3,144 research outputs found
Recommended from our members
The relationship between membrane damage, release of protein and loss of viability in Escherichia coli exposed to high hydrostatic pressure
The aim of this work was to examine a possible association between resistance of two Escherichia coli strains to high hydrostatic pressure and the susceptibility of their cell membranes to pressure-induced damage. Cells were exposed to pressures between 100 and 700 MPa at room temperature (~20C) in phosphate-buffered-saline. In the more pressure-sensitive strain E. coli 8164, loss of viability occurred at pressures between 100 MPa and 300 MPa and coincided with irreversible loss of membrane integrity as indicated by uptake of propidium iodide (PI) and leakage of protein of molecular mass between 9 and 78 kDa from the cells. Protein release increased to a maximum at 400 MPa then decreased, possibly due to intracellular aggregation at the higher pressures. In the pressure-resistant strain E. coli J1, PI was taken up during pressure treatment but not after decompression indicating that cells were able to reseal their membranes. Loss of viability in strain J1 coincided with the transient loss of membrane integrity between approximately 200 MPa and 600 MPa. In E. coli J1 leakage of protein occurred before loss of viability and the released protein was of low molecular mass, between 8 and 11 kDa and may have been of periplasmic origin. In these two strains differences in pressure resistance appeared to be related to differences in the ability of their membranes to withstand disruption by pressure. However it appears that transient loss of membrane integrity during pressure can lead to cell death irrespective of whether cells can reseal their membranes afterwards
Looking for the Top-squark at the Tevatron with four jets
The scalar partner of the top quark is relatively light in many models of
supersymmetry breaking. We study the production of top squarks (stops) at the
Tevatron collider and their subsequent decay through baryon-number violating
couplings such that the final state contains no leptons. Performing a
detector-level analysis, we demonstrate that, even in the absence of leptons or
missing energy, stop masses upto 210 \gev/c^2 can be accessible at the
Tevatron.Comment: 4 pages, 4 embedded figures, RevTe
Permanent spin currents in cavity-qubit systems
In a recent remarkable experiment [P. Roushan et al., Nature Physics 13, 146
(2017)], a spin current in an architecture of three superconducting qubits was
produced during a few microseconds by creating synthetic magnetic fields. The
life-time of the current was set by the typical dissipative mechanisms that
occur in those systems. We propose a scheme for the generation of permanent
currents, even in the presence of such imperfections, and scalable to larger
system sizes. It relies on striking a subtle balance between multiple
nonequilibrium drives and the dissipation mechanisms, in order to engineer and
stimulate chiral excited states which can carry current.Comment: 4 pages, 3 figure
Predicting Performance of Channel Assignments in Wireless Mesh Networks through Statistical Interference Estimation
Wireless Mesh Network (WMN) deployments are poised to reduce the reliance on
wired infrastructure especially with the advent of the multi-radio
multi-channel (MRMC) WMN architecture. But the benefits that MRMC WMNs offer
viz., augmented network capacity, uninterrupted connectivity and reduced
latency, are depreciated by the detrimental effect of prevalent interference.
Interference mitigation is thus a prime objective in WMN deployments. It is
often accomplished through prudent channel allocation (CA) schemes which
minimize the adverse impact of interference and enhance the network
performance. However, a multitude of CA schemes have been proposed in research
literature and absence of a CA performance prediction metric, which could aid
in the selection of an efficient CA scheme for a given WMN, is often felt. In
this work, we offer a fresh characterization of the interference endemic in
wireless networks. We then propose a reliable CA performance prediction metric,
which employs a statistical interference estimation approach. We carry out a
rigorous quantitative assessment of the proposed metric by validating its CA
performance predictions with experimental results, recorded from extensive
simulations run on an ns-3 802.11g environment
A Short Review on Machine Learning in Space Science and Exploration
Machine learning is revolutionizing space exploration by tackling massive datasets, empowering astronauts, and driving scientific breakthroughs. From Deep Space 1's autonomous navigation to the James Webb Space Telescope's AI-assisted exoplanet discovery, Machine learning is transforming the present and shaping the future. With missions like NASA's Parker Solar Probe and the development of AI-powered monitoring systems and astro robots, the possibilities for unravelling the cosmos and democratizing space exploration are limitless. The future of space exploration lies in harnessing the power of ML to unlock the universe's secrets and make them accessible to all
The Potential of Machine Learning for Future Mars Exploration
The pursuit of understanding Mars, our neighboring planet, is rife with challenges that range from treacherous conditions for potential human astronauts to the vast distances that complicate communication. However, a beacon of hope emerges in the form of machine learning, a technological frontier that promises to transform the landscape of Martian exploration. As we embark on this interplanetary journey, the recognition of machine learning's potential is growing. It offers innovative solutions to some of the most pressing challenges, ushering in a new era of autonomous exploration. Imagine rovers and orbiter spacecraft equipped with the ability to analyze Martian data on-site, reducing the need for slow communications with Earth. This revolutionary approach is already in action with rovers like Curiosity, where machine learning enables self-directed exploration and continuous data analysis on the Martian surface. The applications of machine learning extend beyond mere autonomy. They hold the promise of addressing communication limitations, providing greater operational autonomy, and unlocking the mysteries that shroud the Red Planet. From identifying sources of atmospheric gases, such as oxygen and methane, to interpreting geological features like cloud distributions and weather patterns, machine learning is proving itself to be a versatile and indispensable tool in unraveling the complexities of Mars. Venturing deeper into the Martian climate, machine learning becomes a powerful ally. By leveraging this technology to analyze climate data, we have the potential to generate predictive models crucial for planning future surface missions and assessing the habitability of Mars. Additionally, the application of machine learning on Earth offers a unique opportunity to decode uncertainties related to Martian atmospheric interactions, the dynamics of dust storms, and conditions beneath the surface. Anticipating the wealth of data that future Mars missions will yield, the integration of machine learning emerges as a game-changer. Its efficiency in discerning intricate patterns within extensive datasets has the potential to revolutionize our scientific understanding of Mars. As we delve deeper into the mysteries of the Red Planet, machine learning stands as a pivotal catalyst, promising not just incremental but transformative discoveries. It becomes the linchpin in our ongoing quest to answer the age-old question: Did life ever exist on Mars? In the realm of Martian exploration, machine learning is proving to be the technological cornerstone that propels us towards unprecedented scientific revelations
Radio Co-location Aware Channel Assignments for Interference Mitigation in Wireless Mesh Networks
Designing high performance channel assignment schemes to harness the
potential of multi-radio multi-channel deployments in wireless mesh networks
(WMNs) is an active research domain. A pragmatic channel assignment approach
strives to maximize network capacity by restraining the endemic interference
and mitigating its adverse impact on network performance. Interference
prevalent in WMNs is multi-faceted, radio co-location interference (RCI) being
a crucial aspect that is seldom addressed in research endeavors. In this
effort, we propose a set of intelligent channel assignment algorithms, which
focus primarily on alleviating the RCI. These graph theoretic schemes are
structurally inspired by the spatio-statistical characteristics of
interference. We present the theoretical design foundations for each of the
proposed algorithms, and demonstrate their potential to significantly enhance
network capacity in comparison to some well-known existing schemes. We also
demonstrate the adverse impact of radio co- location interference on the
network, and the efficacy of the proposed schemes in successfully mitigating
it. The experimental results to validate the proposed theoretical notions were
obtained by running an exhaustive set of ns-3 simulations in IEEE 802.11g/n
environments.Comment: Accepted @ ICACCI-201
Reliable Prediction of Channel Assignment Performance in Wireless Mesh Networks
The advancements in wireless mesh networks (WMN), and the surge in
multi-radio multi-channel (MRMC) WMN deployments have spawned a multitude of
network performance issues. These issues are intricately linked to the adverse
impact of endemic interference. Thus, interference mitigation is a primary
design objective in WMNs. Interference alleviation is often effected through
efficient channel allocation (CA) schemes which fully utilize the potential of
MRMC environment and also restrain the detrimental impact of interference.
However, numerous CA schemes have been proposed in research literature and
there is a lack of CA performance prediction techniques which could assist in
choosing a suitable CA for a given WMN. In this work, we propose a reliable
interference estimation and CA performance prediction approach. We demonstrate
its efficacy by substantiating the CA performance predictions for a given WMN
with experimental data obtained through rigorous simulations on an ns-3 802.11g
environment.Comment: Accepted in ICACCI-201
- …