Are you a SCEPTIC? SoCial mEdia Precision and uTility in Conferences

We analysed Twitter feeds at an emergency medicine scientific conference to determine the (1) accuracy of disseminated educational messages and the (2) use in providing rapid feedback to speakers. Most speakers were happy for key messages to be tweeted, and the majority of tweets (34/37) represented these accurately. It is important that speakers and conference organisers consider Twitter use and its potential benefits and disadvantages

Adaptive Coordination Offsets for Signalized Arterial Intersections using Deep Reinforcement Learning

One of the most critical components of an urban transportation system is the coordination of intersections in arterial networks. With the advent of data-driven approaches for traffic control systems, deep reinforcement learning (RL) has gained significant traction in traffic control research. Proposed deep RL solutions to traffic control are designed to directly modify either phase order or timings; such approaches can lead to unfair situations -- bypassing low volume links for several cycles -- in the name of optimizing traffic flow. To address the issues and feasibility of the present approach, we propose a deep RL framework that dynamically adjusts the offsets based on traffic states and preserves the planned phase timings and order derived from model-based methods. This framework allows us to improve arterial coordination while preserving the notion of fairness for competing streams of traffic in an intersection. Using a validated and calibrated traffic model, we trained the policy of a deep RL agent that aims to reduce travel delays in the network. We evaluated the resulting policy by comparing its performance against the phase offsets obtained by a state-of-the-practice baseline, SYNCHRO. The resulting policy dynamically readjusts phase offsets in response to changes in traffic demand. Simulation results show that the proposed deep RL agent outperformed SYNCHRO on average, effectively reducing delay time by 13.21% in the AM Scenario, 2.42% in the noon scenario, and 6.2% in the PM scenario. Finally, we also show the robustness of our agent to extreme traffic conditions, such as demand surges and localized traffic incidents

Effects of hydrogen on 4130 steel microstructure during tensile loading

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The hydrogen effects on materials program at NIST-Boulder

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Strain-life testing in hydrogen; Adapting equipment for fully reversed loading of pressure vessel steels in hydrogen

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Is microstructural homogeneity the answer to hydrogen embrittlement resistance?

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Combined high energy X-Ray diffraction and small-angle scattering measurements of strain, dislocation density and porosity near steel fatigue cracks grown in hydrogen

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SSWAP: A Simple Semantic Web Architecture and Protocol for semantic web services

<p>Abstract</p> <p>Background</p> <p>SSWAP (<b>S</b>imple <b>S</b>emantic <b>W</b>eb <b>A</b>rchitecture and <b>P</b>rotocol; pronounced "swap") is an architecture, protocol, and platform for using reasoning to semantically integrate heterogeneous disparate data and services on the web. SSWAP was developed as a hybrid semantic web services technology to overcome limitations found in both pure web service technologies and pure semantic web technologies.</p> <p>Results</p> <p>There are currently over 2400 resources published in SSWAP. Approximately two dozen are custom-written services for QTL (Quantitative Trait Loci) and mapping data for legumes and grasses (grains). The remaining are wrappers to Nucleic Acids Research Database and Web Server entries. As an architecture, SSWAP establishes how clients (users of data, services, and ontologies), providers (suppliers of data, services, and ontologies), and discovery servers (semantic search engines) interact to allow for the description, querying, discovery, invocation, and response of semantic web services. As a protocol, SSWAP provides the vocabulary and semantics to allow clients, providers, and discovery servers to engage in semantic web services. The protocol is based on the W3C-sanctioned first-order description logic language OWL DL. As an open source platform, a discovery server running at <url>http://sswap.info</url> (as in to "swap info") uses the description logic reasoner Pellet to integrate semantic resources. The platform hosts an interactive guide to the protocol at <url>http://sswap.info/protocol.jsp</url>, developer tools at <url>http://sswap.info/developer.jsp</url>, and a portal to third-party ontologies at <url>http://sswapmeet.sswap.info</url> (a "swap meet").</p> <p>Conclusion</p> <p>SSWAP addresses the three basic requirements of a semantic web services architecture (<it>i.e</it>., a common syntax, shared semantic, and semantic discovery) while addressing three technology limitations common in distributed service systems: <it>i.e</it>., <it>i</it>) the fatal mutability of traditional interfaces, <it>ii</it>) the rigidity and fragility of static subsumption hierarchies, and <it>iii</it>) the confounding of content, structure, and presentation. SSWAP is novel by establishing the concept of a canonical yet mutable OWL DL graph that allows data and service providers to describe their resources, to allow discovery servers to offer semantically rich search engines, to allow clients to discover and invoke those resources, and to allow providers to respond with semantically tagged data. SSWAP allows for a mix-and-match of terms from both new and legacy third-party ontologies in these graphs.</p