18 resultados para Layer-by-layer methods


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Cathodoluminescence (CL) studies have previously shown that some secondary fluid inclusions in luminescent quartz are surrounded by dark, non-luminescent patches, resulting from fracture-sealing by late, trace-element-poor quartz. This finding has led to the tacit generalization that all dark CL patches indicate influx of low temperature, late-stage fluids. In this study we have examined natural and synthetic hydrothermal quartz crystals using CL imaging supplemented by in-situ elemental analysis. The results lead us to propose that all natural, liquid-water-bearing inclusions in quartz, whether trapped on former crystal growth surfaces (i.e., of primary origin) or in healed fractures (i.e., of pseudosecondary or secondary origin), are surrounded by three-dimensional, non-luminescent patches. Cross-cutting relations show that the patches form after entrapment of the fluid inclusions and therefore they are not diagnostic of the timing of fluid entrapment. Instead, the dark patches reveal the mechanism by which fluid inclusions spontaneously approach morphological equilibrium and purify their host quartz over geological time. Fluid inclusions that contain solvent water perpetually dissolve and reprecipitate their walls, gradually adopting low-energy euhedral and equant shapes. Defects in the host quartz constitute solubility gradients that drive physical migration of the inclusions over distances of tens of μm (commonly) up to several mm (rarely). Inclusions thus sequester from their walls any trace elements (e.g., Li, Al, Na, Ti) present in excess of equilibrium concentrations, thereby chemically purifying their host crystals in a process analogous to industrial zone refining. Non-luminescent patches of quartz are left in their wake. Fluid inclusions that contain no liquid water as solvent (e.g., inclusions of low-density H2O vapor or other non-aqueous volatiles) do not undergo this process and therefore do not migrate, do not modify their shapes with time, and are not associated with dark-CL zone-refined patches. This new understanding has implications for the interpretation of solids within fluid inclusions (e.g., Ti- and Al-minerals) and for the elemental analysis of hydrothermal and metamorphic quartz and its fluid inclusions by microbeam methods such as LA-ICPMS and SIMS. As Ti is a common trace element in quartz, its sequestration by fluid inclusions and its depletion in zone-refined patches impacts on applications of the Ti-in-quartz geothermometer.

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In this study two commonly used automated methods to detect atmospheric fronts in the lower troposphere are compared in various synoptic situations. The first method is a thermal approach, relying on the gradient of equivalent potential temperature (TH), while the second method is based on temporal changes in the 10 m wind (WND). For a comprehensive objective comparison of the outputs of these methods of frontal identification, both schemes are firstly applied to an idealised strong baroclinic wave simulation in the absence of topography. Then, two case-studies (one in the Northern Hemisphere (NH) and one in the Southern Hemisphere (SH)) were conducted to contrast fronts detected by the methods. Finally, we obtain global winter and summer frontal occurrence climatologies (derived from ERA-Interim for 1979–2012) and compare the structure of these. TH is able to identify cold and warm fronts in strong baroclinic cases that are in good agreement with manual analyses. WND is particularly suited for the detection of strongly elongated, meridionally oriented moving fronts, but has very limited ability to identify zonally oriented warm fronts. We note that the areas of the main TH frontal activity are shifted equatorwards compared to the WND patterns and are located upstream of regions of main WND front activity. The number of WND fronts in the NH shows more interseasonal variations than TH fronts, decreasing by more than 50% from winter to summer. In the SH there is a weaker seasonal variation of the number of observed WND fronts, however TH front activity reduces from summer (DJF) to winter (JJA). The main motivation is to give an overview of the performance of these methods, such that researchers can choose the appropriate one for their particular interest.

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Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.