Multivariate Modeling Techniques Research Articles

Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.

Exploring the use of extended multiplicative scattering correction for near infrared spectra of wood with fungal decay

Extended Multiplicative Signal Correction (EMSC) is a multivariate linear modelling technique for multi-channel measurements that can identify and correct for different types of systematic variation patterns, known or unknown. It is typically used for pre-processing to separate light absorbance spectra, obtained by diffuse reflectance of intact samples, into three main sources of variation: additive variations due to chemical composition (≈Beer's law), mixed multiplicative and additive variations due to physical light scattering (≈Lambert's law) and more or less random measurement noise. The present work evaluates the use of EMSC to pre-process near infrared spectra obtained by hyperspectral imaging of Scots pine sapwood, inoculated with two different basidiomycete fungi and at various degradation stages. The spectral changes due to fungal decay and resulting mass loss are assessed by interpretation of the EMSC parameters and the partial least squares regression (PLSR) results. Including a cellulose (analyte) or bound water (interferent) spectral profile in the EMSC pre-processing model generally improves the predictive performance of the PLS modelling, but it can also make it worse. The inclusion of the additional polynomial baselines does not necessarily lead to a better separation of the physical and chemical effects present in the spectra. The estimated EMSC parameters provide insight into the differences in decay mechanisms. A detailed analysis of the EMSC results highlights advantages and disadvantages of using a complex pre-processing model.

Impact of the total expenditure shocks on food security: VAR model

Purpose. This study examines the causal relationship between total expenditures and food availability and identifies their shocks in food availability in Saudi Arabia. Methodology / approach. The study uses a multivariate modeling technique of the Vector Autoregression (VAR) and its environment, the Granger Causality Test, Forecast Error Variance Decomposition (FEVD), and Impulse Response Function (IRF) for the observation period of 2000–2020 in Saudi Arabia. Results. The results of the Granger causality show that investment expenditure has a significant impact on food availability in Saudi Arabia. However, consumption and government expenditures do affect food availability in Saudi Arabia, but have an indirect effect. The Impulse Response Functions show that the shocks of the selected variables require a long period to reach the long-run equilibrium level and the greatest response of the food availability variable is explained by its own shock and investment expenditure shocks. Originality / scientific novelty. The novelty of this study is related to the investigation of a new model and focus on a new perspective. While traditional food security research has mostly concentrated on agricultural production, availability, and accessibility of food, as well as nutrition and health outcomes factors, this research conveys a new dimension by highlighting the link between total expenditure and food security. Their contribution expands the scope of food security research and highlights the impact of recognising the role of total expenditure in implementing and supporting food security at the household level. Practical value / implications. It is important to design strategies and develop a budgeting plan to allocate a reasonable portion of total consumption and government expenditures on food items. Adding, regularly reviewing, and adapting the budgeting plan based on new challenges, and evolving priorities are essential to address the dynamic nature of food security.

Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data

Modeling forest structure using multi-source satellite data is beneficial to understanding the relationship between vertical and horizontal structure and image features to provide more comprehensive and abundant information for the study of forest structural complexity. This study investigates and models forest structure as a multivariate structure based on sample data and active-passive remote sensing data (Landsat8, Sentinel-2A, and ALOS-2 PALSAR) from the Saihanba Forest in Hebei Province, Northern China, to measure forest structural complexity, relying on a relationship-driven model between field and satellite data. In this study, we considered the effects of the role of satellite variables in different vertical structure types and horizontal structure ranges, used two methods to stepwise select significant variables (stepwise forward selection and Pearson correlation coefficient), and employed a multivariate modeling technique (redundancy analysis) to derive a forest composite structure index (FSI), combining both horizontal and vertical structure attributes. The results show that optical texture can better represent forest structure characteristics, polarization interferometric radar information can represent the vertical structure information of forests, and combining the two can represent 77% of the variance of multiple forest structural attributes. The new FSI can explain 93% of the relationship between stand structure and satellite variables, and the linear fit R2 to the measured data reaches 0.91, which largely shows the situation of the measured data. The generated forest structure map more accurately reflects the complexity of the forest structure in the Saihanba Forest, achieving a supplementary explanation of the measured data.

Multivariate Structural Equation Modeling Techniques for Estimating Reliability, Measurement Error, and Subscale Viability When Using Both Composite and Subscale Scores in Practice

We illustrate how structural equation models (SEMs) can be used to assess the reliability and generalizability of composite and subscale scores, proportions of multiple sources of measurement error, and subscale added value within multivariate designs using data from a popular inventory measuring hierarchically structured personality traits. We compare these techniques between standard SEMs representing congeneric relations between indicators and underlying factors versus SEM-based generalizability theory (GT) designs with simplified essential tau-equivalent constraints. Results strongly emphasized the importance of accounting for multiple sources of measurement error in both contexts and revealed that, in most but not all instances, congeneric designs yielded higher score accuracy, lower proportions of measurement error, greater average subscale score viability, stronger model fits, and differing magnitudes of disattenuated subscale intercorrelations. Extending the congeneric analyses to the item level further highlighted consistent weaknesses in the psychometric properties of negatively versus positively keyed items. Collectively, these findings demonstrate the practical value and advantages of applying GT-based principles to congeneric SEMs that are much more commonly encountered in the research literature and more directly linked to the specific measures being analyzed. We also provide prophecy formulas to estimate reliability and generalizability coefficients, proportions of individual sources of measurement error, and subscale added-value indices for changes made to measurement procedures and offer guidelines and examples for running all illustrated analyses using the lavaan (Version 0.6-17) and semTools (Version 0.5-6) packages in R. The methods described for the analyzed designs are applicable to any objectively or subjectively scored assessments for which both composite and subcomponent scores are reported.

Built environment and the evolution of the “15-minute city”: A 25-year longitudinal study of 200 Swedish cities

This article aims to examine how key features of the built environment have influenced the development of the 15-minute city in Swedish cities. Using longitudinal register data geocoded at a 100-meter level and encompassing the entire population and workplaces, we track the 200 largest Swedish cities over a 25-year period (1992–2017). The 15-minute city is operationalized as the proportion of the population in each city that can access a range of non-work urban social functions within a short distance from home. The study uses various multivariate modeling techniques, including fixed effect models, to measure the effects of changes in the built environment. We thus address significant research gaps by investigating the dynamic nature of the 15-minute city concept, conducting comparisons within and across multiple cities, and considering the influence of the built environment, particularly in smaller cities. Results show that population size has less significance than expected, and that smaller cities can achieve certain criteria of the 15-minute city. However, increasing population density and promoting mixed land use clearly contribute to fostering the 15-minute city. Moreover, cities that to a larger extent were developed before mass motorization tend to exhibit a greater degree of 15-minute city characteristics. The results highlight the importance of making long-term, thoughtful, strategic decisions in urban planning efforts aimed at transforming cities into 15-minute cities.

Applying Resampling and Visualization Methods in Factor Analysis to Model Human Spatial Vision.

Studies have reported different numbers of spatial frequency channels for chromatic and achromatic vision. To resolve the difference, we performed factor analysis, a multivariate modeling technique, on population data of achromatic and chromatic sensitivity. In addition, we included resampling and visualization methods to evaluate models from factor analysis. These routines are complex but widely useful. Therefore we have archived our analysis routines by building smCSF, an open-source software package in R (https://smin95.github.io/dataviz/). Data of 103 normally-sighted adults were analyzed. They included blue-yellow, red-green, and achromatic contrast sensitivity. To obtain the confidence interval of relevant statistical parameters, factor analysis was performed using a resampling method. Then exploratory models were developed. We then performed model selections by fitting them against the empirical data and quantifying the quality of the fits. During the exploratory factor analysis, different statistical tests supported different factor models. These could partially be reasons for why there have been conflicting reports. However, after the confirmatory analysis, we found that a model that included two spatial channels was adequate to approximate the chromatic sensitivity data, whereas that with three channels was so for the achromatic sensitivity data. Our findings provide novel insights about the spatial channels for chromatic and achromatic contrast sensitivity from population data. Also, the analysis and visualization routines have been archived in a computational package to boost the transparency and replicability of science.

Close to home: Family-centered spatial analysis of access to early care and education

This study addresses the issue of equitable access to early care and education (ECE) taking the state of Hawaiʻi as an example. We used spatially-based measures of demand-adjusted slots, cost burden relative to family income, and quality that quantified the supply of ECE services within a five-mile drive, a ten-mile drive, and a 30-min public transit commute from a family's home. Multivariate spatial modeling techniques were used to predict ECE access at the community level, with median income, county of location, population density, and community ethnic composition as predictors. Results revealed some disparities, such as better slot capacity in areas that were densely populated or had a high share of persons of East Asian heritage. We also found promising results relating to slots and quality in low-income communities. The strategic location of Head Start, public preK, and classrooms sponsored by a local philanthropy created conditions where some low-income communities had very favorable access to ECE slots and high-quality programs, relative to the state overall. The spatial methods used in this study are flexible and can be adapted to answer any number of questions about access to community resources for young children and families at different levels of geographic granularity.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

Chemometric Techniques in the Assessment of Ambient Air Quality and Development of Air Quality Index of Coal Mining Complex: A Statistical Approach

This study aims to analyze the regional variation in the source of air pollution, identify the percentage contribution of each pollutant, and distribute the mass contribution of each source category using multivariate analysis. The nine air monitoring sites were successfully divided into three groups using hierarchical agglomerative cluster analysis (HACA) (clusters 1, 2, and 3). The collected meteorological data is non-parametric data for the years 2020–2021 which includes PM2.5, PM10, SO2, NO2, NO, NOx, CO, wind speed, humidity, wind direction, temperature, cloud cover, and surface radiation. The most major air pollution sources were identified using FA. Multiple linear regression (MLR) and principal component regression (PCR) were utilized to create an equation model explaining contaminants' impact in each cluster. However, it was shown that the most important pollutants impacting the value of the air pollutant index (API) are gaseous pollutants (NOx and SO2) and particulate matter (PM10 and PM2.5). Gas and non-gas pollutants have a 65% influence on cluster 1 and meteorological conditions have a 35% effect. Cluster 3 is influenced by 65% particle and non-gas pollutants and 35% weather conditions, compared to Cluster 2 which is 100% affected by gas and particulate pollutants because of its spatial location. This study shows the value of the multivariate modeling technique in minimizing the time and expense associated with monitoring redundant stations and parameters.

Groundwater characterization including prediction of the quality, fluoride, and nitrate occurrence in a typical artisanal mining area in Ghana: A hydrochemical and multivariate statistical approach

Groundwater quality has been compromised by both geogenic and anthropogenic activities. Also, the negative impact of the intake of groundwater with high fluoride and nitrate content has been well established. Artisanal gold mining operations within the Tinga-Maluwe areas go beyond a decade with no knowledge of the level of impact these activities have on the groundwater quality. Hence, this study aims to characterize the groundwater with emphasis on the quality for usage as well as the controls of fluoride and nitrate occurrence in the area using hydrochemistry and multivariate linear regression modeling techniques. This is to enhance groundwater monitoring and stakeholder decision-making processes in the catchment. The concentration order of the cations is Ca2+ > Na+ > Mg2+ > K+ and that of the anions is HCO3− > NO3− > Cl− > SO42− > CO32− > F− with a Na – Cl dominant water type. Apart from F−, NO3−, and TDS exceeding their acceptable limits in Tinga, the overall pollution, water quality, and percentage of pollution indices show that the groundwater is slightly to heavily polluted in Daklupe, Seripe, Tinga, Kablima, Maluwe, Sakpa, and Bekwa-kura communities. Mineral dissolution, especially carbonate minerals, is the dominant process controlling groundwater mineralization from the discriminant diagrams used, and the saturation indices. However, anthropogenic activities impact on the groundwater cannot be precluded from the MLRMs of OPI, WQI, and PPI. The MLRMs show that pH, SO42−, Na+, K+, Ca2+, and Mg2+ are the controls of F− occurrence while that of NO3− is by TDS and pH.

Bivariate copula for flood frequency analysis in Johor river basin

Flooding is a multi-attribute event that is described by many factors such as peak flow and flood volume. It is extremely vital to consider both the flood volume and the flood peak while studying the flood frequency analysis as the univariate analysis cannot accurately portray the flood issue and suffers from an underestimation and an overestimation problem. Traditional univariate and multivariate modeling techniques have several mathematical shortcomings including the inability to distinguish between the marginal and joint behavior of the variables under study. Therefore, the copula function was introduced to tackle the above restriction. Six copula models will be applied in this study to find the best bivariate distribution between the flood variables in Johor River Basin, Malaysia, including Gaussian, Student-t, Clayton, Gumbel, Frank, and Joe. Before that, several marginal distributions were fitted to select the most appropriate distribution for flood variables. It was found that the Pearson Type-III fits both the flood peak flow and the flood volume best. The evaluation of the best univariate marginal distribution and the copula model will be based on Akaike Information Criterion (AIC). Our findings suggested that Frank Copula is more suited to represent the relationship between peak flow and flood volume as it portrays the lowest AIC values of -69.41 and highest log-likelihood values of 35.7, where both values outperform the other proposed copula models. However, future research which considers all three flood variables which are peak flow, volume, and duration should be conducted to attain a more reliable result.

Source apportionment of polychlorinated biphenyls (PCBs) using different receptor models: A case study on sediment from the Portland Harbor Superfund Site (PHSS), Oregon, USA

Multivariate modelling techniques are used by a wide variety of investigations in environmental chemistry. It is surprisingly rare for studies to show a detailed understanding of uncertainties created by modelling or how uncertainties in chemical analysis impact model outputs. It is common to use untrained multivariate models for receptor modelling. These models produce a slightly different output each time they are run. The fact that a single model can provide different results is rarely acknowledged. In this manuscript, we attempt to address this by investigating differences that can be generated using four different receptor models (NMF, ALS, PMF & PVA) to perform source apportionment of polychlorinated biphenyls (PCBs) in surface sediments from Portland Harbor. Results showed that models generally had a strong agreement and identified the same main signatures that represented commercial PCB mixtures, however, subtle differences were identified by; different models, same models but with a different number of end members (EM), and the same model with the same number of end members. As well as identifying different Aroclor-like signatures, the relative proportion of these sources also varied. Depending on which method is selected it may have a significant impact on conclusions of a scientific report or litigation case and ultimately, allocation on who is responsible for paying for remediation. Therefore, care must be taken to understand these uncertainties to select a method that produces consistent results with end members that can be chemically explained. We also investigated a novel approach to use our multivariate models to identify inadvertent sources of PCBs. By using a residual plot produced from one of our models (NMF) we were able to suggest the presence of approximately 30 different potentially inadvertently produced PCBs which account for 6.6 % of the total PCBs in Portland Harbor sediments.

Water quality assessment and pollution source apportionment using multivariate statistical and PMF receptor modeling techniques in a sub-watershed of the upper Yangtze River, Southwest China.

Rapid industrial and agricultural development as well as urbanization affect the water environment significantly, especially in sub-watersheds where the contaminants/constituents present in the pollution sources are complex, and the flow is unstable. Water quality assessment and quantitative identification of pollution sources are the primary prerequisites for improving water management and quality. In this work, 168 water samples were collected from seven stations throughout 2018-2019 along the Laixi River, a vital pollution control unit in the upper reaches of the Yangtze River. Multivariate statistics and positive matrix factorization (PMF) receptor modeling techniques were used to evaluate the characteristics of the river-water quality and reveal the pollution sources. Principal component analysis was employed to screen the crucial parameters and establish an optimized water quality assessment procedure to reduce the analysis cost and improve the assessment efficiency. Cluster analysis further illustrates the spatiotemporal distribution characteristics of river-water quality. Results indicated that high-pollution areas are concentrated in the tributaries, and the high-pollution periods are the spring and winter, which verifies the reliability of the evaluation system. The PMF model identified five and six potential pollution sources in the cold and warm seasons, respectively. Among them, pollution from agricultural activities and domestic wastewater shows the highest contributions (33.2% and 30.3%, respectively) during the cold and warm seasons, respectively. The study can provide theoretical support for pollutant control and water quality improvement in the sub-watershed, avoiding the ecological and health risks caused by the deterioration of water quality.

Utilization and Survival Impact of Hypofractionated Radiotherapy in Stage I Non-small Cell Lung Cancer.

The optimal fractionation schedule in unresected stage I non-small cell lung cancer (NSCLC) unsuitable for stereotactic body radiation therapy is unclear. Given the lack of comparative data regarding nonstereotactic body radiation therapy schemas, we compared overall survival (OS) with hypofractionated radiotherapy (HFRT) versus conventionally fractionated radiotherapy (CFRT) and examined the OS impact of different HFRT doses. This retrospective analysis included 2159 patients from the National Cancer Database diagnosed with stage I (cT1-2aN0M0) NSCLC between 2008 and 2016. Patients underwent CFRT (70≤BED10 [biologically effective dose] <100 Gy10 in ≥30 fractions), low-dose HFRT (LD-HFRT; 70≤BED10 [assuming α/β=10] <100 Gy10 in 11 to 24 fractions), or high-dose HFRT (HD-HFRT; 100≤BED10 ≤120 Gy10 in 6 to 10 fractions). Patients who received surgery, chemotherapy, or immunotherapy were excluded. We compared CFRT versus all HFRT, and separately CFRT versus LD-HFRT and CFRT versus HD-HFRT. OS was evaluated with the Kaplan-Meier estimator, log-rank test, and Cox regression. A total of 63.2% of patients underwent CFRT, 23.5% LD-HFRT, and 13.3% HD-HFRT. OS was significantly longer with HFRT versus CFRT on univariable (28.2 mo [95% CI, 25.6-31.7] vs 26.4 mo [25.0-27.9]; log-rank=0.0025) but not multivariable analysis (MVA; hazard ratio [HR] 0.90; P=0.062). MVA yielded no significant difference in OS between CFRT and LD-HFRT (HR 0.96, P=0.53). OS was significantly longer with HD-HFRT versus CFRT on MVA (HR, 0.75; P=0.003). However, on sensitivity analysis using different multivariable modeling techniques, this did not retain statistical significance (HR, 0.83; P=0.12). For stage I NSCLC, HFRT does not show a robust OS benefit compared with CFRT but may be preferred given the convenience and lower costs.

Use of Hyperspectral Reflectance and Water Quality Indices to Assess Groundwater Quality for Drinking in Arid Regions, Saudi Arabia

Combining hydrogeochemical characterization and a hyperspectral reflectance measurement can provide knowledge for groundwater security under different conditions. In this study, comprehensive examinations of 173 groundwater samples were carried out in Makkah Al-Mukarramah Province, Saudi Arabia. Physicochemical parameters, water quality indices (WQIs), and spectral reflectance indices (SRIs) were combined to investigate water quality and controlling factors using multivariate modeling techniques, such as partial least-square regression (PLSR) and principal component regression (PCR). To measure water quality status, the drinking water quality index (DWQI), total dissolved solids (TDS), heavy metal index (HPI), contamination degree (Cd), and pollution index (PI) were calculated. Standard analytical methods were used to assess nineteen physicochemical parameters. The typical values of ions and metals were as follows: Na2+ &gt; Ca2+ &gt; Mg2+ &gt; K+, Cl− &gt; SO42− &gt; HCO3− &gt; NO3− &gt; CO32−; and Cu &gt; Fe &gt; Al &gt; Zn &gt; Mn &gt; Ni, respectively. The hydrogeochemical characteristics of the examined groundwater samples revealed that Ca-HCO3, Na-Cl, mixed Ca-Mg-Cl-SO4, and Na-Ca-HCO3 were the main mechanisms governing groundwater chemistry and quality under the load of seawater intrusion, weathering, and water-rock interaction. According to the WQIs results, the DWQI values revealed that 2.5% of groundwater samples were categorized as excellent, 18.0% as good, 28.0% as poor, 21.5% as extremely poor, and 30.0% as unfit for drinking. The HPI and Cd values revealed that all groundwater samples had a low degree of contamination and better quality. Furthermore, the PI values showed that the groundwater resources were not affected by metals but were slightly affected by Mn in Wadi Fatimah due to rock–water interaction. Linear regression models demonstrated the significant relationships for the majority of SRIs paired with DWQI (R varied from −0.40 to 0. 75), and with TDS (R varied from 0.46 to 0.74) for the studied wadies. In general, the PLSR and PCR models provide better estimations for DWQI and TDS than the individual SRI. In conclusion, the grouping of WQIs, SRIs, PLSR, PCR, and GIS tools provides a clear image of groundwater suitability for drinking and its controlling elements.

Detection of high-grade glioma clusters: Spatial case-control study using EMR data from 2010-2021.

e14016 Background: High-grade gliomas (HGGs), like Glioblastomas (GBMs), are the most common malignant intracranial tumors with dismal prognosis. Rural areas have higher HGG related mortality and lesser research advancements. The objective of this study was to identify zip codes with disproportionate risk of HGGs compared to other brain malignancies. Methods: Patients’ electronic health data for this case-control study was extracted from West Virginia University Hospital Systems, 2010-2021. HGG cases were defined using diagnoses codes ICD10-C71 and ICD9-191 (excluding low grade gliomas). The control group was defined by using diagnoses codes ICD10-D32, C79.3, C71 (excluding HGG cases) and ICD9-225.2, 225.4, 198.3, 191 (excluding HGG cases). Controls were exact matched four controls to every case based upon patient five-year age categories and gender. Clustering was assessed globally through difference in Ripley’s-K between cases and controls; and locally using scan statistics. Results: The locations of HGGs and other brain tumors were clustered in the northern and northeastern regions of WV. Global cluster analyses detected statistically significant dispersion of HGG cases compared to our control group. Local cluster analysis identified multi-zip code cluster of HGG patients in northeastern WV between 2010-2021. The average relative risk of HGG compared to other brain malignancies was 1.25, indicating a 25% increase in risk of HGGs compared to other brain cancers within this region. While the local cluster was not statistically significant, the zip code level estimates of relative risk within the cluster ranged from 0.00 to 5.01. This indicated that certain zip codes within the cluster had five times the risk of HGGs compared to non-HGG brain malignancies relative to zip codes outside of the cluster. Conclusions: Findings indicate community level disparities in risk of HGGs relative to other brain malignancies. Additionally, results suggest that HGG cases are seen across the state at a more dispersed pattern than for non-HGG related brain tumors. These overall spatial trends could potentially be attributable to differences in clinical practice, access to care, or social determinants of health throughout WV. Spatial multivariable modeling techniques are needed to identify the specific neighborhood level factors associated with disproportionate patterns of risk identified for WV HGG patients.

Evaluation of structural and measurement models of student satisfaction in online learning

&lt;span&gt;This study aimed to evaluate structural models and measurement models of student satisfaction in online learning. This was a quantitative study using a survey research design. Structural model testing was done by examining the relationship between several variables. The variables in question were the learning management system (LMS), admin services, the performance of facilitator lecturers and student satisfaction in learning. The sample used in this study were 149 students. Data analysis was performed using the multivariate Structural Equation Modeling (SEM) technique. The findings of this study indicated that the facilitator lecturer performance is the variable that has the greatest effect in increasing student satisfaction in online learning. Admin service is another variable that has a positive effect on student satisfaction, both direct and indirect effects. The LMS variable does not show a direct effect on student satisfaction, but the LMS has an indirect effect on student satisfaction through the variable facilitator lecturer performance and admin services.&lt;/span&gt;

Psychometric Properties of The Persian Version of Job Design and Work Context Questionnaires Among Employees of the Textile Industry in Qom City

Background &amp; Aims of the Study: Today, the process of humanizing jobs so that the characteristics and working conditions are compatible with the psychological characteristics of employees, has been considered. Content of work and job design is one of the significant concepts in ergonomics, which is one of the impacts of job science and its analysis. The present study was designed to evaluate the reliability and validity of job design questionnaires and work context and then analyze their relationship with musculoskeletal disorders among employees working in the textile industry in Qom in 2019. Materials and Methods: This is a cross-sectional and analytical study. The target population included employees working in the textile industry of Qom, of whom 252 people were included in the study using stratified sampling. The tools used in this study included four questionnaires: demographic, job design, work context, and body map questionnaires. In order to analyze and test the simultaneous correlation of the variables in this study, the multivariate analysis modeling technique of structural equations was used, during which the fit of the measurement model and the reliability of the structures were confirmed by examining the factor loads, Cronbach’s alpha coefficient, and the composite reliability, and its validity by calculating the Average Extracted Variance (AVE) of the convergence validity of the Fornell-Larcker models. This study used Smart PLS and SPSS software, v. 20 software for data analysis. Results: The results of descriptive statistical tests showed that among 252 employees, 74.6% were male, and 25.4% were female. The main variables, i.e., work context and job design and their subscales, had acceptable conditions in terms of validity and reliability. The relationship between job design and musculoskeletal disorders was significant (P&lt;0.05). Conclusion: The result of this study showed that the two Persian versions of the questionnaire, namely the work context questionnaire and the job design questionnaire, have good validity and reliability and can be used to assess working conditions in job environments, especially in Iranian industries.