BIOKEYS – AN INTEGRATED SYSTEM FOR WORKING WITH DATABASE AND POLYCLAVE IDENTIFICATION KEYS OF VARIOUS TAXONOMIC LEVELS

Unlike the other currently available identification software, BioKeys allows the users to create and use own polyclave identification keys of various taxonomic levels. The information of dedicated taxonomic level is stored in the head line of the key file so that BioKeys can understand and search in the right field of the database for the records of matched taxa. The results of database referencing or specimen identification will be displayed in a form of web page. Besides, utilities are available for creating and managing the database, for analyzing the polyclave identification key. An example of using BioKeys is provided with polyclave key of plant families of Magnoliophyta that has been created based on the punched-card system of Bertel Hansen and Knud Rahn (1969)

Study on some groups of microorganism during the forest rehabilitation process in Song Ma district, Son La province

The paper has made assessement on changes of some soil microorganism groups during the process of forest rehabilitation after shifting cultivation and after clear cutting in Song Ma district, Son La province. Sampling has been made in forest areas naturally regenerated for 4-6; 9-11; 14-16 and above 20 years after shifting cultivation and after clear cutting. The results have shown that the total aerobic bacteria, the total actinomycetes and the total microfungi have increased from the rehabilitation stage of 4-6 years (3.12×102; 1.23×103 and 8.05×102 CFU/g respectively in forest areas after shifting cultivation and 4.25×104; 2.32×103 and 7.32×103CFU/g respectively in forest areas after clear cutting) to the rehabilitation stage of ≥ 20 years (3.54×105; 2.23×104 and 2.51×104 CFU/g respectively in forest areas after shifting cultivation and 4.35×106; 2.72×105 and 5.46×105 CFU/g respectively in forest areas after clear cutting). The capacity in free nitrogen fixation, in cellulose decomposition, in phosphates decomposition, and in polysaccharide production of the aerobic bacteria, of actinomycetes, and of microfungi is lowest during the first forest rehabilitation stage (4-6 years), then it grows and reaches the highest value during the stage of≥20 years (5.60×103; 2.75×104; 2.10×103; 1.36×104 CFU/g respectively in forest areas after shifting cultivation and 5.24×103; 3.14×105; 4.27×104; 5.39×104 CFU/g respectively in forest areas after clear cutting)

Investigation and establishment of rational geometric factors of die in the deep drawing without a blank holder

Deep drawing is an operation to transform flat or hollow blanks to create hollow parts of the required shape and size. Deep drawing is an essential operation in sheet-forming technology to manufacture hollow parts. These parts are commonly used in the automobile, aviation, and household appliances industries. To prevent wrinkles on the rim part, a blank holder will be used to compress and flatten the workpiece before the metal is pulled into the die by the punch. Deep drawing without using workpiece holding force is applied to form hollow parts with low depth. In this case, the mould has a simple structure, helping to reduce manufacturing time and costs and making it easier to maintain and repair. The radius of the die is an essential parameter in deep drawing; it greatly affects the quality of products (making wrinkles and tears). However, this study has shown that, in deep drawing without using a blank holder, the cone angle of the die α has a major influence on the quality of the body and rim of the products, such as the products may or may not have wrinkles, either being thinned or thickened. This cone angle also affects the drawing force. This study has determined that, with α>120°, wrinkling begins to appear in the part wall, and wrinkling tends to increase as the angle α increases. The cone angle of the die for quality products is in the range of 100°<α≤120°. The taper angle smaller than 120° simultaneously increases the quality of the products and the cost of manufacturing die, so the most optimal value is determined by simulation and experimental verification as α=120°. The results of this study can be applied to fabricate hollow cylindrical parts with similar shapes when using the deep drawing method without using the blank holde

DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification

Hyperspectral images (HSI) have become popular for analysing remotely sensed images in multiple domain like agriculture, medical. However, existing models struggle with complex relationships and characteristics of spectral-spatial data due to the multi-band nature and data redundancy of hyperspectral data. To address this limitation, we propose a new network called DiffSpectralNet, which combines diffusion and transformer techniques. Our approach involves a two-step process. First, we use an unsupervised learning framework based on the diffusion model to extract both high-level and low-level spectral-spatial features. The diffusion method is capable of extracting diverse and meaningful spectral-spatial features, leading to improvement in HSI classification. Then, we employ a pretrained denoising U-Net to extract intermediate hierarchical features for classification. Finally, we use a supervised transformer-based classifier to perform the HSI classification. Through comprehensive experiments on HSI datasets, we evaluate the classification performance of DiffSpectralNet. The results demonstrate that our framework significantly outperforms existing approaches, achieving state-of-the-art performance.Comment: 18 page

Potential Use of Rice Husk Biochar and Compost to Improve P Availability and Reduce GHG Emissions in Acid Sulfate Soil

© 2020 by the authors. Acid sulfate soil (ASS) has major problems related to phosphorus deficiency and high potential for N2O emissions, as well as strong acidity. The objective of this study was to evaluate the effects of rice husk biochar and compost on P availability and greenhouse gas (GHG) emissions in ASS in in vitro incubation studies. An ASS was amended with two types of rice husk biochar (at rates of 0 g kg-1, 20 g kg-1, and 50 g kg-1, equivalent to 0 Mg ha-1, 20 Mg ha-1, and 50 Mg ha-1, assuming that bulk density was 1 g cm-3 and evenly applied for 10 cm in depth) and compost (at rates of 0 g kg-1, 10 g kg-1, and 20 g kg-1, equivalent to 0 Mg ha-1, 10 Mg ha-1, and 20 Mg ha-1) and incubated. Application of compost increased labile P by 100% and 200% at rates of 10 g kg-1 and 20 g kg-1, respectively. Both biochars showed an increase in NaHCO3-soluble inorganic P by 16% to 30%, decreases in NaOH-soluble inorganic P and NaHCO3-soluble organic P. N2O emissions were significantly decreased by 80% by a biochar with a higher surface area and higher NH4+ adsorption capacity at a rate of 50 g kg-1 as compared with those in un-amended soil. In contrast, compost amendment at a rate of 10 g kg-1 significantly increased N2O emission by 150%. These results suggest that in ASS, whilst compost is more effective in improving P availability, biochar is more effective in mitigating GHG emissions, emphasizing that fundamental characteristics of organic amendments influenced the outcomes in terms of desirable effects

The distribution of Meiofauna community related to salinity gradient in the Ham Luong estuary, Mekong river

Seventeen taxa of meiofauna community in the Ham Luong estuary were investigated and comprised. Free-living nematodes  were the most dominant and diverse group, presenting about 77% in the total of meiofauna density. Meiofauna density varied from 135.7 ± 33.5 inds/10 cm² to 1782.0 ± 199.5 inds/10 cm². The meiofauna density shows a decreasing trend from inland station to the brackish water station and it is increasing at mouth station. Significant differences in meiofauna density, diversity and Hill’s indices were found between stations. The ANOSIM showed significant differences between stations in meiofauna composition (overall R = 0.972, p = 0.1%). The SIMPER analysis clarifies that the average similarity within stations was quite high, changing from EHL3 (76.2%) to EHL1 (86.1%)

VỀ CHỈ SỐ CHÍNH QUY CASTELNUOVO-MUMFORD CỦA VÀNH TỌA ĐỘ CỦA TẬP n + 3 ĐIỂM BÉO KHÔNG SUY BIẾN TRONG KHÔNG GIAN XẠ ẢNH Pn

In this paper we calculate the Castelnuovo-Mumford regularity index of the coordinate ring of n + 3 a set of  non-degenerate fat points in Pn for almost all of cases.Trong bài báo này chúng tôi tính chỉ số chính quy Castelnuovo-Mumford của vành tọa độ của tập n + 3 điểm béo không suy biến trong Pn cho hầu hết các trường hợp của tập điểm

A Complete Method for Reconstructing an Elevation Surface of 3D Point Clouds

Reconstructing the surface of 3D point clouds is a reconstruction from a cloud of 3D points to a triangular mesh. This process approximates a discrete point cloud by a continuous/smooth surface depending on the input data and the applications of users. In this paper, we propose a complete method to reconstruct an elevation surface from 3D point clouds. The method consists of three steps. In the first step, we triangulate an elevation surface of 3D point cloud structured in a 3D grid. In the second step, we remove the outward triangles to deal with concave regions on the boundary of the triangular mesh. In the third step, we reconstruct this surface by filling the hole of triangular mesh. Our method could process very fast for triangulating the surface, preserve the topology and characteristic of the input surface after reconstruction

Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification

Hyperspectral imaging has gained popularity for analysing remotely sensed images in various fields such as agriculture and medical. However, existing models face challenges in dealing with the complex relationships and characteristics of spectral–spatial data due to the multi-band nature and data redundancy of hyperspectral data. To address this limitation, we propose a novel approach called DiffSpectralNet, which combines diffusion and transformer techniques. The diffusion method is able extract diverse and meaningful spectral–spatial features, leading to improvement in HSI classification. Our approach involves training an unsupervised learning framework based on the diffusion model to extract high-level and low-level spectral–spatial features, followed by the extraction of intermediate hierarchical features from different timestamps for classification using a pre-trained denoising U-Net. Finally, we employ a supervised transformer-based classifier to perform the HSI classification. We conduct comprehensive experiments on three publicly available datasets to validate our approach. The results demonstrate that our framework significantly outperforms existing approaches, achieving state-of-the-art performance. The stability and reliability of our approach are demonstrated across various classes in all datasets