921 resultados para direct-subtracting method


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In recent years, surface plasmon-induced photocatalytic materials with tunable mesoporous framework have attracted considerable attention in energy conversion and environmental remediation. Herein we report a novel Au nanoparticles decorated mesoporous graphitic carbon nitride (Au/mp-g-C3N4) nanosheets via a template-free and green in situ photo-reduction method. The synthesized Au/mp-g-C3N4 nanosheets exhibit a strong absorption edge in visible and near-IR region owing to the surface plasmon resonance effect of Au nanoparticles. More attractively, Au/mp-g-C3N4 exhibited much higher photocatalytic activity than that of pure mesoporous and bulk g-C3N4 for the degradation of rhodamine B under sunlight irradiation. Furthermore, the photocurrent and photoluminescence studies demonstrated that the deposition of Au nanoparticles on the surface of mesoporous g-C3N4 could effectively inhibit the recombination of photogenerated charge carriers leading to the enhanced photocatalytic activity. More importantly, the synthesized Au/mp-g-C3N4 nanosheets possess high reusability. Hence, Au/mp-g-C3N4 could be promising photoactive material for energy and environmental applications.

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In this work a direct activation route of zeolites is assessed. It consists of NH4-exchanging the as-synthesized solids before removing the organic template. Calcination afterwards serves to combust the organic template and creates the Brønsted sites directly; thus applying merely a single thermal step. This method simplifies their activation and the material suffers less thermal stress. The approach was particularly effective for microcrystalline beta and ferrierite zeolites. Thorough investigation of the template content and materials' texture points out to three relevant effects that can explain the effective exchange process: partial removal of the template during exchange creates substantial microporosity (ferrierite), the remaining template is reorganized within the pores (ferrierite) and finally, void space can exist due to the non-perfect matching between the network and template (beta). This shorter method appears suited for microcrystalline zeolites; it was ineffective for crystalline MFI types. © 2013 Elsevier B.V. All rights reserved.

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Switched mode power supplies (SMPSs) are essential components in many applications, and electromagnetic interference is an important consideration in the SMPS design. Spread spectrum based PWM strategies have been used in SMPS designs to reduce the switching harmonics. This paper proposes a novel method to integrate a communication function into spread spectrum based PWM strategy without extra hardware costs. Direct sequence spread spectrum (DSSS) and phase shift keying (PSK) data modulation are employed to the PWM of the SMPS, so that it has reduced switching harmonics and the input and output power line voltage ripples contain data. A data demodulation algorithm has been developed for receivers, and code division multiple access (CDMA) concept is employed as communication method for a system with multiple SMPSs. The proposed method has been implemented in both Buck and Boost converters. The experimental results validated the proposed DSSS based PWM strategy for both harmonic reduction and communication.

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In the paper, we construct a composite indicator to estimate the potential of four Central and Eastern European countries (the Czech Republic, Hungary, Poland and Slovakia) to benefit from productivity spillovers from foreign direct investment (FDI) in the manufacturing sector. Such transfers of technology are one of the main benefits of FDI for the host country, and should also be one of the main determinants of FDI incentives offered to investing multinationals by governments, but they are difficult to assess ex ante. For our composite index, we use six components to proxy the main channels and determinants of these spillovers. We have tried several weighting and aggregation methods, and we consider our results robust. According to the analysis of our results, between 2003 and 2007 all four countries were able to increase their potential to benefit from such spillovers, although there are large differences between them. The Czech Republic clearly has the most potential to benefit from productivity spillovers, while Poland has the least. The relative positions of Hungary and Slovakia depend to some extent on the exact weighting and aggregation method of the individual components of the index, but the differences are not large. These conclusions have important implication both the investment strategies of multinationals and government FDI policies.

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In this paper, we construct a composite indicator to estimate the potential of four Central and Eastern European countries (the Czech Republic, Hungary, Poland and Slovakia) to benefit from productivity spillovers from foreign direct investment (FDI) in the manufacturing sector. Such transfers of technology are one of the main benefits of FDI for the host country, and should also be one of the main determinants of FDI incentives offered to investing multinationals by governments, but they are difficult to assess ex ante. For our composite index, we use six components to proxy the main channels and determinants of these spillovers. We have tried several weighting and aggregation methods, and we consider our results robust. According to the analysis of our results, between 2003 and 2007 all four countries were able to increase their potential to benefit from such spillovers, although there are large differences between them. The Czech Republic clearly has the most potential to benefit from productivity spillovers, while Poland has the least. The relative positions of Hungary and Slovakia depend to some extent on the exact weighting and aggregation method of the individual components of the index, but the differences are not large. These conclusions have important implications both for the investment strategies of multinationals and government FDI policies.

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Annual average daily traffic (AADT) is important information for many transportation planning, design, operation, and maintenance activities, as well as for the allocation of highway funds. Many studies have attempted AADT estimation using factor approach, regression analysis, time series, and artificial neural networks. However, these methods are unable to account for spatially variable influence of independent variables on the dependent variable even though it is well known that to many transportation problems, including AADT estimation, spatial context is important. ^ In this study, applications of geographically weighted regression (GWR) methods to estimating AADT were investigated. The GWR based methods considered the influence of correlations among the variables over space and the spatially non-stationarity of the variables. A GWR model allows different relationships between the dependent and independent variables to exist at different points in space. In other words, model parameters vary from location to location and the locally linear regression parameters at a point are affected more by observations near that point than observations further away. ^ The study area was Broward County, Florida. Broward County lies on the Atlantic coast between Palm Beach and Miami-Dade counties. In this study, a total of 67 variables were considered as potential AADT predictors, and six variables (lanes, speed, regional accessibility, direct access, density of roadway length, and density of seasonal household) were selected to develop the models. ^ To investigate the predictive powers of various AADT predictors over the space, the statistics including local r-square, local parameter estimates, and local errors were examined and mapped. The local variations in relationships among parameters were investigated, measured, and mapped to assess the usefulness of GWR methods. ^ The results indicated that the GWR models were able to better explain the variation in the data and to predict AADT with smaller errors than the ordinary linear regression models for the same dataset. Additionally, GWR was able to model the spatial non-stationarity in the data, i.e., the spatially varying relationship between AADT and predictors, which cannot be modeled in ordinary linear regression. ^

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Recent technological developments have made it possible to design various microdevices where fluid flow and heat transfer are involved. For the proper design of such systems, the governing physics needs to be investigated. Due to the difficulty to study complex geometries in micro scales using experimental techniques, computational tools are developed to analyze and simulate flow and heat transfer in microgeometries. However, conventional numerical methods using the Navier-Stokes equations fail to predict some aspects of microflows such as nonlinear pressure distribution, increase mass flow rate, slip flow and temperature jump at the solid boundaries. This necessitates the development of new computational methods which depend on the kinetic theory that are both accurate and computationally efficient. In this study, lattice Boltzmann method (LBM) was used to investigate the flow and heat transfer in micro sized geometries. The LBM depends on the Boltzmann equation which is valid in the whole rarefaction regime that can be observed in micro flows. Results were obtained for isothermal channel flows at Knudsen numbers higher than 0.01 at different pressure ratios. LBM solutions for micro-Couette and micro-Poiseuille flow were found to be in good agreement with the analytical solutions valid in the slip flow regime (0.01 < Kn < 0.1) and direct simulation Monte Carlo solutions that are valid in the transition regime (0.1 < Kn < 10) for pressure distribution and velocity field. The isothermal LBM was further extended to simulate flows including heat transfer. The method was first validated for continuum channel flows with and without constrictions by comparing the thermal LBM results against accurate solutions obtained from analytical equations and finite element method. Finally, the capability of thermal LBM was improved by adding the effect of rarefaction and the method was used to analyze the behavior of gas flow in microchannels. The major finding of this research is that, the newly developed particle-based method described here can be used as an alternative numerical tool in order to study non-continuum effects observed in micro-electro-mechanical-systems (MEMS).

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Background Sucralose has gained popularity as a low calorie artificial sweetener worldwide. Due to its high stability and persistence, sucralose has shown widespread occurrence in environmental waters, at concentrations that could reach up to several μg/L. Previous studies have used time consuming sample preparation methods (offline solid phase extraction/derivatization) or methods with rather high detection limits (direct injection) for sucralose analysis. This study described a faster and sensitive analytical method for the determination of sucralose in environmental samples. Results An online SPE-LC–MS/MS method was developed, being capable to quantify sucralose in 12 minutes using only 10 mL of sample, with method detection limits (MDLs) of 4.5 ng/L, 8.5 ng/L and 45 ng/L for deionized water, drinking and reclaimed waters (1:10 diluted with deionized water), respectively. Sucralose was detected in 82% of the reclaimed water samples at concentrations reaching up to 18 μg/L. The monthly average for a period of one year was 9.1 ± 2.9 μg/L. The calculated mass loads per capita of sucralose discharged through WWTP effluents based on the concentrations detected in wastewaters in the U. S. is 5.0 mg/day/person. As expected, the concentrations observed in drinking water were much lower but still relevant reaching as high as 465 ng/L. In order to evaluate the stability of sucralose, photodegradation experiments were performed in natural waters. Significant photodegradation of sucralose was observed only in freshwater at 254 nm. Minimal degradation (<20%) was observed for all matrices under more natural conditions (350 nm or solar simulator). The only photolysis product of sucralose identified by high resolution mass spectrometry was a de-chlorinated molecule at m/z 362.0535, with molecular formula C12H20Cl2O8. Conclusions Online SPE LC-APCI/MS/MS developed in the study was applied to more than 100 environmental samples. Sucralose was frequently detected (>80%) indicating that the conventional treatment process employed in the sewage treatment plants is not efficient for its removal. Detection of sucralose in drinking waters suggests potential contamination of surface and ground waters sources with anthropogenic wastewater streams. Its high resistance to photodegradation, minimal sorption and high solubility indicate that sucralose could be a good tracer of anthropogenic wastewater intrusion into the environment.

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Recent technological developments have made it possible to design various microdevices where fluid flow and heat transfer are involved. For the proper design of such systems, the governing physics needs to be investigated. Due to the difficulty to study complex geometries in micro scales using experimental techniques, computational tools are developed to analyze and simulate flow and heat transfer in microgeometries. However, conventional numerical methods using the Navier-Stokes equations fail to predict some aspects of microflows such as nonlinear pressure distribution, increase mass flow rate, slip flow and temperature jump at the solid boundaries. This necessitates the development of new computational methods which depend on the kinetic theory that are both accurate and computationally efficient. In this study, lattice Boltzmann method (LBM) was used to investigate the flow and heat transfer in micro sized geometries. The LBM depends on the Boltzmann equation which is valid in the whole rarefaction regime that can be observed in micro flows. Results were obtained for isothermal channel flows at Knudsen numbers higher than 0.01 at different pressure ratios. LBM solutions for micro-Couette and micro-Poiseuille flow were found to be in good agreement with the analytical solutions valid in the slip flow regime (0.01 < Kn < 0.1) and direct simulation Monte Carlo solutions that are valid in the transition regime (0.1 < Kn < 10) for pressure distribution and velocity field. The isothermal LBM was further extended to simulate flows including heat transfer. The method was first validated for continuum channel flows with and without constrictions by comparing the thermal LBM results against accurate solutions obtained from analytical equations and finite element method. Finally, the capability of thermal LBM was improved by adding the effect of rarefaction and the method was used to analyze the behavior of gas flow in microchannels. The major finding of this research is that, the newly developed particle-based method described here can be used as an alternative numerical tool in order to study non-continuum effects observed in micro-electro-mechanical-systems (MEMS).

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Purpose: Computed Tomography (CT) is one of the standard diagnostic imaging modalities for the evaluation of a patient’s medical condition. In comparison to other imaging modalities such as Magnetic Resonance Imaging (MRI), CT is a fast acquisition imaging device with higher spatial resolution and higher contrast-to-noise ratio (CNR) for bony structures. CT images are presented through a gray scale of independent values in Hounsfield units (HU). High HU-valued materials represent higher density. High density materials, such as metal, tend to erroneously increase the HU values around it due to reconstruction software limitations. This problem of increased HU values due to metal presence is referred to as metal artefacts. Hip prostheses, dental fillings, aneurysm clips, and spinal clips are a few examples of metal objects that are of clinical relevance. These implants create artefacts such as beam hardening and photon starvation that distort CT images and degrade image quality. This is of great significance because the distortions may cause improper evaluation of images and inaccurate dose calculation in the treatment planning system. Different algorithms are being developed to reduce these artefacts for better image quality for both diagnostic and therapeutic purposes. However, very limited information is available about the effect of artefact correction on dose calculation accuracy. This research study evaluates the dosimetric effect of metal artefact reduction algorithms on severe artefacts on CT images. This study uses Gemstone Spectral Imaging (GSI)-based MAR algorithm, projection-based Metal Artefact Reduction (MAR) algorithm, and the Dual-Energy method.

Materials and Methods: The Gemstone Spectral Imaging (GSI)-based and SMART Metal Artefact Reduction (MAR) algorithms are metal artefact reduction protocols embedded in two different CT scanner models by General Electric (GE), and the Dual-Energy Imaging Method was developed at Duke University. All three approaches were applied in this research for dosimetric evaluation on CT images with severe metal artefacts. The first part of the research used a water phantom with four iodine syringes. Two sets of plans, multi-arc plans and single-arc plans, using the Volumetric Modulated Arc therapy (VMAT) technique were designed to avoid or minimize influences from high-density objects. The second part of the research used projection-based MAR Algorithm and the Dual-Energy Method. Calculated Doses (Mean, Minimum, and Maximum Doses) to the planning treatment volume (PTV) were compared and homogeneity index (HI) calculated.

Results: (1) Without the GSI-based MAR application, a percent error between mean dose and the absolute dose ranging from 3.4-5.7% per fraction was observed. In contrast, the error was decreased to a range of 0.09-2.3% per fraction with the GSI-based MAR algorithm. There was a percent difference ranging from 1.7-4.2% per fraction between with and without using the GSI-based MAR algorithm. (2) A range of 0.1-3.2% difference was observed for the maximum dose values, 1.5-10.4% for minimum dose difference, and 1.4-1.7% difference on the mean doses. Homogeneity indexes (HI) ranging from 0.068-0.065 for dual-energy method and 0.063-0.141 with projection-based MAR algorithm were also calculated.

Conclusion: (1) Percent error without using the GSI-based MAR algorithm may deviate as high as 5.7%. This error invalidates the goal of Radiation Therapy to provide a more precise treatment. Thus, GSI-based MAR algorithm was desirable due to its better dose calculation accuracy. (2) Based on direct numerical observation, there was no apparent deviation between the mean doses of different techniques but deviation was evident on the maximum and minimum doses. The HI for the dual-energy method almost achieved the desirable null values. In conclusion, the Dual-Energy method gave better dose calculation accuracy to the planning treatment volume (PTV) for images with metal artefacts than with or without GE MAR Algorithm.

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Technological developments in biomedical microsystems are opening up new opportunities to improve healthcare procedures. Swallowable diagnostic capsules are an example of this. In this paper, a diagnostic capsule technology is described based on direct-access sensing of the Gastro Intestinal (GI) fluids throughout the GI tract. The objective of this paper is two-fold: i) develop a packaging method for a direct access sensor, ii) develop an encapsulation method to protect the system electronics. The integrity of the interconnection after sensor packaging and encapsulation is correlated to its reliability and thus of importance. The zero level packaging of the sensor was achieved by using a so called Flip Chip Over Hole (FCOH) method. This allowed the fluidic sensing media to interface with the sensor, while the rest of the chip including the electrical connections can be insulated effectively. Initial tests using Anisotropic Conductive Adhesive (ACA) interconnect for the FCOH demonstrated good electrical connections and functionality of the sensor chip. Also a preliminary encapsulation trial of the flip chipped sensor on a flexible test substrate has been carried out and showed that silicone encapsulation of the system is a viable option.

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Technological developments in biomedical microsystems are opening up new opportunities to improve healthcare procedures. Swallowable diagnostic sensing capsules are an example of these. In none of the diagnostic sensing capsules, is the sensor’s first level packaging achieved via Flip Chip Over Hole (FCOH) method using Anisotropic Conductive Adhesive (ACA). In a capsule application with direct access sensor (DAS), ACA not only provides the electrical interconnection but simultaneously seals the interconnect area and the underlying electronics. The development showed that the ACA FCOH was a viable option for the DAS interconnection. Adequate adhesive formed a strong joint that withstood a shear stress of 120N/mm2 and a compressive stress of 6N required to secure the final sensor assembly in place before encapsulation. Electrical characterization of the ACA joint in a fluid environment showed that the ACA was saturated with moisture and that the ions in the solution actively contributed to the leakage current, characterized by the varying rate of change of conductance. Long term hygrothermal aging of the ACA joint showed that a thermal strain of 0.004 and a hygroscopic strain of 0.0052 were present and resulted in a fatigue like process. In-vitro tests showed that high temperature and acidity had a deleterious effect of the ACA and its joint. It also showed that the ACA contact joints positioned at around or over 1mm would survive the gastrointestinal (GI) fluids and would be able to provide a reliable contact during the entire 72hr of the GI transit time. A final capsule demonstrator was achieved by successfully integrating the DAS, the battery and the final foldable circuitry into a glycerine capsule. Final capsule soak tests suggested that the silicone encapsulated system could survive the 72hr gut transition.

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Objectives: Elevated shame and dissociation are common in dissociative identity disorder (DID) and chronic posttraumatic stress disorder (PTSD) and are part of the constellation of symptoms defined as complex PTSD. Previous work examined the relationship between shame, dissociation, and complex PTSD and whether they are associated with intimate relationship anxiety, relationship depression, and fear of relationships. This study investigated these variables in traumatized clinical samples and a nonclinical community group.

Method: Participants were drawn from the DID (n = 20), conflict-related chronic PTSD (n = 65), and nonclinical (n = 125) populations and completed questionnaires assessing the variables of interest. A model examining the direct impact of shame and dissociation on relationship functioning, and their indirect effect via complex PTSD symptoms, was tested through path analysis.

Results: The DID sample reported significantly higher dissociation, shame, complex PTSD symptom severity, relationship anxiety, relationship depression, and fear of relationships than the other two samples. Support was found for the proposed model, with shame directly affecting relationship anxiety and fear of relationships, and pathological dissociation directly affecting relationship anxiety and relationship depression. The indirect effect of shame and dissociation via complex PTSD symptom severity was evident on all relationship variables.

Conclusion: Shame and pathological dissociation are important for not only the effect they have on the development of other complex PTSD symptoms, but also their direct and indirect effects on distress associated with relationships.

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A new method combining electrospinning of SPEEK and direct spinning of CNT forests has been used to prepare sulfonated poly(ether ether ketone) (SPEEK)/directly spinnable carbon nanotube (dsCNT) composite proton exchange membranes. The SPEEK/dsCNT membrane is more robust than SPEEK alone, and in a fuel cell significantly outperforms both SPEEK and the commercial Nafion 212 membranes.

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Ce mémoire présente deux algorithmes qui ont pour but d’améliorer la précision de l’estimation de la direction d’arrivée de sources sonores et de leurs échos. Le premier algorithme, qui s’appelle la méthode par élimination des sources, permet d’améliorer l’estimation de la direction d’arrivée d’échos qui sont noyés dans le bruit. Le second, qui s’appelle Multiple Signal Classification à focalisation de phase, utilise l’information dans la phase à chaque fréquence pour déterminer la direction d’arrivée de sources à large bande. La combinaison de ces deux algorithmes permet de localiser des échos dont la puissance est de -17 dB par rapport à la source principale, jusqu’à un rapport échoà- bruit de -15 dB. Ce mémoire présente aussi des mesures expérimentales qui viennent confirmer les résultats obtenus lors de simulations.