Robust Affine Invariant Descriptors

An approach is developed for the extraction of affine invariant descriptors by cutting object into slices. Gray values associated with every pixel in each slice are summed up to construct affine invariant descriptors. As a result, these descriptors are very robust to additive noise. In order to establish slices of correspondence between an object and its affine transformed version, general contour (GC) of the object is constructed by performing projection along lines with different polar angles. Consequently, affine in-variant division curves are derived. A slice is formed by points fall in the region enclosed by two adjacent division curves. To test and evaluate the proposed method, several experiments have been conducted. Experimental results show that the proposed method is very robust to noise

Parallel electromagnetic transient simulation of power systems with a high proportion of renewable energy based on latency insertion method

Abstract With the interconnection of regional grids and the increasing penetration of renewable energy, the order of the power system model is getting higher and higher, which brings great challenges to the accuracy and real‐time performance of electromagnetic transient (EMT) simulation. The simulation of power systems with a high proportion of renewable energy requires a sharp increase in the performance of the hardware platform and the parallelism of the simulation algorithm. To solve this problem, the paper proposes a fine‐grained parallel modelling method for power systems with a high proportion of renewable energy, which is inspired by the latency insertion method used in the design of large‐scale integrated circuits. And this method is implemented on GPU to achieve the component‐level and device‐level parallelized simulation of power systems with a high proportion of renewable energy. Finally, the correctness of the proposed algorithm and the CPU‐GPU cooperative architecture is verified based on an improved 118‐bus case. The cascade system of the improved 9‐bus case is used as an example to verify the superiority of the proposed algorithm in terms of simulation efficiency

Structure elucidation and in vitro rat intestinal fermentation properties of a novel sulfated glucogalactan from Porphyra haitanensis

This study was to investigate the structure and rat fecal microbial fermentation properties of a polysaccharide fraction (PHP2) isolated from the red marine alga Porphyra haitanensis. PHP2 was characterized as a sulfated glucogalactan, with a hypothetical backbone structure of -> 4)G alpha(1 -> 6)G4S beta(1 -> 4)Glc(1 -> and a side chain of Man(1 -> 6)Glc. PHP2 had an irregular spherical chain conformation. The 16S rRNA sequence analysis revealed that PHP2 modulated the rat fecal micro-fl ora composition, with a similar effect to inulin, changing the dominant genus (Lactobacillus and Escherichia-Shigella) and promoting the growth of organisms that degrade sulfur-containing polysaccharides, such as Desulfovibrio, Ruminococcaceae_UCG-005, and Ruminococcus_2. PHP2 can promote production of acetic, propionic and butyric acid by rat fecal micro-flora. Prediction of metabolic function suggested that PHP2 could modulate cholesterol metabolism. The sulfated glucogalactan fermentation behavior may be associated with its monosaccharide composition, chain branching and chain conformation. PHP2 appeared to have considerable potential as functional food, and was associated with sulfur-containing polysaccharides in general. (C) 2023 Beijing Academy of Food Sciences. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd

Investigation of the Water Damage Resistance and Storability of a SEBS-Modified Cold-Patching Asphalt Mixture

At present, achieving good storability and water damage resistance remains challenging for cold-patching asphalt mixtures (CAMs). To address this issue, this study selects styrene–ethylene–butadiene–styrene copolymer (SEBS) and diesel as a modifier and diluent, respectively, to improve the water stability and storability of CAMs. The diesel oil content is determined through the Brookfield rotational viscosity test, and the modifier content is obtained through the Marshall stability test. With the empirical formula method, paper trail test, and modified Marshall test, mixed designs of CAMs modified with and without SEBS are established to determine the best cold-patching asphalt content. On this basis, the modification effect of SEBS is verified by comparing the test results of the modified and unmodified CAMs, and the water stability and Marshall stability tests are conducted before and after CAM storage, respectively. Results show that the optimum contents of SEBS and diesel oil are 7.5% and 40% of the base asphalt weight, respectively, and the best modified asphalt content is 4.6% of the mineral material weight in CAM. The Marshall residual stability and freeze–thaw splitting strength ratio of the 7.5% SEBS-modified CAM are increased by 20.1% and 15.7%, respectively, relative to the unmodified CAM, and the storage performance requirement of at least two months can be guaranteed

Prediction model and risk scores of ICU admission and mortality in COVID-19

This study aimed to develop risk scores based on clinical characteristics at presentation to predict intensive care unit (ICU) admission and mortality in COVID-19 patients. 641 hospitalized patients with laboratory-confirmed COVID-19 were selected from 4997 persons under investigation. We performed a retrospective review of medical records of demographics, comorbidities and laboratory tests at the initial presentation. Primary outcomes were ICU admission and death. Logistic regression was used to identify independent clinical variables predicting the two outcomes. The model was validated by splitting the data into 70% for training and 30% for testing. Performance accuracy was evaluated using area under the curve (AUC) of the receiver operating characteristic analysis (ROC). Five significant variables predicting ICU admission were lactate dehydrogenase, procalcitonin, pulse oxygen saturation, smoking history, and lymphocyte count. Seven significant variables predicting mortality were heart failure, procalcitonin, lactate dehydrogenase, chronic obstructive pulmonary disease, pulse oxygen saturation, heart rate, and age. The mortality group uniquely contained cardiopulmonary variables. The risk score model yielded good accuracy with an AUC of 0.74 ([95% CI, 0.63–0.85], p = 0.001) for predicting ICU admission and 0.83 ([95% CI, 0.73–0.92], p<0.001) for predicting mortality for the testing dataset. This study identified key independent clinical variables that predicted ICU admission and mortality associated with COVID-19. This risk score system may prove useful for frontline physicians in clinical decision-making under time-sensitive and resource-constrained environment