9,363 research outputs found
Optimal control under uncertainty and Bayesian parameters adjustments
We propose a general framework for studying optimal impulse control problem
in the presence of uncertainty on the parameters. Given a prior on the
distribution of the unknown parameters, we explain how it should evolve
according to the classical Bayesian rule after each impulse. Taking these
progressive prior-adjustments into account, we characterize the optimal policy
through a quasi-variational parabolic equation, which can be solved
numerically. The derivation of the dynamic programming equation seems to be new
in this context. The main difficulty lies in the nature of the set of controls
which depends in a non trivial way on the initial data through the filtration
itself
Research on the Application of E-commerce to Small and Medium Enterprises (SMEs): the Case of India
SMEs account for a large proportion and play an important role in the development of each country in the world, including India. The globalization will bring many advantages for enterprises however SMEs will face fierce competition at the local, national and International level. In order to maintain and promote the important role of SMEs in the context of increased competition, SMEs have to change and adopt new technologies. E-commerce and digital technologies are bringing opportunities to help SMEs improve their competitiveness, narrow the gap with big enterprises thanks to their fairness and flexibility of the digital business environment. According to UNIDO (2017), India is one of the countries successfully applying e-commerce to SMEs. Contributing to this success is the important role of the Indian government. Therefore, this paper focuses on researching the application of e-commerce to SMEs in terms of the role of government in promoting and creating an ecosystem for SMEs and e-commerce development
An Efficient Method for GPS Multipath Mitigation Using the Teager-Kaiser-Operator-based MEDLL
An efficient method for GPS multipath mitigation is proposed. The motivation for this proposed method is to integrate the Teager-Kaiser Operator (TKO) with the Multipath Estimating Delay Lock Loop (MEDLL) module to mitigate the GPS multipath efficiently. The general implementation process of the proposed method is that we first utilize the TKO to operate on the received signal’s Auto-Correlation Function (ACF) to get an initial estimate of the multipaths. Then we transfer the initial estimated results to the MEDLL module for a further estimation. Finally, with a few iterations which are less than those of the original MEDLL algorithm, we can get a more accurate estimate of the Line-Of-Sight (LOS) signal, and thus the goal of the GPS multipath mitigation is achieved. The simulation results show that compared to the original MEDLL algorithm, the proposed method can reduce the computation load and the hardware and/or software consumption of the MEDLL module, meanwhile, without decreasing the algorithm accuracy
Shear banding of colloidal glasses - a dynamic first order transition?
We demonstrate that application of an increasing shear field on a glass leads
to an intriguing dynamic first order transition in analogy to equilibrium
transitions. By following the particle dynamics as a function of the driving
field in a colloidal glass, we identify a critical shear rate upon which the
diffusion time scale of the glass exhibits a sudden discontinuity. Using a new
dynamic order parameter, we show that this discontinuity is analogous to a
first order transition, in which the applied stress acts as the conjugate field
on the system's dynamic evolution. These results offer new perspectives to
comprehend the generic shear banding instability of a wide range of amorphous
materials.Comment: 4 pages, 4 figure
Pairing effect on the giant dipole resonance width at low temperature
The width of the giant dipole resonance (GDR) at finite temperature T in
Sn-120 is calculated within the Phonon Damping Model including the neutron
thermal pairing gap determined from the modified BCS theory. It is shown that
the effect of thermal pairing causes a smaller GDR width at T below 2 MeV as
compared to the one obtained neglecting pairing. This improves significantly
the agreement between theory and experiment including the most recent data
point at T = 1 MeV.Comment: 8 pages, 5 figures to be published in Physical Review
Ultra-high sensitivity magnetic field and magnetization measurements with an atomic magnetometer
We describe an ultra-sensitive atomic magnetometer using optically-pumped
potassium atoms operating in spin-exchange relaxation free (SERF) regime. We
demonstrate magnetic field sensitivity of 160 aT/Hz in a gradiometer
arrangement with a measurement volume of 0.45 cm and energy resolution per
unit time of . As an example of a new application enabled by such a
magnetometer we describe measurements of weak remnant rock magnetization as a
function of temperature with a sensitivity on the order of 10
emu/cm/Hz and temperatures up to 420C
Numerical Assessment of Fibre Inclusion in a Load Transfer Platform for Pile-Supported Embankments over Soft Soil
© 2016 ASCE. This study presents the results of a numerical investigation in the performance of natural fibre reinforced load transfer platform (NFRLTP) for pile supported embankment construction over soft soil. A numerical analysis based on finite element method (FEM) was carried out on an NFRLTP pile-supported embankment in a two-dimensional plane strain condition. The effects of natural fibre inclusion in the load transfer platform on the stress transfer mechanism, generation and dissipation of excess pore water pressure have been analyzed and discussed in detail. The findings indicate that natural fibre reinforced soil as a load transfer platform facilitated the load transfer process from the embankment to piles, while decreases the intensity of load transferred to soft soil, the excess pore water pressure and the overall settlement
Modeling reactivity to biological macromolecules with a deep multitask network
Most
small-molecule drug candidates fail before entering the market,
frequently because of unexpected toxicity. Often, toxicity is detected
only late in drug development, because many types of toxicities, especially
idiosyncratic adverse drug reactions (IADRs), are particularly hard
to predict and detect. Moreover, drug-induced liver injury (DILI)
is the most frequent reason drugs are withdrawn from the market and
causes 50% of acute liver failure cases in the United States. A common
mechanism often underlies many types of drug toxicities, including
both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes
into reactive metabolites, which then conjugate to sites in proteins
or DNA to form adducts. DNA adducts are often mutagenic and may alter
the reading and copying of genes and their regulatory elements, causing
gene dysregulation and even triggering cancer. Similarly, protein
adducts can disrupt their normal biological functions and induce harmful
immune responses. Unfortunately, reactive metabolites are not reliably
detected by experiments, and it is also expensive to test drug candidates
for potential to form DNA or protein adducts during the early stages
of drug development. In contrast, computational methods have the potential
to quickly screen for covalent binding potential, thereby flagging
problematic molecules and reducing the total number of necessary experiments.
Here, we train a deep convolution neural networkthe XenoSite
reactivity modelusing literature data to accurately predict
both sites and probability of reactivity for molecules with glutathione,
cyanide, protein, and DNA. On the site level, cross-validated predictions
had area under the curve (AUC) performances of 89.8% for DNA and 94.4%
for protein. Furthermore, the model separated molecules electrophilically
reactive with DNA and protein from nonreactive molecules with cross-validated
AUC performances of 78.7% and 79.8%, respectively. On both the site-
and molecule-level, the model’s performances significantly
outperformed reactivity indices derived from quantum simulations that
are reported in the literature. Moreover, we developed and applied
a selectivity score to assess preferential reactions with the macromolecules
as opposed to the common screening traps. For the entire data set
of 2803 molecules, this approach yielded totals of 257 (9.2%) and
227 (8.1%) molecules predicted to be reactive only with DNA and protein,
respectively, and hence those that would be missed by standard reactivity
screening experiments. Site of reactivity data is an underutilized
resource that can be used to not only predict if molecules are reactive,
but also show where they might be modified to reduce toxicity while
retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity
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