Abstract:The identification of species within an ecosystem plays a key role in formulating an inventory for use in the development of conservation management plans. The classification of mangrove species typically involves intensive field surveys, whereas remote sensing techniques represent a cost-efficient means of mapping and monitoring mangrove forests at large scales. However, the coarse spectral resolution of remote sensing technology has up until recently restricted the ability to identify individual species. The more recent development of very high-resolution spatial optical remote sensing sensors and techniques has thus provided new opportunities for the accurate mapping of species within mangrove forests over large areas. When dealing with the complex problems associated with discriminating among species, classifier performance could be enhanced through the adoption of more intrinsic features; such as textural and differential spectral features. This study explored the effectiveness of textural and differential spectral features in mapping mangrove inter-species obtained from WorldView-3 high-spatial-resolution imagery for mangrove species in Hong Kong. Due to the different arrangement of leaves, the branch density, and the average height and size of plants, we found that the differential spectral features could aid in reducing inner-species variability and increasing intra-species separation. Using a combination of textural and differential spectral features thus represents a promising tool for discriminating among mangrove species. Experimental results suggest that combining these features can greatly improve mapping accuracy, thereby providing more reliable mapping results.

First-principles together with statistical mechanics calculations have been performed to study the adsorption behavior of H2O, NH3, CO, and NO2 on the pristine graphene. In the first-principles calculations, we find that the most recent van der Waals (vdW) density functional vdW-DF2 gives even larger binding energies (Eb) that those obtained with the local density approximation, indicating vdW-DF2 may be inappropriate for describing the interaction between these molecules and graphene. With the potential energy curves of the molecules on graphene calculated by the density functional theory, the adsorption capacity (n) of the molecules on the pristine graphene is calculated with the statistical mechanics method. NO2 has the largest n of the order of 108 cm−2 among the four molecules on graphene at room temperature and concentration of 1.0 ppm, but still smaller by almost two order than that on graphene devices estimated from the experimental results. This is probably due to the strong binding of NO2 to the graphene edges with terminating oxygen atoms with Eb as large as 1.0 eV. The calculations of the adsorption capacity of small polar molecules on the pristine graphene and comparison with the experimental values may contribute to the understanding of the mechanism and designing of graphene based gas sensors.

Ag/13X adsorbents were synthesized, characterized and tested for decontamination of gaseous effluents from 131 I 2 at high temperatures. X-ray diffraction patterns showed that the Ag/13X samples maintained a stable structure after calcined at 650°C for 2 h. The decontamination factors achieved with 15 % Ag/13X and 20 % Ag/13X adsorbents for 131 I 2 were nearly close to 10 3 at 650°C. In addition, 15 % Ag/13X had a stable performance for removal of 131 I 2 at 550 and 650°C, even after calcined at 550 and 650°C for over 10 h, which might be suitable for future potential use during nuclear reactor operation or in the case of nuclear accidents.

Virtual Geographic Environment Cognition is the attempt to understand the human cognition of surface features, geographic processes, and human behaviour, as well as their relationships in the real world. From the perspective of human cognition behaviour analysis and simulation, previous work in Virtual Geographic Environments (VGEs) has focused mostly on representing and simulating the real world to create an 'interpretive' virtual world and improve an individual's active cognition. In terms of reactive cognition, building a user 'evaluative' environment in a complex virtual experiment is a necessary yet challenging task. This paper discusses the outlook of VGEs and proposes a framework for virtual cognitive experiments. The framework not only employs immersive virtual environment technology to create a realistic virtual world but also involves a responsive mechanism to record the user's cognitive activities during the experiment. Based on the framework, this paper presents two potential implementation methods: first, training a deep learning model with several hundred thousand street view images scored by online volunteers, with further analysis of which visual factors produce a sense of safety for the individual, and second, creating an immersive virtual environment and Electroencephalogram (EEG)-based experimental paradigm to both record and analyse the brain activity of a user and explore what type of virtual environment is more suitable and comfortable. Finally, we present some preliminary findings based on the first method.

We investigate the transmission of electrons in a single layer graphene system subjected to nanoscale magnetic barriers and wells arranged in the Cantor pre-fractal and the finite periodic distribution. We find that the angular threshold and angular asymmetry of the transmission spectra are closely related to the ratio between the magnitude of the vector potential and the incident energy (|A|/E), which also determine the number and width of the resonant domains for the finite periodically magnetic modulation and the splitting features for the pre-fractal distribution. For the finite periodically magnetic modulation, the position, magnitude, and interval of the oscillatory domains in the conductance spectra are determined by the value |A|. However, due to the disorder of the pre-fractal distribution, the oscillation of the conductance spectrum is less regular compared to the corresponding one of the finite periodic distribution. We also find that the conductance approaches the classical limit in the high-Fermi-energy region but exceeds it in the low-Fermi-energy region, and the critical point of the two regions is negatively correlated with the magnitude of the vector potential.

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