Estimation of illuminance on the south facing surfaces for clear skies in Iran

Background: Daylight availability data are essential for designing effectively day lighted buildings. In respect to no available daylight availability data in Iran, illuminance data on the south facing vertical surfaces were estimated using a proper method. Methods: An illuminance measuring set was designed for measuring vertical illuminances for standard times over 15 days at one hour intervals from 9 a.m. to 3 p.m. at three measuring stations (Hamadan, Eshtehard and Kerman). Measuring data were used to confirm predicted by the IESNA method. Results: Measurement of respective illuminances on the south vertical surfaces resulted in minimum values of 10.5 KLx, mean values of 33.59 KLx and maximum values of 79.6 KLx. Conclusion: In this study was developed a regression model between measured and calculated data of south facing vertical illuminance. This model, have a good linear correlation between measured and calculated values (r= 0.892)

SCREENING FOR A MULTIVARIATE MIXTURE NORMAL DISTRIBUTION

Abstract. The screening problem has been studied by many authors mostly focused on individual multivariate normal model with screening for normal distribution when all or part of the parameters are known, or the performance variable is dichotomous. In this paper a screening method is presented when the screening variable is a mixture of two multivariate normal distributions, meanwhile the performance variable is dichotomous. The method is used for the case when the parameters are known or estimated from separate samples. To reduce the dimensionality of the problem and therefore the scale of the computation, a Fisher's linear discrimination function is applied to find coefficients of a standard linear combination of the variables used in the proposed method. A comparison of methods is made for Conn's syndrome date. The results of the study are equivalent to the predictive screening approach

Modeling the trajectory of CD4 cell count and its effect on the risk of AIDS progression and TB infection among HIV-infected patients using a joint model of competing risks and longitudinal ordinal data

Background: This study was conducted to better understand the influence of prognostic factors and the trend of CD4 cell count on the risk of progression to acquired immunodeficiency syndrome (AIDS) and tuberculosis (TB) infection among patients with human immunodeficiency virus (HIV) in a developing country.  Methods: The information of 1530 HIV-infected patients admitted in Behavioral Diseases Counseling Centers, Tehran, Iran, (2004-2014) was analyzed in this study. A joint model of ordinal longitudinal outcome and competing events is used to model longitudinal measurements of CD4 cell count and the risk of TB-infection and AIDS-progression among HIV patients, simultaneously.  Results: The results revealed that the trend of CD4 cell count had a significant association with the risk of TB-infection and AIDS-progression (p<0.001). Higher ages (p<0.001), the history of being in prison (p=0.013), receiving antiretroviral therapy (ART) (p<0.001) and isoniazid preventive therapy (IPT) (p<0.001) were associated with the positive trend of CD4 cell count. Higher ages were also associated with higher risks of TB (p<0.001) and AIDS-progression (p<0.001). Furthermore, ART (p=.0009) and IPT (p<0.001) were associated with a lower risk of TB-infection. In addition, ART (p<0.001) was associated with a lower risk of AIDS-progression. Moreover, individuals being imprisoned (p=0.001) and abusing alcohol (p=0.012) were more likely to have TB-co-infection.  Conclusions: The used joint model provided a flexible framework for simultaneous studying of the effects of covariates on the level of CD4 cell count and the risk of progression to TB and AIDS. This model also assessed the effect of CD4 trajectory on the hazards of competing events.&nbsp

Diabetic peripheral neuropathy class prediction by multicategory support vector machine model: a cross- sectional study

OBJECTIVES Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients’ demographic characteristics and clinical features. METHODS In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used. RESULTS For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy. CONCLUSIONS The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases

Longitudinal Machine Learning Model for Predicting Systolic Blood Pressure in Patients with Heart Failure

Objective: Systolic blood pressure (SBP) is a powerful prognostic factor in heart failure (HF) patients, which is associated with death and readmission. Therefore, control of blood pressure is an important element for managing these patients. The goal of this study was to compare the performance of classical and machine learning models for predicting SBP and identify important variables related to SBP changes over time. Methods: The information of 483 HF patients was analyzed in this retrospective cohort study. These patients were hospitalized at least twice in Farshchian Heart Center Hamadan province, the west of Iran, between October 2015 and July 2019. We applied a linear mixed-effects model (LMM) and mixed-effects least-square support vector regression (MLS-SVR) for predicting SBP. The performance of both models was assessed by mean absolute error, and root mean squared error. Results: Based on LMM results, there was a significant association between sex, body mass index (BMI), sodium, time, and history of hypertension with SBP changes over time (P-value <0.05). Also, MLS-SVR indicated that the four most important variables were history of hypertension, sodium, BMI, and triglyceride. The performance of MLS-SVR compared to LMM was better in both training and testing datasets. Conclusions: According to our results, BMI, sodium, and history of hypertension were the important variables on SBP changes in both LMM and MLS-SVR models. Also, it seems that MLS-SVR can be used as an alternative for classical longitudinal models for predicting SBP in HF patients

Photocatalytic Removal of Methylbenzene Vapors by MnO2/Al2O3/Fe2O3 Nano composite

Methyl benzene, which has carcinogenic effects, is a volatile organic compound that is widely used in various industries. Nano composites of Mno2/Al203/Fe203, which is a new photocatalyst, have not been applied to remove contaminants from air streams. Therefore, the aim of the present study was to investigate the photocatalytic removal of ethyl benzene by using this nano composite activated by visible light. Morphological characteristics of the synthesized Nano composite in a sol-gel method are determined through XRD, FTIR, and SEM. Through the photocatalyst process and by the use of visible light radiation, the synthesized Nano composite is used to degrade ethyl benzene in the gas phase. In order to estimate the main effects and interaction ones and to optimize the experiment numbers, the response surface method was used. Operational parameters investigated in the study are the initial concentration of contaminants, relative humidity, and the residence time, which were considered in three levels; further, the experiments were designed by Design Expert version 9 software. The results show the Nano composite particle sizes were less than 82 nanometers. The findings also indicate that relative humidity and residence time were effective parameters in removal efficiency of ethyl benzene. This Nano composite, at the optimal conditions, was capable of removing 98.72% of the pollutants, with an initial content of 300 ppm. MnO2/Al2O3/Fe2O3 Nano composite is a suitable catalyst to remove ethyl benzene from air streams. Among the features of the Nano composite are reaction at room temperature and lower production dangerous byproducts, which are the main advantages of this Nano composite as compared with other nano composites

Longitudinal Joint Modelling of Binary and Continuous Outcomes: A Comparison of Bridge and Normal Distributions

Background: Longitudinal joint models consider the variation caused by repeated measurements over time as well as the association among the response variables. In the case of combining binary and continuous response variables using generalized linear mixed models, integrating over a normally distributed random intercept in the binary logistic regression sub-model does not yield to a closed form. In this paper, we assessed the impact of assuming a Bridge distribution for the random intercept in the binary logistic regression submodel and compared the results to that of normal distribution.  Method: The response variables are combined through correlated random intercepts. The random intercept in the continuous outcome submodel follows a normal distribution. The random intercept in the binary outcome submodel follows a normal or Bridge distribution. The estimations were carried out using a likelihood-based approach in direct and conditional joint modeling approaches. To illustrate the performance of the models, a simulation study was conducted Results: Based on the simulation results and regardless of the joint modeling approach, the models with a Bridge distribution for the random intercept of the binary outcome resulted in a slightly more accurate estimations and better performance. Conclusion: In addition to the fact that assuming a bridge distribution for the random intercept in binary logistic regression yields to the same interpretation of parameter estimates in marginal and conditional forms, our study revealed that even if the random intercept of binary logistic regression is normally distributed, assuming a Bridge distribution in the model will result in more accurate results.&nbsp

Photocatalytic Removal of Methylbenzene Vapors by MnO2/Al2O3/Fe2O3 Nano composite

Methyl benzene, which has carcinogenic effects, is a volatile organic compound that is widely used in various industries. Nano composites of Mno2/Al203/Fe203, which is a new photocatalyst, have not been applied to remove contaminants from air streams. Therefore, the aim of the present study was to investigate the photocatalytic removal of ethyl benzene by using this nano composite activated by visible light. Morphological characteristics of the synthesized Nano composite in a sol-gel method are determined through XRD, FTIR, and SEM. Through the photocatalyst process and by the use of visible light radiation, the synthesized Nano composite is used to degrade ethyl benzene in the gas phase. In order to estimate the main effects and interaction ones and to optimize the experiment numbers, the response surface method was used. Operational parameters investigated in the study are the initial concentration of contaminants, relative humidity, and the residence time, which were considered in three levels; further, the experiments were designed by Design Expert version 9 software. The results show the Nano composite particle sizes were less than 82 nanometers. The findings also indicate that relative humidity and residence time were effective parameters in removal efficiency of ethyl benzene. This Nano composite, at the optimal conditions, was capable of removing 98.72% of the pollutants, with an initial content of 300 ppm. MnO2/Al2O3/Fe2O3 Nano composite is a suitable catalyst to remove ethyl benzene from air streams. Among the features of the Nano composite are reaction at room temperature and lower production dangerous byproducts, which are the main advantages of this Nano composite as compared with other nano composites

Predicting the Survival Time for Bladder Cancer Using an Additive Hazards Model in Microarray Data

Background: One substantial part of microarray studies is to predict patients’ survival based on their gene expression profile. Variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. However, these techniques have not been investigated in competing risks setting. This study aimed to investigate the performance of four sparse variable selection methods in estimating the survival time. Methods: The data included 1381 gene expression measurements and clinical information from 301 patients with bladder cancer operated in the years 1987 to 2000 in hospitals in Denmark, Sweden, Spain, France, and England. Four methods of the least absolute shrinkage and selection operator, smoothly clipped absolute deviation, the smooth integration of counting and absolute deviation and elastic net were utilized for simultaneous variable selection and estimation under an additive hazards model. The criteria of area under ROC curve, Brier score and c-index were used to compare the methods. Results: The median follow-up time for all patients was 47 months. The elastic net approach was indicated to outperform other methods. The elastic net had the lowest integrated Brier score (0.137±0.07) and the greatest median of the over-time AUC and C-index (0.803±0.06 and 0.779±0.13, respectively). Five out of 19 selected genes by the elastic net were significant (P<0.05) under an additive hazards model. It was indicated that the expression of RTN4, SON, IGF1R and CDC20 decrease the survival time, while the expression of SMARCAD1 increase it. Conclusion: The elastic net had higher capability than the other methods for the prediction of survival time in patients with bladder cancer in the presence of competing risks base on additive hazards model