Alzheimer's disease (AD) presents a significant challenge in healthcare, necessitating accurate and timely diagnosis for effective management. Resting-state functional magnetic resonance imaging (Rs-fMRI) has emerged as a valuable tool for understanding neural correlates and the early detection of AD. This article reviews recent advancements in utilizing Rs-fMRI in combination with MACHINE learning (ML) techniques for early AD diagnosis. First, we discuss the underlying principles of Rs-fMRI, highlighting its ability to detect alterations in brain functional connectivity (FC) patterns associated with AD. We then explore the potential of ML algorithms, particularly support vector MACHINEs (SVMs), in analyzing Rs-fMRI data and discriminating between AD patients and healthy controls. We indicate the challenges and opportunities in integrating Rs-fMRI and ML, such as in data preprocessing, feature selection, and model interpretation. We also address the importance of large-scale, multi-site studies to validate the robustness and generalizability of the proposed approaches. Overall, the integration of Rs-fMRI and ML holds great promise as a non-invasive, objective, and sensitive DIAGNOSTIC tool for AD, potentially enabling early detection and personalized treatment strategies. However, further studies are warranted to optimize methodologies, enhance interpretability, and facilitate clinical translation.

Background: Caffeine is isolated Methyl-xanthine, Alkaloid stimulants and it is the most commonly used drug in the world. The present study was to investigate the effect of caffeine on VO2max and Electrocardiograph in active male of student college after Bruce exhaustive exercise. Methods: 9 male active aged 25 (mean 20. 9± 1. 37 years, fat percent 11. 21± 3. 79 percentage and BMI 23. 49± 1. 69 kg/m2) allocated into two equal conditions: the supplement (5 mg. kg-1 caffeine) and without caffeine consumtion. After 60 min supplementation, subjects were participated in a single session of exhaustive Bruce Test. Changes in the VO2max and QT interval distance were meatured. Results: The results showed that caffeine had significant effect on the increased levels of VO2max after Bruce exhaustive exercise compared to the control condition. Also, levels of QT interval increased significantly after the exercise (p<0. 05). Conclusion: Caffeine could significantly increase the VO2max and increase QT after Bruce exhaustive test. Maybe, level and severity of exercise increase the ventricular depolarization and repolarization and cardiac performance.

Reservoir permeability is a critical parameter for the evaluation of hydrocarbon reservoirs. There are a lot of well log data related with this parameter. In this study, permeability is predicted using them and a supervised committee MACHINE neural network (SCMNN) which is combined of 30 estimators. All of data were divided in two low and high permeability populations using statistical study. Each estimator of SCMNN was combined of two simple networks to predict permeability in both low and high classes and one gating network, considered as a classifier, classified data to these two classes. Thus, each low and/or high input data would predict in related network. This SCMNN was used to predict permeability on the data of one of petroleum reservoirs of south-west of Iran. 210 samples of this reservoir were available. Because of the fewness of data 80% of them were used as training data and 20% of them were used as validation and testing data. The overall fitting between predicted permeability versus measured ones was qualified through R2 (R=correlation coefficient) to be 97.72% which is considered appropriate. Whereas, R2 in the simple network in the best stat was 84.14%. The high power and efficiency of SCMNN are indicated by lower bias and better R2 in results.