直浇道／横浇道控制浇道系统的应用

最初的阻流是在带锥度的直浇道底部或横浇道与直浇道接合处的阻流截面附近进行,一旦横浇道充满,就由内浇道的截面积来控制流量.

CFD study of turbulent jet impingement on curved surface

The heat transfer and flow characteristics of air jet impingement on a curved surface are investigated with computational fluid dynamics(CFD)approach.The first applied model is a one-equation SGS model for large eddy simulation(LES)and the second one is the SST-SAS hybrid RANS-LES.These models are utilized to study the flow physics in impinging process on a curved surface for different jet-to-surface(h/B)distances at two Reynolds numbers namely,2960 and 4740 based on the jet exit velocity(U_e)and the hydraulic diameter(2B).The predictions are compared with the experimental data in the literature and also the results from RANS k-εmodel.Comparisons show that both models can produce relatively good results.However,one-equation model(OEM)produced more accurate results especially at impingement region at lower jet-to-surface distances.In terms of heat transfer,the OEM also predicted better at different jet-to-surface spacings.It is also observed that both models show similar performance at higher h/B ratios.

Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface

There is a strong drive towards hyperresolution earth system models in order to resolve finer scales of motion in the atmosphere.The problem of obtaining more realistic representation of terrestrial fluxes of heat and water,however,is not just a problem of moving to hyperresolution grid scales.It is much more a question of a lack of knowledge about the parameterisation of processes at whatever grid scale is being used for a wider modelling problem.Hyperresolution grid scales cannot alone solve the problem of this hyperresolution ignorance.This paper discusses these issues in more detail with specific reference to land surface parameterisations and flood inundation models.The importance of making local hyperresolution model predictions available for evaluation by local stakeholders is stressed.It is expected that this will be a major driving force for improving model performance in the future.

Decoding early myelopoiesis from dynamics of core endogenous network

A decade ago mainstream molecular biologists regarded it impossible or biologically ill-motivated to understand the dynamics of complex biological phenomena, such as cancer genesis and progression, from a network perspective. Indeed, there are numerical difficulties even for those who were determined to explore along this direction. Undeterred, seven years ago a group of Chinese scientists started a program aiming to obtain quantitative connections between tumors and network dynamics. Many interesting results have been obtained. In this paper we wish to test such idea from a different angle: the connection between a normal biological process and the network dynamics. We have taken early myelopoiesis as our biological model. A standard roadmap for the cell-fate diversification during hematopoiesis has already been well established experimentally, yet little was known for its underpinning dynamical mechanisms. Compounding this difficulty there were additional experimental challenges, such as the seemingly conflicting hematopoietic roadmaps and the cell-fate inter-conversion events. With early myeloid cell-fate determination in mind, we constructed a core molecular endogenous network from well-documented gene regulation and signal transduction knowledge. Turning the network into a set of dynamical equations, we found computationally several structurally robust states. Those states nicely correspond to known cell phenotypes. We also found the states connecting those stable states.They reveal the developmental routes—how one stable state would most likely turn into another stable state. Such interconnected network among stable states enabled a natural organization of cell-fates into a multi-stable state landscape. Accordingly, both the myeloid cell phenotypes and the standard roadmap were explained mechanistically in a straightforward manner. Furthermore,recent challenging observations were also explained naturally. Moreover, the landscape visually enables a prediction of a pool of additional cell states and developmental routes, including the non-sequential and cross-branch transitions, which are testable by future experiments. In summary, the endogenous network dynamics provide an integrated quantitative framework to understand the heterogeneity and lineage commitment in myeloid progenitors.

Dendrimer-based magnetic resonance imaging agents for brain cancer

Brain cancer is one of the most lethal and difficult-to-treat cancers because of its physical location and biological barriers. The mainstay of brain cancer treatment is surgical resection, which demands precise imaging for tumor localization and delineation. Thanks to advances in bioimaging, brain cancer can be detected earlier and resected more reliably. Magnetic resonance imaging（MRI） is the most common and preferred method to delineate brain cancer, and a contrast agent is often required to enhance imaging contrast.Dendrimers, a special family of synthetic macromolecules,constitute a particularly appealing platform for constructing MRI contrast agents by virtue of their well-defined three-dimensional structure, tunable nanosize and abundant surface terminals, which allow the accommodation of high payloads and numerous functionalities. Tuning the dendrimer size,branching and surface composition in conjunction with conjugation of MRI functionalities and targeting moieties can alter the relaxivity for MRI, overcome the blood-brain barrier and enhance tumor-specific targeting, hence improving the imaging quality and safety profile for precise and accurate imaging of brain tumors. This short review highlights the recent progress, opportunities and challenges in developing dendrimer-based MRI contrast agents for brain tumor imaging.

Information schema constructs for instantiation and composition of system manifestation features

Complementing our previous publications, this paper presents the information schema constructs （ISCs） that underpin the programming of specific system manifestation feature （SMF） orientated information management and composing system models. First, we briefly present （1） the general process of pre-embodiment design with SMFs, （2） the procedures of creating genotypes and phenotypes of SMFs, （3） the specific procedure of instantiation of phenotypes of SMFs, and （4） the procedure of system model management and processing. Then, the chunks of information needed for instantiation of phenotypes of SMFs are discussed, and the ISCs designed for instantiation presented. Afterwards, the information management aspects of system modeling are addressed. Methodologically, system modeling involves （1） placement of phenotypes of SMF in the modeling space, （2） combining them towards the desired architecture and operation, （3） assigning values to the parameters and checking the satisfac- tion of constraints, and （4） storing the system model in the SMFs-based warehouse database. The final objective of the reported research is to develop an SMFs-based toolbox to support modeling of cyber-physical systems （CPSs）.