Fast reproducible identification and large-scale databasing of individual functional cognitive networks

Background  

Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level.     

Some theoretical aspects of elastic wave modeling with a recently developed spectral element method

A spectral element method has been recently developed for solving elastodynamic problems. The numerical solutions are obtained by using the weak formulation of the elastodynamic equation for heterogeneous media, based on the Galerkin approach applied to a partition, in small subdomains, of the original physical domain. In this work, some mathematical aspects of the method and the associated algorithm implementation are systematically investigated. Two kinds of orthogonal basis functions, constructed with Legendre and Chebyshev polynomials, and their related Gauss-Lobatto collocation points are introduced. The related integration formulas are obtained. The standard error estimations and expansion convergence are discussed. An element-by-element pre-conditioned conjugate gradient linear solver in the space domain and a staggered predictor/multi-corrector algorithm in the time integration are used for strong heterogeneous elastic media. As a consequence, neither the global matrices nor the effective force vector is assembled. When analytical formulas are used for the element quadrature, there is even no need for forming element matrix in order to further save memory without losing much in computational efficiency. The element-by-element algorithm uses an optimal tensor product scheme which makes this method much more efficient than finite-element methods from the point of view of both memory storage and computational time requirements. This work is divided into two parts. The first part mainly focuses on theoretical studies with a simple numerical result for the Che-byshev spectral element, and the second part, mainly with the Legendre spectral element, will give the algorithm implementation, numerical accuracy and efficiency analyses, and then the detailed modeling example comparisons of the proposed spectral element method with a pseudo-spectral method, which will be seen in another work by Lin, Wang and Zhang.     

A 100 kHz Narrow-Pulse-Width Mid-Infrared Optical Parametric Oscillator Based on PPMgLN Crystal

We report a narrow pulse width optical parametric oscillator based on periodically poled MgO:LiNbO₃ (PPMgLN) with a high repetition rate under quasi-phase matched conditions. When the maximum pumping power of the 1,064-nm laser was 14.57 W, the acousto-optical (A-O) Q-switch repetition rate was 100 kHz, and the PPMgLN crystal grating period was 29.5 μm. A 1,474-nm signal light output power of 4.21 W and a 3,828 nm idler light output power of 1.547 W were obtained, corresponding to a pulse width of 9.52 ns and 9.65 ns, respectively. The overall optical–optical conversion efficiency was 39.5%. Additionally, by changing the temperature from 25°C to 150°C, a tunable signal wavelength of 1,474–1,499 nm and idler wavelength at 3,676–3,828 nm of the output laser were achieved.     

Analysis of Asymmetric Long-Range Surface-Plasmon Waveguide with High-Confinement Mode

We model and study the asymmetric long-range surface-plasmon waveguides using the finite-element method. We introduce two types of asymmetric structures and discuss their modal properties compared to traditional long-range surface-plasmon waveguides. Although the propagation distance is decreased, the energy-confinement capability is improved for asymmetric long-range waveguiding structures when the geometrical parameters are properly selected. Our simulation result offers guidance for tuning properties of plasmonic waveguides and providing ways for enhancing electromagnetic energy confinement in long-range surface-plasmon waveguides.     

A Novel Method for the Detection of Trace Gases Based on Photo-Acoustic Spectroscopy

We study quartz tuning fork (QTF) characteristics using a 532 nm semiconductor laser with a power of 39 mW and calculate QTF vibrations caused by thermal noise and disturbance of the air using the equipartition theorem; the vibration value is about 1.152 Pm. The signal-to-noise ratio and QTF resonance amplitude acquired experimentally are 104.56 and 214.75 Pm, respectively. In addition, we develop a new photo-acoustic spectroscopic system for detection of trace acetylene using a CW diode laser source with distributed feedback operating near 1,532 nm, measure the absorption spectrum of acetylene employing this system, and show that the method elaborated is more sensitive than photoelectric detectors that provides new directions for research in photo-acoustic spectroscopy.     

Modeling the deformation behavior of nanocrystalline alloy with hierarchical microstructures

A mechanism-based plasticity model based on dislocation theory is developed to describe the mechanical behavior of the hierarchical nanocrystalline alloys. The stress–strain relationship is derived by invoking the impeding effect of the intra-granular solute clusters and the inter-granular nanostructures on the dislocation movements along the sliding path. We found that the interaction between dislocations and the hierarchical microstructures contributes to the strain hardening property and greatly influence the ductility of nanocrystalline metals. The analysis indicates that the proposed model can successfully describe the enhanced strength of the nanocrystalline hierarchical alloy. Moreover, the strain hardening rate is sensitive to the volume fraction of the hierarchical microstructures. The present model provides a new perspective to design the microstructures for optimizing the mechanical properties in nanostructural metals.     

Synthesis and characterization of nanostructured iron compounds prepared from the decomposition of iron pentacarbonyl dispersed into carbon materials with varying porosities

This work describes the production and characterization of carbon-iron nanocomposites obtained from the decomposition of iron pentacarbonyl (Fe(CO)₅) mixed with different carbon materials: a high surface area activated carbon (AC), powdered graphite (G), milled graphite (MG), and carbon black (CB). The nanocomposites were prepared either under argon or in ambient atmosphere, with a fixed ratio of Fe(CO)₅ (4.0 mL) to carbon precursor (2.0 g). The images of scanning electron microscopy and the analysis of textural properties indicated the presence of nanostructured Fe compounds homogeneously dispersed into the different classes of pores of the carbon matrices. The elemental Fe content was always larger for samples prepared in ambient atmosphere, reaching values in the range of 20–32 wt%. On the other hand, samples prepared under argon showed reduced Fe content, with values in the range 5–10 wt% for samples prepared from precursors with low surface area (G, MG, and CB) and a much higher value (~19 wt%) for samples prepared from the precursor of high surface area (AC). Mössbauer spectroscopy and X-ray diffractometry showed that the nanoparticles were mostly composed of iron oxides in the case of the samples prepared in oxygen-rich ambient atmosphere and also for the AC-derived nanocomposite prepared under argon, which is consistent with the large oxygen content of this precursor. For the other precursors, with reduced or no oxygen content, metallic iron and iron carbides were found to be the dominant phases in samples prepared under oxygen-free atmosphere. The samples prepared in ambient atmosphere and the AC-derived sample prepared under argon exhibited superparamagnetic behavior at room temperature, as revealed by temperature-dependent magnetization curves and Mössbauer spectroscopy.     

Automated determination of size and morphology information from soot transmission electron microscope (TEM)-generated images

The thermophoretic sampling of particulates from hot media, coupled with transmission electron microscope (TEM) imaging, is a combined approach that is widely used to derive morphological information. The identification and the measurement of the particulates, however, can be complex when the TEM images are of low contrast, noisy, and have non-uniform background signal level. The image processing method can also be challenging and time consuming, when the samples collected have large variability in shape and size, or have some degree of overlapping. In this work, a three-stage image processing sequence is presented to facilitate time-efficient automated identification and measurement of particulates from the TEM grids. The proposed processing sequence is first applied to soot samples that were thermophoretically sampled from a laminar non-premixed ethylene-air flame. The parameter values that are required to be set to facilitate the automated process are identified, and sensitivity of the results to these parameters is assessed. The same analysis process is also applied to soot samples that were acquired from an externally irradiated laminar non-premixed ethylene-air flame, which have different geometrical characteristics, to assess the morphological dependence of the proposed image processing sequence. Using the optimized parameter values, statistical assessments of the automated results reveal that the largest discrepancies that are associated with the estimated values of primary particle diameter, fractal dimension, and prefactor values of the aggregates for the tested cases, are approximately 3, 1, and 10 %, respectively, when compared with the manual measurements.     

Fabrication of Ni-Co binary oxide/reduced graphene oxide composite with high capacitance and cyclicity as efficient electrode for supercapacitors

Nickel-cobalt binary oxide/reduced graphene oxide (G-NCO) composite with high capacitance is synthesized via a mild method for electrochemical capacitors. G-NCO takes advantages of reduced graphene oxide (RGO) and nickel-cobalt binary oxide. As an appropriate matrix, RGO is beneficial to form homogeneous structure and improve the electron transport ability. The binary oxide owns more active sites than those of nickel oxide and cobalt oxide to promote the redox reaction. Attributed to the well crystallinity, homogeneous structure, increased active sites, and improved charge transfer property, the G-NCO composite exhibits highly enhanced electrochemical performance compared with G-NiO and G-Co₃O₄ composites. The specific capacitance of the G-NCO composite is about 1750 F g^?1 at 1 A g^?1 together with capacitance retention of 79 % (900/1138 F g^?1) over 10,000 cycles at 4 A g^?1. To research its practical application, an asymmetric supercapacitor with G-NCO as positive electrode and activated carbon as negative electrode was fabricated. The asymmetric device exhibits a prominent energy density of 37.7 Wh kg^?1 at a power density of 800 W kg^?1. The modified G-NCO composite shows great potential for high-capacity energy storage.