Statistical Modeling Approach Research Articles

Morphology of the scaphotrapeziotrapezoid joint: A multi-domain statistical shape modeling approach.

The scaphotrapeziotrapezoid (STT) joint is involved in load transmission between the wrist and thumb. A quantitative description of baseline STT joint morphometrics is needed to capture the variation of normal anatomy as well as to guide staging of osteoarthritis. Statistical shape modeling (SSM) techniques quantify variations in three-dimensional shapes and relative positions. The objectives of this study are to describe the morphology of the STT joint using a multi-domain SSM. We asked: (1) What are the dominant modes of variation that impact bone and articulation morphology at the STT joint, and (2) what are the morphometrics of SSM-generated STT joints? Thirty adult participants were recruited to a computed tomography study of normal wrist imaging and biomechanics. Segmentations of the carpus were converted to three-dimensional triangular surface meshes. A multi-domain, particle-based entropy system SSM was used to quantify variation in carpal bone shape and position as well as articulation morphology. Articular surface areas and interosseous proximity distributions were calculated between mesh vertex pairs on adjacent bones within distance (2.0 mm) and surface-normal angular (35°) thresholds. In the SSM, the first five modes of variation captured 76.2% of shape variation and contributed to factors such as bone scale, articular geometries, and carpal tilt. Median interosseous proximities-a proxy for joint space-were 1.39 mm (scaphotrapezium), 1.42 mm (scaphotrapezoid), and 0.61 mm (trapeziotrapezoid). This study quantifies morphological and articular variations at the STT joint, presenting a range of normative anatomy. The range of estimated interosseous proximities may guide interpretation of imaging-derived STT joint space.

Quantifying the carbon sequestration potential of different soil management practices aimed at increasing organic content in soil and reducing the usage of chemical inputs

<p>The global imperative to address climate change and promote sustainable agriculture has underscored the critical role of soil management practices in enhancing carbon sequestration and reducing greenhouse gas emissions. This research focuses on quantifying the carbon sequestration potential of diverse soil management techniques aimed at increasing organic content in soil while minimizing the use of chemical inputs. It integrates a multidisciplinary approach, conducting field experiments across diverse agroecological regions with different soil types and cropping systems. Key practices under investigation include cover cropping, crop rotations, conservation tillage, compost application, organic amendments, integrated pest management (IPM), and precision agriculture. The study assesses their impact on soil organic carbon levels, greenhouse gas emissions, soil health indicators, and crop productivity through comprehensive data collection and analysis techniques. Statistical analyses and modeling are employed to quantify carbon sequestration rates, evaluate treatment effects, and assess environmental and agronomic benefits. These practices are implemented in controlled trials to assess their impact on soil organic carbon (SOC) levels, greenhouse gas emissions (CO2, CH4, N2O), soil health indicators (microbial biomass, nutrient cycling, soil structure), and crop productivity. Data collection and analysis techniques involve comprehensive soil sampling, greenhouse gas flux measurements, soil health assessments, and crop yield monitoring over multiple growing seasons. Statistical analyses and modeling approaches are employed to quantify the carbon sequestration rates, assess treatment effects on soil health and greenhouse gas emissions, and evaluate the overall environmental and agronomic benefits of the soil management practices. The study aims to contribute valuable insights into evidence-based strategies for promoting sustainable soil management practices that enhance carbon sequestration while maintaining or improving agricultural productivity. The findings will have implications for informing agricultural policies, guiding farm management decisions, and advancing research on climate-smart agriculture. By quantifying the carbon sequestration potential of different soil management practices, this research contributes to the broader goal of achieving climate resilience, environmental sustainability, and food security in agricultural systems.</p>

Optimization and Modeling of a Dual-Chamber Microbial Fuel Cell (DCMFC) for Industrial Wastewater Treatment: A Box–Behnken Design Approach

Microbial fuel cells (MFCs) have garnered significant attention due to their capacity to generate electricity using renewable and carbon-neutral energy sources such as wastewater. Extensive experimental work and modeling techniques have been employed to dissect these processes and understand their respective impacts on electricity generation. The driving force is to enhance MFC performance for practical applications commercially. Among the various statistical modeling approaches, one particularly robust tool is the Design of Experiments (DoE). It serves to establish the relationships between different variables that influence MFC performance and allows for the optimization of the MFC configuration and operation for scaled-up performances in terms of bioelectricity generation. This study focused on optimizing microbial fuel cells (MFCs) for bioelectricity production using industrial wastewater treatment, employing the Box–Behnken design (BBD) methodology. Through an analysis of response surface models and ANOVA tests, it was found that a combined approach of reduced quadratic, reduced two-factor interaction, and linear models yielded sound results, particularly in voltage yield, COD removal, and current density. Second-order regression models predicted optimal conditions for various parameters, with surface area, temperature, and catholyte dosage identified as critical input variables for optimization. Under these conditions, conducted by the four-factor and three-level Box–Behnken design methodology in a double-chamber MFC unit considering eight output variables—CCV yield, % COD removal, current density, power density, % TSS removal, % CE, and % PO43−—the optimum values were 700 mV, 54.4%, 54.4 mA/m2, 73.7 mW/m2, 99%, 21.2%, and 100%, respectively. At optimum operating conditions, the results revealed a desirability of 76.6% out of a total of 92 iterations. The paper highlights the effectiveness of statistical ANOVA fit-statistics modeling and optimization in enhancing DCMFC performance, recommending its use as a sustainable bioenergy source. Furthermore, validation results supported the above optimization output response findings and confirmed the viability of biorefinery wastewater as an anolyte for scaling up DCMFC bioelectricity generation.

A Hybrid Experimental Modelling Approach to Solar Photovoltaic Cell Temperature Prediction

Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting especially in commercial power plants. The present study focused on the development of a machine learning based predictive model for solar photovoltaic cell temperature prediction in commercial solar photovoltaic power plants. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for those parameters difficult to measure experimentally and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed for each parameter used to mimic experimentally measured data. Statistical approaches were adopted to analyse the influence of both the dependent and independent variables on solar cell temperature. The analysis included multicollinearity and correlation analysis with the aid of heat maps meant to establish the relationships among the independent and dependent variables. Feature selection and dimensionality reduction was also performed to reduce the input variables and maintain relevant data in the modelling process. Parameters with a strong correlation to each other had some of them eliminated in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.83 °C while an R2 value of 0.93 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed a 3.4% difference in the annual energy generated between a simulation which considered solar cell temperature and that which ignored the solar cell temperature. The study also revealed that this difference is even larger in monetary terms when lifetime energy generation is considered. The present study demonstrated that Random Forests, a machine learning approach to predictive modelling, can handle complex models and can provide models with a higher accuracy compared to statistical modelling approaches. The study recommends the use of solar cell predictive models to improve the accuracy of energy prediction on solar photovoltaic power plants which in turn assists in energy planning and deployment.

Nutrient contents and microbial communities as mediators of the effects of land-use in ecosystem functioning in alpine ecosystems from Central Mexico

Abstract Purpose The interplay between microbial taxonomy, functional diversity, and land-use changes across diverse ecosystems must be better understood. This study explores, through a statistical modeling approach, the intricate relationships between microbial communities, soil properties (nutrient contents and physicochemical parameters), and land-use changes, using stable isotopes ratio (δ13C/δ15N) as ecosystem function indicator. Methods Using Structural Equation Modeling (SEM) this study aims to investigate how soil microbial communities, soil properties and ecosystem function are interconnected in three ecosystems undergoing land-use change in Nevado de Toluca, Mexico. Results Multivariate analyses of microbial communities and soil parameters for the different land-uses reveal significant variation in both microbial composition and soil properties (i.e. nutrient contents) mainly explained by the land-use history of the studied ecosystems. Through the SEM approach, it was possible to disentangle the relative contribution of land-use change, microbial community composition and nutrient contents to the ecosystem function defined as the δ13C/δ15N ratio. Conclusions Microbial communities are the main drivers of soil ecosystem functioning. However, through SEM approach it is possible to formally test direct and indirect relationships impacting microbial communities and the resulting functional consequences, contributing to a mechanistic understanding of the ecological implications of land-use change, aiding in sustainable land-use decision-making.

Permeability modelling in a highly heterogeneous tight carbonate reservoir using comparative evaluating learning-based and fitting-based approaches

Permeability modelling is considered a complex task in reservoir characterization and a key component of reservoir simulation. A common method for permeability modelling involves performing static rock typing (SRT) using routine core analysis data and developing simple fitting-based mathematical relations that link permeability to reservoir rock porosity. In the case of carbonate reservoirs, which are associated with high heterogeneities, fitting-based approaches may fail due to porosity–permeability data scattering. Accurate modelling of permeability using petrophysical well log data seems more promising since they comprise a vast array of information about the intrinsic properties of the geological formations. Furthermore, well log data exhibit continuity throughout the entire reservoir interval, whereas core data are discrete and limited in availability and coverage. In this research work, porosity, permeability and log data of two oil wells from a tight carbonate reservoir were used to predict permeability at un-cored intervals. Machine learning (ML) and fitting models were used to develop predictive models. Then, the developed ML models were compared to exponential and statistical fitting modelling approaches. The integrated ML permeability model based on Random Forest method performed significantly superior to exponential and statistical fitting-based methods. Accordingly, for horizontal and vertical permeability of test samples, the Root Mean Squared Error (RMSE) values were 3.7 and 4.5 for well 2, and 1.7 and 0.86 for well 4, respectively. Hence, using log data, permeability modelling was improved as it incorporates more comprehensive reservoir rock physics. The outcomes of this reach work can be used to improve the distribution of both horizontal and vertical permeability in the 3D model for future dynamic reservoir simulations in such a complex and heterogeneous reservoir system.

Optimization of Process Parameters for Turning Operation on D3 Die Steel

This research aims to determine the optimal Surface Roughness for machining D3 die steel alloy with uncoated carbide inserts. It will do this by studying the most efficient turning parameters, such as cutting speed, feed, and depth of cut. Models have been generated using a variety of statistical modeling approaches, including Genetic Algorithm with Response Surface Methodology. This research aimed to use the regression technique to develop a model that could predict surface roughness. It has also been investigated if the Taguchi Technique may be used to optimize process parameters. To decide the primary boundaries affecting Surface Unpleasantness, we used Signal-to-Noise (S/N) ratio and Analysis of Variance (ANOVA) tests. This paper aims to contribute valuable insights into achieving the best Surface Roughness outcomes in the machining process for D3 die steel alloy with Uncoated Carbide Inserts. The utilization of Genetic Algorithm and Response Surface Methodology showcases a robust approach for modelling intricate parameter interactions. If you know the values of the parameters, you may use the Regression Technique to forecast the surface roughness. Process parameter optimization may be made more systematic with the use of the Taguchi Technique.

Data Mining in Different Fields: A Study

A revolutionary technique for managing massive amounts of data from databases is data mining. These days, information is more commonplace in all types of businesses and industries. For a range of business objectives, fields choose data mining approaches, and data mining is what makes it possible for fields to establish the ideal customer baseline and cultivate long-lasting relationships. Statistical analysis, machine learning, predictive modelling, and database approaches are all combined in knowledge discovery. This review research focuses on and quickly discusses the various domains in which data mining is applied. Here, we discuss several sectors where data mining is relevant in order to comprehend its significance. We also discuss the numerous technologies used in related fields, which depend on user desire. To learn about it, many algorithms are described.

Performance Differences of a Touch-Based Serial Reaction Time Task in Healthy Older Participants and Older Participants With Cognitive Impairment on a Tablet: Experimental Study.

Digital neuropsychological tools for diagnosing neurodegenerative diseases in the older population are becoming more relevant and widely adopted because of their diagnostic capabilities. In this context, explicit memory is mainly examined. The assessment of implicit memory occurs to a lesser extent. A common measure for this assessment is the serial reaction time task (SRTT). This study aims to develop and empirically test a digital tablet-based SRTT in older participants with cognitive impairment (CoI) and healthy control (HC) participants. On the basis of the parameters of response accuracy, reaction time, and learning curve, we measure implicit learning and compare the HC and CoI groups. A total of 45 individuals (n=27, 60% HCs and n=18, 40% participants with CoI-diagnosed by an interdisciplinary team) completed a tablet-based SRTT. They were presented with 4 blocks of stimuli in sequence and a fifth block that consisted of stimuli appearing in random order. Statistical and machine learning modeling approaches were used to investigate how healthy individuals and individuals with CoI differed in their task performance and implicit learning. Linear mixed-effects models showed that individuals with CoI had significantly higher error rates (b=-3.64, SE 0.86; z=-4.25; P<.001); higher reaction times (F1,41=22.32; P<.001); and lower implicit learning, measured via the response increase between sequence blocks and the random block (β=-0.34; SE 0.12; t=-2.81; P=.007). Furthermore, machine learning models based on these findings were able to reliably and accurately predict whether an individual was in the HC or CoI group, with an average prediction accuracy of 77.13% (95% CI 74.67%-81.33%). Our results showed that the HC and CoI groups differed substantially in their performance in the SRTT. This highlights the promising potential of implicit learning paradigms in the detection of CoI. The short testing paradigm based on these results is easy to use in clinical practice.

Thresholds for estuarine compound flooding using a combined hydrodynamic–statistical modelling approach

Abstract. Estuarine compound flooding can happen when extreme sea level and river discharges occur concurrently, or in close succession, inundating low-lying coastal regions. Such events are hard to predict and amplify the hazard. Recent UK storms, including Storm Desmond (2015) and Ciara (2020), have highlighted the vulnerability of mountainous Atlantic-facing catchments to the impacts of compound flooding including risk to life and short- and long-term socio-economic damages. To improve prediction and early warning of compound flooding, combined sea and river thresholds need to be established. In this study, observational data and numerical modelling were used to reconstruct the historic flood record of an estuary particularly vulnerable to compound flooding (Conwy, North Wales). The record was used to develop a method for identifying combined sea level and river discharge thresholds for flooding using idealised simulations and joint-probability analyses. The results show how flooding extent responds to increasing total water level and river discharge, with notable amplification in flood extent due to the compounding drivers in some circumstances, and sensitivity (∼ 7 %) due to a 3 h time lag between the drivers. The influence of storm surge magnitude (as a component of total water level) on the flooding extent was only important for scenarios with minor flooding. There was variability as to when and where compound flooding occurred; it was most likely under moderate sea and river conditions (e.g. 60th–70th and 30th–50th percentiles) and only in the middle-estuary zone. For such cases, joint-probability analysis is important for establishing compound flood risk behaviour. Elsewhere in the estuary, either the sea state (lower estuary) or river flow (upper estuary) dominated the hazard, and single-value probability analysis is sufficient. These methods can be applied to estuaries worldwide to identify site-specific thresholds for flooding to support emergency response and long-term coastal management plans.

Predicting dental anxiety in young adults: classical statistical modelling approach versus machine learning approach

ObjectivesTo predict and identify the key demographic and clinical exposure factors associated with dental anxiety among young adults, and to compare if the traditional statistical modelling approach provides similar results to the machine learning (ML) approach in predicting factors for dental anxiety.MethodsA cross-sectional study of Western Illinois University students. Three survey instruments (sociodemographic questionnaire, modified dental anxiety scale (MDAS), and dental concerns assessment tool (DCA)) were distributed via email to the students using survey monkey. The dependent variable was the mean MDAS scores, while the independent variables were the sociodemographic and dental concern assessment variables. Multivariable analysis was done by comparing the classical statistical model and the machine learning model. The classical statistical modelling technique was conducted using the multiple linear regression analysis and the final model was selected based on Akaike information Criteria (AIC) using the backward stepwise technique while the machine learining modelling was performed by comparing two ML models: LASSO regression and extreme gradient boosting machine (XGBOOST) under 5-fold cross-validation using the resampling technique. All statistical analyses were performed using R version 4.1.3.ResultsThe mean MDAS was 13.73 ± 5.51. After careful consideration of all possible fitted models and their interaction terms the classical statistical approach yielded a parsimonious model with 13 predictor variables with Akaike Information Criteria (AIC) of 2376.4. For the ML approach, the Lasso regression model was the best-performing model with a mean RMSE of 0.617, R2 of 0.615, and MAE of 0.483. Comparing the variable selection of ML versus the classical statistical model, both model types identified 12 similar variables (out of 13) as the most important predictors of dental anxiety in this study population.ConclusionThere is a high burden of dental anxiety within this study population. This study contributes to reducing the knowledge gap about the impact of clinical exposure variables on dental anxiety and the role of machine learningin the prediction of dental anxiety. The predictor variables identified can be used to inform public health interventions that are geared towards eliminating the individual clinical exposure triggers of dental anxiety are recommended.

OLYMPUS-POPGEN: A synthetic population generation model to represent urban populations for assessing exposure to air quality.

With the new individual- and activity-based approaches to simulating exposure to air pollutants, exposure models must now provide synthetic populations that realistically reflect the demographic profiles of individuals in an urban territory. Demographic profiles condition the behavior of individuals in urban space (activities, mobility) and determine the resulting risks of exposure and environmental inequalities. In this context, there is a strong need to determine the relevance of the population modeling methods to reproduce the combinations of socio-demographic parameters in a population from the existing databases. The difficulty of accessing complete, high-resolution databases indeed proves to be very limiting for the ambitions of the different approaches. This work proposes to evaluate the potential of a statistical approach for the numerical modeling of synthetic populations, at the scale of dwellings and including the representation of coherent socio-demographic profiles. The approach is based on and validated against the existing open databases. The ambition is to be able to build upon such synthetic populations to produce a comprehensive assessment of the risk of environmental exposure that can be cross-referenced with lifestyles, indicators of social, professional or demographic category, and even health vulnerability data. The approach implemented here is based on the use of conditional probabilities to model the socio-demographic properties of individuals, via the deployment of a Monte Carlo Markov Chain (MCMC) simulation. Households are assigned to housing according to income and house price classes. The resulting population generation model was tested in the Paris region (Ile de France) for the year 2010, and applied to a population of almost 12 million individuals. The approach is based on the use of census and survey databases. Validation, carried out by comparison with regional census data, shows that the model accurately reproduces the demographic attributes of individuals (age, gender, professional category, income) as well as their combination, at both regional and sub-municipal levels. Notably, population distribution at the scale of the model buildings remains consistent with observed data patterns. The outcomes of this work demonstrate the ability of our approach to create, from public data, a coherent synthetic population with broad socio-demographic profiles. They give confidence for the use of this approach in an activity-based air quality exposure study, and thus for exploring the interrelations between social determinants and environmental risks. The non-specific nature of this work allows us to consider its extension to broader demographic profiles, including health indicators, and to different study regions.

Analyzing the Impact of Road Accidents on Carbon Dioxide Emissions in Freeway Traffic: A Simulation and Statistical Modeling Approach

Roadway accidents significantly contribute to intermittent congestion and increased CO2 emissions on freeways. This research introduces a statistical approach designed to predict the rise in CO2 emissions resulting from traffic disturbances or jams triggered by such incidents. It also assesses the influence of varying levels of accident management effectiveness in different situations. To construct these scenarios, the study employs VISSIM, a traffic modeling software, incorporating diverse factors such as traffic volume, vehicle types, incident durations, and freeway lane counts. It then produces traffic flow characteristics in the form of vehicle paths. The emission estimates are derived by correlating these simulated vehicle paths with emission rates from the MOVES model. The study then applies a regression analysis to examine the connection between the increase in emissions and various influencing factors. The findings indicate that this approach efficiently reflects the impact of variables like accident duration, vehicle mix, and traffic volume on CO2 emissions across different lane configurations. The accuracy of these predictions is also confirmed. The outcomes suggest the model’s potential usage in guiding efforts to lower emissions and determining the optimal duration of incident management, particularly in terms of lane closure, to mitigate emission impacts. This paper also discusses the limitations of the model and the future improvement direction.

Using the surface scattering mechanism from dual-pol SAR data to estimate topsoil particle-sizefractions

Data extracted from Synthetic Aperture Radar (SAR) have been widely employed to estimate soil properties. However, these studies are typically constrained to bare soil conditions, as soil information retrieval in vegetated areas remains challenging. Polarimetric decomposition has emerged as a potentially useful method to separate the scattering contributions of different targets (e.g. canopy/leaves and the underlying soil), which is of significance for areas that are near-permanently covered in low-lying vegetation (e.g. grass) like Ireland – the study area for this investigation. Here, we test the surface scattering mechanism, derived from H-alpha dual-pol decomposition, together with other covariates, to estimate percentages of sand, silt, and clay, over vegetated terrain, using Sentinel 1 data (dual-pol C-band SAR). The statistical modelling approaches evaluated – linear regression (LRM) and tree-based regression models (machine learning) – explicitly consider the compositional nature of soil texture. When compared to the models fitted without surface scattering data, results showed that the inclusion of the surface scattering data improved estimates of silt and clay, with the compositional linear regression model, and estimates of sand and silt fractions with different tree-based models. While not without limitations, our study demonstrated that the polarimetric decomposition method, which is typically used for classification and segmentation purposes, could also be used for soil property estimation, broadening the application of this technique in microwave remote sensing studies.

Thickness and design features of clinical cranial implants-what should automated methods strive to replicate?

New deep learning and statistical shape modelling approaches aim to automate the design process for patient-specific cranial implants, as highlighted by the MICCAI AutoImplant Challenges. To ensure applicability, it is important to determine if the training data used in developing these algorithms represent the geometry of implants designed for clinical use. Calavera Surgical Design provided a dataset of 206 post-craniectomy skull geometries and their clinically used implants. The MUG500+ dataset includes 29 post-craniectomy skull geometries and implants designed for automating design. For both implant and skull shapes, the inner and outer cortical surfaces were segmented, and the thickness between them was measured. For the implants, a 'rim' was defined that transitions from the repaired defect to the surrounding skull. For unilateral defect cases, skull implants were mirrored to the contra-lateral side and thickness differences were quantified. The average thickness of the clinically used implants was 6.0 ± 0.5mm, which approximates the thickness on the contra-lateral side of the skull (relative difference of -0.3 ± 1.4mm). The average thickness of the MUG500+ implants was 2.9 ± 1.0mm, significantly thinner than the intact skull thickness (relative difference of 2.9 ± 1.2mm). Rim transitions in the clinical implants (average width of 8.3 ± 3.4mm) were used to cap and create a smooth boundary with the skull. For implant modelers or manufacturers, this shape analysis quantified differences of cranial implants (thickness, rim width, surface area, and volume) to help guide future automated design algorithms. After skull completion, a thicker implant can be more versatile for cases involving muscle hollowing or thin skulls, and wider rims can smooth over the defect margins to provide more stability. For clinicians, the differing measurements and implant designs can help inform the options available for their patient specific treatment.

Thermal Misfit and Diffusion Induced Stresses of Cu-Al Intermetallics in Microelectronics Wire Bonding

The thermosonic bonding technique is a widely used method for Cu wire interconnections. However, issues arise due to volumetric changes in intermetallic compounds (IMCs) formed at the Cu-Al bonding interface, leading to voids in the Cu-Al IMC layer. This problem is exacerbated after annealing, such as in high-temperature Storage (HTS). In this study, a statistical modelling approach was employed to quantitatively analyse stress, studying the evolution and characteristics of the interfacial microstructure in the thermosonic Cu wire-Al bond pad system. Microstructural analysis focused on Cu-Al IMC crystallography and compositional classification. A stress model was proposed, considering both thermal misfit and diffusion-induced stresses. Results showed that interfacial stress generally increased with higher bonding temperatures. The influence of forming gas supply was relatively minor, with oxide layers minimally impeding Cu-Al interdiffusion during Cu-Al IMC formation. This stress modelling technique hold potential as a valuable failure analysis tool for implementing Cu wire in various industries.

The effect of place on voting behavior: The case of the Arizona proposition to legalize recreational marijuana

AbstractIn 2020, Arizonans approved Proposition 207, the Smart and Safe Arizona Act, which legalized recreational marijuana sales. Previous research has typically used non‐spatial survey data to understand marijuana legalization voting patterns. However, voting behavior can, in part, be shaped by geographic context, or place, which is unaccounted for in aspatial survey data. We use multiscale geographically weighted regression to analyze how place shaped Proposition 207 voting behavior, independently of demographic variations across space. We find significant spatial variability in the sensitivity of voting for Proposition 207 to changes in several of the predictor variables of opposition and support for recreational marijuana legalization. We argue that local statistical modeling approaches provide a more in‐depth understanding of ballot measure voting behavior than the current use of global models.Related ArticlesBranton, Regina, and Ronald J. McGauvran. 2018. “Mary Jane Rocks the Vote: The Impact of Climate Context on Support for Cannabis Initiatives.” Politics &amp; Policy 46(2): 209–32. https://doi.org/10.1111/polp.12248.Brekken, Katheryn C., and Vanessa M. Fenley. 2020. “Part of the Narrative: Generic News Frames in the U.S. Recreational Marijuana Policy Subsystem.” Politics &amp; Policy 49(1): 6–32. https://doi.org/10.1111/polp.12388.Fisk, Jonathan M., Joseph A. Vonasek, and Elvis Davis. 2018. “‘Pot'reneurial Politics: The Budgetary Highs and Lows of Recreational Marijuana Policy Innovation.” Politics &amp; Policy 46(2): 189–208. https://doi.org/10.1111/polp.12246.

Metabolites Associated With Uremic Symptoms in Patients With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study

Rationale & ObjectiveThe toxins contributing to uremic symptoms in patients with CKD are unknown. We sought to apply complementary statistical modeling approaches to data from untargeted plasma metabolomic profiling to identify solutes associated with uremic symptoms in patients with CKD. Study DesignCross-sectional. Setting & Participants1,761 Chronic Renal Insufficiency Cohort (CRIC) participants with CKD not on dialysis. PredictorsMeasurement of 448 known plasma metabolites. OutcomesThe uremic symptoms fatigue, anorexia, pruritus, nausea, paresthesia, and pain were assessed by single items on the Kidney Disease Quality of Life-36 (KDQOL) instrument. Analytical ApproachMultivariable adjusted linear regression, Lasso linear regression, and random forest models were used to identify metabolites associated with symptom severity. After adjustment for multiple comparisons, metabolites selected in at least two of the three modeling approaches were deemed “overall” significant. ResultsParticipant mean eGFR was 43 mL/min/1.73 m2, with 44% self-identifying as female and 41% Non-Hispanic Black. The prevalence of uremic symptoms ranged from 22 – 55%. We identified 17 metabolites for which a higher level was associated with greater severity of at least one uremic symptom, and 9 metabolites inversely associated with uremic symptom severity. Many of these metabolites demonstrated at least a moderate correlation with eGFR (Pearson’s r ≥ 0.5), and some were also associated with risk of developing kidney failure or death in multivariable adjusted Cox regression models. LimitationsLack of a second independent cohort for external validation of our findings. ConclusionsMetabolomic profiling was used to identify multiple solutes associated with uremic symptoms in adults with CKD, but future validation and mechanistic studies are needed.

Analysis of the competitiveness of higher education institutions in Ukraine in the context of recovery and development after the war

The article examines the challenges that arise before the education system in the conditions of military aggression. The changing political and social situation during the conflict seriously affects educational institutions and their ability to provide quality education. The purpose of the study is to create a structural and functional set of criteria for ensuring the processes of forming the competitiveness of educational institutions in the conditions of full-scale military aggression and post-war reconstruction. The article analyzes the effects of military conflict on education infrastructure, access to education, and the psychological well-being of students and staff. She reveals the need to adapt the criteria for assessing the competitiveness of educational institutions pays attention to the flexibility of the educational process and the ability to respond to emergency situations. The article used the method of literature analysis, which made it possible to systematize the results of the analysis of scientific studies, articles, reports, and other sources of information related to the competitiveness of educational institutions in the conditions of military aggression and post-war reconstruction. This allows you to establish the presence of problems, identify factors and determine previous theoretical approaches to the assessment of competitiveness. Analytical methods, including statistical data analysis, modeling, and other analytical approaches for assessing competitiveness, are used to identify relationships and important factors affecting the competitiveness of an educational institution. Based on the use of these methods, a model for assessing the competitiveness of educational institutions was developed. Using the competitive assessment model helps higher education institutions improve their strategic plans. This allows them to identify their strengths and weaknesses, identify competitive opportunities and threats, and develop effective development strategies. The competitiveness assessment model contributes to the improvement of the quality of education.

CVPCNN: Conditionally variational parameterized convolution neural network for HRRP target recognition with imperfect side information

Radar target recognition tasks often incorporate valuable side information (SI) that aids in the recognition task. The effectiveness of SI has been fully verified in traditional statistical modeling approaches. Its full potential remains untapped in deep learning-based high-resolution range profile (HRRP) target recognition tasks. Taking into account the azimuth sensitivity of HRRP, azimuth is chosen as the SI of the proposed model. To integrate SI into deep neural networks, we propose two novel parameterized convolution methods, namely variational parameterized convolution (VPCONV) and the extended conditionally VPCONV (CVPCONV). Specifically, VPCONV makes the convolutional kernel weights as the function of the auxiliary azimuth, which gives the network the capability of adaptively adjusting to changes in azimuth. Acknowledging the challenge of estimating HRRP azimuth accurately in practical scenarios, VPCONV employs a heteroscedastic Gaussian distribution, featuring varying mean and variance, to generalize the estimation error of SI through variational encoding. Moreover, CVPCONV incorporates the properties of the HRRP samples themselves by kernel attention module. Finally, we present a lightweight SI-based network. The experimental results based on the measured HRRPs validate the effectiveness of proposed method across various extended recognition tasks, underscoring the potential of fusing SI via parameterized convolution in advancing target recognition systems.