Global research trends in petrochemical wastewater treatment from 2000 to 2021.

Petrochemical wastewater (PWW) is a huge industrial contaminant that generates a wide range of resistive and poisonous organic pollutants that harm animals and plants in natural water bodies when discharged untreated or partially treated. Therefore, it is vital to develop technologies that are simple, efficient, and profitable for the treatment of oily wastewater. Although much study has been undertaken on the treatment of PWW, there has not been any recent work on bibliometric analysis of global research trends on this issue. A bibliometric analysis will help current and future researchers figure out where the gaps are and how to fill them. The present study's focus is to examine the characteristics and trends of research on oily wastewater treatment with an emphasis on the treatment of PWW. This research was performed on five important aspects, including characterization of research publications, countries' performances and collaborations, an analysis of the best papers with the most citations, keyword analysis (including frequency distribution of the keyword analysis, the transformation of the keyword combination across time, and exploration of changes in rank over time), and journal analysis, according to the 2457 papers in the Science Citation Index Expanded using the Web of Science (WoS) database from 2000 to 2021. For further analysis, the contingency matrix, bump diagram, and inter-temporal network stream were employed.

Forecasting COVID-19 pandemic using optimal singular spectrum analysis.

Coronavirus disease 2019 (COVID-19) is a pandemic that has affected all countries in the world. The aim of this study is to examine the potential advantages of Singular Spectrum Analysis (SSA) for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19, which are the three main variables of interest. This paper contributes to the literature on forecasting COVID-19 pandemic in several ways. Firstly, an algorithm is proposed to calculate the optimal parameters of SSA including window length and the number of leading components. Secondly, the results of two forecasting approaches in the SSA, namely vector and recurrent forecasting, are compared to those from other commonly used time series forecasting techniques. These include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Exponential Smoothing, TBATS, and Neural Network Autoregression (NNAR). Thirdly, the best forecasting model is chosen based on the accuracy measure Root Mean Squared Error (RMSE), and it is applied to forecast 40 days ahead. These forecasts can help us to predict the future behaviour of this disease and make better decisions. The dataset of Center for Systems Science and Engineering (CSSE) at Johns Hopkins University is adopted to forecast the number of daily confirmed cases, deaths, and recoveries for top ten affected countries until October 29, 2020. The findings of this investigation show that no single model can provide the best model for any of the countries and forecasting horizons considered here. However, the SSA technique is found to be viable option for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 based on the number of times that it outperforms the competing models.

A new five-parameter Birnbaum-Saunders distribution for modeling bicoid gene expression data.

An extended version of Birnbaum-Saunders distribution with five parameters is introduced. Theoretical aspects of five-parameter Birnbaum-Saunders distribution and the maximum likelihood estimation of parameters are presented. The reliability and applicability of the proposed distribution is evaluated using both simulation and real-world data namely bicoid gene expression profile. The findings of this research confirm that the newly proposed five-parameter Birnbaum-Saunders distribution can be utilized to describe the distribution of bicoid gene expression profile.

Spatial gradient of bicoid is well explained by Birnbaum-Saunders distribution.

bicoid is a maternally transcribed gene which plays a pivotal role during the early developmental stage of Drosophila melanogaster by acting as an essential input to the segmentation network. Therefore, fundamental insights into gene cross-regulations of segmentation network expect to be unveiled by presenting an accurate mathematical model for bicoid gene expression profile. In this paper, an extended version of Birnbaum-Saunders with four parameters is introduced and evaluated to describe the spatial gradient of this gene. Theoretical aspects of four-parameter Birnbaum-Saunders and the estimated parameters are presented and thoroughly assessed for different embryos. The reliability and validity of the results are evaluated via both simulation studies and real data sets and thereby adding more confidence and value to the findings of this research.