The synthesis, theoretical study and a kinetics and mechanistic investigation are described as a one-pot condensation reaction between benzamide and acetylenic esters in the presence of triphenyphosphine to generate a novel stable phosphorus ylides. For the first time, theoretical calculations have been employed to assign the most stable isomers ( Z or E) of phosphorus ylides 4a-c by AIM and NBO theory, in which Z-4(a,b) are the more stable forms, whereas Z-4c appears as a single isomer. In these cases, the 1H, 13C and 31P NMR spectra of these ylides are consistent with the results obtained from theoretical calculations. Kinetic investigation of the new ylides was undertaken by UV. Useful information was obtained from studies of the effect of solvent, structure of reactants (different alkyl groups within the acetylenic esters), and also the concentration of reactants on the rate of reactions. The proposed mechanism was consistent with the results obtained; from the steady-state approximation, the first step ( k2) of the reaction was recognized as the rate-determining step on the basis of the experimental data.

No abstract

No abstract

The intensive care units (ICUs) are among the most expensive and essential parts of all hospitals for extremely ill patients. This study aims to predict mortality and explore the crucial factors affecting it. Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the patients. In this study, we used a medical dataset, including patients' demographic details, underlying diseases, laboratory disorder, and LOS. Since accurate estimates are required to have optimal results, various data pre-processings as the initial steps are used here. Besides, machine learning models are employed to predict the risk of mortality ICU discharge. For AdaBoost model, these measures are considered AUC= 0.966, sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% making it, AdaBoost, accounts for the highest rate. Our model outperforms other comparison models by using various scenarios of data processing. The obtained results demonstrate that the high mortality can be caused by underlying diseases such as diabetes mellitus and high blood pressure, moderate Pulmonary Embolism Wells Score risk, platelet blood count less than 100000 (mcl), hypertension (HTN), high level of Bilirubin, smoking, and GCS level between 6 and 9.