577 research outputs found
Use of radio base stations to provide ancillary services to the DSO through local flexibility markets
The changes in the energy sector require an appropriate coordination between transmission systems operators (TSOs), distribution systems operators (DSOs) and aggregators. The project SmartNet aims at defining and comparing different TSO-DSO coordination schemes, by implementing dedicated analyses in Italy, Denmark and Spain. This paper describes the pilot project implemented in Spain and presents its main outcomes.The research leading to this article has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 691405
Application of the quantum spin glass theory to image restoration
Quantum fluctuation is introduced into the Markov random fields (MRF's) model
for image restoration in the context of Bayesian approach. We investigate the
dependence of the quantum fluctuation on the quality of BW image restoration by
making use of statistical mechanics. We find that the maximum posterior
marginal (MPM) estimate based on the quantum fluctuation gives a fine
restoration in comparison with the maximum a posterior (MAP) estimate or the
thermal fluctuation based MPM estimate.Comment: 19 pages, 9 figures, 1 table, RevTe
Naive mean field approximation for image restoration
We attempt image restoration in the framework of the Baysian inference.
Recently, it has been shown that under a certain criterion the MAP (Maximum A
Posterior) estimate, which corresponds to the minimization of energy, can be
outperformed by the MPM (Maximizer of the Posterior Marginals) estimate, which
is equivalent to a finite-temperature decoding method. Since a lot of
computational time is needed for the MPM estimate to calculate the thermal
averages, the mean field method, which is a deterministic algorithm, is often
utilized to avoid this difficulty. We present a statistical-mechanical analysis
of naive mean field approximation in the framework of image restoration. We
compare our theoretical results with those of computer simulation, and
investigate the potential of naive mean field approximation.Comment: 9 pages, 11 figure
Image restoration using the Q-Ising spin glass
We investigate static and dynamic properties of gray-scale image restoration
(GSIR) by making use of the Q-Ising spin glass model, whose ladder symmetry
allows to take in account the distance between two spins. We thus give an
explicit expression of the Hamming distance between the original and restored
images as a function of the hyper-parameters in the mean field limit. Finally,
numerical simulations for real-world pictures are carried out to prove the
efficiency of our model.Comment: 27pages, 13figures, revte
A Replica Inference Approach to Unsupervised Multi-Scale Image Segmentation
We apply a replica inference based Potts model method to unsupervised image
segmentation on multiple scales. This approach was inspired by the statistical
mechanics problem of "community detection" and its phase diagram. Specifically,
the problem is cast as identifying tightly bound clusters ("communities" or
"solutes") against a background or "solvent". Within our multiresolution
approach, we compute information theory based correlations among multiple
solutions ("replicas") of the same graph over a range of resolutions.
Significant multiresolution structures are identified by replica correlations
as manifest in information theory overlaps. With the aid of these correlations
as well as thermodynamic measures, the phase diagram of the corresponding Potts
model is analyzed both at zero and finite temperatures. Optimal parameters
corresponding to a sensible unsupervised segmentation correspond to the "easy
phase" of the Potts model. Our algorithm is fast and shown to be at least as
accurate as the best algorithms to date and to be especially suited to the
detection of camouflaged images.Comment: 26 pages, 22 figure
Multi-State Image Restoration by Transmission of Bit-Decomposed Data
We report on the restoration of gray-scale image when it is decomposed into a
binary form before transmission. We assume that a gray-scale image expressed by
a set of Q-Ising spins is first decomposed into an expression using Ising
(binary) spins by means of the threshold division, namely, we produce (Q-1)
binary Ising spins from a Q-Ising spin by the function F(\sigma_i - m) = 1 if
the input data \sigma_i \in {0,.....,Q-1} is \sigma_i \geq m and 0 otherwise,
where m \in {1,....,Q-1} is the threshold value. The effects of noise are
different from the case where the raw Q-Ising values are sent. We investigate
which is more effective to use the binary data for transmission or to send the
raw Q-Ising values. By using the mean-field model, we first analyze the
performance of our method quantitatively. Then we obtain the static and
dynamical properties of restoration using the bit-decomposed data. In order to
investigate what kind of original picture is efficiently restored by our
method, the standard image in two dimensions is simulated by the mean-field
annealing, and we compare the performance of our method with that using the
Q-Ising form. We show that our method is more efficient than the one using the
Q-Ising form when the original picture has large parts in which the nearest
neighboring pixels take close values.Comment: latex 24 pages using REVTEX, 10 figures, 4 table
Variable order porous media equations: Application on modeling the S&P500 and Bitcoin price return
This article reveals a specific category of solutions for the Variable
Order (VO) nonlinear fractional Fokker-Planck equations. These solutions are
formulated using VO -Gaussian functions, granting them significant
versatility in their application to various real-world systems, such as
financial economy areas spanning from conventional stock markets to
cryptocurrencies. The VO -Gaussian functions provide a more robust
expression for the distribution function of price returns in real-world
systems. Additionally, we analyzed the temporal evolution of the anomalous
characteristic exponents derived from our study, which are associated with the
long-range memory in time series data and autocorrelation patterns.Comment: 15 Pages, 3 Figures. Submitted to Physical Review
Image restoration using the chiral Potts spin-glass
We report on the image reconstruction (IR) problem by making use of the
random chiral q-state Potts model, whose Hamiltonian possesses the same gauge
invariance as the usual Ising spin glass model. We show that the pixel
representation by means of the Potts variables is suitable for the gray-scale
level image which can not be represented by the Ising model. We find that the
IR quality is highly improved by the presence of a glassy term, besides the
usual ferromagnetic term under random external fields, as very recently pointed
out by Nishimori and Wong. We give the exact solution of the infinite range
model with q=3, the three gray-scale level case. In order to check our
analytical result and the efficiency of our model, 2D Monte Carlo simulations
have been carried out on real-world pictures with three and eight gray-scale
levels.Comment: RevTex 13 pages, 10 figure
Learning from failure propagation in steel truss bridges
Although truss-type bridge collapses usually have catastrophic consequences, their analysis present opportunities for improving different aspects in the field of bridge engineering, such as structural assessment, structural health monitoring, maintenance and conservation or even design strategies. As the world experiences more extreme events, efforts have been made to design more resilient bridges that can withstand local failures. Forensic techniques have contributed to understanding the causes and risk factors of bridge failures, and the creation of collapse databases has provided valuable insights for preventing such failures. However, these databases often focus on the hazards and do not provide information on initial damage and how it propagates, which is essential for improving the progressive collapse resistance of truss-type bridges. The main novelty of this paper is to present a methodology to identify triggering events leading to progressive collapse on truss-type bridges. It is the first time that a methodology includes a novel database which collects detailed information on initial damages and its propagation, as well as the consequences of the collapse. The methodology was implemented by collecting information from 25 case studies present in the literature. Results have allowed to identify most frequent initial constituted damages states or failures (ICDS) leading progressive collapse. In terms of consequences, results were thoroughly analysed and compared with predictions from different casualty models. The findings showed that the proposed methodology serves as an effective tool for identifying the triggering events of progressive collapse in truss-type bridges.Agencia Estatal de Investigación | Ref. PID2021-124236OBAgencia Estatal de Investigación | Ref. FJC2020–046370-IUniversidade de Vigo/CISU
Plant defense suppression is mediated by a fungal sirtuin during rice infection by \u3ci\u3eMagnaporthe oryzae\u3c/i\u3e
Crop destruction by the hemibiotrophic rice pathogen Magnaporthe oryzae requires plant defense suppression to facilitate extensive biotrophic growth in host cells before the onset of necrosis. How this is achieved at the genetic level is not well understood. Here, we report that a M. oryzae sirtuin, MoSir2, plays an essential role in rice defense suppression and colonization by controlling superoxide dismutase (SOD) gene expression. Loss of MoSir2 function in Δsir2 strains did not affect appressorial function, but biotrophic growth in rice cells was attenuated. Compared to wild type, Δsir2 strains failed to neutralize plant-derived reactive oxygen species (ROS) and elicited robust defense responses in rice epidermal cells that included elevated pathogenesis-related gene expression and granular depositions. Deletion of a SOD-encoding gene under MoSir2 control generated Δsod1 deletion strains that mimicked Δsir2 for impaired rice defense suppression, confirming SOD activity as a downstream output of MoSir2. In addition, comparative protein acetylation studies and forward genetic analyses identified a JmjC domain-containing protein as a likely target of MoSir2, and a Δsir2 Δjmjc double mutant was restored for MoSOD1 expression and defense suppression in rice epidermal cells. Together, this work reveals MoSir2 and MoJmjC as novel regulators of early rice cell infection
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