Supramolecular study, Hirshfeld analysis and theoretical study of 6-methoxyquinoline N-oxide dihydrate

In the crystal structure of 6-methoxyquinoline N-oxide dihydrate, C10H9NO2 center dot 2H(2)O, (I), the presence of two-dimensional water networks is analysed. The water molecules form unusual water channels, as well as two intersecting mutually perpendicular columns. In one of these channels, the O atom of the N-oxide group acts as a bridge between the water molecules. The other channel is formed exclusively by water molecules. Confirmation of the molecular packing was performed through the analysis of Hirshfeld surfaces, and (I) is compared with other similar isoquinoline systems. Calculations of bond lengths and angles by the Hartree-Fock method or by density functional theory B3LYP, both with 6-311++G(d,p) basis sets, are reported, together with the results of additional IR, UV-Vis and theoretical studies

Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation

Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions

Orbital Magnetic Dipole Mode in Deformed Clusters: A Fully Microscopic Analysis

The orbital M1 collective mode predicted for deformed clusters in a schematic model is studied in a self-consistent random-phase-approximation approach which fully exploits the shell structure of the clusters. The microscopic mechanism of the excitation is clarified and the close correlation with E2 mode established. The study shows that the M1 strength of the mode is fragmented over a large energy interval. In spite of that, the fraction remaining at low energy, well below the overwhelming dipole plasmon resonance, is comparable to the strength predicted in the schematic model. The importance of this result in view of future experiments is stressed.Comment: 10 pages, 3 Postscript figures, uses revte

Hot Carrier Transport and Photocurrent Response in Graphene

Strong electron-electron interactions in graphene are expected to result in multiple-excitation generation by the absorption of a single photon. We show that the impact of carrier multiplication on photocurrent response is enhanced by very inefficient electron cooling, resulting in an abundance of hot carriers. The hot-carrier-mediated energy transport dominates the photoresponse and manifests itself in quantum efficiencies that can exceed unity, as well as in a characteristic dependence of the photocurrent on gate voltages. The pattern of multiple photocurrent sign changes as a function of gate voltage provides a fingerprint of hot-carrier-dominated transport and carrier multiplication.Comment: 4 pgs, 2 fg

Method for estimating potential recognition capacity of texture-based biometrics

When adopting an image-based biometric system, an important factor for consideration is its potential recognition capacity, since it not only defines the potential number of individuals likely to be identifiable, but also serves as a useful figure-of-merit for performance. Based on block transform coding commonly used for image compression, this study presents a method to enable coarse estimation of potential recognition capacity for texture-based biometrics. Essentially, each image block is treated as a constituent biometric component, and image texture contained in each block is binary coded to represent the corresponding texture class. The statistical variability among the binary values assigned to corresponding blocks is then exploited for estimation of potential recognition capacity. In particular, methodologies are proposed to determine appropriate image partition based on separation between texture classes and informativeness of an image block based on statistical randomness. By applying the proposed method to a commercial fingerprint system and a bespoke hand vein system, the potential recognition capacity is estimated to around 10^36 for a fingerprint area of 25  mm^2 which is in good agreement with the estimates reported, and around 10^15 for a hand vein area of 2268  mm^2 which has not been reported before

Sea-level constraints on the amplitude and source distribution of Meltwater Pulse 1A.

During the last deglaciation, sea levels rose as ice sheets retreated. This climate transition was punctuated by periods of more intense melting; the largest and most rapid of these—Meltwater Pulse 1A—occurred about 14,500 years ago, with rates of sea-level rise reaching approximately 4 m per century1, 2, 3. Such rates of rise suggest ice-sheet instability, but the meltwater sources are poorly constrained, thus limiting our understanding of the causes and impacts of the event4, 5, 6, 7. In particular, geophysical modelling studies constrained by tropical sea-level records1, 8, 9 suggest an Antarctic contribution of more than seven metres, whereas most reconstructions10 from Antarctica indicate no substantial change in ice-sheet volume around the time of Meltwater Pulse 1A. Here we use a glacial isostatic adjustment model to reinterpret tropical sea-level reconstructions from Barbados2, the Sunda Shelf3 and Tahiti1. According to our results, global mean sea-level rise during Meltwater Pulse 1A was between 8.6 and 14.6 m (95% probability). As for the melt partitioning, we find an allowable contribution from Antarctica of either 4.1 to 10.0 m or 0 to 6.9 m (95% probability), using two recent estimates11, 12 of the contribution from the North American ice sheets. We conclude that with current geologic constraints, the method applied here is unable to support or refute the possibility of a significant Antarctic contribution to Meltwater Pulse 1A