Wall-sheared thermal convection: heat transfer enhancement and turbulence relaminarization

We studied the flow organization and heat transfer properties in two-dimensional and three-dimensional Rayleigh-B\'enard cells that are imposed with different types of wall shear. The external wall shear is added with the motivation of manipulating flow mode to control heat transfer efficiency. We imposed three types of wall shear that may facilitate the single-roll, the horizontally stacked double-roll, and the vertically stacked double-roll flow modes, respectively. Direct numerical simulations are performed for fixed Rayleigh number $Ra = 10^{8}$ and fixed Prandtl number $Pr = 5.3$, while the wall-shear Reynolds number ($Re_{w}$) is in the range $60 \le Re_{w} \le 6000$. Generally, we found enhanced heat transfer efficiency and global flow strength with the increase of $Re_{w}$. However, even with the same magnitude of global flow strength, the heat transfer efficiency varies significantly when the cells are under different types of wall shear. An interesting finding is that by increasing the wall-shear strength, the thermal turbulence is relaminarized, and more surprisingly, the heat transfer efficiency in the laminar state is higher than that in the turbulent state. We found that the enhanced heat transfer efficiency at the laminar regime is due to the formation of more stable and stronger convection channels. We propose that the origin of thermal turbulence laminarization is the reduced amount of thermal plumes. Because plumes are mainly responsible for turbulent kinetic energy production, when the detached plumes are swept away by the wall shear, the reduced number of plumes leads to weaker turbulent kinetic energy production. We also quantify the efficiency of facilitating heat transport via external shearing, and find that for larger $Re_{w}$, the enhanced heat transfer efficiency comes at a price of a larger expenditure of mechanical energy.Comment: 27 pages, 16 figure

Pore-scale statistics of temperature and thermal energy dissipation rate in turbulent porous convection

We report pore-scale statistical properties of temperature and thermal energy dissipation rate in a two-dimensional porous Rayleigh-B\'enard (RB) cell. High-resolution direct numerical simulations were carried out for the fixed Rayleigh number ($Ra$) of $10^{9}$ and the Prandtl numbers ($Pr$) of 5.3 and 0.7. We consider sparse porous media where the solid porous matrix is impermeable to both fluid and heat flux. The porosity ($\phi$) range $0.86 \leq \phi \le 0.98$, the corresponding Darcy number ($Da$) range $10^{-4}<Da<10^{-2}$ and the porous Rayleigh number ($Ra^{*}=Ra\cdot Da$) range $10^{5} < Ra^{*} < 10^{7}$. Our results indicate that the plume dynamics in porous RB convection are less coherent when the solid porous matrix is impermeable to heat flux, as compared to the case where it is permeable. The averaged vertical temperature profiles remain almost a constant value in the bulk, whilst the mean square fluctuations of temperature increases with decreasing porosity. Furthermore, the absolute values of skewness and flatness of the temperature are much smaller in the porous RB cell than in the canonical RB cell. We found that intense thermal energy dissipation occurs near the top and bottom walls, as well as in the bulk region of the porous RB cell. In comparison with the canonical RB cell, the small-scale thermal energy dissipation field is more intermittent in the porous cell, although both cells exhibit a non-log-normal distribution of thermal energy dissipation rate. This work highlights the impact of impermeable solid porous matrices on the statistical properties of temperature and thermal energy dissipation rate, and the findings may have practical applications in geophysics, energy and environmental engineering, as well as other fields that involve the transport of heat through porous media.Comment: 30 pages, 16 figure

A Survey Study on Consumer Perception of Mobile- Commerce Applications

AbstractMobile commerce (m-commerce) can have an important influence on business and society in the future. Hence, m- commerce developers and practitioners must understand consumers’ perception of m-commerce applications in order to better design and deliver m-commerce service. This paper studied Chinese consumers’ perception of m-commerce applications by using the survey methodology. Firstly, 44 mobile applications were adopted on the basis of related study work, and then the web-based questionnaire was employed for obtaining online Chinese consumers’ importance ratings with regard to each mobile application. The survey result is helpful for both academics and practitioners to better design innovative and satisfying m-commerce applications

3′,6′-Bis(ethyl­amino)-2′,7′-dimethyl-2-{2-(E)-[(thio­phen-2-yl)methyl­idene­amino]­eth­yl}spiro­[isoindoline-1,9′-xanthen]-3-one methanol monosolvate

The title compound, C33H34N4O2S·CH3OH, was prepared as a spiro­lactam ring formation of rhodamine 6 G dye for comparison with a ring-opened form. The xanthene and spiro­lactam rings are approximately planar [r.m.s. deviations from planarity = 0.122 (3) and 0.072 (6) Å, respectively]. The dihedral angles formed by the spiro­lactam and thio­phene rings with the xanthene ring system are 89.7 (6) and 86.5 (2)°, respectively. The crystal structure features N—H⋯O and C—H⋯O hydrogen bonds

Fostering Humane Attitudes Toward Animals : An Educational Camp Experience in China

A program for children overcomes detachment from other living beings

3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization

Although deep-learning based methods for monocular pedestrian detection have made great progress, they are still vulnerable to heavy occlusions. Using multi-view information fusion is a potential solution but has limited applications, due to the lack of annotated training samples in existing multi-view datasets, which increases the risk of overfitting. To address this problem, a data augmentation method is proposed to randomly generate 3D cylinder occlusions, on the ground plane, which are of the average size of pedestrians and projected to multiple views, to relieve the impact of overfitting in the training. Moreover, the feature map of each view is projected to multiple parallel planes at different heights, by using homographies, which allows the CNNs to fully utilize the features across the height of each pedestrian to infer the locations of pedestrians on the ground plane. The proposed 3DROM method has a greatly improved performance in comparison with the state-of-the-art deep-learning based methods for multi-view pedestrian detection

Kinetically Controlled Synthesis of Cefaclor with Immobilized Penicillin Acylase in the Presence of Organic Cosolvents

Enzymatic syntheses of cefaclor with immobilized penicillin acylase in organic cosolvents under kinetic control were carried out. KcPGA from Kluyvera citrophila was selected as the best catalyst among the three species of immobilized penicillin acylase. Ethylene glycol, glycerol, methanol, ethyl estate and polyethyleneglycol (PEG) were selected accordingly and cefaclor syntheses were preformed respectively. Best results in terms of yield were obtained in ethylene glycol, with which further studies were investigated and the maximum yield was Y = 93.5 %. The optimal conditions were pH 6.5, temperature θ = 5 °C, 3 mol D-phenylglycine methyl ester (PGME) per mol 7-aminodesacetoxymehtyl-3-chlorocephalosporin acid (7ACCA) and x = 30 % ethylene glycol fraction. Under above mentioned conditions, the yield was Y = 91.1 %

DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities

Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry. Traditionally, software security is safeguarded by designated rule-based detectors that heavily rely on empirical expertise, requiring tremendous effort from software experts to generate rule repositories for large code corpus. Recent advances in deep learning, especially Graph Neural Networks (GNN), have uncovered the feasibility of automatic detection of a wide range of software vulnerabilities. However, prior learning-based works only break programs down into a sequence of word tokens for extracting contextual features of codes, or apply GNN largely on homogeneous graph representation (e.g., AST) without discerning complex types of underlying program entities (e.g., methods, variables). In this work, we are one of the first to explore heterogeneous graph representation in the form of Code Property Graph and adapt a well-known heterogeneous graph network with a dual-supervisor structure for the corresponding graph learning task. Using the prototype built, we have conducted extensive experiments on both synthetic datasets and real-world projects. Compared with the state-of-the-art baselines, the results demonstrate promising effectiveness in this research direction in terms of vulnerability detection performance (average F1 improvements over 10\% in real-world projects) and transferability from C/C++ to other programming languages (average F1 improvements over 11%)

3D Random Occlusion and Multi-layer Projection for Deep Multi-camera Pedestrian Localization

Although deep-learning based methods for monocular pedestrian detection have made great progress, they are still vulnerable to heavy occlusions. Using multi-view information fusion is a potential solution but has limited applications, due to the lack of annotated training samples in existing multi-view datasets, which increases the risk of overfitting. To address this problem, a data augmentation method is proposed to randomly generate 3D cylinder occlusions, on the ground plane, which are of the average size of pedestrians and projected to multiple views, to relieve the impact of overfitting in the training. Moreover, the feature map of each view is projected to multiple parallel planes at different heights, by using homographies, which allows the CNNs to fully utilize the features across the height of each pedestrian to infer the locations of pedestrians on the ground plane. The proposed 3DROM method has a greatly improved performance in comparison with the state-of-the-art deep-learning based methods for multi-view pedestrian detection. Code is available at https://github.com/xjtlu-cvlab/3DROM

Methods used to study the oligomeric structure of G protein-coupled receptors

G-protein coupled receptors (GPCRs), which constitute the largest family of cell surface receptors, were originally thought to function as monomers, but are now recognized as being able to act in a wide range of oligomeric states and indeed, it is known that the oligomerization state of a GPCR can modulate its pharmacology and function. A number of experimental techniques have been devised to study GPCR oligomerization including those based upon traditional biochemistry such as blue-native polyacrylamide gel-electrophoresis (BN-PAGE), co-immunoprecipitation and protein-fragment complementation assays, those based upon resonance energy transfer, fluorescence resonance energy transfer (FRET), time-resolved FRET, FRET spectrometry and bioluminescence resonance energy transfer (BRET). Those based upon microscopy such as fluorescence recovery after photo-bleaching (FRAP), total internal reflection fluorescence microscopy (TIRF), spatial intensity distribution analysis (SpIDA) and various single molecule imaging techniques. Finally with the solution of a growing number of crystal structures, X-ray crystallography must be acknowledged as an important source of discovery in this field. A different, but in many ways complementary approach to the use of more traditional experimental techniques, are those involving computational methods which possess obvious merit in the study of the dynamics of oligomer formation and function. Here we summarize the latest developments which have been made in the methods used to study GPCR oligomerization and give an overview of their application