Designing a novel virtual collaborative environment to support collaboration in design review meetings

Project review meetings are part of the project management process and are organised to assess progress and resolve any design conflicts to avoid delays in construction. One of the key challenges during a project review meeting is to bring the stakeholders together and use this time effectively to address design issues as quickly as possible. At present, current technology solutions based on BIM or CAD are information-centric and do not allow project teams to collectively explore the design from a range of perspectives and brainstorm ideas when design conflicts are encountered. This paper presents a system architecture that can be used to support multi-functional team collaboration more effectively during such design review meetings. The proposed architecture illustrates how information-centric BIM or CAD systems can be made human- and team-centric to enhance team communication and problem solving. An implementation of the proposed system architecture has been tested for its utility, likability and usefulness during design review meetings. The evaluation results suggest that the collaboration platform has the potential to enhance collaboration among multi-functional teams

Methods in angle-resolved photoelectron diffraction: Slab method versus separable propagator cluster approach

We have compared multiple-scattering results of angle-resolved photoelectron diffraction spectra between the exact slab method and the separable propagator perturbation cluster method. In the slab method, the source wave and multiple scattering within strongly scattering layers are expanded in spherical waves while the scattering among different layers is expressed in plane waves. The transformation between spherical waves and plane waves is done exactly. The plane waves are then matched across the solid-vacuum interface to a single outgoing plane wave in the detector's direction. The slab is infinitely extended parallel to the surface. Normal to the surface, enough layers are included to ensure convergence of the calculated intensity. The separable propagator perturbation approach uses two approximations; (i) A separable representation of the Green's-function propagator and (ii) a perturbation expansion of multiple-scattering terms. The cluster size is finite, typically containing 50 atoms or less. Results of this study show that using a cluster of 148 atoms, the largest cluster used to date, the cluster size is still too small for the cluster results on Ni(001) to converge with those of the slab method. Ideas to improve the perturbation expansion cluster method are discussed.published_or_final_versio

Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)

© 1993-2012 IEEE. Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77\%, and at the same time increase the correlation to the true response speed by 19.39-86.47\%

A lung-inspired approach to scalable and robust fuel cell design

A lung-inspired approach is employed to overcome reactant homogeneity issues in polymer electrolyte fuel cells. The fractal geometry of the lung is used as the model to design flow-fields of different branching generations, resulting in uniform reactant distribution across the electrodes and minimum entropy production of the whole system. 3D printed, lung-inspired flow field based PEFCs with N = 4 generations outperform the conventional serpentine flow field designs at 50% and 75% RH, exhibiting a 20% and 30% increase in performance (at current densities higher than 0.8 A cm2) and maximum power density, respectively. In terms of pressure drop, fractal flow-fields with N = 3 and 4 generations demonstrate 75% and 50% lower values than conventional serpentine flow-field design for all RH tested, reducing the power requirements for pressurization and recirculation of the reactants. The positive effect of uniform reactant distribution is pronounced under extended current-hold measurements, where lung-inspired flow field based PEFCs with N = 4 generations exhibit the lowest voltage decay (B5 mV h1). The enhanced fuel cell performance and low pressure drop values of fractal flow field design are preserved at large scale (25 cm2), in which the excessive pressure drop of a large-scale serpentine flow field renders its use prohibitive

Fault-Tolerant Exact State Transmission

We show that a category of one-dimensional XY-type models may enable high-fidelity quantum state transmissions, regardless of details of coupling configurations. This observation leads to a fault- tolerant design of a state transmission setup. The setup is fault-tolerant, with specified thresholds, against engineering failures of coupling configurations, fabrication imperfections or defects, and even time-dependent noises. We propose the implementation of the fault-tolerant scheme using hard-core bosons in one-dimensional optical lattices.Comment: 5 pages and 4 figure

Magnetism and high magnetic-field-induced stability of alloy carbides in Fe-based materials.

Understanding the nature of the magnetic-field-induced precipitation behaviors represents a major step forward towards unravelling the real nature of interesting phenomena in Fe-based alloys and especially towards solving the key materials problem for the development of fusion energy. Experimental results indicate that the applied high magnetic field effectively promotes the precipitation of M23C6 carbides. We build an integrated method, which breaks through the limitations of zero temperature and zero external field, to concentrate on the dependence of the stability induced by the magnetic effect, excluding the thermal effect. We investigate the intimate relationship between the external field and the origins of various magnetics structural characteristics, which are derived from the interactions among the various Wyckoff sites of iron atoms, antiparallel spin of chromium and Fe-C bond distances. The high-magnetic-field-induced exchange coupling increases with the strength of the external field, which then causes an increase in the parallel magnetic moment. The stability of the alloy carbide M23C6 is more dependent on external field effects than thermal effects, whereas that of M2C, M3C and M7C3 is mainly determined by thermal effects

PS-FCN: A Flexible Learning Framework for Photometric Stereo

This paper addresses the problem of photometric stereo for non-Lambertian surfaces. Existing approaches often adopt simplified reflectance models to make the problem more tractable, but this greatly hinders their applications on real-world objects. In this paper, we propose a deep fully convolutional network, called PS-FCN, that takes an arbitrary number of images of a static object captured under different light directions with a fixed camera as input, and predicts a normal map of the object in a fast feed-forward pass. Unlike the recently proposed learning based method, PS-FCN does not require a pre-defined set of light directions during training and testing, and can handle multiple images and light directions in an order-agnostic manner. Although we train PS-FCN on synthetic data, it can generalize well on real datasets. We further show that PS-FCN can be easily extended to handle the problem of uncalibrated photometric stereo.Extensive experiments on public real datasets show that PS-FCN outperforms existing approaches in calibrated photometric stereo, and promising results are achieved in uncalibrated scenario, clearly demonstrating its effectiveness.Comment: ECCV 2018: https://guanyingc.github.io/PS-FC

Public views towards community health and hospital-based outpatient services and their utilisation in Zhejiang, China: a mixed methods study

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Star Formation in the Milky Way. The Infrared View

I present a brief review of some of the most recent and active topics of star formation process in the Milky Way using mid and far infrared observations, and motivated by the research being carried out by our science group using data gathered by the Spitzer and Herschel space telescopes. These topics include bringing together the scaling relationships found in extragalactic systems with that of the local nearby molecular clouds, the synthetic modeling of the Milky Way and estimates of its star formation rate.Comment: 12 pages, 9 figures. To apper in "Cosmic-ray induced phenomenology in star-forming environments: Proceedings of the 2nd Session of the Sant Cugat Forum of Astrophysics" (April 16-19, 2012), Olaf Reimer and Diego F. Torres (eds.

Multi-length scale characterization of compression on metal foam flow-field based fuel cells using X-ray computed tomography and neutron radiography

The mechanical compression of metal foam flow-field based polymer electrolyte fuel cells (PEFCs) is critical in determining the interfacial contact resistance with gas diffusion layers (GDLs), reactant flow and water management. The distinct scale between the pore structure of metal foams and the entire flow-field warrant a multi-length scale characterization that combines ex-situ tests of compressed metal foam samples and in-operando analysis of operating PEFCs using X-ray computed tomography (CT) and neutron radiography. An optimal ‘medium’ compression was found to deliver a peak power density of 853 mW cm−2. The X-ray CT data indicates that the compression process significantly decreases the mean pore size and narrows the pore size distribution of metal foams. Simulation results suggest compressing metal foam increases the pressure drop and gas velocity, improving the convective liquid water removal. This is in agreement with the neutron imaging results that demonstrates an increase in the mass of accumulated liquid water with minimum compression compared to the medium and maximum compression cases. The results show that a balance between Ohmic resistance, water removal capacity and parasitic power is imperative for the optimal performance of metal foam based PEFCs