Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013

Klein tunneling and electron optics in Dirac-Weyl fermion systems with tilted energy dispersion

The outstanding electronic properties of relativistic-like fermions have been extensively studied in solid state systems with isotropic linear dispersions such as graphene. Here, we show that 2D and 3D Dirac-Weyl (DW) materials exhibiting tilted energy dispersions could induce drastically different transport phenomena, compared to the non-tilted case. Indeed, the Klein tunneling of DW fermions of opposite chiralities is predicted to appear along two separated oblique directions. In addition, valley filtering and beam splitting effects are easily tailored by dopant engineering techniques while the refraction of electron waves is dramatically modified by the tilt, thus paving the way for emerging applications in electron optics and valleytronics.Comment: 5 pages, 5 figures and Supplemental Material, submitted for publicatio

The Observability Radius of Networks

This paper studies the observability radius of network systems, which measures the robustness of a network to perturbations of the edges. We consider linear networks, where the dynamics are described by a weighted adjacency matrix, and dedicated sensors are positioned at a subset of nodes. We allow for perturbations of certain edge weights, with the objective of preventing observability of some modes of the network dynamics. To comply with the network setting, our work considers perturbations with a desired sparsity structure, thus extending the classic literature on the observability radius of linear systems. The paper proposes two sets of results. First, we propose an optimization framework to determine a perturbation with smallest Frobenius norm that renders a desired mode unobservable from the existing sensor nodes. Second, we study the expected observability radius of networks with given structure and random edge weights. We provide fundamental robustness bounds dependent on the connectivity properties of the network and we analytically characterize optimal perturbations of line and star networks, showing that line networks are inherently more robust than star networks.Comment: 8 pages, 3 figure

Shaping Giant Membrane Vesicles in 3D-Printed Protein Hydrogel Cages

Giant unilamellar phospholipid vesicles are attractive starting points for constructing minimal living cells from the bottom-up. Their membranes are compatible with many physiologically functional modules and act as selective barriers, while retaining a high morphological flexibility. However, their spherical shape renders them rather inappropriate to study phenomena that are based on distinct cell shape and polarity, such as cell division. Here, a microscale device based on 3D printed protein hydrogel is introduced to induce pH-stimulated reversible shape changes in trapped vesicles without compromising their free-standing membranes. Deformations of spheres to at least twice their aspect ratio, but also toward unusual quadratic or triangular shapes can be accomplished. Mechanical force induced by the cages to phase-separated membrane vesicles can lead to spontaneous shape deformations, from the recurrent formation of dumbbells with curved necks between domains to full budding of membrane domains as separate vesicles. Moreover, shape-tunable vesicles are particularly desirable when reconstituting geometry-sensitive protein networks, such as reaction-diffusion systems. In particular, vesicle shape changes allow to switch between different modes of self-organized protein oscillations within, and thus, to influence reaction networks directly by external mechanical cues

Nanostructurally Controllable Strong Wood Aerogel toward Efficient Thermal Insulation

Eco-friendly materials with superior thermal insulation and mechanical properties are desirable for improved energy- and space-efficiency in buildings. Cellulose aerogels with structural anisotropy could fulfill these requirements, but complex processing and high energy demand are challenges for scaling up. Here we propose a scalable, nonadditive, top-down fabrication of strong anisotropic aerogels directly from wood with excellent, near isotropic thermal insulation functions. The aerogel was obtained through cell wall dissolution and controlled precipitation in lumen, using an ionic liquid (IL) mixture comprising DMSO and a guanidinium phosphorus-based IL [MTBD][MMP]. The wood aerogel shows a unique structure with lumen filled with nanofibrils network. In situ formation of a cellulosic nanofibril network in the lumen results in specific surface areas up to 280 m2/g and high yield strengths >1.2 MPa. The highly mesoporous structure (average pore diameter ∼20 nm) of freeze-dried wood aerogels leads to low thermal conductivities in both the radial (0.037 W/mK) and axial (0.057 W/mK) directions, showing great potential as scalable thermal insulators. This synthesis route is energy efficient with high nanostructural controllability. The unique nanostructure and rare combination of strength and thermal properties set the material apart from comparable bottom-up aerogels. This nonadditive synthesis approach is believed to contribute significantly toward large-scale design and structure control of biobased aerogels.Peer reviewe