The binary Al-Mn system has been critically evaluated based upon available phase equilibrium and thermodynamic data, and optimized model parameters have been obtained giving the Gibbs energies of all phases as functions of temperature and composition. The liquid solution has been modeled with the modified quasichemical model to account for short-range ordering. The results have been combined with those of our previous optimizations of the Al-Mg and Mg-Mn systems to evaluate and optimize the Mg-Al-Mn system. All available data for the ternary system are reproduced with only one small ternary model parameter for the liquid phase.

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The binary Ce – Si and Y – Si systems have been critically evaluated based upon available phase equilibrium and thermodynamic data, and optimized model parameters have been obtained giving the Gibbs energies of all phases as functions of temperature and composition. The liquid solution has been modeled with the Modified Quasichemical Model to account for the short-range ordering. The results have been combined with those of our previous optimizations of the Mg – Si, Mg – Ce and Mg – Y systems to predict the phase diagrams of the Mg – Ce – Si and Mg – Y – Si systems. The predictions have been compared with available data.

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Alzheimer’s Disease (AD) is becoming increasingly prevalent across the globe, and various diagnostic and detection methods have been developed in recent years. Several techniques are available, including Automatic Pipeline Methods and Machine Learning Methods that utilize Biomarker Methods, Fusion, and Registration for multimodality, to pre-process medical scans. The use of automated pipelines and machine learning systems has proven beneficial in accurately identifying AD and its stages, with a success rate of over 95% for single and binary class classifications. However, there are still challenges in multi-class classification, such as distinguishing between AD and MCI, as well as sub-stages of MCI. The research also emphasizes the significance of using multi-modality approaches for effective validation in detecting AD and its stages.