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Hydrogen is an operating system targeting embedded systems (servers, thin clients, SBCs) running on x86. Its written in C and assembly (NASM). There is no support for VGA, its serial-only. My homepage:
MikeOS is an operating system for x86 PCs, written in assembly language. It is a learning tool to show how simple 16-bit, real-mode OSes work, with well-commented code and extensive documentation. It has a BASIC interpreter with 46 instructions, supports over 60 syscalls, could manage a serial terminal connection and output the sound through PC speaker. There is also a file manager, text editor, image viewer and some games
Nightingale is a small operating system with a vaguely UNIX-like userland. It supports multiple processes, loadable kernel modules, networking, and has a (fairly) full featured shell with pipes and file redirection. It has no video support, and only communicates with the outside world via the serial ports and network card.
tachyon is another Hobby OS, longing to support x86_64 only. Currently, it boots on qemu, bochs, virtualbox and real hardware. it has not much to see, really, but a working physical and virtual memory management (still improving), kernel and user threads and some other hardware interfacing... Basic goal was to create everything from scratch with a clean code base. It uses a plugin mechanism to load different supported kernel components.
A unikernel environment based on the Free Pascal compiler and Lazarus IDE, initially targeting single board computers such as Raspberry Pi and also supporting QEMU the design is intended to be portable to other platforms. The modular architecture allows applications to pick and choose what features to use in a project and the compiler produces a bootable kernel image which includes all of the required RTL components. Comes with a comprehensive list of features including pre-emptive threading, multicore support, IPv4 networking, FAT/NTFS/CDFS file systems, USB support, SD/MMC support, drivers for common peripherals such as GPIO, I2C, SPI, PWM, and DMA, C library support, hardware accelerated OpenGL ES and OpenVG graphics and much more. Packaged in an installer download for Windows or as an install script for Linux customized versions of both Free Pascal and Lazarus IDE are included along with full source and a large collection of examples. Our homepage:
She then volunteered to be bait for the Dollmaker, going to each boutique and buying Mermaiden. Quentin pointed out that Felicity must trust The Arrow a lot to be bait for a serial killer. Creeped out, and scared, she vowed to never be bait again, right before she was attacked by the Dollmaker. The Arrow and Quentin scared him off, but not before Felicity was injured. When she'd recovered, she reported the kidnapping of Quentin and Laurel to Oliver, and managed to provide him with their location.
Accurate prediction of catastrophic brittle failure in rocks and in the Earth presents a significant challenge on theoretical and practical grounds. The governing equations are not known precisely, but are known to produce highly non-linear behavior similar to those of near-critical dynamical systems, with a large and irreducible stochastic component due to material heterogeneity. In a laboratory setting mechanical, hydraulic and rock physical properties are known to change in systematic ways prior to catastrophic failure, often with significant non-Gaussian fluctuations about the mean signal at a given time, for example in the rate of remotely-sensed acoustic emissions. The effectiveness of such signals in real-time forecasting has never been tested before in a controlled laboratory setting, and previous work has often been qualitative in nature, and subject to retrospective selection bias, though it has often been invoked as a basis in forecasting natural hazard events such as volcanoes and earthquakes. Here we describe a collaborative experiment in real-time data assimilation to explore the limits of predictability of rock failure in a best-case scenario. Data are streamed from a remote rock deformation laboratory to a user-friendly portal, where several proposed physical/stochastic models can be analysed in parallel in real time, using a variety of statistical fitting techniques, including least squares regression, maximum likelihood fitting, Markov-chain Monte-Carlo and Bayesian analysis. The results are posted and regularly updated on the web site prior to catastrophic failure, to ensure a true and and verifiable prospective test of forecasting power. Preliminary tests on synthetic data with known non-Gaussian statistics shows how forecasting power is likely to evolve in the live experiments. In general the predicted failure time does converge on the real failure time, illustrating the bias associated with the 'benefit of hindsight' in retrospective analyses
Electromagnetic radiation from blazar jets often displays strong variability, extending from radio to γ-ray frequencies. In a few cases, this variability has been characterized using Fourier time lags, such as those detected in the X-rays from Mrk 421 using BeppoSAX. The lack of a theoretical framework to interpret the data has motivated us to develop a new model for the formation of the X-ray spectrum and the time lags in blazar jets based on a transport equation including terms describing stochastic Fermi acceleration, synchrotron losses, shock acceleration, adiabatic expansion, and spatial diffusion. We derive the exact solution for the Fourier transform of the electron distribution and use it to compute the Fourier transform of the synchrotron radiation spectrum and the associated X-ray time lags. The same theoretical framework is also used to compute the peak flare X-ray spectrum, assuming that a steady-state electron distribution is achieved during the peak of the flare. The model parameters are constrained by comparing the theoretical predictions with the observational data for Mrk 421. The resulting integrated model yields, for the first time, a complete first-principles physical explanation for both the formation of the observed time lags and the shape of the peak flare X-ray spectrum. It also yields direct estimates of the strength of the shock and the stochastic magnetohydrodynamical wave acceleration components in the Mrk 421 jet.
Electromagnetic radiation from blazar jets often displays strong variability, extending from radio to γ -ray frequencies. In a few cases, this variability has been characterized using Fourier time lags, such as those detected in the X-rays from Mrk 421 using Beppo SAX. The lack of a theoretical framework to interpret the data has motivated us to develop a new model for the formation of the X-ray spectrum and the time lags in blazar jets based on a transport equation including terms describing stochastic Fermi acceleration, synchrotron losses, shock acceleration, adiabatic expansion, and spatial diffusion. We derive the exact solution formore the Fourier transform of the electron distribution and use it to compute the Fourier transform of the synchrotron radiation spectrum and the associated X-ray time lags. The same theoretical framework is also used to compute the peak flare X-ray spectrum, assuming that a steady-state electron distribution is achieved during the peak of the flare. The model parameters are constrained by comparing the theoretical predictions with the observational data for Mrk 421. The resulting integrated model yields, for the first time, a complete first-principles physical explanation for both the formation of the observed time lags and the shape of the peak flare X-ray spectrum. It also yields direct estimates of the strength of the shock and the stochastic magnetohydrodynamical wave acceleration components in the Mrk 421 jet. less
Bioprosthetic heart valves fail as the result of two simultaneous processes: structural deterioration and calcification. Leaflet deterioration and perforation have been correlated with regions of highest stress in the tissue. The failures have long been assumed to be due to simple mechanical fatigue of the collagen fibre architecture; however, we have hypothesized that local stresses-and particularly dynamic stresses-accelerate local proteolysis, leading to tissue failure. This study addresses that hypothesis. Using a novel, custom-built microtensile culture system, strips of bovine pericardium were subjected to static and dynamic loads while being exposed to solutions of microbial collagenase or trypsin (a non-specific proteolytic enzyme). The time to extend to 30% strain (defined here as time to failure) was recorded. After failure, the percentage of collagen solubilized was calculated based on the amount of hydroxyproline present in solution. All data were analyzed by analysis of variance (ANOVA). In collagenase, exposure to static load significantly decreased the time to failure (P < 0.002) due to increased mean rate of collagen solubilization. Importantly, specimens exposed to collagenase and dynamic load failed faster than those exposed to collagenase under the same average static load (P = 0.02). In trypsin, by contrast, static load never led to failure and produced only minimal degradation. Under dynamic load, however, specimens exposed to collagenase, trypsin, and even Tris/CaCl2 buffer solution, all failed. Only samples exposed to Hanks' physiological solution did not fail. Failure of the specimens exposed to trypsin and Tris/CaCl2 suggests that the non-collagenous components and the calcium-dependent proteolytic enzymes present in pericardial tissue may play roles in the pathogenesis of bioprosthetic heart valve degeneration.
Particularly offshore there is a trend to cluster wind turbines in large wind farms, and in the near future to operate such a farm as an integrated power production plant. Predictability of individual turbine behavior across the entire fleet is key in such a strategy. Failure of turbine subcomponents should be detected well in advance to allow early planning of all necessary maintenance actions; Such that they can be performed during low wind and low electricity demand periods. In order to obtain the insights to predict component failure, it is necessary to have an integrated clean dataset spanning all turbines of the fleet for a sufficiently long period of time. This paper illustrates our big-data approach to do this. In addition, advanced failure detection algorithms are necessary to detect failures in this dataset. This paper discusses a multi-level monitoring approach that consists of a combination of machine learning and advanced physics based signal-processing techniques. The advantage of combining different data sources to detect system degradation is in the higher certainty due to multivariable criteria. In order to able to perform long-term acceleration data signal processing at high frequency a streaming processing approach is necessary. This allows the data to be analysed as the sensors generate it. This paper illustrates this streaming concept on 5kHz acceleration data. A continuous spectrogram is generated from the data-stream. Real-life offshore wind turbine data is used. Using this streaming approach for calculating bearing failure features on continuous acceleration data will support failure propagation detection. 153554b96e