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Why model evacuee decision-making?
No full textThe purpose of this article is to outline the theoretical and practical benefits of representing the evacuee decision-making process within an agent-based simulation tool. This rationale is important as the development of a comprehensive representation of evacuee decision-making will be expensive, requiring a great deal of time, expertise and effort. However, the theoretical and practical benefits are such that this effort is considered warranted. In previous work, Gwynne et al. demonstrated advances in the representation of evacuee performance and the potential for representing evacuee decision-making. Here, we show that realising this potential is critical to progressing future analysis and, in turn, the field. The paper concludes the following: \u2022 Current understanding of evacuee performance suggests a decision-making process often in response to a complex, ambiguous and dynamic environment. This process connects the conditions experienced by an agent and the actions taken. Representing this is important if we are interested in what evacuees do and when they do it. \u2022 Agent-based models have the potential to represent evacuees, their decision-making process, their subsequent actions and the resultant interactions between agents and entities in their environment. \u2022 Generative Social Science (GSS) employs ABM (agent-based models) to produce subject matter insights using retrodiction and prediction. These approaches will help us enhance our subject matter understanding and the computational tools available to quantify evacuee performance, in turn aiding theoretical and practical efforts. The specific benefits of this approach to our understanding and quantification of evacuee performance are described in this paper, which include the expansion of the explanatory value of the tools available and the refinement of theoretical explanations of evacuee behaviour.Peer reviewed: YesNRC publication: Ye
Supporting trust and engagement in personalized learning
No full textThis paper discusses the benefits and challenges associated with supporting learner trust and engagement in personalized learning. Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. Quality of personalization depends on information collected from the learner and stored in a personal profile. The authors elaborate on research and design solutions to support privacy, trust and engagement in the context of the Learning and Performance Support (LPSS) program at the National Research Council of Canada. The paper builds on earlier work by the authors related to learning personalization, trust and privacy, and on the results of user surveys and usability studies of the LPSS system related to trust, privacy and user engagement.Peer reviewed: YesNRC publication: Ye
Transcriptome dynamics associated with resistance and susceptibility against fusarium head blight in four wheat genotypes
No full textBackground: Fusarium head blight (FHB) of wheat in North America is caused mostly by the fungal pathogen Fusarium graminearum (Fg). Upon exposure to Fg, wheat initiates a series of cellular responses involving massive transcriptional reprogramming. In this study, we analyzed transcriptomics data of four wheat genotypes (Nyubai, Wuhan 1, HC374, and Shaw), at 2 and 4 days post inoculation (dpi) with Fg, using RNA-seq technology. Results: A total of 37,772 differentially expressed genes (DEGs) were identified, 28,961 from wheat and 8811 from the pathogen. The susceptible genotype Shaw exhibited the highest number of host and pathogen DEGs, including 2270 DEGs associating with FHB susceptibility. Protein serine/threonine kinases and LRR-RK were associated with susceptibility at 2 dpi, while several ethylene-responsive, WRKY, Myb, bZIP and NAC-domain containing transcription factors were associated with susceptibility at 4 dpi. In the three resistant genotypes, 220 DEGs were associated with resistance. Glutathione S-transferase (GST), membrane proteins and distinct LRR-RKs were associated with FHB resistance across the three genotypes. Genes with unique, high up-regulation by Fg in Wuhan 1 were mostly transiently expressed at 2 dpi, while many defense-associated genes were up-regulated at both 2 and 4 dpi in Nyubai; the majority of unique genes up-regulated in HC374 were detected at 4 dpi only. In the pathogen, most genes showed increased expression between 2 and 4 dpi in all genotypes, with stronger levels in the susceptible host; however two pectate lyases and a hydrolase were expressed higher at 2 dpi, and acetyltransferase activity was highly enriched at 4 dpi. Conclusions: There was an early up-regulation of LRR-RKs, different between susceptible and resistant genotypes; subsequently, distinct sets of genes associated with defense response were up-regulated. Differences in expression profiles among the resistant genotypes indicate genotype-specific defense mechanisms. This study also shows a greater resemblance in transcriptomics of HC374 to Nyubai, consistent with their sharing of two FHB resistance QTLs on 3BS and 5AS, compared to Wuhan 1 which carries one QTL on 2DL in common with HC374.Peer reviewed: YesNRC publication: Ye
Aggregated learning: a vector quantization approach to learning with neural networks
No full textWe establish an equivalence between information bottleneck (IB) learning and an unconventional quantization problem, `IB quantization'. Under this equivalence, standard neural network models correspond to scalar IB quantizers. We prove a coding theorem for IB quantization, which implies that scalar IB quantizers are in general inferior to vector IB quantizers. This inspires us to develop a learning framework for neural networks, AgrLearn, that corresponds to vector IB quantizers. We experimentally verify that AgrLearn applied to some deep network models of current art improves upon them, while requiring less training data. With a heuristic smoothing, AgrLearn further improves its performance, resulting in new state of the art in image classification on Cifar10.Peer reviewed: NoNRC publication: Ye
Optimal scheduling and science delivery of spectra for millions of targets in thousands of fields: the operational concept of the Maunakea spectroscopic explorer (MSE)
No full textThe Maunakea Spectroscopic Explorer (MSE) will each year obtain millions of spectra in the optical to near-infrared, at low (R~3000) to high (R~40000) spectral resolution by observing >3000 spectra per pointing via a highly multiplexed fiber-fed system. Key science programs for MSE include black hole reverberation mapping, stellar population analysis of faint galaxies at high redshift, and sub-km/s velocity accuracy for stellar astrophysics. The architecture of MSE is an assembly of subsystems designed to meet the science requirements and describes what MSE will look like. In this paper we focus on the operations concept of MSE, which describes how to operate a fiber fed, highly multiplexed, dedicated observatory given its architecture and the science requirements. The operations concept details the phases of operations, from selecting proposals within the science community to distributing back millions of spectra to this community. For each phase, the operations concept describes the tools required to support the science community in their analyses and the operations staff in their work. It also highlights the specific needs related to the complexity of MSE with millions of targets to observe, thousands of fibers to position, and different spectral resolution to use. Finally, the operations concept shows how the science requirements on calibration and observing efficiency can be met.Peer reviewed: NoNRC publication: Ye
Track research: longitudinal rail stress management gap analysis research report (phase 1)
No full textRail steel is produced in fixed lengths of between 12 to 120 m. To build track, rails can be joined either mechanically with joint bars and bolts or by welding the sections together as continuously welded rail (CWR). The general industry view is that CWR is preferred for main track construction as it eliminates discontinuities in track support. However, with this type of track construction, the rail steel is fixed in place and not free to expand or contract with changes in temperature. This results in thermally induced stresses in the rail (compressive when hot, and tensile when cold), which can be extremely large and contribute to rail breaks during cold weather conditions, and rail buckles under hot weather conditions. Although North American railways have operated with CWR since the 1950s, there is insufficient information on how rail behaves under stress, how longitudinal stress changes over time, how stress impacts the interaction between track and trains during operations, what exact stress condition(s) would cause broken rail or track buckling, nor how effective are current stress management practices. A literature review of thirty-three technical documents revealed a recurring theme of the difficulty in quantifying the level of stress being experienced by rails in CWR construction. Three potentially game changing technologies for longitudinal rail stress measurement were identified, including frictional strain sensing, X-ray diffraction and fibre optic sensing. These are commercial technologies already used in industries including oil and gas, construction, geotechnical and mining. A way forward is also proposed to test and validate the technologies, and deploy those suitable for the rail operating environment.Peer reviewed: YesNRC publication: Ye
Simulation and numerical modeling of polymers forming processes using NRC\u2019s BlowView software package
No full textBlowView is a 2.5D finite element based simulation software, developed at NRC and dedicated to simulate conventional extrusion blow molding, twin-sheet extrusion blow molding, suction blow molding, stretch blow molding and thermoforming processes. This versatile engineering simulation software is highly automated, flexible and user-friendly, yet allows users in-depth analysis capabilities for a wide range of materials, and includes warpage, optimization and permeability applications. Conventional and twin-sheet extrusion blow molding processes are extensively used in manufacturing automotive parts. These processes consist of three main phases: parison/sheet formation, inflation and part cooling and solidification. The parison/sheet formation is the most critical stage, as the final dimensions and mechanical performance of the part are directly related to the initial extrudate shape, which often requires the use of advanced die shaping technologies such as: VWDS, PWDS, SFDR, DSM, and/or a combination of all four. These complex extrusion technologies are all accessible in the BlowView software, and can be simultaneously synchronized and optimized with the machine programming points. An illustration of the optimization methodology, and the gain in terms of part weight reduction, will be presented for an industrial case study. For stretch blow molding & thermoforming applications, the heating stage is of primary importance. The software\u2019s capability to simulate the complex heating stages for thin gauge roll-fed plastic sheets and plastic preforms, including radiation and preferential heating, as well as the inflation/vacuum stages will be highlighted and discussed. A robust and reliable contact algorithm has been implemented in order to manage the sheet/plug, preform/rod and part/mold contact. Finally, the latest advances in 3D forming will be presented on various blow molded parts to predict the induced welding deformation in the pinch zones, which are subjected to high strain ratios.Peer reviewed: NoNRC publication: Ye
The impact of processing on the cytotoxicity of graphene oxide
No full textIn-house prepared graphene oxide (GO) was processed via base washing, sonication, cleaning and combinations of these processing techniques to evaluate the impact on the flake morphology, composition and cytotoxicity of the material. The flakes of unprocessed GO were relatively planar, but upon base washing, the flakes became textured exhibiting many folds and creases observed by AFM. In addition to the pronounced effect on the topography, base washing increased the C/O ratio and increased the cytotoxicity of GO on all four cell lines studied determined via the WST-8 assay. Sonicating the unprocessed and base washed samples resulted in smaller flakes with a similar topography; the base washed flakes lost the texture previously observed upon sonication. The sonicated samples were more toxic than the unprocessed sample, attributed to the smaller flake size, but were interestingly less toxic than the base washed, unsonicated sample despite the base washed unsonicated sample having a larger flake size. This unexpected finding was confirmed by a second analyst using the same, and a different source of GO and resulted in the conclusion that the morphology of GO greatly impacts the cytotoxicity. Cleaning the GO reduced the amount of nitrogen and sulfur impurities in the sample but had no significant impact on the cytotoxicity of the material. It was observed that nutrient depletion via nanomaterial adsorption was not the route of cytotoxicity for the GO samples studied.Peer reviewed: YesNRC publication: Ye
A new realization of SI for organic chemical measurement: NIST PS1 primary standard for quantitative NMR (Benzoic Acid)
No full textMetrological traceability to common references supports the comparability of chemical measurement results produced by different analysts, at various times, and at separate places. Ideally, these references are realizations of base units of the International System of Units (SI). ISO/IEC 17025 (Clause 6.5) states that traceability of measurement results is a necessary attribute of analytical laboratory competence, and as such, has become compulsory in many industries, especially clinical diagnostics and healthcare. Historically, claims of traceability for organic chemical measurements have relied on calibration chains anchored on unique reference materials with linkage to the SI that is tenuous at best. A first-of-its-kind National Institute of Standards and Technology (NIST) reference material, ultrapure and extensively characterized PS1 Benzoic Acid Primary Standard for quantitative NMR (qNMR), serves as a definitive, primary reference (calibrant) that assuredly links the qNMR spectroscopy technique to SI units. As qNMR itself is a favorable method for accurate, direct characterization of chemical reference materials, PS1 is a standard for developing other traceable standards and is intended to establish traceability for the measurement of thousands of organic chemical species. NIST PS1 will play a critical role in directly promoting accuracy and worldwide comparability of measurement results produced by the chemical measurement community, supporting the soundness of clinical diagnostics, food safety and labeling, forensic investigation, drug development, biomedical research, and chemical manufacturing. Confidence in this link to the SI was established through (i) unambiguous identification of chemical structure; (ii) determinations of isotopic composition and molecular weight; (iii) evaluation of the respective molecular amount by multiple primary measurement procedures, including qNMR and coulometry; and (iv) rigorous evaluation of measurement uncertainty using state-of-the-art statistical methods and measurement models.Peer reviewed: YesNRC publication: Ye
Frequency accuracy measurements for the microwave generator used in the Programmable Josephson Voltage Standard at NRC
No full textStandard frequency and time techniques are used to characterize the frequency accuracy of the commercial microwave generator used in the Programmable Josephson Voltage Standard at National Research Council Canada. Tests are also performed to verify the frequency resolution of the generator.Peer reviewed: YesNRC publication: Ye