80,739 research outputs found
LC compensators based on transmission loss minimization for nonlinear loads
No full textThis material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. Copyright @ 2004 IEEEThis paper presents a method employing the penalty function search algorithm to determine the LC compensator value for the optimal power factor correction in nonsinusoidal systems. The objective of the proposed method is to minimize the transmission loss while the power factor and efficiency are taken as constraints and utilized in order to solve the multiobjective optimization problem by transforming it into a single objective one. Examples show that the load nonlinearity can have a significant impact on optimal compensator sizes
LC compensators for power factor correction of nonlinear loads
No full textThis material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. Copyright @ 2004 IEEEA method is presented for finding the optimum fixed LC compensator for power factor correction of nonlinear loads where both source voltage and load current harmonics are present. The LC combination is selected because pure capacitive capacitors alone would not sufficiently correct the power factor. Optimization minimizes the transmission loss, maximizes the power factor, and maximizes the efficiency. The performance of the obtained compensator is discussed by means of numerical examples
LC-API/MS in Drug Metabolism and Pharmacokinetic Studies
No full textThe use of API interfaces with quadrupole mass spectrometers has been shown to give rise to good sensitivity, selectivity, and robustness for the interfacing of LC to MS. Since their introduction in the 1990s the technique has rapidly become widespread, but at the outset of this research programme, there were still a number of problems associated with it, particularly when dealing with complex sample matrices. The aim of this research programme was to study illustrative examples of the kinds of problems associated with the analysis of biological samples using LC-API-MS in an attempt to arrive at strategies which could be employed to eliminate, or at least compensate for, the problems.
Commonly reported problems include the occurrence of matrix effects - a change in response of the target analyte(s) as a result of the presence in the samples of co /late eluting interferences. An investigation which compared ESI with APCI ionisation illustrated a significant drawback in the accepted methodology for the elimination of matrix effects.
Optimal LC conditions for a number of assays may use non-MS-friendly mobile phases. A simple and convenient solution to this problem was found to be the post column addition of organic modifier, which reproducibly and reliably enhanced sensitivity. This approach was initially used for a range of dihydropyridine calcium channel blockers and was subsequently applied to a range of chiral compounds from different therapeutic groups to illustrate that this was applicable as a generic technique for increasing sensitivity (typically by around an order of magnitude) in low organic mobile phases.
Strategies to develop and validate methods for the determination of endogenous analytes in a biological fluid were investigated. This involved the use of a surrogate matrix, to develop a method for the determination of endogenous testosterone in human serum and the use of non-matrix calibration standards for the successful development and validation of a method for the analysis of indolyl 3 acryloylglycine (IAG) in human urine.
As a result of observations suggesting promotion of ionisation of deltamethrin in liver tissue sample extracts, it was postulated that this was due to the presence of high concentrations of surfactants. After confirming the effect, a series of systematic investigations were performed to attempt to understand the mechanism to be able to utilise this as a general method for the enhancement of signal with low sensitivity analytes. It was found that the type of surfactant and concentration used was directly associated with an increased (or decreased) response.
Although there remain a number of problems associated with the use of LC-API-MS, the work undertaken for this thesis has successfully demonstrated a number of techniques that can be applied to overcome these problems. Knowledge of the nature of the sample undergoing analysis, the required analytical conditions, and where required careful application of one of the techniques described will ensure that a robust method can be readily developed
LC-MS datasets details.
No full textAcute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography-mass spectrometry (LC-MS) datasets investigating the metabolic effects of malaria, leishmaniasis and Zika virus infection were used. The retention time (RT) drift between the datasets was determined using landmarks obtained from the internal standards generally used in the quality control of the LC-MS experiments. Fitted Gaussian Process models (GPs) were used to perform a high level correction of the RT drift between the experiments, which was followed by standard peakset alignment between the samples with corrected RTs of the three LC-MS datasets. Statistical analysis, annotation and pathway analysis of the integrated peaksets were subsequently performed. Metabolic dysregulation patterns common across the datasets were identified, with kynurenine pathway being the most affected pathway between all three fever-associated datasets.</div
Table_1_More Is Not Always Better: Evaluation of 1D and 2D-LC-MS/MS Methods for Metaproteomics.xlsx
No full textMetaproteomics, the study of protein expression in microbial communities, is a versatile tool for environmental microbiology. Achieving sufficiently high metaproteome coverage to obtain a comprehensive picture of the activities and interactions in microbial communities is one of the current challenges in metaproteomics. An essential step to maximize the number of identified proteins is peptide separation via liquid chromatography (LC) prior to mass spectrometry (MS). Thorough optimization and comparison of LC methods for metaproteomics are, however, currently lacking. Here, we present an extensive development and test of different 1D and 2D-LC approaches for metaproteomic peptide separations. We used fully characterized mock community samples to evaluate metaproteomic approaches with very long analytical columns (50 and 75 cm) and long gradients (up to 12 h). We assessed a total of over 20 different 1D and 2D-LC approaches in terms of number of protein groups and unique peptides identified, peptide spectrum matches (PSMs) generated, the ability to detect proteins of low-abundance species, the effect of technical replicate runs on protein identifications and method reproducibility. We show here that, while 1D-LC approaches are faster and easier to set up and lead to more identifications per minute of runtime, 2D-LC approaches allow for a higher overall number of identifications with up to >10,000 protein groups identified. We also compared the 1D and 2D-LC approaches to a standard GeLC workflow, in which proteins are pre-fractionated via gel electrophoresis. This method yielded results comparable to the 2D-LC approaches, however with the drawback of a much increased sample preparation time. Based on our results, we provide recommendations on how to choose the best LC approach for metaproteomics experiments, depending on the study aims.</p
Data_Sheet_1_More Is Not Always Better: Evaluation of 1D and 2D-LC-MS/MS Methods for Metaproteomics.docx
No full textMetaproteomics, the study of protein expression in microbial communities, is a versatile tool for environmental microbiology. Achieving sufficiently high metaproteome coverage to obtain a comprehensive picture of the activities and interactions in microbial communities is one of the current challenges in metaproteomics. An essential step to maximize the number of identified proteins is peptide separation via liquid chromatography (LC) prior to mass spectrometry (MS). Thorough optimization and comparison of LC methods for metaproteomics are, however, currently lacking. Here, we present an extensive development and test of different 1D and 2D-LC approaches for metaproteomic peptide separations. We used fully characterized mock community samples to evaluate metaproteomic approaches with very long analytical columns (50 and 75 cm) and long gradients (up to 12 h). We assessed a total of over 20 different 1D and 2D-LC approaches in terms of number of protein groups and unique peptides identified, peptide spectrum matches (PSMs) generated, the ability to detect proteins of low-abundance species, the effect of technical replicate runs on protein identifications and method reproducibility. We show here that, while 1D-LC approaches are faster and easier to set up and lead to more identifications per minute of runtime, 2D-LC approaches allow for a higher overall number of identifications with up to >10,000 protein groups identified. We also compared the 1D and 2D-LC approaches to a standard GeLC workflow, in which proteins are pre-fractionated via gel electrophoresis. This method yielded results comparable to the 2D-LC approaches, however with the drawback of a much increased sample preparation time. Based on our results, we provide recommendations on how to choose the best LC approach for metaproteomics experiments, depending on the study aims.</p
Longitudinal patterns of behavioral, emotional, and social difficulties and self-concept in adolescents with a history of specific language impairment
No full textPurpose: This study explored the prevalence and stability of behavioral difficulties and self-concepts between 8 and 17 years in a sample of children with a history of specific language impairment (SLI). We investigated whether earlier behavioral, emotional and social difficulties (BESD), self-concepts, language, and literacy abilities predicted behavioral difficulties and self-concepts at 16/17 years.
Method: In this prospective longitudinal study, 65 students were followed up with teacher behavior ratings and individual assessments of language, literacy, and self-concepts at 8, 10, 12, 16, and 17 years.
Results: The students had consistently higher levels of five domains of BESD, which had different trajectories over time, and poorer scholastic competence, whose trajectory also varied over time. Earlier language ability did not predict later behavioral difficulties or self-concepts but the prediction of academic self-concept at 16 by literacy at 10 years approached significance.
Conclusions: We demonstrate the importance of distinguishing domains of behavioral difficulties and self-concept. Language, when measured at 8 or 10 years, was not a predictor of behavior or self-concepts at 16 years, or of self-concepts at 17 years. The study stresses the importance of practitioners addressing academic abilities and different social-behavioral domains in delivering support for adolescents with SLI
Additional file 4 of Targeting KK-LC-1 inhibits malignant biological behaviors of triple-negative breast cancer
No full textAdditional file 4. The role of KK-LC-1 expression in the prognosis of breast cancer patients using the Kaplan–Meier plotter database. A: The effect of increased KK-LC-1 mRNA expression on OS was analyzed in a study including 1089 patients. B: The effect of increased KK-LC-1 mRNA expression on OS was analyzed in a study including 2976 patients. C: The effect of increased KK-LC-1 mRNA expression on OS was analyzed in a study that included 943 patients. D: The effect of increased KK-LC-1 mRNA expression on PPS was analyzed in a study including 180 patients
Cost-effective applications of power factor correction for nonlinear loads
No full textThis material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. Copyright @ 2005 IEEEThe objective of this paper is to propose a new approach for designing passive LC compensators by using the penalty function method as an optimization tool. The performance of the cost-effective passive LC compensator for a constant load depends on the appropriate inductor and capacitor selection. Several design methods are reviewed and a novel design methodology is proposed in this paper. By using the proposed method, the designer can quickly find appropriate parameter values to meet the desired circuit performance. Simulated results show that an appropriate combination of the inductor and capacitor selected by the proposed method can meet the desired power-quality requirement. Different cases of design examples are shown in this paper to verify the performance of the proposed design methodology
palubad/LC-SLIAC: sar
No full text<p># Local incidence angle correction LC-SLIAC for forests in Google Earth Engine </p>
<p>This code repository is an attachment for the article in Remote Sensing: [Paluba et al. (2021): "Land Cover-Specific Local Incidence Angle Correction: A Method for Time-Series Analysis of Forest Ecosystems"](https://www.mdpi.com/2072-4292/13/9/1743/) (doi: 10.3390/rs13091743).<br>The repository contains a folder "javascript_codes" where you can find: <br> - A JavaScript Google Earth Engine (GEE) function "LC-SLIAC.js" to create a SAR image collection where bands have been corrected for effects of terrain<br> - A JavaScript GEE example usage of the function "LC-SLIAC_example.js", where three-month time series chart before and after the application of LC-SLIAC and the corrected image collection are added to the GEE Console</p>
<p>## UPDATE: LC-SLIAC_global<br>After requests from the GEE community, a new version of the code was added, which can be used globally, not only for countries in the European Union. [See the details here](#update-lc-sliac-for-global-use). In the "javascript_codes" folder you can find two more scripts:<br> - A globally usable JavaScript Google Earth Engine (GEE) function "LC-SLIAC_global.js" to create a SAR image collection where bands have been corrected for effects of terrain<br> - A globally usable JavaScript GEE example usage of the function "LC-SLIAC_global_example.js", where three-month time series chart before and after the application of LC-SLIAC and the corrected image (test site in Vietnam)</p>
<p>## About the Land cover-specific local incidence angle correction (LC-SLIAC) in GEE<br>The land cover-specific local incidence angle correction (LC-SLIAC) is based on the linear relationship between the backscatter values and the local incidence angle (LIA) for a given land cover type in the monitored area. Using the combination of CORINE Land Cover and Hansen Global Forest databases, a wide range of different LIAs for a specific forest type can be generated for each individual scene. The algorithm was developed and tested in the cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, Shuttle Radar Topography Mission (SRTM) digital elevation model, as well as CORINE Land Cover and Hansen Global Forest databases. The developed method was created primarily for time-series analysis of forests over mountainous areas. LC-SLIAC was tested in 16 study areas over several protected areas in Central Europe. </p>
<p><b>This methodology is mainly focused on the use in the analysis of forest time series. In general, this method is useful over mountainous areas with moderate to steep sloping terrain, where the variation in LIA values for forests is high.</b> The results after correction by LC-SLIAC showed a statistically significant reduction in variance (of more than 40%) in areas with LIA range > 50° and LIA interquartile range (IQR) > 12°, while in areas with low LIA range (< 30°) and LIA IQR (< 6°), the decrease in variance was very low and statistically not significant. Time series after the correction showed a reduced fluctuation of backscatter values caused by different LIAs in each acquisition path, while this reduction was statistically significant (with up to 95% reduction of variance) in areas with a difference in LIA greater than or equal to 27°.</p>
<p><b>Methodology:</b> The most important step in the methodology of the LC-SLIAC method was to calculate the LIA for every image pixel, where other parameters from the SRTM DEM (slope and aspect) and SAR image (viewing azimuth) needed to be calculated. For SAR images, the active shadow and layover areas were masked out, followed by the generation of forest mask, which was used to select an appropriate number of forest areas to explain the relationship between LIA and backscatter in the linear regression analysis. Figure 1 shows the steps used to generate the corrected image collection.</p>
<p><br>Figure 1. Methodology workflow used in this work</p>
<p> </p>
<p><br># UPDATE: LC-SLIAC for global use<br>After requests from the GEE community, a new version of the code was added, which can be used globally, not only for countries in the European Union. For the "global" version of the LC-SLIAC, the [Copernicus Global Land Cover Layers](https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_Landcover_100m_Proba-V-C3_Global/) (CGLC) was used. The newest version (v 1.8) of the [Hansen Global Forest Change database](https://developers.google.com/earth-engine/datasets/catalog/UMD_hansen_global_forest_change_2020_v1_8/) was used in this update, where data for 2020 are also available.</p>
<p>In the definition of forest type, there are 4 categories in the CGLC:<br> - 111 - Closed forest, evergreen needle leaf. Tree canopy >70 %, almost all needle leaf trees remain green all year. Canopy is never without green foliage.<br> - 112 - Closed forest, evergreen broad leaf. Tree canopy >70 %, almost all broadleaf trees remain green year round. Canopy is never without green foliage.<br> - 113 - Closed forest, deciduous needle leaf. Tree canopy >70 %, consists of seasonal needle leaf tree communities with an annual cycle of leaf-on and leaf-off periods.<br> - 114 - Closed forest, deciduous broad leaf. Tree canopy >70 %, consists of seasonal broadleaf tree communities with an annual cycle of leaf-on and leaf-off periods.</p>
<p><br>### LC-SLIAC function and its usage in GEE<br>You can use this function after the reqirement call require('users/danielp/LC-SLIAC:LC-SLIAC'), i.e.:<br>```ruby<br>require('users/danielp/LC-SLIAC:LC-SLIAC') <br>```<br>or by copying the code in the "LC-SLIA.js" to your code editor and call it with defined parameters.</p>
<p>### LC-SLIAC for global use function and its usage in GEE<br>You can use this function after the reqirement call require('users/danielp/LC-SLIAC:LC-SLIAC_global'), i.e.:<br>```ruby<br>require('users/danielp/LC-SLIAC:LC-SLIAC_global') <br>```<br>or by copying the code in the "LC-SLIA_global.js" to your code editor and call it with defined parameters.</p>
<p>#### Parameters of the function:<br> - ROI (type Geometry)<br> - Define the ROI for what you want to create a time series analysis<br> - startDate (type Date)<br> - Start date of the time series<br> - endDate (type Date)<br> - End date of the time series<br> - *year (type Integer)* - **available only for the LC-SLIAC_global version**<br> - *Year for which the CGLC will be used. Available years: 2015-2019. Hopefully, the 2020 database will be available soon.*<br> - landCoverType (type Integer)<br> - Define the land cover type. Currently tested for coniferous forest (312) and broadleaf forest (311).<br> - For the LC-SLIAC_global function, see the legend [here](#update-lc-sliac-for-global-use)<br> - S1Collection (type ImageCollection, *optional, default: Sentinel-1 Image Collection with VV and VH bands*)<br> - Define the S1 image collection for which you want to apply the LIA correction. Tested and designed for Sentinel-1 data<br> - boudningBoxSize (type Integer, *optional, default: 10000*)<br> - The bounding box size around the selected area to calculate the backscatter-LIA dependence. This area is also used to clip the resulted image collection after the correction.<br> - referenceAngle (type Integer, *optional, deafault: 9999* = mean angle from found LIAs)<br> - Reference angle to which the backscatter values will be corrected. For time series analyses, it is recommended to use the default value - the mean value from the minimum and maximum value of observed LIA (from the available paths).<br> - acquistionMode (type String, optional, *default: 'IW'*)<br> - Acqusition mode for Sentinel-1 data in GEE can be 'IW' (Interferometric Wide Swath), 'EW' (Extra Wide Swath) or 'SM' (Strip Map)</p>
<p>#### Output of the function:<br>The main output of the LC-SLIAC function is the input Sentinel-1 image collection clipped to the predefined study area size by boudningBoxSize, extended by:<br> - Image bands:<br> - LIA - the calculated local incidence angle image<br> - corrected_VH - VH band after LC-SLIAC where active shadow and layover areas are masked out<br> - corrected_VV - VV band after LC-SLIAC where active shadow and layover areas are masked out<br> <br> - Statistical parameters in the properties:<br> - VVscale, VH scale - the scale coefficient calculated from the regression analysis for VV and VH polarization, respectively<br> - VVoffset, VHoffset - the offset coefficient calculated from the regression analysis for VV and VH polarization, respectively<br> - VVnumberOfForestPoints, VHnumberOfForestPoints - the number of forest areas included in the regression analysis for VV and VH polarization, respectively<br> - VHR2, VVR2 - the resulted coefficient of determination (R<sup>2</sup>) of the regression analysis for VV and VH polarization, respectively<br> - VHpValue, VVpValue - the p-value for the regression analysis for VV and VH polarization, respectively<br> - MeanElevationOfForestPoints - mean elevation of selected forest areas for VV and VH polarization, respectively<br> - VV_LIAIQR, VH_LIAIQR - LIA interquartile range (IQR) for forest areas for VV and VH polarization, respectively<br> - LIA_range_VV, LIA_range_VH - LIA range for forest areas for VV and VH polarization, respectively<br> - lookAngleAzimuth - the calculated sensor's look angle</p>
<p><br>#### Important note:<br>For long-term time series analysis, e.g. for the whole Sentinel-1 archive, it is recommended to zoom in to the selected study area, as it is done in the example script (LC-SLIAC_example.js). </p>
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