Modeling of hexavalent chromium removal onto natural zeolite from air stream in a fixed bed column.

The increasing use of hexavalent chromium (Cr(VI)) has exposed large populations to this environmental and occupational carcinogenic agent. Therefore, researchers have been interested in removing this substance through adsorbents. This study aimed to investigate the efficiency of natural zeolite in the direct adsorption of Cr(VI) from airflow and its adsorption modeling. In this study, a nebulizer device produced the Cr(VI) mist. The efficiency of natural zeolite in Cr(VI) adsorption from airflow, modeling of fixed column adsorption, and the effective parameters on adsorption efficiency including the initial concentration of chromium, airflow rate, and adsorption bed depth were studied. To facilitate the prediction of the performance of natural zeolite's adsorption column, Yoon-Nelson, Thomas, BDST, and Buhart-Adams models were used. The results showed that the adsorption capacity diminished with increased airflow rate and initial concentration, while it increased with elevated height of the adsorption bed. Yoon-Nelson, Thomas, and BDST models corresponded to experimental data with a correlation coefficient of 0.9933, but the information of the Buhart-Adams model had a lower correlation coefficient (around 0.6677). In conclusion, natural zeolite can be used as an efficient low-cost adsorbent for directly Cr(VI) removing from the airflow in a fixed bed column.

Investigating the impact of air pollution and temperature changes on emergency admissions (cardiovascular + respiratory) disease in the city of Bojnord, northeastern Iran.

Cardiovascular (CVD) + Respiratory diseases are recognized as the main cause of death worldwide. Fluctuations in temperature and air pollution have been reported as one of the most important causes of cardiovascular & respiratory diseases. Therefore, in the current study, we assessed the relationship between ambient air temperature and pollution on the number of total emergency hospital admission due to cardiovascular and respiratory conditions in the City of Bojnord, northeastern Iran. The meteorological data, including daily temperature, relative humidity and concentrations of five air pollutants CO, NO2, NOX SO2, and PM10 were obtained from online electronic sensors at the Bojnurd meteorological station from 21th March 2018 to 20th March 2020. Statistical analysis, penalized distributed lag non-linear method was applied using R Software. Also, sensitivity analysis test was calculated by using appropriate application. The results of the study revealed that the effect of higher and lower temperatures was observed immediately from the first day and the second week, respectively. Also result showed with increase and decrease temperature, significantly increased the risk of hospitalization by 36% (RR, 1.36; 95% CI (1), 0.95 to 1.95) and 17% (RR, 1.17; 95% CI (1), 0.88 to 1.55) until the lag 25th day, respectively. Based on the results, increasing temperature significantly increased the hospitalization rate of cardiopulmonary patients, but the effect of cold was not significant on the population as well as age and gender subgroups. Study have also proved that there is no significance correlation between air pollutant and Cardiovascular & respiratory diseases.

Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy.

Although tuberculosis (TB) is a disease whose cause, epidemiology and treatment are well known, some infected patients in many parts of the world are still not diagnosed by current methods, leading to further transmission in society. Creating an accurate image-based processing system for screening patients can help in the early diagnosis of this disease. We provided a dataset containing1078 confirmed negative and 469 positive Mycobacterium tuberculosis instances. An effective method using an improved and generalized convolutional neural network (CNN) was proposed for classifying TB bacteria in microscopic images. In the preprocessing phase, the insignificant parts of microscopic images are excluded with an efficient algorithm based on the square rough entropy (SRE) thresholding. Top 10 policies of data augmentation were selected with the proposed model based on the Greedy AutoAugment algorithm to resolve the overfitting problem. In order to improve the generalization of CNN, mixed pooling was used instead of baseline one. The results showed that employing generalized pooling, batch normalization, Dropout, and PReLU have improved the classification of Mycobacterium tuberculosis images. The output of classifiers such as Naïve Bayes-LBP, KNN-LBP, GBT-LBP, Naïve Bayes-HOG, KNN-HOG, SVM-HOG, GBT-HOG indicated that proposed CNN has the best results with an accuracy of 93.4%. The improvements of CNN based on the proposed model can yield promising results for diagnosing TB.