Corrected Akaike Information Criterion Research Articles

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.

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.

Nonlinear modeling of carbon dynamics in soil treated with tannery sludge

In 2020, the Brazilian commercial cattle herd was the largest in the world, representing 14.3% of the worldwide herd, with201.7 million heads. Although this activity yields significant profits, contributing to the economic and social development ofBrazil, it has been the target of concerns, mainly due to the large production of waste/effluents associated to bovine leatherprocessing. In this scenario, mineralization is important because nutrients essential for plant growth are released duringthe process of organic waste decomposition, and the dynamics of carbon release can be described by nonlinear regressionmodels. Thus, this study aimed to model the mineralization of organic carbon in the soil for 6, 12, 24, and 36 megagramsdoses per hectare (Mg ha−1) of tannery sludge using the Stanford &amp; Smith, Cabrera, and Juma nonlinear models. Very clayeysoil samples were used: eutroferric red nitosol (NVef). Mineralized carbon was measured in 21 observations over time untilthe 105th day of incubation. Parameters were estimated using the least squares method. Adjustments were compared usingthe corrected Akaike information criterion (AICc) and the adjusted coefficient of determination (R2aj) as selection criteria. The Cabrera model had the best adjustments for doses 6, 12, and 24 Mg ha−1 and Juma for dose 36 Mg ha−1, based on the selection criteria used. Although the Stanford &amp; Smith model is the most widely used in the literature to model soil carbon dynamics, its use was not the most appropriate for any of the doses evaluated in this study. The higher the dose of tannerysludge, the greater the amount of potentially mineralizable carbon.

Solubility of di-cyclohexylhydrogen phosphonate in supercritical carbon dioxide medium: Application towards the recovery of uranium from aqueous solution

Hydrogen phosphonates are di-esters of phosphonic acids and are widely employed as ligands for recovery of actinide species from different matrices. An alicyclic hydrogen phosphonate, namely, di-cyclohexylhydrogen phosphonate (DCyHeHP) is synthesized; its solubility in supercritical carbon dioxide medium is evaluated towards development of a supercritical fluid extraction (SFE) method for recovery of actinide species. The solubility experiments were carried out at three isotherms (313 K, 323 K, and 333 K) with pressure varying from 10 MPa − 20 MPa, using a dynamic flow method. The mole fraction solubility ranges from 0.2 × 10-3 to 102 × 10-3 in the investigated region. The experimental solubility data was modelled using five semi-empirical equations; Mendez-Santiago, Chrastil, del Valle, Adachi Lu and Sparks equations. The experimental data was found to be self-consistence based on Mendez-Santiago equation. The prediction ability of these models was compared using absolute average relative deviation (AARD) and corrected Akaike Information Criterion (AICc). Among the models employed in the present study, Adachi Lu equation found to better with an AARD and AICc value of 4.2% and 87.2 respectively. Based on the solubility data, SFE method was developed for the selective recovery of uranium from representative fission product elements from an aqueous solution using SCCO2 containing DCyHeHP.

Mathematical model to calculate the total number of decays in Peptide Receptor Radionuclide Therapy using nonlinear mixed effect modelling

The calculation of an individual total number of decays or time-integrated activities (TIAs) of a radiopharmaceutical for kidneys is desirable for dosimetry in molecular radiotherapy. The accuracy of the TIAs calculation relies heavily on the chosen fit model. Therefore, this study aimed to determine the best mathematical model of 177Lu-DOTATATE in peptide receptor radionuclide therapy (PRRT) using the nonlinear mixed effect (NLME) and model selection method. Pharmacokinetics data of 177Lu-DOTATATE in the kidneys of ten PRRT patients were obtained from the literature (PMID: 33443063). Eleven sums of exponential (SOE) functions were fitted to the pharmacokinetics data in the NLME framework. The model selection was performed based on the goodness of fit test and the corrected Akaike Information Criterion (AICc). The goodness of fit was evaluated based on the fitted graphs visualization, coefficient of variations (CV<50%), and the off-diagonal elements of the correlation matrix (-0,8≤CM≤0,8). In general, all SOE functions were successfully fitted to the pharmacokinetic data of 177Lu-DOTATATE in kidneys. The function was selected as the best mathematical model with an AICc weight of 77.58 %.

Public health insurance, frailty, and lack of home support predict rehab discharge following elective anterior cervical discectomy and fusion

BACKGROUND CONTEXTAnterior cervical discectomy and fusion (ACDF) is a commonly-performed and generally well-tolerated procedure used to treat cervical disc herniation. Rarely, patients require discharge to inpatient rehab, leading to inconvenience for the patient and increased healthcare expenditure for the medical system. PURPOSEThe objective of this study was to create an accurate and practical predictive model for, as well as delineate associated factors with, rehab discharge following elective ACDF. STUDY DESIGNThis was a retrospective, single-center, cohort study. PATIENT SAMPLEPatients who underwent ACDF between 2012 and 2022 were included. Those with confounding diagnoses or who underwent concurrent, staged, or nonelective procedures were excluded. OUTCOME MEASURESPrimary outcomes for this study included measurements of accuracy for predicting rehab discharge. Secondary outcomes included associations of variables with rehab discharge. METHODSCurrent Procedural Terminology codes identified patients. Charts were reviewed to obtain additional demographic and clinical characteristics on which an initial univariate analysis was performed. Two logistic regression and two machine learning models were trained and evaluated on the data using cross-validation. A multimodel logistic regression was implemented to analyze independent variable associations with rehab discharge. RESULTSA total of 466 patients were included in the study. The logistic regression model with minimum corrected Akaike information criterion score performed best overall, with the highest values for area under the receiver operating characteristic curve (0.83), Youden's J statistic (0.71), balanced accuracy (85.7%), sensitivity (90.3%), and positive predictive value (38.5%). Rehab discharge was associated with a modified frailty index of 2 (p=.007), lack of home support (p=.002), and having Medicare or Medicaid insurance (p=.007) after correction for multiple hypotheses. CONCLUSIONSNonmedical social determinants of health, such as having public insurance or a lack of support at home, may play a role in rehab discharge following elective ACDF. In combination with the modified frailty index and other variables, these factors can be used to predict rehab discharge with high accuracy, improving the patient experience and reducing healthcare costs.

Extended Kumaraswamy Exponential Distribution with Application to COVID-19 Data set

There are many probability models describing the time related events data. In this study, the exponential distribution is modified by adding one more parameter to get more flexible probability model called Extended Kumaraswamy Exponential (EKwE) distribution using the New Kw-G family (NKwG) of distributions. We have studied some of the statistical characteristics of the model, such as its reliability function, hazard rate function, and quantile function. For testing the applicability of the model, a real data set based on COVID-19 data is taken. The Cramer-von Mises (CVM) approach, Least Square Estimation (LSE), and Maximum Likelihood Estimation (MLE) are used to estimate the model’s parameters. Validity of the model is checked by using P-P plot and Q-Q plot. Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (CAIC), Bayesian Information Criterion (BIC) and Hannan-Quinn Information Criterion (HQIC) are also used for model comparison. Goodness of fit of the proposed model is tested using Kolmogrov-Smirnov (KS), Cramer-Von Mises (CVM) and Anderson-Darling (An) test statistics along with respective p-values. All the analysis of the study is performed by using R programming.

Modeling and Forecasting Inflation in Nigeria: A Time Series Regression with ARIMA Method

This study uses time series regression with autoregressive integrated moving average (ARIMA) modeling to establish a model for forecasting inflation in Nigeria for the period 1981-2020. Akaike Information Criterion Corrected (AICC) and Bayesian Information Criterion (BIC) were used to select the best model among competing models. Through these methods, regression with ARIMA (0,0,1) error was selected as the most parsimonious model for inflation forecasting in Nigeria. The results of the out-sample-forecast show that a high inflation rate will be experienced by the end of 2023, and between 2024 and 2030, the inflation rate will be alternating but will maintain a lower rate than that of 2023.

Comparison between the Four-parameter and Five-parameter Logistics Equations in the Determination of the Limits of Detection of Ochratoxin A

Ochratoxin A, a type of mycotoxin, was detected in plant sample matrices using the "optical waveguide lightmode spectroscopy" (OWLS) method. The calibration curve for the detection of ochratoxin A utilizing "optical waveguide lightmode spectroscopy" (OWLS) displayed a sigmoidal shape; hence, the 5-parameter logistics (5-PL) or 4-parameters logistics (4-PL) model should be used to fit the data rather than a linear model. Through the use of error function analysis and various functions such as Bias Factor, corrected Akaike Information Criterion (AICc), adjusted correlation coefficient (adjR2), Hannan–Quinn information criterion (HQC), Root Mean Square Error (RMSE), Bayesian Information Criterion (BIC), and Accuracy Factor, the distinction between the 5-PL and 4-PL models is found to be inconsistent. The half maximal effective concentration (EC50) confidence intervals overlapped, indicating no difference between the two methods; the 4-PL model was chosen because of its fewer parameters. The Limits of Detection (LOD) calculated using the 4-PL equation was 0.818 ng/mL, with a 95% confidence interval of 0.526-1.143 ng/mL. This value falls within the estimated range of 0.5 to 10 ng/mL found in the first investigation. Not only was the linear component of the data curve successfully represented by this study's use of the 4-PL model, but the model was also successful in representing the entire date curve.

Nodal Electricity Price Forecasting using Exponential Smoothing and Holt’s Exponential Smoothing

The prediction of nodal electricity price (NEP) is a primary step to be done before the bidding process starts in the actual market environment. NEP plays a significant role for the efficient working of the electrical system. NEP follows a common trend as during peak hours when the load is high the price will also be high similarly during off-peak-load times the price will be lower and common to all the node. Thus, accurate forecasting of the NEP can help electricity generation companies to be more proactive in the wholesale electricity market to maximize its overall benefits. In this paper, exponential smoothing (ES), and holt’s exponential smoothing (HES) have been utilized for forecasting the NEP. Furthermore, a comparative analysis between ES and HES has been done considering several alpha values and several trends. The model evaluation and the forecasting performance have been tested using different parameters of ES, and HES techniques such as Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICc), Bayesian Information Criteria (BIC). The performance of the proposed technique has been authenticated efficaciously on average nodal real-time price data collected from ISO New England (BOSTON Zone).

Prevalence of HIV in Kazakhstan 2010-2020 and Its Forecasting for the Next 10 Years.

HIV is a growing public health burden that threatens thousands of people in Kazakhstan. Countries around the world, including Kazakhstan, are facing significant problems in predicting HIV infection prevalence. It is crucial to understand the epidemiological trends of infectious diseases and to monitor the prevalence of HIV in a long-term perspective. Thus, in this study, we aimed to forecast the prevalence of HIV in Kazakhstan for 10 years from 2020 to 2030 by using mathematical modeling and time series analysis. We use statistical Autoregressive Integrated Moving Average (ARIMA) models and a nonlinear epidemic Susceptible-Infected (SI) model to forecast the HIV infection prevalence rate in Kazakhstan. We estimated the parameters of the models using open data on the prevalence of HIV infection among women and men (aged 15-49 years) in Kazakhstan provided by the Kazakhstan Bureau of National Statistics. We also predict the effect of pre-exposure prophylaxis (PrEP) control measures on the prevalence rate. The ARIMA (1,2,0) model suggests that the prevalence of HIV infection in Kazakhstan will increase from 0.29 in 2021 to 0.47 by 2030. On the other hand, the SI model suggests that this parameter will increase to 0.60 by 2030 based on the same data. Both models were statistically significant by Akaike Information Criterion corrected (AICc) score and by the goodness of fit. HIV prevention under the PrEP strategy on the SI model showed a significant effect on the reduction of the HIV prevalence rate. This study revealed that ARIMA (1,2,0) predicts a linear increasing trend, while SI forecasts a nonlinear increase with a higher prevalence of HIV. Therefore, it is recommended for healthcare providers and policymakers use this model to calculate the cost required for the regional allocation of healthcare resources. Moreover, this model can be used for planning effective healthcare treatments.

Analisis Faktor-Faktor yang Menjelaskan Kasus AIDS Provinsi Jawa Timur Menggunakan Model Geographically Weighted Logistic Regression (GWLR)

AIDS is the most chronic phase of HIV infection which can weaken the immune system. In 2020, East Java Province is a province which has the most HIV infections and in the third place for the highest total number of AIDS cases in Indonesia. The purpose of this research is to build a model using Geographically Weighted Logistic Regression (GWLR), and to work out the grouping results of regencies/cities using K-means Clustering Analysis. The variables used in this research are Gini Ratio, L Index of Per Capita Expenditure, Gender Ratio, Dependency Ratio, Gender Development Index, and The Number of Pos Pelayanan KB Desa. The proportion levels of AIDS cases are categorized into 2 categories based on cut-point which has been specified, which 0 as the category of low level with the proportion of AIDS cases is less than 0.0006 and 1 as the category of high level with the proportion of AIDS cases is more than or equal to 0.0006. Parameter estimation for GWLR is using Maximum Likelihood Estimation (MLE) method with Fixed Gaussian as weighted kernel function and optimum bandwidth is determined using Akaike's Information Criterion Corrected (AICc). Z-Score of the most suitable model will be grouped using K-means Clustering Analysis, with Z-score is parameter estimator divided by standard error. Grouping results indicates cluster 1 members tend to be regencies/cities that have gender ratio and dependency ratio as significant variables, meanwhile cluster 2 members tend to be regencies/cities that have only dependency ratio as significant variable.&#x0D;

Comparison between Modified Weighted Pareto Distribution and Many other Distributions

In 2020 one of the researchers in this paper, in his first research, tried to find out the Modified Weighted Pareto Distribution of Type I by using the Azzalini method for weighted distributions, which contain three parameters, two of them for scale while the third for shape.This research compared the distribution with two other distributions from the same family; the Standard Pareto Distribution of Type I and the Generalized Pareto Distribution by using the Maximum likelihood estimator which was derived by the researchers for Modified Weighted Pareto Distribution of Type I, then the Mont Carlo method was used–that is one of the simulation manners for generating random samples data in different sizes ( n= 10,30,50), and in different initial values for each Pareto distribution family being used in the research. A comparison was done by using Akaike Information Criteria, Corrected Akaike Information Criteria, and Bayesian Information Criteria

Top-down and bottom-up effects and relationships with local environmental factors in the water frog–helminth systems in Latvia

Semi-aquatic European water frogs (Pelophylax spp.) harbour rich helminth infra-communities, whose effects on host population size in nature are poorly known. To study top-down and bottom-up effects, we conducted calling male water frog counts and parasitological investigations of helminths in waterbodies from different regions of Latvia, supplemented by descriptions of waterbody features and surrounding land use data. We performed a series of generalized linear model and zero-inflated negative binomial regressions to determine the best predictors for frog relative population size and helminth infra-communities. The highest-ranked (by Akaike information criterion correction, AICc) model explaining the water frog population size contained only waterbody variables, followed by the model containing only land use within 500 m, while the model containing helminth predictors had the lowest rank. Regarding helminth infection responses, the relative importance of the water frog population size varied from being non-significant (abundances of larval plagiorchiids and nematodes) to having a similar weight to waterbody features (abundances of larval diplostomids). In abundances of adult plagiorchiids and nematodes the best predictor was the host specimen size. Environmental factors had both direct effects from the habitat features (e.g., waterbody characteristics on frogs and diplostomids) and indirect effects through parasite-host interactions (impacts of anthropogenic habitats on frogs and helminths). Our study suggests the presence of synergy between top-down and bottom-up effects in the water frog–helminth system that creates a mutual dependence of frog and helminth population sizes and helps to balance helminth infections at a level that does not cause over-exploitation of the host resource.

Applying geographically weighted regression to quantify the impact of impervious surface density on land surface temperature in Ho Chi Minh City, Vietnam

Surface characteristics are believed to be the primary factor causing spatial variation in land surface temperature (LST), especially in a large and rapidly growing city like Ho Chi Minh City. The study aims to estimate the spatial relationship between land surface temperature (LST) and impervious surface density (ISA) by using three statistical techniques: 1) Optimized hot spot analysis; 2) Global linear regression (OLS); and 3) Geographically weighted regression (GWR). LST and ISA were extracted from Landsat 8 OLI of 2020. According to the optimized hot spot analysis’s findings, over 80% of the areas of high ISA values (mean ISA: 0.86, SD: 0.13) overlap with areas of high LST values (mean LST: 36.2°C, SD: 2.04°C), with a correlation coefficient of r=0.775. Likewise, Moran’s I index (I=0.82, Z=72, P<0.001) indicates that LST in Ho Chi Minh City has a significantly spatial dependence, so geographically weighted regression (GWR) and global linear (OLS) models need to be taken into account simultaneously. Coefficient of determination R2 and AICc (Akaike information criterion corrected) were used to compare the models. R2 increases from 0.78 to 0.87, whereas AICc decreases from 8047 to 7033 in OLS and GWR, respectively. GWR has a more substantial explanatory power for the association between LST and ISA and offers superior LST prediction accuracy compared to OLS. Potential urban heat island areas can be accurately detected by combining GWR and optimized hot spot analysis.