In this study 450 cement mortar cubes were cast from 50 different cement samples taken from 9 different cement factories, to develop a mathematical model that can predict Portland cement compressive strength at ages 7 and 28 days within 24 hours only. This is in order to save time and expense, that is lost in waiting for such a long period, and for quality control assurance for both produced cement (in cement factories), and concrete mixes in constructions.In addition, attention has been made on the right choice of variables involved in this model, especially the characteristics of the cement itself (phase composition and fineness). In addition, an attempt has been made to use other variables that are believed to affect compressive strength of Portland cement as the minor oxides MgO, SO3 and soundness. Other variables obtained from chemical analysis of the cement as LOI, IR, and LSF were also included in the model.The most important thing in this study is to get use of the concept of using early age strength to predict Portland cement strength at later ages for the first time. An attempt was made to combine both accelerated strength testing (as an early strength and UPV of cement mortar specimens), with the characteristics of the cement mentioned above, in predicting the compressive strength of cement. It was found that the accelerated strength yields good and high correlation with the compressive strength of cement, especially at the age of 28 days.In this work too, the importance of the ultrasonic pulse velocity (UPV) and mortar density were evident and the usefulness of using these variables in predicting the compressive strength of the cement was proved (because of fixing most of the factors affecting this property). Thus, it is possible to have good results that can be used in the prediction of compressive strength of cement. It was found that using each of the accelerated compressive strength f~c, UPV and density of the mortar cubes yielded high correlation with the compressive strength than any of the other variables. Different combinations of variables were introduced into the model, in order to choose the variables that can significantly predict the cement compressive strength.In this work, it was possible to obtain a model that can predict the cement strength with standard errors of only 1.887 and 1.904 MPa and coefficients of correlation of 0.903 and 0.928, for cement strengths at 7 and 28 days respectively. RI~SUMI~ Dans eette ~tude, 450 cubes de mortier de ciment ont OtO coul& h partir de 50 ~chantillons diffOrents de ciment provenant de 9 cimenteries diffe'rentes, dans le but de d&elopper un modkle mathkmatique capable de prdvoir la r&istance h la compression du ciment Portland h des dges de 7 et 28 jours, seulement en l'espace de 24 heures. Le but est de gagner du temps et de l'argent, h cause de la tr& longue p~riode d'attente, et d'assurer un meilleur contrfle de qualit~ ~ la fois pour Ie ciment produit (dans les cimenteries), et pour les mOlanges de bOtons dans les constructions. En out...

Cardiovascular diseases (CVD) represent a major cause of mortality and morbidity worldwide. To date, many physicians still requesting traditional lipid profile tests (TG, TC, HDL-C, and LDL-C) to confirm the clinical diagnosis related to CVD. However, using these tests may be inadequate for the prediction of CVD risk, especially in intermediate risk. For better clinical practice, laboratory diagnostic alternatives should constantly be evaluated and developed by physicians and laboratory scientists. In this review, we sought to focus on the benefits of lipid ratios (CRI-, CRI-II, AIP, AC, and CHOLindex) in supporting clinical diagnosis and how they can be calculated. To attain this aim, a literature search in reputed databases (PubMed and Scopus) was performed and peer-reviewed research articles were included to conduct this review. Short theoretical and practical notes about each index were accordingly included along with calculation formulas. Thus, the current article can assist new researchers and young physicians to review what supports their knowledge in managing early CVDs.