275 research outputs found
Modern cooling strategies for ultra-deep hydropower mines
The increasing mining depths of South African gold
mine deposits are resulting in ever-increasing heat
loads associated with depth and the geothermal gradient.
Together with changes in the mining horizons
brought about by the depletion of older, shallower, highgrade
reserves, this leads to the need for the continuous
review and redesign of cooling requirements.
Traditionally, cooling requirements were met by using a
combination of cooling strategies, including bulk air
cooling on surface and underground. If this proved to
be insufficient, chilled service water and secondary
remote air-cooling systems were introduced.
This paper reviews these practices in order to provide
a cost-effective means of catering for the introduction
of hydropower at the Gold Fields Ltd South African
operations. Some of the equipment that has been
developed to meet the requirements of both hydropower
and refrigeration includes hydropower fans, cooling
coils and in-stope venturis. These are individually
described and discussed, together with their roles within
the greater strategy.
The planned change in the cooling strategy and the employment of these technologies have effectively doubled
the cooling available, from 10 MW to more than 20
MW, extracted from the hydropower water used to drive
the mining equipment. In conclusion, the cooling strategy
described allows a total heat load of approximately
52 MW to be successfully ventilated and cooled
through the use of combined surface and underground
refrigeration installations, and through the use of
hydropower-chilled water.http://www.mvssa.co.zaam2017Mining Engineerin
In situ oligonucleotide synthesis on poly(dimethylsiloxane): a flexible substrate for microarray fabrication
In this paper, we demonstrate in situ synthesis of oligonucleotide probes on poly(dimethylsiloxane) (PDMS) microchannels through use of conventional phosphoramidite chemistry. PDMS polymer was moulded into a series of microchannels using standard soft lithography (micro-moulding), with dimensions <100 μm. The surface of the PDMS was derivatized by exposure to ultraviolet/ozone followed by vapour phase deposition of glycidoxypropyltrimethoxysilane and reaction with poly(ethylene glycol) spacer, resulting in a reactive surface for oligonucleotide coupling. High, reproducible yields were achieved for both 6mer and 21mer probes as assessed by hybridization to fluorescent oligonucleotides. Oligonucleotide surface density was comparable with that obtained on glass substrates. These results suggest PDMS as a stable and flexible alternative to glass as a suitable substrate in the fabrication and synthesis of DNA microarrays
Predicting Maximum Tree Heights and Other Traits from Allometric Scaling and Resource Limitations
Terrestrial vegetation plays a central role in regulating the carbon and water cycles, and adjusting planetary albedo. As such, a clear understanding and accurate characterization of vegetation dynamics is critical to understanding and modeling the broader climate system. Maximum tree height is an important feature of forest vegetation because it is directly related to the overall scale of many ecological and environmental quantities and is an important indicator for understanding several properties of plant communities, including total standing biomass and resource use. We present a model that predicts local maximal tree height across the entire continental United States, in good agreement with data. The model combines scaling laws, which encode the average, base-line behavior of many tree characteristics, with energy budgets constrained by local resource limitations, such as precipitation, temperature and solar radiation. In addition to predicting maximum tree height in an environment, our framework can be extended to predict how other tree traits, such as stomatal density, depend on these resource constraints. Furthermore, it offers predictions for the relationship between height and whole canopy albedo, which is important for understanding the Earth's radiative budget, a critical component of the climate system. Because our model focuses on dominant features, which are represented by a small set of mechanisms, it can be easily integrated into more complicated ecological or climate models.National Science Foundation (U.S.) (Research Experience for Undergraduates stipend)Gordon and Betty Moore FoundationNational Science Foundation (U.S.) (Graduate Research Fellowship Program)Massachusetts Institute of Technology. Presidential FellowshipEugene V. and Clare Thaw Charitable TrustEngineering and Physical Sciences Research CouncilNational Science Foundation (U.S.) (PHY0202180)Colorado College (Venture Grant Program
Modelling climate change responses in tropical forests: similar productivity estimates across five models, but different mechanisms and responses
Accurately predicting the response of Amazonia to climate change is important for predicting climate change across the globe. Changes in multiple climatic factors simultaneously result in complex non-linear ecosystem responses, which are difficult to predict using vegetation models. Using leaf- and canopy-scale observations, this study evaluated the capability of five vegetation models (Community Land Model version 3.5 coupled to the Dynamic Global Vegetation model – CLM3.5–DGVM; Ecosystem Demography model version 2 – ED2; the Joint UK Land Environment Simulator version 2.1 – JULES; Simple Biosphere model version 3 – SiB3; and the soil–plant–atmosphere model – SPA) to simulate the responses of leaf- and canopy-scale productivity to changes in temperature and drought in an Amazonian forest. The models did not agree as to whether gross primary productivity (GPP) was more sensitive to changes in temperature or precipitation, but all the models were consistent with the prediction that GPP would be higher if tropical forests were 5 °C cooler than current ambient temperatures. There was greater model–data consistency in the response of net ecosystem exchange (NEE) to changes in temperature than in the response to temperature by net photosynthesis (An), stomatal conductance (gs) and leaf area index (LAI). Modelled canopy-scale fluxes are calculated by scaling leaf-scale fluxes using LAI. At the leaf-scale, the models did not agree on the temperature or magnitude of the optimum points of An, Vcmax or gs, and model variation in these parameters was compensated for by variations in the absolute magnitude of simulated LAI and how it altered with temperature. Across the models, there was, however, consistency in two leaf-scale responses: (1) change in An with temperature was more closely linked to stomatal behaviour than biochemical processes; and (2) intrinsic water use efficiency (IWUE) increased with temperature, especially when combined with drought. These results suggest that even up to fairly extreme temperature increases from ambient levels (+6 °C), simulated photosynthesis becomes increasingly sensitive to gs and remains less sensitive to biochemical changes. To improve the reliability of simulations of the response of Amazonian rainforest to climate change, the mechanistic underpinnings of vegetation models need to be validated at both leaf- and canopy-scales to improve accuracy and consistency in the quantification of processes within and across an ecosystem.This research was enabled by a grant from
the Andes–Amazon Initiative of The Gordon and Betty Moore
Foundation. L. Rowland gratefully acknowledges financial support
from the Natural Environment Research Council (UK) for a
NERC PhD studentship, and NERC grant NE/J011002/1; PM
also acknowledges support from ARC FT110100457
Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation
Data-model integration plays a critical role in assessing and improving our
capacity to predict ecosystem dynamics. Similarly, the ability to attach
quantitative statements of uncertainty around model forecasts is crucial for
model assessment and interpretation and for setting field research
priorities. Bayesian methods provide a rigorous data assimilation framework
for these applications, especially for problems with multiple data
constraints. However, the Markov chain Monte Carlo (MCMC) techniques
underlying most Bayesian calibration can be prohibitive for
computationally demanding models and large datasets. We employ an
alternative method, Bayesian model emulation of sufficient statistics, that
can approximate the full joint posterior density, is more amenable to
parallelization, and provides an estimate of parameter sensitivity. Analysis
involved informative priors constructed from a meta-analysis of the primary
literature and specification of both model and data uncertainties, and it
introduced novel approaches to autocorrelation corrections on multiple data
streams and emulating the sufficient statistics surface. We report the
integration of this method within an ecological workflow management software,
Predictive Ecosystem Analyzer (PEcAn), and its application and validation
with two process-based terrestrial ecosystem models: SIPNET and ED2. In a
test against a synthetic dataset, the emulator was able to retrieve the true
parameter values. A comparison of the emulator approach to standard
brute-force MCMC involving multiple data constraints showed that the
emulator method was able to constrain the faster and simpler SIPNET model's
parameters with comparable performance to the brute-force approach but
reduced computation time by more than 2 orders of magnitude. The emulator
was then applied to calibration of the ED2 model, whose complexity precludes
standard (brute-force) Bayesian data assimilation techniques. Both models are
constrained after assimilation of the observational data with the emulator
method, reducing the uncertainty around their predictions. Performance
metrics showed increased agreement between model predictions and data. Our
study furthers efforts toward reducing model uncertainties, showing that the
emulator method makes it possible to efficiently calibrate complex models.</p
Exploring High Aspect Ratio Gold Nanotubes as Cytosolic Agents: Structural Engineering and Uptake into Mesothelioma Cells.
The generation of effective and safe nanoagents for biological applications requires their physicochemical characteristics to be tunable, and their cellular interactions to be well characterized. Here, the controlled synthesis is developed for preparing high-aspect ratio gold nanotubes (AuNTs) with tailorable wall thickness, microstructure, composition, and optical characteristics. The modulation of optical properties generates AuNTs with strong near infrared absorption. Surface modification enhances dispersibility of AuNTs in aqueous media and results in low cytotoxicity. The uptake and trafficking of these AuNTs by primary mesothelioma cells demonstrate their accumulation in a perinuclear distribution where they are confined initially in membrane-bound vesicles from which they ultimately escape to the cytosol. This represents the first study of the cellular interactions of high-aspect ratio 1D metal nanomaterials and will facilitate the rational design of plasmonic nanoconstructs as cytosolic nanoagents for potential diagnosis and therapeutic applications.BLF-Papworth Fellowship from the British Lung Foundation and the Victor Dahdaleh Foundation
Ecosystem heterogeneity and diversity mitigate Amazon forest resilience to frequent extreme droughts
© 2018 The Authors. New Phytologist © 2018 New Phytologist Trust The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2–7 yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss > 20% in 50 yr according to ED2 predictions. Nearly 25% (1.8 million km2) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100
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