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This article reports on the investigation, manufacture, and testing of a liquid crystal (LC)-based tunable terahertz (THz) metamaterial (MM) metal-dielectric-metal (MDM) structure, which has low insertion loss (IL) and large modulation depth (MD). The demonstrated structure consists of two parallel layers of a quartz dielectric surrounding two copper layers. The copper structures were printed on the inner surfaces of the upper and lower surfaces of the quartz substrate, to form periodic arrays of sub-wavelength circular loops. The transmission characteristics and the LC parameters are calculated and analyzed for THz electromagnetic (EM) waves in the frequency range from 220 to 330 GHz. The experimental results show that at 285.45 GHz, 294.8 GHz, 305.91 GHz, and 314.38 GHz, the IL is below 4.08 dB and an intensity MD greater than 70.56% is available for THz EM waves with normal incidence. By varying the voltage applied to the LC layer (0–4.8 V), which contains the MDM structure, the frequency corresponding to the valley is decreased to 285.45 GHz, with a frequency tunability greater than 13.5%. The theoretical calculations and experimental results are in good agreement. The MDM structure shows good prospects for THz modulators and switches, due to its excellent performance and simple planar geometry.

In order to solve the problem of small and dim ship target detection under complex sea-sky background, we propose a target detection algorithm based on sea-sky line detection. Firstly, the paper locates the sea-sky-line based on fully convolutional networks, through which target potential area can be determined and disturbance can be excluded. Then the method based on the mean of four detection gradient is adopted to detect the small and dim ship target. The simulation results show that the method of sea-sky-line detection based on fully convolutional networks can overcome the disadvantages of the traditional methods and is suitable for complex background. The detection method proposed can filter the white noise point on the sea surface and thus can reduce false alarm, through which the detection of small and dim ship can be completed well.

With the continuous increase in the number of motor vehicles and the frequent occurrence of road congestion problems, it has become an important research topic to carry out comprehensive collection of traffic road network status information, processing analysis, prediction, and decision-making recommendation to effectively solve urban traffic problems. The traffic flow is one of the main parameters reflecting the road operation status. It is of great significance to timely and accurately grasp and predict the road traffic situation, which is of great significance for diverting vehicles in advance and improving the operation capacity and efficiency of the road network. Due to the complex spatial structure of the road network, the road traffic flow is non-Euclidean, non-directional, and the change over time is non-stationary, which has a strong time dependence, which leads to greater challenges in traffic flow prediction. This paper comprehensively considers the characteristics of the temporal and spatial correlation of traffic flow, and provides a city traffic flow prediction method based on graph convolutional neural network, which can effectively mine the temporal and spatial dynamic patterns of urban road network traffic flow and realize accurate traffic flow prediction.

A conjugated polymer based on naphthalene-diimide and dithiophene units was synthesized by continuous flow synthesis. Due to the large specific surface area and excellent heat transfer, the polymerization was completed within ∼35 min at 110 °C. Compared to flask synthesis, the continuous flow technology exhibited a high synthesis efficiency and significant reduction in reaction times. The polymer using flow synthesis exhibited higher molecular weight (17.28/35.27 vs. 4.86/13.21 kg mol−1), lower polydispersity indexes (2.0 vs. 2.7), and a more ordered crystalline structure. Consequently, much higher electron mobilities (0.74 vs. 0.11 cm2 V−1 s−1) with Ion/Ioff ratios of 106 were obtained for the polymer using flow synthesis. This study demonstrates that continuous flow synthesis will be promising in the preparation of conjugated polymers with high efficiency.

Currently, traditional feature extraction algorithms have poor data expression and noise robustness. Moreover, traditional recognition methods are gradually falling behind the demand for increasing data, and struggle to extract deep features in targets. By considering the preceding issues, an integrated neural network has been created in this paper for underwater acoustic target recognition via feature fusion learning. Firstly, the short time Fourier transform (STFT) amplitude spectrum, STFT phase spectrum, and bispectrum feature of underwater acoustic signals are extracted and form the input for the network. They not only contain rich information about the target, but also have strong noise robustness. Secondly, an integrated neural network has been designed, which is trained with different features and contains three neural networks. Finally, in the softmax layer of the network, the shuffled frog leaping algorithm (SFLA) is utilized to train the weight coefficients of different networks. Experimental results of the measured data show that the integrated neural network method based on feature fusion has a higher recognition accuracy and stronger noise robustness.

The classification and recognition of the shapes of buildings in map space play an important role in spatial cognition, cartographic generalization, and map updating. As buildings in map space are often represented as the vector data, research was conducted to learn the feature representations of the buildings and recognize their shapes based on graph neural networks. Due to the principles of graph neural networks, it is necessary to construct a graph to represent the adjacency relationships between the points (i.e., the vertices of the polygons shaping the buildings), and extract a list of geometric features for each point. This paper proposes a deep point convolutional network to recognize building shapes, which executes the convolution directly on the points of the buildings without constructing the graphs and extracting the geometric features of the points. A new convolution operator named TriangleConv was designed to learn the feature representations of each point by aggregating the features of the point and the local triangle constructed by the point and its two adjacency points. The proposed method was evaluated and compared with related methods based on a dataset consisting of 5010 vector buildings. In terms of accuracy, macro-precision, macro-recall, and macro-F1, the results show that the proposed method has comparable performance with typical graph neural networks of GCN, GAT, and GraphSAGE, and point cloud neural networks of PointNet, PointNet++, and DGCNN in the task of recognizing and classifying building shapes in map space.

Two small semiconductor molecules based on diketopyrrolopyrrole (DPP) and hemi-isoindigo units with and without fluorination were designed and synthesized for investigating the solution-processed organic field-effect transistors (OFETs). Bottom-gate/top-contact (BG/TC) OFETs were fabricated to explore carrier transport characteristics. The combination of density functional theory (DFT) calculation, grazing incidence X-ray diffraction (GIXD), and atomic force microscopy (AFM) allowed us to understand the relationship between structures and properties. The results revealed the small molecule with fluorination (DPP-T-FMI) exhibited better field-effect performance owing to the improved crystal packing and morphology of thin-films.

The objective of the current study was to discover how processing technology affects beany flavor of soymilk. Four different technology routes, namely soaked soybean-boiling before filtration, soak...

Pulmonary hypertension is called PH for short. It is caused by the pulmonary artery vascular disease leading to pulmonary vascular resistance, and the increase right lung compartment load, which resulting in weakening or even collapse of the right ventricular function. The establishment of rat PH model under the action of monocrotaline is a repeatable, simple and accessible operation technique, which has been widely used in the treatment of pulmonary hypertension. This paper discusses the principle and properties of the PH model on rats under the monocrotaline action.

Cloud computing platforms routinely use virtualization to improve service availability, resiliency, and flexibility. Live migration of Virtual Machines (VM) is a key technique to quickly migrate workloads in response to events such as impending failure or load changes. Despite extensive research, state-of-the-art live migration approaches take a long time to migrate a VM (in the order of tens of seconds for moderately sized VMs) which in turn negatively impacts the application performance during migration. We present Quick Eviction, a new approach to significantly speed up the eviction of a VM from the source host with low impact on VM's performance during migration. Our approach can also improve the effectiveness of VM resilience tools that back up a VM's state in anticipation of system failures. The insight behind Quick Eviction is that the majority of time to evict a VM during migration is due to the transfer of memory contents over the network, sometimes repeatedly. Before migration, Quick Eviction regularly snapshots the VM's memory to a destination or a failover node. During the actual migration, Quick Eviction has to transfer only a small amount of dirtied memory resulting in a very short time to completely evict the VM out of the source. The key challenge is to dynamically adapt the snapshot intervals so as to have minimal impact on application performance during migration. We show that our implementation of Quick Eviction in the KVM/QEMU platform reduces the eviction time of a VM by significant factors for both idle and write-intensive VMs compared to traditional pre-copy algorithm, while maintaining low overheads on application performance and network.

When considering a hypervisor, cloud providers must balance conflicting requirements for simple, secure code bases with more complex, feature-filled offerings. This paper introduces Dichotomy, a new two-layer cloud architecture in which the roles of the hypervisor are split. The cloud provider runs a lean hyperplexor that has the sole task of multiplexing hardware and running more substantial hypervisors (called featurevisors) that implement features. Cloud users choose featurevisors from a selection of lightly-modified hypervisors potentially offered by third-parties in an "as-a-service" model for each VM. Rather than running the featurevisor directly on the hyperplexor using nested virtualization, Dichotomy uses a new virtualization technique called eemeral virtualization which efficiently (and repeatedly) transfers control of a VM between the hyperplexor and featurevisor using memory mapping techniques. Nesting overhead is only incurred when the VM is accessed by the featurevisor. We have implemented Dichotomy in KVM/QEMU and demonstrate average switching times of 80 ms, two to three orders of magnitude faster than live VM migration. We show that, for the featurevisor applications we evaluated, VMs hosted in Dichotomy deliver up to 12% better performance than those hosted on nested hypervisors, and continue to show benefit even when the featurevisor applications run as often as every 2.5~seconds.

In this paper, a sensor based on polyvinylidene fluoride (PVDF) piezoelectric thin film was designed and fabricated to detect wrist motion signals. A series of dynamic experiments have been carried out, including the contrast experiments of different materials and force-charge signal characterization. The experimental results show that when the excitation signal exceeds 15 Hz, the sensitivity of the sensor is always stable at 3.10 pC/N. The signal acquisition experiment of the wrist motion has been carried out by using this sensor. The experiment results show that, with the advantages of small size, good flexibility, and high sensitivity, this wrist PVDF sensor can be used to detect the wrist motion signals with weak amplitude, low frequency, strong interference, and randomness.

A novel kind of zwitterionic polymer, tertiary amine-modified PES (TA-PES), bearing cationic tertiary amine groups and anionic sulfonic groups, was synthesized in this study. The as-synthesized TA-PES was employed to fabricate ultrafiltration membranes by phase inversion in a wet process using pure water or sodium chloride aqueous solution as coagulation bath. It was found that the as-fabricated ultrafiltration membranes exhibited distinct ion-strength-dependent flux due to the inherent zwitterionic characteristics of TA-PES. The permeation flux of TA-PES membranes can be varied dramatically by simply changing the ionic content in coagulation bath. Furthermore, TA-PES membranes displayed superior anti-protein-fouling property and desirable ultrafiltration performance.

(2005). Phytoplankton photosynthesis and plankton community respiration in large shallow Lake Taihu, China. SIL Proceedings, 1922-2010: Vol. 29, No. 1, pp. 395-398.

The day and night vertical distributions of the dominant planktonic crustaceans in Xujiahe Reservoir were investigated on July 7, 1998. Sampling was carried out at Station D in the upstream region, Doupuohe, and Stations LI and LII in the downstream region, Lujiaodao. At Station D and Station LI, Leptodora kindti showed diel vertical migrations. In Daphnia galeata and Diaphanosoma dubia a limited reverse diel vertical migration was found at Station D, and large individuals tended to stay in lower layers during the day. Neutrodiaptomus incongruens seemed to avoid the water column of 0–6 m by day, but a detectable proportion of the population moved upwards at night. Mesocyclops thermocyclopoides showed a clearly defined diel vertical migration behavior at all three stations. The vertical distribution of Thermocyclops crassus was similar at different stations: in daylight hours it was found almost exclusively in the water column below 6 m and during the night a significant proportion of the population shifted upwards.

Virtualization has been widely adopted in recent years in the cloud computing platform to improve server consolidation and reduce operating cost. Virtual Machine (VM) checkpointing refers to the act of saving a persistent snapshot (or checkpoint) of a VM's state at any instant. VM checkpointing can drastically prolong the lifetime and vulnerability of confidential or private user data in applications that execute within VMs. Simply encrypting the checkpoint does not reduce the lifetime of confidential data that should be quickly discarded after its use. In this paper, we present an application-level approach, called Privacy-preserving Checkpointing (PPC), which excludes confidential data from VM checkpoints, instead of encrypting such data. PPC enables an application programmer to register memory locations that represent the origins of confidential data. During the VM's execution, PPC performs information flow analysis to automatically track the propagation of confidential data through the application and various components of the VM, including the guest operating system. During VM checkpointing, the locations identified during the information flow analysis are excluded from the persistent checkpoint. We present the design and implementation of the PPC system in VirtualBox VMs running the commodity Linux operating system. We demonstrate the use of our system using the vim and gedit text editors. We also show that PPC introduces acceptable performance overhead.

Existing algorithms in conic curves cryptosystem are all sequential ones. It is important to have fast parallel algorithms to both encrypt and decrypt data for cryptosystem on conic curves. This paper proposes our own parallel algorithms for conic curves cryptosystem over finite field Fp. Our main works are paralleling the basic multiple-precision integer algorithms and the point-addition operation on conic curves over finite field Fp. We also calculate the speedup ratio based on computing the runtime of sequential arithmetic and parallel arithmetic in this cryptosystem. The performance evaluation demonstrates that our methodology could reduce time complexity and improve efficiency for conic curves cryptosystem over finite field Fp.

This paper is mainly to present a method to extend the network under Open MPI parallel environment. We walk through the Modular Component Architecture (MCA) and three core elements: framework, component and module comprised in the MCA. After an analysis of the point-to-point communication based on the network components in Open MPI, we present a method to extend a network by creating a network component under Byte Transfer Layer (BTL) framework, implementing the basic functions the component must have, configuring the network component to adjust to different architecture platforms and building the whole project to make the new component work in the framework. Finally we successfully extend the network of Open MPI with Hyper Transport (HT). By running a parallel program on the newly extended Open MPI based on HT. We prove the network extend method is accessible.

Virtual machines (VMs) have been widely adopted in cloud platforms to improve server consolidation and reduce operating costs. VM checkpointing is used to capture a persistent snapshot of a running VM and to later restore the VM to a previous state. Although VM checkpointing eases system administration, such as in recovering from a VM crash or undoing a previous VM activity, it can also increase the risk of exposing users’ confidential data. This is because the checkpoint may store a VM’s physical memory pages and disk contents that contain confidential data such as clear text passwords and credit card numbers. This paper presents the design and implementation of SPARC, a Security and Privacy AwaRe virtual machine Checkpointing mechanism. SPARC enables users to selectively exclude users’ confidential data within a VM from being checkpointed. We describe the design challenges in effectively tracking and excluding process-specific memory and disk contents from the checkpoint file for a VM running on the commodity Linux operating system. We also present techniques to track process dependencies due to inter-process communication and to account for such dependencies in SPARC.