Flowfield prediction of airfoil off-design conditions based on a modified variational autoencoder

Airfoil aerodynamic optimization based on single-point design may lead to poor off-design behaviors. Multipoint optimization that considers the off-design flow conditions is usually applied to improve the robustness and expand the flight envelope. Many deep learning models have been utilized for the rapid prediction or reconstruction of flowfields. However, the flowfield reconstruction accuracy may be insufficient for cruise efficiency optimization, and the model generalization ability is also questionable when facing airfoils different from the airfoils with which the model has been trained. Because a computational fluid dynamic evaluation of the cruise condition is usually necessary and affordable in industrial design, a novel deep learning framework is proposed to utilize the cruise flowfield as a prior reference for the off-design condition prediction. A prior variational autoencoder is developed to extract features from the cruise flowfield and to generate new flowfields under other free stream conditions. Physical-based loss functions based on aerodynamic force and conservation of mass are derived to minimize the prediction error of the flowfield reconstruction. The results demonstrate that the proposed model can reduce the prediction error on test airfoils by 30% compared to traditional models. The physical-based loss function can further reduce the prediction error by 4%. The proposed model illustrates a better balance of the time cost and the fidelity requirements of evaluation for cruise and off-design conditions, which makes the model more feasible for industrial applications

Fast buffet onset prediction and optimization method based on a pre-trained flowfield prediction model

The transonic buffet is a detrimental phenomenon occurs on supercritical airfoils and limits aircraft's operating envelope. Traditional methods for predicting buffet onset rely on multiple computational fluid dynamics simulations to assess a series of airfoil flowfields and then apply criteria to them, which is slow and hinders optimization efforts. This article introduces an innovative approach for rapid buffet onset prediction. A machine-learning flowfield prediction model is pre-trained on a large database and then deployed offline to replace simulations in the buffet prediction process for new airfoil designs. Unlike using a model to directly predict buffet onset, the proposed technique offers better visualization capabilities by providing users with intuitive flowfield outputs. It also demonstrates superior generalization ability, evidenced by a 32.5% reduction in average buffet onset prediction error on the testing dataset. The method is utilized to optimize the buffet performance of 11 distinct airfoils within and outside the training dataset. The optimization results are verified with simulations and proved to yield improved samples across all cases. It is affirmed the pre-trained flowfield prediction model can be applied to accelerate aerodynamic shape optimization, while further work still needs to raise its reliability for this safety-critical task.Comment: 44 pages, 20 figure

Domain selection combined with improved cloning strategy for high throughput expression of higher eukaryotic proteins

<p>Abstract</p> <p>Background</p> <p>Expression of higher eukaryotic genes as soluble, stable recombinant proteins is still a bottleneck step in biochemical and structural studies of novel proteins today. Correct identification of stable domains/fragments within the open reading frame (ORF), combined with proper cloning strategies, can greatly enhance the success rate when higher eukaryotic proteins are expressed as these domains/fragments. Furthermore, a HTP cloning pipeline incorporated with bioinformatics domain/fragment selection methods will be beneficial to studies of structure and function genomics/proteomics.</p> <p>Results</p> <p>With bioinformatics tools, we developed a domain/domain boundary prediction (DDBP) method, which was trained by available experimental data. Combined with an improved cloning strategy, DDBP had been applied to 57 proteins from <it>C. elegans</it>. Expression and purification results showed there was a 10-fold increase in terms of obtaining purified proteins. Based on the DDBP method, the improved GATEWAY cloning strategy and a robotic platform, we constructed a high throughput (HTP) cloning pipeline, including PCR primer design, PCR, BP reaction, transformation, plating, colony picking and entry clones extraction, which have been successfully applied to 90 <it>C. elegans </it>genes, 88 Brucella genes, and 188 human genes. More than 97% of the targeted genes were obtained as entry clones. This pipeline has a modular design and can adopt different operations for a variety of cloning/expression strategies.</p> <p>Conclusion</p> <p>The DDBP method and improved cloning strategy were satisfactory. The cloning pipeline, combined with our recombinant protein HTP expression pipeline and the crystal screening robots, constitutes a complete platform for structure genomics/proteomics. This platform will increase the success rate of purification and crystallization dramatically and promote the further advancement of structure genomics/proteomics.</p

Investigation and Analysis of Demand for Intelligent Logistics System With Light Intelligent Packages

With the rapid development of e-commerce logistics, the development of logistics in modern society is facing fierce competition. The expansion of scale and the overflow of waste packaging also increase the burden of the development of logistics enterprises. Based on 512 sample data collected from Mianyang, Yibin and other cities, this paper establishes regression equation by setting independent and dependent variables, and uses linear regression methods such as correlation coefficient, parameter estimation, SPSS regression analysis to analyze. Through the investigation and analysis of demand of light intelligent package in intelligent logistics system, this paper studies the logistics process. In terms of informationization, paperless, optimization of benefit and value of logistics enterprises, and packaging flooding caused by the development of e-commerce logistics, four suggestions are put forward to help the future development of logistics industry

PCAS – a precomputed proteome annotation database resource

BACKGROUND: Many model proteomes or "complete" sets of proteins of given organisms are now publicly available. Much effort has been invested in computational annotation of those "draft" proteomes. Motif or domain based algorithms play a pivotal role in functional classification of proteins. Employing most available computational algorithms, mainly motif or domain recognition algorithms, we set up to develop an online proteome annotation system with integrated proteome annotation data to complement existing resources. RESULTS: We report here the development of PCAS (ProteinCentric Annotation System) as an online resource of pre-computed proteome annotation data. We applied most available motif or domain databases and their analysis methods, including hmmpfam search of HMMs in Pfam, SMART and TIGRFAM, RPS-PSIBLAST search of PSSMs in CDD, pfscan of PROSITE patterns and profiles, as well as PSI-BLAST search of SUPERFAMILY PSSMs. In addition, signal peptide and TM are predicted using SignalP and TMHMM respectively. We mapped SUPERFAMILY and COGs to InterPro, so the motif or domain databases are integrated through InterPro. PCAS displays table summaries of pre-computed data and a graphical presentation of motifs or domains relative to the protein. As of now, PCAS contains human IPI, mouse IPI, and rat IPI, A. thaliana, C. elegans, D. melanogaster, S. cerevisiae, and S. pombe proteome. PCAS is available at CONCLUSION: PCAS gives better annotation coverage for model proteomes by employing a wider collection of available algorithms. Besides presenting the most confident annotation data, PCAS also allows customized query so users can inspect statistically less significant boundary information as well. Therefore, besides providing general annotation information, PCAS could be used as a discovery platform. We plan to update PCAS twice a year. We will upgrade PCAS when new proteome annotation algorithms identified

Singular value decomposition-based robust cubature Kalman filtering for an integrated GPS/SINS navigation system

A new nonlinear robust filter is proposed in this paper to deal with the outliers of an integrated Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) navigation system. The influence of different design parameters for an H∞ cubature Kalman filter is analysed. It is found that when the design parameter is small, the robustness of the filter is stronger. However, the design parameter is easily out of step in the Riccati equation and the filter easily diverges. In this respect, a singular value decomposition algorithm is employed to replace the Cholesky decomposition in the robust cubature Kalman filter. With large conditions for the design parameter, the new filter is more robust. The test results demonstrate that the proposed filter algorithm is more reliable and effective in dealing with the outliers in the data sets produced by the integrated GPS/SINS system

Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization

Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals

Influence mechanism of hydrated cations on surface hydration of slime mineral particles

To elucidate the microscopic mechanisms underlying the impact of hydrated cations on the surface hydration of slime mineral particles (specifically, kaolinite and quartz, the primary minerals in slime), this study focused on constructing two common hydrated cations in slime water: [Na(H2O)5]+ and [Ca(H2O)8]2+. Using density functional theory, the adsorption of these two hydrated cations on the surfaces of kaolinite (001), (\begin{document}$00\overline 1$\end{document}) and α-quartz (001), as well as their competitive adsorption with water molecules were simulated. The simulation results revealed that the adsorption energy of hydrated cations on all three surfaces was over 50% lower than that of water molecules. The adsorption stability on mineral surfaces was as follows: α-quartz (001) surface > kaolinite (001) surface > kaolinite (\begin{document}$00\overline 1$\end{document}) surface. The adsorption energy of the competitively stable configuration was 34%–57% lower than that of a single hydrated cation on kaolinite and quartz. Additionally, the [Ca(H2O)8]2+ configuration exhibited a greater stability than the [Na(H2O)5]+ configuration under both adsorption conditions. When the hydrated cations adsorbed onto three surfaces, strong hydrogen bonds formed with surface, surpassing the strength of hydrogen bonds between water molecules and kaolinite/quartz surfaces. The hierarchy of hydrogen bonds between two hydrated cations on mineral surfaces was as follows: kaolinite (001) surface > α-quartz (001) surface > kaolinite (\begin{document}$00\overline 1$\end{document}) surface. Under a competitive adsorption, the hydrogen bond between [Na(H2O)5]+ and mineral surface strengthened, while the bond between [Ca(H2O)8]2+ and mineral surface weakened. Although hydrogen bonding did not entirely correlate with changes in adsorption energy, electrostatic interactions in the adsorption configuration were identified. The electrostatic interaction in the single adsorption configuration of hydrated cations proved stronger than that in water molecular adsorption. Under a competitive adsorption, the electrostatic interactions between hydrated cations and mineral surfaces intensified, with [Ca(H2O)8]2+ demonstrating stronger interaction than [Na(H2O)5]+. Given the robust adsorption of hydrated cations on the surfaces of kaolinite and quartz, the dehydration of slime particles becomes more challenging. This could increase hydration repulsion between particles, resulting in a more stable dispersion of particles in slime water

Sulfur signaling pathway in cardiovascular disease

Hydrogen sulfide (H2S) and sulfur dioxide (SO2), recognized as endogenous sulfur-containing gas signaling molecules, were the third and fourth molecules to be identified subsequent to nitric oxide and carbon monoxide (CO), and exerted diverse biological effects on the cardiovascular system. However, the exact mechanisms underlying the actions of H2S and SO2 have remained elusive until now. Recently, novel post-translational modifications known as S-sulfhydration and S-sulfenylation, induced by H2S and SO2 respectively, have been proposed. These modifications involve the chemical alteration of specific cysteine residues in target proteins through S-sulfhydration and S-sulfenylation, respectively. H2S induced S-sulfhydrylation can have a significant impact on various cellular processes such as cell survival, apoptosis, cell proliferation, metabolism, mitochondrial function, endoplasmic reticulum stress, vasodilation, anti-inflammatory response and oxidative stress in the cardiovascular system. Alternatively, S-sulfenylation caused by SO2 serves primarily to maintain vascular homeostasis. Additional research is warranted to explore the physiological function of proteins with specific cysteine sites, despite the considerable advancements in comprehending the role of H2S-induced S-sulfhydration and SO2-induced S-sulfenylation in the cardiovascular system. The primary objective of this review is to present a comprehensive examination of the function and potential mechanism of S-sulfhydration and S-sulfenylation in the cardiovascular system. Proteins that undergo S-sulfhydration and S-sulfenylation may serve as promising targets for therapeutic intervention and drug development in the cardiovascular system. This could potentially expedite the future development and utilization of drugs related to H2S and SO2