Effect of Groundwater Pumping on Saltwater Intrusion in a Coastal Plain

Rosana G. Moreira, Editor-in-Chief; Texas A&M UniversityThis is a paper from International Commission of Agricultural Engineering (CIGR, Commission Internationale du Genie Rural) E-Journal Volume 8 (2006): Effect of Groundwater Pumping on Saltwater Intrusion in a Coastal Plain. Manuscript LW 05 003. Vol. VIII. January, 2006

EFFECTS OF Nd2O3 ON THE CRYSTALLIZATION AND PROPERTIES OF GLASS CERAMIC IN Li2O–K2O–Al2O3–SiO2–P2O5 SYSTEM

Glass ceramics (GCs), which often contain a small amount of rare earth oxides to improve their performance, are ideal for dental restorative applications. The aim of this study was to investigate the various effects of Nd2O3 content (0–1 wt%) on crystallization and properties of GC derived from Li2O–K2O–Al2O3–SiO2–P2O5 system. The glass blocks were formed from the molten at 1450 °C. Based on the DTA results, the glass samples were experienced by two–stage heat–treatment (600 °C/ 90 min + 720 °C/ 30 min) to change to ingots. After that, the ingot samples were fired in a hot pressing furnace EP3000 at 930 °C for 30 min. The results of powder X–ray diffraction (XRD) indicated that the final GCs contained crystals such as lithium disilicate (Li2Si2O5 or LS2), lithium metasilicate (Li2SiO3 or LS) and the traces of lithium phosphate (Li3PO4). With increasing Nd2O3 content, the relative amount of LS phase increased slightly while LS2 phase decreased. However, the final GC containing 0.75 wt% Nd2O3 had the highest bending strength at 293 MPa, the lowest chemical solubility and relative high Vicker hardness. These samples had a high degree of crystallization and the highest relative content of desired LS2 phase

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject