Application of Multivariate Techniques for Spatial Drought Modelling using Satellite Rainfall Estimate in Fiji

Monitoring hydrological extremes is essential for developing risk-mitigation strategies. One of the limiting factors for this is the absence of reliable on the ground monitoring networks that capture data on climate variables, which is highly evident in developing states such as Fiji. Fortunately, increasing global coverage of satellite-derived datasets is facilitating utilisation of this information for monitoring dry and wet periods in data sparse regions. In this study, three global satellite rainfall datasets (CHIRPS, PERSIANN-CDR and CPC) were evaluated for Fiji. All satellite products had reasonable correlations with station data, and CPC had the highest correlation with minimum error values. The Effective Drought Index (EDI), a useful index for understanding hydrological extremes, was then calculated. Thereafter, a canonical correlation analysis (CCA) was employed to forecast the EDI using sea surface temperature anomaly (SSTa) data. A high canonical correlation of 0.98 was achieved between the PCs of mean SST and mean EDI, showing the influence of ocean–atmospheric interactions on precipitation regimes in Fiji. CCA was used to perform a hind cast and a short-term forecast. The training stage produced a coefficient of determinant (R2) value of 0.83 and mean square error (MSE) of 0.11. The results in the testing stage for the forecast were more modest, with an R2 of 0.45 and MSE of 0.26. This easy-to-implement system can be a useful tool used by disaster management bodies to aid in enacting water restrictions, providing aid, and making informed agronomic decisions such as planting dates or extents.

An Improved Fusion Paired Group Lasso Structured Sparse Canonical Correlation Analysis Based on Brain Imaging Genetics to Identify Biomarkers of Alzheimer’s Disease

Brain imaging genetics can demonstrate the complicated relationship between genetic factors and the structure or function of the humankind brain. Therefore, it has become an important research topic and attracted more and more attention from scholars. The structured sparse canonical correlation analysis (SCCA) model has been widely used to identify the association between brain image data and genetic data in imaging genetics. To investigate the intricate genetic basis of cerebrum imaging phenotypes, a great deal of other standard SCCA methods combining different interested structed have now appeared. For example, some models use group lasso penalty, and some use the fused lasso or the graph/network guided fused lasso for feature selection. However, prior knowledge may not be completely available and the group lasso methods have limited capabilities in practical applications. The graph/network guided approaches can use sample correlation to define constraints, thereby overcoming this problem. Unfortunately, this also has certain limitations. The graph/network conducted methods are susceptible to the sign of the sample correlation of the data, which will affect the stability of the model. To improve the efficiency and stability of SCCA, a sparse canonical correlation analysis model with GraphNet regularization (FGLGNSCCA) is proposed in this manuscript. Based on the FGLSCCA model, the GraphNet regularization penalty is imposed in our study and an optimization algorithm is presented to optimize the model. The structural Magnetic Resonance Imaging (sMRI) and gene expression data are used in this study to find the genotype and characteristics of brain regions associated with Alzheimer’s disease (AD). Experiment results shown that the new FGLGNSCCA model proposed in this manuscript is superior or equivalent to traditional methods in both artificially synthesized neuroimaging genetics data or actual neuroimaging genetics data. It can select essential features more powerfully compared with other multivariate methods and identify significant canonical correlation coefficients as well as captures more significant typical weight patterns which demonstrated its excellent ability in finding biologically important imaging genetic relations.

Fermi-gas correlators of ADHM theory and triality symmetry

We analytically study the Fermi-gas formulation of sphere correlation functions of the Coulomb branch operators for 3d \mathcal{N}=4𝒩=4 ADHM theory with a gauge group U(N)U(N), an adjoint hypermultiplet and ll hypermultiplets which can describe a stack of NN M2-branes at A_{l-1}Al−1 singularities. We find that the leading coefficients of the perturbative grand canonical correlation functions are invariant under a hidden triality symmetry conjectured from the twisted M-theory. The triality symmetry also helps us to fix the next-to-leading corrections analytically.

The Correlation Between Income Inequality and Population Health

Today, despite the increase in global wealth, the income gap between the rich and the poor gradually widens. This gap is significant in both developed and developing nations. Thus, increasing income inequality adversely affects several socio-economic indicators. Previous studies demonstrated that one of the socio-economic indicators that were negatively affected by income inequality is population health. The income inequality experienced by the individuals or throughout life adversely affects several populations' health outputs, especially life expectancy at birth. The present study aimed to test the correlation between income inequality and population health output indicators with canonical correlation method and based on the most current data available for several nations. To determine the correlation between the two datasets, the 2017 data for 29 European countries and Turkey were analyzed. Canonical correlation analysis revealed a significant correlation between the income inequality and population health indicator datasets.