Fitness Estimates Research Articles

Spatio-temporal characteristics of Tuberculosis in Ghana

Background Tuberculosis (TB) remains a key contributor to global mortality; ranks second as the most fatal infectious disease and seventh among the top ten causes of death in Ghana. There is insufficient literature regarding the utilization of Bayesian hierarchical models for examining the spatial and spatio-temporal dynamics of tuberculosis risk in Ghana. Aim This study addresses this gap by determining TB hotspots regions in Ghana using the Bayesian modeling framework. Methods TB data were obtained from the Ghana Health Service and National Tuberculosis Programme for the 10 administrative regions of Ghana, from 2008 to 2017. Spatial and spatio-temporal TB relative risk for each region were estimated under the Bayesian modeling frameworks. Maps for TB risks were created to visualized regions with TB hotspots. Model fitting and parameter estimation were conducted using integrated nested Laplace approximation via R version 4.3.2. Results Among the baseline predictors, TB cure rate, TB success rate, knowledge about TB, human immunodeficiency virus (HIV) prevalence, percentage of literacy, and high income were found to be most significant predictors of TB risk in Ghana. We noted an increased risk of TB infection in the Northern zone and the Eastern and Greater Accra regions in the Southern zone. Spatio-temporal distribution of TB infection risk was predominantly concentrated in the Southern zone. Clustering of TB risk was observed among neighboring regions. Conclusion Factors influencing tuberculosis (TB) risk in Ghana are TB cure rate, TB success rate, knowledge about TB, HIV prevalence, literacy rate, and income level. The risk distribution was mainly concentrated in the Southern zone, with clusters of TB risk observed among neighboring regions. To achieve a significant reduction in TB cases, it is essential to allocate resources to TB hotspots regions and also implement measures to control significant predictors of TB infection risk.

The impact of temperature, humidity and closing school on the mumps epidemic: a case study in the mainland of China

BackgroundTo control resurging infectious diseases like mumps, it is necessary to resort to effective control and preventive measures. These measures include increasing vaccine coverage, providing the community with advice on how to reduce exposure, and closing schools. To justify such intervention, it is important to understand how well each of these measures helps to limit transmission.MethodsIn this paper, we propose a simple SEILR (susceptible-exposed-symptomatically infectious-asymptomatically infectious-recovered) model by using a novel transmission rate function to incorporate temperature, humidity, and closing school factors. This new transmission rate function allows us to verify the impact of each factor either separately or combined. Using reported mumps cases from 2004 to 2018 in the mainland of China, we perform data fitting and parameter estimation to evaluate the basic reproduction number R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathfrak R}_0$$\\end{document}. As a wide range of one-dose measles, mumps, and rubella (MMR) vaccine programs in China started only in 2008, we use different vaccination proportions for the first Stage I period (from 2004 to 2008) and the second Stage II period (from 2009 to 2018). This allows us to verify the importance of higher vaccine coverage with a possible second dose of MMR vaccine.ResultsWe find that the basic reproduction number R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathfrak R}_0$$\\end{document} is generally between 1 and 3. We then use the Akaike Information Criteria to assess the extent to which each of the three factors contributed to the spread of mumps. The findings suggest that the impact of all three factors is substantial, with temperature having the most significant impact, followed by school opening and closing, and finally humidity.ConclusionWe conclude that the strategy of increasing vaccine coverage, changing micro-climate (temperature and humidity), and closing schools can greatly reduce mumps transmission.

Investigation on the sampling distributions of non-Gaussian wind pressure skewness and kurtosis

Accurate estimation of skewness and kurtosis is crucial for addressing issues related to non-Gaussian wind pressure distribution fitting, signal simulation, and extreme estimation. The skewness and kurtosis determined from finite-length signals are inherent random variables characterized by obvious value fluctuations. Very limited research has been conducted on the sampling distributions of skewness and kurtosis for non-Gaussian wind pressure, motivating the present study. Firstly, the investigation focuses on the non-Gaussian white noise. The expressions of main statistical indicators such as the variance, covariance, correlation coefficient, skewness, and kurtosis of the two sampling distributions are derived and then verified using the Hermite polynomial model. The primary factors influencing the sampling distribution variance (SDV) of white noise are analyzed, including the signal length and marginal moments of the parent distribution. Secondly, the concentration shifts to colored non-Gaussian processes. The composition of SDV is analyzed through theoretical derivation. On this basis, a method for estimating SDV values using the Gaussian process regression model is proposed, with accuracy and feasibility verified based on the long-duration wind pressure data measured in wind tunnel. Furthermore, the relationship between the SDVs of wind pressure processes, signal length, higher-order correlation functions, and marginal moments of the parent process is discussed.

An accurate handheld device to measure log diameter and volume using machine vision technique

Log diameter and volume measurements are crucial processes in the wood supply chain. Conventionally, the measurement mainly focuses on diameters and lengths. Despite the clear definition of conventional measurement methods, obtaining exact diameters is challenging due to the complexity of the measuring process and variability in the organic nature of logs. In this study, a smart handheld device, mainly composed of a single industrial camera, laser projectors, and a small embedded processor, is developed to capture and process log end images. Additionally, a measuring method is proposed, which consists of instance segmentation, contour fitting, and pose estimation, to automatically measure the diameters and estimate the volumes of cut-to-length (CTL) logs by using formulae. Specifically, instance segmentation is used to identify the salient object as a contour in the image space. Contour fitting is then applied to fit the contour into an ellipse, where the major and minor axes represent the long and short diameters of the object, respectively. Pose estimation is utilized to establish the relationship between the image space and physical space. Subsequently, several groups of comparative experiments are conducted with manual measurements as a reference. The experimental results indicate that, for log diameter measurement, approximately 97.33 % of the deviations between device measurement and conventional measurement can be expected to fall within a range of −0.54 cm and 0.61 cm, with mean absolute deviation (MAD) and mean relative deviation (MRD) of 0.25 cm and 0.78 %, respectively. In terms of volume estimations, MAD and MRD are found to be 0.23 and 0.56 %, respectively. The results imply that the device contributes to substantial improvements in accuracy, reliability, and traceability, and it shows promising potential to replace manual measurements.

Accelerated Driving-Training-Based Optimization for Solving Constrained Bi-Objective Stochastic Optimization Problems

The constrained bi-objective stochastic optimization problem (CBSOP) considers the optimization problem with stochastic bi-objective functions subject to deterministic constraints. The CBSOP is part of a set of hard combinatorial optimization problems regarding time complexity. Ordinal optimization (OO) theory provides a commonly recognized structure to handle hard combinatorial optimization problems. Although OO theory may solve hard combinatorial optimization problems quickly, the deterministic constraints will critically influence computing performance. This work presents a metaheuristic approach that combines driving-training-based optimization (DTBO) with ordinal optimization (OO), abbreviated as DTOO, to solve the CBSOP with a large design space. The DTOO approach comprises three major components: the surrogate model, diversification, and intensification. In the surrogate model, the regularized minimal-energy tensor product with cubic Hermite splines is utilized as a fitness estimation of design. In diversification, an accelerated driving-training-based optimization is presented to determine N remarkable designs from the design space. In intensification, a reinforced optimal computing budget allocation is used to find an extraordinary design from the N remarkable designs. The DTOO approach is applied to a medical resource allocation problem in the emergency department. Simulation results obtained by the DTOO approach are compared with three heuristic approaches to examine the performance of the DTOO approach. Test results show that the DTOO approach obtains an extraordinary design with higher solution quality and computational efficiency than the three heuristic approaches.

Comparative study on excess distribution estimation in iid settings

. This study considers excess distribution estimation in iid settings. There are two ways for the estimation; the fitting to the generalized Pareto distribution and the fully non parametric estimation. The fitting estimator is justified by the approximation proven in the extreme value theory; however, the accuracy depends on how extremely large the target is. The non parametric estimator does not need an approximation and has the advantage of wide applicability. This study conducts both theoretical and numerical comparative study on excess distribution estimation. Asymptotic convergence rates of two estimators are obtained, and the mean integrated squared errors are numerically surveyed by simulation study. An illustrative example of Abisko rainfall amount is presented.

Psychometric evaluation of the Chinese version of alcohol relapse risk scale (C-ARRS) in patients with alcohol use disorder

Alcohol use disorder (AUD) is recognized as a chronic relapsing disorder. Alcohol Relapse Risk Scale (ARRS), a multidimensionally self-rating scale, was developed initially by the Japanese to assess the risk of alcohol reuse. The study aimed to validate the reliability and factor structure of the Chinese version of the ARRS (C-ARRS) for patients with AUD. A total of 218 patients diagnosed with AUD according to DSM-5 were recruited for self-administering C-ARRS. We assessed the internal consistency of C-ARRS using Cronbach's α coefficients and examined the factor structure through confirmatory factor analysis (CFA). Additionally, we investigated the concurrent validity by correlating C-ARRS with the Visual Analog Scale of Alcohol Craving (VAS), Penn Alcohol Craving Score (PACS), Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI) scores. CFA demonstrated inadequate data fit for the original 32-item C-ARRS, prompting the development of a revised 27-item version consisting of 6 subscales with satisfactory model fit estimates. The 27-item C-ARRS exhibited favorable internal consistency, with Cronbach's α ranging from 0.611 to 0.798, along with adequate factor loadings. The 27-item C-ARRS scores displayed significant correlations with the scores of VAS, PACS, BDI and BAI (p < .001). Our results indicated favorable reliability and factor structure of the 27-item C-ARRS. The significant correlation between the 27-item C-ARRS and clinical measures (such as depression, anxiety, and craving) demonstrates satisfactory concurrent validity. These observations collectively support the feasibility of using 27-item C-ARRS to assess the risk of alcohol relapse in patients with AUD.

Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency.

Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant R t . These models provide estimates of variant fitness and can be used to forecast changes in variant frequency. We introduce a framework for evaluating real-time forecasts of variant frequencies, and apply this framework to the evolution of SARS-CoV-2 during 2022 in which multiple new viral variants emerged and rapidly spread through the population. We compare models across representative countries with different intensities of genomic surveillance. Retrospective assessment of model accuracy highlights that most models of variant frequency perform well and are able to produce reasonable forecasts. We find that the simple MLR model provides ~0.6% median absolute error and ~6% mean absolute error when forecasting 30 days out for countries with robust genomic surveillance. We investigate impacts of sequence quantity and quality across countries on forecast accuracy and conduct systematic downsampling to identify that 1000 sequences per week is fully sufficient for accurate short-term forecasts. We conclude that fitness models represent a useful prognostic tool for short-term evolutionary forecasting.

Some Estimation Methods for a Random Coefficient in the Gegenbauer Autoregressive Moving-Average Model

The Gegenbauer autoregressive moving-average (GARMA) model is pivotal for addressing non-additivity, non-normality, and heteroscedasticity in real-world time-series data. While primarily recognized for its efficacy in various domains, including the health sector for forecasting COVID-19 cases, this study aims to assess its performance using yearly sunspot data. We evaluate the GARMA model’s goodness of fit and parameter estimation specifically within the domain of sunspots. To achieve this, we introduce the random coefficient generalized autoregressive moving-average (RCGARMA) model and develop methodologies utilizing conditional least squares (CLS) and conditional weighted least squares (CWLS) estimators. Employing the ratio of mean squared errors (RMSE) criterion, we compare the efficiency of these methods using simulation data. Notably, our findings highlight the superiority of the conditional weighted least squares method over the conditional least squares method. Finally, we provide an illustrative application using two real data examples, emphasizing the significance of the GARMA model in sunspot research.

Validation of Abbreviated Science Anxiety Scale in the Indian Context

The abbreviated science anxiety scale1 was validated in the Indian context with 290 secondary school students of grade 8 as the sample subjects. Exploratory factor analysis revealed the original two factors explaining 54.778 % of total variance in science anxity construct. Confirmatory factor analysis validated the factor structure of the construct with excellent goodness of fit estimates like CMIN/DF = 1.830, CFI = 0.969, TLI = 0.957, RMSEA = 0.054 and SRMR = 0.0392, conducted using the estimator maximum likelihood (ML). The floor and ceiling effect estimation for content validity showed that both the effects are absent with estimates way lesser than the benchmark of 15 % at 6.55 % and 0 % respectively. The internal consistency reliability estimation using Cronbach’s alpha found that the five items of the first factor “Learning science anxiety” and four items of the second factor “Science evaluation anxiety” had this estimate at 0.803 and 0.678 respectively, both of which fairly indicate good measurement of reliability of the scale. The education implications of the study are discussed.

From laboratory to industrial use: Understanding process variation during enzymatic protein hydrolysis with dry film fourier-transform infrared spectroscopy

Industries are continuously looking for dedicated sensor solutions for evaluating the chemical composition of products that can provide valuable insights into process control, process optimization, and product quality. A rapid and robust analytical method, Fourier-Transform Infrared (FTIR) spectroscopy, holds significant potential in this regard as it provides detailed compositional values of food nutrients. The main aim of the present study was to use dry film FTIR spectroscopy as an analytical tool to characterize products from an industrial enzymatic protein hydrolysis process and link this to understand industrial process variations. For this purpose, 463 protein hydrolysate samples were obtained from an industrial enzymatic protein hydrolysis plant. In the same period, systematic variations in process parameters such as raw material composition, enzyme type, and water addition, were performed. All samples were analyzed using dry film FTIR spectroscopy. Two subsets containing 200 and 68 hydrolysate samples were chosen for measuring average molecular weight (AMW) and collagen content respectively, providing reference data for constructing calibration models based on partial least squares regression (PLSR). The percentage of low molecular weight constituents was derived from the molecular weight distribution of size exclusion chromatograms of protein hydrolysates and also used in the modeling. Calibration models for the prediction of AMW, low molecular weight constituents, and collagen content were obtained with a good fit and lower estimation errors. Principal component analysis (PCA) of protein hydrolysates' FTIR spectra effectively differentiated process variations (enzyme types and raw materials) without extensive reference analysis. One-factor analysis of variance (ANOVA) tests was used to observe the impact of process variation on product quality. FTIR proved to be a sensitive method for liquid protein analysis and process control with a significant potential in process optimization approaches. To the authors’ knowledge, this is the first time dry film FTIR spectroscopy has been used to evaluate an industrial bioprocess with designed process variations.

Determinants of mosaic chromosomal alteration fitness

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.

A new approach to estimate parameters of the two-parameter Weibull distribution

This study proposes a new approach to estimate the parameters of the two-parameter Weibull distribution in the case of complete (uncensored) or doubly Type II censored samples. The proposed estimators have closed forms and can be adapted to Type II right or Type II left censored samples. The new estimators are strongly consistent and the new shape estimator follows asymptotically a normal and the new scale estimator a lognormal distribution. Bias, mean square and efficiency performance of the new estimators are investigated through simulations and compared to some known estimation methods such as maximum likelihood estimation, method of moments, quantile estimation, maximum goodness of fit estimation, L-moments, U-statistics and bias-corrected maximum likelihood. According to the simulations, the newly proposed estimators seem to have good efficiency performance in comparison to the other methods, particularly in small samples. Furthermore, two real data sets are used to illustrate the new estimators.

OA03 Developing a multivariable prediction model to identify anti-CCP positive individuals among those with non-specific musculoskeletal symptoms in primary care

Abstract Background/Aims A phase of immunologically imminent rheumatoid arthritis (RA) precedes the onset of clinical RA, characterised by increased circulating anti-cyclic citrullinated peptide (anti-CCP) antibodies. Early identification in the pre-clinical phase of RA in primary care (PC), through targeted anti-CCP testing, could prompt secondary care referral for earlier diagnosis, achieving better long-term outcomes. This study aimed to develop a model identifying those likely to be anti-CCP positive amongst people presenting to PC with non-specific musculoskeletal symptoms. Methods Participants were recruited across the UK between 2014 and 2020 to the Leeds ‘Co-ordinated Programme to Prevent Arthritis’, a prospective, observational cohort study. Participants were aged ≥18 years who presented to PC with non-specific musculoskeletal symptoms and no history of clinical synovitis. Fifteen baseline predictors were considered: age; gender; smoking status; first degree relative (FDR) with RA; and patient reported pain in: neck; back; shoulders; elbows; hips; wrists; thumbs; hand and/or fingers; knees; ankles; foot and/or toes. Participants were followed up at 12 months to determine if they had developed RA. Analysis was performed in R. Variable selection was carried out via LASSO logistic regression. Model performance was assessed using area under the receiver operating characteristic curve (ROC AUC); decision curve analysis was utilised to assess clinical utility. Internal validation was carried out via 200 bootstrap re-samples with replacement to estimate corrected model fit estimates. Results Analysis included 6,879 participants; 203 (2.95%) anti-CCP positive. Thirteen predictors were retained (age and ankles omitted); scores for each predictor were: sex (male) [+3], FDR with RA [+3], smoking (ever) [+2], pain in back [-3], neck [-2], knee [-1], wrist [+4], foot/toes [+3], hand/fingers [+3], shoulder [+3], thumb [+1]. With a score of ≥ 11 (≥4% risk) for anti-CCP testing, the ROC AUC was 0.65 (95% CI [0.61-0.69], corrected AUC=0.49). Choosing a threshold of 4% suggested that the benefit of testing one anti-CCP positive patient is 24 times larger than the harm of testing one anti-CCP negative patient. Sensitivity and specificity were 38.42 and 81.88 and positive- and negative-predicted values were 6.06 and 97.76. The net benefit was 0.0040 (corrected=0.0026). This threshold resulted in 19% being tested, corresponding to 39% of all anti-CCP positive participants. 12 month follow-up data were available for 2,480 participants. Out of those who would be tested using our model, 6.68% developed RA, compared with 1.94% if everyone was tested. Out of those tested who were anti-CCP positive, 56.85% developed RA (amongst those tested who were anti-CCP negative, 1.23% developed RA). Conclusion Targeted use of anti-CCP testing in PC may prompt earlier identification of people at risk of RA. Interviews with PC clinicians are currently being undertaken to understand the key behaviours required to support clinicians using this prediction model, alongside heath economic modelling to explore potential cost-effectiveness. Disclosure H.J. Siddle: Grants/research support; HJS has received funding from HEE/NIHR for this research [ICA-SCL-2018-04-ST2-004]. M. Wilson: None. J.L. Nam: None. L. Garcia-Montoya: None. L. Duquenne: None. K. Mankia: Consultancies; Serac Lifesciences. Honoraria; Galapagos, Lilly, Abbvie. Grants/research support; Eli Lilly, Gilead. Other; Attending meetings/travel: Abbvie. P. Emery: Consultancies; Lilly, Galapagos. Honoraria; Novartis, Lilly, Abbvie, Gilead, Galapagos. Grants/research support; Lilly, Samsung. Other; Advisory Board: Lilly, Astra Zeneca, BMS. E.M.A. Hensor: Grants/research support; Institution level grant for the NIHR Leeds Biomedical Research Centre.

Analysis of wind characteristics and wind energy resource assessment for Tonga using eleven methods of estimating Weibull parameters

Analysisof wind characteristics and assessment of wind energy resource is carried out at a location in Tonga with the help of twelve months of measurements carried out at 34 m and 20 m heights above ground level. The daily, monthly and annual averages are computed. The wind shear analysis and its diurnal variation were studied and compared with the temperature variation. The turbulence levels at the two heights were estimated for the entire measurement period as well as for some typical days. For estimating the Weibull parameters, eleven methods were employed along with goodness of fit and error estimates to find the best method. The overall averaged wind speed for the entire period of study is estimated to be 4.41 m/s at 34 m above ground level. The predominant wind direction was south-east for Tonga. ‘Moments’ is seen to be the best method to determine accurate Weibull parameters. The average net annual energy production from one Vergnet 275 kW wind turbine is 198.57 MWh. A payback period of 8.95 years by installing five turbines near the measurement location was estimated, which is very encouraging in terms of investment. Installing wind turbines will lower the heavy reliance on the imported fossil fuels in the country and also help in achieving Sustainable Development Goal 7.

Latent Variable Forests for Latent Variable Score Estimation.

We develop a latent variable forest (LV Forest) algorithm for the estimation of latent variable scores with one or more latent variables. LV Forest estimates unbiased latent variable scores based on confirmatory factor analysis (CFA) models with ordinal and/or numerical response variables. Through parametric model restrictions paired with a nonparametric tree-based machine learning approach, LV Forest estimates latent variable scores using models that are unbiased with respect to relevant subgroups in the population. This way, estimated latent variable scores are interpretable with respect to systematic influences of covariates without being biased by these variables. By building a tree ensemble, LV Forest takes parameter heterogeneity in latent variable modeling into account to capture subgroups with both good model fit and stable parameter estimates. We apply LV Forest to simulated data with heterogeneous model parameters as well as to real large-scale survey data. We show that LV Forest improves the accuracy of score estimation if parameter heterogeneity is present.

Unravelling open innovation determinants and firm performance relationships in CEE and SEE countries

Purpose Open innovation (OI), by now one of the major concepts for the analysis of innovation, is seen as a methodology for collaboratively designing and implementing solutions by engaging stakeholders in an iterative and inclusive service design process. This paper aims to empirically investigate OI capacities, defined as a cooperative, knowledge-sharing innovation ecosystem, and to explore how it can lead to improved performance of firms in Central and Eastern European (CEE) and Southeastern European (SEE) countries.Design/methodology/approach The study builds on the World Bank/European Bank for Reconstruction and Development (EBRD’s) Business Environment Enterprise Performance Survey (BEEPS) dataset for 2009, 2013 and 2019. Primarily, the research model was estimated using log-transformed ordinary least squares (OLS). Taking into consideration that this method might produce substantial bias, yielding misleading inferences, this study is fitting Poisson pseudo maximum likelihood estimators with robust standard errors and instrumental variable/generalized method of moments estimation (IV/GMM) approach for comparative results. Secondarily, the research model was tested using structural equation modelling (SEM) to investigate the relationship between five OI capacities and firm performance.Findings The findings indicate that there is a significant positive relationship between most OI capacities and firm performance, except for innovation, which did not show a statistically significant relationship with firm performance. Specifically, research and development (R&D), knowledge and coopetition are statistically significant and positively associated with firm performance, whereas transformation is statistically significant but negatively associated with firm performance. The IV/GMM estimations’ findings support the view that the firm performance is significantly affected by OI capacities, together with some control variables such as size, age, foreign ownership and year dummy to have a significant impact on firm performance.Originality/value This paper fills an identified gap in the literature by investigating the impact of OI on firm performance executed in the specific CEE and SEE country context.

Evaluating helmet-wearing of single-vehicle overspeeding motorcycle crashes: Insights from temporal instability in parsimonious pooled framework

Objective A lower helmet-wearing rate and overspeeding in Pakistan are critical risk behaviors of motorcyclists, causing severe injuries. To explore the differences in the determinants affecting the injury severities among helmeted and non-helmeted motorcyclists in motorcycle crashes caused by overspeeding behavior, single-vehicle motorcycle crash data in Rawalpindi city for 2017–2019 is collected. Considering three possible crash injury severity outcomes of motorcyclists: fatal injury, severe injury and minor injury, the rider, roadway, environmental, and temporal characteristics are estimated. Methods To provide a mathematically simpler framework, the current study introduces parsimonious pooled random parameters logit models. Then, the standard pooled random parameters logit models without considering temporal effects are also simulated for comparison. By comparing the goodness of fit measure and estimation results, the parsimonious pooled random parameters logit model is suitable for capturing the temporal instability. Then, the non-transferability among helmeted and non-helmeted overspeeding motorcycle crashes is illustrated by likelihood ratio tests and out-of-sample prediction, and two types of models provide robust results. The marginal effects are also calculated. Results Several variables, such as age, cloudy and weekday indicators illustrate temporal instability. Moreover, several variables are observed to only show significance in non-helmeted models, showing non-transferability across helmeted and non-helmeted models. Conclusions More educational campaigns, regulation and enforcement, and management countermeasures should be organized for non-helmeted motorcyclists and overspeeding behavior. Such findings also provide research reference for the risk-compensating behavior and self-selected group issues under overspeeding riding considering the usage of helmets.

Reliability, validity and acceptability of an online clinical reasoning simulator for medical students: An international pilot

Introduction Clinical reasoning skills are essential for decision-making. Current assessment methods are limited when testing clinical reasoning and management of uncertainty. This study evaluates the reliability, validity and acceptability of Practicum Script, an online simulation-based programme, for developing medical students’ clinical reasoning skills using real-life cases. Methods In 2020, we conducted an international, multicentre pilot study using 20 clinical cases with 2457 final-year medical students from 21 schools worldwide. Psychometric analysis was performed (n = 1502 students completing at least 80% of cases). Classical estimates of reliability for three test domains (hypothesis generation, hypothesis argumentation and knowledge application) were calculated using Cronbach’s alpha and McDonald’s omega coefficients. Validity evidence was obtained by confirmatory factor analysis (CFA) and measurement alignment (MA). Items from the knowledge application domain were analysed using cognitive diagnostic modelling (CDM). Acceptability was evaluated by an anonymous student survey. Results Reliability estimates were high with narrow confidence intervals. CFA revealed acceptable goodness-of-fit indices for the proposed three-factor model. CDM analysis demonstrated good absolute test fit and high classification accuracy estimates. Student survey responses showed high levels of acceptability. Conclusion Our findings suggest that Practicum Script is a useful resource for strengthening students’ clinical reasoning skills and ability to manage uncertainty.

Spatio-temporal modeling of high-throughput multispectral aerial images improves agronomic trait genomic prediction in hybrid maize.

Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity.