GIS Data Based Automatic High-Fidelity 3D Road Network Modeling

3D road models are widely used in many computer applications such as racing games and driving simulations_ However, almost all high-fidelity 3D road models were generated manually by professional artists at the expense of intensive labor. There are very few existing methods for automatically generating 3D high-fidelity road networks, especially those existing in the real world. This paper presents a novel approach thai can automatically produce 3D high-fidelity road network models from real 2D road GIS data that mainly contain road. centerline in formation. The proposed method first builds parametric representations of the road centerlines through segmentation and fitting . A basic set of civil engineering rules (e.g., cross slope, superelevation, grade) for road design are then selected in order to generate realistic road surfaces in compliance with these rules. While the proposed method applies to any types of roads, this paper mainly addresses automatic generation of complex traffic interchanges and intersections which are the most sophisticated elements in the road network

Visualization of Traffic Accidents

Traffic accidents have tremendous impact on society. Annually approximately 6.4 million vehicle accidents are reported by police in the US and nearly half of them result in catastrophic injuries. Visualizations of traffic accidents using geographic information systems (GIS) greatly facilitate handling and analysis of traffic accidents in many aspects. Environmental Systems Research Institute (ESRI), Inc. is the world leader in GIS research and development. ArcGIS, a software package developed by ESRI, has the capabilities to display events associated with a road network, such as accident locations, and pavement quality. But when event locations related to a road network are processed, the existing algorithm used by ArcGIS does not utilize all the information related to the routes of the road network and produces erroneous visualization results of event locations. This software bug causes serious problems for applications in which accurate location information is critical for emergency responses, such as traffic accidents. This paper aims to address this problem and proposes an improved method that utilizes all relevant information of traffic accidents, namely, route number, direction, and mile post, and extracts correct event locations for accurate traffic accident visualization and analysis. The proposed method generates a new shape file for traffic accidents and displays them on top of the existing road network in ArcGIS. Visualization of traffic accidents along Hampton Roads Bridge Tunnel is included to demonstrate the effectiveness of the proposed method

Supporting Transportation System Management and Operations Using Internet of Things Technology

Low power wide-area network (LPWAN) technology aims to provide long range and low power wireless communication. It can serve as an alternative technology for data transmissions in many application scenarios (e.g., parking monitoring and remote flood sensing). In order to explore its feasibility in transportation systems, this project conducted a review of relevant literature to understand the current status of LPWAN applications. An online survey that targeted professionals concerned with transportation was also developed to elicit input about their experiences in using LPWAN technology for their projects. The literature review and survey results showed that LPWAN’s application in the U.S. is still in an early stage. Many agencies were not familiar with LPWAN technology, and only a few off-the-shelf LPWAN products are currently available that may be directly used for transportation systems. To conceptually explore data transmission, a set of lab tests, using a primary LPWAN technology, namely LoRa, were performed on a university campus area as well as in a rural area. The lab tests showed that several key factors, such as the mounting heights of devices, distance between the gateway and sensor nodes, and brands of devices affected the LPWAN’s performance. Building upon these efforts, the research team proposed a high-level field test plan for facilitating a potential Phase 2 study that will address primary technical issues concerning the feasibility of transmitting data of different sizes, data transmission frequency, and transmission rate, deployment requirements, etc

Miniaturization in x ray and gamma ray spectroscopy

The paper presents advances in two new sensor technologies and a miniaturized associated electronics technology which, when combined, can allow for very significant miniaturization and for the reduction of weight and power consumption in x-ray and gamma-ray spectroscopy systems: (1) Mercuric iodide (HgI2) x-ray technology, which allows for the first time the construction of truly portable, high-energy resolution, non-cryogenic x-ray fluorescence (XRF) elemental analyzer systems, with parameters approaching those of laboratory quality cryogenic instruments; (2) the silicon avalanche photodiode (APD), which is a solid-state light sensitive device with internal amplification, capable of uniquely replacing the vacuum photomultiplier tube in scintillation gamma-ray spectrometer applications, and offering substantial improvements in size, ruggedness, low power operation and energy resolution; and (3) miniaturized (hybridized) low noise, low power amplification and processing electronics, which take full advantage of the favorable properties of these new sensors and allow for the design and fabrication of advanced, highly miniaturized x-ray and gamma-ray spectroscopy systems. The paper also presents experimental results and examples of spectrometric systems currently under construction. The directions for future developments are discussed

High-Fidelity Roadway Modeling and Simulation

Roads are an essential feature in our daily lives. With the advances in computing technologies, 2D and 3D road models are employed in many applications, such as computer games and virtual environments. Traditional road models were generated by professional artists manually using modeling software tools such as Maya and 3ds Max. This approach requires both highly specialized and sophisticated skills and massive manual labor. Automatic road generation based on procedural modeling can create road models using specially designed computer algorithms or procedures, reducing the tedious manual editing needed for road modeling dramatically. But most existing procedural modeling methods for road generation put emphasis on the visual effects of the generated roads, not the geometrical and architectural fidelity. This limitation seriously restricts the applicability of the generated road models. To address this problem, this paper proposes a high-fidelity roadway generation method that takes into account road design principles practiced by civil engineering professionals, and as a result, the generated roads can support not only general applications such as games and simulations in which roads are used as 3D assets, but also demanding civil engineering applications, which requires accurate geometrical models of roads. The inputs to the proposed method include road specifications, civil engineering road design rules, terrain information, and surrounding environment. Then the proposed method generates in real time 3D roads that have both high visual and geometrical fidelities. This paper discusses in details the procedures that convert 2D roads specified in shape files into 3D roads and civil engineering road design principles. The proposed method can be used in many applications that have stringent requirements on high precision 3D models, such as driving simulations and road design prototyping. Preliminary results demonstrate the effectiveness of the proposed method

Differential binding patterns of anti-sulfatide antibodies to glial membranes

Sulfatide is a major glycosphingolipid in myelin and a target for autoantibodies in autoimmune neuropathies. However neuropathy disease models have not been widely established, in part because currently available monoclonal antibodies to sulfatide may not represent the diversity of anti-sulfatide antibody binding patterns found in neuropathy patients. We sought to address this issue by generating and characterising a panel of new anti-sulfatide monoclonal antibodies. These antibodies have sulfatide reactivity distinct from existing antibodies in assays and in binding to peripheral nerve tissues and can be used to provide insights into the pathophysiological roles of anti-sulfatide antibodies in demyelinating neuropathies

A Novel DNA and Protein Combination COVID-19 Vaccine Formulation Provides Full Protection against SARS-CoV-2 in Rhesus Macaques

The current study aims to develop a safe and highly immunogenic COVID-19 vaccine. The novel combination of a DNA vaccine encoding the full-length Spike (S) protein of SARS-CoV-2 and a recombinant S1 protein vaccine induced high level neutralizing antibody and T cell immune responses in both small and large animal models. More significantly, the co-delivery of DNA and protein components at the same time elicited full protection against intratracheal challenge of SARS-CoV-2 viruses in immunized rhesus macaques. As both DNA and protein vaccines have been proven safe in previous human studies, and DNA vaccines are capable of eliciting germinal center B cell development, which is critical for high -affinity memory B cell responses, the DNA and protein co-delivery vaccine approach has great potential to serve as a safe and effective approach to develop COVID-19 vaccines that provide long-term protection

Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation

Graph neural networks (GNNs) have emerged as a powerful paradigm for embedding-based entity alignment due to their capability of identifying isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart entities usually have non-isomorphic neighborhood structures, which easily causes GNNs to yield different representations for them. To tackle this problem, we propose a new KG alignment network, namely AliNet, aiming at mitigating the non-isomorphism of neighborhood structures in an end-to-end manner. As the direct neighbors of counterpart entities are usually dissimilar due to the schema heterogeneity, AliNet introduces distant neighbors to expand the overlap between their neighborhood structures. It employs an attention mechanism to highlight helpful distant neighbors and reduce noises. Then, it controls the aggregation of both direct and distant neighborhood information using a gating mechanism. We further propose a relation loss to refine entity representations. We perform thorough experiments with detailed ablation studies and analyses on five entity alignment datasets, demonstrating the effectiveness of AliNet.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI 2020

Bulk photovoltaic effect in two-dimensional ferroelectric semiconductor α\alpha-In2_2Se3_3

Bulk photovoltaic effect, which arises from crystal symmetry-driven charge carrier separation, is an intriguing physical phenomenon that has attracted extensive interest in photovoltaic application due to its junction-free photovoltaic and potential to surpass Shockley-Queisser limit. Whereas conventional ferroelectric materials mostly suffer from extremely low photocurrent density and weak photovoltaic response at visible light wavelengths. Emerging two-dimensional ferroelectric semiconductors with coupled visible light absorption and spontaneous polarization characteristics are a promising alternative for making functional photoferroelectrics. Herein, we report the experimental demonstration of the bulk photovoltaic effect behavior based on the 2D ferroelectric semiconductor {$\alpha$-InSe caused by an out-of-plane polarization induced depolarization field. The {$\alpha$-InSe device exhibits enhanced bulk photovoltaic response in the visible light spectrum owing to its narrow bandgap. It was demonstrated that the generated photovoltaic current density was nearly two orders of magnitude greater than conventional bulk ferroelectric materials. These findings highlight the potential of 2D ferroelectric semiconductor materials for bulk photovoltaic applications in a broad spectral region

Pattern Recognition Spiking Neural Network for Classification of Chinese Characters

The Spiking Neural Networks (SNNs) are biologically more realistic than other types of Artificial Neural Networks (ANNs), but they have been much less utilised in applications. When comparing the two types of NNs, the SNNs are considered to be of lower latency, more hardware-friendly and energy-efficient, and suitable for running on portable devices with weak computing performance. In this paper we aim to use an SNN for the task of classifying Chinese character images, and test its performance. The network utilises inhibitory synapses for the purpose of using unsupervised learning. The learning algorithm is a derivative of the traditional Spike-timing-dependent Plasticity (STDP) learning rule. The input images are first pre-processed by traditional methods (OpenCV).Different hyperparameters configurations are tested reaching an optimal configuration and a classification accuracy rate of 93%