Variance Inflation Factor Research Articles

Leveraging Machine Learning for Accurate Anemia Diagnosis Using Complete Blood Count Data

Anemia, a prevalent hematologic disorder, necessitates accurate and timely diagnosis for effective management and treatment. This study explores the application of various machine learning models to classify anemia types using complete blood count (CBC) data. We evaluated multiple models, including DecisionTreeClassifier, ExtraTreeClassifier, RandomForestClassifier, ExtraTreesClassifier, XGBoost, LightGBM, and CatBoost, to identify the most effective approach for anemia diagnosis. The dataset comprised CBC data labeled with anemia diagnoses, sourced from multiple medical facilities. Rigorous data preprocessing was performed, followed by feature selection using methods such as Variance Inflation Factor (VIF), Predictive Power Score (PPS), and feature importance from ensemble models. The models were trained and evaluated using 5-fold cross-validation, with hyperparameter tuning conducted via GridSearchCV. Results demonstrated that the DecisionTreeClassifier achieved the highest balanced accuracy score of 94.17%, outperforming more complex ensemble methods. Confusion matrices validated its robust performance, highlighting its precision and recall. The study underscores the potential of simple decision tree models in medical diagnosis tasks, particularly when datasets are well-preprocessed. These findings have significant implications for clinical practice, suggesting that machine learning can enhance diagnostic accuracy and efficiency. Future work will explore advanced techniques to further improve performance and integration into clinical workflows.

Noninvasive assessment of Ki-67 labeling index in glioma patients based on multi-parameters derived from advanced MR imaging.

To investigate the predictive value of multi-parameters derived from advanced MR imaging for Ki-67 labeling index (LI) in glioma patients. One hundred and nine patients with histologically confirmed gliomas were evaluated retrospectively. These patients underwent advanced MR imaging, including dynamic susceptibility-weighted contrast enhanced MR imaging (DSC), MR spectroscopy imaging (MRS), diffusion-weighted imaging (DWI) and diffusion-tensor imaging (DTI), before treatment. Twenty-one parameters were extracted, including the maximum, minimum and mean values of relative cerebral blood flow (rCBF), relative cerebral blood volume (rCBV), relative mean transit time (rMTT), relative apparent diffusion coefficient (rADC), relative fractional anisotropy (rFA) and relative mean diffusivity (rMD) respectively, and ration of choline (Cho)/creatine (Cr), Cho/N-acetylaspartate (NAA) and NAA/Cr. Stepwise multivariate regression was performed to build multivariate models to predict Ki-67 LI. Pearson correlation analysis was used to investigate the correlation between imaging parameters and the grade of glioma. One-way analysis of variance (ANOVA) was used to explore the differences of the imaging parameters among the gliomas of grade II, III, and IV. The multivariate regression showed that the model of five parameters, including rCBVmax (RC=0.282), rCBFmax (RC=0.151), rADCmin (RC= -0.14), rFAmax (RC=0.325) and Cho/Cr ratio (RC=0.157) predicted the Ki-67 LI with a root mean square (RMS) error of 0. 0679 (R2 = 0.8025).The regression check of this model showed that there were no multicollinearity problem (variance inflation factor: rCBVmax, 3.22; rCBFmax, 3.14; rADCmin, 1.96; rFAmax, 2.51; Cho/Cr ratio, 1.64), and the functional form of this model was appropriate (F test: p=0.682). The results of Pearson correlation analysis showed that the rCBVmax, rCBFmax, rFAmax, the ratio of Cho/Cr and Cho/NAA were positively correlated with Ki-67 LI and the grade of glioma, while the rADCmin and rMDmin were negatively correlated with Ki-67 LI and the grade of glioma. Combining multiple parameters derived from DSC, DTI, DWI and MRS can precisely predict the Ki-67 LI in glioma patients.

The correlation between selenium intake and lung function in asthmatic people: a cross-sectional study.

This study aimed to examine the correlation between selenium intake and lung function in asthmatic people. A total of 4,541 individuals in the US National Health and Nutrition Examination Survey (NHANES) were included in this study. Multivariate linear regression, variance inflation factor, restricted cubic splines and quantile regression were used to analyze the relationship between Se intake and lung function. We divided selenium intake into four levels based on quartiles: Q1: Se ≤ 76.75 mcg/d; Q2: 76.75-105.1 mcg/d; Q3: 105.1-137.65 mcg/d; and Q4: Se ≥137.65 mcg/d. Asthma was negatively associated with the Ratio of Forced Expiratory Volume 1st Second to Forced Vital Capacity (FEV1/FVC) (β = -0.04, 95% CI: -0.06 to -0.02) and FEV1 (β = -215, 95% CI: -340 to -90). Se intake was positively associated with Forced Expiratory Volume 1st Second (FEV1) (β =3.30 95% CI: 2.60 to 4.00) and Forced Vital Capacity (FVC) (β =4.30, 95% CI: 3.50 to 5.10). In asthmatic individuals, the positive effects of Se intake on FVC were enhanced with increasing Se intake, while the positive effects of Se intake on FEV1 varied less dramatically. High Se intake (Q4 level, above 137.65 mcg/d) improved FVC (β = 353, 95% CI: 80 to 626) and FEV1 (β = 543, 95% CI: 118 to 969) in asthmatic patients compared to low Se intake (Q1 level, below 76.75 mcg/d). At the Q2 level (76.75-105.1 mcg/d) and Q4 level (Se ≥137.65 mcg/d) of Se intake, the correlation between FEV1 and asthma disappeared. Our research has revealed a positive correlation between selenium intake and lung function in asthma patients and the strength of this positive correlation is related to the amount of selenium intake. We recommend that asthma patients consume 137.65 mcg to 200 mcg of selenium daily to improve pulmonary function while avoiding the adverse effects of selenium on the human body.

Wildfire susceptibility mapping by incorporating damage proxy maps, differenced normalized burn Ratio, and deep learning algorithms based on sentinel-1/2 data: a case study on Maui Island, Hawaii

ABSTRACT Climate change has contributed to the recent increase in wildfire occurrences, vegetation failures, human health risks, physical damage, and economic losses. Wildfire susceptibility mapping is an essential technique for assessing areas prone to wildfires. In this study, we proposed the combination of the damage proxy map (DPM) and differenced normalized burn ratio (dNBR) method to generate a precise wildfire inventory map and used it to predict areas susceptible to wildfire. The wildfire susceptibility maps were produced using frequency ratio (FR), convolutional neural network (CNN), and long short-term memory (LSTM)-based deep learning and their performances were compared. We implemented the proposed method on Maui Island, Hawaii, where wildfires frequently occur. We started the process by generating a wildfire inventory map from 2019 to 2023 based on the DPM method applied to Sentinel-1 synthetic aperture radar (SAR) data combined with a dNBR map retrieved from Sentinel-2 data. The wildfire inventory was randomly divided into a training dataset (70%) and a testing dataset (30%). Fifteen wildfire-related factors, including topographical, meteorological, land use, environmental, and anthropological factors, were selected to predict wildfires. The wildfire-related factors were selected by conducting study literature and considering spatial correlation analysis based on the FR method, information gain ratio analysis (IGR), and multicollinearity assessment using tolerance (TOL) and variance inflation factor (VIF) metrics. The level of susceptibility of an area to wildfire is divided into five, namely very high, high, moderate, low, and very low. The FR, CNN, and LSTM produced wildfire susceptibility maps with similar patterns, significantly influenced by land use and rainfall factors. The highly susceptible areas are located on gentle slopes covered by agricultural land and unhealthy vegetation, and these areas have low rainfall intensity but receive high levels of solar radiation. Meanwhile, areas with relatively low susceptibility occur in forests with high levels of wet canopy evaporation. The prediction results were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), and the CNN performed slightly better than the FR and LSTM, with AUC values of 0.879, 0.877, and 0.870, respectively. Hence, the use of the CNN algorithm in the proposed method is appropriate, specifically for the study area. In addition, the susceptibility map provides information on susceptible areas on Maui Island, Hawaii, to increase public awareness.

Assessing the environmental factors affecting the sustainability of Aini Falaj system.

This study investigates the spatial distribution patterns and environmental factors influencing the Aini Falaj system in a specific study area. The research findings are presented through the lens of the following four categories: collinearity diagnostics, spatial autocorrelation analysis, kernel density (KD) findings, and multivariate geographically weighted regression (MGWR) analysis. The collinearity diagnostics were applied to examine the interrelationships among 18 independent environmental variables. The results indicate the absence of significant multicollinearity concerns, with most variables showing values below the critical threshold of five for variance inflation factors (VIFs). The selected variables indicate minimal intercorrelation, suggesting that researchers should be confident utilizing them in subsequent modelling or regression analyses. A spatial autocorrelation analysis using Moran's Index revealed positive spatial autocorrelation and significant clustering patterns in the distribution of live and non-functional Aini Falajs. High concentrations of live or dead Falajs tended to be surrounded by neighbouring areas with similar characteristics. These findings provide insights into the ecological preferences and habitat associations of Aini Falajs, thereby aiding conservation strategies and targeted studies. The kernel density (KD) analysis depicted distribution patterns of live and dry Aini Falajs through hotspots and cold spots. Specific regions exhibited high-density areas of live Falajs, indicating favourable environmental conditions or historical factors contributing to their concentrated distribution. Identifying these high-density zones can enhance our understanding of the spatial patterns and potential factors influencing the prevalence and sustainability of Aini Falajs. The multivariate geographically weighted regression (MGWR) models revealed strong associations between the live or dead status of Aini Falajs and environmental factors. The precipitation, topographic wetness index (TWI), aspect and slope exerted positive impacts on the live status, while evaporation, solar radiation, distance to drains and drain density exerted negative influences. Similar associations were observed for the dead status, emphasising the importance of controlling evaporation, shading mechanisms, proper drainage planning and sustainable land-use practices. This study provides valuable insights into the spatial distributions and factors influencing the live and dead status of Aini Falajs, thereby contributing to our understanding of their ecological dynamics and guiding conservation efforts and management strategies.

Effect of chemotherapy on prognosis in patients with primary pancreatic signet ring cell carcinoma: A large real-world study based on machine learning.

Primary pancreatic signet ring cell carcinoma (PSRCC), an extremely rare histologic variant of pancreatic cancer, has a poor prognosis. This study aimed to investigate the prognostic value of chemotherapy in PSRCC. Patients with PSRCC between 2000 and 2019 were identified using the Surveillance Epidemiology and End Results (SEER) database. The main outcomes in this study were cancer-specific survival (CSS) and overall survival (OS). The baseline characteristics of patients were compared using Pearson's Chi-square test. Kaplan-Meier analysis was used to generate the survival curves. Least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression models, and Random Survival Forest model were used to analyze the prognostic variables for OS and CSS. The variance inflation factors (VIFs) were used to analyze whether there was an overfitting problem. A total of 588 patients were identified. Chemotherapy was an independent prognostic factor for OS and CSS, and significantly associated with OS (HR = 0.33, 95% CI = 0.27-0.40, P <0.001) and CSS (HR = 0.32, 95% CI = 0.26-0.39, P <0.001). Chemotherapy showed beneficial effects on OS and CSS in patients with PSRCC and should be recommended in clinical practice.

A nomogram based on conventional and contrast-enhanced ultrasound radiomics for the noninvasively prediction of axillary lymph node metastasis in breast cancer patients.

This study aimed to investigate whether quantitative radiomics features extracted from conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) of primary breast lesions can help noninvasively predict axillary lymph nodes metastasis (ALNM) in breast cancer patients. A total of 111 breast cancer patients with 111 breast lesions were prospectively enrolled. All the included patients received presurgical CUS screening and CEUS examination and were randomly assigned to the training and validation sets at a ratio of 7:3 (n = 78 versus 33). Radiomics features were respectively extracted based on CUS and CEUS using the PyRadiomics package. The max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) analyses were used for feature selection and radiomics score calculation in the training set. The variance inflation factor (VIF) was performed to check the multicollinearity among selected predictors. The best performing model was selected to develop a nomogram using binary logistic regression analysis. The calibration and clinical utility of the nomogram were assessed. The model combining CUS reported ALN status, CUS radiomics score (CUS-radscore) and CEUS radiomics score (CEUS-radscore) exhibited the best performance. The areas under the curves (AUC) of our proposed nomogram in the training and external validation sets were 0.845 [95% confidence interval (CI), 0.739-0.950] and 0.901 (95% CI, 0.758-1). The calibration curves and decision curve analysis (DCA) demonstrated the nomogram's robust consistency and clinical utility. The established nomogram is a promising prediction tool for noninvasive prediction of ALN status. The radiomics features based on CUS and CEUS can help improve the predictive performance.

Bioclimatic and remote sensing factors are better key indicators than local topography and soil: Vegetation composition variability in forests of Pakistan's Spin Ghar Mountain range

The composition, structure and distribution of vegetation are influenced by diverse environmental factors. Such research inquiries provide the initial data for future conservation and management efforts. The study area of North Waziristan district, Khyber Pakhtunkhwa, Pakistan comprises diverse forests of the Spin Ghar Mountain Range (at the border areas of Pakistan and Afghanistan), and a highly remote, mountainous, and unexplored region. There was little information on the complex relationships that existed between the study area's ambient environment and vegetation. This study hypothesized that the varying environmental complexity in the study area and vegetation variety may be significantly correlated, and the ranking of leading influencing factors might enhance our ecological understanding. A total of 61 study sites comprising 183 transects (50 m each) were randomly selected to record the vegetation-environment data from January-2018 to December-2020 (3 years). Monte Carlo permutation testing, hierarchical clustering of study samples, indicator species analysis, and ordination were applied to assess the sampling data. The results indicated that there were a total of 391 vascular plant species which were further classified into seven significantly different (p < 0.05) plant assemblages, each comprising of a distinct species makeup. A variety of different environmental variables (topographic (06), bioclimatic (19), edaphic (09), remote sensing, and anthropogenic predictors (16)) were considered. Simple term effects testing results depicted the significant (p(adj) < 0.05) role of 39 variables initially, whereas, conditional term effects testing (with variance inflation factor (VIF) threshold value of < 10, and forward selecting variables that provided the most unique information) results highlighted the prominent role of eight (08) contributors. The results of the latter analysis ranked the mean temperature of the warmest quarter (Bio10) as the most influencing factor, followed by Normalized Difference Vegetation Index (NDVI), longitude, continuous heat insulation load index (CHILI), precipitation of the warmest quarter (Bio18), global human modification of the terrestrial systems (gHM), organic carbon density (OCD), and annual precipitation (Bio12). This study concluded that the vegetation variability in the study area was significantly correlated with the prevailing environment, and considered bioclimatic and remote sensing factors were better key indicators for any vegetation distribution when the study was conducted on a large spatial scale. Based on these results, the anticipated future variations in climate, particularly global warming, lengthy drought spells, and population explosion might remarkably lead to decline in local plant species richness and distribution. For the study area to guard its priceless biodiversity for future generations, careful and prompt conservation and management planning are required.

Empirical Research on Ethical Leadership and Knowledge Workers’ Innovative Behaviour: The Mediating Role of Job Autonomy

Introduction: This research studied the mediating role of job autonomy in the relationship between ethical leadership and knowledge workers’ innovative behaviour based on self-determination theory. Data were collected from 377 knowledge workers in the IT industry in Sichuan, Henan, and Guangdong province, China. A conceptual model was constructed and tested with the statistical software SMART-PLS. This paper concludes that job autonomy effectively mediates ethical leadership and knowledge workers’ innovative behaviour. The findings also reveal the strong and positive impact of ethical leadership on knowledge workers’ innovative behaviour. This paper enriched the leadership literature and extended the understanding of the predictors of employee innovative behaviour Objective: To investigate the impact of ethical leadership on knowledge workers’ innovative behaviour. To study the relationship between ethical leadership and job autonomy. To examine the impact of job autonomy on knowledge workers’ innovative behaviour. To investigate the mediating effect of job autonomy between ethical leadership and knowledge workers’ innovative behaviour. Theoretical Framework: Knowledge workers are highly skilled workers with professional knowledge, good education, or experience in their respective fields (Davenport, 2006). According to Correia &amp; Dierendonck (2010), three core elements underpin the global meaning framework of knowledge workers: first, knowledge workers perceive their work as a calling; second, they have a strong desire for membership within a community of peers; third, they demand autonomy. These elements are in consistency with SDT. Method: This research used a quantitative approach to test the hypothesis, a closed questionnaire to collect the data, and SMART PLS version 4 to analyze the data. The survey questions were translated into Chinese with a back-translation approach (Brislin, 1986), as the respondents were all Chinese. Results and Discussion: Due to the single source, we first test common method bias. We adopted a full collinearity assessment approach to check the common method bias (Kock, 2015; Kock and Lynn, 2012). In this approach, the constructs were regressed on a new dummy variable with random values. As shown in table 4.1, the variance inflation factor (VIF) is lower than 3.3 (Kock, 2015), the model is indicated free of common method bias. Research Implications: The study constructed a research model to investigate the mediating effect of job autonomy between ethical leadership and knowledge workers' innovative behaviour. Management in knowledge-intensive industries, like the IT industry, can gain inspiration from the research to develop proper management strategies to promote knowledge workers’ innovative behaviour while guarding the ethical line.

Hydrological Variability in the El Cielo Biosphere Reserve, Mexico: A Watershed-Scale Analysis Using Tree-Ring Records

The El Cielo Biosphere Reserve (CBR) stands as a vital forested region in eastern Mexico due to its high biodiversity in flora and fauna and provision of environmental services. This study established a network of 10 ring-width chronologies of different species within the CBR and adjacent watersheds. The objective was to analyze their climatic response and reconstruct the seasonal streamflow contribution of each sub-basin to the main stream, utilizing data from a gauge network of eight hydrological stations located at strategic locations of the CBR. With chronologies ranging from 116 to 564 years, most exhibited association with the accumulated streamflow between January and June. Based on the adjusted R2, Akaike Information Criteria, and Variance Inflation Factor, the stepwise regression procedure was selected among different statistical methods for developing the reconstruction model. In spite of differences in the seasonal reconstructed periods, all the species showed potential to develop hydrological reconstructions as indicated by their common response to streamflow variability, as occurred in the wet years of 1976, 1993, 2000, and 2008, and dry years of 1980, 1982, 1996, and 2011. It was found that the response of the chronologies to gauge records increased as a function of the chronologies’ interseries correlation, average mean sensitivity, and distance of the tree-ring series to the gauge station. Streamflow reconstructions at the sub-basin level allowed a better understanding of the hydroclimatic variability characterizing the CBR, but also suggested the need to increase the network of chronologies for some particular sub-basins lacking tree-ring series to improve the reconstructed models.

Intertumoral Heterogeneity Based on MRI Radiomic Features Estimates Recurrence in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) heterogeneity impacts prognosis, and imaging is a potential indicator. To characterize HCC image subtypes in MRI and correlate subtypes with recurrence. Retrospective. A total of 440 patients (training cohort = 213, internal test cohort = 140, external test cohort = 87) from three centers. 1.5-T/3.0-T, fast/turbo spin-echo T2-weighted, spin-echo echo-planar diffusion-weighted, contrast-enhanced three-dimensional gradient-recalled-echo T1-weighted with extracellular agents (Gd-DTPA, Gd-DTPA-BMA, and Gd-BOPTA). Three-dimensional volume-of-interest of HCC was contoured on portal venous phase, then coregistered with precontrast and late arterial phases. Subtypes were identified using non-negative matrix factorization by analyzing radiomics features from volume-of-interests, and correlated with recurrence. Clinical (demographic and laboratory data), pathological, and radiologic features were compared across subtypes. Among clinical, radiologic features and subtypes, variables with variance inflation factor above 10 were excluded. Variables (P < 0.10) in univariate Cox regression were included in stepwise multivariate analysis. Three recurrence estimation models were built: clinical-radiologic model, subtype model, hybrid model integrating clinical-radiologic characteristics, and subtypes. Mann-Whitney U test, Kruskal-Wallis H test, chi-square test, Fisher's exact test, Kaplan-Meier curves, log-rank test, concordance index (C-index). Significance level: P < 0.05. Two subtypes were identified across three cohorts (subtype 1:subtype 2 of 86:127, 60:80, and 36:51, respectively). Subtype 1 showed higher microvascular invasion (MVI)-positive rates (53%-57% vs. 26%-31%), and worse recurrence-free survival. Hazard ratio (HR) for the subtype is 6.10 in subtype model. Clinical-radiologic model included alpha-fetoprotein (HR: 3.01), macrovascular invasion (HR: 2.32), nonsmooth tumor margin (HR: 1.81), rim enhancement (HR: 3.13), and intratumoral artery (HR: 2.21). Hybrid model included alpha-fetoprotein (HR: 2.70), nonsmooth tumor margin (HR: 1.51), rim enhancement (HR: 3.25), and subtypes (HR: 5.34). Subtype model was comparable to clinical-radiologic model (C-index: 0.71-0.73 vs. 0.71-0.73), but hybrid model outperformed both (C-index: 0.77-0.79). MRI radiomics-based clustering identified two HCC subtypes with distinct MVI status and recurrence-free survival. Hybrid model showed superior capability to estimate recurrence. 3 TECHNICAL EFFICACY STAGE: 2.

Empirical Research on Ethical Leadership and Knowledge Workers’ Innovative Behaviour: The Mediating Role of Job Autonomy

Introduction: This research studied the mediating role of job autonomy in the relationship between ethical leadership and knowledge workers’ innovative behaviour based on self-determination theory. Data were collected from 377 knowledge workers in the IT industry in Sichuan, Henan, and Guangdong province, China. A conceptual model was constructed and tested with the statistical software SMART-PLS. This paper concludes that job autonomy effectively mediates ethical leadership and knowledge workers’ innovative behaviour. The findings also reveal the strong and positive impact of ethical leadership on knowledge workers’ innovative behaviour. This paper enriched the leadership literature and extended the understanding of the predictors of employee innovative behaviour Objective: To investigate the impact of ethical leadership on knowledge workers’ innovative behaviour. To study the relationship between ethical leadership and job autonomy. To examine the impact of job autonomy on knowledge workers’ innovative behaviour. To investigate the mediating effect of job autonomy between ethical leadership and knowledge workers’ innovative behaviour. Theoretical Framework: Knowledge workers are highly skilled workers with professional knowledge, good education, or experience in their respective fields (Davenport, 2006). According to Correia &amp; Dierendonck (2010), three core elements underpin the global meaning framework of knowledge workers: first, knowledge workers perceive their work as a calling; second, they have a strong desire for membership within a community of peers; third, they demand autonomy. These elements are in consistency with SDT. Method: This research used a quantitative approach to test the hypothesis, a closed questionnaire to collect the data, and SMART PLS version 4 to analyze the data. The survey questions were translated into Chinese with a back-translation approach (Brislin, 1986), as the respondents were all Chinese. Results and Discussion: Due to the single source, we first test common method bias. We adopted a full collinearity assessment approach to check the common method bias (Kock, 2015; Kock and Lynn, 2012). In this approach, the constructs were regressed on a new dummy variable with random values. As shown in table 4.1, the variance inflation factor (VIF) is lower than 3.3 (Kock, 2015), the model is indicated free of common method bias. Research Implications: The study constructed a research model to investigate the mediating effect of job autonomy between ethical leadership and knowledge workers' innovative behaviour. Management in knowledge-intensive industries, like the IT industry, can gain inspiration from the research to develop proper management strategies to promote knowledge workers’ innovative behaviour while guarding the ethical line.

Exploring the Association Between Structural Racism and Mental Health: Geospatial and Machine Learning Analysis.

Structural racism produces mental health disparities. While studies have examined the impact of individual factors such as poverty and education, the collective contribution of these elements, as manifestations of structural racism, has been less explored. Milwaukee County, Wisconsin, with its racial and socioeconomic diversity, provides a unique context for this multifactorial investigation. This research aimed to delineate the association between structural racism and mental health disparities in Milwaukee County, using a combination of geospatial and deep learning techniques. We used secondary data sets where all data were aggregated and anonymized before being released by federal agencies. We compiled 217 georeferenced explanatory variables across domains, initially deliberately excluding race-based factors to focus on nonracial determinants. This approach was designed to reveal the underlying patterns of risk factors contributing to poor mental health, subsequently reintegrating race to assess the effects of racism quantitatively. The variable selection combined tree-based methods (random forest) and conventional techniques, supported by variance inflation factor and Pearson correlation analysis for multicollinearity mitigation. The geographically weighted random forest model was used to investigate spatial heterogeneity and dependence. Self-organizing maps, combined with K-means clustering, were used to analyze data from Milwaukee communities, focusing on quantifying the impact of structural racism on the prevalence of poor mental health. While 12 influential factors collectively accounted for 95.11% of the variability in mental health across communities, the top 6 factors-smoking, poverty, insufficient sleep, lack of health insurance, employment, and age-were particularly impactful. Predominantly, African American neighborhoods were disproportionately affected, which is 2.23 times more likely to encounter high-risk clusters for poor mental health. The findings demonstrate that structural racism shapes mental health disparities, with Black community members disproportionately impacted. The multifaceted methodological approach underscores the value of integrating geospatial analysis and deep learning to understand complex social determinants of mental health. These insights highlight the need for targeted interventions, addressing both individual and systemic factors to mitigate mental health disparities rooted in structural racism.

The Raise Regression: Justification, Properties and Application

SummaryMulticollinearity results in inflation in the variance of the ordinary least squares estimators due to the correlation between two or more independent variables (including the constant term). A widely applied solution is to estimate with penalised estimators such as the ridge estimator, which trade off some bias in the estimators to gain a reduction in the variance of these estimators. Although the variance diminishes with these procedures, all seem to indicate that the inference and goodness of fit are controversial. Alternatively, the raise regression allows mitigation of the problems associated with multicollinearity without the loss of inference or the coefficient of determination. This paper completely formalises the raise estimator. For the first time, the norm of the estimator, the behaviour of the individual and joint significance, the behaviour of the mean squared error and the coefficient of variation are analysed. We also present the generalisation of the estimation and the relation between the raise and the residualisation estimators. To have a better understanding of raise regression, previous contributions are also summarised: its mean squared error, the variance inflation factor, the condition number, adequate selection of the variable to be raised, the successive raising, and the relation between the raise and the ridge estimator. The usefulness of the raise regression as an alternative to mitigate multicollinearity is illustrated with two empirical applications.

Determining of variables influencing soil organic carbon content of PNR-Cortadera paramo (Colombia) via remote sensing

The soil organic carbon (SOC) content under climate change scenarios is essential, especially in areas of difficult access such as the high-altitude Tropical paramos. This study aimed to correlate the digital elevation model (DEM) derivatives, spectral indices from Sentinel-1 and Sentinel-2, and WorldClim data with in situ SOC content in the PNR-Cortadera paramo (Boyaca, Colombia). Based on 169 soil samples collected at two depths (0-15 and 15-30 cm) organic carbon was determined using the Walkley-Black method. SOC contents ranged from 25 to 200 t HA-1 at 0-15 cm and from 33 to 466 t ha-1 at 15-30 cm. Altitude, temperature, NDVI, TWI0-15 cm, MRVBF, LS factor0-15 cm and VH band polarization showed the highest correlations and the lowest variance inflation factor. The highest SOC contents are located in the central and southern area of the paramo due to the higher altitude, greater precipitation and presence of vegetation cover.

Effect of Incidence Angle on Temperature Measurement of Solar Panel with Unmanned Aerial Vehicle-Based Thermal Infrared Camera

This study utilizes Thermal Infrared (TIR) imaging technology to detect hotspots in photovoltaic (PV) modules of solar power plants. Unmanned aerial vehicle (UAV)-based TIR imagery is crucial for efficiently analyzing fault detection in solar power plants. This research explores optimal operational parameters for generating high-quality TIR images using UAV technology. In addition to existing variables such as humidity, emissivity, height, wind speed, irradiance, and ambient temperature, newly considered variables including the angle of incidence between the target object and the thermal infrared camera are analyzed for their impact on TIR images. Based on the solar power plant’s tilt (20°) and the location coordinate data of the hotspot modules, the inner and outer products of the vectors were used to obtain the normal vector and angle of incidence of the solar power plant. It was discovered that the difference between measured TIR temperature data and Land Surface Temperature (LST) data varies with changes in the angle of incidence. The analysis presented in this study was conducted using multiple regression analysis to explore the relationships between dependent and independent variables. The Ordinary Least Squares (OLS) regression model employed was able to explain 63.6% of the variability in the dependent variable. Further, the use of the Condition Number (Cond. No.) and the Variance Inflation Factor (VIF) revealed that the multicollinearity among all variables was below 10, ensuring that the independence among variables was well-preserved while maintaining statistically significant correlations. Furthermore, a positive correlation was observed with the actual measured temperature values, while a negative correlation was observed between the TIR image data values and the angle of incidence. Moreover, it was found that an angle of incidence between 15° and 20° yields the closest similarity to LST temperature data. In conclusion, our research emphasizes the importance of adjusting the angle of incidence to 15–20° to enhance the accuracy of TIR imaging by mitigating overestimated TIR temperature values.

Developing and validating a clinlabomics-based machine-learning model for early detection of retinal detachment in patients with high myopia.

Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related retinal detachment (HMRD) is crucial for effective treatment and prevention of additional vision impairment. Consequently, our objective was to streamline and validate a machine-learning model based on clinical laboratory omics (clinlabomics) for the early detection of RD in HM patients. We extracted clinlabomics data from the electronic health records for 24,440 HM and 5607 HMRD between 2015 and 2022. Lasso regression analysis assessed fifty-nine variables, excluding collinear variables (variance inflation factor > 10). Four models based on random forest, gradient boosting machine (GBM), generalized linear model, and Deep Learning Model were trained for HMRD diagnosis and employed for internal validation. An external test of the models was done. Three random data sets were further processed to validate the performance of the diagnostic model. The primary outcomes were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) to diagnose HMRD. Nine variables were selected by all models. Given the AUC and AUCPR values across the different sets, the GBM model was chosen as the final diagnostic model. The GBM model had an AUC of 0.8550 (95%CI = 0.8322-0.8967) and an AUCPR of 0.5584 (95%CI = 0.5250-0.5879) in the training set. The AUC and AUCPR in the internal validation were 0.8405 (95%CI = 0.8060-0.8966) and 0.5355 (95%CI = 0.4988-0.5732). During the external test evaluation, it reached an AUC of 0.7579 (95%CI = 0.7340-0.7840) and an AUCPR of 0.5587 (95%CI = 0.5345-0.5880). A similar discriminative capacity was observed in the three random data sets. The GBM model was well-calibrated across all the sets. The GBM-RD model was implemented into a web application that provides risk prediction for HM individuals. GBM algorithms based on nine features successfully predicted the diagnosis of RD in patients with HM, which will help ophthalmologists to establish a preliminary diagnosis and to improve diagnostic accuracy in the clinic.

Clinical Factors Affecting the Rate of Liver Regeneration in Living Donors after Right Hepatectomy.

Sufficient liver regeneration after a right hepatectomy is important in living donors for preventing postoperative hepatic insufficiency; however, it differs for each living donor so we investigated the clinical factors affecting the rate of liver regeneration after hepatic resection. This retrospective case-control study investigated fifty-four living donors who underwent a right hepatectomy from July 2015 to March 2023. Patients were classified into 2 groups by the remnant/total volume ratio (RTVR): Group A (RTVR < 30%, n = 9) and Group B (RTVR ≥ 30%, n = 45). The peak postoperative level of total bilirubin was more elevated in Group A than in Group B (3.0 ± 1.1 mg/dL vs. 2.3 ± 0.8 mg/dL, p = 0.046); however, no patients had hepatic insufficiency or major complications. The rates of residual liver volume (RLV) growth at Postoperative Week 1 (89.1 ± 26.2% vs. 53.5 ± 23.7%, p < 0.001) were significantly greater in Group A, and its significant predictors were RTVR (β = -0.478, p < 0.001, variance inflation factor (VIF) = 1.188) and intraoperative blood loss (β = 0.247, p = 0.038, VIF = 1.182). In conclusion, as the RLV decreases, compensatory liver regeneration after hepatic resection becomes more prominent, resulting in comparable operative outcomes. Further studies are required to investigate the relationship between hematopoiesis and the rate of liver regeneration.

Prevalence and Factors Associated With Adolescent Pregnancy Among an Indigenous Ethnic Group in Rural Nepal: A Community-based Cross-sectional Study.

The Chepang people, an indigenous ethnic group in Nepal, experience substantial marginalization and socioeconomic disadvantages, making their communities among the most vulnerable in the region. This study aimed to determine the prevalence and factors associated with adolescent pregnancy in the Chepang communities of Raksirang Rural Municipality, Makwanpur District, Bagmati Province, Nepal. A cross-sectional study was conducted from October 2022 to April 2023 among 231 Chepang women selected using simple random sampling from Raksirang Rural Municipality. A semi-structured questionnaire was used for interviewing the mothers. Bivariate and multivariate logistic regression analyses were performed, using odds ratios with 95% confidence intervals (CIs). Variables with a variation inflation factor of more than 2 and a p-value of more than 0.25 were excluded from the final model. The study revealed that the prevalence rate of adolescent pregnancy among Chepang women was 71.4% (95% CI, 65.14 to 77.16). A large percentage of participants (72.7%) were married before the age of 18 years. Poor knowledge of adolescent pregnancy (adjusted odds ratio [aOR], 10.3; 95% CI, 8.42 to 14.87), unplanned pregnancy (aOR, 13.3; 95% CI, 10.76 to 19.2), and lack of sex education (aOR, 6.57; 95% CI, 3.85 to 11.27) were significantly associated with adolescent pregnancy. The prevalence of adolescent pregnancy among the Chepang community was high. These findings highlighted the importance of raising awareness about the potential consequences of adolescent pregnancy and implementing comprehensive sexuality education programs for preventing adolescent pregnancies within this community.

Expected credit loss modeling

This article proposes a method for modeling the probability of default, describes the statistical evaluation of the model, and presents a model of the software implementation algorithm. The algorithm automatically selects from the group of regression models where the models are both linear regression and various modifications of semi-logarithmic models and lag models for macro factors Xi,t,Xi,t-1, ...,Xi,t-TStatistical analysis is carried out using the coefficient of determination R-squared, p-value, VIF (variance inflation factor).The relevance of this topic is determined by the need for banking organizations to comply with international standards, such as International Financial Reporting Standards (IFRS 9) and the Agreement on Banking Supervision and Capital (Basel 3). These standards define credit risk assessment requirements and capital requirements. Adherence to these standards is important not only for ensuring the stability and reliability of the financial system, but also for maintaining the trust of clients and investors. Compliance with international standards also makes banks competitive in the global market and promotes investment inflows and the development of the financial sector.IFRS 9 can be presented in various mathematical models. The article proposes an approach to choosing the appropriate model for forecasting the probability of default. The described model selection method allows banks to choose the optimal default forecast assessment model within the framework of the given standard. This contributes to a more accurate and reliable assessment of credit risk, in accordance with regulatory requirements, which will provide banks with the means for better forecasting and management of financial resources, as well as risk reduction.The model selection methodology saves a significant amount of time and resources, since the search for the optimal model occurs automatically. This allows us to react more quickly to changes in the economic environment, improve decision-making strategies and manage credit risks, which is of great importance for financial institutions in a competitive environment.There is currently a war going on in Ukraine, and forecasting using current methods becomes a difficult task due to unpredictable stressful situations for the economy. In such conditions, standard models may not be sufficiently adapted to account for increased risk and volatility. The proposed approach allows finding more conservative forecasting models that can be useful in unstable periods and war.