150 research outputs found
Climatic Analysis of Wind Patterns to Enhance Sailors’ Performance during Races
he impact of environmental and meteorological conditions when dealing with sport performance has been demonstrated by several studies carried out in recent years. Among the meteorological variables with the greatest effect are temperature, humidity, precipitation, and wind direction and speed. This research focused on analyzing and forecasting the wind patterns occurring in Enoshima Bay (Japan). In particular, the objective of this study was to provide support and guidance to sailors in the preparation of the race strategy, thanks to an in-depth knowledge of these meteorological variables. To do this, an innovative method was used. First, through the combined use of Weather Research and Forecasting (WRF) and CALMET models, a simulation was performed, in order to reconstruct an offshore database of a recent 10-year period (2009–2018) over the race area, inside the bay. Subsequently, the verification of hind-cast was performed: the wind data measured at sea were compared with the data extracted from the CALMET database to verify the validity of the model. The verification was performed through three statistical indexes: BIAS, MAE, and PCC. The analysis showed mixed results, depending on the examined pattern, but made it possible to identify the days that best simulated the reality. Then, the wind data from the selected days were summarized and collected in plots, tables, and maps to design a decision support service (DSS), in order to provide athletes with the necessary information in a simple and effective way. In conclusion, we state that the application of this method extends beyond the sports field. Indeed, the study of wind patterns may be necessary in the design of actions to contrast and adapt to climate change, particularly in coastal areas
Spin–orbit coupling controlling the superconducting dome of artificial superlattices of quantum wells
While it is known that a resonant amplification of Tc in two-gap
superconductors can be driven by using the Fano-Feshbach resonance tuning the
chemical potential near a Lifshitz transition, little is known on tuning the Tc
resonance by cooperative interplay of the Rashba spin-orbit coupling (RSOC)
joint with phonon mediated (e-ph) pairing at selected k-space spots. Here we
present first-principles quantum calculation of superconductivity in an
artificial heterostructure of metallic quantum wells with 3 nm period where
quantum size effects give two-gap superconductivity with RSOC controlled by the
internal electric field at the interface between the nanoscale metallic layers
intercalated by insulating spacer layers. The key results of this work show
that fundamental quantum mechanics effects including RSCO at the nanoscale
(Mazziotti et al Phys. Rev. B, 103, 024523, 2021) provide key tools in applied
physics for quantitative material design of unconventional high temperature
superconductors at ambient pressure. We discuss the superconducting domes where
Tc is a function of either the Lifshitz parameter (?) measuring the distance
from the topological Lifshitz transition for the appearing of a new small Fermi
surface due to quantum size effects with finite spin-orbit coupling and the
variable e-ph coupling g in the appearing second Fermi surface linked with the
softening of the phonon energy cut off.Comment: 13 pages, 8 figure
Regional indices of socio-economic and health inequalities: a tool for public health programming
Abstract OBJECTIVES. The aim was to provide an affordable method for computing socio-economic deprivation indices at regional level, to reveal the specific aspects of the relationship between socio-economic (SE) inequalities and health outcomes. The Umbria region Socio-Health Index (USHI) was computed and compared to the Italian National Deprivation Index at Umbria region level (NDI-U).METHODS. The USHI was computed by applying factor analysis to census tract SE variables correlated to the general mortality and validated in comparison with the NDI-U.RESULTS. Overall mortality presented linear positive USHI trends, while trends for NDI-U resulted non-linear or not-significant. Similar and relevant results were obtained for specific causes of death by deprivation groups, gender and age.CONCLUSIONS. The USHI better describes a local population by SE health-related status. Therefore, policy makers could adopt this method to obtain a better picture of SE-associated health conditions in regional population and target strategies for reducing inequalities in health
Network science Ising states of matter
Network science provides very powerful tools for extracting information from
interacting data. Although recently the unsupervised detection of phases of
matter using machine learning has raised significant interest, the full
prediction power of network science has not yet been systematically explored in
this context. Here we fill this gap by providing an in-depth statistical,
combinatorial, geometrical and topological characterization of 2D Ising
snapshot networks (IsingNets) extracted from Monte Carlo simulations of the 2D
Ising model at different temperatures, going across the phase transition. Our
analysis reveals the complex organization properties of IsingNets in both the
ferromagnetic and paramagnetic phases and demonstrates the significant
deviations of the IsingNets with respect to randomized null models. In
particular percolation properties of the IsingNets reflect the existence of the
symmetry between configurations with opposite magnetization below the critical
temperature and the very compact nature of the two emerging giant clusters
revealed by our persistent homology analysis of the IsingNets. Moreover, the
IsingNets display a very broad degree distribution and significant
degree-degree correlations and weight-degree correlations demonstrating that
they encode relevant information present in the configuration space of the 2D
Ising model. The geometrical organization of the critical IsingNets is
reflected in their spectral properties deviating from the one of the null
model. This work reveals the important insights that network science can bring
to the characterization of phases of matter. The set of tools described hereby
can be applied as well to numerical and experimental data.Comment: 17 pages, 18 figure
Non-parametric learning critical behavior in Ising partition functions: PCA entropy and intrinsic dimension
We provide and critically analyze a framework to learn critical behavior in
classical partition functions through the application of non-parametric methods
to data sets of thermal configurations. We illustrate our approach in phase
transitions in 2D and 3D Ising models. First, we extend previous studies on the
intrinsic dimension of 2D partition function data sets, by exploring the effect
of volume in 3D Ising data. We find that as opposed to 2D systems for which
this quantity has been successfully used in unsupervised characterizations of
critical phenomena, in the 3D case its estimation is far more challenging. To
circumvent this limitation, we then use the principal component analysis (PCA)
entropy, a "Shannon entropy" of the normalized spectrum of the covariance
matrix. We find a striking qualitative similarity to the thermodynamic entropy,
which the PCA entropy approaches asymptotically. The latter allows us to
extract -- through a conventional finite-size scaling analysis with modest
lattice sizes -- the critical temperature with less than error for both
2D and 3D models while being computationally efficient. The PCA entropy can
readily be applied to characterize correlations and critical phenomena in a
huge variety of many-body problems and suggests a (direct) link between
easy-to-compute quantities and entropies.Comment: Corrected affiliation informatio
The interplay of microscopic and mesoscopic structure in complex networks
Not all nodes in a network are created equal. Differences and similarities
exist at both individual node and group levels. Disentangling single node from
group properties is crucial for network modeling and structural inference.
Based on unbiased generative probabilistic exponential random graph models and
employing distributive message passing techniques, we present an efficient
algorithm that allows one to separate the contributions of individual nodes and
groups of nodes to the network structure. This leads to improved detection
accuracy of latent class structure in real world data sets compared to models
that focus on group structure alone. Furthermore, the inclusion of hitherto
neglected group specific effects in models used to assess the statistical
significance of small subgraph (motif) distributions in networks may be
sufficient to explain most of the observed statistics. We show the predictive
power of such generative models in forecasting putative gene-disease
associations in the Online Mendelian Inheritance in Man (OMIM) database. The
approach is suitable for both directed and undirected uni-partite as well as
for bipartite networks
Analysis of meteorology-chemistry interactions during air pollution episodes using online coupled models within AQMEII Phase-2
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).This study reviews the top ranked meteorology and chemistry interactions in online coupled models recommended by an experts’ survey conducted in COST Action EuMetChem and examines the sensitivity of those interactions during two pollution episodes: the Russian forest fires 25 Jul -15 Aug 2010 and a Saharan dust transport event from 1 Oct -31 Oct 2010 as a part of the AQMEII phase-2 exercise. Three WRF-Chem model simulations were performed for the forest fire case for a baseline without any aerosol feedback on meteorology, a simulation with aerosol direct effects only and a simulation including both direct and indirect effects. For the dust case study, eight WRF-Chem and one WRF-CMAQ simulations were selected from the set of simulations conducted in the framework of AQMEII. Of these two simulations considered no feedbacks, two included direct effects only and five simulations included both direct and indirect effects. The results from both episodes demonstrate that it is important to include the meteorology and chemistry interactions in online-coupled models. Model evaluations using routine observations collected in AQMEII phase-2 and observations from a station in Moscow show that for the fire case the simulation including only aerosol direct effects has better performance than the simulations with no aerosol feedbacks or including both direct and indirect effects. The normalized mean biases are significantly reduced by 10-20% for PM10 when including aerosol direct effects. The analysis for the dust case confirms that models perform better when including aerosol direct effects, but worse when including both aerosol direct and indirect effects, which suggests that the representation of aerosol indirect effects needs to be improved in the model.Peer reviewedFinal Published versio
Improving the deterministic skill of air quality ensembles
<p><strong>Abstract.</strong> Forecasts from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as the model itself (e.g. physical parameterization, chemical mechanism). Multi-model ensemble forecasts can improve the forecast skill provided that certain mathematical conditions are fulfilled. We demonstrate through an intercomparison of two dissimilar air quality ensembles that unconditional raw forecast averaging, although generally successful, is far from optimum. One way to achieve an optimum ensemble is also presented. The basic idea is to either add optimum weights to members or constrain the ensemble to those members that meet certain conditions in time or frequency domain. The methods are evaluated against ground level observations collected from the EMEP and Airbase databases.<br><br> The two ensembles were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). Verification statistics shows that the deterministic models simulate better O<sub>3</sub> than NO<sub>2</sub> and PM<sub>10</sub>, linked to different levels of complexity in the represented processes. The ensemble mean achieves higher skill compared to each station's best deterministic model at 39&#8201;%&#8211;63&#8201;% of the sites. The skill gained from the favourable ensemble averaging has at least double the forecast skill compared to using the full ensemble. The method proved robust for the 3-monthly examined time-series if the training phase comprises 60 days. Further development of the method is discussed in the conclusion.</p>
Modeled deposition of nitrogen and sulfur in Europe estimated by 14 air quality model systems: evaluation, effects of changes in emissions and implications for habitat protection
The evaluation and intercomparison of air quality models is key to reducing model errors and uncertainty. The projects AQMEII3 and EURODELTA-Trends, in the framework of the Task Force on Hemispheric Transport of Air Pollutants and the Task Force on Measurements and Modelling, respectively (both task forces under the UNECE Convention on the Long Range Transport of Air Pollution, LTRAP), have brought together various regional air quality models to analyze their performance in terms of air concentrations and wet deposition, as well as to address other specific objectives. This paper jointly examines the results from both project communities by intercomparing and evaluating the deposition estimates of reduced and oxidized nitrogen (N) and sulfur (S) in Europe simulated by 14 air quality model systems for the year 2010. An accurate estimate of deposition is key to an accurate simulation of atmospheric concentrations. In addition, deposition fluxes are increasingly being used to estimate ecological impacts. It is therefore important to know by how much model results differ and how well they agree with observed values, at least when comparison with observations is possible, such as in the case of wet deposition. This study reveals a large variability between the wet deposition estimates of the models, with some performing acceptably (according to previously defined criteria) and others underestimating wet deposition rates. For dry deposition, there are also considerable differences between the model estimates. An ensemble of the models with the best performance for N wet deposition was made and used to explore the implications of N deposition in the conservation of protected European habitats. Exceedances of empirical critical loads were calculated for the most common habitats at a resolution of 100  ×  100 m2 within the Natura 2000 network, and the habitats with the largest areas showing exceedances are determined. Moreover, simulations with reduced emissions in selected source areas indicated a fairly linear relationship between reductions in emissions and changes in the deposition rates of N and S. An approximate 20 % reduction in N and S deposition in Europe is found when emissions at a global scale are reduced by the same amount. European emissions are by far the main contributor to deposition in Europe, whereas the reduction in deposition due to a decrease in emissions in North America is very small and confined to the western part of the domain. Reductions in European emissions led to substantial decreases in the protected habitat areas with critical load exceedances (halving the exceeded area for certain habitats), whereas no change was found, on average, when reducing North American emissions in terms of average values per habitat
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