bayes theorem

Removing dyes from wastewater is crucial for environmental protection and public health. Dyes used in various industries can be toxic and persistent, posing threats to aquatic ecosystems and human health.

This study examined the efficiency of beech wood-derived activated carbon (BW-AC) in removing Acid Red 18 (AR18) and Methylene Blue (MB) dyes. A batch adsorption procedure was used to investigate the impacts of various operational variables, including pH, contact time, adsorbent dosage, and initial dye concentration.

Changes in pH did not significantly affect removal efficiency. However, increasing contact time and adsorbent dosage notably enhanced removal rates, achieving nearly complete removal after 180 minutes. Various statistical metrics were used to identify the best-fitting model, including the corrected Akaike information criterion (AICc) and Bayesian information criterion (BIC). The general order kinetic model provided the best fit to the experimental data based on its AICc values (range: -45.42 to 12.435) and BIC values (range: -48.16 to 6.67) for AR18. For MB dye, the ranges were -5.81 to 7.057 for AICc and -9.58 to 3.282 for BIC. Regarding the adsorption isotherms, the correlation capabilities of the models, as assessed by AICc and BIC, were ranked as follows for AR18: Freundlich, Liu, and Langmuir; while for MB dye, the ranking was Langmuir, Liu, and Freundlich.

The results demonstrate that BW-AC is a highly promising adsorbent for dye removal from aqueous solutions, offering a sustainable, environmentally friendly, and cost-effective solution for water and industrial wastewater treatment.

Congenital hypothyroidism (CH) is one of the most prevalent preventable causes of mental retardation. Studies show that the incidence rate of CH is very high in Iran. Disease mapping is a tool for visually expressing the frequency, incidence, or relative risk of illness. The present study aimed to model CH counts considering the effects of the neighborhood in towns and perform mapping based on the relative risk.

In this historical cohort study, data of all neonates diagnosed with CH with TSH level ≥5 mIU/L between March 21, 2017, and March 20, 2018, in health centers in Guilan, Iran were used. The number of neonates with CH was zero in most towns of Guilan Province. The Bayesian spatial zero-inflated Poisson (ZIP) regression model was employed to investigate the effect of the town’s neighborhood on the relative risk of CH incidence. Then, the map of the posterior mean of the relative risk for CH incidence was provided. The analysis was performed using OpenBUGS and Arc GIS software programs.

The relative risk of CH incidence was high in the West of Guilan. Moreover, the goodness-of-fit criterion indicated that it is more appropriate to fit the Bayesian spatial ZIP model to these data than the common model.

Considering the high relative risk of CH in the Western towns of Guilan Province, it is better to check important risk factors in this region.

A pre-surgical evaluation of cognitive functions in patients with mesial temporal lobe epilepsy (mTLE) is critical. The limitations of the usual brain analysis model were resolved by the spatial Bayesian variable selection (SBVS) method. An Ising and Dirichlet Process (Ising-DP) model considers SBVS and the grouping of a large number of voxels. The present study aimed to identify brain areas involved in episodic memory in patients with right mTLE and controls via the Ising-DP model. The model was extended to include between-subject factors (BSFs), and the results were compared with other classical methods. The present cross-sectional study was conducted on 15 patients with right mTLE and 20 controls in Tehran, Iran, in 2018. During functional magnetic resonance imaging, the subjects were tested with the face-encoding memory task, followed by a recognition memory test. The participants demographic factors such as age, sex, marital status, area of residence, and years of schooling were considered to comprise BSFs. The independent t test, the chi-square test, and the correlation test were conducted using the SPSS software (version 20.0). The image processing was carried out using SPM (version 12.0) and MATLAB (version R2014a). The Ising-DP model appropriately (R2=0.642) detected activated hippocampal areas. The model adjusted for BSFs indicated a better fit by the significant effect of age (P[γ]>0.91), sex (P[γ]>0.87), and years of schooling (P[γ]>0.89). The heat maps exhibited decreased activation in the right hippocampal region in the patients compared with the controls (p <0.0001). Right hippocampal activity had a significant positive correlation with the recognition memory test in the mTLE group (r=0.665) and the control group (r=0.593). The Ising-DP model was sufficiently sensitive to detect activated areas in our patients with right mTLE during the face-encoding memory task. Since the model adjusted for BSFs improved sensitivity, we recommend the use of more detailed BSFs such as seizure history in future research.

Recognizing the factors affecting the number of decayed and filled teethhas a major role in oral health. Dental data usually suffer from over-dispersion and excess zero frequencies. The purpose of this study was to use theConway-Maxwell-Poisson (COM-Poisson) model to determine some of the factors affecting the number of decayed and filled teeth.

In this cross-sectional study, a sample of 1000 people from a cohort study in Shahrekord City, Iran, aged 35-70 years, was selected through systematic sampling. The data were analyzed using the Bayesian approach through Markov chain Monte Carlo (MCMC) simulation by OpenBUGS. Zero-inflated Poisson (ZIP), COM-Poisson model, and zero-inflated Com-Poisson (ZICMP) model were fitted on the data and compared using the deviance information criterion (DIC).

The mean numbers of decayed and filled teeth were 0.77 ± 1.63 and 4.37 ± 4.62, respectively. The Com-Poisson and ZICMP showed to be better fit for the number of decayed and filled teeth, respectively. Those people who were younger, male, smokers, diabetics, did not floss, and did not use mouthwash had significantly more number of decayed teeth (P < 0.05). Those people who were younger, female, non-diabetics, non-smokers, employed, literate, had less body mass index (BMI), flossed, and got higher score of quality of life had significantly more number of filled teeth (P < 0.05).

By controlling such factors as education, BMI, flossing, using mouthwash, smoking, diabetes, and quality of life, we could improve the oral health.