194 research outputs found
Hierarchical amino acid utilization and its influence on fermentation dynamics: rifamycin B fermentation using Amycolatopsis mediterranei S699, a case study
BACKGROUND: Industrial fermentation typically uses complex nitrogen substrates which consist of mixture of amino acids. The uptake of amino acids is known to be mediated by several amino acid transporters with certain preferences. However, models to predict this preferential uptake are not available. We present the stoichiometry for the utilization of amino acids as a sole carbon and nitrogen substrate or along with glucose as an additional carbon source. In the former case, the excess nitrogen provided by the amino acids is excreted by the organism in the form of ammonia. We have developed a cybernetic model to predict the sequence and kinetics of uptake of amino acids. The model is based on the assumption that the growth on a specific substrate is dependent on key enzyme(s) responsible for the uptake and assimilation of the substrates. These enzymes may be regulated by mechanisms of nitrogen catabolite repression. The model hypothesizes that the organism is an optimal strategist and invests resources for the uptake of a substrate that are proportional to the returns. RESULTS: Stoichiometric coefficients and kinetic parameters of the model were estimated experimentally for Amycolatopsis mediterranei S699, a rifamycin B overproducer. The model was then used to predict the uptake kinetics in a medium containing cas amino acids. In contrast to the other amino acids, the uptake of proline was not affected by the carbon or nitrogen catabolite repression in this strain. The model accurately predicted simultaneous uptake of amino acids at low cas concentrations and sequential uptake at high cas concentrations. The simulated profile of the key enzymes implies the presence of specific transporters for small groups of amino acids. CONCLUSION: The work demonstrates utility of the cybernetic model in predicting the sequence and kinetics of amino acid uptake in a case study involving Amycolatopsis mediterranei, an industrially important organism. This work also throws some light on amino acid transporters and their regulation in A. mediterranei .Further, cybernetic model based experimental strategy unravels formation and utilization of ammonia as well as its inhibitory role during amino acid uptake. Our results have implications for model based optimization and monitoring of other industrial fermentation processes involving complex nitrogen substrate
A simulation scenario based mixed integer programming approach to airline reserve crew scheduling under uncertainty
The environment in which airlines operate is uncertain for many reasons, for example due to the effects of weather, traffic or crew unavailability (due to delay or sickness). This work focuses on airline reserve crew scheduling under crew absence uncertainty and delay for an airline operating a single hub and spoke network. Reserve crew can be used to cover absent crew or delayed connecting crew. A fixed number of reserve crew are available for scheduling and each requires a daily standby duty start time. This work proposes a mixed integer programming approach to scheduling the airline’s reserve crew. A simulation of the airline’s operations with stochastic journey time and crew absence inputs (without reserve crew) is used to generate input disruption scenarios for the mixed integer programming simulation scenario model (MIPSSM) formulation. Each disruption scenario corresponds to a record of all of the disruptions that may occur on the day of operation which are solvable by using reserve crew. A set of disruption scenarios form the input of the MIPSSM formulation, which has the objective of finding the reserve crew schedule that minimises the overall level of disruption over the set of input scenarios. Additionally, modifications of the MIPSSM are explored, a heuristic solution approach and a reserve use policy derived from the MIPSSM are introduced. A heuristic based on the proposed MIPSSM outperforms a range of alternative approaches. The heuristic solution approach suggests that including the right disruption scenarios is as important as the quantity of disruption scenarios that are added to the MIPSSM. An investigation into what makes a good set of scenarios is also presented
Nash equilibria in fisher market
Much work has been done on the computation of market equilibria. However due to strategic play by buyers, it is not clear whether these are actually observed in the market. Motivated by the observation that a buyer may derive a better payoff by feigning a different utility function and thereby manipulating the Fisher market equilibrium, we formulate the Fisher market game in which buyers strategize by posing different utility functions. We show that existence of a conflict-free allocation is a necessary condition for the Nash equilibria (NE) and also sufficient for the symmetric NE in this game. There are many NE with very different payoffs, and the Fisher equilibrium payoff is captured at a symmetric NE. We provide a complete polyhedral characterization of all the NE for the two-buyer market game. Surprisingly, all the NE of this game turn out to be symmetric and the corresponding payoffs constitute a piecewise linear concave curve. We also study the correlated equilibria of this game and show that third-party mediation does not help to achieve a better payoff than NE payoffs
Image sensing with multilayer, nonlinear optical neural networks
Optical imaging is commonly used for both scientific and technological
applications across industry and academia. In image sensing, a measurement,
such as of an object's position, is performed by computational analysis of a
digitized image. An emerging image-sensing paradigm breaks this delineation
between data collection and analysis by designing optical components to perform
not imaging, but encoding. By optically encoding images into a compressed,
low-dimensional latent space suitable for efficient post-analysis, these image
sensors can operate with fewer pixels and fewer photons, allowing
higher-throughput, lower-latency operation. Optical neural networks (ONNs)
offer a platform for processing data in the analog, optical domain. ONN-based
sensors have however been limited to linear processing, but nonlinearity is a
prerequisite for depth, and multilayer NNs significantly outperform shallow NNs
on many tasks. Here, we realize a multilayer ONN pre-processor for image
sensing, using a commercial image intensifier as a parallel optoelectronic,
optical-to-optical nonlinear activation function. We demonstrate that the
nonlinear ONN pre-processor can achieve compression ratios of up to 800:1 while
still enabling high accuracy across several representative computer-vision
tasks, including machine-vision benchmarks, flow-cytometry image
classification, and identification of objects in real scenes. In all cases we
find that the ONN's nonlinearity and depth allowed it to outperform a purely
linear ONN encoder. Although our experiments are specialized to ONN sensors for
incoherent-light images, alternative ONN platforms should facilitate a range of
ONN sensors. These ONN sensors may surpass conventional sensors by
pre-processing optical information in spatial, temporal, and/or spectral
dimensions, potentially with coherent and quantum qualities, all natively in
the optical domain
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
We train and validate a semi-supervised, multi-task LSTM on 57,675
person-weeks of data from off-the-shelf wearable heart rate sensors, showing
high accuracy at detecting multiple medical conditions, including diabetes
(0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep
apnea (0.8298). We compare two semi-supervised train- ing methods,
semi-supervised sequence learning and heuristic pretraining, and show they
outperform hand-engineered biomarkers from the medical literature. We believe
our work suggests a new approach to patient risk stratification based on
cardiovascular risk scores derived from popular wearables such as Fitbit, Apple
Watch, or Android Wear.Comment: Presented at AAAI 201
Detonating Cord for Flux Compression Generation using Electrical Detonator No. 33
The paper highlights the use of electrical detonators for magnetic flux compression generator applications which requires synchronisation of two events with precise time delay of tens of ms and jitter within a few ms. These requirements are generally achieved by exploding bridge wire type detonators which are difficult to develop and are not commercially available. A technique has been developed using commercially available electrical detonator no. 33 to synchronise between peak of seed current in stator coil and detonation of explosive charge in armature. In present experiments, electrical signal generated by self-shorting pin due to bursting of electrical detonator has been used to trigger the capacitor discharge and the detonating cord of known length has been used to incorporate predetermined delay to synchronise the events. It has been demonstrated that using electrical detonator and known length of detonating cord, the two events can be synchronised with predetermined delay between 31 and 251 ms with variation of ± 0.5ms. The technique developed is suitable for defence applications like generation of high power microwaves using explosive driven magnetic flux compression generators.Defence Science Journal, 2011, 61(1), pp.19-24, DOI:http://dx.doi.org/10.14429/dsj.61.3
Scaling on-chip photonic neural processors using arbitrarily programmable wave propagation
On-chip photonic processors for neural networks have potential benefits in
both speed and energy efficiency but have not yet reached the scale at which
they can outperform electronic processors. The dominant paradigm for designing
on-chip photonics is to make networks of relatively bulky discrete components
connected by one-dimensional waveguides. A far more compact alternative is to
avoid explicitly defining any components and instead sculpt the continuous
substrate of the photonic processor to directly perform the computation using
waves freely propagating in two dimensions. We propose and demonstrate a device
whose refractive index as a function of space, , can be rapidly
reprogrammed, allowing arbitrary control over the wave propagation in the
device. Our device, a 2D-programmable waveguide, combines photoconductive gain
with the electro-optic effect to achieve massively parallel modulation of the
refractive index of a slab waveguide, with an index modulation depth of
and approximately programmable degrees of freedom. We used a
prototype device with a functional area of to perform
neural-network inference with up to 49-dimensional input vectors in a single
pass, achieving 96% accuracy on vowel classification and 86% accuracy on -pixel MNIST handwritten-digit classification. This is a scale beyond
that of previous photonic chips relying on discrete components, illustrating
the benefit of the continuous-waves paradigm. In principle, with large enough
chip area, the reprogrammability of the device's refractive index distribution
enables the reconfigurable realization of any passive, linear photonic circuit
or device. This promises the development of more compact and versatile photonic
systems for a wide range of applications, including optical processing, smart
sensing, spectroscopy, and optical communications
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