Different Number Of Sites Research Articles

Persistent revivals in a system of trapped bosonic atoms

Dynamical signatures of quantum chaos are observed in the survival probability of different initial states, in a system of cold atoms trapped in a linear chain with site noise and open boundary conditions. It is shown that chaos is present in the region of small disorder, at intermediate energies. The study is performed with different number of sites and atoms: 7, 8 and 9, but focusing on the case where the particle density is one. States of the occupation basis with energies in the chaotic region are evolved at long times. Remarkable differences in the behavior of the survival probability are found for states with different energy-eigenbasis participation ratio (PR). Whereas those with large PR clearly exhibit the characteristic random-matrix correlation hole before equilibration, those with small PR present a marginal or even no correlation hole which is replaced by revivals lasting up to the stage of equilibration, suggesting a connection with the quantum scarring phenomenon.

Quantum droplets in two-dimensional optical lattices

We study the stability of zero-vorticity and vortex lattice quantum droplets (LQDs), which are described by a two-dimensional (2D) Gross–Pitaevskii (GP) equation with a periodic potential and Lee–Huang–Yang (LHY) term. The LQDs are divided in two types: onsite-centered and offsite-centered LQDs, the centers of which are located at the minimum and the maximum of the potential, respectively. The stability areas of these two types of LQDs with different number of sites for zero-vorticity and vorticity with S = 1 are given. We found that the μ–N relationship of the stable LQDs with a fixed number of sites can violate the Vakhitov–Kolokolov (VK) criterion, which is a necessary stability condition for nonlinear modes with an attractive interaction. Moreover, the μ–N relationship shows that two types of vortex LQDs with the same number of sites are degenerated, while the zero-vorticity LQDs are not degenerated. It is worth mentioning that the offsite-centered LQDs with zero-vorticity and vortex LQDs with S = 1 are heterogeneous.

Assessing temporal data partitioning scenarios for estimating reference evapotranspiration with machine learning techniques in arid regions

Recently, data driven machine learning techniques has been widely applied for modeling reference evapotranspiration (ETo) values under various climatic conditions taking into account the different number of sites and available data length. A major issue with applying those models is the proper selection of training/testing data sets. Although some spatial generalization approaches have been recommended for this purpose, there are no specified recommended local (temporal) data partitioning strategies for machine learning based ETo estimation. The present study evaluates different hold-out and k-fold validation temporal data partitioning strategies when using gene expression programming (GEP) technique to estimate daily ETo in arid regions. The k-fold validation strategies considered annual, monthly and growing season period patterns as test data sets. Although commonly used partitioning of the available patterns into training and testing sets gave accurate results, statistical analysis showed that the results obtained through k-fold validation assessment were more reliable. A two-block partitioning strategy with chronologic data selection for training and testing provided the most accurate results among the hold-out procedures (mean scatter index (SI) value of 0.162). Fixing the extreme ETo values as training data set in hold-out procedures provided the less accurate results with considerable over/underestimation of the ETo values (mean SI value was 0.506). Results on the basis of hold-out approaches can be biased or only partially valid depending on selection of the test data from the time series. K-fold validation yielded the lowest over/underestimations of ETo values. Further, considering monthly patterns as minimum affordable test size produced higher error magnitudes among k-fold validation strategies, while considering the complete patterns of one growing season provided more accurate results among k-fold validation strategies.

DNA METHYLATION CLOCKS IN MOUSE

One of important limiting factors in aging research is the time required to measure the effect of an intervention on lifespan. But this situation is now changing due to a recent discovery of DNA methylation-based markers (DNAm clocks). We developed a whole lifespan multi-tissue DNAm clock for mice with R2 =0.89. We also carried out comparative analyses of the available mouse DNAm clocks (single- or multi-tissue, based on different number of sites, based on one genomic locus or multi-loci). In general, tissue specific clocks are more accurate than muti-tissue clocks. We applied these tools to a variety of experimental systems, ranging from interventions to rejuvenation approaches, and analyzed various mouse tissues and public datasets. We further applied DNAm clocks to newly sequenced sets of blood and liver samples. Multi-loci blood clock outperforms other clocks when applied to blood samples, and the liver and multi-tissue clocks show similar precision on liver.

Regional air pollution mixtures across the continental US

A limited literature body have estimated regional differences in air pollution mixtures. More comprehensive analyses are necessary to accurately depict differences in air pollution characteristics over space and time. Our objective is to further these efforts by investigating spatial differences of air pollution mixtures across the US. We employed spatially constrained clustering approach (based on k-means algorithm) to group air pollution monitoring sites that exhibit distinct pollutant profiles or mixtures in the US over 9 years (2008–2016). We accounted for 20 chemical components of PM2.5. The resulting clusters of pollution mixtures are characterized and validated based on source emissions represented by land-use information. Our analysis resulted in 27 clusters with different number of sites. For example, the cluster 1 has 14 sites and it covers part of the southeast, including the states of North Carolina, South Carolina, Georgia, and Florida. The southwest has a very prominent cluster with 8 sites (cluster 26), covering part of the Louisiana, Mississippi, Texas, and Arkansas. In the west coast, two clusters were highlighted in our analysis, cluster 3 in California and cluster 7 in Washington and part of Oregon. Both clusters with 5 sites. We estimated that Cu, Se, NO3−, Cr, and Ba were the top five species that divided the study area into cluster of sites more effectively. Observing the concentration ratios (concentrations of the species i/concentration of PM2.5) for some of these clusters, our results show that clusters 3 and 7 in the west coast represent sites with high Na ratios. Cluster 13 in the northwest and part of the Midwest represents sites with high SO42− ratio. The cluster 16 with a single site in northeast has the highest SO42− ratio, representing almost the third quartile of the SO42− ratio. This is one of the few studies focused on spatial patterns analysis to estimate regions that exhibit distinct pollutant mixtures on a large scale. We expect that further investigations can use our findings to analyze the relationship between areas that exhibit distinct pollutant mixtures and the impact of regulations, climate change, and health effects in the US.

Effect of number and properties of specific sites on alumina surfaces for Pt-Al2O3 catalysts

In this work, how the number and properties of specific sites on alumina surfaces affect the specific interaction between Pt and alumina was investigated by using X-ray diffraction, ethanol temperature programmed desorption, diffuse reflectance infrared Fourier transform spectroscopy, H2 chemisorption, scanning transmission electron microscopy and benzene hydrogenation reaction. Here, we chose two sets of model aluminas having different number of sites with the identical properties and different properties of sites with the same number based on ethanol TPD. The H2 chemisorption results for the model aluminas show that H/Pt are all similar for low Pt loadings, but significantly different for high Pt loadings. For 1 wt% Pt/Al2O3, the number of specific sites on all the aluminas was sufficient to disperse all the Pt, leading to only highly dispersed Pt clusters (∼1 nm). However, at 10 wt% Pt/Al2O3, the number of Pt atoms is greater than that of the specific sites on the alumina surface, resulting in a bimodal distribution of large agglomerated Pt (>10 nm) and highly dispersed Pt clusters (<3 nm) revealed by XRD and TEM. Overall, the results clearly demonstrated that Pt shows higher dispersion with increasing number of sites and interaction strength, because the Pt atoms can interact with specific sites on alumina in greater numbers and more strongly. However, these Pt dispersion changes do not represent the gradual change in Pt cluster sizes, but the relative population change of small (<3 nm) and large agglomerated Pt clusters (>10 nm) under bimodal distribution. The number of large agglomerated Pt clusters decreased with increasing number of sites and interaction strength. This fundamental understanding provides an important perspective for designing Al2O3-based supported catalysts.

Spatial Upscaling of Sparse Soil Moisture Observations Based on Ridge Regression

Available ground-based observation networks for the validation of soil moisture remote sensing products are commonly sparse; thus, ground truth determinations are difficult at the validated remote sensing pixel scale. Based on the consistency of temporal trends between ground truth and in situ measurements, it is feasible to estimate ground truth by building a linear relationship between temporal sparse ground observations and truth samples. Herein, auxiliary remote sensing data with a moderate spatial resolution can be transformed into truth samples depending on the stronger representation of remote sensing data to spatial heterogeneity in the validated pixel relative to limited sites. When solving weighting coefficients for the relationship model, the underlying correlations among the in situ measurements cause the multicollinearity problem, leading to failed predictions. An upscaling algorithm called ridge regression (RR) addresses this by introducing a regularization parameter. With sparse sites, the RR method is tested in two cases employing six and nine sites, and compared with the ordinary least squares and the arithmetic mean. The upscaling results of the RR method show higher prediction accuracies compared to the other two methods. When the RR method is used, the six-site case has the same estimation accuracy as the nine-site case due to maintaining the diversity of in situ measurements through the analysis of the ridge trace and variance inflation factor (VIF). Thus, the ridge trace and VIF analysis is considered as the optimal selection method for the existing observation networks if the RR method will be used in future validation work. With a different number of sites, the RR method always displays the best estimation accuracy and is not sensitive to the number of sites, which indicates that the RR method can potentially upscale sparse sites. However, if the sites are too few, e.g., one to four, it is difficult to perform the upscaling method.

Partial chiral symmetry-breaking as a route to spectrally isolated topological defect states in two-dimensional artificial materials

Bipartite quantum systems from the chiral universality classes admit topologically protected zero modes at point defects. However, in two-dimensional systems these states can be difficult to separate from compacton-like localized states that arise from flat bands, formed if the two sublattices support a different number of sites within a unit cell. Here we identify a natural reduction of chiral symmetry, obtained by coupling sites on the majority sublattice, which gives rise to spectrally isolated point-defect states, topologically characterized as zero modes supported by the complementary minority sublattice. We observe these states in a microwave realization of a dimerized Lieb lattice with next-nearest neighbour coupling, and also demonstrate topological mode selection via sublattice-staggered absorption.

Hierarchical model for distributed seismicity

A cellular automata model for the interaction between seismic faults in an extended region is presented. Faults are represented by boxes formed by a different number of sites and located in the nodes of a fractal tree. Both the distribution of box sizes and the interaction between them is assumed to be hierarchical. Load particles are randomly added to the system, simulating the action of external tectonic forces. These particles fill the sites of the boxes progressively. When a box is full it topples, some of the particles are redistributed to other boxes and some of them are lost. A box relaxation simulates the occurrence of an earthquake in the region. The particle redistributions mostly occur upwards (to larger faults) and downwards (to smaller faults) in the hierarchy producing new relaxations. A simple and efficient bookkeeping of the information allows the running of systems with more than fifty million faults. This model is consistent with the definition of magnitude, i.e., earthquakes of magnitude m take place in boxes with a number of sites ten times bigger than those boxes responsible for earthquakes with a magnitude m-1 which are placed in the immediate lower level of the hierarchy. The three parameters of the model have a geometrical nature: the height or number of levels of the fractal tree, the coordination of the tree and the ratio of areas between boxes in two consecutive levels. Besides reproducing several seismicity properties and regularities, this model is used to test the performance of some precursory patterns.

Entanglement of the Heisenberg chain with the next-nearest-neighbor interaction

The features of the concurrences of the nearest-neighbor and the next-nearest-neighbor sites for one-dimensional Heisenberg model with the next-nearest-neighbor interaction are studied both at the ground state and finite temperatures respectively. Both concurrences are found to exhibit different behaviors at the ground state, which is clarified from the point of view of the correlation function. The threshold temperature with respective to different number of sites and the thermal concurrences of the system up to 12 sites are studied numerically.

Bounds on models with one latticized extra dimension

We study an extension of the standard model with one latticized extra dimension accessible to all fields. The model is characterized by the size of the extra dimension and the number of sites, and contains a tower of massive particles. At energies lower than the mass of the new particles there are no tree-level effects. Therefore, bounds on the scale of new physics can only be set from one-loop processes. We calculate several observables sensitive to loop-effects, such as the $\rho$ parameter, $b\to s \gamma$, $Z\to b\bar b$, and the $B^0\rightleftharpoons\bar{B}^0$ mixing, and use them to set limits on the lightest new particles for different number of sites. It turns out that the continuous result is rapidly reached when the extra dimension is discretized in about 10 to 20 sites only. For small number of sites the bounds placed on the usual continuous scenario can be reduced by roughly a factor of 10%--25%, which means that the new particles can be as light as $320 {GeV}$. Finally, we briefly discuss an alternative model in which fermions do not have additional modes.

Kinetics and modeling of 1-phenyl-1,2-propanedione hydrogenation

Kinetics and modeling of 1-phenyl-1,2-propanedione hydrogenation over cinchonidine-modified Pt/Al 2O 3 catalyst is reported. Hydrogenation experiments carried out in a pressurized autoclave (288 K, 1.2–6.5 bar hydrogen) revealed interesting kinetic effects which inspired the model development. The enantioselectivity towards the (R)-configuration, as well as the reaction rate and regioselectivity, depended on the modifier concentration having a maximum. The enantio- and regioselective effects were explained by the kinetic model, which assumes different number of sites for adsorption of the carbonyl groups of the 1-phenyl-1,2-propanedione as well as for the cinchonidine adsorbed in flat and tilted modes. The number of adsorption sites needed for the different species were obtained from molecular considerations and the hydrogenation rate constants were determined along with the adsorption parameters by non-linear regression analysis. A comparison of model predictions with experimental data revealed that the model accounts for the kinetic regularities.

Catalyst deactivation by site coverage through multi-site reaction mechanisms

Catalyst deactivation by site coverage is stochastically described for single- and dual-site reaction mechanisms for the main and the coking reactions of a catalytic process. Nonlinear relations between the deactivation function and the degree of site coverage are obtained for dual-site mechanisms. When the main and the coking reactions involve a different number of sites, the respective deactivation functions are not identical. When the coking reaction occurs on dual sites, the deactivation rate of the coking reaction depends on the number of sites in the cluster and therefore becomes structure sensitive, a feature that cannot be revealed by the classic phenomeno-logical approach.

On the development of a catalyst fouling model

A model of catalyst fouling is presented. It is based on a reaction network in which a hydrocarbon molecule is sequentially adsorbed onto a small group of active sites. Each intermediate species in this sequence is bonded to a different number of sites and treated as an independent fouling precursor. Thus, a series of parallel reactions are competing to foul the surface. The rate of each parallel step differs by an exponential factor equivalent to about 2 kcal/mol decrease in the fouling activation energy for each additional site involved in the rate determining step of fouling, and a simple power-model activity factor. The model provides an explanation of empirical fouling correlations and the apparent “variable reaction order” of fouling. The model is extended to include catalysts that are pretreated by a poisoning agent such as sulfur.

Monte Carlo study of U(1) and SU(2) gauge fields on four-dimensional random lattices

The asymptotic behavior of the random lattice gauge model is examined. Numerical results of the specific heat of U(1) and SU(2) on the random lattices of a different number of sites in four dimensions are reported and the phase structure is discussed.