Total Number Of Connections Research Articles

Returns to workplace connectivity: evidence from Brazil

ABSTRACT We empirically investigate the quantitative effect of workplace connectivity on workers’ wages. Connectivity measures a worker’s rank within the workplace’s connection hierarchy, considering the number of co-workers in the same occupation and firm (workplace) and the relative tenure of immediate co-workers. We construct a connectivity index as the ratio of a worker’s tenure-weighted connections to the total number of connections in the workplace, using a worker’s job tenure and the connections formed over time to obtain a relative measurement of their connectivity. Using a unique dataset from the Brazilian Annual Social Information Report (RAIS), we estimate the (fixed effect) wage equation, controlling for work and firm heterogeneity. Our results show positive and statistically significant returns to connectivity. The effect is particularly pronounced for workers in administrative services and industrial goods production, those at the bottom of the connectivity distribution, and those in medium-sized firms. We also find that a worker’s connectivity is more relevant for wages than their tenure.

Effects of Combined Stress of High Density Polyethylene Microplastics and Chlorimuron-ethyl on Soybean Growth and Rhizosphere Bacterial Community

With the vigorous development of agriculture in China， plastic mulch film and pesticides are widely used in agricultural production. However， the accumulation of microplastics （formed by the degradation of plastic mulch film） and pesticides in soil has also caused many environmental problems. At present， the environmental biological effects of microplastics or pesticides have been reported， but there are few studies on the combined effects on crop growth and the rhizosphere soil bacterial community. Therefore， in this study， the high density polyethylene microplastics （HDPE， 500 mesh） were designed to be co-treated with sulfonylurea herbicide chlorimuron-ethyl to study their effects on soybean growth. In addition， the effects of the combined stress of HDPE and chlorimuron-ethyl on soybean rhizosphere soil bacterial community diversity， structure composition， microbial community network， and soil function were investigated using high-throughput sequencing technology， interaction network， and PICRUSt2 function analysis to clarify the combined toxicity of HDPE and chlorimuron-ethyl to soybean. The results showed that the half-life of chlorimuron-ethyl in soil was prolonged by the 1% HDPE treatment （from 11.5 d to 14.3 d）， and the combined stress of HDPE and chlorimuron-ethyl had more obvious inhibition effects on soybean growth than that of the single pollutant or control. The HiSeq 2 500 sequencing showed that the rhizosphere bacterial community of soybean was composed of 20 phyla and 312 genera under combined stress， the number of phyla and genera was significantly less than that of the control and single pollutant treatment， and the relative abundances of bacteria with potential biological control and plant growth-promoting characteristics （such as Nocardioides and Sphingomonas） were reduced. Alpha diversity analysis showed that the combined stress significantly reduced the richness and diversity of the soybean rhizosphere bacterial community， and Beta diversity analysis showed that the combined stress significantly changed the structure of the bacterial community. The dominant flora of the rhizosphere bacterial community were regulated， and the abundances of secondary functional layers such as amino acid metabolism， energy metabolism， and lipid metabolism were reduced under combined stress by the analysis of LEfSe and PICRUSt2. It was inferred from the network analysis that the combined stress of HDPE and chlorimuron-ethyl reduced the total number of connections and network density of soil bacteria， simplified the network structure， and changed the important flora species to maintain the stability of the network. The results above indicated that the combined stress of HDPE and chlorimuron-ethyl significantly affected the growth of soybean and changed the rhizosphere bacterial community structure， soil function， and network structure. Compared with that of the single pollutant treatment， the potential risk of combined stress was greater. The results of this study can provide guidance for evaluating the ecological risks of polyethylene microplastics and chlorimuron-ethyl and for the remediation of contaminated soil.

Network-based restoration strategies maximize ecosystem recovery

Redressing global patterns of biodiversity loss requires quantitative frameworks that can predict ecosystem collapse and inform restoration strategies. By applying a network-based dynamical approach to synthetic and real-world mutualistic ecosystems, we show that biodiversity recovery following collapse is maximized when extirpated species are reintroduced based solely on their total number of connections in the original interaction network. More complex network-based strategies that prioritize the reintroduction of species that improve ‘higher order’ topological features such as compartmentalization do not provide meaningful performance improvements. These results suggest that it is possible to design nearly optimal restoration strategies that maximize biodiversity recovery for data-poor ecosystems in order to ensure the delivery of critical natural services that fuel economic development, food security, and human health around the globe.

Total Rainbow Connection Number Of Shackle Product Of Antiprism Graph (〖AP〗_3)

Function if is said to be k total rainbows in , for each pair of vertex there is a path called with each edge and each vertex on the path will have a different color. The total connection number is denoted by trc defined as the minimum number of colors needed to make graph to be total rainbow connected. Total rainbow connection numbers can also be applied to graphs that are the result of operations. The denoted shackle graph is a graph resulting from the denoted graph where t is number of copies of G. This research discusses rainbow connection numbers rc and total rainbow connection trc(G) using the shackle operation, where is the antiprism graph . Based on this research, rainbow connection numbers rc shack , and total rainbow connection trc shack for .

Comparison of Soil Bacterial Communities under Canopies of Pinus tabulaeformis and Populus euramericana in a Reclaimed Waste Dump

To compare the effects of different remediation tree species on soil bacterial communities and provide a theoretical basis for the selection of ecosystem function promotion strategies after vegetation restoration, the characteristic changes in soil bacterial communities after Pinus tabulaeformis and Populus euramericana reclamation were explored using high-throughput sequencing and molecular ecological network methods. The results showed that: (1) With the increase in reclamation years, the reclaimed soil properties were close to the control group, and the soil properties of Pinus tabulaeformis were closer to the control group than those of P. euramericana. (2) The dominant bacteria under the canopies of P. tabulaeformis and P. euramericana was the same. Proteobacteria, Actinobacteria, Acidobacteria, Chloroflexi, Gemmatimonadetes, Planctomycetes, Bacteroidetes, and Cyanobacteria were the dominant bacteria in the restored soil, accounting for more than 95% of the total abundance. The average values of the Shannon diversity index, Simpson diversity index, Chao 1 richness estimator, and abundance-based coverage estimator of the bacterial community in the P. euramericana reclaimed soil were higher than those in the P. tabulaeformis reclaimed soil. The influence of reclamation years on the bacterial community of samples is greater than that of species types. (3) The results of ecological network construction showed that the total number of nodes, total number of connections, and average connectivity of the soil bacterial network under P. euramericana reclamation were greater than those under P. tabulaeformis reclamation. The bacterial molecular ecological network under P. euramericana was more abundant. (4) Among the dominant bacteria, the relative abundance of Actinobacteria was negatively correlated with soil pH, soil total nitrogen content, and the activities of urease, invertase, and alkaline phosphatase, while the relative abundance of Proteobacteria and Bacteroidetes was positively correlated with these environmental factors. The relationship between the soil bacterial community of P. tabulaeformis and P. euramericana and the environmental factors is not completely the same, and even the interaction between some environmental factors and bacteria is opposite.

CBFL: A Communication-Efficient Federated Learning Framework From Data Redundancy Perspective

Federated learning (FL) is an emerging machinelearning framework, which enables multiple mobile users to collaboratively train a global model without uploading their local sensitive data. Due to limited network bandwidth, communication efficiency has become a significant bottleneck for the implementation of FL. Existing works attempt to improve this situation by reducing the total bits transferred for each client update via data compression. However, these research works are only from the perspective of update parameters to reduce the transmission of redundant parameters. They do not explore the intrinsic reasons for redundant parameters. In this article, we propose a coreset-based FL (CBFL) framework. Instead of training model on full datasets with a regular network model, CBFL uses a much smaller well-matched evolutionary network model on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coreset</i> . CBFL indirectly reduces the total transmission bits for each client while achieving a similar accuracy as training with full datasets. CBFL includes a novel distributed coreset construction and adaptive model evolution algorithms. The network model is adaptively adjusted during this process, which dynamically removes the least important connections from the current model. Experimental results with various datasets and models show that CBFL is able to find an optimized evolutionary model that has about 10% of the total number of connections in the original regular model while only about 2% degradation in model accuracy.

Fission Mechanism of 235U+n Reaction According to the Symmetrical Atomic Nucleus Model

In this study, the variants of structures are considered for fission fragments of the 235U nucleus caused by thermal neutrons, depending on differences in the initial configuration of proton, neutron and alpha particle compositions, according to a symmetrical model developed for the atomic nucleus. The proposed model is based on the principles of spatial symmetry and the analysis of the binding energy of the nucleus, taking into account the quark structure of nucleons. For the first time, the number of alpha particles in the composition of the 235U nucleus is considered to be 44 and the total number of connections between nucleons is 292. The work compares the binding energy of fragments of the atomic nucleus 235U, which have the same number of protons and neutrons in their composition, but a different number of alpha particles. The results obtained are the basis for an experimental study on the energy characteristics of various fission options of the 235U+n reaction, which is of interest for improving the efficiency of nuclear power sources.

Bank Competition Using Networks: A Study on an Emerging Economy

Interconnectedness among banks is a key distinguishing feature of the banking system. It helps mitigate liquidity problems but on the other hand, acts as a curse in propagating systemic risk at times of distress. Thus, as banks cannot function in isolation, this study uses the Contemporary Theory of Networks to examine banking competition in India for five distinct economic phases, emphasizing upon the Global Financial Crisis (GFC) and the ongoing COVID-19 pandemic. This paper proposes a Market Power Network Index (MPNI), which uses network parameters to measure banks’ market power. This network structure shows a formation of bank clusters that are involved in competition. Specifically, network properties, such as centroid, average path length, the distance of a node from the centroid, the total number of connections in the inter-bank market, and network density, do go on to explain banking competition. It is interesting to note that crisis periods witness a lower level of competition, with GFC bearing the least competition. The ongoing COVID-19 pandemic shows a lower trend, but it is of a higher magnitude than GFC. It was also found that big-sized, profitable, capital adequate, and public banks dominate the banking system. Notably, this study was conducted on a sample of 33 listed Indian banks from April 2008 to December 2020.

Influence network design via multi-level optimization considering boundedly rational user behaviours in social media networks

Social media networks have been playing an increasingly more important role for both socialization and information diffusion. Political campaign can gain more supporters by attracting more mass attention and influencing them directly, while commercial campaigns can increase their companies’ profits by expanding social media connection with new users. To build the optimal network structure to influence the whole, this paper studies mathematical models to simulate the users’ behaviours interacting with others in the information provider’s network. The behaviours of concerns include information re-posting and following/unfollowing other users. Linear threshold propagation model is used to determine the re-posting actions, Boundedly Rational User Equilibrium (BRUE) models are used to determine the following or unfollowing actions. Hence, the topology of the network changes and depends on the information provider’s plan to post various kinds of information. A three-level optimization model is proposed to maximize total number of connections, the goal of the top level. The second level simulates user behaviours under BRUE. The third level maximizes the each user’s utility defined in the second level. This paper solves this problem using exact algorithms for a small-scale synthetic network. For a large-scale problem, this paper uses heuristic algorithms based on large neighbourhood search. This paper also discusses possible reasons why the BRUE model may be a more accurate simulation of users’ actions compared to game theory. Comparisons from the BRUE model to game theoretical model show that the BRUE model performs significantly better than game theoretical model.

Contact network uncertainty in individual level models of infectious disease transmission

Infectious disease transmission between individuals in a heterogeneous population is often best modelled through a contact network. This contact network can be spatial in nature, with connections between individuals closer in space being more likely. However, contact network data are often unobserved. Here, we consider the fit of an individual level model containing a spatially-based contact network that is either entirely, or partially, unobserved within a Bayesian framework, using data augmented Markov chain Monte Carlo (MCMC). We also incorporate the uncertainty about event history in the disease data. We also examine the performance of the data augmented MCMC analysis in the presence or absence of contact network observational models based upon either knowledge about the degree distribution or the total number of connections in the network. We find that the latter tend to provide better estimates of the model parameters and the underlying contact network.

Design of Feedforward Neural Networks in the Classification of Hyperspectral Imagery Using Superstructural Optimization

Artificial Neural Networks (ANNs) have been used in a wide range of applications for complex datasets with their flexible mathematical architecture. The flexibility is favored by the introduction of a higher number of connections and variables, in general. However, over-parameterization of the ANN equations and the existence of redundant input variables usually result in poor test performance. This paper proposes a superstructure-based mixed-integer nonlinear programming method for optimal structural design including neuron number selection, pruning, and input selection for multilayer perceptron (MLP) ANNs. In addition, this method uses statistical measures such as the parameter covariance matrix in order to increase the test performance while permitting reduced training performance. The suggested approach was implemented on two public hyperspectral datasets (with 10% and 50% sampling ratios), namely Indian Pines and Pavia University, for the classification problem. The test results revealed promising performances compared to the standard fully connected neural networks in terms of the estimated overall and individual class accuracies. With the application of the proposed superstructural optimization, fully connected networks were pruned by over 60% in terms of the total number of connections, resulting in an increase of 4% for the 10% sampling ratio and a 1% decrease for the 50% sampling ratio. Moreover, over 20% of the spectral bands in the Indian Pines data and 30% in the Pavia University data were found statistically insignificant, and they were thus removed from the MLP networks. As a result, the proposed method was found effective in optimizing the architectural design with high generalization capabilities, particularly for fewer numbers of samples. The analysis of the eliminated spectral bands revealed that the proposed algorithm mostly removed the bands adjacent to the pre-eliminated noisy bands and highly correlated bands carrying similar information.

Foundations and Trends in Localization Technologies — Part I [Scanning the Issue

The advent of new wireless technologies in recent years has created a tremendous increase in the demand for wireless connectivity. The estimated number of mobile device connections exceeds eight billion globally, more than the population of the world, and the growth trend is rapidly increasing as the developing world penetration rate is still in its infancy. With the Internet-of-Things (IoT), it is expected that the total number of connections will reach over fifty billion in the next few years. In conjunction with wireless connectivity technology, the need for location awareness is paramount.

Improved Problem Table for Targeting Hydrogen Network with Single Intermediate Header

This paper introduces the generalized Improved Problem Table for flowrate targeting of hydrogen networks with single intermediate header. It consists of a stepwise procedure to locate the minimum flowrate targets for a hydrogen network, i.e., hydrogen utility, hydrogen header, waste hydrogen streams, as well as feed and product streams of purification system, prior to the design of hydrogen network. Three scenarios with direct reuse/recycling and purification reuse/recycling are analyzed for a literature case study to elucidate the proposed method. In scenarios A and C, the optimal flowrate targets are identical with those for the conventional hydrogen network without hydrogen header. The total number of direct connections between hydrogen sources and sinks are greatly reduced while the total number of connections is almost unchanged. In scenario B, the total number of direct connections between hydrogen sources and sinks is eliminated at the cost of a greatly increased flowrate of hydrogen utility.

Evaluating the impact of fast-fMRI on dynamic functional connectivity in an event-based paradigm.

The human brain is known to contain several functional networks that interact dynamically. Therefore, it is desirable to analyze the temporal features of these networks by dynamic functional connectivity (dFC). A sliding window approach was used in an event-related fMRI (visual stimulation using checkerboards) to assess the impact of repetition time (TR) and window size on the temporal features of BOLD dFC. In addition, we also examined the spatial distribution of dFC and tested the feasibility of this approach for the analysis of interictal epileptiforme discharges. 15 healthy controls (visual stimulation paradigm) and three patients with epilepsy (EEG-fMRI) were measured with EPI-fMRI. We calculated the functional connectivity degree (FCD) by determining the total number of connections of a given voxel above a predefined threshold based on Pearson correlation. FCD could capture hemodynamic changes relative to stimulus onset in controls. A significant effect of TR and window size was observed on FCD estimates. At a conventional TR of 2.6 s, FCD values were marginal compared to FCD values using sub-seconds TRs achievable with multiband (MB) fMRI. Concerning window sizes, a specific maximum of FCD values (inverted u-shape behavior) was found for each TR, indicating a limit to the possible gain in FCD for increasing window size. In patients, a dynamic FCD change was found relative to the onset of epileptiform EEG patterns, which was compatible with their clinical semiology. Our findings indicate that dynamic FCD transients are better detectable with sub-second TR than conventional TR. This approach was capable of capturing neuronal connectivity across various regions of the brain, indicating a potential to study the temporal characteristics of interictal epileptiform discharges and seizures in epilepsy patients or other brain diseases with brief events.

Rainbow total-coloring of complementary graphs and Erdos-Gallai type problem for the rainbow total-connection number

Rainbow total-coloring of complementary graphs and Erdos-Gallai type problem for the rainbow total-connection number

Equilibrium distributions in entropy driven balanced processes

For entropy driven balanced processes we obtain final states with Poisson, Bernoulli, negative binomial and Pólya distributions. We apply this both for complex networks and particle production. For random networks we follow the evolution of the degree distribution, Pn, in a system where a node can activate k fixed connections from K possible partnerships among all nodes. The total number of connections, N, is also fixed. For particle physics problems Pn is the probability of having n particles (or other quanta) distributed among k states (phase space cells) while altogether a fixed number of N particles reside on K states.

Left Anterior Temporal Lobe and Bilateral Anterior Cingulate Cortex Are Semantic Hub Regions: Evidence from Behavior-Nodal Degree Mapping in Brain-Damaged Patients.

Although the distributed-plus-hub organization of semantic memory has been proposed for several years, it remains unclear which hubs other than the anterior temporal lobe are included in the semantic system. Here, we identified such hubs from an innovative network perspective. The voxelwise nodal degree values were correlated with the performance of general and modality-specific semantic tasks in 86 patients with brain damage. We observed that the left anterior temporal lobe and bilateral anterior cingulate cortex could be semantic hubs because their decreased nodal degree values were significantly correlated with the severity of the deficit in semantic performance. The two hub regions might contribute to semantic representational and control processes, respectively. These findings offer new evidence for the distributed-plus-hub theory.

On rainbow total-coloring of a graph

A path P connecting two vertices u and v in a total-colored graph G is called a rainbow total-path between u and v if all elements in V(P)∪E(P), except for u and v, are assigned distinct colors. The total-colored graph G is rainbow total-connected if it has a rainbow total-path between every two vertices. The rainbow total-connection number, denoted by rtc(G), of a graph G is the minimum colors such that G is rainbow total-connected. It was shown that rtc(G)≤m(G)+n′(G), and the equality holds if and only if G is a tree, where n′(G) is the number of inner vertices of G. In this paper, we show that rtc(G)≠m(G)+n′(G)−1,m(G)+n′(G)−2 and characterize the graphs with rtc(G)=m(G)+n′(G)−3. With this result, the following sharp upper bound holds: for a connected graph G, if G is not a tree, then rtc(G)≤m(G)+n′(G)−3; moreover, the equality holds if and only if G belongs to five graph classes. We also investigate the Nordhaus–Gaddum-type lower bounds for the rainbow total-connection number of a graph and derive that if G is a connected graph of order n≥8, then rtc(G)+rtc(G¯)≥6 and rtc(G)rtc(G¯)≥9. An example is given to show that both of these bounds are sharp.

Analysis of electricity consumption under a photovoltaic micro-grid system in India

For a planner of decentralized renewable energy systems with micro-grids, an accurate load prediction is imperative for proper sizing of the system. For an accurate load projection, the following questions need to be answered: How many households actually get connected to the micro-grid? How much electricity do they actually use? What factors affect their consumption level? How does their consumption level change over time? In this study, an attempt has been made to identify answers to these questions by looking into one of the PV micro-grid rural electrification projects in Sundarban area of West Bengal, India. It is found that the number of beneficiary households has been on increasing trends indicating existence of large potential demands. The monthly electricity consumption of the average household is turned out to be within the range of 0.42–0.66kWh/day thus not showing any trend of increase over time. In order to identify factors that affect consumption level, a regression model has been developed with two explanatory variables, namely, the total number of connections and the average ambient temperature during supply hours (night). Application of the model would be effective in predicting additional load requirement in case grid extension is planned under the same project to accommodate potential demands. Results of this study would also be applicable to optimization of expansion process of micro-grid systems.

Epidemic spreading model of complex dynamical network with the heterogeneity of nodes

In this paper, we model epidemic spreading by considering the mobility of nodes in complex dynamical network based on mean field theory using differential equations. Moreover, a resistance factor which can characterise the impact of individual's difference on the propagation dynamics in complex dynamical network is proposed by considering the influence of total number of connections and the continuous time to remain in contact. The effect of heterogeneity on the evolution process of propagation dynamics is explored by simulation. Extensive simulations are conducted to study the key influence parameters and the influence of them on the spreading dynamics, which are helpful to the understanding of epidemic spreading mechanism and the designing of effective control strategies.