Variance Inflation Factor Research Articles

Utilizing Machine Learning Algorithms for the Development of Gully Erosion Susceptibility Maps: Evidence from the Chotanagpur Plateau Region, India

Gully erosion is a serious environmental threat, compromising soil health, damaging agricultural lands, and destroying vital infrastructure. Pinpointing regions prone to gully erosion demands careful selection of an appropriate machine learning algorithm. This choice is crucial, as the complex interplay of various environmental factors contributing to gully formation requires a nuanced analytical approach. To develop the most accurate Gully Erosion Susceptibility Map (GESM) for India’s Raiboni River basin, researchers harnessed the power of two cutting-edge machine learning algorithm: Extreme Gradient Boosting (XGBoost) and Random Forest (RF). For a comprehensive analysis, this study integrated 24 potential control factors. We meticulously investigated a dataset of 200 samples, ensuring an even balance between non-gullied and gullied locations. To assess multicollinearity among the 24 variables, we employed two techniques: the Information Gain Ratio (IGR) test and Variance Inflation Factors (VIF). Elevation, land use, river proximity, and rainfall most influenced the basin’s GESM. Rigorous tests validated XGBoost and RF model performance. XGBoost surpassed RF (ROC 86% vs. 83.1%). Quantile classification yielded a GESM with five levels: very high to very low. Our findings reveal that roughly 12% of the basin area is severely affected by gully erosion. These findings underscore the critical need for targeted interventions in these highly susceptible areas. Furthermore, our analysis of gully characteristics unveiled a predominance of V-shaped gullies, likely in an active developmental stage, supported by an average Shape Index (SI) value of 0.26 and a mean Erosivness Index (EI) of 0.33. This research demonstrates the potential of machine learning to pinpoint areas susceptible to gully erosion. By providing these valuable insights, policymakers can make informed decisions regarding sustainable land management practices.

Bayesian regression modeling and inference of energy efficiency data: the effect of collinearity and sensitivity analysis

The majority of research predicted heating demand using linear regression models, but they did not give current building features enough context. Model problems such as Multicollinearity need to be checked and appropriate features must be chosen based on their significance to produce accurate load predictions and inferences. Numerous building energy efficiency features correlate with each other and with heating load in the energy efficiency dataset. The standard Ordinary Least Square regression has a problem when the dataset shows Multicollinearity. Bayesian supervised machine learning is a popular method for parameter estimation and inference when frequentist statistical assumptions fail. The prediction of the heating load as the energy efficiency output with Bayesian inference in multiple regression with a collinearity problem needs careful data analysis. The parameter estimates and hypothesis tests were significantly impacted by the Multicollinearity problem that occurred among the features in the building energy efficiency dataset. This study demonstrated several shrinkage and informative priors on likelihood in the Bayesian framework as alternative solutions or remedies to reduce the collinearity problem in multiple regression analysis. This manuscript tried to model the standard Ordinary Least Square regression and four distinct Bayesian regression models with several prior distributions using the Hamiltonian Monte Carlo algorithm in Bayesian Regression Modeling using Stan and the package used to fit linear models. Several model comparison and assessment methods were used to select the best-fit regression model for the dataset. The Bayesian regression model with weakly informative prior is the best-fitted model compared to the standard Ordinary Least Squares regression and other Bayesian regression models with shrinkage priors for collinear energy efficiency data. The numerical findings of collinearity were checked using variance inflation factor, estimates of regression coefficient and standard errors, and sensitivity of priors and likelihoods. It is suggested that applied research in science, engineering, agriculture, health, and other disciplines needs to check the Multicollinearity effect for regression modeling for better estimation and inference.

Calculating the power of a planned individual participant data meta-analysis to examine prognostic factor effects for a binary outcome.

Collecting data for an individual participant data meta-analysis (IPDMA) project can be time consuming and resource intensive and could still have insufficient power to answer the question of interest. Therefore, researchers should consider the power of their planned IPDMA before collecting IPD. Here we propose a method to estimate the power of a planned IPDMA project aiming to synthesise multiple cohort studies to investigate the (unadjusted or adjusted) effects of potential prognostic factors for a binary outcome. We consider both binary and continuous factors and provide a three-step approach to estimating the power in advance of collecting IPD, under an assumption of the true prognostic effect of each factor of interest. The first step uses routinely available (published) aggregate data for each study to approximate Fisher's information matrix and thereby estimate the anticipated variance of the unadjusted prognostic factor effect in each study. These variances are then used in step 2 to estimate the anticipated variance of the summary prognostic effect from the IPDMA. Finally, step 3 uses this variance to estimate the corresponding IPDMA power, based on a two-sided Wald test and the assumed true effect. Extensions are provided to adjust the power calculation for the presence of additional covariates correlated with the prognostic factor of interest (by using a variance inflation factor) and to allow for between-study heterogeneity in prognostic effects. An example is provided for illustration, and Stata code is supplied to enable researchers to implement the method.

Predictors of successful transition of adolescents and young adults living with HIV from pediatric to adult-oriented care in southern Ethiopia: a retrospective cohort study

BackgroundThe introduction of highly active antiretroviral therapy has significantly improved the life expectancies of children and adolescents living with HIV, leading to an increased number transitioning to adult care. However, there has been a lack of studies in Ethiopia focusing on factors influencing the success of this transition. Therefore, this study aimed to determine predictors of a successful transition from pediatric to adult HIV clinics among adolescents and young adults living with HIV in health facilities in southern Ethiopia.MethodsA retrospective cohort study included 337 adolescents and young adults who transitioned to adult-oriented HIV care. Successful transition was defined as having a viral load of less than 1000 copies/ml and maintaining care during the first year post-transition. Patients’ antiretroviral therapy (ART) cards and monitoring charts were reviewed. Secondary data analysis was conducted using a multivariable binary logistic regression model to identify predictors of a successful transition. Using the variance inflation factor, we checked for multi-collinearity between variables and assessed model fitness with the Hosmer and Lemeshow goodness-of-fit test. Adjusted Odds Ratio (AOR) with 95% confidence intervals (CI) and P-value ≤ 0.05 measured the strength of association and statistical significance.ResultsOf 337 participants, 230 (68.25%) successfully transitioned (95% CI = 63.25, 73.25). Transitioning at age 18 or older (AOR = 4.25; 95% CI = 2.29, 7.87), residing in an urban area (AOR = 1.78; 95% CI = 1.04, 3.02), and being on antiretroviral therapy for more than two years (AOR = 4.25; 95% CI = 1.17, 4.94; P < 0.017) were identified as positive predictors and opportunistic infection (AOR = 0.34; 95% CI = 0.15, 0.75; P < 0.008) was identified as a negative predictor for a successful transition from pediatric to adult ART clinic.ConclusionThis study sheds light on the challenges faced by HIV patients transitioning from pediatric to adult care, with less than 70% successfully navigating this critical phase. Factors such as age at transition, residence, duration of ART, and the presence of opportunistic infections were identified as key predictors of successful transition. The findings underscore the urgent need for tailored interventions, including standardized transition plans that address age and urban/rural disparities, to enhance transition outcomes for adolescents and young adults living with HIV in the region.

Construction of a nomogram with IrAE and clinic character to predict the survival of advanced G/GEJ adenocarcinoma patients undergoing anti-PD-1 treatment.

This study aimed to develop and validate a survival prediction model and nomogram to predict survival in patients with advanced gastric or gastroesophageal junction (G/GEJ) adenocarcinoma undergoing treatment with anti-programmed cell death 1 receptor (PD-1). This model incorporates immune-related adverse events (irAEs) alongside common clinical characteristics as predictive factors. A dataset comprising 255 adult patients diagnosed with advanced G/GEJ adenocarcinoma was assembled. The irAEs affecting overall survival (OS) to a significant degree were identified and integrated as a candidate variable, together with 12 other candidate variables. These included gender, age, Eastern cooperative oncology group performance status (ECOG PS) score, tumor stage, human epidermal growth factor receptor 2 (HER2) expression status, presence of peritoneal and liver metastases, year and line of anti-PD-1 treatment, neutrophil-to-lymphocyte ratio (NLR), controlling nutritional status (CONUT) score, and Charlson comorbidity index (CCI). To mitigate timing bias related to irAEs, landmark analysis was employed. Variable selection was performed using the least absolute shrinkage and selection operator (LASSO) regression to pinpoint significant predictors, and the variance inflation factor was applied to address multicollinearity. Subsequently, a Cox regression analysis utilizing the forward likelihood ratio method was conducted to develop a survival prediction model, excluding variables that failed to satisfy the proportional hazards (PH) assumption. The model was developed using the entire dataset, then internally validated through bootstrap resampling and externally validated with a cohort from another Hospital. Furthermore, a nomogram was created to delineate the predictive model. After consolidating irAEs from the skin and endocrine systems into a single protective irAE category and applying landmark analysis, variable selection was conducted for the prognostic prediction model along with other candidate variables. The finalized model comprised seven variables: ECOG PS score, tumor stage, HER2 expression status in tumor tissue, first-line anti-PD-1 treatment, peritoneal metastasis, CONUT score, and protective irAE. The overall concordance index for the model was 0.66. Calibration analysis verified the model's accuracy in aligning predicted outcomes with actual results. Clinical decision curve analysis indicated that utilizing this model for treatment decisions could enhance the net benefit regarding 1- and 2-year survival rates for patients. This study developed a prognostic prediction model by integrating common clinical characteristics of irAEs and G/GEJ adenocarcinoma. This model exhibits good clinical practicality and possesses accurate predictive ability for overall survival OS in patients with advanced G/GEJ adenocarcinoma.

Variations in water use efficiency and carbon and nitrogen concentrations in red heart Chinese fir.

Temperature can significantly (P < 0.05) affect plant growth by modifying water use strategies, which are determined by intrinsic water use efficiency (WUEi). Red Heart Chinese Fir (Cunninghamia lanceolata) is one of the most important ecological and economic plantation species in China. However, the C. lanceolata water use strategy in response to increased temperatures and uneven temporal distribution of precipitation during the growing season is rarely reported. In a 7-year-old C. lanceolata plantation, differences in WUEi and C and N concentrations in different organs were analysed by anova, and the δ13C stable isotope, C, and N concentrations in stems determined at different tree heights. Stepwise regression and variance inflation factor were used to remove autocorrelated factors, and structural equation modelling was then used to explore relationships between WUEi and climate and biological factors. WUEi differed significantly between leaf and branch at different standardized precipitation evapotranspiration indices (SPEI). WUEi and N concentration decreased with age. The highest WUEi in branches and leaves were 92.7 and 88.4 μmol·mol-1 in 2020 (SPEI = 0.00), respectively. δ13C increased with relative tree height but N concentration and C/N ratio were not affected. Air temperatures has increased in between 2014 and 2020. WUEi and N concentration decreased with increasing branch and leaf age, but C concentration increased. SPEI significantly positively affected WUEi (P < 0.05), and WUEi was significantly negatively related to C concentration, which is consistent with the trade-off between C and water.

HIV-related Perceptions, Knowledge, Professional Ethics, Institutional Support, and HIV/AIDS-related Stigma in Health Services in West Sumatra, Indonesia: An Empirical Evaluation Using PLS-SEM.

The aim of this study was to investigate the significance of associations between knowledge, professional ethics, institutional support, perceptions regarding HIV/AIDS, and HIV/AIDS-related stigma among health workers in West Sumatra, Indonesia. We conducted a cross-sectional study involving health workers at public hospitals and health centers in West Sumatra in June 2022. The Health Care Provider HIV/AIDS Stigma Scale was employed to assess the stigma associated with HIV/AIDS. To estimate and evaluate the model's ability to explain the proposed constructs, we utilized the standardized partial least squares structural equation model (PLS-SEM). In total, 283 individuals participated in this study (average age, 39 years). The majority were female (91.2%), nearly half were nurses (49.5%), and 59.4% had been working for more than 10 years. The study revealed that HIV/AIDS-related stigma persisted among health workers. The PLS-SEM results indicated that all latent variables had variance inflation factors below 5, confirming that they could be retained in the model. Knowledge and professional ethics significantly contributed to human immunodeficiency virus (HIV)-related stigma, with an effect size (f²) of 0.15 or greater. In contrast, perceived and institutional support had a smaller impact on HIV-related stigma, with an effect size (f²) of at least 0.02. The R 2 value for health worker stigma was 0.408, suggesting that knowledge, professional ethics, institutional support, and perceived support collectively explain 40.8% of the variance in stigma. Improving health workers' understanding of HIV, fostering professional ethics, and strengthening institutional support are essential for reducing HIV-related stigma in this population.

Mapping and modeling groundwater potential using machine learning, deep learning and ensemble learning models in the Saiss basin (Fez-Meknes region, Morocco)

The Saïss basin in the Fez-Meknes region of Morocco, covering approximately 2100 km2, faces increased water demand due to population growth, economic development, and climate change, making groundwater a crucial resource. This study aims to delineate areas with groundwater potential (GWP) and evaluate the performance of various machine learning, deep learning, and hybrid ensemble models in predicting GWP. Using a dataset of 440 springs and wells, and 20 groundwater conditioning factors (GWCF) including topographical, hydrological, geological, and hydrogeological features, the study employed multi-collinearity analysis, variance inflation factor (VIF), tolerance (Tol) assessments, and an Information Gain (IG) test to analyze these factors. The study compared the performance of three machine learning algorithms (Gaussian Naive Bayes (GNB), k-Nearest Neighbors (KNN), Gradient Boosting Classifier (GBC)), three deep learning algorithms (Deep Learning Neural Networks (DLNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)), and a hybrid ensemble model (Random Forests (RF), Support Vector Machine (SVM), Logistic Regression (LR)) using the area under the receiver operating characteristic curve (ROC-AUC) as the evaluation metric. The results showed that the hybrid ensemble model had the highest AUC of 0.86, followed by GBC (AUC = 0.85), DLNN (AUC = 0.84), CNN (AUC = 0.83), KNN (AUC = 0.79), RNN (AUC = 0.78), and GNB (AUC = 0.75). The study revealed that 45% of the Saïss Basin exhibits high to very high GWP, particularly in Ain Taoujdat, Haj Kaddour, and Boufekrane districts, with lithology, slope, and transmissivity being the most influential factors. The resulting GWP map can guide decision-makers in planning well and borehole drilling for drinking water and agriculture, as well as artificial recharge projects, thus promoting sustainable groundwater management in the Saïss basin.

Evaluation of Total Risk-Weighted Assets in Islamic Banking through Fintech Innovations

The assessment of total risk-weighted assets (LTRWAs) in the banking sector is of the utmost importance. It serves as a critical component for regulatory compliance, risk management, and capital adequacy. By accurately assessing LTRWAs, banks can effectively meet regulatory requirements, efficiently allocate capital resources, and proactively manage risks. Moreover, the accurate assessment of LTRWAs supports performance evaluation and fosters investor confidence in the financial stability of banks. This study presents statistical analyses and machine learning methods to identify factors influencing LTRWAs. Data from Bahrain, Jordan, Qatar, the United Arab Emirates, and Yemen, spanning from 2010 to 2021, was utilized. Various statistical tests and models, including ordinary least squares, fixed effect, random effect, correlation, variance inflation factor, tolerance tests, and fintech models, were conducted. The results indicated significant impacts of the unemployment rate, inflation rate, natural logarithm of the loan-to-asset ratio, and natural logarithm of total assets on LTRWAs in regression models. The dataset was divided into a training group (90% of the data) and a testing group (10% of the data) to evaluate the predictive capabilities of various fintech models, including an adaptive network-based fuzzy inference system (ANFIS), a hybrid neural fuzzy inference system (HyFIS), a fuzzy system with the heuristic gradient descent (FS.HGD), and fuzzy inference rules with the descent method (FIR.DM) models. The selection of the optimal model is contingent upon assessing its performance according to specific error criteria. The HyFIS model outperformed others with lower errors in predicting LTRWAs. Independent t-tests confirmed statistically significant differences between original and predicted LTRWA for all models, with HyFIS showing closer predictions. This study provides valuable insights into LTRWA prediction using advanced statistical and machine learning techniques, based on a dataset from multiple countries and years.

Machine Learning-Driven Landslide Susceptibility Mapping in the Himalayan China–Pakistan Economic Corridor Region

The reliability of data-driven approaches in generating landslide susceptibility maps depends on data quality, analytical method selection, and sampling techniques. Selecting optimal datasets and determining the most effective analytical methods pose significant challenges. This study assesses the performance of seven machine learning classifiers in the Himalayan region of the China–Pakistan Economic Corridor, utilizing statistical techniques and validation metrics. Thirteen geo-environmental variables were analyzed, including topographic (8), land cover (1), hydrological (1), geological (2), and meteorological (1) factors. These variables were evaluated for multicollinearity, feature importance, and their influence on landslide incidences. Our findings indicate that Support Vector Machines and Logistic Regression were highly effective, particularly near fault zones and roads, due to their effectiveness in handling complex, non-linear terrain interactions. Conversely, Random Forest and Logistic Regression demonstrated variability in their results. Each model distinctly identified landslide susceptibility zones ranging from very low to very high risk. Significant conditioning variables such as elevation, rainfall, lithology, slope, and land use were identified, reflecting the unique geomorphological conditions of the Himalayas. Further analysis using the Variance Inflation Factor and Pearson correlation coefficient showed minimal multicollinearity among the variables. Moreover, evaluations of Area Under the Receiver Operating Characteristic Curve (AUC-ROC) values confirmed the strong predictive capabilities of the models, with the Random Forest Classifier performing exceptionally well, achieving an AUC of 0.96 and an F-Score of 0.86. This study shows the importance of model selection based on dataset characteristics to enhance decision-making and strategy effectiveness.

On-farm cereal rye biomass estimation using machine learning on images from an unmanned aerial system

This study assesses the potential of using multispectral images collected by an unmanned aerial system (UAS) on machine learning (ML) frameworks to estimate cereal rye (Secale cereal L.) biomass. Multispectral images and ground-truth cereal rye biomass data were collected from 15 farmers’ fields up to three times between March and May in northwest Ohio. Images were processed to derive 13 vegetation indices (VIs). Out of 13 VIs, six optimal sets of VIs, including excess green (ExG), normalized green red difference index (NGRDI), soil adjusted vegetation index (SAVI), blue green ratio (B_G_ratio), red-edge triangular vegetation index (RTVI), and normalized difference red-edge (NDRE) were selected using the variance inflation factor (VIF) based feature selection approach. Six regression models including a multiple linear regression (MLR), elastic net (ENET), multivariate adaptive regression splines (MARS), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGB) were investigated for estimation of cereal rye biomass based on the VIs. For most of the models, the six selected VIs performed better than or similar to the full set of 13 VIs with R2 ranging from 0.24 to 0.59 and RMSE ranging from 83.13 to 91.89 g/m2 during 10-fold cross-validation. During independent accuracy assessment with the selected set of VIs, XGB exhibited the highest R2 (0.67) and lowest RMSE (83.13 g/m2) and MAE (48.13 g/m2) followed by RF and ENET. For all the models, the agreement between observed and predicted biomass was high for biomass less than or equal to 200 g/m2 but decreased for biomass greater than 200 g/m2. When field-collected structural features were integrated with the selected VIs, the models showed improved performance, with R2 and RMSE of the models reaching up to 0.82 and 61.67 g/m2 respectively. Among the six VIs, SAVI showed the strongest impact on the model prediction for the best-performing RF and XGB regression models. The findings of this study demonstrate the potential of precisely estimating and mapping cereal rye biomass based on UAS-captured multispectral images. Timely information on cover crop growth can facilitate numerous decision-making processes, including planning the planting operations, and management of nutrients, weeds, and soil moisture to improve agronomic and environmental outcomes.

The Effect of Individual Entrepreneurial Orientation on Entrepreneurial Intention Among University Students

This study aims to evaluate the impact of individual entrepreneurial orientation on the entrepreneurial intention of Algerian university students. Specifically, it focuses on three dimensions of individual entrepreneurial orientation: risk-taking, innovativeness, and proactiveness. To achieve the main purpose of the study and test its hypotheses, the researchers employed a descriptive-analytical method, and a survey was conducted using a questionnaire on a sample of 333 second-year master’s students at the Faculty of Economics, Commercial and Management Sciences in Jijel University. The questionnaire consists of 15 statements: 3 – regarding risk-taking, 3 – innovativeness, 3 –proactiveness, and 6 – individual entrepreneurial orientation. The opinions of the sample (students) were measured using a five-point Likert scale. Cronbach’s alpha coefficient was applied to test the reliability of the study instrument. To test internal consistency, Pearson’s correlation coefficient was calculated to measure the correlation between each questionnaire item and the dimension to which it belongs. Variance Inflation Factor (VIF) and the Durbin-Watson test were also relied upon to ensure the independence of the independent variables and the absence of multiple linear correlation or autocorrelation between the residuals. Multiple regression analysis was used to evaluate the effect of the independent variables on the dependent variables to test the study hypotheses accurately. The predictive capacity of the regression model is confirmed by the results of the ANOVA regression analysis. Statistical analyses were carried out using SPSS, version 28. The findings indicate that both risk-taking and proactiveness have a direct and positive influence on entrepreneurial intention. However, innovativeness does not demonstrate any statistically significant effect on entrepreneurial intention. In light of the obtained results, a set of recommendations meant to foster the individual entrepreneurial orientation and entrepreneurial intention of university students was proposed.

Equations for estimating binary mixture toxicity: 3-methyl-2-butanone with a series of electrophiles.

Mixture toxicity was determined for 32 binary combinations. One chemical was the non-reactive, non-polar narcotic 3-methyl-2-butanone (always chemical A) and the other was a potentially reactive electrophile (chemical B). Bioluminescence inhibition in Allovibrio fischeri was measured at 15-, 30-, and 45-minutes of exposure for A, B, and the mixture (MX). Concentration-response curves (CRCs) were developed for each chemical and used to develop predicted CRCs for the concentration addition (CA) and independent action (IA) mixture toxicity models. Also, MX CRCs were generated and compared with model predictions using the 45-minute data. Classification of observed mixture toxicity used three specific criteria: 1) predicted IA EC50 vs. CA EC50 values at 45-minutes, 2) consistency of 45-minute MX CRC fit to IA, CA, or otherwise at three effect levels (EC25, EC50 and EC75), and 3) the known/suspected mechanism of toxicity for chemical B. Mixture toxicity was then classified into one of seven groupings. As a result of the predicted IA EC50 being more toxic than the predicted CA EC50, IA represented the greater toxic hazard. For this reason, non-sham MXs having toxicity consistent with CA were classified as being "coincident" with CA rather than mechanistically-consistent with CA. Multiple linear regression analyses were performed to develop equations that can be used to estimate the toxicity of other 3M2B-containing binary mixtures. These equations were developed from the data for both IA and CA, at each exposure duration and effect level. Each equation had a coefficient of determination (r2) above 0.950 and a variance inflation factor <1.2. This approach can potentially reduce the need for mixture testing and is amenable to other model systems and to assays that evaluate toxicity at low effect levels.

Do Retailers Compete Against Informal Firms? A Comparison of the Periods of the Global Crisis and Post-Crisis

This study investigates the transformation of competitive dynamics between formal sector retailers and their informal sector counterparts in Eastern European and Central Asian countries after the 2008-2009 global financial crisis. The absence of extensive government bailouts in these regions intensified the economic repercussions of the crisis. This study used data from the BEEPS II and BEEPS IV (i.e. Business Environment and Enterprise Performance Survey) surveys conducted by the European Bank for Reconstruction and Development and the World Bank with a focus on informal competition in Eastern Europe and Central Asia based on data from owners or top-managers of companies working in this region. To investigate the impact of the global crisis on informal competition for retailers, survey results in 2008 (“crisis period”) were compared with the data in 2013 (“post-crisis period”). Non-parametric tests, specifically the Mann-Whitney-Wilcoxon tests, were used to compare the 2008 and 2013 results. Binary logistic regressions were used to identify factors influencing the perception of competition from the informal sector (Pearson correlation coefficients and assessed Variance Inflation Factors to detect multicollinearity issues). Findings reveal a significant shift post-crisis shift with a reduced proportion of formal sector retailers competing with informal firms. Notably, this effect is statistically insignificant for retailers with sole proprietorships and partnerships and those with internationally recognized quality certifications. Moreover, research highlights a diminished perception of informal competitors as substantial hindrances to retailers’ business operations post-crisis. However, this observation is not statistically significant for retail businesses structured as partnerships. Logistic regression indicates that smaller firms, firms led by a more experienced top manager, and firms with a female top manager are more prone to perceive competition from the informal sector. Furthermore, this study delves into the factors shaping retailers’ perceptions across post-Soviet and other transition countries, revealing differences in the impact of firm size and female ownership. This nuanced investigation contributes depth to the current literature concerning the interconnections between formal and informal retail sectors. It offers a comparative analysis focusing on post-Soviet states and other transitioning nations in Eastern Europe and Central Asia.

Effects of multicollinearity and data granularity on regression models of stream temperature

Water temperature is a key factor influencing biota of stream ecosystems. Hence, it is important to comprehend the environmental drivers of stream temperature for robust prediction of conditions and effective management of stream communities. Linear regression models are commonly used for predictive purposes, but their predictive capacity and interpretability can be significantly affected by their complexity and the structure of input data. In some cases, researchers may be obligated to favor prediction power or interpretability while compromising the other. Therefore, insight into relationships between model fit, correlation among predictor variables (i.e., multicollinearity), and level of temporal aggregation of data (i.e., data granularity) may be helpful to reduce such trade-offs. In this paper, we investigated these relationships within a hierarchical set of multiple linear regression (MLR) models examining environmental factors influencing stream temperature dynamics. Our findings showed that as the number of predictor variables (i.e., model complexity) increased, the magnitude of multicollinearity in MLR models increased, but model fit also increased. The results also revealed that using data averaged over longer time frames (i.e., coarser data granularity) yielded high multicollinearity, as indexed by variance inflation factor values (VIF) for all model predictors. This led to higher variance in parameter estimates (i.e., parameter instability) and potential challenges in model interpretation as the sign of parameter estimates changed in many streams examined. Multicollinearity was not the only reason for these changes in the sign of parameter estimates as they were also observed in simple linear regression models across varying levels of data granularity. Based on our findings, we conclude that the selection of data granularity is an important consideration in multiple regression modeling, with profound implications for model interpretability.

Effect of women empowerment on treatment seeking practice for sexually transmitted infections among women in Ethiopia

BackgroundSexually transmitted infections (STI) are public health problems in Ethiopia. Women have a higher chance of acquiring STI. STI complications are more severe in women compared to men. Despite that, treatment seeking for STI among women is poor. Woman empowerment and gender related factors may be playing a role for treatment seeking practice for STI. However, there are no studies that assess the association between these factors and treatment seeking practice for STI among married reproductive age women in Ethiopia. Therefore, this analysis was designed to explore this association in Ethiopia.MethodsThis analysis used the 2016 Ethiopian demographic and health survey (EDHS) data. The 2016 EDHS collected data about STI treatment seeking practice for STI among other variables. Data was analyzed using STATA 17.0. Sampling weights were applied to improve the representativeness of the samples. Descriptive statistics were computed to describe the characteristics of the women. Binary and multivariable logistic regression models were fitted to identify the association between treatment seeking practice for STI and predictor variables. Multicollinearity was checked using variance inflation factors before running the multivariable logistic regression.ResultsIn this study, about 28% (95%CI: 20.87, 36.77) married reproductive age women with STI or STI symptoms sought treatment from the formal sector. Women whose husband attended secondary and higher education (AOR, 8.52; 95%CI 1.42, 51.21), and women with higher women empowerment scores (AOR 1.38, 95%CI 1.06, 1.81) had higher odds of treatment seeking for STI or STI symptoms. On the other hand, women who believe wife beating is justified had lower odds (AOR 0.32; 95%CI 0.15, 0.68) of treatment seeking for STI or STI symptoms.ConclusionsTreatment seeking practice for STI among married reproductive age women in Ethiopia is low. The Ministry of Health and development partners shall conduct further research to identify barriers for treatment seeking practice. Gender variables (women empowerment and belief that wife beating is justified) were significantly associated with STI treatment seeking practice among married reproductive age women. STI prevention and control strategies shall include women empowerment and gender issues as essential component in STI prevention, treatment, and control activities.

Development of a model for iron concentrate tonnage with least angle regressions – An industrial trial

ABSTRACT The optimal concentrate assay and weight recovery are among the most important factors in mineral processing plants, particularly iron ore beneficiation plants. The optimal balance between concentrate assay and total recovery is typically determined through chemical analysis and application of the Davis tube test in the laboratory. Consequently, the level of concentrate assay and tonnage is regulated in the plant by (a) sampling and chemical analysis in the lab and (b) application of weighing equipment such as belt scales. Belt scales also have a random error and specified standard deviation in weighing and are not completely reliable. Modifying the assay obscures the variation in concentrate weight. The best subset method can be used to extract the relation between concentrate tonnage and process variables; however, this method is unsuitable due to the variance inflation factor (VIF). In this investigation, to circumvent VIF, simple models using least angle regressions (LARS), Lasso, and LAR are presented utilising 2655 data (for each variable) collected from the Gohar Zamin beneficiation iron ore plant. The highest model’s R-square was 66.51%, and it predicted that if the concentrate assay decreases from 68.41% to 67%, the total concentrate tonnage can increase by 33800–35300 tons per year with a 95% prediction interval for the 2 million-ton capacity plant.

Identifying heterogeneity for increasing the prediction accuracy of machine learning models

In recent years, the significance of machine learning in agriculture has surged, particularly in post-harvest monitoring for sustainable aquaculture. Challenges like heterogeneity, irrelevant variables and multicollinearity hinder the implementation of smart monitoring systems. However, this study focuses on investigating heterogeneity among drying parameters that determine the moisture content removal during seaweed drying due to its limited attention, particularly within the field of agriculture. Additionally, a heterogeneity model within machine learning algorithms is proposed to enhance accuracy in predicting seaweed moisture content removal, both before and after the removal of heterogeneity parameters and also after the inclusion of single-eliminated heterogeneity parameters. The dataset consists of 1914 observations with 29 independent variables, but this study narrows down to five: Temperature (T1, T4, T7), Humidity (H5), and Solar Radiation (PY). These variables are interacted up to second-order interactions, resulting in 55 variables. Variance inflation factor and boxplots are employed to identify heterogeneity parameters. Two predictive machine learning models, namely random forest and elastic net are then utilized to identify the 15 and 20 highest important parameters for seaweed moisture content removal. Evaluation metrics (MSE, SSE, MAPE, and R-squared) are used to assess model performance. Results demonstrate that the random forest model outperforms the elastic net model in terms of higher accuracy and lower error, both before and after removing heterogeneity parameters, and even after reintroducing single-eliminated heterogeneity parameters. Notably, the random forest model exhibits higher accuracy before excluding heterogeneity parameters.

Development and validation of a prediction model for falls among older people using community-based data.

The aim of this study was to develop a calculation tool to predict the risk of falls 1year in the future and to find the cutoff value for detecting a high risk based on a database of individuals who participated in a community-based group exercise. We retrospectively reviewed a total of 7726 physical test and Kihon Checklist data from 2381 participants who participated in community-based physical exercise groups. We performed multilevel logistic regression analysis to estimate the odds ratio of falls for each risk factor and used the variance inflation factor to assess collinearity. We determined a cutoff value that effectively distinguishes individuals who are likely to fall within a year based on both sensitivity and specificity. The final model included variables such as age, sex, weight, balance, standing up from a chair without any aid, history of a fall in the previous year, choking, cognitive status, subjective health, and long-term participation. The sensitivity, specificity, and best cutoff value of our tool were 68.4%, 53.8%, and 22%, respectively. Using our tool, an individual's risk of falls over the course of a year could be predicted with acceptable sensitivity and specificity. We recommend a cutoff value of 22% for use in identifying high-risk populations. The tool may benefit healthcare workers in screening community-dwelling older adults with various levels of risk for falls and support physicians in planning preventative and follow-up care.

Landslide susceptibility assessment and mapping using new ensemble model

Landslides pose significant impact on human life and society such as loss of livelihood, destruction of infrastructure, and damage to natural resources around the world. Due to existing complications in conditional factors of landslide, mapping and predicting landslide occurrences with high accuracy needs more attention. In light of this, we aim to develop an ensemble landslide susceptibility model named as support vector regression–grasshopper optimization algorithm (SVR–GOA). This model is validated along with other landslide susceptibility models such as artificial neural network (ANN), boosted regression tree (BRT), and elastic net models. The present study carried out over the Kalaleh Basin in Iran, in which we selected 140 landslides with 16 conditional factors to construct a geographic database of the region. The multicollinearity analysis was done on the hazard conditioning factors using variance inflation factor and tolerance indices. Similarly, significance of these factors and their association with selected locations were identified through random forest method. The state of the art of the study is implementing SVR-GOA in landslide susceptibility mapping, including this model we use other landslide models such as ANN, BRT, and elastic net for validation and development using the area under the curve (AUC), kappa, and root mean squared error values. Our results show lithology, slope degree, rainfall, topography position index, topography wetness index, surface area, and landuse/landcover were found to be the most influential conditioning factors. We have also observed that, despite accurate prediction, SVR-GOA outperforms the others by showing the highest AUC values around AUC = 0.930 and others show ANN (AUC = 0.833), BRT (AUC = 0.822) and elastic net (AUC = 0.726) respectively. This innovative approach to landslide mapping using SVR-GOA ensembles would enhance the advancement of landslide research at multiple scales.