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The primary objective of this investigation is to establish a clear correlation between air quality and the prevalence of respiratory conditions, this is done by employing deep learning (DL) methodologies. RNN was compared against alternative methods such as GNN, CNN, feed-forward technique (DL), k-nearest neighbours, linear regression, decision tree, and neural network. Performance evaluations were conducted employing an Octuple crossvalidation approach, with the root mean square error (RMSE) employed to perform comparative analysis. RMSE serves as the primary metric for evaluating regression models, additional criteria for model comparison include computational efficiency, scalability, assessing model performance with larger datasets; and generalization to new data. While RMSE's simplicity and sensitivity to large errors are strengths, limitations include sensitivity to outliers and overlooking distributional differences. The data was collected on two major features, the first being the Air quality dataset which contained various gases present in the atmosphere that affect the air quality, and the second being the hospital patient dataset which indicates the number of people suffering from respiratory diseases due to the air quality. The study acknowledges the superior performance of the Recurrent Neural Network (RNN) model but suggests the need for new learning methods handling small datasets and explores efficient, scalable approaches like distributed and federated learning. [ABSTRACT FROM AUTHOR]

Phenylketonuria (PKU) is an autosomal recessive hereditary disorder due to deficiency of enzyme phenylalanine hydroxylase or the cofactor tetrahydrobiopterin. Its late manifestation leads to irreversible neurological changes. The aim of our work is to emphasize the difference in presentation in late diagnosis of the disease verses classical presentation making it difficult to correctly diagnose and also point out the reasons for late diagnosis and missed cases in India. A 9-year-old patient presented with global developmental delay and severe behavioral problems. Hypertonia and spasticity with low intelligence quotient (IQ) were seen. Baseline investigations such as renal function, thyroid function, electrolytes, uric acid, folate, and blood adrenocorticotropic hormone level were within range; hence, magnetic resonance imaging (MRI) was advised which revealed areas of bilateral demyelination suggesting metabolic leukodystrophy. PKU was thereafter confirmed with metabolic profile and clinical exome study. Early routine screening of all newborns for common inherited and metabolic disorders should be mandatory to later prevent irreversible damage. Cases of delayed diagnosis deviate considerably from classical clinical and radiological findings of the disease making correct and prompt diagnosis difficult. Education of parents and prenatal counseling should be encouraged. [ABSTRACT FROM AUTHOR]

Functionally graded materials or functionally gradient materials (FGM) are characterized as an anisotropic material whose physical properties varies continuously because the dimensions vary randomly or strategically to realize the specified characteristics. The general properties of the functionally gradient material are different from the properties of any of the individual parent materials which forms it. In this case aluminium and ceramic material FGM is considered, as ceramic materials displays better wear, corrosion and temperature resistance. Material modelling, geometric modelling and finite element modelling are completed for the disc subjected to inertia force because of the rotation of the disk, and thermal load. Then the numerical problem is solved using the finite element software ANSYS 18.1. A functionally graded annular disc is analyzed for different profiles like convex, concave, linearly varying and uniform with different angular velocity, and ceramic material considering exponentially varying material properties. [ABSTRACT FROM AUTHOR]