Changing Trends in the Clinical Presentation and Incidence of Molar Pregnancy in Saudi Arabia: A 30-Year Retrospective Analysis.

Background Molar pregnancy (MP) incidence and clinical presentation vary significantly worldwide. Recent trends show changes in its clinical representation and incidence, particularly with the adoption of early diagnosis using first-trimester ultrasonography, which has reduced the prevalence of classical second-trimester presentations. This study aimed to analyze the changes in clinical presentation and incidence of MP among the Saudi population over the past 30 years.

Investigating the impact of smoking habits through photoplethysmography analysis.

Smoking is widely recognized as a significant risk factor in the progression of arterial stiffness and cardiovascular diseases. Valuable information related to cardiac arrhythmias and heart function can be obtained by analyzing biosignals such as the electrocardiogram (ECG) and the photoplethysmogram (PPG). The PPG signal is a non-invasive optical technique that can be used to evaluate the changes in blood volume, and thus it can be linked to the health of the vascular system.

Knowledge Mapping of the Literature on Fiber-Reinforced Geopolymers: A Scientometric Review.

This study examined the bibliographic data on fiber-reinforced geopolymers (FRGPs) using scientometrics to determine their important features. Manual review articles are inadequate in their capability to connect various segments of literature in an ordered and systematic manner. Scientific mapping, co-citation, and co-occurrence are the difficult aspects of current research.

Mechanical and Durability Evaluation of Metakaolin as Cement Replacement Material in Concrete.

Due to the increasing prices of cement and its harmful effect on the environment, the use of cement has become highly unsustainable in concrete. There is a considerable need for promoting the use of cement replacement materials. This study investigates the effect of variable percentages of metakaolin (MK) on the mechanical and durability performance of concrete.

Application of Soft-Computing Methods to Evaluate the Compressive Strength of Self-Compacting Concrete.

This research examined machine learning (ML) techniques for predicting the compressive strength (CS) of self-compacting concrete (SCC). Multilayer perceptron (MLP), bagging regressor (BR), and support vector machine (SVM) were utilized for analysis. A total of 169 data points were retrieved from the various published articles.