Statistical models used in geophysical, environmental, and climate science applications must reflect the curvature of spatial domain global data. Over past few decades, statisticians have developed covariance that capture temporal behavior these data sets. Though geodesic distance is most natural metric for measuring on surface a sphere, mathematical limitations compelled to use chordal compute matrix many instead, which may cause physically unrealistic distortions. Therefore, functions...

Wind has the potential to make a significant contribution future energy resources. Locating sources of this renewable on global scale is however extremely challenging, given difficulty store very large data sets generated by modern computer models. We propose statistical model that aims at reproducing data-generating mechanism an ensemble runs via Stochastic Generator (SG) annual wind data. introduce evolutionary spectrum approach with spatially varying parameters based large-scale...

There is a growing interest in developing covariance functions for processes on the surface of sphere because wide availability data globe. Utilizing one‐to‐one mapping between Euclidean distance and great circle distance, isotropic positive definite space can be used as sphere. This approach, however, may result physically unrealistic distortion especially large distances. We consider several classes parametric sphere, defined with either or investigate their impact upon spatial prediction....

Quantifying the uncertainty of wind energy potential from climate models is a time-consuming task and requires considerable computational resources.A statistical model trained on small set runs can act as stochastic approximation original model, assess considerably faster than by resorting to for additional runs.While Gaussian have been widely employed means approximate simulations, Gaussianity assumption not suitable winds at policy-relevant (i.e., subannual) time scales.We propose...

This study aims to improve the performance of voice spoofing attack detection through self-supervised pre-training. Supervised learning needs appropriate input variables and corresponding labels for constructing machine models that are be applied. It is necessary secure a large number labeled datasets supervised processes. However, labeling requires substantial inputs time effort. One methods managing this requirement learning, which uses pseudo-labeling without necessity human input....

This study examines the impact of incorporating cryptocurrencies into global asset portfolios using ensemble approaches and a tracing strategy. We considered cryptocurrency ratios 1%, 3%, 5% for including cryptocurrencies. Benchmarking was performed classical portfolio optimization strategies such as minimum variance (MVP), maximum diversification (MDP), equal risk contribution (ERCP), hierarchical parity (HRP). The methods we evaluated were equally weighted (EWP), linear combination (LCP),...

There is a growing interest in developing covariance functions for processes on the surface of sphere due to wide availability data globe. Utilizing one-to-one mapping between Euclidean distance and great circle distance, isotropic positive definite space can be used as sphere. This approach, however, may result physically unrealistic distortion especially large distances. We consider several classes parametric sphere, defined with either or investigate their impact upon spatial prediction....

Hepatitis A is a water-borne infectious disease that frequently occurs in unsanitary environments. However, paradoxically, those who have spent their infancy sanitary environment are more susceptible to hepatitis because they do not the opportunity acquire natural immunity. In Korea, prevalent of distribution uncooked seafood, especially during hot and humid summers. general, transmission known be dynamically affected by socioeconomic, environmental, weather-related factors heterogeneous...

Quantifying the uncertainty of wind energy potential from climate models is a very time-consuming task and requires considerable amount computational resources. A statistical model trained on small set runs can act as stochastic approximation original model, be used to assess considerably faster than by resorting for additional runs. While Gaussian have been widely employed means approximate simulations, Gaussianity assumption not suitable winds at policy-relevant time scales, i.e.,...

As the risk posed by climate change becomes increasingly evident, countries across world are constantly seeking alternative energy sources. Wind has substantial potential for future portfolios without having negative impacts on environment. In developing nationwide and worldwide plans, understanding spatio-temporal pattern of wind is crucial. We analyze vectors in region East Asia from fifth-generation ECMWF atmospheric reanalysis. To model vectors, we consider Tukey g-and-h...

Wind has the potential to make a significant contribution future energy resources. Locating sources of this renewable on global scale is however extremely challenging, given difficulty store very large data sets generated by modern computer models. We propose statistical model that aims at reproducing data-generating mechanism an ensemble runs via Stochastic Generator (SG) annual wind data. introduce evolutionary spectrum approach with spatially varying parameters based large-scale...