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This study aims to predict poor glycemic control during Ramadan among non-fasting patients with diabetes using machine learning models.First, we conducted three consultations, before, during, and after Ramadan to assess demographics, diabetes history, caloric intake, anthropometric and metabolic parameters. Second, machine learning techniques (Logistic Regression, Support Vector Machine, Naive Bayes, K-nearest neighbor, Decision Tree, Random Forest, Extra Trees Classifier and Catboost) were trained using the data to predict poor glycemic control among patients. Then, we conducted several simulations with the best performing machine learning model using variables that were found as main predictors of poor glycemic control.The prevalence of poor glycemic control among patients was 52.6%. Extra tree Classifier was the best performing model for glycemic deterioration (accuracy = 0.87, AUC = 0,87). Caloric intake evolution, gender, baseline caloric intake, baseline weight, BMI variation, waist circumference evolution and Total Cholesterol serum level after Ramadan were selected as the most significant for the prediction of poor glycemic control. We determined thresholds for each predicting factor among which this risk is present.The clinical use of our findings may help to improve glycemic control during Ramadan among patients who do not fast by targeting risk factors of poor glycemic control.

This paper deals with a numerical and experimental study of a square enclosure filled with air and differentially heated. The heated vertical wall of the enclosure contains a 1 cm thickness layer of a phase change material (PCM), whereas the opposite one is cooled. The dimensions of the experimental air cavity are 10 cm × 10 cm with a width of 10 cm. The PCM layer, initially molten, is discharged in the air within the cavity. Temperature measurements were performed inside the air cavity as well as the PCM and the PCM/air interface during the charging and discharging processes. A numerical model based on the coupled Navier-Stokes and energy equations for the air inside the cavity and the PCM layer was developed to examine the perturbation of the phase change on heat transfer at the PCM/air interface. The results showed a notable disagreement between the calculated and measured temperature in the cavity and PCM, when the density change during the solidification process has not been taken into account. A new approach based on volume contraction during solidification has been proposed and successfully tested to overcome this issue. Moreover, the simulations demonstrated that the heat transfer in the cavity is affected significantly when the phase change occurs.

We present in this work an enhanced insulated container using phase change material (PCM) solidification process as a Keeping Warm System (KWS). A square enclosure with one vertical PCM wall whereas the other one kept at ambient temperature has been taken as a simplified configuration of the KWS. The solidification of the PCM releases latent heat inducing a natural convection air flow inside the cavity and compensating the heat loss through the cold wall. A transient, two-dimensional numerical model has been developed to study the coupled heat transfer in the PCM and air inside the enclosure. The results show the effect of the PCM on the time wise variations of the gas average temperature and the Nusselt number at both cold and hot sides of the cavity. The comparison with the case without latent heat shows that the heat retention in the cavity has been significantly enhanced by the presence of a PCM wall.