Honey is a natural product made by honeybees, its composition depends on factors such as climate, soil and plant source. In this study, the nutritional parameters, phenolic composition, antioxidant activity and antibacterial ability of 30 different types of honey of different botanical and geographical origins in Vietnam were investigated. The study focused on the characterization and evaluation of the influence of plant origin and geographical location on physical–chemical properties and biological activities (antioxidant and antibacterial). The obtained results show that all honey samples meet quality standards according to international standards and Vietnamese standards, except for some exceptions recorded in moisture, 5-hydroxymethylfurfural (HMF) value and ash. These samples were explored for the detection of 13 polyphenols by using high-performance liquid chromatography (HPLC). The classification of honey samples collected from different regions and botanical sources was performed by principal component analysis (PCA), and it was observed that certain phenolic compounds contributed to the identification of honey samples. In addition, the correlation between physicochemical properties, chemical composition and biological activity of most honeys was also first clarified in this study. Overall, our data provide an overview data set and essential results in creating a database on the world honey trait map.
This paper presents an automatic method for computing an anisotropic 2D shape distribution on an arbitrary 2-manifold mesh. Our method allows the user to specify the direction as well as the density of the distribution. Using a pre-computed lookup table, our method can efficiently detect collision among the shapes to be distributed on the 3D mesh. In contrast to existing approaches, which usually assume the 2D objects are isotropic and have simple geometry, our method works for complex 2D objects and can guarantee the distribution is conflict-free, which is a critical constraint in many applications. It is able to compute multi-class shape distributions in parallel. Our method does not require global parameterization of the input 3D mesh. Instead, it computes local parameterizations on the fly using geodesic polar coordinates. Thanks to a recent breakthrough in geodesic computation, the local parameterization can be computed at low cost. As a result, our method can be applied to models with complicated geometry and topology. Experimental results on a wide range of 3D models and 2D anisotropic shapes demonstrate the good performance and effectiveness of our method.
Computing geodesics on meshes is a classical problem in computational and differential geometry. It measures the length of the local shortest path with minimum curvature along the surface between two points. Previous studies have documented many important properties of geodesics, such as being a distance metric, locally isotropic and invariant to isometric transformation. With those properties, geodesic distance could be considered as a more general definition of Euclidean distance. Thus, it plays an important role in constructing various algorithms, solving many real-world problems and developing countless applications in a wide range of fields, including computer graphic, digital geometric processing, computer vision and image processing, etc.Although computing discrete geodesics has been widely studied, there are still many difficult barriers in finding of geodesic distances and paths, including high computational cost, dependence on quality of the input mesh and scalability for other input data types with less connectivity information. Effectively computing discrete geodesics overcoming those issues can be a key to open larger doors to solving a lot of related problems.Traditionally, the proposed geodesic algorithms have tended to focus on geodesics between vertices rather than between arbitrary points on input mesh surfaces. Geodesics between two arbitrary points could be computed in a simple but inaccurate way using interpolation or in other way using re-triangulation which always is very expensive and can globally change the topology of the input meshes. The lack of this ability in computing geodesics can diminish its area of applications.This thesis aims at developing efficient and flexible geodesic algorithms to address the challenges of computing geodesics at vertices/surface points on large real-world models with high time performance for interactive applications. Our contributions include: i
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