| 21281. |
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| 21282. |
- Sandgren, Sofia
(författare)
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The Malmö Study
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Annan publikation (övrigt vetenskapligt/konstnärligt)
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| 21283. |
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| 21284. |
- Sandhi, Arifin, 1986-
(författare)
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ARSENIC REMOVAL BY PHYTOFILTRATION AND SILICON TREATMENT : A POTENTIAL SOLUTION FOR LOWERING ARSENIC CONCENTRATIONS IN FOOD CROPS
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Use of arsenic-rich groundwater for crop irrigation can increase the arsenic (As) content in food crops and act as a carcinogen, compromising human health. Using aquatic plant based phytofiltration is a potential eco-technique for removing arsenic from water. The aquatic moss species Warnstorfia fluitans grows naturally in mining areas in northern Sweden, where high concentrations of arsenic occur in lakes and rivers. This species was selected as a model for field, climate chamber and greenhouse studies on factors governing arsenic removal and arsenic phytofiltration of irrigation water. The arsenic and silicon (Si) concentrations in soil, water and plant samples were measured by AAS (atomic absorption spectrophotometry), while arsenite and arsenate species were determined using AAS combined with high pressure liquid chromatography (HPLC) with an anion exchange column. The arsenic content in grains of hybrid and local aromatic rice (Oryza sativa) cultivars with differing arsenic accumulation factor (AF) values was investigated in an arsenic hotspot in Bangladesh. The results showed that arsenic AF was important in identifying arsenic-safer rice cultivars for growing in an arsenic hotspot. The study based on silicon effect on arsenic uptake in lettuce showed that arsenic accumulation in lettuce (Lactuca sativa) could be reduced by silicon addition. The aquatic moss had good phytofiltration capacity, with fast arsenic removal of up to 82% from a medium with low arsenic concentration (1 µM). Extraction analysis showed that inorganic arsenic species were firmly bound inside moss tissue. Absorption of arsenic was relatively higher than adsorption in the moss. Regarding effects of different abiotic factors, plants were stressed at low pH (pH 2.5) and arsenic removal rate was lower from the medium, while arsenic efflux occurred in arsenate-treated medium at low (12°C) and high (30°C) temperature regimes. Besides these factors, low oxygenation increased the efficiency of arsenic removal from the medium. Finally, combining W. fluitans as a phytofilter with a lettuce crop on a constructed wetland significantly reduced the arsenic content in edible parts (leaves) of lettuce. Thus W. fluitans has great potential for use as an arsenic phytofilter in temperate regions.
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| 21285. |
- Sandholm, Thomas
(författare)
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Autoregressive Time Series Forecasting of Computational Demand
- 2007
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- We study the predictive power of autoregressive moving average models when forecasting demand in two shared computational networks, PlanetLab and Tycoon. Demand in these networks is very volatile, and predictive techniques to plan usage in advance can improve the performance obtained drastically. Our key finding is that a random walk predictor performs best for one-step-ahead forecasts, whereas ARIMA(1,1,0) and adaptive exponential smoothing models perform better for two and three-step-ahead forecasts. A Monte Carlo bootstrap test is proposed to evaluate the continuous prediction performance of different models with arbitrary confidence and statistical significance levels. Although the prediction results differ between the Tycoon and PlanetLab networks, we observe very similar overall statistical properties, such as volatility dynamics.
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| 21286. |
- Sandholm, Thomas, 1974-
(författare)
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Managing Service Levels in Grid Computing Systems : Quota Policy and Computational Market Approaches
- 2007
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- We study techniques to enforce and provision differentiated service levels in Computational Grid systems. The Grid offers simplified provisioning of peak-capacity for applications with computational requirements beyond local machines and clusters, by sharing resources across organizational boundaries. Current systems have focussed on access control, i.e., managing who is allowed to run applications on remote sites. Very little work has been done on providing differentiated service levels for those applications that are admitted. This leads to a number of problems when scheduling jobs in a fair and efficient way. For example, users with a large number of long-running jobs could starve out others, both intentionally and non-intentionally. We investigate the requirements of High Performance Computing (HPC) applications that run in academic Grid systems, and propose two models of service-level management. Our first model is based on global real-time quota enforcement, where projects are granted resource quota, such as CPU hours, across the Grid by a centralized allocation authority. We implement the SweGrid Accounting System to enforce quota allocated by the Swedish National Allocations Committee in the SweGrid production Grid, which connects six Swedish HPC centers. A flexible authorization policy framework allows provisioning and enforcement of two different service levels across the SweGrid clusters; high-priority and low-priority jobs. As a solution to more fine-grained control over service levels we propose and implement a Grid Market system, using a market-based resource allocator called Tycoon. The conclusion of our research is that although the Grid accounting solution offers better service level enforcement support than state-of-the-art production Grid systems, it turned out to be complex to set the resource price and other policies manually, while ensuring fairness and efficiency of the system. Our Grid Market on the other hand sets the price according to the dynamic demand, and it is further incentive compatible, in that the overall system state remains healthy even in the presence of strategic users.
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| 21287. |
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| 21288. |
- Sandholm, Thomas, 1974-
(författare)
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Statistical Methods for Computational Markets : Proportional Share Market Prediction and Admission Control
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- We design, implement and evaluate statistical methods for managing uncertainty when consuming and provisioning resources in a federated computational market. To enable efficient allocation of resources in this environment, providers need to know consumers' risk preferences, and the expected future demand. The guarantee levels to offer thus depend on techniques to forecast future usage and to accurately capture and model uncertainties. Our main contribution in this thesis is threefold; first, we evaluate a set of techniques to forecast demand in computational markets; second, we design a scalable method which captures a succinct summary of usage statistics and allows consumers to express risk preferences; and finally we propose a method for providers to set resource prices and determine guarantee levels to offer. The methods employed are based on fundamental concepts in probability theory, and are thus easy to implement, as well as to analyze and evaluate. The key component of our solution is a predictor that dynamically constructs approximations of the price probability density and quantile functions for arbitrary resources in a computational market. Because highly fluctuating and skewed demand is common in these markets, it is difficult to accurately and automatically construct representations of arbitrary demand distributions. We discovered that a technique based on the Chebyshev inequality and empirical prediction bounds, which estimates worst case bounds on deviations from the mean given a variance, provided the most reliable forecasts for a set of representative high performance and shared cluster workload traces. We further show how these forecasts can help the consumers determine how much to spend given a risk preference and how providers can offer admission control services with different guarantee levels given a recent history of resource prices.
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| 21289. |
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| 21290. |
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