Educational Program Boosts Constipation Knowledge in Coronary Artery Disease Patients

Constipation is a prevalent condition causing substantial morbidity and commonly presenting to healthcare providers, with associations reported between constipation and cardiovascular disease (CVD). This study aims to evaluate the effect of an instructional program on constipation knowledge in patients with coronary artery disease (CAD). Conducted at Nasiriyah Heart Center from January 2, 2023, to March 19, 2024, this quantitative, quasi-experimental study used purposive sampling to select 64 CAD patients with constipation. These were divided into a study group (n=32) and a control group (n=32). The program's content validity and the knowledge test were confirmed by a panel of 17 experts, and reliability was assessed using a test-retest approach. Statistical analysis using SPSS version 25 revealed highly significant differences in knowledge scores between pre- and post-tests and between the study and control groups (p<0.01). The study group's mean knowledge score improved from 1.56 to 2.50, while the control group's score remained virtually unchanged (1.57 to 1.62). The instructional program significantly enhanced patients' knowledge about constipation. The study recommends broad health education efforts on constipation, physical activity, dietary intake, and toileting habits through various media to elevate patients' knowledge

Artificial Neural Networks Modeling of Dynamic Adsorption From Aqueous Solution

The aim of this work is to use multilayered perceptron artificial neural networks (MLP-ANN) and multiple linear regressions (MLR) models to predict the dynamic adsorption of the complex system of adsorbent-adsorbate in solid-liquid phase. A set of 1859 data points were used. For the (MLP-ANN), nine neurons were used in the input layer, sixteen neurons at hidden layer and one was used in the output layer. Levenberg Marquardt learning (LM) algorithm, logarithmic sigmoid transfer function and linear transfer function were used at the hidden and output layer respectively. The comparison of the obtained results in term of root mean square error (RMSE) and correlation coefficient (R) using the (MLP-ANN) and MLR models revealed the superiority of the (MLP-ANN) model in predicting of dynamic adsorption process.The statistic results showed a correlation coefficient R = 0.991 with root mean square error RMSE= 0.0521for the (MLP-ANN) model and R= 0.80 with RMSE=0.237for the MLR model. Thus, it can be suggested that the artificial neural network model gave far better and more significant results