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A case worker with the New South Wales Department of Family and Community Services has been charged over the sexual abuse of a nine-year-old girl.
The 54-year-old man has been stood down as an employee after he was arrested this afternoon and charged with four counts of aggravated sexual assault.
He will face Newcastle Bail Court tomorrow.
Meanwhile, the girl has been removed from the man's partner's home and placed in care.
In a statement, Community Services Minister Pru Goward said the Ombudsman would oversee the investigation of the case following the police investigation.
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The Interstate Highway 80 off-ramp at Fifth Street in San Francisco has reopened following a crash this morning, according to the California Highway Patrol.
The crash occurred at the westbound off-ramp around 3:22 a.m.
There were injuries in the crash.
Copyright © 2014 by Bay City News, Inc. Republication, re-transmission or reuse without the express written consent of Bay City News, Inc. is prohibited.

subset: commoncrawl


text:
colorful skull legging
Very comfortable and soft as cotton
Perfect for yoga, Dance, working, running
Midweight soft, stretchy, smooth feel
Made from high quality SUPPLEX material that is designed to stretch to fit just about any body type.
Material is Polyester/Elastane which is extremely flexible, fade resistant, holds shape, lighter weight, quick drying, wash durability and resists wrinkling
Machine wash cold with like colors, tumble dry low.
One size fits most in a S-M
Polyester 90%
Elastane 10%


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The Cambridge Public Library is pleased to announce a new poetry series this fall, in honor of Louisa Solano, owner of the Grolier Book Shop from 1974 to 2006 and patron of the poetry community. Poets will read from their selected works at the Main Library, 449 Broadway in Cambridge, Massachusetts. Each reading will begin at 6:30 p.m. in the Lecture Hall Books will be available for purchase and signing.
Ms. Solano has made a significant donation to the Cambridge Room, the Cambridge Public Library's Archives and Special Collections, including a substantial collection of signed first editions from members of the Cambridge poetry community as well as material related to the Grolier Book Shop. The Grolier Book Shop, founded in 1927, is the oldest continuously run bookstore devoted to selling poetry and poetry criticism. Under Solano's ownership, the Grolier sponsored an annual national poetry contest, the Grolier Poetry Prize, launching the careers of new poets. During her tenure, Solano also founded a popular reading series at Harvard University's Adams House. Solano's knowledge of poetry and her philosophy of stocking the shelves with difficult-to-find publications and small, avant-garde presses made the Grolier a destination for established and emerging poets, and lovers of literature.
The Louisa Solano Poetry Series at the Cambridge Public Library will feature the following poets:
Tuesday, Oct. 1t
Robert Pinsky, Poet Laureate from 1997-2000
Gail Mazur, Founding Director of Blacksmith House Poetry Series in Cambridge
David Ferry, 2012 National Book Award Winner for Poetry
Frank Bidart reads from Metaphysical Dog: Poems, his latest collection
"Louisa's donation is a wonderful addition to the Cambridge Public Library's Archives and Special Collections," said Alyssa Pacy, the Library's Archivist. "Many of these publications are rare and out of print. I know that they will be a valuable resource for researchers for generations to come. As I have been going through the signed editions, nearly every inscription reads 'For Louisa, in gratitude and admiration.' Those words are a testament to Louisa's lifelong support of poets and show her impact on the poetry community."
For more information, visit their website: www.cambridgepubliclibrary.org or call 617-349-4040


text:
These are amazing!! They are from pinterest also. I love taking these places because everyone asks about the beans!!
ARKANSAS GREEN BEANS (adapted from AllRecipes; makes 10 servings)
- 5 (15-ounce) cans green beans, drained (note: you can substitute a similar amount of frozen green beans, about 4 (12-ounce) bags, thawed, or you can use fresh green beans, too)
- 12 slices bacon
- 2/3 cup brown sugar
- 1/4 cup butter, melted
- 7 teaspoons soy sauce
- 1 1/2 teaspoons garlic powder
Preheat oven to 350 degrees. Place the drained green beans in a 9×13″ baking pan.
Cook bacon in a frying pan on your stove top or in a microwave on a microwave-safe plate until bacon strips are cooked enough to chop into large pieces (they should be done, but not real crispy). Sprinkle the bacon pieces on top of the green beans.
Combine the brown sugar, melted butter, soy sauce, and garlic powder in a small bowl. Pour this mixture over the green beans and bacon. Bake uncovered at 350 degrees for 40 minutes. Remove from oven and serve.


text:
Welcome to the Lancing Pest Control ciyt page for Pest Control Finder.co.uk. Find an exterminator in Lancing or pest control specialist in Lancing to help you identify a pest and eradicate the problem quickly. Many of the pest control firms listed here can offer environmentally responsible pest control products and solutions to treat infestations from cockroaches, termites, spiders, mice, bats, ants and more.
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Flat 3
Offers: Chemical and non-chemical treatments for bed bugs, cockroaches, carpet beetles, fleas, ants, moths, ticks, mice, and others.
Areas: London, Ealing, Uxbridge, Harrow, Wood Green, Romford, Stratford, Ilford, West End of London, Shepherd's Bush, Knightsbridge, Hounslow, Kingston upon Thames, Sutton, Croydon, Bromley, Surrey
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The Distributive Effects of Joining the Global Economy in Iran: the Application of ARDL Model
In this paper the relationship between globalization and distribution of income in Iran with using of time series data over 1977-2007 is investigated. For this purpose, we used Auto Regressive Distributed Lag (ARDL) model and long-run and short-run relations between involved variables in model are considered. The trade intensity index is used as measure of globalization. Results indicated that, there is long-run relationship and co-integration between involved variables and the Gini coefficient. Furthermore, accompanied by other variables globalization, has a positive and significant effect on dependent variable. Value of error correction coefficient is equal to -0.90 and statistically significant and suggests a high speed of convergence to equilibrium.
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DOI: https://doi.org/10.5296/ber.v2i2.2001
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Author's name. ISBN. Title. Lampooning a real human being on the World Wide Web. I don't know Miss Roth or her work, but seeing another person, even the foolish and ignorant, vilified in this matter is truly sad, and unprofessional.I've loved this blog. Please tell me this is a marketing ploy, Miss Snark, and you haven't gone from clever to cruel.
whaddaya mean "to" cruel? Like I've been nice before? You must have been reading someone else's blog.
This person wrote me this exact email. I emailed her back and said "You know all these emails go up on the blog, right?". And she ANSWERED, and said "yes, if you can't help me maybe someone else can".
I redacted her address and phone number and email. She didn't even have sense enough to say post this without that. Sometimes the ice cold bucket of disdain is the only thing that brings someone to their senses.
The thing is, she didn't query my agency, she queried THIS BLOG. At some point, you get what you ask for and this honey, is it.
No of course it's not kind. No it's not nice. Yes, it's cruel. So fucking what. This is not nursery school and you don't get a certificate for participation and a hug for remembering to flush the toilet. Bobby Knight throws chairs; I throw words. If you don't like it, go play for Notre Dame.


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|Particle beam and crabbing and deflecting structure
|March, 2010
|Diffuse light pharmaceutical inspection system and process
|November, 2005
|Armbuster et al.
|January, 2009
|Stephenson et al.
|Image sensor assembly and a carriage using the same
|August, 2003
|Cargo inspection system for Special Nuclear Materials (SNM)
|April, 2010
|Covering of scintillation detector with a reflective coating
|May, 2006
|Dosimeter for sun radiation
|September, 2001
|Faran et al.
|September, 2009
|Khurgin et al.
|May, 2009
|Uhlemann et al.
|Shielded transport for multiple brachytheapy implants with integrated measuring and cutting board
|April, 2005
|Terwilliger et al.
|Method for the chemical separation of GE-68 from its daughter Ga-68
|January, 2009
|Fitzsimmons et al.
This application claims the benefit of U.S. Provisional Application No. 60/239,969, entitled "Near Infrared Chemical Imaging Microscope" filed Oct. 13, 2000.
This work is supported by the National Institute of Standards and Technology (NIST) under the Advanced Technology Program (ATP) award (Contract Number 70NANB8H4021)
The present invention is related to near-infrared (NIR) microscopes for spectroscopic and image analysis, and, in particular, to microscopes useful for both NIR spectroscopy, NIR chemical imaging and NIR volumetric chemical imaging.
NIR spectroscopy is a mature, non-contact, non-destructive analytical characterization tool that has been widely applied to a broad range of materials. The NIR region of the electromagnetic spectrum encompasses radiation with wavelengths of 0.78 to 2.5 μm (12,800 to 4,000 cm−1). NIR spectra result from the overtone and combination bands of fundamental mid-infrared (MIR) bands. Among the many desirable characteristics, NIR is used to rapidly obtain both qualitative and quantitative information about the molecular makeup of a material. Digital imaging, on the other hand, provides a means to obtain optical (i.e., spatial—morphological, topographical, etc.) information about a material. By combining the spatial information obtained from digital imagery and the spectral information obtained from NIR spectroscopy, the chemical makeup of complex material matrices can be mapped out in both two and three spatial dimensions. NIR chemical imaging combines NIR spectroscopy and digital imaging for the molecular-specific analysis of materials. A NIR chemical imaging microscope apparatus employing NIR absorption molecular spectroscopy for materials characterization is disclosed.
State-of-the-Art Instrumentation
NIR microscopes are used to obtain NIR absorption, transmittance or reflectance spectra (e.g., NIR microspectra) from samples ranging in size between 1 and 1000 μm. These instruments are typically equipped with a digital camera to visually locate a region of interest on a sample upon which a NIR light beam from a Fourie transform (FT) spectrometer is focused. Reflective optics are used to direct the transmitted or reflected light from the sample to a NIR detector. The output is a NIR absorption spectrum collected in transmittance or reflectance mode.
NIR chemical imaging can be considered an extension of NIR microspectroscopy. Much of the imaging performed since the development of the first NIR microprobes has involved spatial scanning of samples beneath NIR microscopes in order to construct NIR "maps" of surfaces. In point by point scanning with NIR microscopes, the NIR light beam is focused onto the surface of a sample or apertured to illuminate a small region of a sample and a spectrum from each spatial position is collected. Images are obtained by rastering the sample through the focused or apertured NIR light beam and the spectra recorded are then reconstructed to form an image. Although point scanning produces images based on NIR contrast, long experimental times are common since the duration of the experiment is proportional to the number of image pixels. As a direct result, point scan images are captured at low image definition, which relates directly to the limited utility of the technique as an imaging tool for the routine assessment of material morphology. The spatial resolution of the image is limited by the size of the NIR illumination spot on the sample (no less than 1 μm) and the rastering mechanism, which requires the use of moving mechanical parts that are challenging to operate reproducibly.
NIR imaging cameras have been used in photography for decades. Until recently, however, it has not been easily accessible to those not versed in traditional photographic processes. By using optical filters (e.g., cold filters) that block the visible wavelengths (0.4-0.78 μm), charge-coupled devices (CCDs) used in digital cameras and camcorders can be used to sense NIR light out to around 1100 nm. Other regions of the NIR spectrum can be viewed using devices such as indium gallium arsenide (InGaAs—0.9 μm to 1.7 μm) and indium antimonide (InSb—1.0 μm to 5.0 μm) focal plane array (FPA) detectors. These integrated wavelength NIR imaging approaches allow one to study relative light intensities of objects over broad ranges of the NIR spectrum, but useful chemical information is unattainable without the use of some type of discrete wavelength filtering device.
The use of dielectric interference filters in combination with NIR FPAs is one method in which chemical information can be obtained from a sample. To form NIR chemical images, a NIR light beam is defocused to illuminate a wide field of view and the reflected or transmitted light from the illuminated area is imaged onto a two-dimensional NIR detector. A selection of discrete dielectric interference filters provided in a filter wheel, or a linearly variable or circularly variable format can be positioned in front of a broadband NIR light source, or in front of the NIR FPA itself in order to collect NIR wavelength resolved images. Typically, the use of several fixed bandpass filters is required to access the entire NIR spectrum. The spatial resolution of the NIR image approaches that of the optical microscope, while spectral resolution of several nanometers has been demonstrated. Key limitations of the dielectric filter approach include the need for a multitude of discrete filters to provide appreciable free spectral range, or the reliance on moving mechanical parts in employing continuously tunable dielectric interference filters as a requirement to form wavelength resolved images. While moving mechanical assemblies can be engineered they add cost and complexity to NIR chemical imaging systems. Alternatives to moving mechanical assemblies are generally more cost effective and provide performance advantages.
Acousto-optic tunable filters (AOTFs) have been employed as no-moving-parts imaging spectrometers for NIR imaging. The AOTF is a solid-state device that is capable of functioning from the UV to the mid-IR depending on the choice of the filter's crystal material. Operation of the AOTF is based on the interaction of light with a traveling acoustic sound wave in an anisotropic crystal medium. The incident light is diffracted with a narrow spectral bandpass when an rf signal is applied to the device. By changing the applied rf frequency under computer control the spectral passband can be tuned rapidly with the benefit of non-moving parts.
For use in NIR chemical imaging, AOTFs have distinct limitations. AOTFs have imaging performance that is degraded appreciably from diffraction-limited conditions due to dispersion effects and image shifting effects. Furthermore, AOTFs suffer from temperature instability and exhibit nonlinear properties that complicate their use as imaging spectrometers.
An aim of NIR chemical imaging technology development has been to develop a NIR imaging technique that combines diffraction-limited spatial resolution with high spectral resolution. NIR chemical imaging techniques have only recently achieved a degree of technological maturity that allow the collection of high resolution (spectral and spatial) data with the advent of the liquid crystal (LC) imaging spectrometers. In general, LC devices provide diffraction-limited spatial resolution. The spectral resolution of the LC imaging spectrometer is comparable to that provided by dispersive monochromator and Fourier transform interferometers. In addition, LC technology provides high out of band rejection, broad free spectral range, moderate transmittance, high overall etendue and highly reproducible random access computer controlled tuning.
Under normal NIR imaging operation, LC imaging spectrometers allow NIR chemical images of samples to be recorded at discrete wavelengths (energies). A spectrum is generated corresponding to thousands of spatial locations at the sample surface by tuning the LC imaging spectrometer over a range of wavelengths and collecting NIR images systematically. Contrast is generated in the images based on the relative amounts of NIR absorption, transmittance or reflectance that is generated by the different species located throughout the sample. Since a high quality NIR spectrum is generated for each pixel location, a wide variety of chemometric analysis tools, both univariate and multivariate, can be applied to the NIR image data to extract pertinent information. Correlative multivariate routines are particularly powerful when applied to chemical images collected from samples intentionally seeded with a known standard material. This approach of incorporating calibration standards within an image field of view can be extended to quantitative chemical image analysis. In addition, digital image analysis procedures can also be applied to high image quality NIR chemical images to perform routine particle analysis in both two (2D) and three (3D) spatial dimensions. Volumetric 3D NIR chemical image analysis can be performed very effectively using numerical deconvolution computational strategies.
To address the need for a device that can provide video imaging, NIR spectroscopy and high resolution (spatial and spectral) NIR chemical imaging in two and three spatial dimensions, a novel NIR chemical imaging microscope has been developed that is NIR chemical imaging capable.
The microscope design uses NIR optimized liquid crystal (LC) imaging spectrometer technology for wavelength selection. The NIR optimized refractive microscope is used in conjunction with infinity-corrected objectives to form the NIR image on the detector with or without the use of a tube lens. An integrated parfocal analog color CCD detector provides real-time sample positioning and focusing. The color image and the NIR image are fused in software. In one configuration, the NIR microscope may be used as a volumetric imaging instrument through the means of moving the sample through focus, collecting images at varying focal depths and reconstructing a volumetric image of the sample in software, or through the means of keeping the sample fixed and changing the wavelength dependent depth of penetration in conjunction with a refractive tube lens with a well characterized chromatic effect. The output of the microscope can be coupled to a NIR spectrometer either via direct optical coupling or via a fiber optic. A Chemical Imaging Addition Method seeds the sample with a material of known composition, structure and/or concentration and then generates the NIR image suitable for qualitative and quantitative analysis. The microscope generates NIR chemical image data that is analyzed and visualized using chemical image analysis software in a systematic and comprehensive manner. While this invention has been demonstrated on a microscope optic platform, the novel concepts are also applicable to other image gathering platforms, namely fiberscopes, macrolens systems and telescopes.
FIG. 1 shows a schematic diagram of the near-infrared (NIR) chemical imaging microscope
FIG. 2 shows a diagram of the chemical imaging data analysis cycle performed in software.
FIG. 3 is a digital brightfield image of a CdZnTe semiconductor material decorated with tellurium inclusions.
FIG. 4 an NIR microscopic transmittance image of a CdZnTe semiconductor material decorated with tellurium inclusions.
FIG. 5A illustrates a raw NIR image frame of a CdZnTe wafer sample.
FIG. 5B illustrates an NIR image frame of the sample of FIG. 5A in which the threshold value for the image was set too low.
FIG. 5C illustrates an NIR image frame of the sample of FIG. 5A in which the threshold value for the image was set too high.
FIG. 5D illustrates an NIR image frame of the sample of FIG. 5A in which the threshold value for the image was set to an intermediate level.
FIG. 6A is the original raw image of four adjacent regions of interest on a CdZnTe wafer.
FIG. 6B is the background-corrected image corresponding to the four adjacent regions of interest of the CdZnTe wafer of FIG. 6A.
FIG. 6C is the binarized image corresponding to the four adjacent regions of interest of the CdZnTe wafer of FIG. 6A.
FIG. 7 is a three-dimensional view of tellurium inclusions in a CdZnTe wafer.
The NIR chemical imaging microscope combines in a single platform a NIR optimized refractive optical microscope base, which is equipped with NIR optimized infinity-corrected microscope objectives, an automated XYZ translational microscope stage and quartz tungsten halogen (QTH) lamps to secure and illuminate samples for NIR spectroscopy and imaging, an analog color charge-coupled device (CCD) detector for ordinary optical image collection and digital image collection, a NIR LC imaging spectrometer for NIR chemical image wavelength selection and a room temperature or optionally cooled NIR FPA for NIR image capture.
FIG. 1 is a schematic diagram of the NIR chemical imaging microscope. NIR illumination is directed to the sample in a reflected light configuration using a QTH source or other broadband white light source, including metal halide or Xe arc lamps 1 or a transmitted light configuration using QTH or suitable NIR source 2 of an NIR optimized refractive optical microscope platform 3. The reflected or transmitted NIR light is collected from the sample positioned on the automated XYZ translational microscope stage 4 through an infinity-corrected NIR optimized microscope objective 5.
Ordinary optical imagery of the sample can be obtained using a mirror or beamsplitter or prism arrangement inserted into turret 6 and collecting an image with an analog or digital color or monochrome charge-coupled device (CCD) or CMOS detector 7. In NIR chemical imaging mode, the magnified NIR image is coupled through a NIR LC imaging spectrometer 8 and collected on a room temperature or cooled NIR focal plane array (FPA) detector 9. The FPA is typically comprised of indium gallium arsenide (InGaAs), but may be comprised of other NIR sensitive materials, including platinum silicide (PtSi), indium antimonide (InSb) or mercury cadmium telluride (HgCdTe). Using a beamsplitting element inserted into turret 6, NIR and ordinary optical imagery can be collected with an analog monochrome or color CCD detector 7 and NIR FPA 9 simultaneously.
A central processing unit 10, typically a Pentium computer, is used for NIR chemical image collection and processing. The analog color CCD 7, NIR FPA 9, automated XYZ translational microscope stage 4 controlled via a controller 12 and NIR LC imaging spectrometer 8 (through LC imaging spectrometer controller 11) are operated with commercial software, such as Acquisition Manager (ChemIcon Inc.) in conjunction with ChemImage (ChemIcon Inc.).
By introducing a polarization sensitive beam splitting element in the optical path prior to the NIR LC imaging spectrometer 8 (not shown in schematic diagram), a portion of the NIR light from the sample may be coupled to a remote NIR spectrometer (also not shown in schematic diagram).
Preferably, NIR optimized liquid crystal (LC) imaging spectrometer technology is used for wavelength selection. The LC imaging spectrometer may be of the following types: Lyot liquid crystal tunable filter (LCTF); Evans Split-Element LCTF; Solc LCTF; Ferroelectric LCTF; Liquid crystal Fabry Perot (LCFP); or a hybrid filter technology comprised of a combination of the above-mentioned LC filter types or the above mentioned filter types in combination with fixed bandbass and bandreject filters comprised of dielectric, rugate, holographic, color absorption, acousto-optic or polarization types.
One novel component of this invention, is that a NIR optimized refractive microscope is used in conjunction with infinity-corrected objectives to form the NIR image on the detector without the use of a tube lens. The microscope can be optimized for NIR operation through inherent design of objective and associated anti-reflective coatings, condenser and light source. To simultaneously provide high numerical apertures the objective should be refractive. To minimize chromatic aberration, maximize throughput and reduce cost the conventional tube lens can be eliminated, while having the NIR objective form the NIR image directly onto the NIR focal plane array (FPA) detector, typically of the InGaAs type. The FPA can also be comprised of Si, SiGe, PtSi, InSb, HgCdTe, PdSi, Ge, analog vidicon types. The FPA output is digitized using an analog or digital frame grabber approach.
An integrated parfocal analog CCD detector provides real-time sample positioning and focusing. An analog video camera sensitive to visible radiation, typically a color or monochrome CCD detector, but may be comprised of a CMOS type, is positioned parfocal with the NIR FPA detector to facilitate sample positioning and focusing without requiring direct viewing of the sample through conventional eyepieces. The video camera output is typically digitized using a frame grabber approach.
The color image and the NIR image are fused using software. While the NIR and visible cameras often generate images having differing contrast, the sample fields of view can be matched through a combination of optical and software manipulations. As a result, the NIR and visible images can be compared and even fused through the use of overlay techniques and correlation techniques to provide the user a near-real time view of both detector outputs on the same computer display. The comparitive and integrated views of the sample can significantly enhance the understanding of sample morphology and architecture. By comparing the visible, NIR and NIR chemical images, additional useful information can be acquired about the chemical composition, structure and concentration of species in samples.
The NIR microscope can be used as a volumetric imaging instrument through the means of moving the sample through focus in the Z, axial dimension, collecting images in and out of focus and reconstructing a volumetric image of the sample in software. For samples having some volume (bulk materials, surfaces, interfaces, interphases), volumetric chemical imaging in the NIR has been shown to be useful for failure analysis, product development and routine quality monitoring. The potential also exists for performing quantitative analysis simultaneous with volumetric analysis. Volumetric imaging can be performed in a non-contact mode without modifying the sample through the use of numerical confocal techniques, which require that the sample be imaged at discrete focal planes. The resulting images are processed and reconstructed and visualized. Computional optical sectioning reconstruction techniques based on a variety of strategies have been demonstrated, including nearest neighbors and iterative deconvolution.
An alternative to sample positioning combined with computation reconstruction is to employ a tube lens in the image formation path of the microscope which introduces chromatic aberration. As a result the sample can be interrogated as a function of sample depth by exercising the LC imaging spectrometer, collecting images at different wavelengths which penetrate to differing degrees into bulk materials. These wavelength dependent, depth dependent images can be reconstructed to form volumetric images of materials without requiring the sample to be moved, again through application of computational optical sectioning reconstruction algorithms.
The output of the microscope can be coupled to a NIR spectrometer either via direct optical coupling or via a fiber optic cable. This allows conventional spectroscopic tools to be used to gather NIR spectra for traditional, high speed spectral analysis. The spectrometers can be of the following types: fixed filter spectrometers; grating based spectrometers; Fourier Transform spectrometers; or Acousto-Optic spectrometers
A novel method that is readily employed by the disclosed microscope invention is a method described as the Chemical Imaging Addition Method which involves seeding the sample with a material of known composition, structure and/or concentration and then generating the NIR image suitable for qualitative and quantitative analysis. The Chemical Imaging Addition Method is a novel extension of a standard analytical chemical analysis technique, the Standard Addition Method. A common practice in quantitative chemical analysis is to construct a standard calibration curve which is a plot of analytical response for a particular technique as a function of known analyte concentration. By measuring the analytical response from an unknown sample, an estimate of the analyte concentration can then be extrapolated from the calibration curve. In the Standard Addition Method, known quantities of the analyte are added to the samples and the increase in analytical response is measured. When the analytical response is linearly related to concentration, the concentration of the unknown analyte can be found by plotting the analytical response from a series of standards and extrapolating the unknown concentration from the curve. In this graph, however, the x-axis is the concentration of added analyte after being mixed with the sample. The x-intercept of the curve is the concentration of the unknown following dilution. The primary advantage of the standard addition method is that the matrix remains constant for all samples.
While the Standard Addition Method is used specifically for quantitative analysis, the Chemical Imaging Addition Method can be used for qualitative and quantitative analysis. The Chemical Imaging Addition Method relies upon spatially isolating analyte standards in order to calibrate the Chemical Imaging analysis. In chemical imaging, thousands of linearly independent, spatially-resolved spectra are collected in parallel of analytes found within complex host matrices. These spectra can then be processed to generate unique contrast intrinsic to analyte species without the use of stains, dyes, or contrast agents. Various spectroscopic methods including near-infrared (NIR) absorption spectroscopy can be used to probe molecular composition and structure without being destructive to the sample. Similarly, in NIR chemical imaging the contrast that is generated reveals the spatial distribution of properties revealed in the underlying NIR spectra.
The Chemical Imaging Addition Method can involve several data processing steps, typically including, but not limited to:
1. Ratiometric correction in which the sample NIR image is divided by the background NIR image to produce a result having a floating point data type.
2. The divided image is normalized by dividing each intensity value at every pixel in the image by the vector norm for its corresponding pixel spectrum. Where the vector norm is the square root of the sum of the squares of pixel intensity values for each pixel spectrum. Normalization is applied for qualitative analysis of NIR chemical images. For quantitative analysis, normalization is not employed, but relies instead on the use of partial least squares regression (PLSR) techniques.
3. Correlation analysis, including Euclidian Distance and Cosine correlation analysis (CCA) are established multivariate image analysis techniques that assess similarity in spectral image data while simultaneously suppressing background effects. More specifically, CCA assesses chemical heterogeneity without the need for training sets, identifies differences in spectral shape and efficiently provides chemical image based contrast that is independent of absolute intensity. The CCA algorithm treats each pixel spectrum as a projected vector in n-dimensional space, where n is the number of wavelengths sampled in the image. An orthonormal basis set of vectors is chosen as the set of reference vectors and the cosine of the angles between each pixel spectrum vector and the reference vectors are calculated. The intensity values displayed in the resulting CCA images are these cosine values, where a cosine value of 1 indicates the pixel spectrum and reference spectrum are identical, and a cosine value of 0 indicates the pixel spectrum and the reference spectrum are orthogonal (no correlation). The dimensions of the resulting CCA image is the same as the original image because the orthonormal basis set provides n reference vectors, resulting in n CCA images.
4. Principal component analysis (PCA) is a data space dimensionality reduction technique. A least squares fit is drawn through the maximum variance in the n-dimensional dataset. The vector resulting from this least squares fit is termed the first principal component (PC) or the first loading. After subtracting the variance explained from the first PC, the operation is repeated and the second principal component is calculated. This process is repealed until some percentage of the total variance in the data space is explained (normally 95% or greater). PC Score images can then be visualized to reveal orthogonal information including sample information, as well as instrument response, including noise. Reconstruction of spectral dimension data can then be performed guided by cluster analysis, including without PCs that describe material or instrument parameters that one desires to amplify or suppress, depending on the needs of the sensing application.
Effective materials characterization with the disclosed NIR chemical imaging microscope invention typically requires application of a multitude of software procedures to the NIR chemical image. A schematic of the chemical image analysis cycle is shown in FIG. 2. A fairly comprehensive description of the variety of steps used to process chemical images is described below.
Until recently, seamless integration of spectral analysis, chemometric analysis and digital image analysis has not been commercially available. Individual communities have independently developed advanced software applicable to their specific requirements. For example, digital imaging software packages that treat single-frame gray-scale images and spectral processing programs that apply chemometric techniques have both reached a relatively mature state. One limitation to the development of chemical imaging, however, has been the lack of integrated software that combines enough of the features of each of these individual disciplines to have practical utility.
Historically, practitioners of chemical imaging were forced to develop their own software routines to perform each of the key steps of the data analysis. Typically, routines were prototyped using packages that supported scripting capability, such as Matlab, IDL, Grams or LabView. These packages, while flexible, are limited by steep learning curves, computational inefficiencies, and the need for individual practitioners to develop their own graphical user interface (GUI). Today, commercially available software does exist that provides efficient data processing and the ease of use of a simple GUI.
Software that meets these goals must address the entirety of the chemical imaging process. The chemical imaging analysis cycle illustrates the steps needed to successfully extract information from chemical images and to tap the full potential provided by chemical imaging systems. The cycle begins with the selection of sample measurement strategies and continues through to the presentation of a measurement solution. The first step is the collection of images. The related software must accommodate the full complement of chemical image acquisition configurations, including support of various spectroscopic techniques, the associated spectrometers and imaging detectors, and the sampling flexibility required by differing sample sizes and collection times. Ideally, even relatively disparate instrument designs can have one intuitive GUI to facilitate ease of use and ease of adoption.
The second step in the analysis cycle is data preprocessing. In general, preprocessing steps attempt to minimize contributions from chemical imaging instrument response that are not related to variations in the chemical composition of the imaged sample. Some of the functionalities needed include: correction for detector response, including variations in detector quantum efficiency, bad detector pixels and cosmic events; variation in source illumination intensity across the sample; and gross differentiation between spectral lineshapes based on baseline fitting and subtraction. Examples of tools available for preprocessing include ratiometric correction of detector pixel response; spectral operations such as Fourier filters and other spectral filters, normalization, mean centering, baseline correction, and smoothing; spatial operations such as cosmic filtering, low-pass filters, high-pass filters, and a number of other spatial filters.
Once instrument response has been suppressed, qualitative processing can be employed. Qualitative chemical image analysis attempts to address a simple question, "What is present and how is it distributed?". Many chemometric tools fall under this category. A partial list includes: correlation techniques such as cosine correlation and Euclidean distance correlation; classification techniques such as principal components analysis, cluster analysis, discriminant analysis, and multi-way analysis; and spectral deconvolution techniques such as SIMPLISMA, linear spectral unmixing and multivariate curve resolution.
Quantitative analysis deals with the development of concentration map images. Just as in quantitative spectral analysis, a number of multivariate chemometric techniques can be used to build the calibration models. In applying quantitative chemical imaging, all of the challenges experienced in non-imaging spectral analysis are present in quantitative chemical imaging, such as the selection of the calibration set and the verification of the model. However, in chemical imaging additional challenges exist, such as variations in sample thickness and the variability of multiple detector elements, to name a few. Depending on the quality of the models developed, the results can range from semi-quantitative concentration maps to rigorous quantitative measurements.
Results obtained from preprocessing, qualitative analysis and quantitative analysis must be visualized. Software tools must provide scaling, automapping, pseudocolor image representation, surface maps, volumetric representation, and multiple modes of presentation such as single image frame views, montage views, and animation of multidimensional chemical images, as well as a variety of digital image analysis algorithms for look up table (LUT) manipulation and contrast enhancement.
Once digital chemical images have been generated, traditional digital image analysis can be applied. For example, Spatial Analysis and Chemical Image Measurement involve binarization of the high bit depth (typically 32 bits/pixel) chemical image using threshold and segmentation strategies. Once binary images have been generated, analysis tools can examine a number of image domain features such as size, location, alignment, shape factors, domain count, domain density, and classification of domains based on any of the selected features. Results of these calculations can be used to develop key quantitative image parameters that can be used to characterize materials.
The final category of tools, Automated Image Processing, involves the automation of key steps or of the entire chemical image analysis process. For example, the detection of well defined features in an image can be completely automated and the results of these automated analyses can be tabulated based on any number of criteria (particle size, shape, chemical composition, etc). Automated chemical imaging platforms have been developed that can run for hours in an unsupervised fashion.
This invention incorporates a comprehensive analysis approach that allows user's to carefully plan experiments and optimize instrument parameters and should allow the maximum amount of information to be extracted from chemical images so that the user can make intelligent decisions.
As the demand for high quality, low cost X-ray, γ-ray and imaging detector devices increases, there is a need to improve the quality and production yield of semiconductor materials used in these devices. One effective strategy for improving semiconductor device yield is through the use of better device characterization tools that can rapidly and nondestructively identify defects at early stages in the fabrication process. Early screening helps to elucidate the underlying causes of defects and to reduce downstream costs associated with processing defect laden materials that are ultimately scrapped. The present invention can be used to characterize tellurium inclusion defects in cadmium zinc telluride (CdZnTe) semiconductor materials based on near infrared imaging. With this approach, large area wafers can be inspected rapidly and non-destructively in two and three spatial dimensions by collecting NIR image frames at multiple regions of interest throughout the wafer using an automated NIR imaging system. The NIR image frames are subjected to image processing algorithms including background correction and image binarization. Particle analysis is performed on the binarized images to reveal tellurium inclusion statistics, sufficient to pass or fail wafers. In addition, data visualization software is used to view the tellurium inclusions in two and three spatial dimensions.
The present invention has been used to automatically inspect tellurium inclusions in CdZnTe. Compound semiconductors are challenging to fabricate. There are several steps along the manufacturing process in which defects can arise. The chemical nature associated with semiconductor defects often plays a vital role in device performance. Device fabrication and device processing defects can be difficult and time consuming to measure during manufacturing. Unfortunately, defective devices are often left undiagnosed until latter stages in the manufacturing process because of the inadequacy of the metrology tools being used. This results in low production yields and high costs which can be an impediment to growth in the semiconductor device market potential.
There is a general need in the semiconductor industry for metrology technologies that can nondestructively assess semiconductor material defects and ultimately increase manufacturing yields. A potential solution is to develop a high throughput screening system capable of fusing multiple chemical imaging modalities into a single instrument. Chemical imaging combines digital imaging and molecular spectroscopy for the chemical analysis of materials. A modality of based on near-infrared (NIR) chemical imaging can be used to inspect tellurium inclusions in CdZnTe compound semiconductor materials.
CdZnTe is a leading material for use in room temperature X-ray detectors, γ-ray radiation detectors and imaging devices. Applications for these devices include nuclear diagnostics, digital radiography, high-resolution astrophysical X-ray and γ-ray imaging, industrial web gauging and nuclear nonproliferation. These devices are often decorated with microscopic and macroscopic defects limiting the yield of large-size, high-quality materials. Defects commonly found in these materials include cracks, grain boundaries, twin boundaries, pipes, precipitates and inclusions. CdZnTe wafers are often graded based on the size and number of Te inclusion defects present.
The definition used by Rudolph and Muhlberg for tellurium inclusions (i.e., tellurium-rich domains in the 1-50 μm size range that originate as a result of morphological instabilities at the growth interface as tellurium-rich melt droplets are captured from the boundary layer ahead of the interface) has been adopted and is used herein. There have been numerous studies on the composition and distribution of tellurium inclusions in CdZnTe material. It has been demonstrated that the presence of tellurium inclusions can impair the electronic properties of CdZnTe materials—consequently degrading the end-product device performance.
The current procedure used by low volume semiconductor manufacturers for characterizing tellurium inclusions in CdZnTe is labor intensive, susceptible to human error and provides little information on inclusions in the 1-5 μm size scale. Inclusions are viewed and counted manually by a human operator using an IR microscope platform. When an inclusion is identified that is suspected to exceed a specified size limit, a Polaroid film photograph is taken. An overlay of a stage micrometer is laid over the photograph to determine the size. This analysis is relatively time consuming, often taking several minutes to characterize a region of interest from a large wafer.
The present invention can be used for automated characterization of microscale tellurium inclusions in CdZnTe based on volumetric NIR chemical imaging. The system takes advantage of the fact that CdZnTe is transparent to infrared wavelengths (>850 nm). When viewing CdZnTe with an infrared focal plane array (IR-FPA) through a NIR LC imaging spectrometer, tellurium inclusions appear as dark, absorbing domains. The invention images wafers in two and three spatial dimensions capturing raw infrared images at each region of interest. Images are automatically background equilibrated, binarized and processed. The processed data provides particle statistical information such as inclusion counts, sizes, density, area and shape. The system provides a rapid method for characterizing tellurium inclusions as small as 0.5 μm while virtually eliminating the subjectivity associated with manual inspection.
Sample Description
Tellurium-rich CdZnTe samples were produced by a commercial supplier (eV Products) for analysis. Samples containing high tellurium inclusion densities were purposely acquired to effectively demonstrate the capabilities of the automated tellurium inclusions mapping system. The CdZnTe materials were grown by the Horizontal Bridgeman (HB) method and contained a nominal zinc cation loading concentration of 4% and an average etch pit density of 4×104/cm2. The materials displayed a face A <111> orientation and were polished on both sides. Sample thicknesses ranged from approximately 1 mm to 15 mm. No further sample preparation was necessary for the automated tellurium inclusion mapping analysis.
Data Collection
Volumetric maps of the tellurium inclusions in the CdZnTe samples were obtained by first placing the sample on the XYZ-translational stage of the automated mapping system. NIR image frames were then captured through the LC imaging spectrometer at a wavelength that maximized the Te precipitate contrast relative to the surrounding CdZnTe matrix in the X-Y direction at multiple regions of interest across the samples. Depth profiling was achieved by translating the sample focus under the microscope at user-defined increments. This process was then repeated in an iterative fashion until the entire wafer was characterized.
Data Processing
Once imaging data was collected, ChemImage was used to process the data. For each wafer, the software generates a background-corrected grayscale image, a binarized image using the threshold value selected for each frame of the image, a montage view of the binarized image and particle statistics. The particle statistics table includes information such as particle counts, particle sizes, particles densities, and a number of geometrical parameters such as particle area and particle aspect ratios.
NIR Imaging
FIGS. 3 and 4, respectively, show a digital macro brightfield image and a raw NIR microscopic transmittance image of a CdZnTe semiconductor material with numerous tellurium inclusions. The left half of the wafer has been polished. The tellurium inclusions appear as dark spots in the microscopic NIR image. The raw NIR microscopic image was acquired using the automated near-infrared tellurium inclusion volumetric mapping system.
Background Correction and Image Binarization
The automated particle analysis begins by applying a background correction preprocessing routine to the raw image frames. One of the biggest problems with the raw images collected is the gradually varying background across each image frame. As a result, a particle in one area of a frame may have a higher intensity value than the background of another area of that frame.
FIGS. 5A-5D illustrate the difficulty associated with selecting a threshold value for an image with a widely varying background. In FIGS. 5A-5D, regions 1 and 2 have mean intensity values of approximately 2600 and 1950, respectively. The whole of region 1 is primarily a particle whereas region 2 is primarily background with a small particle in the center. FIG. 5A shows a raw NIR image frame collected from a single region of interest in a CdZnTe wafer. At wavelengths longer than approximately 850 nm, CdZnTe is transparent while tellurium inclusions remain opaque. A NIR image of the sample is light where there are no precipitates and dark where there are precipitates. In FIG. 5B, the threshold value is set low enough (value=1520) that the particle in region 2 is correctly identified, but most of the remaining particles are not found. In FIG. 5C, the threshold value is set high enough (value=2470) so that all particles are detected. Unfortunately, a large area of the frame is incorrectly identified as one very large particle. FIG. 5D displays the case in which the threshold is set to an intermediate value (value=1960). Many of the particles are correctly identified, but the particle in region 2 is identified as being larger than it actually is.
To address this issue, a background correction step is used to force the background to be essentially constant across a given image frame. The procedure applies a moving window across the image frame and smoothes the resulting background before subtracting it from the frame. Other operations such as low pass filtering and selective removal of bad camera pixels are also applied.
The second step in the automated particle analysis is the selection of the threshold value resulting in the binarized image which best reflects the number and size of particles actually present in the sample being imaged. A human operator would typically approach this problem by trying multiple threshold values and comparing the resulting binarized images to the actual image to see which binarized image best matches their perception of the particles in the actual image. The algorithm employed by the NIR chemical imaging microscope system takes essentially the same approach. A series of threshold values are used to generate binarized images. Each binarized image is submitted to a routine that finds the particles present in the image. A set of particle morphology rules was developed to determine the point at which the threshold value identifies the particles consistent with results obtained by a trained human operator. This threshold value is then further refined with using derivative operations.
FIGS. 6A-6C show montage views of raw, background-corrected, and binarized NIR image frames, respectively, corresponding to four adjacent regions of interest from a CdZnTe wafer. A visual inspection of these images suggests that the particle analysis adequately identifies the particles in an automated fashion.
Volumetric Reconstruction and Visualization
It is of particular interest to the semiconductor manufacturing industry to view defects, including tellurium inclusions in this example, in a three dimensional volumetric view. Individual binarized image frames generated at discrete axial planes of focus have been reconstructed into a volumetric view allowing users to view tellurium inclusions in three-dimensional space.
FIG. 7 shows a 3D volumetric view of tellurium inclusions in CdZnTe generated from 50 individual image slices. FIG. 7 is constructed using a nearest neighbors computational approach for volume reconstruction. Improved results can be obtained using more sophisticated strategies that deconvolve the entire image volume using iterative deconvolution approaches. The staring time of the sensor used to gather the volumetric data was less than 1 sec. The total acquisition time for the data generated in this figure was well under a minute. Note how the inclusions tend to form in planes described as veils. These veils are believed to be subgrain boundaries within the CdZnTe material. Grain boundaries provide low energy nucleation sites for the inclusions to form during the growth process.
Table 1 provides tabulated statistical information on the volumetric data shown in FIG. 7.
|Particle Statistics
|Slice Number and Depth (μm)
|0 (0)
|10 (89.77)
|20 (189.52)
|30 (289.26)
|40 (389.01)
|50 (488.75)
|# of Inclusions
|Mean Diameter (μm)
|Density (Inclusions/cm2)
|Area (μm2)
|Perimeter (μm)
|Shape Factor
|Maximum Chord Length
|Feret 1 Diameter
|Feret 2 Diameter
|Aspect Ratio
Defects such as tellurium inclusions affect the electrical properties in CdZnTe semiconductor materials, degrading end-product device performance. Having the ability to rapidly and non-invasively identify and quantify tellurium inclusion defects at critical stages in the fabrication process provides semiconductor manufacturers with information that will enable them to optimize the manufacturing process and reduce production costs. The Automated NIR Volumetric Mapping System described here is capable of providing such information. The system provides qualitative and quantitative information about tellurium inclusions present in CdZnTe wafers in two and three spatial dimensions. This system boasts improved spatial resolution (˜0.5 μm) compared to systems currently used by many semiconductor manufacturers and it virtually eliminates the subjectivity associated with human counting and sizing measurements. Whole wafers are capable of being characterized in minutes.
While in the above example, the present invention has been demonstrated in connection with the characterization of semiconductors, it is to be expressly understood that the present invention can also be used in the characterization of other materials including, but not limited to, food and agricultural products, paper products, pharmaceutical materials, polymers, thin films and in medical uses.
Although present preferred embodiments of the invention have been shown and described, it should be distinctly understood that the invention is not limited thereto but may be variously embodied within the scope of the following claims.


text:
Well here we have it the group stage are over and it's down to the serious business.
Quarter Finals
06 Oct - 15:00 Australia - England
06 Oct - 21:00 New Zealand - France
07 Oct - 15:00 South Africa - Fiji
07 Oct - 21:00 Argentina - Scotland
Well here we have it the group stage are over and it's down to the serious business.
Quarter Finals
06 Oct - 15:00 Australia - England
06 Oct - 21:00 New Zealand - France
07 Oct - 15:00 South Africa - Fiji
07 Oct - 21:00 Argentina - Scotland
They all look such huge mismatches. This isn't going to be fun.
South Africa
The Future of International Cricket - Rohit Sharma, Suresh Raina, Ravi Bopara, Tim Southee, Ross Taylor, Shahriar Nafees, Raqibul Hasan, Salman Butt, JP Duminy
Proud Supporter of the Bangladeshi Tigers
Ryan ten Doeschate - A Legend in the Making
MSN: email@example.com
I dunno. They all look fairly clear-cut on paper, but only really SA/Fiji is a cast-iron certainty. France are unlikely to upset the ABs, but it is possible & with Wilkinson & Paterson respectively England and Scotland have chances in their games. If either advance it will doubtless be by playing a forward-orientated game and it won't be pretty, but the end justifies the means.
If a mod is reading this, maybe it could be stickied & the group threads cut loose?
Cricket Web's current Premier League Tipping Champion
- As featured in The Independent.
"Raab gives the 5pm press conference with the air of a man who strongly suspects theres a chalk penis drawn on his back, but is just going to butch it out by facing forward till the bell goes." - The Guardian's Marina Hyde on the UK's supply prime minister
I don't think Fiji have any real chance against South Africa, wouldn't surprise me if they lost by 50 points. Australia shouldn't have much trouble brushing aside England, while the same should be true for the All Blacks and France. But, I think France have that ability to step it up when it counts (99 semi) and they've just got that unpredictability and flair. If the AB's and Australia win, will we meet each other in the semi-final?. Argentina should get past Scotland, if they can continue playing to the standard they have been lately.
New Zealand
South Africa
IF England can pull off some 10 man rugby they could upset Australia. I don't rate France's chances at all. Our forwards have dominated them time and time again and their backline sucks.
I'd be very wary of a cornered France though, our chances are greatly increased by playing this game in Cardiff.
South Africa
Argentina ...those two are the ones i would feel most certain of...
I would love England to win,and the last game Australia played wasnt impressive so we might have some slim chance if the wind was in the right direction!!!
And New Zealand cos its my second team (thanks to some rather gorgeous lookin guy playing for them..) ...i wouldnt write off France though...
And once the storm is over you wont remember how you made it through,how you managed to survive. You wont even be sure,in fact,whether the storm is really over. But one thing is certain. When you come out of the storm you wont be the same person who walked in. Haruki Murakami .
We made a bit of a heavy weather beating the USA, IMO.
France are no hope against the All Blacks! Laporte has coached every ounce of flair out of them!
Thanks to Auntie:
England: Jason Robinson; Paul Sackey, Mathew Tait, Andy Farrell, Josh Lewsey; Jonny Wilkinson, Andy Gomarsall; Andrew Sheridan, Mark Regan, Phil Vickery (captain), Simon Shaw, Ben Kay, Martin Corry, Lewis Moody, Nick Easter.
Replacements: George Chuter, Matt Stevens, Lawrence Dallaglio, Joe Worsley, Peter Richards, Olly Barkley, Dan Hipkiss.
Well, the good news is that Jase Robinson is back. Other changes made are possibly more debateable. I totally see the logic in bringing back a bigger centre (the idea of Mortlock running at Barkley & Tait was, frankly, scary), but Farrell? Had his best game for us v Tonga coming off the bench as an impact player. Hipkiss to start at 13 with maybe Cetty at 12 (who has the virtue of being right-footed too).
The presence of Lol & Worsley on the bench suggests we're abandoning any pretence at a 15-man game (no specialist openside in the 22 at all) and are going to try to smash Oz up front. Vickery starting in the place of the infinitely more dynamic Stevens, presumably on the grounds of his (arguably) better scrummaging seems to support this too.
From the BBC....
Farrell ruled out of England side
England lose centre Andy Farrell for the World Cup quarter-final against Australia because of injury.
All south semis for me.
Poor northern hemisphere.
Batsman for Cricketweb Blue
OD: -62---56----15-----1342----32.73-----10------0-------99 (seriously)
RIP Fardin 1990-2006
......England replace Farrell with Catt
England name Mike Catt at inside centre in place of the injured Andy Farrell for Saturday's quarter-final against Australia....
Probably a good job that CW went down this afternoon.
A True Champion - Bob. Rest in peace. 15/04/06
"People today have too big a devil and too small a God"
- Stephen Currie
"The Lord is my light and my salvation; whom shall I fear? The Lord is the strength of my life; of whom shall I be afraid?" Psalm 27:1
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text:
1981 Porsche 911 SC Euro Version
Sale price: $43,900.00 make an offer
Technical specifications, photos and description:
Comprehensive Extended Warranty Available
This 1981 Porsche 911 SC Coupe Euro Version. Finished Moss Green Metallic With Brown Leather Interior.
Driven Only 52. 00 Documented Miles.
Upgraded 3. 2L Engine.
Recent Top Engine Rebuilt.
Brand New Seals And Weather Stripping All Around The Vehicle.
Fitted With All Standard And Special Options As Follows:
Factory Gold Color Fuchs Wheels
Fresh Set of Pirelli P7 Tires
Color Keyed Side Decals
Whale Tail
TRADES ACCEPTED NON SMOKER SUPER SHARP LEASE//FINANCE RATES AVAILABLE If you have any questions about this or any of my other cars please feel free to contact me. Thank you for your interest and I look forward to hearing from you. 305-949-4100 steven@kesslermotorcars. com Click Here for a CARFAX Record Check
BUY WITH CONFIDENCE Kessler Auto Group. -STEVEN KESSLER- formerly New York City's only authorized Ferrari- Maserati- Lotus- Alfa Romeo dealer. continues to offer from it's South Florida location only the finest automobiles for your driving investment and motoring pleasure. Our Car Buying Philosophy is We rather over pay for the Right Car. Than Under Pay for the Wrong Car! Additional Photos
click on a thumbnail to view full-size photos 1981 Porsche 911 SC Euro Version 2-Door Coupe Exterior Color: Moss Green Metallic Interior Color: Brown Stock Number: 16331 Mileage: 52. 00 Fuel: Gasoline Transmission: 5 Speed Manual Warranty: As Is - No Warranty Title: Clear VIN: 00000000000000000 CARFAX: FREE CARFAX Record Check Name: Steven Kessler Main Phone: (305) 949-4100 Email: steven@kesslermotorcars. com Vehicle Features & Options Order CARFAX Report Find out the history
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Shipping Information Get a Free Shipping Price Estimate: Destination Zip Code: powered by We recommend ShipMyVehicle. com for vehicle shipping. Dealership Information Kessler Auto Group. Inc Main Phone: (305) 949-4100 Alt. Phone: (305) 606-0666 Email: STEVEN@KESSLERMOTORCARS. COM Business Hours Weekdays: 9:00 AM to 5:00 PM Sat: 9:00 AM to 2:00 PM Sun: By Appointment Only Location 2024 NE 155th St.
Miami. FL 33162
Dealership Map 1- Placing a Bid: Your bid constitutes a legally binding contract to purchase this vehicle. Please do not bid if you're not seriously interested or financially able to purchase this vehicle. Please read eBay's User Agreement
Kessler Auto Group reserves the right to. /p> Obtain and verify the registered information of all users who bid on this auction. Cancel any and all bids at our discretion. or end the auction early if necessary.
2- Bidders Age: You must be 18 years of age or older to Bid.
3- Special eBay Bid Retraction Rules: Please read eBay's "Retracting a Bid"If you place a bid before the last 12-hour period of the auction: You may retract that bid before that last 12-hour period but only for exceptional circumstances. You will not be allowed to retract that bid during the last 12-hour period of the auction. If you place a bid during the last 12-hour period of the auction: You will be allowed to retract the bid for exceptional circumstances but only if you do so within one hour after placing the bid.
4- Funds & Financing: For help in arranging for a Pre-Approved loan or for any questions please e-mail or phone Steven at
305-949-4100 prior to bidding.
5- Buyers Inspection: Kessler Auto Group. Inc. and it's representatives have made every effort to accurately and fairly describe the vehicle to you and disclose any known information regarding the vehicle. We will never describe a car as "perfect" and buyer should have expectations of "typical" dings and or chips and scratches that are consistent with the year and mileage on the vehicle. Buyers are provided with all additional items that were provided to us with the vehicle. Buyers are to expect that NOT all used vehicles will be provided with full sets of keys. books. CD magazines. Navigation Discs. floor mats or the like. NOR will all cars be accompanied by detailed service records etc. . Kessler Auto Group. Inc. will not be held responsible to provide any additional items other than represented as with the car unless detailed in writing by an authorized representative. Kessler Auto Group. Inc. welcomes an independent inspection of the vehicle prior to purchase. All costs and arrangements for such inspections are the responsibility of the buyer. All inspections are to be performed on premises at Kessler Auto Group showroom or at a local shop in Miami. FL. Please call if you need recommendations of inspection services available in Miami. Kessler Auto Group. Inc. will review any issues or concerns that develop as a result of an inspection and will make repairs . adjustments or concessions at their sole discretion.
Mileage listed may increase due to test drives or other usage.
6- Representations and Warranties made by seller: This vehicle is being sold as is. where is. with no warranty. expressed written or implied. The seller shall not be responsible for the correct description. authenticity. genuineness. or defects herein. and makes no warranty in connection therewith. No allowance or set aside will be made on account of any incorrectness. imperfection. defect or damage. Any descriptions or representations are for identification purposes only and are not to be construed as a warranty of any type. It is the responsibility of the buyer to have thoroughly inspected the vehicle. and to have satisfied himself or herself as to the condition and value and to purchase based upon that judgment solely. The seller shall and will make every reasonable effort to disclose any known defects associated with this vehicle at the buyer's request prior to the close of sale. Seller assumes no responsibility for any repairs regardless of any oral statements about the vehicle. Seller makes no guarantee or warranty. expressed. written or implied of the vehicles ability to pass any state emissions inspection. In the event a vehicle fails state emissions inspection. Kessler Auto Group. Inc. bears no responsibility whatsoever. Remaining manufacturers warranty if applicable may apply.
7- Extended Warranty: An extended warranty is available for purchasing. Kessler Auto Group. Inc will help to apply for coverage for the vehicle but will not be responsible in any way for claims. We are not afiliated with any extended warranty provider. Any claims or other communication regarding extended warranties will be between you and the extended warranty provider. not with Kessler Auto Group. Inc.
8- Taxes and Registration fees: Out of state buyers are responsible for all state. county. city taxes and fees. as well as title service fees in the state that the vehicle will be registered. All taxes and fees must be paid in full in order for vehicle to be titled and registered. Dealer fee of $399 will be charged to all sales as a documentation fee for each retail sale which includes a temporary tag when applicable. motor vehicle documents and transfers and miscellaneous expenses including clerical fees. notary fees and Federal Express and US mail charges. This fee will not be discounted or waived under any circumstance. Buyers are responsible for all state. county and local taxes and fees as well as any registration and title fees applicable in and required by the state in which the buyer will register and title the vehicle. Buyer is responsible for any expenses . tax and fees should they export the car outside of the US. . Florida Residents Will Be Charged Applicable State Sales Tax. Buyer Responsible For Shipping Charges.
9- Title Information: Vehicles titles may be held by banks or lenders as collateral for loans. In many cases there is a delay in receiving the original instruments up to 21 days from the time we pay a vehicle off. While we usually have all titles in our possession at closing. there are occasions where we may be waiting for them to arrive. If payment is made by cashier's or personal checks we will hold all titles for 10 days or until funds have cleared.
10- Shipping & Delivery:
11- Finalizing your Purchase:Kessler Auto Group will contact the successful high bidder by e-mail after the auction closes. Successful high bidder MUST communicate with Steven at Kessler Auto Group by e-mail or phone
305-949-4100 within 24 hours of the auction ending to make arrangements to complete their transaction. If we cannot confirm your intention to buy or the sale is not completed within 5 days. we reserve the right to relist this vehicle or sell to any other qualified buyer. In order to secure bid on vehicle. Successful bidder (BUYER) must within 24 hours of bid closing send to Seller a Deposit in the amount of 10% by major credit card. cash in person or bank certified funds. Within 72 hours of bid closing. Buyer must send balance of funds by bank wire transfer. cash in person. bank certified funds to Seller. At time of sending initial deposit. Buyer MUST fax copy of their State issued valid Driver Licence. Furthermore. before said vehicle is released for shipment to Buyer. all other Sale related and title related paperwork must be signed and returned complete to Seller.
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text:
Corsair Vengeance Blue LED 64GB DDR4 2666 Memory Kit 4x 16GB
64GB (4x16GB) Corsair DDR4 Vengeance LED, PC4-21300 (2666), Non-ECC Unbuffered, CAS 16-18-18-35, Blue LED, XMP 2.0, 1.2V
By DPD
to your specified address. |
Receive SMS with one-hour delivery window
Weekend, timed and European delivery options are available at checkout
Have your parcel delivered by DPD to your specified address. Receive SMS with one-hour delivery windowWeekend, timed and European delivery options are available at checkout
Collect instore
Collect from our Bolton store, BL6 6PE |
Order online, collect from our Bolton store (25-28 Enterprise Park, Middlebrook, Horwich, Bolton, BL6 6PE)
Protect against installation damage for 28 days.
More info
ScanProtect is an enhanced warranty specifically designed and offered by Scan to reduce the risk of any mishap or damage to components during installation.
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- 48HR REPLACEMENT If you need to return this item, your replacement will be dispatched within 2 working days of your product arriving back at Scan. More info
Corsair DDR4 Vengeance LED 64GB (4x16GB) 2666MHz Memory (Blue)
Corsair DDR4 Vengeance LED RAM
Outshine the rest of the memory competition with the DDR4 Vengeance LED from Corsair. Corsair Vengeance LED DDR4 memory features a sleek, precision-engineered light bar to keep your high-end components coordinated. This collection of four memory modules combines to make a huge 64GB of DDR4 RAM, at 16GB per module. Such a large amount helps with heavy processing, traffic flow and accessing multiple programs and applications at once, among other memory heavy tasks. A higher frequency such as this Vengeance LED 2666MHz will allow for a higher bit count to be transferred per second, which ensures faster data reading times from your rig. With such a high MHz count you can be sure the memory's performance is more than enough for your digital games and other media.
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text:
Every single day, thousands of working people, women, and children experience terrible exploitation, opression and suffer in near silence. But with your help, we can put a stop to this.
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text:
Revision history for Perl extension Class::Meta.
- Fixed broken rt.cpan.org email address.
- Added missing call to `build()` in the synopsis. Thanks to Laurnet
Dami for the report (RT #72058).
- Fixed a couple of typos in the Pod, with thanks to Gregor Herrmann and
the Debian project (RT #78163).
- Require Test::Pod 1.41 to run the Pod tests.
- Removed redundant bug reporting email address from the documentation.
- Fixed tests failures on Windows (hopefully). Thanks to Eric Brine for
the suggested fix.
- Moved repostitory to [GitHub](https://github.com/theory/class-meta/).
- Switched to a "traditional" `Makefile.PL`.
- Fixed POD formatting typo in Class::Meta::Type with thanks to Gregor
Herrmann (RT #36143).
- Fixed a warning from Perl 5.11 (I think).
- Removed the spelling test from the distribution, since it can
fail on systems without a spell program or using a different locale.
Reported by Andreas Koenig.
- Fixed test failures on Perl 5.6. Reported by Slaven Rezic and many
others via cpan-testers.
- Required constraints are now enforced by the constructor, as well as
the individual attributes themselves. Frankly, it was a bug that this
isn't the way it worked before, although at the time I thought it was
a feature. This is a backwards compatibility change, but see the next
point. Reported by Jon Swartz.
- Added support for an optional subroutine reference to be passed as the
final argument to constructors created by Class::Meta. The object
being constructed will be passed to the anonymous sub after all of the
specified and default values have been set, but before required
constraints are enforced. This allows developers to have a scope
within which to work before required attributes throw an exception.
Yes, I did borrow this idea from Ruby.
- Added a link to the Subversion repository.
- Moved the "Justification" section of the documentation nearer to the
bottom. Suggested by Jon Swartz.
- The "view" parameter (PUBLIC, PRIVATE, PROTECTED, TRUSTED) is now
enforced for methods when the method body is passed via the "code"
parameter. Reported by Jon Swartz.
- Added the `trusted` method to Class::Meta::Class.
- You can now use strings to specify constant values when declaring
class attributes, methods, etc. The strings correspond to their
equivalent constant names, such as "PUBLIC", "PRIVATE", "GET", "SET",
"OBJECT", "CLASS", and so on. The corresponding constant value will,
however, be stored in the resulting Class::Meta object.
- A more useful exception is now thrown when an attribute is added with
a missing or unknown type.
- Added the `default_type` parameter to `new()`, and the corresponding
attribute to Class::Meta::Class. This value will be used for the data
type of attributes created without a data type. Borrowed from
- Many of the parameters to `Class::Meta->new` are now passed on to
suclasses if they are not explicitly specified by those subclasses.
This makes it easier to get the same settings (e.g., for Class::Meta
subclasses or for the default type) without typing the same things
over and over in subclasses.
- Added a spelling test. This has the benefit of reducing the number of
annoying spelling errors in the documentation.
- Added the "configure_requires", and "recommends" parameters to
- Updated the copyright.
- Documented the 'name' parameter to new().
- The 'name' attribute of classes now defaults to the key name with
underscores replaced with spaces and each word capitalized by the
ucfirst operator. So "foo" will become "Foo" and "contact_type" will
become "Contact Type".
- Added an example and brief discussion of Class::Meta::Express to the
docs. Suggested by Jonathan Swartz.
- Added 'code' parameter to "new_constructor()" to install a constructor
written by the user. This essentially works just like the 'code'
method parameter.
- Fixed tests that fail with Data::Types 0.06 and later.
- Added 'is' parameter to the attribute constructor as an alias for
'type'. If both 'type and 'is' are passed to the attribute
constructor, 'is' will be used.
- Added the boolean method is() to the attribute class to compare a
- Documented the 'create' parameter to "new_constructor()".
- Data types in defined in the Class::Meta::Types namespace are now
automatically loaded if they are used in an attribute and not
already loaded.
- The constructor method created by Class::Meta::Constructor no longer
assigns the default value to an attribute if that attribute has
already been set by another attribute accessor. This is useful, for
example, when the setting of one attribute triggers the setting of
another attribute, as when a public attribute implicitly sets a
private attribute.
- Added "code" parameter to add_method(), so that a new method can be
defined right in the call to add_method(), and Class::Meta::Method
will install it. Inspired by Ovid's Class::Meta::Declare.
- Constructors created by Class::Meta now iterate over the attributes
when assigning arguments or defaults in the order in which the
were defined, rather than randomly.
- The "attributes()", "constructors()", and "methods()" methods of
Class::Meta::Class now return *all* objects, including private and
trusted objects, when called from the class that defined those
- Minor optimizations to the constructor created by Class::Meta.
- Added "keys" method to allow all Class Object keys to be fetched.
- Added "args" and "returns" parameters to "add_method()" to allow
methods to be better described.
- Added "clear" method to delete Class Object keys.
- Fixed accessor generation for "once" attributes with a default so that
a value can be passed to a constructor and properly assigned to the
"once" attribute instead of its default, rather than throwing a
read-only exception.
- Added "default_builder()" class method to Class::Meta::Type. This
allows a default builder other than "default" to be specified when
none is explicitly passed to Class::Meta::Type->add(), such as when
data types are implicitly created for attributes that reference
objects of Class::Meta classes.
- Changed the "constructors()", "attributes()", and "methods()" methods
in Class::Meta::Class so that any classes thta inherit from
Class::Meta::Class are never considered to be the caller.
- Added the "trusted" parameter to "new()" to identify trusted packages.
Added a new constant, TRUSTED, for the "view" parameter to identfy
constructors, attributes, and methods that can be used by trusted
- Fixed documentation to reflect that the "class()" method in the
Constructor class requires the package name as its first argument.
- Modified "add_method()" so that methods can optionally be
automatically created by "build()". Patch from Tim Canfield.
- A call to "build()" now deletes unneeded references to objects,
freeing up a bit of memory.
- Changed minimum required Perl version to 5.6.1. Certain tests are
disabled in this version, since the version of Carp included in
Perl 5.6 lacks the @CARP_NOT feature. Everything should work fine,
however. Suggested by Tim Canfield.
- Added "parents()" method to Class::Meta::Class to return the class
objects for any classes that the class inherits from.
- Attributes that use an alias to set their types will now have the
alias converted to the canonical type key.
- Fixed typo in Class::Meta that named the generated method "class()"
instead of "my_class()". Reported by Curtis Poe.
- Documented "bool" synonym for the "boolean" data type in the
Class::Meta library documentation (it was already documented in
Class::Meta::Types::Boolean, of course).
- Fixed broken links to Class::Meta::Types classes in Class::Meta
library documentation. Reported by Curtis Poe.
- Classes created by Class::Meta will now be used as data types. This
saves the developer having to generate classes *and* add the new
classes as data types when objects of a class will be attributes of
another class.
- Added "class_validation_generator()" class method to Class::Meta::Type
so that a custom object validation generator can be specified instead
of the default. This simplifies specifying objects as data types
without custom creating validation checks for every one, and is
especially useful with the new implicit Class::Meta class data types,
as it will be used to generate the validation checks.
- Accessor builder classes that don't properly load will now correctly
cause Class::Meta to die.
- Class::Meta::Class->handle_error() now joins multiple arguments it
receives into a single string to be passed to the error handler
code reference.
- Class::Meta::Attribute now correctly finds attribute accessors that
were not created by Class::Meta (that is, when the "create" parameter
is set to NONE), provided that the build_attr_set() and
build_attr_get() functions of the accessor builder package can
find them (as the accessor builders include with Class::Meta can).
- Fixed test failures on Windows in "t/errors.t".
- Added "abstract" attribute to class objects to identify abstract
(a.k.a. "virtual") classes. Constructors generated by Class::Meta
will throw an exception if they are used to try to construct an
object in an abstract class.
- Added "for_key()" class method to Class::Meta to return a
Class::Meta::Class class for a class key.
- Eliminated '"my" variable $objs masks earlier declaration in same
scope' warning.
- Remembered to actually apply the patch taht fixes the tests under
Windows. Sheesh!
- Subclasses of Class, Constructor, Attribute, and Method can now call
"SUPER::new()" and "SUPER::build()" without getting errors.
- Changed implementation of Class::Meta::Class so that its attributes
are stored in the object hash itself. This brings it in line with
the implementation of Constructor, Attribute, and Method, thus
making subclassing consistent with those classes. However it also
required that references to its contents be changed in all the other
classes, as well. Hence the bump to 0.40.
- Finally, truly got the tests fixed for Windows. Without question.
The fix was even tested, first! Thanks to Robert Rothenberg for
his persistence.
- Minor doc fixes, repoted by Jesse Vincent.
- Added simple example for a default value code reference. Suggested
by Jesse Vincent.
- Fixed the names of the included types classes in the documentation
of Class::Meta::Type. Spotted by Dan Kubb.
- Fixed a few documentation references to a "class()" method to
reference the correctly named "my_class()" method.
- Fixed failing tests on Win32. For real this time, I hope! Reported
by Robert Rothenberg's CPAN testing.
- Added build() method to Class::Meta::Method to parallel the same
method in Class::Meta::Attribute and Class::Meta::Constructor.
It's a no-op, but will be called when Class::Meta::build() is
called, so it could be useful for subclasses. Inspired by a
suggestion by Mark Jaroski.
- Added POD coverage test.
- Documented undocumented methods and functions. Most of these are
actually protected methods, but they will be of interest to those
creating their own subclasses or accessor generators Class::Meta.
- Fixed failing tests on Win32. Reported by Robert Rothenberg's CPAN
- Added "override" parameter to "add_attribute()" so that subclasses
can override attributes in their parent classes.
- Fixed the MANIFEST so that the new semi-affordance accessor generation
actually works. Reported by Mark Jaroski.
- Moved extra code to prevent AccessorBuilder from pointing to
Constructor in the default (croak) error handler from AccessorBuilder
to the default error handler. Carp is a PITA.
- Added "handle_error()" class method to Class::Meta. This method is
used by Class::Meta classes when no Class::Meta::Class object is
- Added semi-affordance accessor generation.
- Modified arguments passed to check code references. Now, in addition
to the new value to be assiged to the attribute, the object being
assigned to and the Class::Meta::Attribute object that describes the
attribute are passed. If the attribute is a class attribute, then the
second argument is a hash reference containing the existing value and
the name of the package.
- Thanks the the presence of the attribute object as an argument to
check code references, the name of the attribute is now included in
exceptions thrown for "once" and "required" attributes.
- Added "class" accessors to Constructor, Attribute, and Method, to
return the Class object for the class in which the constructor,
attribute, or method was defined.
- Added "error_handler" parameter to Class::Meta->new to be called for
fatal errors.
- Added default_error_handler() class method to Class::Meta to act
as the default error handler when no "error_handler" parameter is
passed to Class::Meta->new.
- Added more documentation to the Class::Meta synopsis that highlights
the generated constructor and attribute accessors, as well as the
introspection API.
- Fixed documentation to reflect that the introspection class method
installed in a generated class is called my_class(), not class().
Thanks to Marcus Ramberg for the spot!
- Documented the "required" attribute of Class::Meta::Attribute.
- Added "once" attribute to Class::Meta::Attribute. This attribute
indicates whether an attribute value can be set to a defined value
only once.
- Renamed the call_get() and call_set() methods of
Class::Meta::Attribute to simply get() and set().
- Private and protected constructors generated by Class::Meta are now
truly private and protected.
- Class::Meta no longer generates constructors when they're added with
create => 0.
- For default accessors, object and class attribute accessors were
- Private and protected attributes now are truly private and protected
if they're constructed by the accessor builder packages that come with
- The call_get() and call_set() methods of Class::Meta::Attribute and
the call() methods of Class::Meta::Constructor and
Class::Meta::::Method now use goto to execute the true methods. This
removes the call to call_get() or call_set() or call() from the call
stack trace, and makes it possible for the private and protected
checks to always work properly.
- The class "name" attribute now defaults to be the same as the key
if it is not explicitly set.
- The constructor generated by Class::Meta no longer attempts to set
class attributes.
- A package name now must be passed to the Class::Meta::Constructor's
call() method as the first argument. This is allow for proper support
for inheritance.
- Accessor generators now create accessors for class attributes as
class attributes, instead of as object attributes.
- Added link to rt.cpan.org for reporting bugs.
- Added distribution information to all modules.
- The package attribute now properly defaults to the package
calling Class::Meta->new.
- Class::Meta::Class->construtors now works.
- Initial public release.


text:
NEW DELHI (Reuters) - India's finance minister has banned government officials from holding conferences at five-star hotels, restricted travel and ordered a freeze on hiring to fill vacant posts.
A single-minded political veteran who commands both fear and respect in Indian officialdom, P. Chidambaram is squeezing government ministries hard to cut spending wherever they can, and quickly, to help rein in a widening fiscal deficit.
He is a man under pressure and with an eye on the clock.
Four weeks ago to the day, he set himself an ambitious target: to hold the government's fiscal deficit for 2012/2013 to 5.3 percent of gross domestic product, even as skeptical private economists forecast a deficit closer to 6 percent.
But a series of revenue-raising setbacks since October 29 now means it will be almost impossible for the government to meet that target, economists say, and some finance ministry officials privately agree. That increases the risk that credit rating agencies could downgrade India to junk in the coming months.
"This has taken on a very great sense of urgency," said Rajiv Biswas, chief Asia economist at market information and analytics company IHS, as he called on Chidambaram to draw up a credible medium-term road-map for cutting the deficit.
The deficit reduction plan unveiled by Chidambaram last month was panned by economists for being short on specifics and putting a firewall around fuel subsidies and expensive social welfare programs for the country's millions of poor.
A month earlier a deficit reduction panel appointed by Chidambaram had urged the government to cut such spending. Their language was dramatic: India was on the edge of a "fiscal precipice" and the economy was "flashing red lights", they said.
The government is pursuing a "band-aid approach" to deficit reduction, favoring quick fixes instead of implementing structural reforms to slash the deficit, said economist Rajeev Malik of CLSA in Singapore, who is sticking to a deficit forecast of 6 percent of GDP.
Financial markets are already expecting the Indian government to overshoot its target and hit around 5.6 percent of GDP, which helped push benchmark 10-year bond yields to the highest in nearly three months late last week.
But the big unknown is the response of the rating agencies, which have repeatedly warned India to get its finances in order.
The agencies are unlikely to reveal their thinking until after Chidambaram unveils his budget in February, analysts said.
But in October, Standard & Poor's said India still faced a one-in-three chance of a downgrade within the next 24 months. Such an outcome would hurt investor sentiment and push up overseas borrowing costs for Indian companies.
Chidambaram, 67, a lanky politician with a disarming smile that belies a sharp tongue and an intolerance for time-wasting, charmed financial markets with his can-do attitude and burst of economic reforms in September, after years of policy inaction by Prime Minister Manmohan Singh's weak coalition government.
India's benchmark BSE index .BSESN rallied more than 6 percent after the reforms were announced in mid-September. But concerns over implementation, the fiscal deficit and falling foreign fund inflows have since pushed it down 3.3 percent.
"We believe that this is the beginning of the realization that a sustainable turnaround in India's growth prospects would require considerable effort, well beyond the burst of measures seen in September," Deutsche Bank said last week in an analyst note headlined "Reality Check".
Chidambaram's deficit reduction plan banks heavily on raising billions of dollars by auctioning off cellphone airwaves and selling shares in state companies.
Neither effort is going particularly well.
The government raised less than a quarter of its 400 billion rupee ($7.3 billion) target in a 2G spectrum auction in mid-November. A second auction is planned before March, but a senior government official told Reuters there would likely be at least a 200 billion rupee shortfall.
India succeeded in raising 8.1 billion rupees ($147 million) by selling shares of state-run Hindustan Copper Ltd on (HCPR.NS) Friday, although the deal was supported by buying from state institutions.
To put the deal in context: New Delhi aims to raise 300 billion rupees by selling shares in state companies this fiscal year, which ends in March. Excluding the latest sale, it has managed just 1.25 billion rupees so far.
The government is staring at an overall shortfall of nearly 500 billion rupees in revenues this year, the government official said, speaking on condition of anonymity because of the sensitivity of the subject. This may require additional borrowing from the market.
Chidambaram's battle to tame the deficit takes place against the backdrop of a continued economic slowdown, and a fractious parliament where the government has lost its majority after its biggest coalition ally withdrew support to oppose its reforms.
Manufacturing is contracting and exports are falling. India's October trade deficit of nearly $21 billion was its worst on record.
And a second round of reforms aimed at liberalising the pension and insurance sectors has fallen victim to gridlock in parliament. It is not clear if the measures, long sought by investors, will be passed in the current winter session.
But Chidambaram, who began his second stint as finance minister in August, gives no appearance of being disheartened and as recently as Saturday was confidently predicting he would be able to contain the deficit to 5.3 percent of GDP.
Inside his ministry, officials said the target looks daunting but they have had no word of a revision from the minister. Instead, he has intensified pressure on them to find ways of meeting the target, they said.
Chidambaram's credibility is not yet on the line, said analysts. In fact, perhaps the opposite. His credentials as an economic reformer during two previous stints as finance minister are buying him time to pull India back from the fiscal precipice.
Addtional reporting by Frank Jack Daniel in NEW DELHI, Swati Bhat, Sumeet Chatterjee and Subhadip Sircar in MUMBAI; Editing by Alex Richardson