Sir koi windows h Jo 1gb k andar ho? Ek ka link jyada see jyada 12 ghante Tak hi on rehta h uske baad download fail ho jata hai. Mere 2 din ka net khatam ho gya is chakkar me. Please 1gb k andar koi windows batao
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I had a family photo, and i may have accidently saved it down in photoshop as a compressed file, like, the original photo i took 5mp and was 2.5mb, and i still have a copy of the photo, but its only 1024x768 now, its still quite printable but i wouldnt mind the original back, but i may have either saved that over the top of the original or just deleted it. This happened about 3 years ago and since then i have formatted and reinstalled windows many times, i have no idea what the original was called ect. Should i just give up hope, or is it possible to get it back? i mean the photo wasnt priceless or anything, i took about 5 photos of that same shot so i wouldnt bother paying heaps for a pro to get it back but this one was the best i took. Thanks... USRM000107 04:57, 10 October 2007 (UTC)Reply[reply]
So - is there anything I can download or create from Linux (or at a pinch) another WinXP machine that will enable me to restore the boot stuff? Remember I can run Linux, download from the web and write CD's, I can even mount the WinXP drive under Linux and look at the contents - and I have access to a WinXP machine at work (but not the original CD's for it). I also have the product ID thingy to install Windows with if I can get an ISO of the CD or something.
Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.
Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612
This paper presents a study on the efficacy of highly porous nanofibrous membranes for application in membrane-based absorbers and desorbers. Permeability studies showed that membranes with a pore size greater than about one micron have a sufficient permeability for application in the absorber heat exchanger. Membranes with smaller pores were found to be adequate for the desorber heat exchanger. The membranes were implemented in experimental membrane-based absorber and desorber modules and successfully tested. Parametric studies were conducted on both absorber and desorber processes. Studies on the absorption process were focused on the effects of water vapor pressure, cooling water temperature,more and the solution velocity on the absorption rate. Desorption studies were conducted on the effects of wall temperature, vapor and solution pressures, and the solution velocity on the desorption rate. Significantly higher absorption and desorption rates than in the falling film absorbers and desorbers were achieved. Published by Elsevier Ltd. less
Hard, brittle and wear-resistant materials like ceramics pose a problem when being machined using conventional machining processes. Machining ceramics even with a diamond cutting tool is very difficult and costly. Near net-shape processes, like laser evaporation, produce micro-cracks that require extra finishing. Thus it is anticipated that ceramic machining will have to continue to be explored with new-sprung techniques before ceramic materials become commonplace. This numerical investigation results from the numerical simulations of the thermal and mechanical modeling of simultaneous material removal from hard-to-machine materials using both laser ablation and conventional tool cutting utilizing the finite element method. The model is formulated using a two dimensional, planar, computational domain. The process simulation acronymed, LAHM (Laser Ablation Hybrid Machining), uses laser energy for two purposes. The first purpose is to remove the material by ablation. The second purpose is to heat the unremoved material that lies below the ablated material in order to ``soften'' it. The softened material is then simultaneously removed by conventional machining processes. The complete solution determines the temperature distribution and stress contours within the material and tracks the moving boundary that occurs due to material ablation. The temperature distribution is used to determine the distance below the phase change surface where sufficient ``softening'' has occurred, so that a cutting tool may be used to remove additional material. The model incorporated for tracking the ablative surface does not assume an isothermal melt phase (e.g. Stefan problem) for laser ablation. Both surface absorption and volume absorption of laser energy as function of depth have been considered in the models. LAHM, from the thermal and mechanical point of view is a complex machining process involving large deformations at high strain rates, thermal effects of the laser, removal of
Naturally occurring water is a promising candidate for achieving broadband absorption. In this work, by virtue of the optically transparent character of the water, the water-based metamaterial absorbers (MAs) are proposed to achieve the broadband absorption at microwave frequencies and optical transparence simultaneously. For this purpose, the transparent indium tin oxide (ITO) and polymethyl methacrylate (PMMA) are chosen as the constitutive materials. The water is encapsulated between the ITO backed plate and PMMA, serving as the microwave loss as well as optically transparent material. Numerical simulations show that the broadband absorption with the efficiency over 90% in the frequency band of 6.4-30 GHz and highly optical transparency of about 85% in the visible region can be achieved and have been well demonstrated experimentally. Additionally, the proposed water-based MA displays a wide-angle absorption performance for both TE and TM waves and is also robust to the variations of the structure parameters, which is much desired in a practical application.
The aim of this study was to evaluate the damage tolerance of different zirconia-based materials. Bars of one hard machined and one soft machined dental zirconia and an experimental 95% zirconia 5% alumina ceramic were subjected to 100,000 stress cycles (n = 10), indented to provoke cracks on the tensile stress side (n = 10), and left untreated as controls (n = 10). The experimental material demonstrated a higher relative damage tolerance, with a 40% reduction compared to 68% for the hard machined zirconia and 84% for the soft machined zirconia.
A non-destructive and novel in situ acoustic sensor approach based on the sound absorption spectra was developed for identifying and classifying different seed types. The absorption coefficient spectra were determined by using the impedance tube measurement method. Subsequently, a multivariate statistical analysis, i.e., principal component analysis (PCA), was performed as a way to generate a classification of the seeds based on the soft independent modelling of class analogy (SIMCA) method. The results show that the sound absorption coefficient spectra of different seed types present characteristic patterns which are highly dependent on seed size and shape. In general, seed particle size and sphericity were inversely related with the absorption coefficient. PCA presented reliable grouping capabilities within the diverse seed types, since the 95% of the total spectral variance was described by the first two principal components. Furthermore, the SIMCA classification model based on the absorption spectra achieved optimal results as 100% of the evaluation samples were correctly classified. This study contains the initial structuring of an innovative method that will present new possibilities in agriculture and industry for classifying and determining physical properties of seeds and other materials. PMID:22163455 2ff7e9595c
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