1,627 research outputs found
TAGGED an Agricultural Education Immersion Program
According to the 2014 National Association of Agricultural Educator’s (NAAE) National Teach Ag Campaign Report, a total of 96 agricultural teaching positions went unfilled across the nation. California is located in the region with the highest number of unfilled positions. 739 teachers left the profession nation-wide, testifying to the growing issue of retention in the field of Agricultural Education. In order to help further recruitment efforts for students to consider agricultural education as a profession, the findings of this research will support the need for an Agricultural Education Immersion Program.
This project outlines a plan for an Agricultural Education Immersion Program that can be implemented during the summer for high school students entering their sophomore through senior year. This program will work in conjunction with the national and state Teach Ag Campaign efforts and with the NAAE STAR Program
Phase transition in a class of non-linear random networks
We discuss the complex dynamics of a non-linear random networks model, as a
function of the connectivity k between the elements of the network. We show
that this class of networks exhibit an order-chaos phase transition for a
critical connectivity k = 2. Also, we show that both, pairwise correlation and
complexity measures are maximized in dynamically critical networks. These
results are in good agreement with the previously reported studies on random
Boolean networks and random threshold networks, and show once again that
critical networks provide an optimal coordination of diverse behavior.Comment: 9 pages, 3 figures, revised versio
El delito de aborto
Fil: Alvarez Aldana, Enrique A. Universidad de Buenos Aires. Facultad de Derecho. Cátedra Derecho Penal II. Buenos Aires, ArgentinaTemas de Derecho Penal I
Stable and unstable attractors in Boolean networks
Boolean networks at the critical point have been a matter of debate for many
years as, e.g., scaling of number of attractor with system size. Recently it
was found that this number scales superpolynomially with system size, contrary
to a common earlier expectation of sublinear scaling. We here point to the fact
that these results are obtained using deterministic parallel update, where a
large fraction of attractors in fact are an artifact of the updating scheme.
This limits the significance of these results for biological systems where
noise is omnipresent. We here take a fresh look at attractors in Boolean
networks with the original motivation of simplified models for biological
systems in mind. We test stability of attractors w.r.t. infinitesimal
deviations from synchronous update and find that most attractors found under
parallel update are artifacts arising from the synchronous clocking mode. The
remaining fraction of attractors are stable against fluctuating response
delays. For this subset of stable attractors we observe sublinear scaling of
the number of attractors with system size.Comment: extended version, additional figur
Ciclo Gonádico y Condición del Mejillón Ischadium recurvum (Rafinesque, 1820) en la Laguna Mecoacán, Tabasco, México
Kualitas Pelayanan Rekomendasi Penelitian Bagi Mahasiswa Strata Satu (S-1) di Badan Pelayanan Perizinan Terpadu (Bp2t) Provinsi Riau
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Canalizing Kauffman networks: non-ergodicity and its effect on their critical behavior
Boolean Networks have been used to study numerous phenomena, including gene
regulation, neural networks, social interactions, and biological evolution.
Here, we propose a general method for determining the critical behavior of
Boolean systems built from arbitrary ensembles of Boolean functions. In
particular, we solve the critical condition for systems of units operating
according to canalizing functions and present strong numerical evidence that
our approach correctly predicts the phase transition from order to chaos in
such systems.Comment: to be published in PR
Equivalence between an optomechanical system and a Kerr medium
We study the optical bistability of an optomechanical system in which the
position of a mechanical oscillator modulates the cavity frequency. The
steady-state mean-field equation of the optical mode is identical to the one
for a Kerr medium, and thus we expect it to have the same characteristic
behavior with a lower, a middle, and an upper branch. However, the presence of
position fluctuations of the mechanical resonator leads to a new feature: the
upper branch will become unstable at sufficiently strong driving in certain
parameter regimes. We identify the appropriate parameter regime for the upper
branch to be stable, and we confirm, by numerical investigation of the quantum
steady state, that the mechanical mode indeed acts as a Kerr nonlinearity for
the optical mode in the low-temperature limit. This equivalence of the
optomechanical system and the Kerr medium will be important for future
applications of cavity optomechanics in quantum nonlinear optics and quantum
information science
Detection of weak forces based on noise-activated switching in bistable optomechanical systems
We propose to use cavity optomechanical systems in the regime of optical
bistability for the detection of weak harmonic forces. Due to the
optomechanical coupling an external force on the mechanical oscillator
modulates the resonance frequency of the cavity and consequently the switching
rates between the two bistable branches. A large difference in the cavity
output fields then leads to a strongly amplified homodyne signal. We determine
the switching rates as a function of the cavity detuning from extensive
numerical simulations of the stochastic master equation as appropriate for
continuous homodyne detection. We develop a two-state rate equation model that
quantitatively describes the slow switching dynamics. This model is solved
analytically in the presence of a weak harmonic force to obtain approximate
expressions for the power gain and signal-to-noise ratio that we then compare
to force detection with an optomechanical system in the linear regime
Entropy of complex relevant components of Boolean networks
Boolean network models of strongly connected modules are capable of capturing
the high regulatory complexity of many biological gene regulatory circuits. We
study numerically the previously introduced basin entropy, a parameter for the
dynamical uncertainty or information storage capacity of a network as well as
the average transient time in random relevant components as a function of their
connectivity. We also demonstrate that basin entropy can be estimated from
time-series data and is therefore also applicable to non-deterministic networks
models.Comment: 8 pages, 6 figure
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