Hierarchical Partitioning Research Articles

An enhanced method for improving the accuracy of small failure probability of structures

This paper presents a hierarchical partitioning strategy (HP) for probability space to improve the accuracy of metamodel and avoid the memory problems of computer in dealing with small failure probability events. By the continuous partition of probability space, the construction of adaptive Kriging metamodel is intelligently divided into two steps. Thereafter, the well-trained Kriging via two steps is used to approximate the relationship between the extreme value of response and basic random variables of the system. By combining the probability density evolution method with well-trained Kriging metamodel, the reliability of the investigated stochastic system can be readily obtained with a one-dimensional integration operation. Subsequently, a new reliability analysis method is proposed, called HP-AK-PDEM (Hierarchical Partitioning strategy-based Adaptive Kriging combining Probability Density Evolution Method). To demonstrate the accuracy and efficiency of the proposed method, three analytical performance functions with nonlinear features and a ten-story shear-frame structure are addressed. The comparative study of different reliability methods is carried out as well. Numerical results demonstrate that the presented method has better accuracy and higher efficiency in dealing with the problems of small and rare failure probability issues encountered in engineering structures.

Life course, genetic, and neuropathological associations with brain age in the 1946 British Birth Cohort: a population-based study

A neuroimaging-based biomarker termed the brain age is thought to reflect variability in the brain's ageing process and predict longevity. Using Insight 46, a unique narrow-age birth cohort, we aimed to examine potential drivers and correlates of brain age. Participants, born in a single week in 1946 in mainland Britain, have had 24 prospective waves of data collection to date, including MRI and amyloid PET imaging at approximately 70 years old. Using MRI data from a previously defined selection of this cohort, we derived brain-predicted age from an established machine-learning model (trained on 2001 healthy adults aged 18-90 years); subtracting this from chronological age (at time of assessment) gave the brain-predicted age difference (brain-PAD). We tested associations with data from early life, midlife, and late life, as well as rates of MRI-derived brain atrophy. Between May 28, 2015, and Jan 10, 2018, 502 individuals were assessed as part of Insight 46. We included 456 participants (225 female), with a mean chronological age of 70·7 years (SD 0·7; range 69·2 to 71·9). The mean brain-predicted age was 67·9 years (8·2, 46·3 to 94·3). Female sex was associated with a 5·4-year (95% CI 4·1 to 6·8) younger brain-PAD than male sex. An increase in brain-PAD was associated with increased cardiovascular risk at age 36 years (β=2·3 [95% CI 1·5 to 3·0]) and 69 years (β=2·6 [1·9 to 3·3]); increased cerebrovascular disease burden (1·9 [1·3 to 2·6]); lower cognitive performance (-1·3 [-2·4 to -0·2]); and increased serum neurofilament light concentration (1·2 [0·6 to 1·9]). Higher brain-PAD was associated with future hippocampal atrophy over the subsequent 2 years (0·003 mL/year [0·000 to 0·006] per 5-year increment in brain-PAD). Early-life factors did not relate to brain-PAD. Combining 12 metrics in a hierarchical partitioning model explained 33% of the variance in brain-PAD. Brain-PAD was associated with cardiovascular risk, and imaging and biochemical markers of neurodegeneration. These findings support brain-PAD as an integrative summary metric of brain health, reflecting multiple contributions to pathological brain ageing, and which might have prognostic utility. Alzheimer's Research UK, Medical Research Council Dementia Platforms UK, Selfridges Group Foundation, Wolfson Foundation, Wellcome Trust, Brain Research UK, Alzheimer's Association.

Association of the Heart Rate Variability Response to Active Standing with the Severity of Calcific Aortic Valve Disease: Novel Insights of a Neurocardiovascular Pathology.

The aim of this work was to obtain insights of the participation of the autonomic nervous system in different stages of calcific aortic valve disease (CAVD) by heart rate variability (HRV) analysis. Studying subjects with no valve impairments and CAVD patients, we also sought to quantify the independent contribution or explanatory capacity of the aortic valve echocardiographic parameters involved in the HRV changes caused by active standing using hierarchical partitioning models to consider other variables or potential confounders. We detected smaller adjustments of the cardiac autonomic response at active standing caused specifically by the aortic valve deterioration. The highest association (i.e., the highest percentage of independent exploratory capacity) was found between the aortic valve area and the active standing changes in the short-term HRV scaling exponent α1 (4.591%). The valve’s maximum pressure gradient echocardiographic parameter was present in most models assessed (in six out of eight models of HRV indices that included a valve parameter as an independent variable). Overall, our study provides insights with a wider perspective to explore and consider CAVD as a neurocardiovascular pathology. This pathology involves autonomic-driven compensatory mechanisms that seem generated by the aortic valve deterioration.

Recommendations to enhance breeding bird diversity in managed plantation forests determined using LiDAR.

Widespread afforestation is a crucial component of climate mitigation strategies worldwide. This presents a significant opportunity for biodiversity conservation if forests are appropriately managed. Within forests, structural and habitat diversity are known to be critical for biodiversity but pragmatic management recommendations are lacking. We make a comprehensive assessment of the effects of habitat variables on bird populations using data from over 4000 ha of forested landscape. We combine high-resolution remote sensing data with comprehensive management databases to classify habitat attributes and measure the response of six taxonomic and functional diversity metrics: species richness, Shannon diversity, functional richness, functional evenness, functional divergence, and functional dispersion. We use a novel approach that combines hierarchical partitioning analysis with linear models to determine the relative importance of different habitat variables for each bird diversity metric. The age class of forest stands was consistently the most important variable across all bird diversity metrics, outperforming other structural measures such as horizontal and vertical heterogeneity and canopy density. Shrub density and gap fraction were each significantly associated with one bird diversity metric. In contrast, variables describing within-stand structural heterogeneity (vertical and horizontal) were generally less important while tree species identity (e.g., conifer or broadleaved) was not significant for any bird diversity metric. Each of the six bird diversity metrics had different patterns of independent variable importance and significance, emphasizing the need to consider multiple diversity metrics in biodiversity assessments. Similarly, the optimal resolution for remote sensing metrics varied between structural variables and bird diversity metrics, suggesting that the use of remote sensing data in biodiversity studies could be greatly improved by first exploring different resolutions and data aggregations. Based on the results from this comprehensive study, we recommend that managers focus on creating habitat diversity at the between-, rather than exclusively within-stand scale, such as by creating a matrix of different age classes, to maximize bird diversity. This recommendation for forest managers is powerful yet pragmatic in its simplicity.

Ecological restoration programs dominate vegetation greening in China

Many ecological restoration programs have been implemented in China during the last two decades. At the same time, the vegetation has turned green significantly in China. However, few studies have directly evaluated the contribution of the ecological restoration programs to vegetation greening in comparison with the contribution of climate change using high-resolution data of afforestation areas at the national scale. We used newly compiled high-resolution data on yearly forest plantation and mountain closure, the daily climate data from the 2480 meteorological stations and GIMMS 3g NDVI data. We used a multiple linear regression model to compare the influence of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We then used the hierarchical variance partitioning method to evaluate the relative contribution of temperature, precipitation, and ecological restoration programs on NDVI dynamics. We found a significant greening trend in China from 1999 to 2015 with an annual increase rate of 0.0017 yr−1 in the mean growing season NDVI. The ecological restoration programs dominated the vegetation greening in northern China and the southern coastal regions, indicating a good performance of restoration programs in these regions. In contrast, temperature or precipitation dominated the vegetation greening in southwestern China, Inner Mongolia and the implementation regions of several ecological restoration programs in northeastern China. Among the ecological restoration programs except the Three-North Shelterbelt Forest Program, the effect of ecological restoration programs on vegetation greening was stronger than the total effects of temperature and precipitation changes. Our study presents a systematic assessment on the contribution of ecological restoration programs to the vegetation greening in China, accessed the role on vegetation greening of different ecosystem restoration programs. We analyzed the reasons for the differences in the contribution of different ecological restoration programs to vegetation greening and provided insights facilitating policy makers to prioritize future restoration planning.

Hierarchical partitioning of multiple niche dimensions among ecomorphs, species and sexes in Puerto Rican anoles

AbstractAssemblages of co‐occurring closely related species tend to vary in one or more niche dimensions, but niche partitioning also occurs at levels of biological organization above and below the species level. Niche space occupied by a community may involve variation among groups of species, such as guilds or ecomorphs, and among sexes or individuals within species. We examined variation in structural microhabitat, thermal habitat, and prey size and composition in an assemblage of six anole lizards belonging to three habitat specialist ecomorph categories in a Puerto Rican rainforest. As predicted, ecomorphs were most distinct in structural microhabitat, while species within ecomorphs varied mainly in thermal habitat. Ecological sex dimorphism was limited, and considerable variation in all dimensions was unexplained or potentially related to individual‐level niche variation. Our analysis largely corroborates the view of anole adaptive radiation and community assembly as involving structural habitat divergence among ecomorphs and secondary divergence among species using different climatic niches. A hierarchical approach to niche variation presents opportunities for taking a comparative approach to understanding how ecological diversity is partitioned within communities.

Evolution and Climate Drivers of NDVI of Natural Vegetation during the Growing Season in the Arid Region of Northwest China

Vegetation plays an important role in linking water, atmosphere, and soil. The dynamic change in vegetation is an important indicator for the regulation of the terrestrial carbon balance and climate change. This study applied trend analysis, detrended correlation analysis, and the Hierarchical Partitioning Algorithm (HPA) to GIMMS NDVI3g data, meteorological data, and natural vegetation types for the period 1983 to 2015 to analyze the temporal and spatial changes in NDVI during the growing season and its driving factors in the arid region of northwestern China. The results showed that: (1) the growing season length (GSL) was delayed, with a regional trend of 8 d/33 a, due to a significant advancement in the start of the growing season (SOS, −7 d/33 a) and an insignificant delay to the end of growing season (EOS, 2 d/33 a). (2) The regional change in NDVI was mainly driven by temperature and precipitation, contributing to variations in NDVI of forest of 36% and 15%, respectively, and in the NDVI of grassland, of 35% and 21%, respectively. In particular, changes to forested land and medium-coverage grassland (Mgra) were closely related to temperature and precipitation, respectively. (3) The spatial distribution of the mean NDVI of forest was closely related with precipitation, temperature, and solar radiation, with these meteorological variables explaining 20%, 15%, and 10% of the variation in NDVI, respectively. Precipitation and solar radiation explained 29% and 17% of the variation in the NDVI of grassland, respectively. The study reveals the spatial–temporal evolution and driving mechanism of the NDVI of natural vegetation in the arid region of Northwest China, which can provide theoretical and data support for regional vegetation restoration and conservation.

Elevational patterns of amphibian functional and phylogenetic structures in eastern Nepal Himalaya

AbstractAimExploring the mechanism underlying community assembly processes is considered as one of the central topics for biogeography. As one of the biodiversity hotspots, Nepal Himalaya has attracted increasing attention during the past decade. However, the main processes shaping amphibian assemblages in this area are still unclear. Here, we examined the elevational patterns of amphibian functional and phylogenetic structures to understand the main mechanism driving amphibian assemblages along an elevational gradient in eastern Nepal Himalaya.LocationEastern Nepal Himalaya.MethodsAmphibian taxonomic, functional and phylogenetic diversity were calculated, and their responses to the increasing elevations were investigated based on the linear regressions. The environmental determinants of amphibian diversity were revealed based on the incorporation of generalized linear models and hierarchical partitioning analyses. In addition, the mechanism driving amphibian assemblages from low to high elevations was tested by calculating the mean pairwise functional distance and the mean pairwise phylogenetic distance indices.ResultsAmphibian taxonomic, functional and phylogenetic diversity had contrast responses to the increasing elevations, which were strongly determined by variables such as air temperature, the number of trees, leaf litter cover and water pH. Interestingly, the trends of amphibian functional and phylogenetic diversity along an elevational gradient can be changed after controlling the effects of species richness. Moreover, determining process played a dominant role in shaping amphibian assemblages with the increasing elevations.ConclusionsOur results revealed the elevational patterns of amphibian functional and phylogenetic structures in eastern Nepal Himalaya, associated with the change in environmental variables. We also highlighted the dominant role of determining process in shaping amphibian assemblages. Overall, our results can help ecologists and managers better understand and formulate amphibian diversity conservation. In addition, our results also enriched the fundamental knowledge to understand the general patterns of community assembly rules in montane ecosystems.

Soil texture and pH exhibit important effects on biological nitrogen fixation in paddy soil

Biological nitrogen fixation (BNF) is an important source of soil nitrogen. However, for dry-wet alternative paddy soil, the combined effects of multiple driving factors, including soil physicochemical properties and the soil microbiome, on BNF are not fully understood. In this study, three types of paddy soil were collected to determine the BNF amount by field-based 15N2 labeling experiments. Soil nitrogen fixing microbiome was analyzed using high-throughput sequencing based on the nifH gene. Random forest analysis, co-occurrence network analysis and hierarchical partitioning were performed to screen the key diazotrophic community and critical soil properties. The results revealed that rice nitrogen acquisition from BNF ranged from 1.62 % to 7.80 %. Three key diazotrophic genera, Rubrivivax, Frankia and Azospirillum, significantly affected BNF. The BNF amount in the 0–1 cm soil layer was significantly higher than that in the 1–15 cm soil layer, which was consistent with the relative abundance of Cyanobacteria. The soil silt content and pH were positively correlated with the BNF amount by mediating the key diazotrophic microbiome. These results indicated that the BNF amount plays an important role in rice nitrogen acquisition and paddy soil nitrogen sources, which might be regulated by soil texture and soil pH.

Genetic Diversity of Wild Rice (Oryza longistaminata) in Ghana

Oryza longistaminata is a wild rice taxon and an excellent source of genetic variation that remains largely unexploited. This study was conducted to understand the genetic diversity between and within O. longistaminata populations, collected from different geographic locations in Ghana. In this study, SSR markers were used to determine the intra-specific variability in O. longistaminata, and how the variation is partitioned within and between different populations. Analysis of molecular variance revealed high levels of polymorphism (95.9%) within the populations. The overall genetic diversity for all the loci in the six populations was high (Shannon's Information Index I = 0.579, Nei's unbiased expected heterozygosity, He = 0.405). Hierarchical partitioning also indicated a high genetic diversity between and within populations, with some level of relatedness between samples taken from the Savannah agroecological area. The high genetic diversity observed in this study offer a great opportunity for screening useful traits among the populations of O. longistaminata in Ghana for rice breeding programs.

Joint gravity and magnetic inversion with trans-dimensional alpha shapes and autoregressive noise models

Typical geophysical inverse problems are ill-posed and non-unique which causes challenges for interpretation. To address these issues, deterministic inversion methods often apply constraints to parameter values, which control the effective number of parameters. However, such approaches can inhibit inference on complex structural boundaries. Bayesian trans-dimensional (trans-D) parametrizations for Earth structure partition space based on data information with the ability to adapt the parametrization locally to data information. Therefore, trans-D approaches can avoid under- or over-parametrizing regions of the model. Nonetheless, these parametrizations depend on the choice of partitioning types, such as Voronoi nodes or wavelet decomposition. In addition, trade-offs exist between spatial resolution and correlated data errors. We present a hierarchical model that treats both spatial and data noise parametrizations as trans-D to better incorporate trade-offs between noise and structure into uncertainty quantification. This includes a hierarchical spatial partitioning based on linear and nearest-neighbor interpolations and alpha shapes. The alpha shapes provide advantages for the inversion of potential field data by permitting flexibility in the shapes of structures of interest. The trans-D autoregressive noise model quantifies the impact of correlated noise on geophysical parameter estimates. We compare these methods with nested Voronoi partitioning and show differences in uncertainties, data fit, and parsimony of the parametrizations. Studies on simulated data show well-resolved structures and successful decorrelation of data residuals while requiring few parameters. The inversion of field data infers basement and salt broadly consistent with previous studies, but results show additional details that are consistent with independent geological knowledge.

Impact of cyanobacterial bloom intensity on plankton ecosystem functioning measured by eukaryotic phytoplankton and zooplankton indicators

Cyanobacterial blooms are global threats to freshwater ecosystem functioning, human health, and ecoservices. We assessed impacts of cyanobacterial bloom intensity on plankton ecosystem functioning using eukaryotic phytoplankton and zooplankton indicators and associated key physicochemical data collected from four seasons of two years at 24 evenly distributed sites in Lake Taihu that has year-around cyanobacterial blooms. Our analyses involved comparison of four site-groups with different bloom intensities and analyzing all sampling sites together using comparison, hierarchical partitioning analysis, generalized additive mixed model, and structural equation model. We found that cyanobacterial abundance positively associated with TP and temperature (negatively with TN:TP), while phytoplankton positively associated with TN. There was an inverse relation trend between relative abundances of phytoplankton and cyanobacteria, but there was no clear trend between absolute abundances of phytoplankton and cyanobacteria. Rotifers were most dominant when cyanobacteria were unabundant, while cladocerans presented higher abundance when cyanobacteria were in high abundance. Phytoplankton functional richness and species richness negatively and zooplankton functional richness and species richness positively associated with cyanobacterial bloom intensity. Cyanobacterial bloom intensity negatively associated with resource use efficiencies (RUEs) of phytoplankton and rotifers, and positively associated with RUE of cladocerans. Our analytical approach of integrating comparison of site-groups and analyzing all sites together uncovered how cyanobacterial bloom intensity shifted and altered physicochemical and biological conditions and plankton ecosystem functioning, and identified the mechanism and strength of the interactive linkages among physicochemical and biological indicators. Although our results may be different from oligotrophic lakes or reservoirs, our findings provide new insights in understanding the impacts of cyanobacterial bloom intensity on the dynamics of plankton communities and ecosystem functioning for polymictic eutrophic lakes, which may have broad application in enhancing the knowledge of this subject and provides the science base for managing polymictic eutrophic lake water quality and ecosystem functioning.

Hierarchical adaptive low‐rank format with applications to discretized partial differential equations

AbstractA novel framework for hierarchical low‐rank matrices is proposed that combines an adaptive hierarchical partitioning of the matrix with low‐rank approximation. One typical application is the approximation of discretized functions on rectangular domains; the flexibility of the format makes it possible to deal with functions that feature singularities in small, localized regions. To deal with time evolution and relocation of singularities, the partitioning can be dynamically adjusted based on features of the underlying data. Our format can be leveraged to efficiently solve linear systems with Kronecker product structure, as they arise from discretized partial differential equations (PDEs). For this purpose, these linear systems are rephrased as linear matrix equations and a recursive solver is derived from low‐rank updates of such equations. We demonstrate the effectiveness of our framework for stationary and time‐dependent, linear and nonlinear PDEs, including the Burgers' and Allen–Cahn equations.

A warmer winter followed by a colder summer contributed to a longer recovery time in the high latitudes of Northeast China

A drought is an extreme moisture deficit event caused by meteorological factors that destroys the structure and function of ecosystems. The recovery time (RT) is a critical metric that describes the responses of ecosystems to drought. However, the factors influencing ecosystem RTs are still unclear at the seasonal scale, especially the influence of the climatic state and the biological processes behind it during the recovery period. We selected a severe drought that occurred in Northeast China (NEC) from October 2018 to April 2019 (Win18-Spr19) and observed the vegetation indicator dynamics during and after the drought. Hierarchical partitioning and partial correlation analysis were used to quantify the contributions of the influencing factors to the RT. Abnormally high temperatures, and low precipitation and snowfall led to this drought event, which caused greenness loss in more than half of the ecosystems. Nearly half of the ecosystems recovered within 4 months, while regions with RTs longer than 9 months were concentrated in high-latitude regions, accounting for approximately 6% of the analysed ecosystems. The RTs were significantly negatively correlated with temperature anomalies in the high-latitude regions of NEC. This result indicated that ecosystems that experienced greater temperature reductions tended to have longer RTs, reflecting the negative impacts of low growing season temperatures on ecosystem recovery in the high-latitude regions of NEC. The abnormal climate pattern in which a warmer winter is followed by a colder summer threatens ecosystems: the vegetation loss caused by the warm winter drought event and slowing growth due to low summertime temperature. This climate pattern places ecosystems under unfavorable environmental conditions over long durations, including both the drought period and the long subsequent recovery period.

Volt-VAR control for distribution networks with high penetration of DGs: An overview

Continuously improving the ability to accept distributed renewable energies is the trend of future grid development, and a large number of papers have been published in recent years to study the problem of Volt-VAR control (VVC) for distribution networks with high penetration of distributed generations. This paper summarizes the relevant modeling and solution methods for VVC problems, mainly including VVC based on multiple time scales, hierarchical partitioning, multi-stage and network reconstruction, in conjunction with the operational characteristics of distribution networks containing distributed renewable energies; meanwhile, it analyzes the advantages and disadvantages of traditional optimization methods, heuristic intelligent algorithms and random variable processing methods used to solve VVC problems, and then introduces the application of model-free deep reinforcement learning as a latest decision method in VVC of distribution networks. Most of the models and methods compiled in this article are from the research results of the last three years and have some reference value.

Hierarchical network partition of DC power systems containing a large number of switches

In order to realize the high-efficiency and automatic network partition of dc power systems that contain a large number of switches, this paper presents a hierarchical partition method. It consists of two layers. The first layer is the partition into sub-systems, its optimality criteria are optimal trade-off between the effects of uneven switch distribution and the number of cutting branches, then, a method of transferring switch nodes between the adjacent sub-systems is used. The second layer is the partition into sub-networks, its optimality criteria are optimal trade-off between the effects of uneven switch distribution and the number of state-space-nodal (SSN) nodes, then, the method of merging adjacent switch nodes at multiple points simultaneously is adopted. Further, a complete set of automatic hierarchical network partition method of dc power system is formed. Finally, a typical MVDC integrated power system and a modified 39-bus dc power system are taken as the examples to verify the effectiveness of the proposed method. Real-time simulation performance shows that the proposed method can significantly reduce the simulation step, balance the consumed simulation resources and improve the partition efficiency compared with traditional methods.

Low carbon availability in paleosols nonlinearly attenuates temperature sensitivity of soil organic matter decomposition.

Temperature sensitivity (Q10 ) of soil organic matter (SOM) decomposition is an important parameter in models of the global carbon (C) cycle. Previous studies have suggested that substrate quality controls the intrinsic Q10 , whereas environmental factors can impose large constraints. For example, physical protection of SOM and its association with minerals attenuate the apparent Q10 through reducing substrate availability and accessibility ([S]). The magnitude of this dampening effect, however, has never been quantified. We simulated theoretical Q10 changes across a wide range of [S] and found that the relationship between Q10 and the log10 -transformed [S] followed a logistic rather than a linear function. Based on the unique Holocene paleosol chronosequence (7 soils from ca. 500 to 6900years old), we demonstrated that the Q10 decreased nonlinearly with soil age up to 1150years, beyond which Q10 remained stable. Hierarchical partitioning analysis indicated that an integrated C availability index, derived from principal component analysis of DOC content and parameters reflecting physical protection and mineral association, was the main explanatory variable for the nonlinear decrease of Q10 with soil age. Microbial inoculation and 13 C-labelled glucose addition showed that low C availability induced by physical protection and minerals association attenuated Q10 along the chronosequence. A separate soil incubation experiment indicated that Q10 increased exponentially with activation energy (Ea ) in the modern soil, suggesting that SOM chemical complexity regulates Q10 only when C availability is high. In conclusion, organic matter availability strongly decreased with soil age, whereas Michelis-Menten kinetics defines the Q10 response depending on C availability, but Arrhenius equation describes the effects of increasing substrate complexity.

Different Clustering Algorithms in Data Mining

Clustering is the grouping together of similar data items into clusters. Clustering analysis is one of the main analytical methods in data mining; the method of clustering algorithm will influence the clustering results directly. This paper discusses the various types of algorithms like Hierarchical Clustering Algorithms Partitioning Method Nearest Neighbor algorithm K-Mean (A centroid based Technique) Density-Based clustering etc. This paper also deals with the problems of clustering algorithm such as time complexity and accuracy to provide the better results based on various environments.

Environmental filtering dominated the antibiotic resistome assembly in river networks

River networks play important roles in dissemination of antibiotic resistance genes (ARGs). The occurrence, diversity, and abundance of ARGs in river networks have been widely investigated. However, the assembly processes that shaped ARGs profiles across space and time are largely unknown. Here, the dynamics of ARGs profiles in river networks (Taihu Basin) were revealed by high-throughput quantitative PCR followed by multiple statistical analyses to assess the underlying ecological processes. The results revealed clear variations for ARGs profiles across wet, normal, and dry seasons. Meanwhile, a significant negative correlation (p < 0.01) was observed between the similarity of ARGs profiles and geographic distance, indicating ARGs profiles exhibited distance-decay patterns. Null model analysis showed that ARGs profiles were mainly assembled via deterministic processes. Redundancy analysis followed by hierarchical partitioning revealed that environmental attributes (mainly pH and temperature) were the major factors affecting the dynamics of ARGs profiles. Together, these results indicated that environmental filtering was the dominant ecological process that shaped ARGs profiles. This study enhances our understanding how the antibiotic resistome is assembled in river networks and will be beneficial for the development of management strategies to control ARGs dissemination.

Unified understanding and new results of controllability model of multi‐agent systems

AbstractIn this article, the effects of different models on the controllability of leader‐follower dynamics system based on Laplacian matrix are studied, and the controllability results applicable to general model are obtained. Our research is based on three different control modes, namely, broadcast control, control, and non‐broadcast control, which cover all the control modes in the general model. Specifically, we first identify the relation of different control, that is, if the system is controllable under different control signals, then increasing the number of control signals does not change the controllability of the system. Furthermore, it is proved that in a fixed topology, when the number of control inputs is increased, the controllability of the system is not correlated with the controllability of the original system, which appears counter‐intuitive. But the spectral radius of the Gramian matrix of the controllable system is increased. In addition, we analyze the condition number of Gramian matrix. Based on the above conclusions, we put forward the concept of hierarchical equivalent partition (HEP), and prove that under the general model, the existence of HEP makes the system uncontrollable. Finally, we expect to find a way to make the system controllable under the general model. Therefore, we obtain sufficient conditions for the system to be controllable. As a by‐product, we find that there is no completely uncontrollable graph when the general model with broadcast control is removed.