Weak Clustering Research Articles

Whole Genome Sequencing Detects Minimal Clustering Among Escherichia coli Sequence Type 131-H30 Isolates Collected From United States Children's Hospitals.

We applied whole genome sequencing to identify putative transmission clusters among clinical multidrug-resistant Escherichia coli sequence type 131-H30 isolates from 4 United States children's hospitals. Of 126 isolates, 17 were involved in 8 putative transmission clusters; 4 clusters showed evidence of healthcare-associated epidemiologic linkages. Geographic clustering analyses showed weak geographic clustering.

Holographic Observations of Centimeter-Scale Nonuniformities within Marine Stratocumulus Clouds

Abstract Data collected with a holographic instrument [Holographic Detector for Clouds (HOLODEC)] on board the High-Performance Instrumented Airborne Platform for Environmental Research Gulfstream-V (HIAPER GV) aircraft from marine stratocumulus clouds during the Cloud System Evolution in the Trades (CSET) field project are examined for spatial uniformity. During one flight leg at 1190 m altitude, 1816 consecutive holograms were taken, which were approximately 40 m apart with individual hologram dimensions of 1.16 cm × 0.68 cm × 12.0 cm and with droplet concentrations of up to 500 cm−3. Unlike earlier studies, minimally intrusive data processing (e.g., bypassing calculation of number concentrations, binning, and parametric fitting) is used to test for spatial uniformity of clouds on intra- and interhologram spatial scales (a few centimeters and 40 m, respectively). As a means to test this, measured droplet count fluctuations are normalized with the expected standard deviation from theoretical Poisson distributions, which signifies randomness. Despite the absence of trends in the mean concentration, it is found that the null hypothesis of spatial uniformity on both spatial scales can be rejected with compelling statistical confidence. Monte Carlo simulations suggest that weak clustering explains this signature. These findings also hold for size-resolved analysis but with less certainty. Clustering of droplets caused by, for example, entrainment and turbulence, is size dependent and is likely to influence key processes such as droplet growth and thus cloud lifetime.

Functional brain network characteristics are associated with epilepsy severity in childhood absence epilepsy.

While cognitive impairments are not generally considered to be part of the childhood absence epilepsy (CAE) syndrome, some recent studies report cognitive, mainly attentional, deficits. Here we set out to investigate the whole brain functional network of children with CAE and controls. Furthermore, the possible relation of the functional network abnormalities with epilepsy and neurocognitive characteristics is studied. Seventeen children with childhood CAE (aged 9.2 ± 2.1 years) and 15 controls (aged 9.8 ± 1.8 years) were included. Resting state functional MRI was acquired to study the functional network. Using graph theoretical analysis, three global metrics of the functional network were investigated: the characteristic path length, the clustering coefficient, and the small-worldness. A multivariable linear regression model including age, sex, and subject motion as covariates was used to investigate group differences in the graph metrics. Subsequently, relations of the graph metrics with epilepsy and neurocognitive characteristics were assessed. Longer path lengths, weaker clustering and a lower small-world network topology were observed in children with CAE compared to controls. Moreover, longer path lengths were related to a longer duration of CAE and a higher number of absence seizure per hour. Clustering and small-worldness were not significantly related to epilepsy or neurocognitive characteristics. The organization of the functional network of children with CAE is less efficient compared to controls, and is related to disease duration. These preliminary findings suggest that CAE is associated with alterations in the functional network.

Mating system, population genetics, and phylogeography of the devil’s garden ant, Myrmelachista schumanni, in the Peruvian Amazon

Devil’s gardens are a remarkable feature of Amazonian rainforests. These clearings result from the cultivation of ant-plants by their symbiotic ant, Myrmelachista schumanni. Each devil’s garden is inhabited by a single M. schumanni colony, often with millions of workers and thousands of queens. Through a combination of field surveys and microsatellite genotyping, we examined M. schumanni colony structure, mating system, dispersal, and phylogeography. We discovered that the reproduction of M. schumanni is weakly seasonal, exhibits facultative polyandry, and involves split sex ratios potentially leading to sex-biased dispersal. Surprisingly, we observed only very weak clustering of genetic variation, either within or between devil’s gardens. We hypothesize that the apparent absence of geographical structure results from the unusually high level of genetic differentiation between colonies. This study adds intriguing observations to the scarce literature about the reproduction and phylogeography of Amazonian ants.

A Case Study on the Influence of Multiple Users on the Effective Channel in a Massive MIMO System

We investigate the importance of weak clusters when modeling a wireless massive MIMO channel. We do this by studying the influence of densely spaced terminals and the number of base-station antennas for a zero-forcing precoded massive MIMO system. In particular, we focus on the influence on the correlation and imbalance between the signals at the terminal antennas, the effective channel-gain, the eigenvalue distributions and the number of clusters. The study is based on measured radio-channels from terminal prototypes with integrated antennas connected to a massive MIMO testbed. We further evaluate the advantage of using block-diagonalized zero-forcing compared to conventional zero-forcing in a massive MIMO system. Unexpectedly, terminals with low antenna envelope correlation coefficient may benefit significantly from block-diagonal zero-forcing in a massive MIMO system. The main conclusion is that weaker clusters are important when modeling multi-user scenarios.

Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering for Genomic Predictive Pattern Analytics

Gene expression data clustering is a significant problem to be resolved as it provides functional relationships of genes in a biological process. Finding co-expressed groups of genes is a challenging problem. To identify interesting patterns from the given gene expression data set, a Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering (TCS-MSGABC) Model is proposed. TCS-MSGABC model comprises two processes namely feature selection and clustering. In first process, Tanimoto Coefficient Similarity Measurement based Feature selection (TCSM-FS) is introduced to identify relevant gene features based on the similarity value for performing the genomic expression clustering. Tanimoto Coefficient Similarity Value ranges from ‘ ’ to ‘ ’ where ‘ ’ is highest similarity. The gene feature with higher similarity value is taken to perform clustering process. After feature selection, Mean Shift Gentle Adaptive Boosted Clustering (MSGABC) algorithm is carried out in TCS-MSGABC model to cluster the similar gene expression data based on the selected features. The MSGABC algorithm is a boosting method for combining the many weak clustering results into one strong learner. By this way, the similar gene expression data are clustered with higher accuracy with minimal time. Experimental evaluation of TCS-MSGABC model is carried out on factors such as clustering accuracy, clustering time and error rate with respect to number of gene data. The experimental results show that the TCS-MSGABC model is able to increases the clustering accuracy and also minimizes clustering time of genomic predictive pattern analytics as compared to state-of-the-art works.

Phylogenetic structure and formation mechanism of shrub communities in arid and semiarid areas of the Mongolian Plateau.

The mechanisms of species coexistence within a community have always been the focus in ecological research. Community phylogenetic structure reflects the relationship of historical processes, regional environments, and interactions between species, and studying it is imperative to understand the formation and maintenance mechanisms of community composition and biodiversity. We studied the phylogenetic structure of the shrub communities in arid and semiarid areas of the Mongolian Plateau. First, the phylogenetic signals of four plant traits (height, canopy, leaf length, and leaf width) of shrubs and subshrubs were measured to determine the phylogenetic conservation of these traits. Then, the net relatedness index (NRI) of shrub communities was calculated to characterize their phylogenetic structure. Finally, the relationship between the NRI and current climate and paleoclimate (since the Last Glacial Maximum, LGM) factors was analyzed to understand the formation and maintenance mechanisms of these plant communities. We found that desert shrub communities showed a trend toward phylogenetic overdispersion; that is, limiting similarity was predominant in arid and semiarid areas of the Mongolian Plateau despite the phylogenetic structure and formation mechanisms differing across habitats. The typical desert and sandy shrub communities showed a significant phylogenetic overdispersion, while the steppified desert shrub communities showed a weak phylogenetic clustering. It was found that mean winter temperature (i.e., in the driest quarter) was the major factor limiting steppified desert shrub phylogeny distribution. Both cold and drought (despite having opposite consequences) differentiated the typical desert to steppified desert shrub communities. The increase in temperature since the LGM is conducive to the invasion of shrub plants into steppe grassland, and this process may be intensified by global warming.

Cosmology with stacked cluster weak lensing and cluster–galaxy cross-correlations

ABSTRACT Cluster weak lensing is a sensitive probe of cosmology, particularly the amplitude of matter clustering σ8 and matter density parameter Ωm. The main nuisance parameter in a cluster weak lensing cosmological analysis is the scatter between the true halo mass and the relevant cluster observable, denoted $\sigma _{\ln M_\mathrm{ c}}$. We show that combining the cluster weak lensing observable ΔΣ with the projected cluster–galaxy cross-correlation function wp,cg and galaxy autocorrelation function wp,gg can break the degeneracy between σ8 and $\sigma _{\ln M_\mathrm{ c}}$ to achieve tight, per cent-level constraints on σ8. Using a grid of cosmological N-body simulations, we compute derivatives of ΔΣ, wp,cg, and wp,gg with respect to σ8, Ωm, $\sigma _{\ln M_\mathrm{ c}}$, and halo occupation distribution (HOD) parameters describing the galaxy population. We also compute covariance matrices motivated by the properties of the Dark Energy Survey cluster and weak lensing survey and the BOSS CMASS galaxy redshift survey. For our fiducial scenario combining ΔΣ, wp,cg, and wp,gg measured over 0.3−30.0 h−1 Mpc, for clusters at z = 0.35−0.55 above a mass threshold Mc ≈ 2 × 1014 h−1 M⊙, we forecast a $1.4{{\ \rm per\ cent}}$ constraint on σ8 while marginalizing over $\sigma _{\ln M_\mathrm{ c}}$ and all HOD parameters. Reducing the mass threshold to 1 × 1014 h−1 M⊙ and adding a z = 0.15−0.35 redshift bin sharpens this constraint to $0.8{{\ \rm per\ cent}}$. The small-scale (rp < 3.0 h−1 Mpc) ‘mass function’ and large-scale (rp > 3.0 h−1 Mpc) ‘halo-mass cross-correlation’ regimes of ΔΣ have comparable constraining power, allowing internal consistency tests from such an analysis.

Dark Energy Survey Year 1 results: validation of weak lensing cluster member contamination estimates from P(z) decomposition

ABSTRACT Weak lensing source galaxy catalogues used in estimating the masses of galaxy clusters can be heavily contaminated by cluster members, prohibiting accurate mass calibration. In this study, we test the performance of an estimator for the extent of cluster member contamination based on decomposing the photometric redshift P(z) of source galaxies into contaminating and background components. We perform a full scale mock analysis on a simulated sky survey approximately mirroring the observational properties of the Dark Energy Survey Year One observations (DES Y1), and find excellent agreement between the true number profile of contaminating cluster member galaxies in the simulation and the estimated one. We further apply the method to estimate the cluster member contamination for the DES Y1 redMaPPer cluster mass calibration analysis, and compare the results to an alternative approach based on the angular correlation of weak lensing source galaxies. We find indications that the correlation based estimates are biased by the selection of the weak lensing sources in the cluster vicinity, which does not strongly impact the P(z) decomposition method. Collectively, these benchmarks demonstrate the strength of the P(z) decomposition method in alleviating membership contamination and enabling highly accurate cluster weak lensing studies without broad exclusion of source galaxies, thereby improving the total constraining power of cluster mass calibration via weak lensing.

A37 Transmission success of dengue virus type 1 lineages in a dynamic virus population: An evolutionary view

Arbovirus transmission involves an interplay between host, virus, and environmental factors. Because of the complexity of interactions, the transmission success of arboviruses could either be a function of viral fitness or be stochastic. In the present study, using 1,963 envelope (E) gene sequences and 239 whole genomes, we conducted a large-scale molecular epidemiological analysis of a dengue virus type 1 (DENV-1) population to understand the transmission success, evolution, and dispersal patterns of different lineages of DENV-1 circulating in Singapore from 2011 to 2016. The study population was highly dynamic and heterogeneous. However, only a handful of genetically distinct strains (n = 6) established sustained transmission, but at variable levels of dominance. Phylogeographic analysis revealed a weak spatial clustering and 35 well-supported diffusion pathways, implying widespread and complex dispersal of these strains in local settings. Yet, the dominant strains were neither evolving faster than less dominant ones nor under positive selection. These observations suggested that lineage dominance was likely to be stochastic and opportunistically driven by non-viral factors such as host immune pressure and vector abundance. Our findings, therefore, emphasize the implications of understanding the vector and human factors in parallel to virus dynamics on continuing efforts to control the arbovirus disease transmission in endemic regions.

Spectroscopy of Globular Clusters in the Spheroidal Dwarf Galaxy IKN

Moderate resolution spectra of four globular clusters in the dwarf spheroidal galaxy IKN obtained with the 6-m telescope of the Special Astrophysical Observatory have been used to determine the radial velocities, ages, and metallicities of the clusters, and also to derive the first approximate estimates of the abundances of Mg, Ca, and C. Cross-correlation with radial-velocity standards, fitting of the observed spectra with model spectra, diagnostic diagrams based on the Lick absorption indices, and comparison of the spectra and absorption indices with those of Galactic globular clusters are applied. The integrated spectrum of the two bright clusters IKN4 and IKN5, which are close to the center of the galaxy in projection on the celestial sphere, yields the heliocentric radial velocity Vh = 38 ± 30 km/s, age T =12.6 ± 2 Gyr, metallicity [Fe/H] = −2.1 ± 0.2 dex, and abundance of α-process elements [α/Fe] ∼ 0.5 dex. The integrated spectrum of the two weaker clusters IKN1 and IKN3, which are far from the center of the galaxy, yielded the radial velocity Vh = −39 ± 50 km/s. Despite the low signal-to-noise ratio in the integrated spectrum for IKN1 and IKN3, all the analysis methods applied indicate that these two objects have approximately the same age and metallicity as IKN4 and IKN5. According to the measured Lick indices \({{\rm{H}}_{\delta {\rm{F}}}}\) and Hβ, the studied globular clusters in IKN have blue horizontal branches.

Spatial Organization of Human Population and Wastewater Treatment Plants in Urbanized River Basins

AbstractDischarge from multiple wastewater treatment plants (WWTPs) distributed in urbanized river basins contributes to impairments of river water‐quality and aquatic ecosystem integrity, with size and location of WWTPs determined by population distribution within a river basin. Here we used geo‐referenced data for WWTPs in Germany to investigate the spatial organization of three attributes of interest in this study: population, population equivalents (the aggregated population served by each WWTP), and the number/sizes of WWTPs. To this end, we selected as case studies three large urbanized river basins (Weser, Elbe, and Rhine), home to about 70% of the population in Germany. We employed fractal river networks as structural platforms to examine the spatial patterns from two perspectives: spatial hierarchy (stream order) and patterns along longitudinal flow paths (width function). Moreover, we proposed three dimensionless scaling indices to quantify (1) human settlement preferences by stream order, (2) non‐sanitary flow contribution to total wastewater treated at WWTPs, and (3) degree of centralization in WWTPs locations. Across the three river basins, we found scale‐invariant distributions for each of the three attributes with stream order, quantified using extended Horton scaling ratios. We found a weak downstream clustering of population in the three basins. Variations in population equivalent clustering among different class‐sizes of WWTPs reflected the size, number, and locations of urban agglomerations in these river basins. We discussed the applicability of this approach to other large urbanized basins to analyze spatial organization of population and WWTPs.

CO2 emissions patterns of 26 cities in the Yangtze River Delta in 2015: Evidence and implications

As a country with the highest CO2 emissions and at the turning point of socio-economic transition, China's effort to reduce CO2 emissions will be crucial for climate change mitigation. Yet, due to geospatial variations of CO2 emissions in different cities, it is important to develop city-specific policies and tools to help control and reduce CO2 emissions. The key question is how to identify and quantify these variations so as to provide reference for the formulation of the corresponding mitigation policies. This paper attempts to answer this question through a case study of 26 cities in the Yangtze River Delta. The CO2 emissions pattern of each city is measured by two statistics: Gini coefficient to describe its quantitative pattern and Global Moran's I index to capture its spatial pattern. It is found that Gini coefficients in all these cities are all greater than 0.94, implying a highly polarized pattern in terms of quantity; and the maximum value for Global Moran's I index is 0.071 with a standard deviation of 0.021, indicating a weak spatial clustering trend but strong difference among these cities. So, it would be more efficient for these cities at current stage to reduce CO2 emissions by focusing on the large emission sources at certain small localities, particularly the very built-up areas rather than covering all the emission sources on every plot of the urban prefectures. And by a combination of these two metrics, the 26 cities are regrouped into nine types with most of them are subject to type HL and ML. These reclassification results then can serve as reference for customizing mitigation policies accordingly and positioning these policies in a more accurate way in each city.

Roof-Crush Protection Design of Automotive Bodies Using Clustering and Pattern Recognition

Computer-aided engineering (CAE) tools play an indispensable role in the vehicle development process. However, it is difficult to accurately predict the relationships and behavior of automotive bodies in vehicle crashes owing to high-order nonlinearity and numerous design variables of the automotive body structure. In this study, clustering and pattern recognition techniques were used to develop a novel optimization design of an automotive body considering roof crushing by vehicle rollover. The large-scale data were clustered to find the strong and weak clusters, and new response surface models were acquired by clustering analysis to achieve better performance than the response surface model of traditional optimization. For an efficient robust design, clusters with weak performance were excluded from the optimum solution. Finally, it was confirmed that the solutions by the proposed optimization technique were better than those obtained by the traditional optimum method based on a comparative analysis by various cluster combinations.

The evolution of life-history theory: a bibliometric analysis of an interdisciplinary research area

The term ‘life-history theory’ is a familiar label in several disciplines. Life-history theory has its roots in evolutionary models of the fitness consequences of allocating energy to reproduction, growth and self-maintenance across the life course. Increasingly, the term is also used in the conceptual framing of psychological and social-science studies. As a scientific paradigm expands its range, its parts can become conceptually isolated from one another, even to the point that it is no longer held together by a common core of shared ideas. Here, we investigate the literature invoking the term ‘life-history theory’ using quantitative bibliometric methods based on patterns of shared citation. Results show that the literature up to and including 2010 was relatively coherent: it drew on a shared body of core references and had only weak cluster divisions running along taxonomic lines. The post-2010 literature is more fragmented: it has more marked cluster boundaries, both between the human and non-human literatures, and within the human literature. In particular, two clusters of human research based on the idea of a fast–slow continuum of individual differences are bibliometrically isolated from the rest. We also find some evidence suggesting a decline over time in the incidence of formal modelling. We point out that the human fast–slow continuum literature is conceptually closer to the non-human ‘pace-of-life’ literature than it is to the formal life-history framework in ecology and evolution.

The Wendelstein Weak Lensing (WWL) pathfinder: accurate weak lensing masses for Planck clusters

ABSTRACT We present results from the Wendelstein Weak Lensing (WWL) pathfinder project, in which we have observed three intermediate-redshift Planck clusters of galaxies with the new 30 arcmin × 30 arcmin wide field imager at the 2 m Fraunhofer Telescope at Wendelstein Observatory. We investigate the presence of biases in our shear catalogues and estimate their impact on our weak lensing mass estimates. The overall calibration uncertainty depends on the cluster redshift and is below 8.1–16 per cent for z ≈ 0.27–0.77. It will decrease with improvements on the background sample selection and the multiplicative shear bias calibration. We present the first weak lensing mass estimates for PSZ1 G109.88+27.94 and PSZ1 G139.61+24.20, two Sunyaev-Zeldovich (SZ)-selected cluster candidates. Based on Wendelstein colours and SDSS (Sloan Digital Sky Survey) photometry, we find that the redshift of PSZ1 G109.88+27.94 has to be corrected to z ≈ 0.77. We investigate the influence of line-of-sight structures on the weak lensing mass estimates and find upper limits for two groups in each of the fields of PSZ1 G109.88+27.94 and PSZ1 G186.98+38.66. We compare our results to SZ and dynamical mass estimates from the literature, and in the case of PSZ1 G186.98+38.66 to previous weak lensing mass estimates. We conclude that our pathfinder project demonstrates that weak lensing cluster masses can be accurately measured with the 2 m Fraunhofer Telescope.

Factors Influencing Sun Basking in Red Wood Ants (Formica polyctena): a Field Experiment on Clustering and Phototaxis

We monitored nest interior and surface temperatures together with two aspects of the sun-basking behavior of wood ants: aggregation (by regular monitoring of occurrence and density of clusters) and phototaxis (tendency to move from shade to sun or vice versa, recorded as a response to artificial shading during sunny weather), using ten nests of Formica polyctena near Vimperk Czechia. Dense clusters occurred only in spring; weak clusters rarely occur the rest of the year. Statistical analysis showed that timing of dense clusters is affected by both environmental parameters (light intensity, insulation status of the nest) and internal nest factors (inner nest temperature, ant population size, nest volume). Phototaxis changed from positive to negative during the year. In spring, ant workers performed the sun basking readily and when shaded moved into the sun. In summer, however, the ants avoided sun basking and aggregated in the shade. The shift from sun basking to sun avoidance was driven mainly by nest surface temperature. The switch occurred when the temperature of sun-exposed nest surface reached 42.8 °C, which is two degrees higher than the experimentally measured lethal temperature (LD50) for red wood ants. This shows that two basic components of sun basking, aggregation and sun exposure, are driven by a different set of environmental conditions and their interplay is likely to maintain balance between the needs of the colony to heat up the nest and risk for individual workers arising from overheating during prolonged sun exposure.

Abstract P3-07-06: Evidence for tumor heterogeneity and clonal evolution during invasive progression in breast cancer

Abstract Purpose: Intratumoral heterogeneity is well recognized to be an important driver of treatment resistance and metastasis. We undertook this N of three study to measure the degree of heterogeneity in three large preinvasive lesions, all with invasive components to determine the relationship between tumor heterogeneity, spatial distribution, clonal evolution, and invasive progression. Methods: We identified patients A, B, C with extensive DCIS measuring 7.5 cm, 6 cm, and 7 cm associated with 0.3 cm, 3.8cm, and 3.4 cm of an invasive component and 0, 7 and 1 positive lymph node, respectively. We sequenced the tumor sample for Case A from 32 unique blocks with precise geospatial localization; invasive cancer was identified in 3 of 32 blocks. Case B had 26 blocks sequenced with invasive cancer in 13 of 26 blocks. Case C had 23 blocks sequenced with invasive in 11 of 23 blocks. For germline reference, we sequenced DNA from an uninvolved tissue from each case. NGS libraries were made from FFPE derived DNA (20-40ng) for full exome sequencing. Variant calling was performed by GATK HaplotypeCaller, Platypus and Mutect. Identified somatic mutations were annotated with Oncotator and pathway enrichment analysis was performed with Bioconductor. To investigate the clonal evolution and progression history, phylogenetic trees were constructed in R and sub-clonal analysis was performed with Treeomics. Results: The sequence data was analyzed with Platypus, MuTect and GATK HaplotypeCaller. The somatic mutation sites were concatenated into one sequence for each sample. Both neighbor-joining trees and maximum parsimony trees were built for each case. Phylogenetic analysis and sub-clonal analysis support the multi-clonal invasion model of invasive cells, in which invasive cancer can evolve from multiple clades, either early or late in the evolutionary history, independently. Dense sampling allowed reconstruction of the temporal order of mutations that accumulated in the cell lineage of the invasive cancers. Furthermore, phylogeny and sub-clone spatial analysis revealed that distant regions may be closely genetically related and showed a weak spatial sub-clone clustering pattern, which is consistent with the predictions of Big Bang model. For driver genes, we find that except for SETD2 in Case B, the majority of driver gene mutations are sub-clonal. Somatic mutations on ATP-binding cassette (ABC) transporter pathway was found in all cases. Conclusions: Extensive sampling and sequencing of tumors yields important insights about tumor heterogeneity and tumor progression of DCIS to invasive cancer. Variable invasive propensity was identified, with foci of invasion were geospatially associated with preinvasive regions of progressively higher mutational load. Citation Format: Ding Y, Marks J, King L, Hardman T, Hall A, Mallo D, Rodrigo A, Maley C, Hwang S. Evidence for tumor heterogeneity and clonal evolution during invasive progression in breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-07-06.

The role of the tachyonic instability in Horndeski gravity

The tachyonic instability is commonly associated with mass terms having a negative sign and evolving faster than the Hubble scale, leading to an unstable low-k regime. In the cosmological exploration of modified gravity, it is seldom taken into account, with more focus given to the popular no-ghost and no-gradient conditions. The latter though are intrinsically high-k statements. Here we combine all three conditions into a full set of requirements that we show to guarantee stability on the whole range of cosmological scales. We then explore the impact of the different conditions on the parameter space of scalar-tensor gravity, with particular emphasis on the no-tachyon one. We focus on Horndeski gravity and also consider separately the two subclasses of f(R) and Generalized Brans Dicke theories. We identify several interesting features, for instance in the parameter space of designer f(R) on a wCDM background, shedding light on previous findings. When looking at the phenomenological functions Σ and μ, associated to the weak lensing and clustering potential respectively, we find that in the case of Generalized Brans Dicke the no-tachyon condition clearly cuts models with μ , Σ>1. This effect is less prevalent in the Horndeski case due to the larger amount of free functions in the theory.

The Evaluation Method of Particle Clustering Phenomena in RAP

The particle clustering phenomena in reclaimed asphalt pavement (RAP) particles is one of the most important factors to affect the efficient recycling of asphalt concrete. In this study, the particle composition, clustering degree, crushing properties and clustering stability of RAP were studied by extraction test and cantabro-crushing test. It was found that the particles above 4.75 mm were composed mainly of small particles with a large degree of cluster and poor stability. The coarse particles (>4.75 mm) had a great influence on the variation of 4.75 mm sieve. Quantitative indexes of Percentage Loss rate (PL) and Stability Index (w) were proposed to evaluate the clustering degree and the stability of RAP. It provided a meaningful reference for the comparison of different RAP and the different crushing processes. In addition, the RAP could be divided into three kinds of structures, including weak cluster structure, strong cluster structure and old aggregate. The process of crushing the RAP was divided into three stages, which are weak structure-dominated, strong structure-dominated and the broken old aggregate. The weak structure had the largest degree of cluster and worst stability, resulting in a large variability of RAP, and it should be avoided in the crushing process.