411 research outputs found

    Variations in Atmospheric 13C/14C Ratios within a Radiocarbon Preparatory Lab and Implications for AMS Measurements

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    At CAMS, we focus on measuring carbon isotopes (12/13/14C) for a variety of applications. High precision radiocarbon AMS measurements are used for measuring atmospheric radiocarbon. The problem with performing high precision radiocarbon AMS measurements is that there’s almost always background contamination introduced during preparation for the AMS. For graphite preparation, dry ice is used in an isopropyl-dry ice mixture to condense H2O in the CO2 extraction and reduction lines. Dry ice is composed of 12/13C and releases carbon into the atmosphere as it sublimes. The dry ice for preparation is held in containers inside and outside the lab. We believe this may be a possible source of background contamination during the graphite preparatory phase for AMS. This experiment will provide data for quality control for ultra precise measurements at CAMS, LLNL. We measured the 13C/14C ratios of air at multiple locations within the graphitization lab and compared them across the lab and to atmosphere to test the following hypotheses: H1: 13C/14C ratios of lab air vary from atmosphere. H2: 13C/14C ratios vary across sites within the lab. We found that air in the graphite lab varies from atmosphere and at different sites within the lab. The dry ice containers are releasing depleted 13C and 14C into the lab air and affect lab sites close to them. This could lead to contamination when weighing out samples when CO2 can remain adsorbed to the sample during preparation. It is recommended that the dry ice containers be removed from the lab to reduce background contamination levels

    A Method for Investigating “Instructional Familiarity” and Discerning Authentic Learning

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    Background: Presently, most medical educators rely exclusively on item difficulty and discrimination indices to investigate an item’s psychometric quality and functioning. We argue “instructional familiarity” effects should also be of primary concern for persons attempting to discern the quality and meaning of a set of test scores. Aim: There were four primary objectives of this study: (1) Revisit Haladyna and Roid’s conceptualization of “instructional sensitivity” within the context of criterion‑referenced assessments, (2) provide an overview of “instructional familiarity” and its importance, (3) reframe the concept for a modern audience concerned with medical school assessments, and (4) conduct an empirical evaluation of a medical school examination in which we attempt to investigate the instructional effects on person and item measures. Subjects and Methods: This study involved a medical school course instructor providing ratings of instructional familiarity (IF) for each mid‑term examination item, and a series of psychometric analyses to investigate the effects of IF on students’ scores and item statistics. The methodology used in this study is based primarily on a mixed‑method, “action research” design for a medical school course focusing on endocrinology. Rasch measurement model; correlation analysis. Results: The methodology presented in this article was evidenced to better discern authentic learning than traditional approaches that ignore valuable contextual information about students’ familiarity with exam items. Conclusions: The authors encourage other medical educators to adopt this straightforward methodology so as to increase the likelihood of making valid inferences about learning.Keywords: Action research, Assessment, Medical education, Psychometrics, Testin

    Exactness of Acoustic Wave Fronts Calculated via Fresnel-Kirchhoff Integrals

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    Higher Educatio

    The concept of social justice and righteousness in the eighth century prophets

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    This item was digitized by the Internet Archive. Thesis (M.A.)--Boston Universityhttps://archive.org/details/theconceptofsoci00hed

    Linear and circular k-combinations with restrictions

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    For several years problems involving k-combinations of n objects have filled the literature. Most of these have concerned combinations taken on a line, but selections arranged on a circle are also of interest. The aim here is to organize and discuss in some detail these linear combinations in addition to the circular varieties. Various restrictions will be placed on the combinations. Finally, an apparently unlikely relationship will be established between k-combinations and the Fibonacci and Lucas sequences. Any intermediate mathematics student with only a minimum of experience in the field should have little difficulty with this discussion. Familiarity with only the basic concepts is assumed. The books of Riordan [11] as well as Netto [10] are recommended references. For notation, terminology, and basic combinatorial results, we follow Riordan

    From the Top: Surface-derived Carbon Fuels Greenhouse Gas Production at Depth in a Neotropical Peatland

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    Tropical peatlands play an important role in global carbon (C) cycling but little is known about factors driving carbon dioxide (CO2) and methane (CH4) emissions from these ecosystems, especially production below the surface. This study aimed to identify source material and processes regulating C emissions from deep in a Neotropical peatland on the Caribbean coast of Panama. We hypothesized that: 1) surface derived organic matter transported down the soil profile is the primary C source for respiration products at depth and 2) high lignin content results in hydrogenotrophic methanogenesis as the dominant CH4 production pathway throughout the profile. We used radiocarbon isotopes to determine whether CO2 and CH4 at depth (measured to 2 m) are produced from modern substrates or ancient deep peat, and we used stable C isotopes to identify the dominant CH4 production pathway. Peat organic chemistry was characterized using 13C solid state nuclear magnetic resonance spectroscopy (13C-NMR). We found that deep peat respiration products had radiocarbon signatures that were more similar to surface dissolved organic C (DOC) than deep solid peat. Radiocarbon ages for deep peat ranged from 1200 – 1800 yrBP at the sites measured. These results indicate that surface derived C was the dominant source for gas production at depth in this peatland, likely because of vertical transport of DOC from the surface to depth. Carbohydrates did not vary with depth across these sites, whereas lignin, which was the most abundant compound (55–70 % of C), tended to increase with depth. These results suggest that there is no preferential decomposition of carbohydrates, but preferential retention of lignin. Stable isotope signatures of respiration products indicated that hydrogenotrophic rather than acetoclastic methanogenesis was the dominant production pathway of CH4 throughout the peat profile. These results suggest, even C compounds that are typically considered vulnerable to decomposition (i.e., carbohydrates) are preserved deep in these tropical peats, highlighting the importance of anaerobic, waterlogged conditions for preserving tropical peatland C

    IndoorWaze: A Crowdsourcing-Based Context-Aware Indoor Navigation System

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    Indoor navigation systems are very useful in large complex indoor environments such as shopping malls. Current systems focus on improving indoor localization accuracy and must be combined with an accurate labeled floor plan to provide usable indoor navigation services. Such labeled floor plans are often unavailable or involve a prohibitive cost to manually obtain. In this paper, we present IndoorWaze, a novel crowdsourcing-based context-aware indoor navigation system that can automatically generate an accurate context-aware floor plan with labeled indoor POIs for the first time in literature. IndoorWaze combines the Wi-Fi fingerprints of indoor walkers with the Wi-Fi fingerprints and POI labels provided by POI employees to produce a high-fidelity labeled floor plan. As a lightweight crowdsourcing-based system, IndoorWaze involves very little effort from indoor walkers and POI employees. We prototype IndoorWaze on Android smartphones and evaluate it in a large shopping mall. Our results show that IndoorWaze can generate a high-fidelity labeled floor plan, in which all the stores are correctly labeled and arranged, all the pathways and crossings are correctly shown, and the median estimation error for the store dimension is below 12%
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