A Continuous Automated Vault Inventory System (CAVIS) for accountability monitoring of stored nuclear materials

Nearly all facilities that store hazardous (radioactive or non-radioactive) materials must comply with prevailing federal, state, and local laws. These laws usually have components that require periodic physical inspections to insure that all materials remain safely and securely stored. The inspections are generally labor intensive, slow, put personnel at risk, and only find anomalies after they have occurred. The system described in this paper was developed for monitoring stored nuclear materials resulting from weapons dismantlement, but its applications extend to any storage facility that meets the above criteria. The traditional special nuclear material (SNM) accountability programs, that are currently used within most of the Department of Energy (DOE) complex, require the physical entry of highly trained personnel into SNM storage vaults. This imposes the need for additional security measures, which typically mandate that extra security personnel be present while SNM inventories are performed. These requirements increase labor costs and put additional personnel at risk to radiation exposure. In some cases, individuals have received radiation exposure equivalent to the annual maximum during just one inventory verification. With increasing overhead costs, the current system is rapidly becoming too expensive to operate, the need for an automated method of inventory verification is evident. The Continuous Automated Vault Inventory System (CAVIS) described in this paper was designed and prototyped as a low cost, highly reliable, and user friendly system that is capable of providing, real-time weight, gamma. and neutron energy confirmation from each item stored in a SNM vault. This paper describes the sensor technologies, the CAVIS prototype system (built at Y- 12 for highly enriched uranium storage), the technical requirements that must be achieved to assure successful implementation, and descriptions of sensor technologies needed for a plutonium facility

The effect of elevated CO2 on diel leaf growth cycle, leaf carbohydrate content and canopy growth performance of Populus deltoides

Image sequence processing methods were applied to study the effect of elevated CO2 on the diel leaf growth cycle for the first time in a dicot plant. Growing leaves of Populus deltoides, in stands maintained under ambient and elevated CO2 for up to 4 years, showed a high degree of heterogeneity and pronounced diel variations of their relative growth rate (RGR) with maxima at dusk. At the beginning of the season, leaf growth did not differ between treatments. At the end of the season, final individual leaf area and total leaf biomass of the canopy was increased in elevated CO2. Increased final leaf area at elevated CO2 was achieved via a prolonged phase of leaf expansion activity and not via larger leaf size upon emergence. The fraction of leaves growing at 30–40% day−1 was increased by a factor of two in the elevated CO2 treatment. A transient minimum of leaf expansion developed during the late afternoon in leaves grown under elevated CO2 as the growing season progressed. During this minimum, leaves grown under elevated CO2 decreased their RGR to 50% of the ambient value. The transient growth minimum in the afternoon was correlated with a transient depletion of glucose (less than 50%) in the growing leaf in elevated CO2, suggesting diversion of glucose to starch or other carbohydrates, making this substrate temporarily unavailable for growth. Increased leaf growth was observed at the end of the night in elevated CO2. Net CO2 exchange and starch concentration of growing leaves was higher in elevated CO2. The extent to which the transient reduction in diel leaf growth might dampen the overall growth response of these trees to elevated CO2 is discussed

Green leaf volatiles and oxygenated metabolite emission bursts from mesquite branches following light-dark transitions

Jardine K, Barron-Gafford GA, Norman JP, et al. Green leaf volatiles and oxygenated metabolite emission bursts from mesquite branches following light-dark transitions. Photosynthesis Research. 2012;113(1-3):321-333.Green leaf volatiles (GLVs) are a diverse group of fatty acid-derived compounds emitted by all plants and are involved in a wide variety of developmental and stress-related biological functions. Recently, GLV emission bursts from leaves were reported following light-dark transitions and hypothesized to be related to the stress response while acetaldehyde bursts were hypothesized to be due to the 'pyruvate overflow' mechanism. In this study, branch emissions of GLVs and a group of oxygenated metabolites (acetaldehyde, ethanol, acetic acid, and acetone) derived from the pyruvate dehydrogenase (PDH) bypass pathway were quantified from mesquite plants following light-dark transitions using a coupled GC-MS, PTR-MS, and photosynthesis system. Within the first minute after darkening following a light period, large emission bursts of both C-5 and C-6 GLVs dominated by (Z)-3-hexen-1-yl acetate together with the PDH bypass metabolites are reported for the first time. We found that branches exposed to CO2-free air lacked significant GLV and PDH bypass bursts while O-2-free atmospheres eliminated the GLV burst but stimulated the PDH bypass burst. A positive relationship was observed between photosynthetic activity prior to darkening and the magnitude of the GLV and PDH bursts. Photosynthesis under (CO2)-C-13 resulted in bursts with extensive labeling of acetaldehyde, ethanol, and the acetate but not the C-6-alcohol moiety of (Z)-3-hexen-1-yl acetate. Our observations are consistent with (1) the "pyruvate overflow" mechanism with a fast turnover time ( 3 h) responsible for the C-6 alcohol moiety of (Z)-3-hexen-1-yl acetate via the 13-lipoxygenase pathway. We conclude that our non-invasive method may provide a new valuable in vivo tool for studies of acetyl-CoA and fatty acid metabolism in plants at a variety of spatial scales

Quantifying ecological memory in plant and ecosystem processes.

The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18-28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time-series data and facilitating new hypothesis generation and additional research

Quantifying ecological memory in plant and ecosystem processes

The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18-28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time-series data and facilitating new hypothesis generation and additional research

Differential daytime and night-time stomatal behavior in plants from North American deserts

Night-time stomatal conductance (g night) occurs in many ecosystems, but the g night response to environmental drivers is relatively unknown, especially in deserts. Here, we conducted a Bayesian analysis of stomatal conductance (g) (N=5013) from 16 species in the Sonoran, Chihuahuan, Mojave and Great Basin Deserts (North America). We partitioned daytime g (g day) and g night responses by describing g as a mixture of two extreme (dark vs high light) behaviors. Significant g night was observed across 15 species, and the g night and g day behavior differed according to species, functional type and desert. The transition between extreme behaviors was determined by light environment, with the transition behavior differing between functional types and deserts. Sonoran and Chihuahuan C4 grasses were more sensitive to vapor pressure difference (D) at night and soil water potential (ψ soil) during the day, Great Basin C3 shrubs were highly sensitive to D and ψ soil during the day, and Mojave C3 shrubs were equally sensitive to D and ψ soil during the day and night. Species were split between the exhibition of isohydric or anisohydric behavior during the day. Three species switched from anisohydric to isohydric behavior at night. Such behavior, combined with differential D, ψ soil and light responses, suggests that different mechanisms underlie g day and g night regulation