Nanoparticle Formation in a Mixture of Fe, C, O[2] in Low-temperature Plasma in a Magnetic Field

The paper presents the results of researching a magnetic field influence on the formation of dispersed particles from the mixture of Fe+C+N[2]+Ar+O[2] at the temperature of more than 4000K. To optimize the composition of the plasmaforming gas, thermodynamic modeling was performed. The research establishes that an external magnetic field has a significant effect on the formation of a dispersed phase in the mixture of carbon and iron vapor. For example, in a powder obtained without a magnetic field, X-ray diffraction shows up to 95% C. In a powder obtained in the magnetic field of 15 mT, C (up to 50%), Fe[3]O[4] (up to 45%), Fe[2]O[3] (up to 15%), and FeO (less than 5%) are recorded. The observed results are explained by the coagulation of nanoparticles in the magnetic field

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)1.

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Implementation of the Least-Squares Lattice with Order and Forgetting Factor Estimation for FPGA

A high performance RLS lattice filter with the estimation of an unknown order and forgetting factor of identified system was developed and implemented as a PCORE coprocessor for Xilinx EDK. The coprocessor implemented in FPGA hardware can fully exploit parallelisms in the algorithm and remove load from a microprocessor. The EDK integration allows effective programming and debugging of hardware accelerated DSP applications. The RLS lattice core extended by the order and forgetting factor estimation was implemented using the logarithmic numbers system (LNS) arithmetic. An optimal mapping of the RLS lattice onto the LNS arithmetic units found by the cyclic scheduling was used. The schedule allows us to run four independent filters in parallel on one arithmetic macro set. The coprocessor containing the RLS lattice core is highly configurable. It allows to exploit the modular structure of the RLS lattice filter and construct the pipelined serial connection of filters for even higher performance. It also allows to run independent parallel filters on the same input with different forgetting factors in order to estimate which order and exponential forgetting factor better describe the observed data. The FPGA coprocessor implementation presented in the paper is able to evaluate the RLS lattice filter of order 504 at 12 kHz input data sampling rate. For the filter of order up to 20, the probability of order and forgetting factor hypotheses can be continually estimated. It has been demonstrated that the implemented coprocessor accelerates the Microblaze solution up to 20 times. It has also been shown that the coprocessor performs up to 2.5 times faster than highly optimized solution using 50 MIPS SHARC DSP processor, while the Microblaze is capable of performing another tasks concurrently

GSFAP adaptive filtering using log arithmetic for resource-constrained embedded systems

Adaptive filters are widely used in digital signal processing for such applications as system identification, noise cancellation, and in areas such as digital communication systems. Traditionally, small resource-constrained embedded systems have used the least computationally intensive filter adaptive algorithms based on least mean squares (LMS).The power-normalized version (NLMS) is typical example. More complex adaptive algorithms, such as recursive least squares (RLS), are usually too computationally expensive for implementation in small embedded systems.Our work deals with a floating-point-like implementation of the Gauss-Seidel fast affine projection (GSFAP) algorithm and shows that FPGAs are a highly suitable platform for more computationally intensive adaptive algorithms. FAP based algorithms are characterized by better adaptation properties than NLMS with only a slightly higher complexity, providing some compromise between the slow convergence of NLMS and the computational complexity of RLS.We present the design of an optimized core which implements GSFAP. To reduce the resource requirements we use logarithmic arithmetic, rather than conventional floating point, within the custom core. Our design makes effective use of the pipelined logarithmic addition units, and takes advantage of the very low cost of logarithmic multiplication and division.The resource requirements of the resulting GSFAP core are slightly higher than the requirements for the corresponding NLMS core. However, experiments show that GSFAP has adaptation properties much superior to NLMS which is demonstrated on a noise/echo cancellation example