Search

Isolation and analysis of circulating tumor cells (CTCs) from peripheral blood provides a potential way to detect and characterize cancer. Existing technologies to separate or capture CTCs from whole blood still have issues with sample throughput, separation efficiency or stable efficiency at different flow rates. Here, we proposed a new concept to capture rare CTCs from blood by integrating a triangular prism array-based capture apparatus with streamline-based focus-separation speed reduction design. The focus-separation design could focus and maintain CTCs, while removing a considerable proportion of liquid (98.9%) containing other blood cells to the outlet, therefore, a high CTC capture efficiency could be achieved in the trap arrays with a high initial flow rate. It is worth mentioning that the new design works well over a wide range of flow rates, so it does not require the stability of the flow rate. The results showed that this novel integrated chip can achieve a sample throughput from 5 to 40 mL h-1 with a stable and high CTC capture efficiency (up to 94.8%) and high purity (up to 4 log white blood cells/WBC depletion). The clinical experiment showed that CTCs including CTC clusters were detected in 11/11 (100.0%) patients (mean = 31 CTCs mL-1, median = 25 CTCs mL-1). In summary, our chip enriches and captures CTCs based on physical properties, and it is simple, cheap, fast, and efficient and has low requirements on flow rate, which is very suitable for large-scale application of CTC testing in clinics.

This paper mainly introduces the design scheme and the concrete realization method of small satellite launch site remote testing project. The paper analyzes the management process and technology realization of satellite remote testing from three aspects, namely the workflow, management mode and technology realization. The remote testing system solves the difficulties under the condition of long distance and narrow bandwidth, such as stability, security and flashover of remote control and multi-channel parallel transmission of audio and video data across the network segment. Through remote testing of launch site, it significantly saves manpower. It is conducive to providing technical support for the launch site with more back superior resources. With the overall popularization and application of launch site remote testing, the work interface is delivered to AIT, further promoting the separation of design and production as well as model development management mode transformation.

In order to improve the data mining and information scheduling capabilities of geo-information system (GIS), it is necessary to optimise GIS information feature estimation and perform GIS information feature extraction, so a GIS information feature estimation algorithm based on big data analysis is proposed. In this algorithm, the piecewise linear estimation method is adopted to reconstruct feature data in the GIS information database in group, and associated information fusion is performed to the GIS data in the database, and adaptive scheduling is performed to the GIS information feature database through the cascaded distributed scheduling method; according to the spatial distribution of geographic information, vector adjustment is performed to the cluster centre, and the frequent item mining method is adopted to extract features of GIS information, and then sequential processing is adopted to the extracted feature quantity of GIS information; the regularised power density spectrum estimation method is adopted to perform unbiased estimation to GIS information feature data. Simulation results show that in GIS information feature estimation, the proposed method can provide estimation with low bias and high accuracy, so it has good GIS information scheduling capability and precision.

In order to reduce the interference of redundant information during geographic information system query and improve the accuracy of information query, a geographic information system query optimisation algorithm based on redundant data deletion and filtering technology is proposed. In this method, a distributed data query structure model of geographic information system is constructed; adaptive group matching of query intervals is conducted through the segment fusion method, and then key information points after group matching are located through the association rule feature information fusion method. Finally, the high-order cumulant feature extraction method is adopted to achieve accurate information query of geographic information system. Simulation results show that in geographic information system query under redundant data infection, this method can provide accuracy 8.75% higher than that provided by the FCM method and 12.98% higher than that provided by the support vector machine algorithm, which indicates that this method can provide strong anti-interference ability of query and cost less overhead.

Today's data centers are facing extreme challenges in providing low latency and high throughput simultaneously for their applications. However, the fair sharing congestion control algorithms, together with the hash-based load balancing mechanisms, seriously impede the satisfaction of such requirements. In this paper, we use threshold-based signaling and marking schemes to seamlessly integrate load balancing with congestion control protocols. Through adaptive per-packet load balancing, we evenly split network traffic across multiple paths. Meanwhile, we adaptively react to ECN in the case of congestion, and prioritize flows according to their latency requirements. Using realistic data center workloads, we show that, compared to the state-of-the-art load balancing and TCP, our approach improves the application throughput and mean flow completion time by 30–50% and 40–80%, respectively.

Highly virtualized data center have placed many new and unique requirements on the networking fabric. Conventional network protocols limit the scale, latency, throughput of cloud networks. To solve these problems, the large layer 2 technology is proposed. The large layer 2 technology such as TRILL and SPB has obvious advantages in scalability, convergence and resource utilization compared with the traditional layer 2 technology. However, the disadvantages of employing the large layer 2 technology are barely addressed. To study the overhead of large layer 2 networking, we setup a data center environment with 6 core switches and conduct experiments to compare the performance of the representative large layer 2 protocol-TRILL and the typical layer 2 technology-STP. We also evaluate the intra-VLAN forwarding and inter-VLAN forwarding of TRILL. From our experiments, it is observed that the forwarding efficiency of TRILL is 30.16% lower than STP on average. It is also observed that the inter-VLAN traffic forwarding of TRILL averagely costs about 54% more time and consumes about 5% more CPU than intra-VLAN with a fixed packet generation rate. This is the first study towards unveiling the inefficiency of large layer 2 approach in data center networks as far as we know.

Recently, there has been renewed interest in latency as a primary metric for mainstream data center applications. In this paper, we propose a coordinated mechanism for congestion control and multi-path transmission to reduce flow completion time in data centers. The approach leverages ECN to adaptively react to congestion, prioritizes flows according to their timeliness requirements, and evenly distributes network traffic across multiple paths. Through a real implementation and simulations, we show that our approach can effectively reduce flow completion time under different workflow patterns. Compared to existing solutions based on TCP and flow hashing, our approach achieves an improvement of 30% in application throughput and a reduction of up to 90% in 99th percentile flow completion time for latency-sensitive flows, and a reduction of 50%-80% in average completion time for long background flows.

It is common that many enterprises tend to operate several datacenters applying virtualization technologies from different vendors. However, for virtual machine migration across different vendors, currently there is no feasible method to transfer the corresponding network policies as well. In this paper, we show a cross-vendor network policy adaptor (CVNPA), which is able to cooperate with today's cloud computing platforms and to provide seamless network policy migration across virtual switches from different vendors. We experiment CVNPA in our simulated cloud computing environment with OpenStack as their cloud computing platform. The results show that CVNPA can transfer corresponding network policy over heterogeneous virtual switches efficiently and automatically during the process of VM migration with little overhead.

While virtualization is giving great advantages in cloud computing, power metering for virtual machines (VMs) becomes one of the challenges raised. VM power can hardly be measured purely in hardware, so linear power models have been built to infer power consumption at runtime from resource usage. Linear models are simple and of low overhead in implementation. However, power consumption is not exactly linear with certain resource usage and linearity does not necessarily hold across multiple workloads. We propose a new method, VPower, which is effective for more workloads without increasing complexity. In this method, a two dimensional lookup table (LUT) is constructed for each VM, which gives an empirical power value at certain CPU utilization and last-level-cache (LLC) miss rate. Experiments are performed on VMs running SPEC CPU2006 benchmarks. It is shown that the VM power estimated reflects the variability of the two factors effectively, and the per-VM error is about 2.61W.

Encoding topology information into logical addresses is a new trend in data center network (DCN) as it can help to improve the scalability and routing efficiency. Regretfully, to configure large-scale data center manually is not only time-consuming but also error-prone. Because of this, research in auto-configuration becomes getting more attention. Previous works in auto-configuration generally neglect the properties of given topology and cause high computational complexity. In this paper, we propose a new auto-configuration algorithm called Pivot-based Fast Automatic Configuration (PFAC). Leveraging properties of the given data center network topology, PFAC can assign addresses to physical devices in the data center automatically and efficiently. The experiments show that the processing time of PFAC is reduced by about 30% compared to previous works.