Oak Ridge National Laboratory

About: Oak Ridge National Laboratory is a facility organization based out in Oak Ridge, Tennessee, United States. It is known for research contribution in the topics: Neutron & Ion. The organization has 31868 authors who have published 73724 publications receiving 2633689 citations. The organization is also known as: ORNL.

Fish Behavior in Relation to Passage through Hydropower Turbines: A Review

Abstract: We evaluated the literature on fish behavior as it relates to passage of fish near or through hydropower turbines Our goal was to foster compatibility of engineered systems with the normal behavior patterns of fish species and life stages such that passage into turbines and injury in passage are minimized In particular, we focused on aspects of fish behavior that could be used for computational fluid dynamics (CFD) modeling of fish trajectories through turbine systems Salmon smolts approaching dams are generally surface oriented and follow flow They can be diverted from turbines by spills or bypasses, with varying degrees of effectiveness Smolts typically become disoriented in dam forebays Those smolts drawn into turbine intakes orient vertically to the ceilings but are horizontally distributed more evenly, except as they are affected by intake-specific turbulence and vortices Smolts often enter intakes while oriented with their heads upstream, but they may change orientation in the flow f

Covalently bonded three-dimensional carbon nanotube solids via boron induced nanojunctions

Abstract: The establishment of covalent junctions between carbon nanotubes (CNTs) and the modification of their straight tubular morphology are two strategies needed to successfully synthesize nanotube-based three-dimensional (3D) frameworks exhibiting superior material properties. Engineering such 3D structures in scalable synthetic processes still remains a challenge. This work pioneers the bulk synthesis of 3D macroscale nanotube elastic solids directly via a boron-doping strategy during chemical vapour deposition, which influences the formation of atomic-scale "elbowg" junctions and nanotube covalent interconnections. Detailed elemental analysis revealed that the "elbowg" junctions are preferred sites for excess boron atoms, indicating the role of boron and curvature in the junction formation mechanism, in agreement with our first principle theoretical calculations. Exploiting this materialĝ€™s ultra-light weight, super-hydrophobicity, high porosity, thermal stability, and mechanical flexibility, the strongly oleophilic sponge-like solids are demonstrated as unique reusable sorbent scaffolds able to efficiently remove oil from contaminated seawater even after repeated use.

Productivity and compensatory responses of yellow-poplar trees in elevated C0 2

Abstract: INCREASED forest growth in response to globally rising CO2 concentrations could provide an additional sink for the excess carbon added to the atmosphere from fossil fuels1,2. The response of trees to increased CO2, however, can be expected to be modified by the interactions of other environmental resources and stresses, higher-order ecological interactions and internal feedbacks inherent in the growth of large, perennial organisms3,4. To test whether short-term stimulation of tree growth by elevated CO2 can be sustained without inputs from other environmental resources, we grew yellow-poplar (Liriodendron tulipifera L.) saplings for most of three growing seasons with continuous exposure to ambient or elevated concentrations of atmospheric CO2. Despite a sustained increase in leaf-level photosynthesis and lower rates of foliar respiration in CO2-enriched trees, whole-plant carbon storage did not increase. The absence of a significant growth response is explained by changes in carbon allocation patterns, specifically a relative decrease in leaf production and an increase in fine root production. Although these compensatory responses reduced the potential increase in carbon storage in increased CO2 concentrations, they also favour the efficient use of resources over the longer term.

Toward more realistic projections of soil carbon dynamics by Earth system models

Abstract: Soil carbon (C) is a critical component of Earth system models (ESMs), and its diverse representations are a major source of the large spread across models in the terrestrial C sink from the third to fifth assessment reports of the Intergovernmental Panel on Climate Change (IPCC). Improving soil C projections is of a high priority for Earth system modeling in the future IPCC and other assessments. To achieve this goal, we suggest that (1) model structures should reflect real-world processes, (2) parameters should be calibrated to match model outputs with observations, and (3) external forcing variables should accurately prescribe the environmental conditions that soils experience. First, most soil C cycle models simulate C input from litter production and C release through decomposition. The latter process has traditionally been represented by first-order decay functions, regulated primarily by temperature, moisture, litter quality, and soil texture. While this formulation well captures macroscopic soil organic C (SOC) dynamics, better understanding is needed of their underlying mechanisms as related to microbial processes, depth-dependent environmental controls, and other processes that strongly affect soil C dynamics. Second, incomplete use of observations in model parameterization is a major cause of bias in soil C projections from ESMs. Optimal parameter calibration with both pool- and flux-based data sets through data assimilation is among the highest priorities for near-term research to reduce biases among ESMs. Third, external variables are represented inconsistently among ESMs, leading to differences in modeled soil C dynamics. We recommend the implementation of traceability analyses to identify how external variables and model parameterizations influence SOC dynamics in different ESMs. Overall, projections of the terrestrial C sink can be substantially improved when reliable data sets are available to select the most representative model structure, constrain parameters, and prescribe forcing fields.

Use of poisson regression models in estimating incidence rates and ratios

Abstract: Summarizing relative risk estimates across strata of a covariate is commonly done in comparative epidemiologic studies of incidence or mortality. Conventional Mantel-Haenszel and rate standardization techniques used for this purpose are strictly suitable only when there is no interaction between relative risk and the covariate, and tests for interaction typically are limited to examination for departures from linearity. Poisson regression modeling offers an alternative technique which can be used for summarizing relative risk and for evaluating complex interactions with covariates. A more general application of Poisson regression is its utility in modeling disease rates according to postulated etiologic mechanisms of exposures or according to disease expression characteristics in the population. The applications of Poisson regression analysis to problems of summarizing relative risk and disease rate modeling are illustrated with examples of cancer incidence and mortality data, including an example of a nonlinear model predicted by the multistage theory of carcinogenesis.