Average Degree Of Connectivity Research Articles

A comparative analysis of the robustness of multimodal comprehensive transportation network considering mode transfer: A case study

<abstract> <p>China has built a nationwide transportation network, but there needs to be a smooth connection and transfer between different modes. Five networks are constructed to explore the characteristics of a multimodal comprehensive transportation network (CNet) in Jiangsu Province based on the optimized modeling method and multisource data. Statistical and robustness characteristics are analyzed for CNet and other single-mode networks including the highway, railway, navigation channel and airway networks (HNet, RNet, NNet and ANet, respectively). The research results show following: (ⅰ) In Jiangsu, CNet, HNet, RNet and NNet are not scale-free networks and do not have small-world properties. However, ANet is the opposite. (ⅱ) The five networks in Jiangsu are robust to the random attack and their robustness changes during the attack. However, their robustness is different under different calculated attacks. For all attack strategies, CNet is the most robust. (ⅲ) In Jiangsu, the three optimized methods enhance the robustness significantly. The network failure is delayed by 12.34, 2.79 and 2.44%, respectively. The average connectivity degree is improved by 265.69, 52.95 and 32.54%, respectively. The more hubs there are with powerful transfer capacity, the stronger the network robustness. The results reveal the key points of the construction of a multimodal comprehensive transportation system and can guide the design and optimization of it.</p> </abstract>

Application of thifluzamide alters microbial network structure and affects methane cycle genes in rice-paddy soil.

Thifluzamide is an effective agent for controlling rice sheath blight and has a long half-life in soil. However, the effects of thifluzamide on the abundance of microbes harboring methane-cycle genes and soil microbial community assembly patterns are not well known. Thus, we conducted a three-month indoor mesocosm experiment to ascertain the effects of thifluzamide (0.05, 0.5, and 5 mg kg-1 soil; 0.05 mg kg-1 soil being recommended) on bacterial and archaeal community structure and on the abundance of methanogen genes using two typical paddy soils: sandy soil from Hangzhou (HZ) and loam sandy soil from Jiansanjiang (JSJ). The effects of thifluzamide on soil microorganisms were related to soil type. In JSJ loam sandy soil, thifluzamide significantly increased bacterial α diversity after 7-30 d and archaeal α diversity at 30 and 60 d. In HZ sandy soil, however, α diversity did not change significantly. Network analysis showed that thifluzamide-treated soils possessed more complex networks with more total nodes and links, a higher average degree of connectivity, and more keystone species. Thifluzamide application increased the number of keystone species associated with methane production in both types of paddy soil. A relatively greater number of modules were significantly negatively correlated with mcrA abundance in the HZ T10 network, but more modules were positively correlated with mcrA abundance in the JSJ T100 network. The half-life of thifluzamide varied for the different doses, i.e., from 152.0 to 419.6 d. The results reveal that methane-cycle genes, soil microbiome assembly, and interactions among microbial species all change in response to thifluzamide stress.

Roles of biochar in cement-based stabilization/solidification of municipal solid waste incineration fly ash

Low-carbon stabilization/solidification (S/S) is of increasing importance for sustainable treatment of hazardous wastes. In this study, we integrated carbon-negative rice husk biochar (RBC) and yard waste biochar (YBC) as green additives into the binder for S/S of municipal solid waste incineration (MSWI) fly ash. Experimental results illustrated that the addition of both biochars promoted cement hydration reaction via pozzolanic reaction and internal curing. In particular, the incorporation of 10 wt% RBC (rich in activated Si) significantly increased the content of C-S-H gel from 41.6 wt% (control sample) to 52.0 wt% and increased the average degree of connectivity of C-S-H gel from 1.43 to 1.52 as indicated by quantitative X-ray diffraction and 29Si nuclear magnetic resonance analysis. The incorporation of RBC and YBC (20 or 30 wt%) enhanced the immobilization efficiency of potentially toxic elements in MSWI fly ash due to the additional hydration products and high adsorption ability of biochar. For instance, in R-80FA and Y-80FA samples (namely, 20 wt% binder dosage, of which RBC or YBC accounted for 10 wt% of binder), the immobilization efficiency for Pb could reach 96.2% and comply with the leachability limit. The biochar-modified S/S blocks achieved comparable strength to the cement-based S/S blocks, presenting a mechanically stable solidified matrix for engineering application. Therefore, this study expands the emerging application of biochar and demonstrates that biochar-augmented binder can ensure low-carbon and high-performance S/S of hazardous materials.

Rhizobacterial communities and crop development in response to long-term tillage practices in maize and soybean fields on the Loess Plateau of China

Conservation tillage, that is, reduced or zero tillage, can change the soil ecological environment and rhizosphere bacterial communities. Here, we investigated temporal variations in rhizosphere bacterial communities during crop development and their relationships with crop growth characteristics under three tillage practices (Plow tillage (PT), chisel plough tillage (CPT), and zero tillage (ZT)) in maize and soybean fields on the Loess Plateau of China. Conservation tillage (CPT and ZT) practices significantly increased the contents of soil moisture (3.62–13.13%), soil organic carbon (29.19–39.67%), and nitrogen (38.69–60.23%) and decreased soil pH (1.15–3.76%). The overall responses of rhizosphere bacterial community composition to tillage practice and phenological period were taxonomically consistent across maize and soybean fields, and changes were predominantly observed in the relative abundances of dominant bacterial phyla. Co-occurrence network analysis indicated that the average connectivity degree among 16S operational taxonomic units was higher under PT (38.92% and 73.60%) practice than under CPT and ZT practices. CPT practice tended to increase the total biomass and nitrogen accumulation of maize and soybean compared with PT practice, whereas ZT practice significantly (P < 0.05) decreased them. Partial least squares path modeling analysis showed that temporal variations in the rhizosphere bacterial communities (explained variations in maize and soybean fields were 94.7% and 92.7%, respectively) were regulated by tillage practices and phenological periods, and partly explained the changes in growth dynamics of maize and soybean (0.161 and 0.031, respectively). Overall, CPT practice could improve the soil ecological environment and promote crop growth in the China’s Loess Plateau. Therefore, we recommend that CPT practice should be adopted in semi-arid agricultural areas to improve the potential relationships between the underground and aboveground in agroecosystems.

Organ System Network Disruption Is Associated With Poor Prognosis in Patients With Chronic Liver Failure.

BackgroundA healthy individual has a high degree of functional connectivity between organ systems, which can be represented graphically in a network map. Disruption of this system connectivity is associated with mortality in life-threatening acute illnesses, demonstrated by a network approach. However, this approach has not been applied to chronic multisystem diseases and may be more reliable than conventional individual organ prognostic scoring methods. Cirrhosis is a chronic disease of the liver with multisystem involvement. Development of an efficient model for prediction of mortality in cirrhosis requires a profound understanding of the pathophysiologic processes that lead to poor prognosis. In the present study, we use a network approach to evaluate the differences in organ system connectivity between survivors and non-survivors in a group of well-characterized patients with cirrhosis.Methods201 patients with cirrhosis originally referred to the Clinic five at the University Hospital of Padova, with 13 clinical variables available representing hepatic, metabolic, haematopoietic, immune, neural and renal organ systems were retrospectively enrolled and followed up for 3, 6, and 12 months. Software was designed to compute the correlation network maps of organ system interaction in survivors and non-survivors using analysis indices: A. Bonferroni corrected Pearson’s correlation coefficient and B. Mutual Information. Network structure was quantitatively evaluated using the measures of edges, average degree of connectivity and closeness, and qualitatively using clinical significance. Pair-matching was also implemented as a further step after initial general analysis to control for sample size and Model for End-Stage Liver Disease (MELD-Na) score between the groups.ResultsThere was a higher number of significant correlations in survivors, as indicated by both the analysis indices of Bonferroni corrected Pearson’s correlation coefficient and the Mutual Information analysis. The number of edges, average degree of connectivity and closeness were significantly higher in survivors compared to non-survivors group. Pair-matching for MELD-Na was also associated with increased connectivity in survivors compared to non-survivors over 3 and 6 months follow up.ConclusionThis study demonstrates the application of a network approach in evaluating functional connectivity of organ systems in liver cirrhosis, demonstrating a significant degree of network disruption in organ systems in non-survivors. Network analysis of organ systems may provide insight in developing novel prognostic models for organ allocation in patients with cirrhosis.

The response of nitrogen cycling and bacterial communities to E. coli invasion in aquatic environments with submerged vegetation

The effects of exogenous Escherichia coli on nitrogen cycling (N-cycling) in freshwater remains unclear. Thus, seven ecosystems, six with submerged plants-Potamogeton crispus (PC) and Myriophyllum aquaticum (MA)-and one with no plants were set up. Habitats were assessed before and after E. coli addition (107colony-forming units/mL). E. coli colonization of freshwater ecosystems had significant effects on bacterial community structure in plant surface biofilms and surface sediments (ANOVA, P<0.05). It reduced the relative abundance of nitrosification bacteria (-70.94±26.17%) and nitrifiers (-47.86±23.68%) in biofilms which lead to significant reduction of ammoxidation in water (P<0.05). The N-cycling intensity from PC systems was affected more strongly by E. coli than were MA systems. Furthermore, the coupling coefficient of exogenous E. coli to indigenous N-cycling bacteria in sediments (6.061, average connectivity degree) was significantly weaker than that in biofilms (9.852). Additionally, at the genus level, E. coli were most-closely associated with N-cycling bacteria such as Prosthecobacter, Hydrogenophaga, and Bacillus in sediments and biofilms according to co-occurrence bacterial network (Spearman). E. coli directly changed their abundance, so that the variability of species composition of N-cycling bacterial taxa was triggered, as well. Overall, exogenous E. coli repressed ammoxidation, but promoted ammonification and denitrification. Our results provided new insights into how pathogens influence the nitrogen cycle in freshwater ecosystems.

Analysis of degree characteristics in airport networks

Flight operation data provides a record of air traffic behaviour in detail, and has become an important resource for studying air traffic patterns. In this paper, the degree characteristic of air traffic networks is studied using flight operation data in order to explain air traffic behaviour. The degree indexes were founded from the perspective of network structure and network function based on complex network theory and traffic flow theory. The connectivity degree, traffic degree and delay degree were established, and the characteristics of these three degrees were studied based on American flight data from different days in 2015 over different time periods of each day. Research shows that at all scales the number degrees, volume degrees and duration degrees are all of power-law distribution, and the power exponents are different depending on the scale, date and index. The power exponents fluctuate from 0.5 to 1.5. In general, the connectivity degree exponent is greater than that of the traffic degree and the delay degree. The duration degree exponent is greater than the traffic degree. The exponents are greater on holidays than they are on non-holidays, as well as during non-working hours than during working hours of the day. Compared with non-holidays, the average connectivity degree and delay degree are larger on holidays, and the average traffic degree is smaller. This reveals that most airports only provide flights to one airport, and the airports’ numbers and flight volumes both decrease on holidays. The power indexes of the delay degree and traffic degree are basically the same. The main mode of traffic (arrivals or departures) during each time period is also studied.

Topology determines force distributions in one-dimensional random spring networks.

Networks of elastic fibers are ubiquitous in biological systems and often provide mechanical stability to cells and tissues. Fiber-reinforced materials are also common in technology. An important characteristic of such materials is their resistance to failure under load. Rupture occurs when fibers break under excessive force and when that failure propagates. Therefore, it is crucial to understand force distributions. Force distributions within such networks are typically highly inhomogeneous and are not well understood. Here we construct a simple one-dimensional model system with periodic boundary conditions by randomly placing linear springs on a circle. We consider ensembles of such networks that consist of N nodes and have an average degree of connectivity z but vary in topology. Using a graph-theoretical approach that accounts for the full topology of each network in the ensemble, we show that, surprisingly, the force distributions can be fully characterized in terms of the parameters (N,z). Despite the universal properties of such (N,z) ensembles, our analysis further reveals that a classical mean-field approach fails to capture force distributions correctly. We demonstrate that network topology is a crucial determinant of force distributions in elastic spring networks.

Comparison study of small world network between patients with brain concussion and diffuse axonal injury

Objective To analyze the properties and difference of small world network in patients with concussion and diffuse axonal injury by resting-state functional MRI (rs-fMRI). Methods A prospective study was conducted, in which 13 patients with brain concussion and 9 patients with diffuse axonal injury were admitted to Neurosurgery Department of Wuhan General Hospital of PLA from December 2013 to December 2015, and 14 healthy people were set as controls. The rs-fMRI data in those 3 groups were collected and preprocessed. Brain areas were labeled by AAL90 and Dosenbach160. The correlation coefficients of the brain regions were calculated. The small world was assessed and network parameters were compared between the 3 groups. Results The brain network of patients with brain concussion, diffuse axonal injury and normal subjects were in line with the characteristics of small world. Compared with controls, a higher situation of clustering coefficient, global efficiency, local efficiency and average connectivity degree (all P<0.05) were presented in patients with brain concussion and the same parameters were lower (all P<0.05) in patients with diffuse axonal injury. Conclusion The patients with brain concussion and diffuse axonal injury both have small world networks, but the parameters might vary, which could be due to compensation or decompensation of brain functions. Key words: Brain concussion; Diffuse axonal injury; Magnetic resonance imaging, functional; Brain functional network; Small world

Corollary discharge, auditory hallucinations and schizophrenia – a structural network analysis

IntroductionCorollary discharges (CDs) are the reason most people cannot tickle themselves. They are the brain's way of distinguishing whether a stimulus is associated with one's own actions or something else. In neural terms, CDs are copies of motor plans that are propagated to sensory cortex where they can be compared with inputs. A range of phenomena associated with schizophrenia from auditory hallucinations to visual processing difficulties to the ability of patients to tickle themselves can be explained as pathologies in CD mechanisms. Auditory hallucinations for example involve patients failing to perceive themselves as the author of their own inner speech.Objectives and aimsTo test whether schizophrenia is associated with a structural network disruption that could impair CD signals involved in language processing, adolescents with schizophrenia were examined using magnetic resonance imaging and compared to healthy controls.MethodsA graph theoretical approach was used to analyse the connectivity in networks centered on:– Broca's area;– Wernicke's area.Connectivity information was acquired using diffusion tensor imaging (DTI).ResultsCompared to healthy controls, adolescents with schizophrenia displayed a lower average degree of connectivity with the left inferior frontal gyrus (Broca's area). No significant differences were found in the degree of connectivity with the right inferior frontal gyrus and the superior temporal gyrus bilaterally (Wernicke's area).ConclusionsThe results suggest a link between schizophrenia and impairment to areas where CDs associated with inner speech plausibly originate.Disclosure of interestThe authors have not supplied their declaration of competing interest.

A Cluster-Based Consensus Algorithm in a Wireless Sensor Network

In this paper, we propose an average connectivity degree cluster (ACDC) scheme gossip algorithm to improve the convergence speed and the accuracy of the consensus, when a common decision is needed for a certain phenomenon in a distributed network. We analyze the effects of the initial value, the network topology (regular and irregular), and the number of clusters on the algorithm convergence rate as well as the accuracy of the value when reaching consensus. A utility function is developed based on two parameters, iteration and relative error, to help the network designers make an optimal decision based on their requirements. An irregular sensor model which is based on the degree of irregular (DOI) radius is introduced to evaluate the robustness of the algorithm. The simulation results demonstrate that for any initial value and network topology, the proposed ACDC gossip algorithm can yield results that are 50% closer to the real average value than the referenced standard gossip and grid cluster gossip algorithms. With different DOI values, our ACDC gossip algorithm can still reach lower relative error compared with other gossip algorithms, which demonstrates that our algorithm is robust enough to be executed in the network.

Toward collinearity-aware and conflict-friendly localization for wireless sensor networks

Localization aims at determining node positions and is essential for many applications in wireless sensor networks (WSNs). Most existing localization protocols adopt graph rigidity theory as the theoretical basis. The rigidity theory assumes that every three nodes are noncollinear in a two-dimensional graph; this assumption, however, may not always hold in WSNs. A lack of node collinearity verification places a limitation on localization accuracy. Furthermore, existing localization protocols explore only distance constraints for localization, giving rise to another limitation on localization percentage. Against these limitations, this paper presents two approaches toward collinearity-aware and conflict-friendly rigidity-based localization for WSNs. The proposed approaches are expected to increase both localization accuracy and percentage of traditional rigidity-based localization protocols. First, to achieve collinearity-awareness, we investigate node collinearity and propose a detection method to mitigate localization errors induced by probably collinear nodes. Second, to achieve conflict-friendliness, besides distance constraints, we explore distance conflicts to eliminate position ambiguities. Distance conflicts relax the sufficient condition of 3-connectivity for localizability to 2-connectivity; this relaxation can significantly improve localization percentage. For example, trilateration using distance conflicts yields a higher efficacy in both convex and non-convex WSNs and requires only a 25% lower average connectivity degree to locate 95% of sensors. The proposed approaches can be conveniently incorporated into existing localization protocols with small overhead. We validate their effectiveness of enhancing localization accuracy and percentage through both real and simulation experiments.

Coherent periodic activity in excitatory neural networks : the role of network connectivity

Neural collective oscillations have been observed in very many context in brain circuits, ranging from ubiquitous γ-oscillations to θ-rythm in the hyppocampus. The origin of these oscillations is commonly associated to the balance between excitation and inhibition in the network, while purely excitatory circuits are believed to lead to “unstructured population bursts” [1]. However, coherent activity patterns have been observed also in “in vivo” measurements of the developing rodent neocortex and hyppocampus for a short period after birth, despite the fact that at this early stage the nature of the involved synapses is essentially excitatory [2]. Of particular interest are the so-called giant depolarizing potentials (GDPs), recurrent oscillations which repetitevely synchronizes a relatively small assembly of neurons and whose degree of synchrony is orchestrate by hub neurons [3]. Furthermore, in their recent study Bonifazi et al. [3] found that the functional connectivity of developing hippocampal networks is characterized by a power-law distribution of the output links with an exponent δ ~ 1.1 – 1.3. On the other hand, computational and theoretical studies of excitatory networks of leaky integrate-and-fire (LIF) neurons have revealed a regime characterized by coherent periodic activity at a macroscopic level both in fully [4] as well as in diluted networks [5]. This regime is characterized by a partial synchronization (PS) among the neurons reflected by a periodic behaviour of collective observables, while the single neuron evolution is quasi-periodic in time. The random dilution in the neuronal connectivity induces a weak form of chaos which vanishes for sufficiently large networks, therefore the PS regime appears to be robust with respect to this kind of disorder [6]. In the same framework, namely pulse-coupled LIF excitatory networks, we have analyzed the role of the average degree of connectivity for the stability of the PS regime. In particular, we have considered directed random network with Erdos-Renyi topology with an average connectivity C corresponding to that of a finite scale-free network characterized by a power-law distribution with a decay exponent 1 <δ < 2. This amounts to have C which scales as N^(2-δ) with the number of neurons in the network. Therefore as two limiting cases massively connected networks are recovered for δ = 1 (where C is proportional to N) and for δ=2 sparse networks (where C is constant). Our analysis has revealed that, for a fixed system size N, coherent periodic dynamics can be observed only for a connectivity C larger than a critical value Г=Г(N) and that this value is independent from the exponent δ. Furthermore, in the thermodynamic limit Г tends to an asymptotic value which depends only how the random connections are generated, namely statically or dynamically. The emergence of collective dynamics is controlled simply by the number of afferent synapses, independent of all the other details in the network topology, in agreement with what found for the synchronization of bursting neurons [6]. Finally, we have found indications that these systems are weakly chaotic, in the sense that in the thermodynamic limit completely regular dynamical behaviours are recovered [5]. Nonetheless, the degree of chaoticity increases by increasing δ and in the sparse network limit we expect to observe a constant maximal Lyapunov exponent, independent by N.

Monte Carlo simulation of particle aggregation and gelation: I. Growth, structure and size distribution of the clusters

Lattice and off-lattice Monte Carlo simulations of diffusion-limited cluster aggregation and gelation were done over a broad range of concentrations. The large-scale structure and the size distribution of the clusters are characterized by a crossover at a characteristic size (m(c)). For m < m(c), they are the same as obtained in a dilute DLCA process and for m >> m(c) they are the same as obtained in a static percolation process. m(c) is determined by the overlap of the clusters and decreases with increasing particle concentration. The growth rate of large clusters is a universal function of time reduced by the gel time. The large-scale structural and temporal properties are the same for lattice and off-lattice simulations. The average degree of connectivity per particle in the gels formed in off-lattice simulations is independent of the concentration, but its distribution depends on the concentration.