Low Local Density Research Articles

Negative density-dependent parasitism in a group-living carnivore.

Animals living at high population densities commonly experience greater exposure to disease, leading to increased parasite burdens. However, social animals can benefit immunologically and hygienically from cooperation, and individuals may alter their socio-spatial behaviour in response to infection, both of which could counteract density-related increases in exposure. Consequently, the costs and benefits of sociality for disease are often uncertain. Here, we use a long-term study of a wild European badger population (Meles meles) to investigate how within-population variation in host density determines infection with multiple parasites. Four out of five parasite taxa exhibited consistent spatial hotspots of infection, which peaked among badgers living in areas of low local population density. Combined movement, survival, spatial and social network analyses revealed that parasite avoidance was the likely cause of this negative density dependence, with possible roles for localized mortality, encounter-dilution effects, and micronutrient-enhanced immunity. These findings demonstrate that animals can organize their societies in space to minimize parasite infection, with important implications for badger behavioural ecology and for the control of badger-associated diseases.

Modeling Cyclic Voltammetry of Reduced Graphene Oxide/ Aramid Nanofiber Structural Supercapacitors

Cyclic voltammetry is an important technique to characterize the electrochemical performance of electrical energy storage devices under various conditions. The cyclic voltammetry characterization also can be obtained through physical modeling and simulations to improve the understanding of experimental measurements and to guide the optimization of the electrode and electrolyte. In this study, the physics based generalized Poisson-Nernst-Planck model is used to simulate cyclic voltammetry measurements of reduced graphene oxide (rGO)/ aramid nanofiber (ANF) structural supercapacitors through a multiphysics computational modeling. The cyclic voltammetry is simulated for a supercapacitor with a binary electrolyte and two identical electrodes with “layered” and “house of cards” nanostructures. The capacitance and resistance values are determined for different compositions of aramid nanofiber. The modeling results show a very good agreement with experimental measurements. The results demonstrate an ideal supercapacitor's behavior which are compared to various electrode kinetics such as Butler-Volmer equation and Tafel equation with very low local current density. Modeling cyclic voltammetry can provide useful information about kinetics and thermodynamics of structural supercapacitors which enables better design and control over the performance and life cycle of the supercapacitors. Comparison of predicted and measured voltammograms enables determination of unknown system parameters and material properties such as diffusion coefficient, resistance and capacitance of the structural supercapacitors. Figure 1

A New General Paradigm for Understanding and Preventing Li Metal Penetration through Solid Electrolytes

Summary The use of lithium (Li) or sodium (Na) metal anodes together with highly ion-conductive solid electrolytes (SEs) could provide batteries with a step improvement in volumetric and gravimetric energy densities. Unfortunately, these SEs face significant technical challenges, in large part because Li and Na dendrites can penetrate through SEs, leading to short circuits. The ability of such a soft material (Li or Na metal) to penetrate through ceramic is surprising from the point of view of models widely used in the Li-battery field. We introduce a concept, new to the battery field, for preventing penetration of lithium dendrites through SEs by putting the SE surfaces into a state of residual compressive stress. For a sufficiently high compressive stress, cracks have difficulty forming, and cracks that do form are forced to close, inhibiting dendrite penetration. This approach is widely used to solve commercially important stress corrosion cracking problems in metals and static fatigue problems in ceramics and glasses (e.g., Gorilla Glass). However, the technique will not be useful for SEs if the Li-ion transport rate through a SE is substantially reduced when the SE is under compression. Our molecular dynamics calculations for Li-ion transport through a common SE demonstrate that the introduction of even very high residual compressive stresses (∼10 GPa) has only a modest effect on Li-ion transport kinetics, suggesting that this approach is viable and capable of providing a new paradigm for developing high-performance and mechanically stable SEs.

Time‐Traveling Larvae Ensure Survival in Small, Synchronously Fluctuating Populations

The tiny gall midge Contarinia vincetoxici is a long-lived insect, although its active life, as adult–egg–growing larva, is brief. Eggs are laid in young flower buds of a swallow-wort, which then develop into conspicuous galls harboring the developing larvae. Mature larvae fall to the ground where they enter a long dormancy, which may last over a decade. Gall populations were monitored in a large host plant patch system over 12 years. Despite poor colonization rates, low local gall densities, and large synchronous density fluctuations, almost all patches seemed to be continually inhabited due to the large hidden underground pool of time-traveling larvae. This photograph illustrates the article “Time-traveling larvae ensure survival in small, synchronously fluctuating populations” by Christer Solbreck and Olof Widenfalk published in Ecosphere. https://doi.org/10.1002/ecs2.3086.

Structure‐Controlled Li Metal Electrodes for Post‐Li‐Ion Batteries: Recent Progress and Perspectives

AbstractLi dendrite formation deteriorates cyclability and poses a safety hazard, hindering the widespread use of Li metal as the ultimate anode material for post‐Li‐ion batteries. Hence, the underlying reasons of this phenomenon and ways to suppress it have been extensively investigated, which has resulted in the establishment of corresponding theoretical models and their practical applications. Herein, several representative models (e.g., the Chazalviel model) of Li dendrite growth are explained, and the key technologies of structure‐controlled framework and Li metal usage allowing to realize low local current densities and improved electrochemical performance are covered with the practical (dis)advantages due to material characteristics, electrode and cell design, and even manufacturing processes. In particular, the use of Li metal powder and patterned Li metal is discussed in conjunction with corresponding applications (e.g., protection layers, functional additives, and salts in the electrolyte) and advantages.

McDPC: multi-center density peak clustering

Density peak clustering (DPC) is a recently developed density-based clustering algorithm that achieves competitive performance in a non-iterative manner. DPC is capable of effectively handling clusters with single density peak (single center), i.e., based on DPC’s hypothesis, one and only one data point is chosen as the center of any cluster. However, DPC may fail to identify clusters with multiple density peaks (multi-centers) and may not be able to identify natural clusters whose centers have relatively lower local density. To address these limitations, we propose a novel clustering algorithm based on a hierarchical approach, named multi-center density peak clustering (McDPC). Firstly, based on a widely adopted hypothesis that the potential cluster centers are relatively far away from each other. McDPC obtains centers of the initial micro-clusters (named representative data points) whose minimum distance to the other higher-density data points are relatively larger. Secondly, the representative data points are autonomously categorized into different density levels. Finally, McDPC deals with micro-clusters at each level and if necessary, merges the micro-clusters at a specific level into one cluster to identify multi-center clusters. To evaluate the effectiveness of our proposed McDPC algorithm, we conduct experiments on both synthetic and real-world datasets and benchmark the performance of McDPC against other state-of-the-art clustering algorithms. We also apply McDPC to perform image segmentation and facial recognition to further demonstrate its capability in dealing with real-world applications. The experimental results show that our method achieves promising performance.

Time-dependent potential impurity in a topological insulator

We consider periodically driven potential impurities coupled to the surface states of a two-dimensional topological insulator. The problem is addressed by means of two models, out which the first model is an effective continuum Hamiltonian for the surface states, whereas the Kane-Mele lattice model is our second approach. While both models result in drastic changes in the local density of electron states with increasing amplitude and frequency of the driving field, the linearly low energy local density of electron states remains in the continuum model, however, with an increased Fermi velocity. The spectrum of the continuum model remains gapless under the emergence of new impurity resonances near the Fermi energy. The Kane-Mele lattice model represents a finite size system, with edge states appearing at the boundary of the system. We, thus, consider the impurity at two different positions, one at the boundary and one at the center of the lattice. In the former case, a reduction and broadening of the low energy local density of electron states result with increasing amplitude of the driving field. On the other hand, there are no new resonances emerging in the spectrum. In the latter case, the spectrum is gapped both in the absence of the impurity as well as for weak amplitudes of the driving field, while the gap tends to fill up with impurity states with increasing amplitude.

Bayesian quantification of ecological determinants of outcrossing in natural plant populations: Computer simulations and the case study of biparental inbreeding in English yew.

The mating system is a central parameter of plant biology because it shapes their ecological and evolutionary properties. Therefore, determining ecological variables that influence the mating system is important for a deeper understanding of the functioning of plant populations. Here, using old concepts and recent statistical developments, we propose a new statistical tool to make inferences about ecological determinants of outcrossing in natural plant populations. The method requires codominant genotypes of seeds collected from maternal plants within different locations. Using extensive computer simulations, we demonstrated that the method is robust to the issues expected for real-world data, including the Wahlund effect, inbreeding and genotyping errors such as allele dropout and allele misclassification. Furthermore, we showed that the estimates of ecological effects and outcrossing rates can be severely biased if genotyping errors and genetic differentiation are not treated explicitly. Application of the new method to the case study of a dioecious tree (Taxus baccata) allowed revealing that female trees that grow in lower local densities have a greater tendency towards mating with relatives. Moreover, we also demonstrated that biparental inbreeding is higher in populations that are characterized by a longer mean distance between trees and a smaller mean trunk perimeter. We found these results to agree with both the theoretical predictions and the history of English yew.

Spatial Density Peak Clustering for Hyperspectral Image Classification With Noisy Labels

The “ noisy label ” problem is one of the major challenges in hyperspectral image (HSI) classification. In order to address this problem, a spatial density peak (SDP) clustering-based method is proposed to detect mislabeled samples in the training set. Specifically, the proposed methods consist of the following steps: first, the correlation coefficients among the training samples in each class are estimated. In this step, instead of measuring the correlation coefficients by considering individual samples, all neighbor samples or $K$ representative neighbor samples in a local window surrounding each training sample are considered. By this way, the spatial contextual information could be used, and two versions of the proposed method, i.e., measuring the correlation coefficients using all neighbor samples or $K$ representative samples, are referred as SDP and $K$ -SDP, respectively. Second, with the correlation coefficients calculated above, the local density of each training sample can be obtained by the DP clustering algorithm. Finally, those mislabeled samples which usually have lower local densities in each class are able to be identified by a defined decision function. The effectiveness of the proposed detection method is evaluated using a series of spectral and spectral-spatial classification methods on several real hyperspectral data sets.

Structural features of high-local-density water molecules: Insights from structure indicators based on the translational order between the first two molecular shells.

The two-liquids scenario for liquid water assumes the existence of two competing preferential local molecular structural states characterized by either low or high local density. While the former is expected to present good local order thus involving privileged structures, the latter is usually regarded as conforming a high-entropy unstructured state. A main difference in the local arrangement of such "classes" of water molecules can be inferred from the degree of translational order between the first and second molecular shells. This is so, since the low-local-density molecules present a clear gap between the first two shells while in the case of the high-local-density ones, one or more molecules from the second shell have collapsed toward the first one, thus populating the intershell region. Some structural indicators, like the widely employed local structure index and the recently introduced ζ index, have been devised precisely on the basis of this observation, being successful in detecting well-structured low-local-density molecules. However, the nature of the high-local-density state has been mainly disregarded over the years. In this work we employ molecular dynamics simulations for two water models (the extended simple point charge model and the five-site model) at the liquid and supercooled regimes combined with the inherent dynamics approach (energy minimizations of the instantaneous configurations) in order to both rationalize the detailed structural and topological information that these indicators provide and to advance in our understanding of the high-density state.

The stability of spruce treelines on the eastern Tibetan Plateau over the last century is explained by pastoral disturbance

Most Tibetan people make their living through pastoralism, with livestock grazing being widespread throughout the Tibetan Plateau. The Tibetan Plateau is also home to the northern hemisphere’s highest treelines but the effects of human activities, in particular pastoralism, on these treelines and their influence on the treeline responses to climate change remain unclear. To disentangle the effects of pastoral disturbances and climate on treeline dynamics, we surveyed three plots at grazing-disturbed Balfour spruce (Picea likiangensis var. balfouriana) treelines in the Yushu region (eastern Tibetan Plateau) and compared this data with published results of five undisturbed spruce treeline sites. Spatiotemporal changes in the position and structure of treelines during the past 400 years were reconstructed using dendrochronology. Tree recruitment was significantly linked to long-term summer and winter temperature. Under low local population densities and grazing pressure but consistent climate warming, disturbed treelines shifted upwards by 23.9–82.6 m during the 1611–1910 period. However, under higher local population densities, increased grazing pressure and climate warming over the last century, disturbed treelines showed no significant upward shift. In contrast, undisturbed treelines shifted upward by 25–68.5 m over the last century, thus implying that pastoralism was suppressing treeline expansion in disturbed sites. Together, these results underscore the importance of anthropogenic disturbances in influencing the structure and position of alpine treelines on the Tibetan Plateau. The role played by human disturbance should be carefully considered when evaluating the potential for treeline advance under future climate warming.

A Novel Density Peaks Clustering Halo Node Assignment Method Based on K-Nearest Neighbor Theory

The density peaks clustering (DPC) algorithm is not sensitive to the recognition of halo nodes. The halo nodes at the edge of the density peaks clustering algorithm has a lower local density. The outliers are distributed in halo nodes. The novel halo identification method based on density peaks clustering algorithm utilize the advantage of DBSCAN algorithm to quickly identify outliers, which improved the sensitivity to halo nodes. However, the identified halo nodes cannot be effectively assigned to adjacent clusters. Therefore, this paper will use K-nearest neighbor (KNN) algorithm to classify the identified halo nodes. K-nearest neighbor is the simplest and most efficient classification method. The KNN algorithm has the advantages of high accuracy, insensitivity to outliers and no input hypothesis data. Hence, we proposed a novel density peaks clustering halo node assignment algorithm based on K-nearest neighbor theory (KNN-HDPC). KNN-HDPC can grasp the internal relations between outliers and cluster nodes more deeply, so as to dig out the deeper relations between nodes. Experimental results demonstrate that the proposed algorithm can effectively cluster and reclassify a large number of complex data. We can quickly dig out the potential relationship between noise points and cluster points. The improved algorithm has higher clustering accuracy than the original DPC algorithm, and essentially has more robust clustering results.

Effects of resonant scattering of the Si IV doublet near 140 nm in a solar active region

Aims. In a previous study we analysed the C IV 1548.189 Å and 1550.775 Å lines observed with the Solar Ultraviolet Measurements of Emitted Radiation (SUMER), showing cases where the 1548.189 Å spectral profile was noticeably different from the 1550.775 Å one, profiles that we dubbed differentially shaped profiles. We explained this differential behaviour by an important radiative contribution, affecting multiple plasma motions happening at the instrument sub-resolution scale. In the present study we examine more general cases where radiative effects may contribute to the emission from the transition region of an active region. Here we analyse the lines Si IV 1393.757 Å and 1402.772 Å observed with the Interface Region Imaging Spectrograph (IRIS). Methods. We study active region NOAA 12529, observed with IRIS on 18 April 2016. Using sorting techniques we selected individual profiles for which the intensity line ratio 1393.757 Å/1402.772 Å is significantly higher or lower than 2 and we also tracked differentially shaped profiles. We analyse the physical conditions that create these profiles and in some cases we estimate electron densities. Results. We found more than 4000 individual profiles with line ratios higher than 2, about 500 profiles for which the line ratio is in the range 1.3–1.6, and 15 differentially shaped profiles. Line ratios higher than 2, are found along loops, and mostly at the y = 250 to 300″ part of the plage. There, we estimated the incident radiation and derived electron densities that can vary from 109 to a few times 1011 cm−3, depending on the plasma temperature. For the low line ratios, the sources are concentrated at the periphery of the active region plage, mostly along fibrils and present optical depths, τ, between 1.5 and 3. in most cases. The electron densities calculated from these Si IV profiles are comparable with electron densities derived using the O IV 1399.766 Å-1401.163 Å ratios. Conclusions. We found that about 2.4% of the individual profiles for which we can perform a Gaussian fit present a line ratio higher than 2. In profiles with a high line ratio, the resonant scattering appears to be due to the combination of an average incident radiation field with a relatively low local electron density and not due to the vicinity of an ephemeral strong light source. As far as low intensity ratios are concerned, non-negligible optical depths are found at the edge of the plage, near the footpoints of fibrils that are oriented towards quiet Sun areas, where the electron density can be as high as (7 − 9) × 1011 cm−3 if we assume a plasma in ionization equilibrium.

Ultrahigh-Capacity and Long-Life Lithium-Metal Batteries Enabled by Engineering Carbon Nanofiber-Stabilized Graphene Aerogel Film Host.

A safe, high-capacity, and long-life Li metal anode is highly desired due to recent developments in high-energy-density Li-metal batteries. However, there are still rigorous challenges associated with the undesirable formation of Li dendrites, lack of suitable host materials, and unstable chemical interfaces. Herein, a carbon nanofiber-stabilized graphene aerogel film (G-CNF film), inspired by constructional engineering, is constructed. As the host material for Li deposition, the G-CNF film features a large surface area, porous structure, and a robust skeleton that can render low local current density. This allows for dendrite-free Li deposition and mitigation of problems associated with large volume change. Importantly, the G-CNF film can keep high Li plating/stripping efficiency at nearly 99% for over 700 h with an areal capacity of 10 mA h cm-2 (the specific capacity up to 2588 mA h g-1 based on the total mass of carbon host and Li metal). The symmetric cells can stably run for more than 1000 h with low voltage hysteresis. The full cell with the LiFePO4 cathode also delivers enhanced capacity and lowered overpotential. As two-in-one host materials for both cathodes and anodes in Li-O2 batteries, the battery exhibits a capacity of 1.2 mA h cm-2 .

Heterogeneous microstructures and corrosion resistance of biomedical Co-Cr-Mo alloy fabricated by electron beam melting (EBM)

We have investigated the spatial distribution of microstructures of a Co-Cr-Mo alloy rod fabricated by Electron Beam Melting (EBM) method along built height. The topside of the rod is rich in γ-fcc phase and consists of fine grains with high local distortion density. The bottom part has an ε-hcp single phase and consists of relatively coarser grains with low local distortion density. The middle part of the rod consisted of the mixture of both phases. The mean grain size increases from 56 μm (at the top of the rod) to 159 μm (at the bottom), and is accompanied by a decrease in the γ-fcc phase fraction. On the other hand, a large number of precipitates including the main M23X6 phase and minor phases (η-phase and π-phase) were observed. The area fraction of the precipitates increases gradually from 5.26% (at the top) to 8.73% (at the bottom), and the relative proportion of each phase fluctuates at different positions. The hardness of the top side is lower than that of the bottom side. As a result, the hardness of the samples, as well as the area fraction of precipitates formed in the samples, increases gradually from top to bottom of the rod, while corrosion resistance is uniformly high throughout the rod almost independently of the location. The mechanism behind the formation of phase distribution is discussed in terms of thermodynamic phase stability and kinetics of phase transformation accompanying the thermal history during the post-solidification process.

Heterogeneity in local density allows a positive evolutionary relationship between self-fertilisation and dispersal.

Despite empirical evidence for a positive relationship between dispersal and self-fertilization (selfing), theoretical work predicts that these traits should always be negatively correlated, and the Good Coloniser Syndrome of high dispersal and selfing (Cf. Baker's Law) should not evolve. Critically, previous work assumes that adult density is spatiotemporally homogeneous, so selfing results in identical offspring production for all patches, eliminating the benefit of dispersal for escaping from local resource competition. We investigate the joint evolution of dispersal and selfing in a demographically structured metapopulation model where local density is spatiotemporally heterogeneous due to extinction-recolonization dynamics. Selfing alleviates outcrossing failure due to low local density (an Allee effect) while dispersal alleviates competition through dispersal of propagules from high- to low-density patches. Because local density is spatiotemporally heterogeneous in our model, selfing does not eliminate heterogeneity in competition, so dispersal remains beneficial even under full selfing. Hence the Good Coloniser Syndrome is evolutionarily stable under a broad range of conditions, and both negative and positive relationships between dispersal and selfing are possible, depending on the environment. Our model thus accommodates positive empirical relationships between dispersal and selfing not predicted by previous theoretical work and provides additional explanations for negative relationships.

A local cores-based hierarchical clustering algorithm for data sets with complex structures

Hierarchical clustering is of great importance in data analysis. Although there are a number of hierarchical clustering algorithms including agglomerative methods, divisive methods and hybrid methods, most of them are sensitive to noise points, suffer from high computational cost and cannot effectively discover clusters with complex structures. When recognizing patterns from complex structures, humans intuitively tend to discover obvious clusters in dense regions firstly and then deal with objects on the border. Inspired by this idea, we propose a local cores-based hierarchical clustering algorithm called HCLORE. The proposed method first partitions the data set into several clusters by finding local cores, instead of optimizing an objective function through iteration like K-means; then temporarily removes points with lower local density, so that the boundary between clusters is clearer; after that merges clusters according to a newly defined similarities between clusters; and finally points with lower local density are assigned to the same clusters as their local cores belong to. The experimental results on synthetic data sets and real data sets show that our algorithm is more effective and efficient than existing methods when processing data sets with complex structures.

Hosting Ultrahigh Areal Capacity and Dendrite-free Lithium via Porous Scaffold

Lithium (Li) metal is one of the most attractive anode materials for next-generation energy storage batteries. However, the undesirable Li dendrite growth usually leads to low Coulombic efficiency (CE) and safety hazards. Here, we employ reduced graphene oxide (rGO) infiltrated Ni foam (rGO@Ni) as a three-dimensional (3D) current collector to suppress Li dendrites. After Ni foam is soaked up with rGO, the enlarged working area conduces to a low local effective current density and a homogeneous Li deposition, therefore rendering a dendrite-free morphology. Compared with the Cu foil and Ni foam electrodes, the rGO@Ni electrode achieves enhanced CE and longer cycle life. Even for an extreme areal capacity of 20 mAh cm–2, so far the highest areal capacity has been reported in the literatures, the rGO@Ni current collector not only merely still functions after 50 cycles, but also delivers an enhanced CE of above 95%. Moreover, the symmetrical cell runs for 1100 h without fluctuation. The exceptional electrochem...

Accumulated Relative Density Outlier Detection For Large Scale Traffic Data

Outlier detection (OD) has been popularly developed in many fields such as medical diagnosis, network intrusion detection, fraud detection and military surveillance. This paper presents an accumulated relative density (ARD) OD method to identify outliers which possess relatively low or high local density. Previously, many density-based OD methods, such as local outlier factor (LOF) and Local Correlation Integral (LOCI), are applied to detect outliers which have low relative density in the data set. Relative local density (RLD) is measured and then compared with each other by statistics to label abnormities. In the proposed ARD method, a big circle centered at every data point is formed first. This big circle covers some data points with its radius. Then, for each encapsulated point inside this big circle, a small circle centered at itself is defined. Afterward, the ratio of number of covered data points inside the small circle of that particular point to the average number of data points in all small circles is defined as the RLD. After RLDs of all data points are calculated, a point whose RLD deviates greatly from the mean of all RLDs will be labeled as an outlier, otherwise as inliers. This ARD method was evaluated by a real world traffic data set which was originally represented as spatial-temporal (ST) traffic flow signals. The ST signals were processed by a principal component analysis (PCA) to reduce its dimension into two-dimensional 2D data points. An average 95% detection success rate (DSR) of OD can be achieved by this method.

Do large forest trees tend towards high species mingling?

Effects caused by human management and natural disturbances as well as by ecological mechanisms such as those described, for example, by the Janzen-Connell and the herd immunity hypotheses play an important role in maintaining species diversity. These processes are often accompanied by local size-hierarchy effects and as a likely consequence of them we hypothesised that large plants generally have a tendency to show high species mingling, where plants and their nearest neighbours are heterospecific. To carry out a first benchmark study in forests from different parts of Europe, Africa and North America, we selected spatial data from twelve forest ecosystems and analysed the mingling situation based on an index of spatial species mingling. Using stem diameter at breast height and stem diameter differentiation among the nearest neighbours as explanatory variables we then applied logistic regression to explain mingling probability. Overall we found significant support for the expectation that large trees and trees growing at low local densities often (75% of all analysed forest stands) have indeed a tendency towards high species mingling. This supports our expectation that the tendency of larger trees towards high species mingling generally is either a consequence of disturbances/forest management or of the aforementioned ecological processes, although a few forest stands also deviated from the expected pattern. We also found that size differentiation and species mingling are strongly related in local neighbourhoods. Thus the results of our study strongly support the view that local species richness promotes local size hierarchy irrespective of climate zone. This leads to situations where in local neighbourhoods with large size diversity there is also high mingling and vice versa. This allows conservation to focus on maintaining or improving one diversity aspect, e.g. size diversity, whilst obtaining the other, e.g. species mingling, as a by-product.