Corrected Akaike Information Criterion Research Articles

COMPARATIVE ANALYSIS OF TRIANGULAR FUZZY HIDDEN MARKOV MODELS AND TRADITIONAL HIDDEN MARKOV MODELS

This study optimizes Traditional Hidden Markov Models (THMMs) using Triangular Fuzzy Membership Functions, resulting in Triangular Fuzzy Hidden Markov Models (TFHMMs) that tolerate ambiguous observations and gradual state transitions in agricultural data prediction. Using oilseed area data from 1992 to 2022, we compare TFHMMs to traditional HMMs, focusing on predicting accuracy using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Corrected Akaike Information Criterion (AICc), and Hannan-Quinn Information Criterion (HQIC). Our research includes stationary parameters to improve model stability and uses the Viterbi technique to find optimal state sequences, which improves forecast interpretability. The results show that THMMs outperform TFHMMs at capturing the complicated patterns of agricultural data, with lower prediction errors and more reliability. This work emphasizes the potential of fuzzy logic in not improving probabilistic models for agricultural forecasting, providing a more delicate and accurate approach to analyzing and predicting agricultural trends.

Modified Half-Cauchy Chen (MHCC) Distribution with Applications to Lifetime Dataset

In this article, we have recommended an innovative versatile distribution called Modified Half-Cauchy Chen distribution by modifying half-Cauchy Chen distribution. The recommended distribution's various properties are derived and analyzed. The recommended distribution's parameters are ascertained by applying the maximum likelihood estimation (MLE) approach. Furthermore, the performance of the Modified Half-Cauchy Chen distribution is compared against other distributions using various statistical measures. These measures consist of the Corrected Akaike Information Criterion (CAIC), the Kolmogorov-Smirnov (K-S) test, the Bayesian Information Criterion (BIC), and the Akaike Information Criterion (AIC). The results consistently demonstrate the superior fit of the Modified Half-Cauchy Chen distribution to the data. The goodness-of-fit analysis is carried out on a real data set in order to evaluate the innovative distribution's applicability. The Modified half-Cauchy Chen distribution is shown to perform better than a few other known distributions. R programming software is used to help with every computation.

Constraints on power law and exponential models in f(Q) gravity

In this paper, we observationally test the f(Q) gravity model at both background and perturbation levels using Pantheon+, Hubble measurements, and Redshift Space Distortion Data. We obtain the best-fit parameters by solving numerically the modified Friedmann equations for two distinct cosmological models of f(Q) gravity namely the Power law and Exponential models. This involves performing a Markov Chain Monte Carlo analysis for these specific forms of f(Q). To evaluate the statistical significance of the f(Q) gravity models, we use the Bayesian and corrected Akaike Information Criteria. Our results indicate that the Exponential model in f(Q) gravity is statistically favored over both the Power-law model and the ΛCDM model.

Modeling of Some Egg Characteristics in Henna Partridges

In this study, the distributions of egg count (daily and cumulative), width, length and weight values of chukar partridges obtained in two different egg production seasons were statistically modeled. For this purpose, in modeling cumulative egg production curves; Logistic, Gompertz, Gamma, Schunute, Brody, Richard, Negative Exponential, von Bertalanffy, Cubic Piecewise and Cubic models were used, and in evaluating daily egg production curves; Gompertz, Logistic, Richard, McNally, Gamma, Cubic Piecewise, Quadratic, Quadratic Piecewise and Modified Compartmental models were used. In modeling egg width values; Gompertz, Gamma, Cubic Piecewise and Cubic models were used, in modeling length values; Logistic, Gamma, Cubic Piecewise and Cubic models were used, and in modeling weight values; McNally, Gamma, Cubic Piecewise and Cubic models were used. In all modeling studies, as comparison and evaluation criteria; Error Mean Square, Corrected Coefficient of Determination, Accuracy Factor, Deviation Factor, Durbin-Watson, Akaike Information Criterion, Corrected Akaike Information Criterion and Bayesian Information Criterion were taken into consideration. As a result of the study; Gamma was determined as the most suitable model for modeling cumulative egg yields and length values, McNally for modeling daily egg yields and weight values, and Gompertz and Gamma models for modeling egg width values.

A New Extension of Topp-Leone Distribution (NETD) Using Generalized Logarithmic Function

Study’s Excerpt New Extension of Topp-Leone Distribution (NETD), derived using a generalized logarithm function, expanding the Topp-Leone family of distributions, is introduced. Mathematical properties of the NETD, including its quantile function, moments, Renyi entropy, and order statistics were investigated. The model demonstrates superior goodness-of-fit over existing models across four real-world datasets. Full Abstract In this study, we introduce a novel statistical model termed the New Extension of Topp-Leone Distribution (NETD), constructed using a generalized logarithm function. The derivation of the cumulative density function (CDF) and the probability density function (PDF) for this new distribution is thoroughly detailed. A validity test was conducted to confirm the legitimacy of the proposed NETD, and the results affirm that it is indeed a valid probability distribution. We investigate several key mathematical and statistical properties of the NETD, including the quantile function, moments, Renyi entropy, probability-weighted moments, and order statistics. These properties provide a comprehensive understanding of the distribution's behaviour and characteristics. To estimate the parameters of the NETD, we applied the Maximum Likelihood Estimation (MLE) method, which is known for its efficiency and asymptotic properties. The flexibility, versatility, and performance of the NETD were evaluated using four diverse real-world datasets. Through the application of model selection criteria such as the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Corrected Akaike Information Criterion (CAIC), and Hannan-Quinn Information Criterion (HQIC), we demonstrated that the NETD exhibits superior goodness-of-fit compared to existing variants of the Topp-Leone distribution. Our findings suggest that the NETD is a robust and adaptable distribution, capable of effectively modelling a wide range of empirical data. This new extension not only broadens the applicability of the Topp-Leone family of distributions but also enhances the toolkit available for statistical analysis and modelling in various fields.

New solubility data of Amoxapine (anti-depressant) drug in supercritical CO2: Application of cubic EoSs

The solubility of the anti-depressant drug, Amoxapine drug, in supercritical carbon dioxide has been studied. The experiments were performed by the simple and static procedure at P = 120–300 bar and T(K) = 311, 323, 333, and 343. The drug solubilities (mole fraction) ranged from 0.44 × 10−6 to 7.81 × 10−6. The drug solubilities were modeled, first using two cubic equations of state (Peng-Robinson and Soave-Redlich-Kwong EoSs with van der Waals mixing rule) and additionally by 17 semi-empirical density-based correlations (Stahl et al., Chrastil, Kumar-Johnston, Garlapati-Madras (model I and II), Sung-Shim, del Valle and Aguilera, Adachi-Lu, Ch and Madras, Hezave and Lashkarbolooki, Mendez-Santiago-Teja, Bartle et al., Jafari Nejad et al., Hozhabr et al., Keshmiri et al., Khansary et al. and Sodeifian et al.). The results revealed that cubic EoSs and semi-empirical density-based correlations can be used to obtain satisfactory drug solubility correlations at various temperatures and pressures. Between cubic EoSs, Peng-Robinson EoS (with Akaike Information Criterion is −692.12), and among 17 density-based correlations, the Stahl et al. correlation (with AARD% = 4.16 %, corresponding corrected Akaike Information Criterion is −700.98 and Radj = 0.9963) gave much better accuracy with the experimental data. The thermodynamic consistency test based on Gibbs-Durham's theory was performed by using both cubic EoSs and all experimental data were examined. Furthermore, the solvation, vaporization, and total enthalpies of Amoxapine-CO2 were calculated by Chrastil, KJ, and Bartle et al. correlations.

EFFECTS OF THE CLIMATE GRADIENT ONF THE WOOD DENSITY OF Cedrela odorata L. IN THE NEOTROPICAL REGION

Understanding wood properties is crucial for optimizing its technological potential, enhancing both the effectiveness of use and product quality. Among these properties, density is regarded as one of the most significant. Numerous studies have documented the relationship between wood density and climate. However, the influence of climatic factors on the density variation of native species remains largely unexplored. Cedrela odorata L., a species traditionally logged in Brazil and widely distributed across the Neotropical region, requires further analysis to understand its potential response to varying climate patterns, thereby informing its management. This study aimed to determine the relationship between the basic density of Cedrela odorata L. and the climatic conditions in the Neotropical region. A comprehensive database was compiled from published scientific literature. Statistical analyses were conducted using Generalized Linear Models (GLM), with the best models selected based on the corrected Akaike Information Criterion (AICc). The presence of spatial patterns was assessed using Moran’s Index to verify spatial autocorrelation. The study identified significant relationships between the wood density of C. odorata and variables such as mean annual temperature and annual precipitation. The findings indicate a strong interaction between the climatic variables analyzed and the basic density of Cedrela odorata L. wood, with density being more sensitive to mean annual temperature.

Unraveling the Relationship between Soil Nutrients and Maize Leaf Disease Occurrences in Mopani District Municipality, Limpopo Province, South Africa

Maize is a staple crop important for food security that millions globally depend upon as an energy source, primarily due to its high starch and fat content. For growth and disease resistance, maize production requires a balanced intake of essential nutrients, including nitrogen (N), phosphorus (P) and potassium (K). This study investigated the relationship between soil nutrient levels and maize disease occurrences in the Mopani District Municipality, Limpopo Province, South Africa. Soil and maize leaves were collected using a systematic sampling approach. Grids of 10 × 10 m were created, covering a maize field. Forty soil samples were collected a day before the planting date and sent to the laboratory for analysis of N, P and K. During the tasseling stage of the maize plant, 40 maize leaf samples were collected and sent to the laboratory for disease identification. Maize leaves were classified as healthy, southern corn leaf blight (Bipolaris maydis), northern corn leaf blight (Exserohilum turcicum), maize streak disease (Maize streak virus), nitrogen-deficient or phosphorus-deficient. Generalized Linear Models (GLMs) with a corrected Akaike Information Criterion (AICc) showed a significant relationship between low soil nutrient levels of N, P and K and maize disease occurrence (p < 0.0001). The interaction of the N*P*K model had the lowest AIC value (AICc = 28.53), indicating the necessity of considering synergistic effects in maize disease management. All the model performances had a delta AICc = 0. These findings highlight the significance of comprehensive soil management strategies in enhancing the disease resistance, well-being and yields of maize crops.

Modeling the Impacts of Climate Change on Potential Distribution of Betula luminifera H. Winkler in China Using MaxEnt

Betula luminifera H. Winkler, a fast-growing broad-leaved tree species native to China’s subtropical regions, possesses significant ecological and economic value. The species’ adaptability and ornamental characteristics make it a crucial component of forest ecosystems. However, the impacts of global climate change on its geographical distribution are not well understood, necessitating research to predict its potential distribution shifts under future climate scenarios. Our aims were to forecast the impact of climate change on the potential suitable distribution of B. luminifera across China using the MaxEnt model, which is recognized for its high predictive accuracy and low sample data requirement. Geographical coordinate data of B. luminifera distribution points were collected from various databases and verified for redundancy. Nineteen bioclimatic variables were selected and screened for correlation to avoid overfitting in the model. The MaxEnt model was optimized using the ENMeval package, and the model accuracy was evaluated using the Akaike Information Criterion Correction (delta.AICc), Training Omission Rate (OR10), and Area Under the Curve (AUC). The potential distribution of B. luminifera was predicted under current and future climate scenarios based on the Shared Socio-economic Pathways (SSPs). The optimized MaxEnt model demonstrated high predictive accuracy with an AUC value of 0.9. The dominant environmental variables influencing the distribution of B. luminifera were annual precipitation, minimum temperature of the coldest month, and standard deviation of temperature seasonality. The potential suitable habitat area and its geographical location were predicted to change significantly under different future climate scenarios, with complex dynamics of habitat expansion and contraction. The distribution centroid of B. luminifera was also predicted to migrate, indicating a response to changing climatic conditions. Our findings underscore the importance of model optimization in enhancing predictive accuracy and provide valuable insights for the development of conservation strategies and forest management plans to address the challenges posed by climate change.

Modeling non-stationarity in extreme rainfall data and implications for climate adaptation: A case study from southern highlands region of Tanzania

The Southern Highlands region of Tanzania has witnessed an increased frequency of severe flash floods. This study examines rainfall data of four stations (Iringa, Mbeya, Rukwa, and Ruvuma) spanning 30 years (1991–2020) to investigate drivers of extreme rainfall and non-stationarity behavior. The Generalized Extreme Value (GEV) model, commonly used in hydrological studies, assumes constant distribution parameters, which may not be true due to climate variability, potentially leading to bias in extreme quantile estimation. Recent studies have introduced a technique for constructing non-stationary Intensity-Duration-Frequency (IDF) rainfall curves. The method incorporates trends in the parameters of the GEV distribution, only using time as a covariate. However, uncertainty exists about whether time is the most suitable covariate, highlighting the need to explore all potential covariates for modeling non-stationarity. The aim of this study is to assess the influence of other time-varying covariates on extreme daily rainfall events, considering seasonality and climate change in the rainfall data. Specifically, five processes (i.e., local temperature changes (LTC), urbanization, annual Global Temperature Anomaly (GTA), the Indian Ocean Dipole (IOD), and the El Niño-Southern Oscillation (ENSO) cycle) were studied as drivers of extreme rainfall events. Sixty two non-stationary GEV models are developed based on these covariates and their combinations, alongside two non-stationary GEV models using the time covariate to capture the seasonality of the unimodal rainfall in the region, and one stationary GEV model (S0). With the use of corrected Akaike Information Criterion (AICc), the best model for each duration (i.e., 1-, 3-, and 5-days) of rainfall series is chosen. Results indicate that local processes (i.e., LTC and urbanization) are the optimal covariates for 1 day-duration rainfall, while global processes (i.e, IOD, ENSO cycle, and GTA) are identified as the most suitable covariates for 3, and 5 day-duration rainfall across all stations. The identified best non-stationary model (with their best covariates) are then used to develop non-stationary rainfall IDF curves for all stations. According to the analysis of non-stationary extreme values, the return periods of extreme rainfall events concluded a notable decrease in comparison to the stationary approach. The study also revealed strong correlations between global climate indices (ENSO, IOD, GTA) and long-duration extreme rainfall in Tanzania’s Southern Highlands. Local factors like Urbanization and temperature changes also show significant associations with 1-day duration events. These findings emphasize the need for integrated climate forecasting to inform effective adaptation strategies. Finally, the study addresses associated uncertainties in our predictions of forthcoming extreme rainfall events through rigorous analysis. The study demonstrated that return levels for extreme rainfall events exhibit a rising trend with increasing return period, indicating heightened intensity over longer time spans, whereas, a relative uncertainty analysis illustrate escalating uncertainty with increasing return periods, emphasizing challenges in long-term prediction.

Modeling dynamics of acute HIV infection incorporating density-dependent cell death and multiplicity of infection.

Understanding the dynamics of acute HIV infection can offer valuable insights into the early stages of viral behavior, potentially helping uncover various aspects of HIV pathogenesis. The standard viral dynamics model explains HIV viral dynamics during acute infection reasonably well. However, the model makes simplifying assumptions, neglecting some aspects of HIV infection. For instance, in the standard model, target cells are infected by a single HIV virion. Yet, cellular multiplicity of infection (MOI) may have considerable effects in pathogenesis and viral evolution. Further, when using the standard model, we take constant infected cell death rates, simplifying the dynamic immune responses. Here, we use four models-1) the standard viral dynamics model, 2) an alternate model incorporating cellular MOI, 3) a model assuming density-dependent death rate of infected cells and 4) a model combining (2) and (3)-to investigate acute infection dynamics in 43 people living with HIV very early after HIV exposure. We find that all models qualitatively describe the data, but none of the tested models is by itself the best to capture different kinds of heterogeneity. Instead, different models describe differing features of the dynamics more accurately. For example, while the standard viral dynamics model may be the most parsimonious across study participants by the corrected Akaike Information Criterion (AICc), we find that viral peaks are better explained by a model allowing for cellular MOI, using a linear regression analysis as analyzed by R2. These results suggest that heterogeneity in within-host viral dynamics cannot be captured by a single model. Depending on the specific aspect of interest, a corresponding model should be employed.

Experimental study of the effect of geodesic curvature on turbulent transport in magnetically confined plasma

An experimental study has demonstrated the impact of the geodesic curvature of the magnetic field line on turbulent ion-heat transport in magnetically confined plasma using the large helical device. Statistical analyses with corrected Akaike Information Criterion and multiple regression have revealed that the geodesic curvature indicates a dominant contribution to the ion-heat transport. Geodesic curvature dependence of the zonal-flow effect is evaluated by using a gyrokinetic-simulation-based reduced model. Then, the analysis implies a significant enhancement of the zonal-flow effect with a small geodesic curvature. These two independent analyses indicated the possibility of external zonal-flow control with the geodesic curvature of the magnetic field.

The Generalized Odd Maxwell-Kumaraswamy Distribution: Its Properties and Applications

This study introduces a novel family of continuous probability distributions using Alzaatreh‘s technique to explore the Generalized Odd Maxwell-Generated (GOM-G) distribution family. Within the Generalized Odd Maxwell family, the GOM-Kumaraswamy distribution is presented as extended form of the Kumaraswamy distribution. The investigation thoroughly examines the cumulative distribution function, probability density function, hazard, and survival functions, as well as mixture representations of the Generalized Odd Maxwell-Kumaraswamy distribution. The suggested distribution's structural properties, including moments, skewness, kurtosis, probability-weighted moments, entropies, stress-strength models, and order statistics, are derived. Model parameters are estimated through the maximum likelihood method, and simulation studies evaluate the performance of maximum likelihood estimations using the quantile function. Employing two real-life datasets, goodness-of-fit measures such as Akaike Information Criterion (AIC), Corrected Akaike Information Criterion, Bayesian Information Criterion (BIC), Hannan-Quinn Information Criterion (HQIC), and chi-square goodness-of-fit tests demonstrate the adaptability and flexibility of the GOM-Kumaraswamy distribution against competing distributions, including Kumaraswamy-Kumaraswamy and Kumaraswamy-Burr III. The results reveal that the GOM-Kumaraswamy distribution exhibits the lowest AIC, CAIC, BIC, and HQIC values and the highest goodness-of-fit values compared to other models, suggesting its superiority as the preferred fit for both dataset forms. This proposed distribution contributes to practical applications, unveiling its potential significance in modeling real-world phenomena within the domains of hydrology and engineering.

Statistical analysis of air quality dataset of Kathmandu, Nepal, with a New Extended Kumaraswamy Exponential Distribution.

This paper aims to create a new probability distribution and conducts statistical analysis on air quality dataset from Kathmandu. Using this innovative distribution, we have studied the ground reality of air quality conditions of Kathmandu, Nepal. In our research, we have developed a new probability distribution known as the New Extended Kumaraswamy Exponential Distribution by introducing an additional shape parameter to the Extended Kumaraswamy Exponential (EKwE) Distribution. Statistical characteristics such as cumulative distribution function, probability density function, hazard function, reversed hazard function, skewness, kurtosis, survival function, and hazard rate function are studied. The suggested model is non-normal and positively skewed with increasing and inverted bathtub-shaped hazard rate curves. To assess the model's suitability, we utilized a real dataset comprising air quality data from Kathmandu, Nepal, during the year 2021. Study shows that the air quality data exhibit an increasing failure rate, but the P2.5, P10, and total suspended particle concentrations exhibited its lowest levels during the monsoon season and its highest levels during the winter season. Parameters of the model are estimated by using the least square estimation (LSE), maximum likelihood estimation (MLE), and Cramér-von Mises (CVM) approach for P10 at Ratnapark Station, Kathmandu. To assess the model's validity, P-P plots and Q-Q plots are employed. Model comparisons are carried out using Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (CAIC), Bayesian Information Criterion (BIC), and Hannan-Quinn Information Criterion (HQIC). Furthermore, the goodness of fit of the proposed model is evaluated using test statistics such as Anderson-Darling (A2) test, Cramér-von Mises (CVM) test, and the Kolmogorov-Smirnov (KS) test along with their respective p-values. From the findings, we have found that the air quality status of Kathmandu, Nepal, was found to be poor. Proposed distribution provides a better fit with greater flexibility for forecasting air quality data and conducting reliability data analyses. Dataset is analyzed and visualized using R programming.

Geographical variation in hotspots of stunting among under-five children in Ethiopia: A geographically weighted regression and multilevel robust Poisson regression analysis.

Childhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey. The current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association. The prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran's I = 0.40, p<0.001). significant hotspot areas of stunting were identified in the west and south Afar, Tigray, Amhara and east SNNPR regions. In the local model, no maternal education, poverty, child age 6-23 months and male headed household were predictors associated with spatial variation of stunting among under five children in Ethiopia. In the multivariable multilevel robust Poisson regression the prevalence of stunting among children whose mother's age is >40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6-23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting. In Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

Modified Exponential Power distribution with properties and applications

We have developed the Modified Exponential power distribution, a new and adaptable probability distribution, in this work. An additional shape parameter is added to the exponential power distribution to create this distribution. Numerous statistical properties of suggested model are derived and analyzed. Cramer-Von-Mises (CVME), maximum likelihood (MLE), and least-squares (LSE) are used to estimate the model's parameters. P-P and Q-Q charts are used to assess the validity of the model. Several information criteria are applied in model comparisons, including the Bayesian Information Criterion (BIC), the Hannan-Quinn Information Criterion (HQIC), the Corrected Akaike Information Criterion (CAIC), and the Akaike Information Criterion (AIC). These criteria help determine the best model by balancing goodness-of-fit with model complexity. Test statistics, together with their corresponding p-values, are also utilized to evaluate the recommended model's goodness of fit. These tests consist of the Kolmogorov-Smirnov (KS), Cramer-Von Mises (CVM), and Anderson-Darling (An) tests. R programming is used for the dataset analysis and visualization.

Modelling the Effect of mercury on the Growth Rate of an SDS-degrading Pseudomonas sp. strain Maninjau1

The SDS-degrading bacterium Pseudomonas sp. strain Maninjau1 experienced significant inhibition by mercury. Observations of bacterial growth at varying mercury concentrations revealed a sigmoidal pattern, with lag periods extending from 7 to 12 hours. Increasing mercury concentrations progressively impeded growth, with a concentration of 1.0 mg/L nearly halting bacterial activity. To analyze these effects, the modified Gompertz model was employed to determine growth rates across different mercury concentrations. These rates were then subjected to several models, including the modified Han-Levenspiel, Wang, Liu, Shukor, modified Andrews, and Amor models. Among these, the Amor model failed to appropriately fit the growth curves. Statistical analysis indicated that the Shukor model performed the best, evidenced by the lowest values for Root Mean Square Error (RMSE) and Akaike’s Information Criterion corrected (AICc), the highest adjusted correlation coefficient (adR2), and values of Accuracy Factor (AF) and Bias Factor (BF) closest to unity. The parameters obtained from the Shukor model, which are mmax (h-1) and Sm (mg L-1) and n which represent maximum growth rate, critical heavy metal ion concentration and empirical constant values were 0.187, 1.126 and 2.406, respectively. The Shukor model allows for the prediction of the critical heavy metals concentration which can completely inhibited bacterial growth. This robust modeling approach underscores the Shukor model's suitability for predicting the impact of mercury on the growth dynamics of Pseudomonas sp. strain Maninjau1 under toxic stress conditions.

Estimation of the Xrama Distribution Parameter Under Complete and Progressive Type-II Censored Schemes

In this paper, a two-component mixture of Exponential and Rama distribution named Xrama distribution is introduced and discussed. The distributional properties such as moment and the related measures including other characteristics namely quantile function, mean residual life function, Bonferroni curve, Lorenz curve, order statistics, stochastic ordering, and entropy were derived. Simulation study for both complete and progressive type-2 censored schemes is used to illustrate and analyze the behavior of the model. The proposed model’s performance is assessed through a comparison with various other lifetime distributions using negative log-likelihood, Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Hannan–Quinn information criterion on an actual dataset. The results reveal that, when modeling real lifetime data, the suggested model exhibits greater flexibility than the competing models examined in this study.

Exploiting carboxymethyl cellulose-starch/alumina nano gel to eliminate Fe(III) from ore leachates of rare earth elements

AbstractThe present study describes the development of a novel nano gel of Carboxymethyl Cellulose Starch and Alumina (CMC-St/Al2O3) to purify the leach liquor of rare earth elements (REEs) minerals from Fe(III), which is considered the most contaminating metal in the REE liquor. CMC-St/Al2O3 nano gel was recognized by different analytical techniques such as FT-IR, SEM, TEM, X-ray diffraction, particle size, and thermal analysis. In a batch study, the best conditions for purifying REE from Fe(III) contaminants using CMC-St/Al2O3 nano gel were determined. For an initial concentration of 100 mg L−1, 97.6% of Fe(III) was efficiently adsorbed onto CMC-St/Al2O3 after 15.0 min, pH = 2, and 25 °C. Due to the competition effect, the sorption efficiencies of the binary systems decreased to 68.4%, 72.97%, and 84.71% for the systems Fe(III)/La(III), Fe(III)/Sr(II), and Fe(III)/Cs(I), respectively. 99.9% of Fe(III) is eluted by 0.50 mol L−1 H2SO4. The sorption process was fitted with pseudo-second-order and the Langmuir model based on the error functions: Coefficient of determination (R2), Reduced Chi-square (χ2), The sum of square errors (SSE), and Corrected Akaike Information Criterion (AICc) as well as Residual error plots. The sorption process was spontaneous and exothermic. Finally, a CMC-St/Al2O3 nano gel was used to separate Fe(III) from the monazite liquor minerals and Sela leachate, mineralized from the Rosetta area and Gabal El Sela at Halaib environs, Egypt, with efficiencies of 89.03 and 92.7%, respectively.

On evaluating the hypothesis of shape similarity between soil particle-size distribution and water retention function

Two pedotransfer functions (PTFs) are available in the literature enabling the soil water retention function (WRF) to be estimated from knowledge of the soil particle-size distribution (PSD), oven-dry soil bulk density (b), and saturated soil water content (s): i) the Arya and Heitman model (PTF-AH) and ii) the Mohammadi and Vanclooster model (PTF-MV). These physico-empirical PTFs rely on the hypothesis of shape similarity between PSD and WRF, and do not require the calibration of the input parameters. In the first stage, twenty-seven PSD models were evaluated using 4,128 soil samples collected in Campania (southern Italy). These models were ranked according to the root mean square residuals (RMSR), corrected Akaike Information Criterion (AICc), and adjusted coefficient of determination (R2adj). In the second stage, three subsets of PSD and WRF data (DS-1, DS-2, and DS-3), comprising 282 soil samples, were used to evaluate the two PTFs using the best three PSD models selected in the first stage. The hypothesis of shape similarity was assumed as acceptable only when the RMSR value was lower than the field standard deviation of the WRFs (*), which is viewed as a tolerance threshold and computed from the physically-based scaling approach proposed by Kosugi and Hopmans (1998). In the first study area (DS-1), characterized by a fairly uniform, loamy textured volcanic soil, the PTF-AH outperformed the PTF-MV and both PTFs provided reasonable performance within the acceptance threshold (i.e., RMSR &lt; *). In the other two heterogeneous field sites (DS-2 and DS-3, characterized by soil textural classes that span from clay and clay-loam to loam and even sandy-loam soils), the PTF-MV (with 3% to 6% RMSR surpassing *) outperformed the PTF-AH (with 8% to 30% RMSR surpassing *) and the majority of RMSR values were larger than those obtained in the original studies. The mean relative error (MRE) revealed that the PTF-MV systematically underestimates the measured WRFs, whereas the PTF-AH provided negative MRE values indicating an overall overestimation. The outcomes of our study provide a critical evaluation when using calibration-free PTFs to predict WRFs over large areas.