658 research outputs found
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A systematic method of parameterisation estimation using data assimilation
In numerical weather prediction, parameterisations are used to simulate missing physics in the model. These can
be due to a lack of scientific understanding or a lack of computing power available to address all the known
physical processes. Parameterisations are sources of large uncertainty in a model as parameter values used
in these parameterisations cannot be measured directly and hence are often not well known; and the
parameterisations themselves are also approximations of the processes present in the true atmosphere. Whilst
there are many efficient and effective methods for combined state/parameter estimation in data assimilation
(DA), such as state augmentation, these are not effective at estimating the structure of parameterisations.
A new method of parameterisation estimation is proposed that uses sequential DA methods to estimate errors
in the numerical models at each space-time point for each model equation. These errors are then fitted to
pre-determined functional forms of missing physics or parameterisations that are based upon prior information.
We applied the method to a one-dimensional advection model with additive model error, and it is shown that
the method can accurately estimate parameterisations, with consistent error estimates. Furthermore, it is shown
how the method depends on the quality of the DA results. The results indicate that this new method is a powerful
tool in systematic model improvement
Tubular carbonate concretions as hydrocarbon migration pathways? Examples from North Island, New Zealand
Cold seep carbonate deposits are associated with the development on the sea floor of distinctive chemosyn¬thetic animal communities and carbonate minerali¬sation as a consequence of microbially mediated anaerobic oxidation of methane. Several possible sources of the methane exist, identifiable from the carbon isotope values of the carbonate precipitates. In the modern, seep carbonates can occur on the sea floor above petroleum reservoirs where an important origin can be from ascending thermogenic hydrocar¬bons. The character of geological structures marking the ascent pathways from deep in the subsurface to shallow subsurface levels are poorly understood, but one such structure resulting from focused fluid flow may be tubular carbonate concretions.
Several mudrock-dominated Cenozoic (especially Miocene) sedimentary formations in the North Island of New Zealand include carbonate concretions having a wide range of tubular morphologies. The concretions are typically oriented at high angles to bedding, and often have a central conduit that is either empty or filled with late stage cements. Stable isotope analyses (δ13C, δ18O) suggest that the carbonate cements in the concretions precipitated mainly from ascending methane, likely sourced from a mixture of deep thermogenic and shallow biogenic sources. A clear link between the tubular concretions and overlying paleo-sea floor seep-carbonate deposits exists at some sites.
We suggest that the tubular carbonate concretions mark the subsurface plumbing network of cold seep systems. When exposed and accessible in outcrop, they afford an opportunity to investigate the geochemical evolution of cold seeps, and possibly also the nature of linkages between subsurface and surface portions of such a system. Seep field development has implications for the characterisation of fluid flow in sedimentary basins, for the global carbon cycle, for exerting a biogeochemical influence on the development of marine communities, and for the evaluation of future hydrocarbon resources, recovery, and drilling and production hazards. These matters remain to be fully assessed within a petroleum systems framework for New Zealand’s Cenozoic sedimentary basins
An analysis of the Research Fellowship Scheme of the Royal College of Surgeons of England.
BACKGROUND: The Research Fellowship Scheme of the Royal College of Surgeons of England commenced in 1993 with the aim of exposing selected surgical trainees to research techniques and methodology, with the hope of having an impact on surgical research and increasing the cadre of young surgeons who might decide to pursue an academic career in surgery. Over 11 million pounds sterling (approximately US 20 million dollars) has been invested in 264 fellowships. The College wished to evaluate the impact of the Scheme on the careers of research fellows, surgical research, and patient care. As the 10th anniversary of the Scheme approached. STUDY DESIGN: Two-hundred and sixty research fellows whose current addresses were available were sent a questionnaire. Two-hundred and thirty-eight (91.5%) responded. RESULTS: Three-quarters of the research fellows conducted laboratory-based research, with most of the remainder conducting patient-based clinical research. One-third of the fellows who have reached consultant status have an academic component to their post. The total number of publications based on fellowship projects was 531, with a median impact factor of 3.5. Almost all fellows had been awarded a higher degree or were working toward this. Half of the fellows received subsequent funding for research, mostly awarded by national or international funding bodies. CONCLUSIONS: The Research Fellowship Scheme of the Royal College of Surgeons of England has successfully supported many trainee surgeons in the initial phase of their research career. It has helped surgical research by increasing the pool of surgeons willing to embark on an academic career. Indirectly, patient care has benefited by promoting an evidence-based culture among young surgeons. Such schemes are relevant to surgical training programs elsewhere if more young surgeons are to be attracted into academic surgery
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Reinforcement Learning for Optimization of COVID-19 Mitigation Policies
The year 2020 has seen the COVID-19 virus lead to one of the worst global pandemics in history. As a result, governments around the world are faced with the challenge of protecting public health, while keeping the economy running to the greatest extent possible. Epidemiological models provide insight into the spread of these types of diseases and predict the effects of possible intervention policies. However, to date, the even the most data-driven intervention policies rely on heuristics. In this paper, we study how reinforcement learning (RL) can be used to optimize mitigation policies that minimize the economic impact without overwhelming the hospital capacity. Our main contributions are (1) a novel agent-based pandemic simulator which, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community; and (2) an RL-based methodology for optimizing fine-grained mitigation policies within this simulator. Our results validate both the overall simulator behavior and the learned policies under realistic conditions.Integrative Biolog
Identifying Metocean Drivers of Turbidity Using 18 Years of MODIS Satellite Data: Implications for Marine Ecosystems under Climate Change
Turbidity impacts the growth and productivity of marine benthic habitats due to light limitation. Daily/monthly synoptic and tidal influences often drive turbidity fluctuations, however, our understanding of what drives turbidity across seasonal/interannual timescales is often limited, thus impeding our ability to forecast climate change impacts to ecologically significant habitats. Here, we analysed long term (18-year) MODIS-aqua data to derive turbidity and the associated meteorological and oceanographic (metocean) processes in an arid tropical embayment (Exmouth Gulf in Western Australia) within the eastern Indian Ocean. We found turbidity was associated with El Niño Southern Oscillation (ENSO) cycles as well as Indian Ocean Dipole (IOD) events. Winds from the adjacent terrestrial region were also associated with turbidity and an upward trend in turbidity was evident in the body of the gulf over the 18 years. Our results identify hydrological processes that could be affected by global climate cycles undergoing change and reveal opportunities for managers to reduce impacts to ecologically important ecosystems
Long-term spatial variations in turbidity and temperature provide new insights into coral-algal states on extreme/marginal reefs
Globally, coral reefs are under threat, with many exhibiting degradation or a shift towards algal-dominated regimes following marine heat waves, and other disturbance events. Marginal coral reefs existing under naturally extreme conditions, such as turbid water reefs, may be more resilient than their clear water counterparts as well as offer some insight into how reefs could look in the future under climate change. Here, we surveyed 27 benthic habitats across an environmental stress gradient in the Exmouth Gulf region of north Western Australia immediately following a marine heatwave event. We used multidecadal remotely sensed turbidity (from an in-situ validated dataset) and temperature, to assess how these environmental drivers influence variability in benthic communities and coral morphology. Long-term turbidity and temperature variability were associated with macroalgal colonisation when exceeding a combined threshold. Coral cover was strongly negatively associated with temperature variability, and positively associated with depth, and wave power, while coral morphology diversity was positively associated with turbidity. While moderate turbidity (long-term average ~ 2 mg/L suspended matter) appeared to raise the threshold for coral bleaching and macroalgal dominance, regions with higher temperature variability (> 3.5 °C) appeared to have already reached this threshold. The region with the least turbidity and temperature variability had the highest amount of coral bleaching from a recent heatwave event and moderate levels of both these variables may confer resilience to coral reefs
Prevalence of interactions and influence of performance constraints on kick outcomes across Australian Football tiers: Implications for representative practice designs
Introduction: Representative learning design is a key feature of the theory of ecological dynamics, conceptualising how task constraints can be manipulated in training designs to help athletes self-regulate during their interactions with information-rich performance environments. Implementation of analytical methodologies can support representative designs of practice environments by practitioners recording how interacting constraints influence events, that emerge under performance conditions. To determine key task constraints on kicking skill performance, the extent to which interactions of constraints differ in prevalence and influence on kicking skills was investigated across competition tiers in Australian Football (AF).
Method: A data sample of kicks (n = 29,153) was collected during junior, state-level and national league matches. Key task constraints were recorded for each kick, with performance outcome recorded as effective or ineffective. Rules were based on frequency and strength of associations between constraints and kick outcomes, generated using the Apriori algorithm.
Results: Univariate analysis revealed that low kicking effectiveness was associated with physical pressure (37%), whereas high efficiency emerged when kicking to an open target (70%). Between-competition comparisons showed differences in constraint interactions through seven unique rules and differences in confidence levels in shared rules.
Discussion: Results showed how understanding of key constraints interactions, and prevalence during competitive performance, can be used to inform representative learning designs in athlete training programmes. Findings can be used to specify how the competitive performance environment differs between competition tiers, supporting the specification of information in training designs, representative of different performance levels
Design of photoactivatable metallodrugs : selective and rapid light-induced ligand dissociation from half-sandwich [Ru([9]aneS3)(N–N′)(py)]2+ complexes
The synthesis of the inert Ru(II) half-sandwich coordination compounds, [Ru([9]aneS3)(bpy)(py)][PF6]2 (1, [9]aneS3 = 1,4,7-trithiacyclononane, bpy = 2,2′-bipyridine, py = pyridine), [Ru([9]aneS3)(en)(py)][PF6]2 (2, en = 1,2-diaminoethane), and [Ru([9]aneN3)(en)(dmso-S)][PF6]2 (3, [9]aneN3 = 1,4,7-triazacyclononane), is reported along with the X-ray crystal structure of 1. We investigated whether these complexes have photochemical properties which might make them suitable for use as pro-drugs in photochemotherapy. Complexes 1 and 2 underwent rapid (minutes) aquation with dissociation of the pyridine ligand in aqueous solution when irradiated with blue light (λ = 420 or 467 nm). The photodecomposition of 3 was much slower. All complexes readily formed adducts with 9-ethylguanine (9-EtG) when this model nucleobase was present in the photolysis solution. Similarly, complex 1 formed adducts with the tripeptide glutathione (GSH), but only when photoactivated. HPLC and MS studies of 1 showed that irradiation promoted rapid formation of 1:1 (major) and 1:2 (minor) adducts of the oligonucleotide d(ATACATGCTACATA) with the fragment {Ru([9]aneS3)(bpy)}2+. Density functional theory (DFT) calculations and time-dependent DFT reproduced the major features of the absorption spectra and suggested that the lowest-lying triplet state with 3MLCT character, which is readily accessible via intersystem crossing, might be responsible for the observed dissociative behavior of the excited states. These complexes are promising for further study as potential photochemotherapeutic agents
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