Hannan-Quinn Criterion Research Articles

Biosorption of Zn (II) onto Rice Husk Ash: Isothermal Remodelling

Zn is a heavy metal often found at elevated concentrations in effluents as high as 300 ppm from the rubber processing industry as it is used for the vulcanization of rubber. The rice milling process produces rice husk as a by-product. It is one of the most important agricultural leftovers in terms of volume. The data of the sorption isotherm of Zn (II) (CV) sorption onto rice husk ash, which was plotted using linearized plots of isothermal models were reanalyzed using isothermal models using nonlinear regression. As the datapoints were small, isotherms with parameters of only up to three were utilized to prevent overfitting. The nineteen models were Henry, Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, Jovanovic, Redlich-Peterson, Sips, Toth, Hill, Khan, BET, Vieth-Sladek, Radke-Prausnitz, Brouers–Sotolongo, Fritz-Schlunder III, Unilan, Fowler-Guggenheim and Moreau. Statistical analysis based on error function analyses such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), accuracy factor (AF), bias factor (BF), Bayesian Information Criterion (BIC), corrected AICc (Akaike Information Criterion), and Hannan-Quinn Criterion (HQC) showed that Freundlich, followed by Langmuir and the Jovanovic models as the best top three models. The value of the maximum monolayer adsorption capacity for Zn binding to rice husk ash according to the Langmuir’s parameter qmL was 7.33 mg g-1 (95% Confidence interval from5.464 to 9.187), while bL (L mg-1), the Langmuir model constants was 0.011 L mg-1 (95% C.I. from 0.030 to 0.182). As the Freundlich equation is unable to model the maximum adsorption, the Halsey rearrangement of the Freundlich equation was utilized and gave the estimated maximum absorption of 7.20 mg g-1, which is very close to the experimental value. The constant values obtained in this study, especially the Langmuir model are largely different from the previously reported linearized approach.

Mercury Sorption onto Rice Husk Ash: An Isothermal Remodelling

The rice milling process produces rice husk as a by-product. It is one of the most important agricultural leftovers in terms of volume. The data of the sorption isotherm of Hg (II) (CV) sorption onto rice husk ash, which was plotted using linearized plots of isothermal models were reanalyzed using isothermal models using nonlinear regression. As the datapoints were small, nineteen isotherm models with parameters of only up to three were utilized to prevent overfitting. The models were Henry, Langmuir, Freundlich, Temkin, Dubinin-Radushkevich, Jovanovic, Redlich-Peterson, Sips, Toth, Hill, Khan, BET, Vieth-Sladek, Radke-Prausnitz, Brouers–Sotolongo, Fritz-Schlunder III, Unilan, Fowler-Guggenheim and Moreau. Statistical analysis based on error function analyses such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), accuracy factor (AF), bias factor (BF), Bayesian Information Criterion (BIC), corrected AICc (Akaike Information Criterion), and Hannan-Quinn Criterion (HQC) showed that Freundlich model was the best model. The value of the maximum monolayer adsorption capacity for Hg binding to rice husk ash according to the Langmuir’s parameter qmL was 3.998 mg g-1 (95% Confidence interval from 2.473 to 5.523), while bL (L mg-1), the Langmuir model constants was 0.067 L mg-1 (95% C.I. from 0.001 to 0.134). The Freundlich model is unable to forecast the maximal adsorption capacity. The Halsey rearrangement of the Freundlich equation gave the estimated maximum absorption of 3.39 mg g-1, which is very close to the experimental value. The nonlinear regression method provides parameter values within the 95% confidence interval, facilitating improved comparability with prior research.

Adsorption of Sodium Dodecyl Sulfate onto Activated Coconut Shell-Based Adsorbent: Isothermal Remodelling

A remodelling evaluation was conducted on the sorption isotherm data for SDS adsorption onto activated coconut shell using nonlinear regression. A total of nineteen models, including BET, Brouers-Sotolongo, Dubinin-Radushkevich, Fowler-Guggenheim, Freundlich, Fritz-Schlunder III, Hill, Henry, Jovanovic, Khan, Langmuir, Moreau, Radke-Prausnitz, Redlich-Peterson, Sips, Temkin, Toth, Unilan, and Vieth-Sladek, were employed to determine the best fit through nonlinear regression. All models were found to exhibit good fits to the data, except for the Fowler-Guggenheim, Henry and Dubinin-Radushkevich models. Statistical analysis based on error function assessments, including accuracy factor (AF), bias factor (BF), root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), Bayesian Information Criterion (BIC), corrected AICc (Akaike Information Criterion), and Hannan-Quinn Criterion (HQC), revealed that the best performance was achieved by the Moreau model followed by (descending order) Unilan, Redlich-Peterson, Fritz-Schlunder III, Toth, and Langmuir. The maximum adsorption capacity estimates given by the Moreau and Langmuir models were better in line with experimental findings. The value of the maximum monolayer adsorption capacity for SDS binding to activated coconut shell according to the Langmuir’s parameter qmL was 81.93 mg g-1 (95% Confidence interval from 76.422 to 87.440), while bL (L mg-1), the Langmuir model constants was 0.10 L mg-1 (95% C.I. from 0.070 to 0.133). The value of the maximum monolayer adsorption capacity for SDS binding to activated coconut shell according to the Moreau’s parameter qmM was 90.82 mg g-1 (95% Confidence interval from 76.336 to 105.303), while b (L mg-1), the Moreau’s model constant was 0.15 L mg-1 (95% C.I. from 0.089 to 0.203) and l, another Moreau’s dimensionless constant was 0.3 (95% C.I. from -0.049 to 0.646).

Isothermal Remodelling of the Biosorption of Congo Red onto Kaolin

A remodeling analysis was conducted on the sorption isotherm data for Congo Red adsorption onto kaolin using nonlinear regression. (CR) also known as 1-naphthalenesulfonic acid, 3,3′-(4,4′-biphenylenebis(azo)) bis(4-amino-) disodium salt, is a synthetic anionic azo dye widely utilized in various industrial sectors, including rubber, plastic, textiles, paper, and printing. This dye is of significant interest due to its versatile applications and unique chemical properties. To prevent overfitting due to the limited data points, isotherm models with up to three parameters were utilized. Statistical analysis based on error function assessments, including root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), accuracy factor (AF), bias factor (BF), Bayesian Information Criterion (BIC), corrected AICc (Akaike Information Criterion), and Hannan-Quinn Criterion (HQC), revealed that the best performance was achieved by the Freundlich model, followed by the Langmuir and Jovanovic models, which ranked as the top three models.The best isotherm model was found to be the Unilan followed by (descending order) Brouers–Sotolongo, Hill, Sips and Langmuir. The Unilan maximum adsorption capacity, shows large deviation from the experimentally observed value with a large 95% confidence interval, indicating poor fitting parameters despite being the best model based on the error function analysis. The next best model was Brouers–Sotolongo with a maximum adsorption capacity, qmBS of 5.48 mg g-1 (95% confidence interval from 4.791 to 6.172) which agrees with experimental observations. The value of the maximum monolayer adsorption capacity for Congo Red binding to kaolin according to the Langmuir’s parameter qmL was 5.49 mg g-1 (95% Confidence interval from 5.018 to 5.967), while bL (L mg-1), the Langmuir model constants was 0.5 L mg-1 (95% C.I. from 0.285 to 0.710).

Analysis of the Influence of Temperature and Humidity on Rainfall in Sindh Province by Vector Autoregression (VAR)

The effects of climate change are relatively local, although it is a worldwide problem. Since climate change has already occurred, it has had a wide variety of effects in almost all regions of the country and has also had an impact on many economic sectors. Rainfall, temperature, cloud cover, wind speed, humidity, and heavy sunlight are the main climatic variables. In order to understand the subsequent changes of these climatic variables, the behaviour of these variables should be studied. which also helps to implement significant policies. Investigating the behaviour of the climatic factors in the past, present, and future is a major problem. Primary goal of this research project was to create an adequate vector autoregression (VAR) model that could forecast monthly temperature, humidity, and rainfall at three meteorological stations in Sindh Province, Pakistan. The Kwiatkowski–Phillips-Schmidt–Shin, Phillips–Perron, and Augmented Dickey–Fuller tests have all verified the stationarity of time series variables. Order of the VAR model was determined by applying Schwarz information criteria, Hannan-Quinn information criteria, Akaike information criteria, final prediction error, and likelihood ratio test. Ordinary least squares approach was the method utilized to estimate the parameters of the model. It was determined that the optimal models for this study were VAR (8) and VAR (7). The structural analyses were performed using the forecast error variance decomposition and impulse response function. These structural studies show that, in the future, humidity, temperature, and rainfall will all be endogenous. The research indicates that humidity and temperature both favour rainfall. When temperature and humidity are both high, rainfall is greatest, and when they are both low, it is minimum. This study suggests that the correlation between temperature and relative humidity holds negligible influence over changes in rainfall patterns.

Primary Growth Models Investigation of Pseudomonas nitroreducens Growth on Octylphenol Polyethoxylates

This study evaluated the degradation of octylphenol polyethoxylates by Pseudomonas nitrore-ducens TX1 using secondary kinetics analysis. Nonlinear kinetic regression was employed through the utilization of curve-fitting software in order to fit the digitized growth degradation data. A comprehensive analysis was conducted using various statistical metrics including root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accu-racy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian Information Criterion (BIC), and Hannan-Quinn information criterion (HQC). The accuracy and statistical analysis of the kinetic models used showed that only the Huang, Baranyi Roborts, modified Gompertz, Bu-chanan-3-phase, modified Richards and Von Bertalanffy model fit the data, with modified Logis-tics having the best model with low RMSE and AICc values, highest adjusted R2 values, and Bias Factor and Accuracy Factor values closest to unity. The calculated values for the modified Logis-tics constant maximum growth rate (m), maximum growth value (A) and lag period (l), were 0.179 (h-1), 2.199 and 13.015 h, respectively. Growth curve of the bacterium on varying concen-trations of this compound can then be modelled using this model and the maximum growth rate value can be utilized for secondary modelling works further revealing important parameters.

Modelling income distributions based on theoretical distributions derived from normal distributions

In income modelling studies, such well-known distributions as the Dagum, the lognormal or the Zenga distributions are often used as approximations of the observed distributions. The objective of the research described in the article is to verify the possibility of using other type of distributions, i.e. asymmetric distributions derived from normal distribution (ND) in the context of income modelling. Data from the 2011 EU-SILC survey on the monthly gross income per capita in Poland were used to assess the most important characteristics of the discussed distributions. The probability distributions were divided into two groups: I – distributions commonly used for income modelling (e.g. the Dagum distribution) and II – distributions derived from ND (e.g. the SU Johnson distribution). In addition to the visual evaluation of the usefulness of the analysed probability distributions, various numerical criteria were applied: information criteria for econometric models (such as the Akaike Information Criterion, Schwarz’s Bayesian Information Criterion and the Hannan-Quinn Information Criterion), measures of agreement, as well as empirical and theoretical characteristics, including a measure based on quantiles, specifically defined by the authors for the purposes of this article. The research found that the SU Johnson distribution (Group II), similarly to the Dagum distribution (Group I), can be successfully used for income modelling.

Isothermal Modelling of the Adsorption of Crystal violet onto Modified Charred Rice Husk

The rice milling process produces rice husk as a by-product. It is one of the most important agricultural leftovers in terms of volume. The data of the sorption isotherm of crystal violet (CV) sorption onto modified charred rice husk, which was plotted using linearized plots of isothermal models were reanalyzed using twenty isothermal models using nonlinear regression. Nineteen models — Henry, Langmuir, Freundlich, Jovanovic, Redlich-Peterson, Sips, Toth, Hill, Khan, BET, Vieth-Sladek, Radke-Prausnitz, Fritz-Schlunder III, Unilan, Baudu, Marczewski-Jaroniec, Fritz-Schluender IV, Weber-van Vliet and Fritz-Schluender V – fitted the data best using non-linear regression. Statistical analysis based on error function analyses such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), accuracy factor (AF), bias factor (BF), Bayesian Information Criterion (BIC), corrected AICc (Akaike Information Criterion), and Hannan-Quinn Criterion (HQC) showed that Jovanovic model was the best model. The maximal adsorption capacity in the Jovanovic model, expressed in milligrams per gram (mg/g), is denoted by qmJ while Kj is the Jovanovic constant, and the calculated values were 55.979 mg/L (95% confidence interval; 52.556 to 59.403) and 0.010 (95% confidence interval; 0.008 to 0.012), respectively. The nonlinear regression method provides parameter values within the 95% confidence interval, facilitating improved comparability with prior research.

Impacts of monetary policies on the real estate bubble in Hanoi, Vietnam

The development of the real estate market always goes hand in hand with the fluctuation of the economy. In recent years, this market has experienced many recessions and «freezes» associated with the appearance of a real estate bubble. To approach this issue, this paper studies and gives an overview of the real estate bubble and the impact of monetary policies on the real estate bubble in Vietnam. This paper’s purpose is to identify and measure the influence of monetary policies, including interest rates, credit and money supply, on the real estate bubble in Ha Noi. The vector autoregression model (VAR) is used to test the interaction of the variables in the model. Dickey-Fuller test (DF) is applied to determine the stationarity of the variables, while the Akaike information criterion (AIC), Likelihood Ratio (LR), Final prediction error (FPE), Hannan-Quinn information criterion (HQ) and Schwarz criterion (SC) are used to find optimal lag of the model; then Granger causality test is utilized to determine the two-way correlation between variables. The results showed that the real estate bubble reacted quickly to shocks from macroeconomic factors representing the monetary policy, consisting of interbank interest rates, credit growth, and money supply growth. Thus, it is concluded that monetary policy is not only the cause of formation, but also one of the effective solutions to deflate the real estate bubble. AcknowledgmentThis research is funded by Vietnam Ministry of Education and Training (MOET) under grant number [B2022-NTH-03].

Multivariate Garch Models Comparison in Terms of the Symmetric and Asymmetric Models

Modelling the Multivariate Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model included both symmetric and asymmetric processes. The information includes monthly data for the Consumer Price Index, Crude Oil Price, Exchange Rate, and Inflation Rate from January 2005 to December 2021. For the analysis, E-view Statistical software was employed. The aforementioned four macroeconomic variables show a tendency for volatility to cluster across time. Both symmetrical and asymmetrical processes had the volatility condition. The residual recursive plot was used to analyse the structural fluctuation in the series. The plot showed continual movement in the inflation rate as well as a downward and upward movement in the consumer price index, exchange rate, and crude oil price. The Akaike Information Criterion (AIC), Hannan-Quinn Information Criterion (HQIC), and Schwarz Information Criterion were chosen as the best models based on information criteria (SIC). Symmetric and asymmetric modelling techniques were used to create the Economic Variables Multivariate GARCH (M-GARCH) models. To calculate the covariance and correlation between the four variables, M-GARCH models were utilised. The main finding of the estimation of all M-GARCH models is that the symmetric models (Diagonal BEKK and Constant Conditional Correlation, or "CCC") have the lowest values of the model information criteria compared to the asymmetric models (Diagonal BEKK and CCC), while the asymmetric models (Diagonal VECH) have the lowest values of the model information criteria compared to the symmetric models (Diagonal VECH). Based on the findings, it was discovered that while analysing the interaction between the four economic variables returns series, the Symmetric Diagonal BEKK and CCC model outperformed the Asymmetric Diagonal VECH model.

Non-linear Regression Models for Predicting Biogas Yields from Selected Bio-wastes

The benefits of biogas as alternative energy to other fossil fuel sources, due to its renewability, environmentally friendly nature, health benefits, etc., cannot be overemphasized. There are numerous models for predicting biogas production rate from bio-materials, including the modified Gompertz equation. These models are primarily dependent on specific biomass parameters. When any of these parameters, like the slurry volume, changes, another round of experiments must be conducted and curve fitted before biogas yield predictions can be made. This could be time-consuming and costly. Using experimentally published data, simple empirical models can be developed for predicting biogas yields over a range of input parameters. This will eliminate the need for always performing experiments before biogas yield predictions can be made. In light of this, scarce literature provides explicit models for predicting biogas yield over a range of parameters based on published data. This study developed non-linear regression models using published data on parameters that affect biogas yields, like the slurry volume, carbon-to-nitrogen ratio, temperature, total solids, volatile solids, hydraulic retention time, and pH. The data covered seven readily available bio-wastes, including cow dung, cow dung with plant waste, cow dung with poultry dung, poultry dung with grass, pig dung, and plant wastes. On validation of the models, the results showed that the models had a relatively low standard error of estimates, Akaike information criterion, Schwarz criterion, and Hannan-Quinn information criterion. Furthermore, the coefficients of determination, R2, were between 94.62 and 98.93%. The percentage average absolute deviation (% AAD) for each model was less than 7 %. The non-linear models were found to adequately predict the biogas yields within the limits of the available data set.

Time Series Analysis of Nigeria External (Foreign) Reserves

Economy of a country can absorb shock and as well boost confidence through external reserves. Hence, external reserves play an important role to the extent that it helps in stabilizing the country’s economy. This study focuses on modeling the Nigeria external reserves using time series technique. 30-year data were extracted from the Central Bank of Nigeria (CBN) bulletin from 1990 – 2019. Some economic tools used to diagonize the data are Augmented Dickey Fuller (ADF) test, unit root tests Kwaitkowshi – philips – Schmiot – Shin test in order to ascertain the stationary of the data. Meanwhile, Auto Regression Integrated Moving Average (ARIMA) model was used as model for prediction whereby Akaike Information Criterion (AIC) and Hannan-Quinn Information Criterion (HQIC) were used as model diagnostic checking. At original level, the data showed an upward trend and found out to be non-stationary. When further examined using the diagnostic economic tools, at first difference the data were found to maintain a state of equilibrium. Also, model diagnostic checking revealed that ARIMA (2,1,7) was found to be the appropriate optimal model and thereby used for forecast for the next five years. Hence, the forecasted values revealed that the Nigeria external reserves will continue to increase steadily. Consequently, government should put in place legal policies that will enhance, increase accumulation and proper management of external reserves.

Comparison of Kinetic Models Applied for Transport Description in Polymer Inclusion Membranes.

Five mathematical models for transport description in polymer inclusion membranes (PIMs) were presented and compared via regression analysis. The applicability of the models was estimated through the examination of experimental data of Zn(II), Cd(II), Pb(II), and Cu(II) ions transported by typical carriers. In four kinetic models, a change in the feed and stripping solution volume was taken into account. The goodness of fit was compared using the standard error of the regression, Akaike information criterion (AIC), Bayesian (Schwarz) information criterion (BIC), and Hannan-Quinn information criterion (HQC). The randomness distribution in the data was confirmed via a nonparametric runs test. Based on these quantities, appropriate models were selected.

Forecasting Inflation Rate in the Philippines Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model

The Philippines has experienced a long period of rapid economic growth. However, inflation has now become a major concern, with the Philippines currently experiencing high inflation. High inflation is a threat to strong economic growth, particularly to people's purchasing power. This study aims to predict the inflation rate in the Philippines using Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The historical data on the inflation rate of the Philippines, comprising 135 observations from January 2012 to March 2023, was obtained from the official website of the Philippine Statistic Authority (PSA). The study followed the Box Jenkins Methodology, which consists of 4 stages: Identification, Estimation, Diagnostic Checking, and Forecasting to develop a SARIMA model. It was then revealed that the best SARIMA model for predicting the inflation rate in the Philippines is SARIMA (1,1,1) (0,0,1)_12 based on the following criteria: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan-Quinn Information Criterion (HQIC), σ^2, and the Highest Log Likelihood. This chosen model has a Mean Absolute Percentage Error (MAPE) of 8.17% which is within the acceptable range of 25%. This shows an indication that the forecast is acceptably accurate. The best SARIMA model was then used to forecast the inflation rate in the Philippines from April 2023 to March 2024, and based on the result of the forecasting, it appears that the inflation rate in the Philippines is expected to decline gradually over the next 12 months, from 6.93% in April 2023 to 4.85% in March 2024. These findings could provide insights that the government can use to make decisions about monetary policies and help the economy of the Philippines to improve.

Modelling the Kinetics of Tartrazine Sorption by Bottom Ash

Bottom ash is the solid residue left over from municipal waste combustion or incineration in a Municipal Waste Incineration Furnace. Its use as a sorption agent, particularly for dye sorption, is a new and important application. Linearized adsorption kinetics has drawbacks such as inaccurate representation of the parameters' 95 percent confidence interval output, unbalanced attention to potential outliers, and magnification of errors may result in inaccurate parameter values. In this study, we used nonlinear regression to investigate 16 adsorption kinetics models of tartrazine by bottom ash. The pseudo-second order was the best model based on the Bias and Accuracy factor near unity, but based on other error function analysis, this model performs equally well with the exponential and fractal-like pseudo-second order based other error functions such as Root-Mean-Square Error (RMSE), adjusted coefficient of determination (adjR2), Marquardt’s percent standard deviation (MPSD), Bayesian Information Criterion (BIC), Hannan-Quinn Information Criterion (HQC), and especially the corrected Akaike Information Criterion (AICc) function as the absolute difference is 5 absolute unit making discriminatory activity difficult. Furthermore, because the pseudo-second order and exponential models have only two parameters, they are less complicated according to Occam's razor. Because the pseudo-second order model is more popular and has more applications than the less well-known exponential model, we chose it as the best model for tartrazine sorption to bottom ash. Kinetic analysis using the PSO model gave a value of equilibrium adsorption capacity, qe of 21.88 mg g-1 (95% confidence interval (C.I.), 20.93 to 22.84) and k2 (g/(mg.sec)) of 0.00002 (95%, C.I., 0.00001 to 0.00002).

Modelling the Kinetics of Tartrazine Sorption by the Rhizopus arrhizus Biomass

Rhizopus arrhizus is utilized in many biotechnological applications such as in the manufacturing of enzymes, including pectinases, amylases, proteases, cellulases, and phytases, and metabolites such as lactic acid, ethanol and fumaric acids. Its spent biomass is useful in the food and feed industry. Its usage as a biosorption agent especially dye sorption is an emerging and important application. In this study we explore 16 adsorption kinetics model of tartrazine by R. arrhizus using nonlinear regression. Based on the statistical indicators especially penalty-based error functions such as adjusted coefficient of determination (R2), Root-Mean-Square Error (RMSE), corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC), Hannan-Quinn Information Criterion (HQC) and Marquardt's percentage standard deviation (MPSD) shows that the pseudo-2nd order (PSO) was the best model followed by pseudo-nth order and Fractal-like Pseudo-2nd Order. The parameter of the PSO model gave a value of equilibrium adsorption capacity, qe of 9.367 mg g-1 (95% confidence interval (C.I.), 9.250 to 9.485) and k2 (g/(mg.min)) of 0.037 (95%, C.I., 0.032 to 0.041). The nonlinear regression exercise allows the uncertainty determination of the parameters to be carried out.

Modeling and analysis of recovery time for the COVID-19 patients: a Bayesian approach

The ongoing pandemic of COVID-19 has changed every aspect of life. Most of the people who become a victim of COVID-19 experience mild to moderate symptoms, but some people may become seriously ill. This illness, sometimes, may lead to a very painful death. The Fréchet distribution is one of the flexible distribution for survival time. Hence, in this article, the recovery time of COVID-19 patients is modeled by a new Fréchet-exponential (FE) distribution, and the parameters of the distribution are estimated in the classical and Bayesian paradigms. Since the Bayes estimators using informative priors are not in the closed form, the Lindley and Tierney–Kadane approximation methods are used for their evaluation. The results obtained through simulation studies and the COVID-19 data set assess the superiority of the Bayes estimators over the classical estimators in terms of minimum risks. Mathematically and graphically, it is shown that our proposed model appropriately fits the data set. The minimum values of Akaike information criterion, Bayesian information criterion, corrected Akaike information criterion, and Hannan-Quinn information criterion proves that the FE distribution better fit than the competitors’ distribution for the data set about the recovery time of COVID-19 patients.

Removal of Heavy Metals (Pb2+, Zn2+, Cu2+, Hg2+, Co2+, Cr2+ and Cd2+) From Water by Trimethyl Phenyl Ammonium Cation (TMPA+) Based Organoclay: Experimental and Statistical Test

In this study, trimethyl phenyl ammonium cation (TMPA+) intercalated bentonite was used as an adsorbent for adsorptive removal of seven heavy metal ions namely: Pb (II), Zn (II), Cu (II), Hg (II), Cr (II), Co (II) and Cd (II) in aqueous solution via batch adsorption process. The developed adsorbent, ab initio has been characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FTIR). The operational parameters evaluated on the adsorption efficiency were solution pH, initial metal ions concentration, temperature, adsorbent dosage, and contact time. The adsorption of Pb (II), Zn (II), Cu (II), Hg (II), Cr (II), Co (II) and Cd (II) ions were found to have attained equilibrium positions in 120 min for the concentration range tested, respectively. Two generally applied linearized adsorption isotherm models; Langmuir and Freundlich tested, when compared, revealed that Langmuir isotherm fitted well to the experimental data judging from the higher correlation coefficient values (R2 ) and lower values of the error functions (chi-square (χ2), the sum of square error (ERRSQ/SSE) and the sum of absolute error (EABS)) with monolayer adsorption capacities of 17.66, 21.12, 14.64, 14.98, 16.60, 13.81 and 15.50 mg/g for Pb (II), Zn (II), Cu (II), Hg (II), Cr (II), Co (II) and Cd (II) ions, respectively. Adsorption kinetic models investigated by pseudo-first-order and pseudo-second-order showed the conformity of pseudo-second-order model to the process of adsorption as informed by the higher values R2 and Adj, R2 , maximum log-likelihood and smaller ERRSQ/SSE, χ2, Akaike information criterion (AIC), Bayesian information criterion (BIC), and Hannan-Quinn information criterion (HQIC). Result from kinetic data revealed the adsorption mechanism of Pb (II), Zn (II), Cu (II), Hg (II), Cr (II), Co (II) and Cd (II) ions onto Bt-TMPA as a chemical adsorption.

Comparative Study of the Error Trend and Seasonal Exponential Smoothing and ARIMA Model Using COVID-19 Death Rate in Nigeria

Background: COVID-19 has claimed the lives of millions of people in Nigeria and around the world during the last two years. It is a recognized global health crisis of our day, as well as a persistent threat to the earth. The goal of this study was to examine the trend and fit an Error Trend and Seasonal (ETS) exponential smoothing and Autoregressive Integrated Moving Average (ARIMA) model to Nigeria's COVID-19 daily fatalities.Methods: A dataset of daily COVID-19 confirmed fatality cases was used in the investigation. Data was acquired from the Nigerian Centre for Disease Control (NCDC) web database between the 10th of July 2020 and the 2nd of December 2021. The ARIMA model and twelve (12) ETS exponential smoothing techniques were investigated using a dataset of COVID-19 pandemic deaths in Nigeria. The ARIMA and ETS exponential smoothing algorithms were evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Hannan Quinn Information Criterion (HQC), and Average Mean Squared Error (AMSE) selection criteria.Result: The ARIMA (0,1,0) model was the best time series modeling for the coronavirus (COVID-19) epidemic in Nigeria since it had the lowest AIC=2863.51, BIC=2866.90, HQ = 2866.90, and AMSE = 0.55471 values.Conclusion: The ARIMA (0,1,0) model is preferred above the other thirteen (13) competing models based on daily confirmed COVID-19 deaths in Nigeria. This research would assist the Nigerian government in better understanding the pestilence's evolution pattern and providing adequate provisions, prompt mediation, and treatment to prevent additional deaths caused by the virus.

Kinetic Study of Malathion Biosorption Using Dry Cells of an Isolated Bacillus sp. S14

Pesticides Pollutants are a major ecological issue because they kill organisms that are not their intended targets. Even in trace amounts, their diversity, toxicity, and durability are harmful to natural systems. High levels of malathion in the air, water, or food can make breathing difficult, tighten the chest, cause nausea, cramps, diarrhoea, watery eyes, impaired vision, salivation, perspiration, headaches, and even cause death. Two kinetic models—pseudo-1st- and pseudo-2nd order were used to examine the sorption isotherm of malathion onto Bacillus sp. S14, and they were fitted using non-linear regression. The pseudo-1st order model was found to be the best model by statistical analysis based on root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), accuracy factor (AF), corrected AICc (Akaike Information Criterion), Bayesian information criterion (BIC), and Hannan-Quinn information criterion (HQC). A kinetic study employing the pseudo-1st order model at 150 PPM yielded an equilibrium sorption capacity qe of 4.19 mg/g (95% confidence interval from 4.137448 to 4.257148) and a pseudo-1st-order rate constant, k1 of 0.53. (95 percent confidence interval from 0.510371 to 0.559508). Further analysis is required to give evidence for the chemisorption mechanism commonly associated with this kinetic.