Accurate estimation of the satellite-based global terrestrial latent heat flux (LE) at high spatial and temporal scales remains a major challenge. In this study, we introduce a Bayesian model averaging (BMA) method to improve satellite-based global terrestrial LE estimation by merging five process-based algorithms. These are the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product a...

Infection with filarial parasites is associated with T cell hyporesponsiveness, which is thought to be partly mediated by their ability to induce regulatory T cells (Tregs) during human infections. This study investigates the functional capacity of Tregs from different groups of filarial patients to suppress filaria-specific immune responses during human filariasis. Microfilaremic (MF), chronic...

Motion estimation and compensation is an essential part of existing video coding systems. The mesh-based motion estimation (MME) produces smoother motion field, better subjective quality (free from blocking artifacts), and higher peak signal-to-noise ratio (PSNR) in many cases, especially at low bitrate video communications, compared to the conventional block matching algorithm (BMA). However, ...

Mediumand long-term runoff forecasting is essential for hydropower generation and water resources coordinated regulation in the Yellow River headwaters region. Climate change has a great impact on runoff within basins, and incorporating different climate information into runoff forecasting can assist in creating longer lead-times in planning periods. In this paper, a multimodel approach was dev...

Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However for datasets where the number of variables p is large (e.g. p > 5, 000) the algorithm can become prohibitively expensive, computationally. Another method which is popular for hig...

Naive Bayes (NB) is well-known to be a simple but effective classifier, especially when combined with feature selection. Unfortunately, feature selection methods are often greedy and thus cannot guarantee an optimal feature set is selected. An alternative to feature selection is to use Bayesian model averaging (BMA), which computes a weighted average over multiple predictors; when the different...

Harmful algal blooms (HABs) are a worldwide problem that have been increasing in frequency and extent over the past several decades. HABs severely damage aquatic ecosystems by destroying benthic habitat, reducing invertebrate and fish populations, and affecting larger species such as dugong that rely on seagrasses for food. Few statistical models for predicting HAB occurrences have been develop...

Bayesian model averaging (BMA) is a statistical way of postprocessing forecast ensembles to create predictive probability density functions (PDFs) for weather quantities. It represents the predictive PDF as a weighted average of PDFs centered on the individual bias-corrected forecasts, where the weights are posterior probabilities of the models generating the forecasts and reflect the forecasts...

A new block matching algorithm (BMA) especially appropriate for large search areas is proposed. Motion vectors of causally adjacent blocks can be credible motion vector candidates in continuous motion fields. However, they are not helpful for searching complex or random motions. In order to remedy this problem, we propose a new two-step hierarchical block matching algorithm using spatial correl...

OBJECTIVE The objective of this prospective study was assessment of efficacy of bone marrow aspirate (BMA) (containing plasma rich in growth factors and mesenchymal stem cells) injection in treatment of tennis elbow. MATERIALS AND METHODS A total of 30 adult patients of previously untreated tennis elbow were administered single injection of BMA. This concentrate was made by centrifugation of ...