1,491 research outputs found

    EFFECTS OF LOW-TEMPERATURE AND PHYSIOLOGICAL AGE ON SUPEROXIDE-DISMUTASE IN WATER HYACINTH (EICHHORNIA-CRASSIPES SOLMS)

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    Superoxide dismutase activity in water hyacinth leaves was not sensitive to small changes in environmental pH, but declined markedly with greater pH changes. KCN inhibited superoxide dismutase activity, suggesting that the enzyme was mainly composed of the Cu-Zn form. Low temperature (2-degrees-C) treatment caused a decline in superoxide dismutase activity. This effect became more pronounced as the treatment time was prolonged. Furthermore, the decline was much more significant than reductions of glucose-6-phosphate dehydrogenase activity or respiration under comparable conditions. With increasing physiological age, superoxide dismutase activity declined and was significantly lower in old than in young leaves. Therefore, superoxide dismutase activity might be employed as one of physiological parameters in studying leaf senescence

    Probing Compressed Bottom Squarks with Boosted Jets and Shape Analysis

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    A feasibility study is presented for the search of the lightest bottom squark (sbottom) in a compressed scenario, where its mass difference from the lightest neutralino is 5 GeV. Two separate studies are performed: (1)(1) final state containing two VBF-like tagging jets, missing transverse energy, and zero or one bb-tagged jet; and (2)(2) final state consisting of initial state radiation (ISR) jet, missing transverse energy, and at least one bb-tagged jet. An analysis of the shape of the missing transverse energy distribution for signal and background is performed in each case, leading to significant improvement over a cut and count analysis, especially after incorporating the consideration of systematics and pileup. The shape analysis in the VBF-like tagging jet study leads to a 3σ3\sigma exclusion potential of sbottoms with mass up to 530 (462)530 \, (462) GeV for an integrated luminosity of 300300 fb−1^{-1} at 14 TeV, with 5%5\% systematics and PU =0 (50)= 0 \, (50).Comment: 5 pages, 4 figures. Discussions and references updated, reach for PU=50 case give

    Allelopathic effects of the submerged macrophyte Potamogeton malaianus on Scenedesmus obliquus

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    Allelopathic effects of the submerged macrophyte Potamogeton malaianus on Scenedesmus obliquus were assessed using a twophase approach under controlled laboratory conditions. In the co- culture experiment ( phase I), the growth of S. obliquus at two different initial cell densities was significantly inhibited by P. malaianus. Moreover, the growth inhibition was dependent on the biomass density of P. malaianus. Antioxidant enzymes ( SOD, CAT and POD), MDA, APA, total soluble protein, protein electrophoretic pattern and morphology of S. obliquus were determined after the coculture experiment was terminated. The activities of SOD, CAT, POD and APA at the low initial cell density were stimulated, the contents of MDA and total soluble protein were increased, and some special protein bands disappeared in P. malaianus treatments. The macrophyte had no effect on the activities of SOD and APA at the high initial cell density, but significantly influenced other physiological parameters of S. obliquus with the increase of biomass density. The morphology of S. obliquus showed no difference in the macrophyte treatments and the controls, and the cultures were dominated by 4- celled coenobia. The results indicated P. malaianus had significant allelopathic effects on the growth and physiological processes of S. obliquus. Moreover, the allelopathic effects depended on initial algal cell density, biomass density of the macrophyte, and their interaction. In the experiment of P. malaianus culture filtrates ( phase II), filtrates from combined culture of plant and S. obliquus at the low initial cell density exhibited no apparent growth inhibitory effect on S. obliquus. The result showed that initial addition of growth- inhibiting plant filtrates had no allelopathic effect on S. obliquus. We concluded that the allelopathic effects on S. obliquus were found in the presence of P. malaianus, but not in P. malaianus filtrates. However, the absence of allelopathic effect on S. obliquus might be due to the very low concentrations of allelochemicals in the filtrates.Allelopathic effects of the submerged macrophyte Potamogeton malaianus on Scenedesmus obliquus were assessed using a twophase approach under controlled laboratory conditions. In the co- culture experiment ( phase I), the growth of S. obliquus at two different initial cell densities was significantly inhibited by P. malaianus. Moreover, the growth inhibition was dependent on the biomass density of P. malaianus. Antioxidant enzymes ( SOD, CAT and POD), MDA, APA, total soluble protein, protein electrophoretic pattern and morphology of S. obliquus were determined after the coculture experiment was terminated. The activities of SOD, CAT, POD and APA at the low initial cell density were stimulated, the contents of MDA and total soluble protein were increased, and some special protein bands disappeared in P. malaianus treatments. The macrophyte had no effect on the activities of SOD and APA at the high initial cell density, but significantly influenced other physiological parameters of S. obliquus with the increase of biomass density. The morphology of S. obliquus showed no difference in the macrophyte treatments and the controls, and the cultures were dominated by 4- celled coenobia. The results indicated P. malaianus had significant allelopathic effects on the growth and physiological processes of S. obliquus. Moreover, the allelopathic effects depended on initial algal cell density, biomass density of the macrophyte, and their interaction. In the experiment of P. malaianus culture filtrates ( phase II), filtrates from combined culture of plant and S. obliquus at the low initial cell density exhibited no apparent growth inhibitory effect on S. obliquus. The result showed that initial addition of growth- inhibiting plant filtrates had no allelopathic effect on S. obliquus. We concluded that the allelopathic effects on S. obliquus were found in the presence of P. malaianus, but not in P. malaianus filtrates. However, the absence of allelopathic effect on S. obliquus might be due to the very low concentrations of allelochemicals in the filtrates

    FPGA-accelerated machine learning inference as a service for particle physics computing

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    New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.Comment: 16 pages, 14 figures, 2 table

    Ion–Conducting Ceramic Membrane Reactors for the Conversion of Chemicals

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    Ion–conducting ceramic membranes, such as mixed oxygen ionic and electronic conducting (MIEC) membranes and mixed proton–electron conducting (MPEC) membranes, have the potential for absolute selectivity for specific gases at high temperatures. By utilizing these membranes in membrane reactors, it is possible to combine reaction and separation processes into one unit, leading to a reduction in by–product formation and enabling the use of thermal effects to achieve efficient and sustainable chemical production. As a result, membrane reactors show great promise in the production of various chemicals and fuels. This paper provides an overview of recent developments in dense ceramic catalytic membrane reactors and their potential for chemical production. This review covers different types of membrane reactors and their principles, advantages, disadvantages, and key issues. The paper also discusses the configuration and design of catalytic membrane reactors. Finally, the paper offers insights into the challenges of scaling up membrane reactors from experimental stages to practical applications

    Fast convolutional neural networks on FPGAs with hls4ml

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    We introduce an automated tool for deploying ultra low-latency, low-power deep neural networks with convolutional layers on FPGAs. By extending the hls4ml library, we demonstrate an inference latency of 5 Ό5\,\mus using convolutional architectures, targeting microsecond latency applications like those at the CERN Large Hadron Collider. Considering benchmark models trained on the Street View House Numbers Dataset, we demonstrate various methods for model compression in order to fit the computational constraints of a typical FPGA device used in trigger and data acquisition systems of particle detectors. In particular, we discuss pruning and quantization-aware training, and demonstrate how resource utilization can be significantly reduced with little to no loss in model accuracy. We show that the FPGA critical resource consumption can be reduced by 97% with zero loss in model accuracy, and by 99% when tolerating a 6% accuracy degradation.Comment: 18 pages, 18 figures, 4 table

    Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs

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    We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks. The two complementary FPGA designs are based on OpenCL, a framework for writing programs that execute across heterogeneous platforms, and hls4ml, a high-level-synthesis-based compiler for neural network to firmware conversion. We evaluate and compare the resource usage, latency, and tracking performance of our implementations based on a benchmark dataset. We find a considerable speedup over CPU-based execution is possible, potentially enabling such algorithms to be used effectively in future computing workflows and the FPGA-based Level-1 trigger at the CERN Large Hadron Collider.Comment: 8 pages, 4 figures, To appear in Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020
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