silicate, respectively. Concerning fungicides, Shenzy 34% gave noticeable control in disease reduction followed by Evito 48% and Fentobein 32.5%, respectively. Usage of antioxidants as chemical inducers for enhancing plant resistance and capability of defying diseases is well recommended as fungicide alternatives due to their safe influence on human health. But, fungicides are still the most widespread used compounds in disease management strategies, based on their compliant application, reliable and efficient results than any other safer chemical or natural compound which controls the disease by reducing the losses, not by eradicating the disease in which fungicides can do successfully

Evaluation of different chemicals to control Erysiphe betae the causal pathogen of sugar beet powdery mildew

Survey on sugar beet plantations at Minia and Assiut governorates, Egypt revealed that powdery mildew disease was the most epidemic threat on sugar beet plantations.  It was noticed that the highest Area Under Powdery Mildew Progress Curve (AUPMPC) value was detected in Abnob locality, Assiut governorate while the lowest one was found in Maghagha locality, Minia governorate. Data revealed that five months’ post collection conidia of Erysiphe betae failed to infect sugar beet leaves cultivar FD.0807. Results of conidial germination showed that the percent germination in darkness was lower than in light. Also a high percentage of germinating conidia formed appressorium on dry glass slides. The examination of powdery mildew infected sugar beet leaves using scanning electron microscopy showed that the fungus penetrates the epidermis of the leaves by the haustoria which are folded in many patches forming a complex web almost completely covers the leaf. Field experiment was conducted to evaluate three chemical compounds containing plant macronutrients, along with five fungicides against powdery mildew disease. Results showed that sodium bicarbonate achieved the best disease control among the macronutrient-containing compounds followed by calcium chloride and potassium silicate, respectively. Sodium bicarbonate achieved the highest total soluble solids (TSS) percentage and root weight at all rates of application followed by calcium chloride, while potassium silicate achieved the least TSS % and root weight. Concerning fungicides, Bellis 38%WG gave noticeable result in disease reduction followed by Collis 30% SC and Tilt 25% EC, respectively. The results showed that the highest TSS % and root weight were detected in the roots of sugar beet plants treated with Bellis 38% fungicide followed by Collis 30%. Meanwhile, the lowest significant of TSS % and root weight was detected after treatment with Permatrol 99%

Extended Gompertz Distribution: Properties and Estimation under Complete and Censored Data

In this paper, a new flexible model with three-parameter alternative to exponential and Gompertz distributions is proposed. Some of its statistical properties are derived including quantities, moments, incomplete moments, moment of residual and reversed residual life. The parameters are estimated using the maximum likelihood method based on complete and Type II right censored data. We assess the performance of estimators in terms of bias and mean square error using simulation study. Finally, three real data sets are analyzed to illustrate the flexibility of the proposed model

Degradation and energy performance evaluation of mono-crystalline photovoltaic modules in Egypt

Abstract Degradation reduces the capability of solar photovoltaic (PV) production over time. Studies on PV module degradation are typically based on time-consuming and labor-intensive accelerated or field experiments. Understanding the modes and methodologies of degradation is critical to certifying PV module lifetimes of 25 years. Both technological and environmental conditions affect the PV module degradation rate. This paper investigates the degradation of 24 mono-crystalline silicon PV modules mounted on the rooftop of Egypt's electronics research institute (ERI) after 25 years of outdoor operation. Degradation rates were determined using the module's performance ratio, temperature losses, and energy yield. Visual inspection, I–V characteristic measurement, and degradation rate have all been calculated as part of the PV evaluation process. The results demonstrate that the modules' maximum power ( $${P}_{max}$$ P max ) has decreased in an average manner by 23.3% over time. The degradation rates of short-circuit current ( $${I}_{sc}$$ I sc ) and maximum current ( $${I}_{m}$$ I m ) are 12.16% and 7.2%, respectively. The open-circuit voltage ( $${V}_{oc}$$ V oc ), maximum voltage ( $${V}_{m}$$ V m ), and fill factor ( $$FF$$ FF ) degradation rates are 2.28%, 12.16%, and 15.3%, respectively. The overall performance ratio obtained for the PV system is 85.9%. After a long time of operation in outdoor conditions, the single diode model's five parameters are used for parameter identification of each module to study the effect of aging on PV module performance

Bayesian Inferential Approaches and Bootstrap for the Reliability and Hazard Rate Functions under Progressive First-Failure Censoring for Coronavirus Data from Asymmetric Model

This paper deals with the estimation of the parameters for asymmetric distribution and some lifetime indices such as reliability and hazard rate functions based on progressive first-failure censoring. Maximum likelihood, bootstrap and Bayesian approaches of the distribution parameters and reliability characteristics are investigated. Furthermore, the approximate confidence intervals and highest posterior density credible intervals of the parameters are constructed based on the asymptotic distribution of the maximum likelihood estimators and Markov chain Monte Carlo technique, respectively. In addition, the delta method is implemented to obtain the variances of the reliability and hazard functions. Moreover, we apply two methods of bootstrap to construct the confidence intervals. The Bayes inference based on the squared error and LINEX loss functions is obtained. Extensive simulation studies are conducted to evaluate the behavior of the proposed methods. Finally, a real data set of the COVID-19 mortality rate is analyzed to illustrate the estimation methods developed here

Bivariate Discrete Odd Generalized Exponential Generator of Distributions for Count Data: Copula Technique, Mathematical Theory, and Applications

In this article, a new family of bivariate discrete distributions is proposed based on the copula concept, in the so-called bivariate discrete odd generalized exponential-G family. Some distributional properties, including the joint probability mass function, joint survival function, joint failure rate function, median correlation coefficient, and conditional expectation, are derived. After proposing the general class, one special model of the new bivariate family is discussed in detail. The maximum likelihood approach is utilized to estimate the family parameters. A detailed simulation study is carried out to examine the bias and mean square error of maximum likelihood estimators. Finally, the importance of the new bivariate family is explained by means of two distinctive real data sets in various fields

Effect of Fuzzy Time Series on Smoothing Estimation of the INAR(1) Process

In this paper, the effect of fuzzy time series on estimates of the spectral, bispectral and normalized bispectral density functions are studied. This study is conducted for one of the integer autoregressive of order one (INAR(1)) models. The model of interest here is the dependent counting geometric INAR(1) which is symbolized by (DCGINAR(1)). A realization is generated for this model of size n = 500 for estimation. Based on fuzzy time series, the forecasted observations of this model are obtained. The estimators of spectral, bispectral and normalized bispectral density functions are smoothed by different one- and two-dimensional lag windows. Finally, after the smoothing, all estimators are studied in the case of generated and forecasted observations of the DCGINAR(1) model. We investigate the contribution of the fuzzy time series to the smoothing of these estimates through the results

Effect of Fuzzy Time Series on Smoothing Estimation of the INAR(1) Process

In this paper, the effect of fuzzy time series on estimates of the spectral, bispectral and normalized bispectral density functions are studied. This study is conducted for one of the integer autoregressive of order one (INAR(1)) models. The model of interest here is the dependent counting geometric INAR(1) which is symbolized by (DCGINAR(1)). A realization is generated for this model of size n = 500 for estimation. Based on fuzzy time series, the forecasted observations of this model are obtained. The estimators of spectral, bispectral and normalized bispectral density functions are smoothed by different one- and two-dimensional lag windows. Finally, after the smoothing, all estimators are studied in the case of generated and forecasted observations of the DCGINAR(1) model. We investigate the contribution of the fuzzy time series to the smoothing of these estimates through the results

A New Statistical Technique to Enhance MCGINAR(1) Process Estimates under Symmetric and Asymmetric Data: Fuzzy Time Series Markov Chain and Its Characteristics

Several models for time series with integer values have been published as a result of the substantial demand for the description of process stability having discrete marginal distributions. One of these models is the mixed count geometric integer autoregressive of order one (MCGINAR(1)), which is based on two thinning operators. This study examines how the estimates of the spectral density functions of the MCGINAR(1) model are affected by fuzzy time series Markov chain (FTSMC). Regarding this study’s context, the higher-order moments, central moments and spectral density functions of MCGINAR(1) are computed. The anticipated realizations of the generated realizations for this model are obtained based on FTSMC. In the case of generated and anticipated realizations, several lag windows are used to smooth the spectral density estimators. The generated realization estimates are compared with the anticipated realization estimates using the MSE to ascertain the FTSMC’s role in improving the estimation process

Synthesis, in vitro cytotoxicity activity against the human cervix carcinoma cell line and in silico computational predictions of new 4-arylamino-3-nitrocoumarin analogues

Halawa AH, Eliwa EM, Hassan AA, et al. Synthesis, in vitro cytotoxicity activity against the human cervix carcinoma cell line and in silico computational predictions of new 4-arylamino-3-nitrocoumarin analogues. JOURNAL OF MOLECULAR STRUCTURE. 2020;1200: UNSP 127047.A new series of 4-arylamino-3-nitrocoumarin analogues (4-18) have been synthesized and characterized by sophisticated spectroscopic techniques (H-1 NMR, C-13 NMR) and mass spectrometry. All the new synthesized compounds were evaluated for their in vitro cytotoxic activity against the human cervix carcinoma cell line (KB-3-1) using resazurin assay with (+)-griseofulvin as the positive control (IC50 = 19 mu M). Among them, thiazolidinylidene derivative 17a that bearing malononitrile unit displayed the best cytotoxic potency with IC50 value of 21 mu M. Also, in silico docking simulation studies were conducted on human DNA topoisomerase 1 (Top1) (PDB: 1T8I) to explore and interpret the interaction pattern between the selected compounds and target enzyme as well confirm the acquired cytotoxicity results. In addition to the above, in silico predictions of physicochemical properties, ADME (absorption, distribution, metabolism and excretion) parameters, oral toxicity and indication of toxicity targets were implemented for some title compounds. (C) 2019 Published by Elsevier B.V