Bayesian Information Criterion Research Articles

Simple Regression Models: A Comparison using Criteria Measures

The study is on simple regression models: a comparison using criteria measures. The source of the dataset used for this study was extracted from records of the Federal Medical Centre, Owerri, Imo State, on weight of babies and hemoglobin level of mothers. The response variable is weight of babies while the explanatory variable is hemoglobin level of mothers. Eleven simple regression models—Linear, Growth, Quadratic, Polynomial, Logarithmic, Hyperbolic, Power, Exponential Growth, Square Root, Sinusoidal and Arctangent—were stated and employed for the study. For ease of data analysis, E-views package was implemented. Three model selection criteria measures for comparison, known as Akaike Information Criterion (AIC), Schwarz Information Criterion (SIC) and Hannan-Quinn Information Criterion (HQIC), were employed. The result of the study showed that, when it comes to analyzing the association between baby weight and mothers' hemoglobin levels, the exponential growth regression model performs better than the other ten models that were examined. Therefore, researchers should investigate other models that were not included in this analysis and compare the findings using goodness of fit metrics other than the criteria measures used in this work.

Modeling the dispersion measure—redshift relation for fast radio bursts

This theoretical work investigates different models to predict the redshift of fast radio bursts (FRBs) from their observed dispersion measure (DM) and other reported properties. We performed an extended revision of the FRBs with confirmed galaxy hosts in the literature and built the most updated catalog to date. With this sample of FRBs, we propose four models that relate the DM and z: a linear trend (inspired by the Macquart relation), a log-parabolic function, a power law, and an interpolation for DM that includes z and the position of the host galaxy of the transient. The latter model has the highest success rate according to the metrics implemented: likelihood, median of the z difference, and the Akaike and Bayesian information criteria. Although the performance of model D is closely followed by the power law and linear models, the former has the advantage of accounting for anisotropies in the dispersion measure due to the angular coordinates. Conversely, the log-parabolic formula performs poorly in this task but provides a good prediction for FRBs with low DM at a low redshift. Additionally, we use the reported galaxy properties of the hosts to establish a connection between the observed DM with the star formation rate (SFR) and stellar mass (Ms) of the galaxies where the FRBs reside. In both cases, we find a weak correlation. Although the studied correlations are well-motivated, the sample of FRBs is not statistically significant enough to draw solid conclusions in this second part of our work. With the advent of new facilities devoted to studying the localization and nature of these transients, we will get access to new data that will enrich the proposed models and give us hints on the astrophysical origin and evolution of FRBs.

Topp-Leone Exponentiated Pareto Distribution: Properties and Application to Covid-19 Data

AbstractThis paper proposes a new Topp-Leone Exponentiated Pareto (TLEtP) distribution. The new distribution family is derived by expanding the Topp Leone-G family of distributions with additional positive shape parameters. The corresponding density and distribution functions are derived and shown. Some of the derived mathematical properties of the distribution include quantile function, ordinary and incomplete moments generating function (mgf), hazard function, survival function, odd function, probability weighted moment, and distribution of order statistic. The parameters of the distribution are estimated using Maximum Likelihood method. The proposed distribution’s validity is demonstrated by fitting two sets of real data and comparing the results with two existing same-family distributions, the Topp-Leone Pareto type I(TLPI) and Pareto (P), with the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC), respectively. The comparison of the proposed Topp-Leone Exponentiated Pareto (TLEtP) to the Topp-Leone Pareto type I(TLPI) and Pareto (P) distribution demonstrate that the TLEtP distribution offers a better fit for the data sets than the other two distributions.

78 Estimates of genetic parameters for weight, wool, and carcass traits of Rambouillet sheep

Abstract The Rambouillet sheep breed is the foundation to most western range sheep in the U.S. Through the National Sheep Improvement Program (NSIP), Rambouillet breeders seek to improve economically relevant traits. However, the parameters used in the NSIP genetic evaluation were last estimated two decades ago. Our aim was to re-estimate genetic parameters for body weight (BW), carcass, and wool traits. Data were provided by NSIP, which included BW at birth (BWT; n = 28,817), weaning [WWT; n = 5,633; age: 68 (SD 16) day], early post-weaning [PWTE; n = 23,303; 128 (SD 22) d], late post-weaning [PWTL; n = 7,689; 201 (SD 45) d] and as yearlings [YWT; n = 5,830; 375 (SD 33) d]. Yearling ultrasound scans of 12th rib backfat depth (YCF; n = 2,447) and eye muscle depth (YEMD; n = 2,457) were also available. Yearling wool traits were fiber curvature (YFC; n = 6,009), fiber diameter (YFD; n = 9,880), fiber diameter coefficient of variation (YFDCV; n = 8,803), greasy fleece weight (YGFW; n = 11,859) and staple length (YSL; n = 11,270). The pedigree consisted of 36,297 individuals. Animals with phenotypic records were progeny of 333 sires and 1,727 dams. A succession of univariate animal models was fitted. The most comprehensive model included direct and maternal additive genetic effects, their genetic covariance, and maternal permanent environmental and residual effects. Phenotypes were pre-adjusted for birth-rearing type category and, as covariates, dam age and age at recording. Contemporary group—a concatenation of flock, year, season, management group, sex, and, excluding BWT, recording date—was the only fixed effect fitted in the model. Model selection was based on the log-likelihood ratio test, and Akaike and Bayesian information criteria. The data structure, namely dams and their daughters and granddaughters contributing data, allowed reliable testing of the maternal effect terms. The direct-maternal genetic covariance did not explain variation for any trait (P > 0.18). The most parsimonious model for BW traits included direct and maternal additive genetic, and maternal permanent environmental, effects. For carcass and wool traits, only direct additive genetic effects were included in the final model. Such was unsurprising, as maternal effects often diminish as animals age. For the final models fitted (Table 1), direct heritabilities (± SE) for body weights ranged from 0.14 ± 0.01 (BWT) to 0.38 ± 0.04 (PWTL); the maternal heritabilities ranged from 0.04 ± 0.02 (PWTL; YWT) to 0.13 ± 0.01 (BWT). For carcass traits, direct heritabilities were 0.13 ± 0.04 (YCF) and 0.31 ± 0.05 (YEMD). For wool traits, they ranged from 0.37 ± 0.02 (YSL) to 0.58 ± 0.02 (YFD). These re-estimated parameter values are consistent with those used by NSIP, with the exception of PWTL (greater) and YSL (lower). With genomic data accumulating in Rambouillet, further refinements in parameter estimation are planned.

82 Relationships between performance traits and osteochondrosis of the hock in Standardbred racehorses

Abstract Equine osteochondrosis (OC) is a developmental orthopedic disease that results in abnormal articular cartilage and often the formation of osteochondral bone chips (osteochondrosis dissecans). Standardbreds, a breed predominately used in harness racing, are prone to the development of OC. This study aimed to quantify the relationship between OC and key performance traits, fastest mile time, annual earnings, and consistency in race times over career, using a cohort of 347 North American Standardbred horses. Radiographic examination of the tarsi was previously performed to identify horses as OC-affected (n = 155) or healthy (n = 192). Performance data was acquired from the United States Trotting Association (USTA) detailing horse identification, performance, and lifetime race record. Linear mixed multiple regression models were constructed to evaluate the relationship between OC and performance. A total of 24 performance related variables (e.g., age, gait, track) were evaluated for inclusion in the model through subset variable selection and goodness of fit as determined by Schwarz’s Bayesian Information Criterion (SBC). Preliminary results indicate that healthy horses exhibit significantly faster mile times (P < 0.001) compared with their OC-affected counterparts. However, no significant association was detected between OC status and annual earnings (P > 0.2) or consistency in race times over career (P > 0.2). Ongoing analyses are currently being conducted to assess additional predictors and performance variables for a potential association with osteochondrosis. This study emphasizes the importance of implementing effective strategies for OC management and improving breeding selection practices when producing offspring with desired performance characteristics.

Arima Model to Predict the Prevalence of Diabetes Type 1 and Type 2 Patients: A Case Study of Jos University Teaching Hospital

Diabetes Mellitus is a huge burden for human health, increasing number of patient is likely to result in rising demand for the medical emergencies. Due to limited number of hospitals with standard laboratory test kits to differentiate between type 1 and type 2 diabetes it is important to forecast the future incidences and prepare with proper resource planning. The monthly number of Diabetes patients obtained from Jos University Teaching Hospital is fitted by autoregressive integrated moving average (ARIMA) model. Dataset starting from January, 2010 to December,2020. Using ARIMA, several models were evaluated based on the Bayesian Information Criterion (BIC) and Ljung-Box Q statistics. ARIMA(3, 1, 1) is found to be better and used to describe and predict the future trends of Diabetes  type 1 and ARIMA(1,1,1) is a better model to predict the future prevalence of diabetes type 2. Therefore, the proposed model will help in the appropriate planning and allocation of resources for emergencies.

Estimation and comparison of Weibull-Normal distribution with some other probability models using Bayesian method of estimation

In statistical applications the Normal distribution is adjudged to be the best. Recent studies Terna (2017) using classical method indicated that Weibull-Normal distribution outperformed the Normal distribution. In this study we used the non-classical Bayesian method of estimation to estimate and compare the Weibull-Normal distribution with some other distributions including Normal and Gamma-Normal distributions. This study derived explicit expressions for basic statistical properties such as moments, moment generating function, the characteristic function, reliability analysis and the distribution of order statistics. It looks at estimation of confidence intervals for the parameters of the Weibull-Normal distribution and estimated the parameters of the new distribution using a non-classical approach for the purpose of theoretical comparisons. The two other distributions whose parameters were also estimated by using Bayesian estimation are the normal distribution and gamma distribution as well as the combination Gamma-Normal distribution. Based on the analyses and interpretations of the results obtained it was found that the parameters and other general properties of Normal distribution gives a better fit than other distributions. R-software was used; the models were written as an R code in R program using the rjags library, the distribution parameters were obtained from a Gibbs sampling of a Bayesian Fit for data set I and data set II. The criteria used in R for comparisons were the negative log-likelihood, AIC (Akaike information criterion), CAIC (Consistent Akaike Information Criterion) and BIC (Bayesian information Criterion).

Governing accelerating Universe via newly reconstructed Hubble parameter by employing empirical data simulations

A new parametrization of the Hubble parameter is proposed to explore the issue of the cosmological landscape. The constraints on model parameters are derived through the Markov Chain Monte Carlo (MCMC) method by employing a comprehensive union of datasets such as 34 data points from cosmic chronometers (CC), 42 points from baryonic acoustic oscillations (BAO), a recently updated set of 1701 Pantheon+ (P22) data points derived from Type Ia supernovae (SNeIa), and 162 data points from gamma-ray bursts (GRBs). Furthermore, the models are compared by using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), so that a comparative assessment of model performance can be available. Additionally, we compare the Dainotti relation via Gaussian likelihood analysis versus new likelihoods and Calibration of the Dainotti relation through a model-independent method. The kinematic behavior of the models is also investigated by encompassing the transition from deceleration to acceleration and the evolution of the jerk parameter. From the analysis of the parametric models, it is strongly indicated that the Universe is currently undergoing an accelerated phase with diagnostics of the model validating the quintessence phase.

A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.

This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.

Geographical Variations in Early Onset Colorectal Cancer in the United States between 2001 and 2020.

Colorectal cancer remains the second leading cause of cancer-related death in the US. As early-onset colorectal cancer (EO-CRC) becomes more prevalent in the US, research attention has shifted towards identifying at-risk populations. Previous studies have highlighted the rising rate of early-onset adenocarcinoma (ADC) and neuroendocrine tumors (NET) in the US. However, data on geographical variations of EO-CRC are scarce. Hence, our study aims to analyze time trends in EO-CRC incidence rates across various US regions and to assess these trends by sex and histopathological subtypes (ADC and NET). We analyze data spanning from 2001 to 2020 from the United States Cancer Statistics (USCS) database, covering nearly 98% of the US population. Using SEER*Stat software version (8.4.2, NCI), we calculated EO-CRC incidence rates among adults aged 20-54 years, adjusting for the age standard 2000 US population. The rates were categorized by sex and US geographical regions into west, midwest, northeast, and south. Time trends, reported as annual percentage change (APC) and average APC (AAPC), were generated via Joinpoint Regression software (v.5.0.2, NCI) utilizing the weighted Bayesian Information Criteria "BIC" method to generate the best-fit trends with a two-sided p-value cutoff at 0.05. The rates were also stratified by histopathology into ADC and NET. Between 2001 and 2020, a total of 514,875 individuals were diagnosed with early-onset CRC in the US, with 54.78% being men. Incidence rates and trends varied across geographical regions. In the western region (comprising 106,685 patients, 54.85% men), incidence rates significantly increased in both women (AAPC = 1.37, p < 0.001) and men (AAPC = 1.34, p < 0.001). Similarly, in the midwestern region (with 110,380 patients, 55.46% men), there were significant increases in incidence rates among women (AAPC = 1.06, p < 0.001) and men (AAPC = 1.35, p < 0.001). The northeastern region (with 94,758 patients, 54.53% men) also witnessed significant increases in incidence rates for both women (AAPC = 0.71, p < 0.001) and men (AAPC = 0.84, p < 0.001). In contrast, the southern region (with 203,052 patients, 54.48% men) experienced slower increases in incidence rates among both women and men (AAPC = 0.25, p < 0.05 in women; AAPC = 0.66, p < 0.05 in men). When stratified by histopathology, incidence rates for adenocarcinomas (ADC) increased in all regions, most notably in the west (AAPC = 1.45, p < 0.05), and least in the south (AAPC = 0.46, p < 0.05). Conversely, for neuroendocrine tumors (NET), while incidence rates increased similarly across all regions, the pace was notably faster compared to ADC, particularly in the west (AAPC = 3.26, p < 0.05) and slower in the south (AAPC = 2.24, p < 0.05) Discussion: Our analysis of nationwide US data spanning two decades and encompassing over half a million early-onset CRC patients, representing nearly 98% of the US population, highlights significant temporal variation in incidence rates across various geographical regions. The most substantial increases in incidence rates were observed in the west, while the least pronounced changes were noted in the south, affecting both men and women. These trends persisted across the main CRC histopathological subtypes, with NET exhibiting a notably swifter pace of increase compared with ADC. These findings hold important implications for public health strategies and underscore the need for targeted interventions to address the rising burden of early-onset CRC across different regions in the US.

Constraining on the non-standard cosmological models combining the observations of high-redshift quasars and BAO

In this work, we studied four types of cosmological models with different mechanisms driving the accelerated expansion of the universe, include Braneworld models, Chaplygin Gas models, Emergent Dark Energy models, and cosmological torsion models. Considering that the dynamics of these models at low redshifts are very similar and difficult to distinguish, we used the latest and largest UV and X-ray measurements of quasars (QSOs) observations covering the range of redshift 0.009<z<7.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.009<z<7.5$$\\end{document}. However, the high intrinsic dispersion of this sample and the degeneracy between cosmological model parameters, we added 2D-BAO and 3D-BAO datasets to help us constrain the parameters of these cosmological models. Our results suggest that standard cold dark matter scenario may not be the best cosmological model preferred by the high-redshift observations. The Generalized Chaplygin Gas (GCG) and cosmological constant plus torsion (named Case II) models perform best by Akaike Information Criterion (AIC), but the Λ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Lambda $$\\end{document}CDM is the best cosmological model preferred by Bayesian Information Criterion (BIC). Our work also supports that the Phenomenologically Emergent Dark Energy and cosmological torsion models may alleviate the Hubble tension, the reported value of the Hubble constant obtained from QSO+BAO datasets combination lies between Planck 2018 observations and local measurements from the SH0ES collaboration, while other cosmological models all support that the Hubble constant tends to be closer to recent Planck 2018 results, but these model are penalized by information criterion.

Forecasting the spread of COVID-19 based on policy, vaccination, and Omicron data.

The COVID-19 pandemic caused by the novel SARS-COV-2 virus poses a great risk to the world. During the COVID-19 pandemic, observing and forecasting several important indicators of the epidemic (likenew confirmed cases, new cases in intensive care unit, andnew deaths for each day) helped prepare the appropriate response (e.g., creating additional intensive care unit beds, andimplementing strict interventions). Various predictive models and predictorvariables have been used to forecast these indicators. However, the impact of prediction models and predictorvariables on forecasting performance has not been systematically well analyzed. Here,we compared the forecasting performance using a linear mixed model in terms of prediction models (mathematical, statistical, andAI/machine learning models) and predictor variables (vaccination rate, stringency index, andOmicron variantrate) for seven selected countries with the highest vaccination rates. We decided on our best models based on the Bayesian Information Criterion (BIC) and analyzed the significance of each predictor. Simple models were preferred. The selection of the bestprediction models and the use of Omicron variantrate were considered essential in improving prediction accuracies. For the test dataperiod before Omicron variantemergence, the selection of the bestmodels was the most significant factor in improving prediction accuracy. For the test period after Omicron emergence, Omicron variantrate use was considered essential in deciding forecasting accuracy. For prediction models, ARIMA, lightGBM, and TSGLM generally performed well in both test periods. Linear mixed models with country as a random effect has proven that the choice of prediction models and the use of Omicron data was significant in determining forecasting accuracies for the highly vaccinated countries. Relatively simple models, fit with either prediction model or Omicron data, produced best results in enhancing forecasting accuracies with test data.

Incremental transfer learning for spatial autoregressive model with linear constraints

Transfer learning is generally regarded as a beneficial technique for utilizing external information to enhance learning performance on target tasks. However, current research on transfer learning in high-dimensional regression models does not take into account both the location information of the data and the explicit utilization of prior knowledge. In the framework of transfer learning, this study seeks to resolve the spatial autoregressive problem and investigate the impact of introducing linear constraints. In this paper, a two-step transfer learning approach and a transferable source detection algorithm based on cross-validation are proposed when the input dimensions of the source and target datasets are the same. When the input dimensions are different, this paper suggests a straightforward and workable incremental transfer learning method. Additionally, for the estimating model developed under this method, Karush–Kuhn–Tucker (KKT) conditions and degrees of freedom are determined, and a Bayesian Information Criterion (BIC) is created for choosing hyperparameters. The effectiveness of the proposed methods is proven by numerical calculations, and the performance of the model in transfer learning estimation is improved by the addition of linear constraints.

Effective removal of microplastics by filamentous algae and its magnetic biochar: Performance and mechanism

In recent years, filamentous algae blooms and microplastics (MPs) pollution have become two major ecological and environmental problems in urban water systems. In order to solve these two problems at the same time, this study explored the loading capacity of MPs on fresh filamentous algae, and successfully synthesized magnetic filamentous algae biochar loading with Fe3O4 by hydrothermal method, with the purpose of removing MPs from water. The magnetic filamentous algal biochar was characterized by scanning electron microscopy (SEM), Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and so on. Experiments on adsorption kinetics, adsorption isotherms and optimum pH were carried out to explore the adsorption mechanism of MPs on magnetic filamentous algal biochar. The adsorption kinetics and adsorption isotherm models were evaluated, and the selection criterion for the appropriate model was determined by using the residual sum of squares (RSS) and Bayesian information criterion (BIC). Microscope images revealed that fresh filamentous algae could interact with MPs in the form of entanglement, adhesion and encapsulation. The average load of MPs in filamentous algae samples was 14.1 ± 5 items/g dry weight. The theoretical maximum adsorption capacities of polystyrene MPs (PS-MPs) by raw biochar (A500) and magnetic biochar with Fe3O4 (M2A500) were 176.99 mg/g and 215.58 mg/g, respectively. The adsorbent materials gave better reusability because they could be reused up to five times. Overall, these findings have provided new insights into the use of filamentous algae for in situ remediation of fluvial MPs pollution, as well as feasible strategies for the recycling of algal waste.

Assessing reproductive performance and predictive models for litter size in Landrace sows under tropical conditions.

Litter size and piglet loss at birth significantly impact piglet production and are closely associated with sow parity. Understanding how these traits vary across different parities is crucial for effective herd management. This study investigates the patterns of the number of born alive piglets (NBA), number of piglet losses (NPL), and the proportion of piglet losses (PPL) at birth in Landrace sows under tropical conditions. Additionally, it aims to identify the most suitable model for describing these patterns. A dataset comprising 2,322 consecutive reproductive records from 258 Landrace sows, spanning parities from 1 to 9, was analyzed. Modeling approaches including 2nd and 3rd degree polynomial models, the Wood gamma function, and a longitudinal model were applied at the individual level to predict NBA, NPL, and PPL. The choice of the best-fitting model was determined based on the lowest mean and standard deviation of the difference between predicted and actual values, Akaike information criterion (AIC), and Bayesian information criterion (BIC). Sow parity significantly influenced NBA, NPL, and PPL (p<0.0001). NBA increased until the 4th parity and then declined. In contrast, NPL and PPL decreased until the 2nd parity and then steadily increased until the 8th parity. The 2nd and 3rd degree polynomials, and longitudinal models showed no significant differences in predicting NBA, NPL, and PPL (p>0.05). The 3rd degree polynomial model had the lowest prediction standard deviation and yielded the smallest AIC and BIC. The 3rd degree polynomial model offers the most suitable description of NBA, NPL, and PPL patterns. It holds promise for applications in genetic evaluations to enhance litter size and reduce piglet loss at birth in sows. These findings highlight the importance of accounting for sow parity effects in swine breeding programs, particularly in tropical conditions, to optimize piglet production and sow performance.

Comparison of Nonlinear Models to Describe the Growth of Tuj and Romanov x Tuj (F1) Lambs

This study aimed to identify the most suitable model for explaining weight changes in purebred Tuj (n=35) and Romanov x Tuj (RoxTuj) (F1) (n=25) lambs using non-linear models. Single-born lambs of both breeds and genders were included in the evaluation. Five different non-linear growth models were compared: Brody, Gompertz, Logistic, Richards, and Weibull. The best model for describing growth was chosen based on four criteria: coefficient of determination (R²), mean square error (MSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Models with the highest R² and the lowest MSE, AIC, and BIC values were considered the best fit for the data. It was observed that the Brody model had the highest R2 and lowest MSE, AIC and BIC values for Tuj and ROxTuj (F1) female and male lambs. The Gompertz, Logistic, and Richards models exhibited similar predictive performance. In contrast, the Weibull model produced significantly different results compared to the other models when predicting weight changes. Therefore, the Brody model was identified as the most effective model for explaining growth patterns in both Tuj and RoxTuj (F1) lambs.

Short‐term predictions of PM10 and NO2 concentrations in urban environments based on ARIMA search grid modeling

AbstractAir pollution poses a persistent challenge for urban management departments and policymakers due to its significant health and economic impacts. Various cities worldwide have implemented diverse strategies and initiatives to enhance air quality monitoring and modeling standards. However, the outcomes of these efforts often manifest over the long term, leading to a preference for short‐term statistical methods. The autoregressive integrated moving average (ARIMA) search grid modeling approach has gained widespread use for forecasting air quality. This paper presents a comprehensive time series analysis conducted to predict air quality in urban areas of Budapest, Hungary, with a focus on nitrogen dioxide (NO2) and particulate matter (PM10), using air quality data spanning from 2018 to 2022 for four monitoring categories: Urban traffic, industrial background, urban background, and suburban background. The study employs the ARIMA search grid method to forecast concentrations of these pollutants at multiple air quality monitoring stations based on Akaike information criteria (AIC) and the Bayesian information criteria (BIC) criteria along with the results of augmented Dickey–Fuller (ADF) test. The results demonstrate varying levels of forecast accuracy across different stations, indicating the model's effectiveness in short‐term predicting of air quality. These findings are essential for assessing the reliability of air quality forecasts in Budapest and can inform decisions regarding air quality management and the development of strategies to address air pollution and particulate matter concerns in the region.

Contralateral Neck-shaft Angle Lower Than 130° Is Associated With Clinical Failure in Nongeriatric Individuals: Analysis of the National Femoral Neck Fracture Database of 1066 Patients.

Treatment of femoral neck fractures in patients who are nongeriatric (≤ 60 years) is challenging because of high failure rates. Anatomic parameters influence the biomechanical environment for fracture healing, but their associations with clinical prognosis remains unclear. (1) Which anatomic parameter that is identifiable on pelvic radiographs shows a statistical correlation with a higher risk of clinical failure defined as nonunion, avascular necrosis (AVN), reoperation, and functional failure (decrease in Harris hip score reaching the minimum clinically important difference) in the screw fixation of femoral neck fractures among nongeriatric patients? (2) How does the influence of anatomic parameters on clinical prognosis manifest: directly or mediated by additional mechanisms? This retrospective, multicenter study used a nationwide database in China. Between January 2014 and December 2020, we evaluated 1066 patients with femoral neck fractures with a median age of 53 years (interquartile range 46 to 56) and median follow-up period of 62 months. Anatomic parameters including femoral neck-shaft angle (NSA), femoral head radius, femoral neck width, femoral offset, acetabular center-edge angle, and acetabular sharp angle were variables of interest. The primary outcome was clinical failure including nonunion, AVN, reoperation, and functional failure (decrease in Harris hip score reaching the minimum clinically important difference). Risk factors for failure were first filtered using the Bayesian information criterion and then assessed with multiple regression adjusting for confounders. The mediation effect was further explored using model-based causal mediation analysis with a quasi-Bayesian Monte Carlo method. Of all anatomic parameters we assessed, the contralateral NSA was associated with clinical failure, after adjusting for all potential covariates and confounding variables (adjusted odds ratio 0.92 [95% confidence interval 0.89 to 0.95]; p < 0.001). The optimal threshold for the NSA was 130°, with the highest Youden index of 0.27. Patients with an NSA < 130° (41% [441 of 1066]) demonstrated an increased occurrence of nonunion (15% [68 of 441] versus 5% [33 of 625]; p < 0.001), AVN (32% [141 of 441] versus 22% [136 of 625]; p < 0.001), functional failure (25% [110 of 441] versus 15% [93 of 625]), and reoperations (28% [122 of 441] versus 13% [79 of 625]). The impact of an NSA less than 130° on clinical failure was direct and substantially mediated by the type of displaced fracture (mediation proportion: 18.7%). In our study of screw fixations for femoral neck fractures among nongeriatric patients, we identified that a contralateral NSA < 130° correlates with an increased risk of clinical failure including nonunion, AVN, functional failure, and reoperation. The effect is either direct or mediated through displaced fracture types. This is important for surgeons in order to recognize the elevated rate of clinical failure and nature of the challenging biomechanical environment, which should guide them in refining surgical details and selecting appropriate fixation and rehabilitation plans. Approaches to managing these fractures require further validation with large-scale clinical trials. Level III, prognostic study.

A novel flexible exponent power-X family of distributions with applications to COVID-19 mortality rate in Mexico and Canada.

This paper aims to introduce a novel family of probability distributions by the well-known method of the T-X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan-Quinn information criterion (HQINC), the Cramer-von-Misses (CRMI), and the ANDA (Anderson-Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study.

Use of risk chart algorithms for the identification of psoriatic arthritis patients at high risk for cardiovascular disease: findings derived from the project CARMA cohort after a 7.5-year follow-up period.

To assess the predictive value of four cardiovascular (CV) risk algorithms for identifying high-risk psoriatic arthritis (PsA) patients. Evaluation of patients with PsA enrolled in the Spanish prospective project CARdiovascular in RheuMAtology. Baseline data of 669 PsA patients with no history of CV events at the baseline visit, who were followed in rheumatology outpatient clinics at tertiary centres for 7.5 years, were retrospectively analysed to test the performance of the Systematic Coronary Risk Assessment (SCORE), the modified version (mSCORE) European Alliance of Rheumatology Associations (EULAR) 2015/2016, the SCORE2 algorithm (the updated and improved version of SCORE) and the QRESEARCH risk estimator version 3 (QRISK3). Over 4790 years of follow-up, there were 34 CV events, resulting in a linearised rate of 7.10 per 1000 person-years (95% CI 4.92 to 9.92). The four CV risk scales showed strong correlations and all showed significant associations with CV events (p<0.001). SCORE, mSCORE EULAR 2015/2016 and QRISK3 effectively differentiated between low and high CV risk patients, although the cumulative rate of CV events observed over 7.5 years was lower than expected based on the frequency predicted by these risk scales. Additionally, model improvement was observed when combining QRISK3 with any other scale, particularly the combination of QRISK3 and SCORE2, which yielded the lowest Akaike information criterion (411.15) and Bayesian information criterion (420.10), making it the best predictive model. Risk chart algorithms are very useful for discriminating PsA at low and high CV risk. An integrated model featuring QRISK3 and SCORE2 yielded the optimal synergy of QRISK3's discrimination ability and SCORE2's calibration accuracy.