In this work molecularly imprinted polymer (MIP)-aptamer (Apt) hybrid recognition sensor based on nanocomposites of reduced graphene oxide (rGO) and gold nanoparticles (AuNPs) was reported. The glassy carbon electrode (GCE) was first modified with rGO and AuNPs. Then thrombin-Apt composites were obtained by the incubation of thiolated thrombin aptamer and template thrombin. Subsequently thrombin-aptamer composites were immobilized onto AuNPs/rGO/GCE through the Au-S bond between AuNPs and thiol group at the terminal of aptamer. Finally MIP-Apt hybrid recognition sensor was prepared by the electropolymerization of thionine. Using the polythionine as the probe, cyclic voltammetry, electrochemical impedance spectroscopy and differential pulse voltammetry were performed to characterize the MIP-Apt sensor. Adsorption kinetic model was established to obtain kinetic binding constants and equilibrium current. Due to hybrid recognition of MIP and Apt, MIP-Apt sensor has faster rebinding rate, better selectivity and sensitivity, comparing with non-molecularly imprinted sensor and non-aptamer molecularly imprinted sensor. The MIP-Apt sensor exhibited good linear response from 2.5 × 10 −9 mg/mL to 1.3 × 10 −6 mg/mL for thrombin with a detection limit of 1.6 × 10 −10 mg/mL.

Cinnamon essential oil (CEO) was extracted by three different methods: steam distillation (SD), ultrasound-assisted steam distillation (UASD) and microwave-assisted steam distillation (MASD). The volatiles in CEO were separated and identified by gas chromatography–mass spectrometry (GC-MS), and the differences in volatiles among the three different methods were further analyzed through principal component analysis. The results showed that 36 individual volatile components were present in the CEO from the three different methods. In general, the numbers of aldehydes, esters, alcohols, terpenes, aromatics and ketones were 6, 3, 7, 17, 2, and 1, respectively. The most abundant volatile component was determined to be cinnamic aldehyde. The content of total cinnamic aldehydes, which determines the price of CEO, was the highest among the three methods in the UASD sample (85.633%). Moreover, the highest yield (8.33‰) of essential oil was extracted by the UASD method. Therefore, UASD was the best way for CEO extraction in this research and was recommended for future industrial applications.

Asymmetric multicore processors (AMP) offer multiple types of cores under the same programming interface. Extracting the full potential of AMPs requires intelligent scheduling decisions, matching each thread with the right kind of core, the core that will maximize performance or minimize wasted energy for this thread. Existing OS schedulers are not up to this task. While they may handle certain aspects of asymmetry in the system, none can handle all runtime factors affecting AMPs for the general case of multi-threaded multi-programmed workloads. We address this problem by introducing COLAB, a general purpose asymmetry-aware scheduler targeting multi-threaded multi-programmed workloads. It estimates the performance and power of each thread on each type of core and identifies communication patterns and bottleneck threads. With this information, the scheduler makes coordinated core assignment and thread selection decisions that still provide each application its fair share of the processor's time. We evaluate our approach using both the GEM5 simulator on four distinct big.LITTLE configurations and a development board with ARM Cortex-A73/A53 processors and mixed workloads composed of PARSEC and SPLASH2 benchmarks. Compared to the state-of-the art Linux CFS and AMP-aware schedulers, we demonstrate performance gains of up to 25% and 5% to 15% on average, together with an average 5% energy saving depending on the hardware setup.

This paper presents an automatic k-means clustering solution targeting the Sunway TaihuLight supercomputer. We first introduce a multi-level parallel partition approach that not only partitions by dataflow and centroid, but also by dimension, which unlocks the potential of the hierarchical parallelism in the heterogeneous many-core processor and the system architecture of the supercomputer. The parallel design is able to process large-scale clustering problems with up to 196,608 dimensions and over 160,000 targeting centroids, while maintaining high performance and high scalability. Furthermore, we propose an automatic hyper-parameter determination process for k-means clustering, by automatically generating and executing the clustering tasks with a set of candidate hyper-parameter, and then determining the optimal hyper-parameter using a proposed evaluation method. The proposed auto-clustering solution can not only achieve high performance and scalability for problems with massive high-dimensional data, but also support clustering without sufficient prior knowledge for the number of targeted clusters, which can potentially increase the scope of k-means algorithm to new application areas.

This paper presents a novel design and implementation of k-means clustering algorithm targeting the Sunway TaihuLight supercomputer. We introduce a multi-level parallel partition approach that not only partitions by dataflow and centroid, but also by dimension. Our multi-level (nkd) approach unlocks the potential of the hierarchical parallelism in the SW26010 heterogeneous many-core processor and the system architecture of the supercomputer.Our design is able to process large-scale clustering problems with up to 196,608 dimensions and over 160,000 targeting centroids, while maintaining high performance and high scalability, significantly improving the capability of k-means over previous approaches. The evaluation shows our implementation achieves performance of less than 18 seconds per iteration for a largescale clustering case with 196,608 data dimensions and 2,000 centroids by applying 4,096 nodes (1,064,496 cores) in parallel, making k-means a more feasible solution for complex scenarios.