936 resultados para Ultra-trace analysis


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Major, minor and trace elemental contents in northeast China soybeans were determined by using inductively, coupled plasma atomic emission spectrometry (ICP-AES). Three different sample digestion methods including two wet digestions, HNO3-HClO4 and HNO3-H2SO4 and a dry ash method were compared. Owing to the high oil content in soybeans, long time is needed and access acid should be added, with mixed acid digestion methods, which may result in higher sample blank. Therefore, the dry ask method would be more proper for the pre-treatment of soybean samples. Potassium and phosphorus are major elements in soybeans, so the effect of potassium and phosphorus on the other elements was investigated. Results showed that the potassium and phosphorus did not affect the determination. of other trace elements. There are not significant differences in trace elemental contents for the eleven northeast China soybeans.

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Hybrid linear analysis (HLA) was applied to resolution of overlapping spectra of Fe3+-salicylfluorone and Al3+-salicylfluorone complexes and simultaneous spectrophotometric determination of Fe3+ and Al3+. The absorbance matrix of 7 standard mixtures at 41 measuring points ranged from the wavelength of 550 nm to 630 nm was used for calibration. To avoid the effect of interaction between the two components on the determination, the column vector of K matrix obtained from the standard mixtures with least squares was used as the pure spectrum of component. The recoveries of the two elements for the analysis of the synthetic samples were 93.3% similar to 107.5% in the range of the concentration ratio of Fe3+:Al3+ = 10:1 to 1:8. Comparing with the partial least squares (PIS) model, the HLA method was simple, accuracy and precise.

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This article applied [HEH(HEP)] (2-ethyhexyldrogen-2-ethylhexyl phosphonate)extraction chromatography to separate 14 rare earth impurities from ultra-highly pure Er2O3 and Ho2O3, and then the impurities were determined by atomic emission spectrometry. The average percentage recovery for each element is in the range of 70%similar to 140%. The relative standard deviations of the determination are +/-3.3%similar to 2.2%. This method can be applied to the determination of the trace amounts of rare eath impurities in Er2O3 and Ho2O3 with a purity of 99.999 9%-99.999 99%.

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Fitzgerald, S., Simon, B., and Thomas, L. 2005. Strategies that students use to trace code: an analysis based in grounded theory. In Proceedings of the First international Workshop on Computing Education Research (Seattle, WA, USA, October 01 - 02, 2005). ICER '05. ACM, New York, NY, 69-80

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In this paper, we explore the application of cooperative communications in ultra-wideband (UWB) wireless body area networks (BANs), where a group of on-body devices may collaborate together to communicate with other groups of on-body equipment. Firstly, time-domain UWB channel measurements are presented to characterize the body-centric multipath channel and to facilitate the diversity analysis in a cooperative BAN (CoBAN). We focus on the system deployment scenario when the human subject is in the sitting posture. Important channel parameters such as the pathloss, power variation, power delay profile (PDP), and effective received power (ERP) crosscorrelation are investigated and statistically analyzed. Provided with the model preliminaries, a detailed analysis on the diversity level in a CoBAN is provided. Specifically, an intuitive measure is proposed to quantify the diversity gains in a single-hop cooperative network, which is defined as the number of independent multipaths that can be averaged over to detect symbols. As this measure provides the largest number of redundant copies of transmitted information through the body-centric channel, it can be used as a benchmark to access the performance bound of various diversity-based cooperative schemes in futuristic body sensor systems.

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We explore the potential application of cognitive interrogator network (CIN) in remote monitoring of mobile subjects in domestic environments, where the ultra-wideband radio frequency identification (UWB-RFID) technique is considered for accurate source localization. We first present the CIN architecture in which the central base station (BS) continuously and intelligently customizes the illumination modes of the distributed transceivers in response to the systempsilas changing knowledge of the channel conditions and subject movements. Subsequently, the analytical results of the locating probability and time-of-arrival (TOA) estimation uncertainty for a large-scale CIN with randomly distributed interrogators are derived based upon the implemented cognitive intelligences. Finally, numerical examples are used to demonstrate the key effects of the proposed cognitions on the system performance

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Geologic and environmental factors acting over varying spatial scales can control
trace element distribution and mobility in soils. In turn, the mobility of an element in soil will affect its oral bioaccessibility. Geostatistics, kriging and principal component analysis (PCA) were used to explore factors and spatial ranges of influence over a suite of 8 element oxides, soil organic carbon (SOC), pH, and the trace elements nickel (Ni), vanadium (V) and zinc (Zn). Bioaccessibility testing was carried out previously using the Unified BARGE Method on a sub-set of 91 soil samples from the Northern Ireland Tellus1 soil archive. Initial spatial mapping of total Ni, V and Zn concentrations shows their distributions are correlated spatially with local geologic formations, and prior correlation analyses showed that statistically significant controls were exerted over trace element bioaccessibility by the 8 oxides, SOC and pH. PCA applied to the geochemistry parameters of the bioaccessibility sample set yielded three principal components accounting for 77% of cumulative variance in the data
set. Geostatistical analysis of oxide, trace element, SOC and pH distributions using 6862 sample locations also identified distinct spatial ranges of influence for these variables, concluded to arise from geologic forming processes, weathering processes, and localised soil chemistry factors. Kriging was used to conduct a spatial PCA of Ni, V and Zn distributions which identified two factors comprising the majority of distribution variance. This was spatially accounted for firstly by basalt rock types, with the second component associated with sandstone and limestone in the region. The results suggest trace element bioaccessibility and distribution is controlled by chemical and geologic processes which occur over variable spatial ranges of influence.

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This study introduces an inexact, but ultra-low power, computing architecture devoted to the embedded analysis of bio-signals. The platform operates at extremely low voltage supply levels to minimise energy consumption. In this scenario, the reliability of static RAM (SRAM) memories cannot be guaranteed when using conventional 6-transistor implementations. While error correction codes and dedicated SRAM implementations can ensure correct operations in this near-threshold regime, they incur in significant area and energy overheads, and should therefore be employed judiciously. Herein, the authors propose a novel scheme to design inexact computing architectures that selectively protects memory regions based on their significance, i.e. their impact on the end-to-end quality of service, as dictated by the bio-signal application characteristics. The authors illustrate their scheme on an industrial benchmark application performing the power spectrum analysis of electrocardiograms. Experimental evidence showcases that a significance-based memory protection approach leads to a small degradation in the output quality with respect to an exact implementation, while resulting in substantial energy gains, both in the memory and the processing subsystem.

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Wearable devices performing advanced bio-signal analysis algorithms are aimed to foster a revolution in healthcare provision of chronic cardiac diseases. In this context, energy efficiency is of paramount importance, as long-term monitoring must be ensured while relying on a tiny power source. Operating at a scaled supply voltage, just above the threshold voltage, effectively helps in saving substantial energy, but it makes circuits, and especially memories, more prone to errors, threatening the correct execution of algorithms. The use of error detection and correction codes may help to protect the entire memory content, however it incurs in large area and energy overheads which may not be compatible with the tight energy budgets of wearable systems. To cope with this challenge, in this paper we propose to limit the overhead of traditional schemes by selectively detecting and correcting errors only in data highly impacting the end-to-end quality of service of ultra-low power wearable electrocardiogram (ECG) devices. This partition adopts the protection of either significant words or significant bits of each data element, according to the application characteristics (statistical properties of the data in the application buffers), and its impact in determining the output. The proposed heterogeneous error protection scheme in real ECG signals allows substantial energy savings (11% in wearable devices) compared to state-of-the-art approaches, like ECC, in which the whole memory is protected against errors. At the same time, it also results in negligible output quality degradation in the evaluated power spectrum analysis application of ECG signals.