866 resultados para spatial variations in sulfie generation


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We explored the temporal and spatial variations in airborne Alternaria spore quantitative and phenological features in Europe using 23 sites with annual time series between 3 and 15 years. The study covers seven countries and four of the main biogeographical regions in Europe. The observations were obtained with Hirst-type spore traps providing time series with daily records. Site locations extend from Spain in the south to Denmark in the north and from England in the West to Poland in the East. The study is therefore the largest assessment ever carried out for Europe concerning Alternaria. Aerobiological data were investigated for temporal and spatial patterns in their start and peak season dates and their spore indices. Moreover, the effects of climate were checked using meteorological data for the same period, using a crop growth model. We found that local climate, vegetation patterns and management of landscape are governing parameters for the overall spore concentration, while the annual variations caused by weather are of secondary importance but should not be neglected. The start of the Alternaria spore season varies by several months in Europe, but the peak of the season is more synchronised in central northern Europe in the middle of the summer, while many southern sites have peak dates either earlier or later than northern Europe. The use of a crop growth model to explain the start and peak of season suggests that such methods could be useful to describe Alternaria seasonality in areas with no available observations.

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We present a detailed experimental study of the onset of rippling in highly crystalline carbon nanotubes. Modeling has shown that there should be a material constant, called the critical length, describing the dependence of the critical strain on the nanotube outer radius. Surprisingly, we have found very large variations, by a factor of three, in the critical length. We attribute this to a supporting effect from the inner walls in multiwalled concentric nanotubes. We provide an analytical expression for the maximum deflection prior to rippling, which is an important design consideration in nanoelectromechanical systems utilizing nanotubes.

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Thesis (Ph.D.)--University of Washington, 2016-08

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This study seeks to understand how the physiological constraints of diving may change on a daily and seasonal basis. Dive data were obtained from southern elephant seals (Mirounga leonina) from South Georgia using satellite relay data loggers. We analysed the longest (95th percentile) dive durations as proxies for physiological dive limits. A strong, significant relationship existed between the duration of these dives and the time of day and week of year in which they were performed. The depth of the deepest dives also showed a significant, but far less consistent, relationship with local time of day and season. Changes in the duration of the longest dives occurred irrespective of their depth. Dives were longest in the morning (04:00-12:00 h) and shortest in the evening (16:00-00:00 h). The size of the fluctuation varied among animals from 4.0 to 20.0 min. The daily pattern in dive depth was phase-shifted in relation to the diurnal rhythm in dive duration. Dives were deeper at midday and shallower around midnight. Greater daily changes in duration occurred in seals feeding in the open ocean than in those foraging on the continental shelf. The seasonal peak in the duration of the longest dives coincided with austral midwinter. The size of the increase in dive duration from autumn/spring to winter ranged from 11.5 to 30.0 min. Changes in depth of the longest dives were not consistently associated with particular times of year. The substantial diurnal and seasonal fluctuations in maximum dive duration may be a result of changes in the physiological capacity to remain submerged, in addition to temporal changes in the ecological constraints on dive behaviour. We speculate about the role of melatonin as a hormonal mediator of diving capability.

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BACKGROUND: Invasive meningococcal disease is a significant cause of mortality and morbidity in the UK. Administration of chemoprophylaxis to close contacts reduces the risk of a secondary case. However, unnecessary chemoprophylaxis may be associated with adverse reactions, increased antibiotic resistance and removal of organisms, such as Neisseria lactamica, which help to protect against meningococcal disease. Limited evidence exists to suggest that overuse of chemoprophylaxis may occur. This study aimed to evaluate prescribing of chemoprophylaxis for contacts of meningococcal disease by general practitioners and hospital staff. METHODS: Retrospective case note review of cases of meningococcal disease was conducted in one health district from 1st September 1997 to 31st August 1999. Routine hospital and general practitioner prescribing data was searched for chemoprophylactic prescriptions of rifampicin and ciprofloxacin. A questionnaire of general practitioners was undertaken to obtain more detailed information. RESULTS: Prescribing by hospital doctors was in line with recommendations by the Consultant for Communicable Disease Control. General practitioners prescribed 118% more chemoprophylaxis than was recommended. Size of practice and training status did not affect the level of additional prescribing, but there were significant differences by geographical area. The highest levels of prescribing occurred in areas with high disease rates and associated publicity. However, some true close contacts did not appear to receive prophylaxis. CONCLUSIONS: Receipt of chemoprophylaxis is affected by a series of patient, doctor and community interactions. High publicity appears to increase demand for prophylaxis. Some true contacts do not receive appropriate chemoprophylaxis and are left at an unnecessarily increased risk

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The level of Lake Victoria has, since 1961, reached a height which caused serious flood damage. Already the financial implications are considerable for Kenya, Tanzania and Uganda. If further rises can be anticipated, expenditure on flood control measures to the tune of several million pounds sterling must be envisaged. If such rises should lead to uncontrolled discharge at the Owen Falls Dam site because of overshooting, downstream districts of Uganda and the Sudan may be seriously flooded. All this merits a thorough study, and any indication of the future behaviour of lake levels, even when associated with a low probability, must be taken into account. In these circumstances the Water Development Department of Kenya approached the East African Meteorological Department in November, 1964, on behalf of all parties concerned with the request to study the meteorological background of the Iake level variation, with a view to forecasting future behaviour.

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Using water quality management programs is a necessary and inevitable way for preservation and sustainable use of water resources. One of the important issues in determining the quality of water in rivers is designing effective quality control networks, so that the measured quality variables in these stations are, as far as possible, indicative of overall changes in water quality. One of the methods to achieve this goal is increasing the number of quality monitoring stations and sampling instances. Since this will dramatically increase the annual cost of monitoring, deciding on which stations and parameters are the most important ones, along with increasing the instances of sampling, in a way that shows maximum change in the system under study can affect the future decision-making processes for optimizing the efficacy of extant monitoring network, removing or adding new stations or parameters and decreasing or increasing sampling instances. This end, the efficiency of multivariate statistical procedures was studied in this thesis. Multivariate statistical procedure, with regard to its features, can be used as a practical and useful method in recognizing and analyzing rivers’ pollution and consequently in understanding, reasoning, controlling, and correct decision-making in water quality management. This research was carried out using multivariate statistical techniques for analyzing the quality of water and monitoring the variables affecting its quality in Gharasou river, in Ardabil province in northwest of Iran. During a year, 28 physical and chemical parameters were sampled in 11 stations. The results of these measurements were analyzed by multivariate procedures such as: Cluster Analysis (CA), Principal Component Analysis (PCA), Factor Analysis (FA), and Discriminant Analysis (DA). Based on the findings from cluster analysis, principal component analysis, and factor analysis the stations were divided into three groups of highly polluted (HP), moderately polluted (MP), and less polluted (LP) stations Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective river water quality management. This study also shows the effectiveness of these techniques for getting better information about the water quality and design of monitoring network for effective management of water resources. Therefore, based on the results, Gharasou river water quality monitoring program was developed and presented.

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Observations of H3+ in the Galactic diffuse interstellar medium (ISM) have led to various surprising results, including the conclusion that the cosmic-ray ionization rate (zeta_2) is about 1 order of magnitude larger than previously thought. The present survey expands the sample of diffuse cloud sight lines with H3+ observations to 50, with detections in 21 of those. Ionization rates inferred from these detections are in the range (1.7+-1.0)x10^-16 s^-1 < zeta_2 < (10.6+-6.8)x10^-16 s^-1 with a mean value of zeta_2=(3.3+-0.4)x10^-16 s^-1. Upper limits (3sigma) derived from non-detections of H3+ are as low as zeta_2 < 0.4x10^-16 s^-1. These low upper-limits, in combination with the wide range of inferred cosmic-ray ionization rates, indicate variations in zeta_2 between different diffuse cloud sight lines. Calculations of the cosmic-ray ionization rate from theoretical cosmic-ray spectra require a large flux of low-energy (MeV) particles to reproduce values inferred from observations. Given the relatively short range of low-energy cosmic rays --- those most efficient at ionization --- the proximity of a cloud to a site of particle acceleration may set its ionization rate. Variations in zeta_2 are thus likely due to variations in the cosmic-ray spectrum at low energies resulting from the effects of particle propagation. To test this theory, H3+ was observed in sight lines passing through diffuse molecular clouds known to be interacting with the supernova remnant IC 443, a probable site of particle acceleration. Where H3+ is detected, ionization rates of zeta_2=(20+-10)x10^-16 s^-1 are inferred, higher than for any other diffuse cloud. These results support both the concept that supernova remnants act as particle accelerators, and the hypothesis that propagation effects are responsible for causing spatial variations in the cosmic-ray spectrum and ionization rate. Future observations of H3+ near other supernova remnants and in sight lines where complementary ionization tracers (OH+, H2O+, H3O+) have been observed will further our understanding of the subject.

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The temperature of the mantle and the rate of melt production are parameters which play important roles in controlling the style of crustal accretion along mid-ocean ridges. To investigate the variability in crustal accretion that develops in response to variations in mantle temperature, we have conducted a geophysical investigation of the Southeast Indian Ridge (SEIR) between the Amsterdam hotspot and the Australian-Antarctic Discordance (88 degrees E-118 degrees E). The spreading center deepens by 2100 m from west to east within the study area. Despite a uniform, intermediate spreading rate (69-75 mm yr-l), the SEIR exhibits the range in axial morphology displayed by the East Pacific Rise and the Mid-Atlantic Ridge (MAR) and usually associated with variations in spreading rate. The spreading center is characterized by an axial high west of 102 degrees 45'E, whereas an axial valley is prevalent east of this longitude. Both the deepening of the ridge axis and the general evolution of axial morphology from an axial high to a rift valley are not uniform. A region of intermediate morphology separates axial highs and MAR-like rift valleys. Local transitions in axial morphology occur in three areas along the ridge axis. The increase in axial depth toward the Australian-Antarctic Discordance may be explained by the thinning of the oceanic crust by similar to 4 km and the change in axial topography. The long-wavelength changes observed along the SEIR can be attributed to a gradient in mantle temperature between regions influenced by the Amsterdam and Kerguelen hot spots and the Australian-Antarctic Discordance. However, local processes, perhaps associated with an heterogeneous mantle or along-axis asthenospheric flow, may give rise to local transitions in axial topography and depth anomalies.

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Sea- level variations have a significant impact on coastal areas. Prediction of sea level variations expected from the pre most critical information needs associated with the sea environment. For this, various methods exist. In this study, on the northern coast of the Persian Gulf have been studied relation to the effectiveness of parameters such as pressure, temperature and wind speed on sea leve and associated with global parameters such as the North Atlantic Oscillation index and NAO index and present statistic models for prediction of sea level. In the next step by using artificial neural network predict sea level for first in this region. Then compared results of the models. Prediction using statistical models estimated in terms correlation coefficient R = 0.84 and root mean square error (RMS) 21.9 cm for the Bushehr station, and R = 0.85 and root mean square error (RMS) 48.4 cm for Rajai station, While neural network used to have 4 layers and each middle layer six neurons is best for prediction and produces the results reliably in terms of correlation coefficient with R = 0.90126 and the root mean square error (RMS) 13.7 cm for the Bushehr station, and R = 0.93916 and the root mean square error (RMS) 22.6 cm for Rajai station. Therefore, the proposed methodology could be successfully used in the study area.

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This study examined variations in the Fulton condition factor, chemical composition, and stable isotopes of carbon and nitrogen in the Brazilian freshwater fish cachara (Pseudoplatystoma fasciatum), comparing farmed and wild fish in different seasons. Values for energy, protein, moisture, and Fulton's condition factor were higher for farmed than for wild fish in the rainy season, indicating better nutritional quality; however, these differences were not observed in the dry season. Likewise, we found significant enhancement of delta(15)N in farmed fish in the rainy season but not in the dry season, whereas enhancement of delta(13)C was observed in both seasons. The combined measurement of delta(13)C and delta(15)N provided traceability under all conditions. Our findings show that stable isotope analysis of C and N can be used to trace cachara origin, and that seasonal variations need to be considered when applying chemical and isotopic authentication of fish and fish products. (C) 2010 Elsevier Ltd. All rights reserved.

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Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

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The mechanical behaviour and performance of a ductile iron component is highly dependent on the local variations in solidification conditions during the casting process. Here we show a framework which combine a previously developed closed chain of simulations for cast components with a micro-scale Finite Element Method (FEM) simulation of the behaviour and performance of the microstructure. A casting process simulation, including modelling of solidification and mechanical material characterization, provides the basis for a macro-scale FEM analysis of the component. A critical region is identified to which the micro-scale FEM simulation of a representative microstructure, generated using X-ray tomography, is applied. The mechanical behaviour of the different microstructural phases are determined using a surrogate model based optimisation routine and experimental data. It is discussed that the approach enables a link between solidification- and microstructure-models and simulations of as well component as microstructural behaviour, and can contribute with new understanding regarding the behaviour and performance of different microstructural phases and morphologies in industrial ductile iron components in service.