Complex segregation occurs in a binary particle system with differing particle sizes and densities,particularly when the larger particles are heavier(S–D system,i.e.,size minus density system).Predicting the segregat...
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Complex segregation occurs in a binary particle system with differing particle sizes and densities,particularly when the larger particles are heavier(S–D system,i.e.,size minus density system).Predicting the segregation pattern driven by multiple mechanisms simultaneously is often *** study explores the segregation mechanisms in a quasi-2D circular drum containing a S–D system,realizing a transition between the S-core and Core-and-band patterns by adjusting the drum rotation *** the transition of the segregation pattern,only the S-core pattern chiefly driven by the percolation mechanism is initially *** the rotation speed increases,the buoyancy mechanism and particle diffusion gradually strengthen,jointly driving the formation of the Core-and-band pattern.A dimensionless strength ratio,λ=H/h,where H and h respectively represent the diffusion and buoyancy strengths at length scales,is introduced to elucidate this *** Core-and-band pattern emerges whenλreached 1.4.
A computational analysis of genome-scale transcriptomic data collected on -1,700 tissue samples of three cancer types： breast carcinoma, colon adenocarcinoma and lung adenocarcinoma, revealed that each tissue consist...
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A computational analysis of genome-scale transcriptomic data collected on -1,700 tissue samples of three cancer types： breast carcinoma, colon adenocarcinoma and lung adenocarcinoma, revealed that each tissue consists of （at least） two major subpopulations of cancer cells with different capabilities to handle fluctuating Oz levels. The two populations have distinct genomic and transcriptomic characteristics, one accelerating its proliferation under hypoxic conditions and the other proliferating faster with higher O2 levels, referred to as the hypoxia and the reoxygenation subpopulations, respectively. The proportions of the two subpopulations within a cancer tissue change as the average 02 level changes. They both contribute to cancer development but in a complementary manner. The hypoxia subpopulation tends to have higher proliferation rates than the reoxygenation one as well as higher apoptosis rates; and it is largely responsible for the acidic environment that enables tissue invasion and provides protection against attacks from T-cells. In comparison, the reoxygenation subpopulation generates new extracellular matrices in support of further growth of the tumor and strengthens cell-cell adhesion to provide scaffolds to keep all the cells connected. This subpopulation also serves as the major source of growth factors for tissue growth. These data and observations strongly suggest that these two major subpopulations within each tumor work together in a conjugative relationship to allow the tumor to overcome stresses associated with the constantly changing Oz level due to repeated growth and angiogenesis. The analysis results not only reveal new insights about the population dynamics within a tumor but also have implications to our understanding of possible causes of different cancer phenotypes such as diffused versus more tightly connected tumor tissues.
As multi-core processors with tens or hundreds of cores begin to grow, system optimization issues once faced only by the High-Performance Computing (HPC). To satisfy the requirement, one can leverage multi-core archit...
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As multi-core processors with tens or hundreds of cores begin to grow, system optimization issues once faced only by the High-Performance Computing (HPC). To satisfy the requirement, one can leverage multi-core architectures to parallelize traffic monitoring so as to progress information processing capabilities over traditional uni-processor architectures. In this paper an effective scheduling framework for multi-core processors that strike a balance between control over the system and an effective network traffic control mechanism for high-performance computing is proposed. In the proposed Cache Fair Thread Scheduling (CFTS), information supplied by the user to guide threads scheduling and also, where necessary, gives the programmer fine control over thread placement. Cloud computing has recently received considerable attention, as a promising approach for delivering network traffic services by improving the utilization of data centre resources. The primary goal of scheduling framework is to improve application throughput and overall system utilization in cloud applications. The resultant aim of the framework is to improve fairness so that each thread continues to make good forward progress. The experimental results show that the parallel CFTS could not only increase the processing rate, but also keep a well performance on stability which is important for cloud computing. This makes, it an effective network traffic control mechanism for cloud computing.
When transferring the geometric constraint equation group into the optimization model, we need a method to jump out of the local beat solution so that we can find a global best solution. Considering the speed and glob...
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When transferring the geometric constraint equation group into the optimization model, we need a method to jump out of the local beat solution so that we can find a global best solution. Considering the speed and global capability, we adopt compound particle group optimization algorithm. Particle swarm optimization algorithm is a kind of evolution computation technology based on group intelligence. In all the evolution computations heuristic function should be included to control its one's own characteristic. These parameters are usually correlated with the specific problem and are defined by the users. Suitable parameter choice needs user abundant experience and correct judgment on the information offered by the problem. More important thing is that these heuristic parameters will influence the convergence characteristic of the algorithm. Because of this even experienced users may choose the not appropriate parameter and then make the problem unable to get effective solution. It needs to carry on some research on these parameters more and more. Here we choose the control parameters as an optimization question in the particle swarm algorithm. Thus heuristic function in the PSO can be controlled by the ordinal genetic algorithm and we form the composite particle swarm optimization algorithm. And we use this algorithm into the geometric constraint solving successfully
Virtual instrumentation is the use of customizable software and modular measurement hardware to create user-defined measurement systems, called virtual instruments. The primary difference between hardware instrumentat...
Virtual instrumentation is the use of customizable software and modular measurement hardware to create user-defined measurement systems, called virtual instruments. The primary difference between hardware instrumentation and virtual instrumentation is that LabVIEW software is used to replace a large amount of hardware. The software enables complex and expensive hardware to be replaced by already purchased computer hardware. Virtual Instrumentation is established using LabVIEW. Laboratory Virtual Instrument Engineering Workbench (LabVIEW) is a system-design platform and development environment for a visual programming language from National Instruments. The graphical language is named “G”; not to be confused with G-code. Originally released for the Apple Macintosh in 1986, LabVIEW is commonly used for data acquisition, instrument control, and industrial automation on a variety of Operating Systems (OSs), including Microsoft Windows, various versions of Unix, Linux, and MacOS. Simple timer Circuit, Traffic Light Control System, Automotive Safety assurance, Speed, Direction control of DC motor and Library Database File Management are realized by implementing VI programs. Select switch, Looping concepts, Case and Flat Sequence structures, String Manipulation, Clusters, File IO systems and Arduino interfacing through DAQ are used to implement all Simulation Models. Software Developed timer circuit and Automotive Safety assurance overcome the accuracy problem of conventional circuits. Designed Library Management system enhances ease of tracking. Conventional control of DC motor relies on hardware accessories such as Field and Armature rheostats and it requires manual implementation, whereas Virtual instrumentation makes software based control and it improves the system reliability. Fetching of Real time data and manipulation addresses the flexibility issue of hardware based Traffic Light Control system.
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