2,242 research outputs found
Wireless Chipless Liquid Sensing using a Slotted Cylindrical Resonator
This thesis presents a comprehensive study on the application of a slotted cylindrical resonator for the wireless assessment of liquids. Using simple geometry and measurement techniques, a method for the sensing of liquids within non-metal pipes is established, allowing for the prospect of non-contact, real-time, wireless monitoring of industrial liquid processes with no requirement for samples. The main contribution of this work is the development of a thorough understanding of the geometry, as well as an extensive presentation of measured data using liquids of wide-ranging properties. A full parametric and sensitivity study obtained through theory, simulation and measurement provides analysis on every aspect of the proposed sensor, including a number of potential future research topics. The slotted cylinder is placed directly on-pipe, requiring no additional circuitry, power or support structure, and is excited wirelessly by an external antenna. Its resonant frequency is very sensitive to the permittivity within the sensor cavity, and is shown to operate well across a relatively large range of permittivity. The structure is highly adaptable, even for fixed pipe dimensions, and simple adjustments provide a method for the tuning of resonant frequency and sensitivity control. Additionally, the placement of multiple sensors in close proximity allows for the measurement of high-loss liquids, which may otherwise not be possible. A number of measurement techniques for level sensing are presented, covering both frequency and amplitude detection methods using single and multiple sensors, where the geometry is shown to be highly sensitive to very small changes in liquid level. Measurements detecting relatively small changes in liquid temperature provide a further potential application of the sensor. The simultaneous monitoring of multiple liquids is easily achieved using a single measurement system, vastly reducing the complexity inherent in large-scale industrial processes. The sensor is shown to be resilient to changes in polarisation and position relative to the measurement antenna, as well larger read distances compared with other passive sensors
A New Spin on the Weak Gravity Conjecture
The mild form of the Weak Gravity Conjecture states that quantum or
higher-derivative corrections should decrease the mass of large extremal
charged black holes at fixed charge. This allows extremal black holes to decay,
unless protected by a symmetry (such as supersymmetry). We reformulate this
conjecture as an integrated condition on the effective stress tensor capturing
the effect of quantum or higher-derivative corrections. In addition to charged
black holes, we also consider rotating BTZ black holes and show that this
condition is satisfied as a consequence of the -theorem, proving a spinning
version of the Weak Gravity Conjecture. We also apply our results to a
five-dimensional boosted black string with higher-derivative corrections. The
boosted black string has a near-horizon geometry and,
after Kaluza-Klein reduction, describes a four-dimensional charged black hole.
Combining the spinning and charged Weak Gravity Conjecture we obtain positivity
bounds on the five-dimensional Wilson coefficients that are stronger than those
obtained from charged black holes alone.Comment: 29 pages + appendices, 4 figure
Residue Geometry Networks: A Rigidity-Based Approach to the Amino Acid Network and Evolutionary Rate Analysis.
Amino acid networks (AANs) abstract the protein structure by recording the amino acid contacts and can provide insight into protein function. Herein, we describe a novel AAN construction technique that employs the rigidity analysis tool, FIRST, to build the AAN, which we refer to as the residue geometry network (RGN). We show that this new construction can be combined with network theory methods to include the effects of allowed conformal motions and local chemical environments. Importantly, this is done without costly molecular dynamics simulations required by other AAN-related methods, which allows us to analyse large proteins and/or data sets. We have calculated the centrality of the residues belonging to 795 proteins. The results display a strong, negative correlation between residue centrality and the evolutionary rate. Furthermore, among residues with high closeness, those with low degree were particularly strongly conserved. Random walk simulations using the RGN were also successful in identifying allosteric residues in proteins involved in GPCR signalling. The dynamic function of these residues largely remain hidden in the traditional distance-cutoff construction technique. Despite being constructed from only the crystal structure, the results in this paper suggests that the RGN can identify residues that fulfil a dynamical function.A.S.F. is supported
by a Doctoral Research Award from Microsoft Research. S.E.A. is supported by The Royal Society (UK). D.J.C. is
supported by a Marie Curie International Outgoing Fellowship within the 7th European Community Framework
Programme. A.W.C. is supported by the Winton Programme for the Physics of Sustainability.This is the final version of the article. It first appeared from Nature Publishing Group at http://dx.doi.org/10.1038/srep33213
Chipless Liquid Sensing Using a Slotted Cylindrical Resonator
A method for the wireless sensing of the permittivity and level of liquids is presented. The use of a simple, thin-film slotted cylindrical cavity wrapped around a standard polytetrafluoroethylene pipe is proposed. Wireless interrogation of the slot excites a resonant mode whose frequency is dependent on the liquid currently present within the pipe. The proposed method allows for measurements to be taken in situ with no requirement for taking samples of potentially hazardous liquids. The device is capable of sensing materials of high relative permittivity, including water, as well as very lossy liquids. A comprehensive set of results is presented, including measurements of butanol, ethanol, methanol and water, for several device configurations. The proposed sensor is also shown to be sensitive to small changes in liquid level, allowing for accurate water level measurements down to 0:1 ml. This sensor is a good candidate for very low-cost, low-complexity real-time monitoring of liquids
Toward ab initio optical spectroscopy of the Fenna-Matthews-Olson complex
We present progress toward a first-principles parametrization of the Hamiltonian of the FennaâMatthewsâOlson pigmentâprotein complex, a molecule that has become key to understanding the role of quantum dynamics in photosynthetic exciton energy transfer. To this end, we have performed fully quantum mechanical calculations on each of the seven bacteriochlorophyll pigments that make up the complex, including a significant proportion of their protein environment (more than 2000 atoms), using linear-scaling density functional theory exploiting a recent development for the computation of excited states. Local pigment transition energies and interpigment coupling between optical transitions have been calculated and are in good agreement with the literature consensus. Comparisons between simulated and experimental optical spectra point toward future work that may help to elucidate important design principles in these nanoscale devices
Reverse engineering of force integration during mitosis in the Drosophila embryo
The mitotic spindle is a complex macromolecular machine that coordinates accurate chromosome segregation. The spindle accomplishes its function using forces generated by microtubules (MTs) and multiple molecular motors, but how these forces are integrated remains unclear, since the temporal activation profiles and the mechanical characteristics of the relevant motors are largely unknown. Here, we developed a computational search algorithm that uses experimental measurements to âreverse engineer' molecular mechanical machines. Our algorithm uses measurements of length time series for wild-type and experimentally perturbed spindles to identify mechanistic models for coordination of the mitotic force generators in Drosophila embryo spindles. The search eliminated thousands of possible models and identified six distinct strategies for MTâmotor integration that agree with available data. Many features of these six predicted strategies are conserved, including a persistent kinesin-5-driven sliding filament mechanism combined with the anaphase B-specific inhibition of a kinesin-13 MT depolymerase on spindle poles. Such conserved features allow predictions of forceâvelocity characteristics and activationâdeactivation profiles of key mitotic motors. Identified differences among the six predicted strategies regarding the mechanisms of prometaphase and anaphase spindle elongation suggest future experiments
Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation
Cosmic Dawn (CD) and Epoch of Reionization (EoR) are epochs of the Universe
which host invaluable information about the cosmology and astrophysics of X-ray
heating and hydrogen reionization. Radio interferometric observations of the
21-cm line at high redshifts have the potential to revolutionize our
understanding of the universe during this time. However, modeling the evolution
of these epochs is particularly challenging due to the complex interplay of
many physical processes. This makes it difficult to perform the conventional
statistical analysis using the likelihood-based Markov-Chain Monte Carlo (MCMC)
methods, which scales poorly with the dimensionality of the parameter space. In
this paper, we show how the Simulation-Based Inference (SBI) through Marginal
Neural Ratio Estimation (MNRE) provides a step towards evading these issues. We
use 21cmFAST to model the 21-cm power spectrum during CD-EoR with a
six-dimensional parameter space. With the expected thermal noise from the
Square Kilometre Array (SKA), we are able to accurately recover the posterior
distribution for the parameters of our model at a significantly lower
computational cost than the conventional likelihood-based methods. We further
show how the same training dataset can be utilized to investigate the
sensitivity of the model parameters over different redshifts. Our results
support that such efficient and scalable inference techniques enable us to
significantly extend the modeling complexity beyond what is currently
achievable with conventional MCMC methods.Comment: 15 pages, 9 figures. Accepted for publication in MNRA
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