Understanding the substrate specificity of HIV-1 protease plays an essential role in the prevention of HIV infection. A variety of computational models have thus been developed to predict substrate sites that are cleaved by HIV-1 protease, but most of them normally follow a supervised learning scheme to build classifiers by considering experimentally verified cleavable sites as positive samples and unknown sites as negative samples. However, certain noisy can be contained in the negative set, as false negative samples are possibly existed. Hence, the performance of the classifiers is not as accurate as they could be due to the biased prediction results. In this work, unknown substrate sites are regarded as unlabeled samples instead of negative ones. We propose a novel positive-unlabeled learning algorithm, namely PU-HIV, for an effective prediction of HIV-1 protease cleavage sites. Features used by PU-HIV are encoded from different perspectives of substrate sequences, including amino acid identities, coevolutionary patterns and chemical properties. By adjusting the weights of errors generated by positive and unlabeled samples, a biased support vector machine classifier can be built to complete the prediction task. In comparison with state-of-the-art prediction models, benchmarking experiments using cross-validation and independent tests demonstrated the superior performance of PU-HIV in terms of AUC, PR-AUC, and F-measure. Thus, with PU-HIV, it is possible to identify previously unknown, but physiologically existed substrate sites that are able to be cleaved by HIV-1 protease, thus providing valuable insights into designing novel HIV-1 protease inhibitors for HIV treatment.
This paper proposes a method for optimising the hull form of ocean-going trawlers to decrease resistance and consequently reduce the energy consumption. The entire optimisation process was managed by the integration of computer-aided design and computational fluid dynamics (CFD) in the CAESES software. Resistance was simulated using the CFD solver and STAR-CCM+. The ocean-going trawler was investigated under two main navigation conditions: trawling and design. Under the trawling condition, the main hull of the trawler was modified using the Lackenby method and optimised by NSGA-II algorithm and Sobol + Tsearch algorithm. Under the design condition, the bulbous bow was changed using the free-form deformation method, and the trawler was optimised by NSGA-Ⅱ. The best hull form is obtained by comparing the ship resistance under various design schemes. Towing experiments were conducted to measure the navigation resistance of trawlers before and after optimisation, thus verifying the reliability of the optimisation results. The results show that the proposed optimisation method can effectively reduce the resistance of trawlers under the two navigation conditions.
A numerical model based on the two-phase flow model for incompressible viscous fluid with a complex free surface has been developed in this study. The two-step projection method is employed to solve the Navier–Stokes equations in the numerical solutions, and finite difference method on a staggered grid is used throughout the computation. The two-order accurate volume of fluid (VOF) method is used to track the distorted and broken free surfaces. The two-phase model is first validated by simulating the dam break over a dry bed, in which the numerical results and experimental data agree well. Then 2-D fluid sloshing in a horizontally excited rectangular tank at different excitation frequencies is simulated using this two-phase model. The results of this study show that the two-phase flow model with VOF method is a potential tool for the simulation of nonlinear fluid sloshing. These studies demonstrate the capability of the two-phase model to simulate free surface flow problems with considering air movement effects.
Floating offshore wind turbines are easily affected by typhoons in the deep sea, which may cause serious damage to their structure. Therefore, it is necessary to study further the dynamic response of wind turbine structures under typhoons. This paper took the 5MW floating offshore wind turbine developed by the National Renewable Energy Laboratory (NREL) as the research object. Based on the motion theory of platforms in waves, a physical model with a scale ratio of 1:120 was established, and a hydraulic cradle was used to simulate the effect of waves on the turbines. The dynamic response characteristics of offshore wind turbines under typhoons are systematically studied. The research results clarified that the turbine structure is mainly affected by wave loads under typhoons, and its motion response reaches its maximum value under the action of extreme wave loads. The research results of this paper can provide reference value for the design of offshore wind turbine structures under typhoons.
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