Xi'an Jiaotong University

About: Xi'an Jiaotong University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Heat transfer & Dielectric. The organization has 85440 authors who have published 99682 publications receiving 1579683 citations. The organization is also known as: '''Xi'an Jiaotong University''' & Xi'an Jiao Tong University.

Microwave assisted extraction of secondary metabolites from plants: Current status and future directions

Abstract: Plant secondary metabolites are known to be an important source of foods, fragrances, pigment, drugs and so on. Extraction is one of the crucial steps for research and development of plant secondary metabolites. Over the past 25 years, a large number of manuscripts of microwave assisted extraction have been published and lots of remarkable results have been achieved. However, there are still many theoretical and technical barriers in the area of microwave assisted extraction of plant secondary metabolites, which need to be overcome. This paper reviews recent advances in microwave assisted extraction of plant secondary metabolites, such as flavonoids, quinones, phenylpropanoids, terpenoids and alkaloids. Principles and mechanisms, apparatuses and equipment, advantages and disadvantages of microwave assisted extraction are also summarized. The last part of the paper introduces new and emerging technologies of microwave technique, and then suggests strategies for further research into microwave assisted extraction of plant secondary metabolites.

A filled function method for finding a global minimizer of a function of several variables

Abstract: The concept of a filled function is introduced. We construct a particular filled function and analyze its properties. An algorithm for global minimization is generated based on the concept and properties of the filled function. Some typical examples with 1 to 10 variables are tested and computational results show that in most cases this algorithm works better than the tunneling algorithm. The advantages and disadvantages are analyzed and further research directions are discussed.

Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement

Abstract: In this paper, we propose a novel generic image prior-gradient profile prior, which implies the prior knowledge of natural image gradients. In this prior, the image gradients are represented by gradient profiles, which are 1-D profiles of gradient magnitudes perpendicular to image structures. We model the gradient profiles by a parametric gradient profile model. Using this model, the prior knowledge of the gradient profiles are learned from a large collection of natural images, which are called gradient profile prior. Based on this prior, we propose a gradient field transformation to constrain the gradient fields of the high resolution image and the enhanced image when performing single image super-resolution and sharpness enhancement. With this simple but very effective approach, we are able to produce state-of-the-art results. The reconstructed high resolution images or the enhanced images are sharp while have rare ringing or jaggy artifacts.

Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design

Abstract: One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical. We review how methods from the information sciences enable us to accelerate the search and discovery of new materials. In particular, active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations. The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data. We discuss several utility functions and demonstrate their use in materials science applications, impacting both experimental and computational research. We summarize by indicating generalizations to multiple properties and multifidelity data, and identify challenges, future directions and opportunities in the emerging field of materials informatics.