Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Mass production and commercial availability are prerequisites for the viability and wide application of graphene. The exfoliation of graphite to give graphene is one of the most promising ways to achieve large-scale production at an extremely low cost. This review focuses on discussing different exfoliation techniques based on a common mechanical mechanism; because a deep understanding of the exfoliation mechanism can provide fruitful information on how to efficiently achieve high-quality graphene by optimizing exfoliation techniques. We highlight the recent progress on mechanical exfoliation for graphene production during the last decade. The emphasis is set on the widely used sonication method with the latest insight into sonication-induced defects, the newly explored ball milling method, the fluid dynamics method that has emerged in the last three years, and the innovative supercritical fluid method. We also give an outlook on how to achieve high-quality graphene efficiently using mechanical exfoliation techniques. We hope this review will point towards a rational direction for the scalable production of graphene.

A review on mechanical exfoliation for the scalable production of graphene

We present a critical review that encompasses the fundamentals and state-of-the-art knowledge of barium titanate-based piezoelectrics. First, the essential crystallography, thermodynamic relations, and concepts necessary to understand piezoelectricity and ferroelectricity in barium titanate are discussed. Strategies to optimize piezoelectric properties through microstructure control and chemical modification are also introduced. Thereafter, we systematically review the synthesis, microstructure, and phase diagrams of barium titanate-based piezoelectrics and provide a detailed compilation of their functional and mechanical properties. The most salient materials treated include the (Ba,Ca)(Zr,Ti)O3, (Ba,Ca)(Sn,Ti)O3, and (Ba,Ca)(Hf,Ti)O3 solid solution systems. The technological relevance of barium titanate-based piezoelectrics is also discussed and some potential market indicators are outlined. Finally, perspectives on productive lines of future research and promising areas for the applications of these ma...

BaTiO3-based piezoelectrics: Fundamentals, current status, and perspectives

The discovery of ultrahigh piezoelectricity in relaxor-ferroelectric solid solution single crystals is a breakthrough in ferroelectric materials. A key signature of relaxor-ferroelectric solid solutions is the existence of polar nanoregions, a nanoscale inhomogeneity, that coexist with normal ferroelectric domains. Despite two decades of extensive studies, the contribution of polar nanoregions to the underlying piezoelectric properties of relaxor ferroelectrics has yet to be established. Here we quantitatively characterize the contribution of polar nanoregions to the dielectric/piezoelectric responses of relaxor-ferroelectric crystals using a combination of cryogenic experiments and phase-field simulations. The contribution of polar nanoregions to the room-temperature dielectric and piezoelectric properties is in the range of 50-80%. A mesoscale mechanism is proposed to reveal the origin of the high piezoelectricity in relaxor ferroelectrics, where the polar nanoregions aligned in a ferroelectric matrix can facilitate polarization rotation. This mechanism emphasizes the critical role of local structure on the macroscopic properties of ferroelectric materials.

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The origin of ultrahigh piezoelectricity in relaxor-ferroelectric solid solution crystals

In order to fulfill their potential for applications, it will be necessary to develop large-scale production methods for two-dimensional (2D) inorganic nanosheets. Here we demonstrate the large-scale shear-exfoliation of molybdenum disulfide nanosheets in aqueous surfactant solution using a kitchen blender. Using standard procedures, we measure how the MoS2 concentration and production rate scale with processing parameters. However, we also use recently developed methods based on optical spectroscopy to simultaneously measure both nanosheet lateral size and thickness, allowing us to also study the dependence of nanosheet dimensions on processing parameters. We found the nanosheet concentration and production rates to depend sensitively on the mixing parameters (the MoS2 concentration, Ci; the mixing time, t; the liquid volume, V; and the rotor speed, N). By optimizing mixing parameters, we achieved concentrations and production rates as high as 0.4 mg/mL and 1.3 mg/min, respectively. Conversely, the nanos...

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Large-Scale Production of Size-Controlled MoS2 Nanosheets by Shear Exfoliation

Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder (VAE) for classification. The decoder of the VAE generates a Gaussian distribution, so it can be used to fit the Gaussian distribution of EEG signals. A new representation of input was developed by combining the time, frequency, and channel information from the EEG signal, and the CNN-VAE method was designed and optimized accordingly for this form of input. In this network, the classification of the extracted CNN features is performed via the deep network VAE. Our framework, with an average kappa value of 0.564, outperforms the best classification method in the literature for BCI Competition IV dataset 2b with a 3% improvement. Furthermore, using our own dataset, the CNN-VAE framework also yields the best performance for both three-electrode and five-electrode EEGs and achieves the best average kappa values 0.568 and 0.603, respectively. Our results show that the proposed CNN-VAE method raises performance to the current state of the art.

EEG Classification of Motor Imagery Using a Novel Deep Learning Framework.

We investigated the effect of surfactants and their concentration on the final graphene concentration via the liquid-phase exfoliation method. Six typical surfactants including ionic and non-ionic ones are explored and the optimized concentration for each surfactant is determined. For ionic surfactants, the graphene concentration increases with surfactant addition and then decreases after reaching its maximum value. In contrast, for non-ionic surfactants, graphene concentration firstly increases with surfactant concentration and then saturates. The different mechanisms of ionic and non-ionic surfactants in stabilizing graphene dispersions are explained by the theory for colloidal stability. Surfactant molecules can adhere to exfoliated graphene sheets and provide an available repulsive force for their stabilization. The as-prepared graphene sheets are verified to be highly exfoliated through transmission electron microscopy and atomic force microscopy studies. The defect level is investigated by Raman spectra and X-ray photoelectron spectroscopy.

The effect of surfactants and their concentration on the liquid exfoliation of graphene

This paper presents a capacitive rotary encoder for both angular position and angular speed measurements. The encoder is mainly composed of three parts: the transmitting segments; a pair of reflecting electrodes; and a pair of receiving electrodes. The transmitting segments together with four mutual quadrature carrier voltages provide a modulated electric field. The reflecting electrodes, which are patterned sinusoidally can encode the angular position to a phase/frequency modulated signal based on quadrature modulation. The modulated signal is then digitally decoded to the angular position in a field programmable gate array processor based on the quadrature demodulation and the coordinate rotational digital computer algorithm. Through a universal serial bus, the digital angular position is transmitted to a computer for further analysis in National Instruments' LabVIEW software. A prototype of the capacitive encoder shows that its precision is better than 0.006° and the resolution is 0.002°. The dynamic nonlinearity is evaluated at ±0.4° when the rotor is rotating at 1000 r/min.

A Capacitive Rotary Encoder Based on Quadrature Modulation and Demodulation

Multiobject tracking is a basic task in video analysis. Due to the strict requirements on efficiency and resource consumption, most of the applications on edge devices are online or near-online methods. Besides motion modeling, appearance information is also widely used for tracking. However, the influence of occlusion is usually ignored. In this article, spatial–temporal co-occurrence constraints (STCCs) features are introduced to resist occlusions by exploring the rich spatial and temporal information of tracklets. In addition, a novel blockchain-based near-online framework called co-occurrence constraints tracklet tracker (CoCTs) is proposed for cross-camera tracking. It inherits the advantages of the blockchain technology in sharing information. Based on blockchain, an efficient association mechanism and a reliable information sharing method are introduced. Experimental results show that CoCT performs high computational efficiency and low resource consumption. In the edge computing environment, it achieves real-time performance on cross-camera tracking. On the MOT17 benchmark, our method shows the state-of-the-art results compared with other online trackers.

Near-Online Tracking With Co-Occurrence Constraints in Blockchain-Based Edge Computing

Acoustic communication is important for the reproductive behaviour of frogs. Using acoustic playback experiments, Shen et al. show that calls from male concave-eared frogs (Odorrana tormota) evoke vocal responses and phonotaxis from females, but the females show no ultrasonic sensitivity.

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Shuai Wang

Papers

EEG Classification of Motor Imagery Using a Novel Deep Learning Framework.

The effect of surfactants and their concentration on the liquid exfoliation of graphene

A Capacitive Rotary Encoder Based on Quadrature Modulation and Demodulation

Near-Online Tracking With Co-Occurrence Constraints in Blockchain-Based Edge Computing

Ultrasonic frogs show extraordinary sex differences in auditory frequency sensitivity