Average Link Research Articles

Hypergraph Convolutional Networks with Multi-Ordering Relations for Cross-Document Event Coreference Resolution

Recognizing the coreference relationship between different event mentions in the text (i.e., event coreference resolution) is an important task in natural language processing. It helps to understand the association between various events in the text, and plays an important role in information extraction, question answering systems, and reading comprehension. Existing research has made progress in improving the performance of event coreference resolution, but there are also some shortcomings. For example, most of the existing methods analyze the event data in the document in a serial processing mode, without considering the complex relationship between events, and it is difficult to mine the deep semantics of events. To solve these problems, this paper proposes a cross-document event co-reference resolution method (HGCN-ECR) based on hypergraph convolutional neural networks. Firstly, the BiLSTM-CRF model was used to label the semantic role of the events extracted from a number of scientific and technical kinds of literature. According to the labeling results, the trigger words and non-trigger words of the event were determined, and the multi-document event hypergraph was constructed around the event trigger words. Then hypergraph convolutional neural networks are used to learn higher-order semantic information in multi-document event hypergraphs, and multi-head attention mechanisms are introduced to understand the hidden features of different event relationship types by treating each event relationship as a set of separate attention mechanisms. Finally, the feed-forward neural network and the average link clustering method are used to calculate the coreference score of events and complete the coreference event clustering, and the cross-document event coreference resolution is realized. The experimental results show that the cross-document event co-reference resolution method is superior to the baseline model.

On the use of vibrations and temperatures for the monitoring of plastic chain conveyor systems

In industrial automation, the transportation of unit loads plays a crucial role. This paper addresses the under-explored area of plastic chain conveyors, a versatile and efficient means of transport within production processes. Despite their widespread use, these conveyors suffer from wear and tear due to continuous contact between the chain and guide rails, often leading to unscheduled shutdowns and increased costs. The literature on monitoring solutions for such systems is sparse, particularly for plastic chains. This paper aims to bridge this gap by surveying the most common wear mechanisms for these systems and presenting a physical model that describes the interaction between the chain links and guide rails, based on chain of masses, springs and dampers and the Archard wear model. This model allows to relate the variations due to wear with other physical quantities, such as passage frequencies of the links and heat generated due to friction. The comprehensive model provides analytical formulas to understand the vibrations induced by the chain on the platform and the guide-rail temperatures. To validate the model and evaluate how the measurements are affected by the degradation of the system, an extensive experimental campaign was conducted on a conveyor test rig, acquiring vibration and temperature data. The end goal is to identify and discuss methodologies suitable for estimating the wear mechanisms of conveyor systems, with a focus on the constraints of industrial environments. Hence, this study aims to lay the groundwork for defining monitoring solutions that enhance the maintenance efficiency of plastic chain conveyor systems. The results suggest that the power Spectral Densities (PSD) of the platform’s accelerations are sufficient to estimate the average links pitch, which in turn could be used to assess the chain elongation. Besides, the local temperatures confirmed the model observations, making them suitable for estimating the slide-rail thinning.

Measuring Entanglement in Physical Networks.

The links of a physical network cannot cross, which often forces the network layout into nonoptimal entangled states. Here we define a network fabric as a two-dimensional projection of a network and propose the average crossing number as a measure of network entanglement. We analytically derive the dependence of the average crossing number on network density, average link length, degree heterogeneity, and community structure and show that the predictions accurately estimate the entanglement of both network models and of real physical networks.

An intelligent clustering scheme based on whale optimization algorithm in flying ad hoc networks

Due to the progress of unmanned aerial vehicles (UAVs), this new technology is widely applied in military and civilian areas. Multi-UAV networks are often known as flying ad hoc networks (FANETs). Due to these applications, FANET must ensure communication stability and have high scalability. These goals are achieved by presenting clustering techniques in FANETs. However, the characteristics of these networks, like high-mobility nodes, limited energy, and dynamic topology, have created great challenges in two important processes of clustering protocols, namely cluster construction and the selection of cluster heads. In this paper, an intelligent clustering scheme based on the whale optimization algorithm called ICW is suggested in flying ad hoc networks. Firstly, each UAV specifies its hello interval based on the lifespan of adjacent links to guarantee the adaptability of ICW to FANET. Then, a centralized clustering process is done using a whale optimization algorithm (WOA) to find the best cluster centers on the network. To determine the membership of each UAV in a cluster, ICW employs a new criterion, i.e. closeness ratio, so that each UAV joins a cluster with the best closeness ratio. In addition, the evaluation of each whale is carried out based on a fitness function, consisting of three components, namely the number of isolated clusters, the ratio of inter-cluster distance to intra-cluster distance, and cluster size. Then, a cluster head is selected for each cluster based on a score value. This score is dependent on the weighted sum of four metrics, namely remaining energy, the average link lifespan between each UAV and its neighbors, neighbor degree, and the average distance between each UAV and its neighbors. In the last step, two routing processes, namely intra-cluster routing and inter-cluster routing, are introduced in FANET. Then, the evaluation and implementation of ICW is performed through the NS2 simulator. After completing the simulation process, ICW is compared to MWCRSF, DCM, and GWO, and the evaluation results are presented in two scenarios, namely network evaluation in the clustering process and network evaluation in the routing process. Accordingly, in the first scenario, ICW has low clustering time and a high cluster lifetime. In the second scenario, ICW optimizes energy consumption, network longevity, packet delivery rate, routing overhead, and delay compared to other approaches. However, throughput in ICW is about 3.9% lower than that in MWCRSF.

A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble

Clustering ensembles can obtain more superior final results by combining multiple different clustering results. The qualities of the points, clusters, and partitions play crucial roles in the consistency of the clustering process. However, existing methods mostly focus on one or two aspects of them, without a comprehensive consideration of the three aspects. This paper proposes a three-level weighted clustering ensemble algorithm namely unified point-cluser-partition algorithm (PCPA). The first step of the PCPA is to generate the adjacency matrix by base clusterings. Then, the central step is to obtain the weighted adjacency matrix by successively weighting three layers, i.e., points, clusters, and partitions. Finally, the consensus clustering is obtained by the average link method. Three performance indexes, namely F, NMI, and ARI, are used to evaluate the accuracy of the proposed method. The experimental results show that: Firstly, as expected, the proposed three-layer weighted clustering ensemble can improve the accuracy of each evaluation index by an average value of 22.07% compared with the direct clustering ensemble without weighting; Secondly, compared with seven other methods, PCPA can achieve better clustering results and the proportion that PCPA ranks first is 28/33.

Stable and efficient networks with neighborhood‐influenced externalities

AbstractThis paper analyzes the incentives of individuals to add or sever links that imply the formation of stable and efficient networks when a society is partitioned into groups. In terms of group partitioning, we determine cost topology by arranging a model in which a pair of players pays equally for the link connecting them and in which such a cost depends on the neighborhood composition of the pair when they belong to different groups. To be more precise, the cost of a link between players can be reduced if at least one of these players has neighbors from the group the other player belongs to. We examine specific network structures (i.e., minimal networks, minimally connected networks, complete networks, majority complete networks, and complete bipartite networks) when they are stable and efficient. Our analysis demonstrates how players' distribution among groups modifies the conditions of stability and efficiency. More significantly, we identify some fascinating phenomena which sharply contrast with most literature dealing with stable and efficient networks: (i) the nonminimal network can be stable in the absence of a benefit decay through the path; (ii) a player may prefer to link with players in other groups with a higher average link cost abandoning connection with the partners from her own group; (iii) it is impossible to ensure that the complete network will be efficient for partition with certain characteristics irrespective of the decay factor and the value of costs. The numerical examples are provided to illustrate our theoretical findings.

Transportation Simulation Modeling and Location-Based Services Data Completion Based on a Data and Model Dual-Driven Approach

The evolving economic and technological landscape has brought about significant changes in travel behaviors and traffic patterns. These changes have led to the emergence of complex, multi-modal travel demands that interact with transportation networks, posing new challenges in transportation analysis and control. The primary objective of this study is to address these challenges by improving transportation modeling and data completeness using advanced modeling tools and transportation big data. We propose a dual-driven simulation model that integrates transportation simulation and big data. The approach begins by utilizing initial Location-Based Services (LBS) data to establish a mesoscopic multi-modal simulation model, which is then calibrated. This calibrated model is then employed to complete the missing trajectories of the LBS data. The innovative aspect of this dual-driven simulation model lies in its novel approach to constructing transportation models and completing LBS data, thereby enhancing both the simulation accuracy and the results of missing path completion. We conduct tests using the urban area of Hangzhou as an example, and the results show that the Normalized Root Mean Square Error (NRMSE) between the average link speeds in the simulation model and in real world observation is reduced to 24.1%. In the LBS data completion process, our proposed method achieves a travel mode identification accuracy of 95.3% for private car travel. Compared to the two baseline methods, the average accuracy of completed trajectories increases by 6.31% and 2.46%, respectively.

Identification of Quantitative Trait Loci Controlling Root Morphological Traits in an Interspecific Soybean Population Using 2D Imagery Data.

Roots are the hidden and most important part of plants. They serve as stabilizers and channels for uptaking water and nutrients and play a crucial role in the growth and development of plants. Here, two-dimensional image data were used to identify quantitative trait loci (QTL) controlling root traits in an interspecific mapping population derived from a cross between wild soybean 'PI366121' and cultivar 'Williams 82'. A total of 2830 single-nucleotide polymorphisms were used for genotyping, constructing genetic linkage maps, and analyzing QTLs. Forty-two QTLs were identified on twelve chromosomes, twelve of which were identified as major QTLs, with a phenotypic variation range of 36.12% to 39.11% and a logarithm of odds value range of 12.01 to 17.35. Two significant QTL regions for the average diameter, root volume, and link average diameter root traits were detected on chromosomes 3 and 13, and both wild and cultivated soybeans contributed positive alleles. Six candidate genes, Glyma.03G027500 (transketolase/glycoaldehyde transferase), Glyma.03G014500 (dehydrogenases), Glyma.13G341500 (leucine-rich repeat receptor-like protein kinase), Glyma.13G341400 (AGC kinase family protein), Glyma.13G331900 (60S ribosomal protein), and Glyma.13G333100 (aquaporin transporter) showed higher expression in root tissues based on publicly available transcriptome data. These results will help breeders improve soybean genetic components and enhance soybean root morphological traits using desirable alleles from wild soybeans.

Quorum sensing bacteria improve microbial networks stability and complexity in wastewater treatment plants

Quorum-sensing bacteria (QSB) are crucial factors for microbial communication, yet their ecological role in wastewater treatment plants (WWTPs) remains unclear. Here, we developed a method to identify QSB by comparing 16S rRNA gene sequences. QSB in 388 activated sludge samples collected from 130 WWTPs across China primarily were identified as rare taxa and conditionally rare taxa. A co-occurrence network shared by all sludge communities revealed that QSB exhibited higher average clustering coefficient (0.46) than non-QSB (0.15). Individual sludge networks demonstrated that quorum sensing microbiomes were positively correlated with network robustness and network complexity, including average clustering coefficient and link density. We confirmed that QSB keystones and QSB nodes have a positive impact on network complexity by influencing network modularity through a structural equation model. Meanwhile, QSB communities directly contributed to maintaining network robustness (r = 0.29, P < 0.05). Hence, QSB play an important role in promoting network complexity and stability. Furthermore, QSB communities were positively associated with the functional composition of activated sludge communities (r = 0.33, P < 0.01), especially the denitrification capacity (r = 0.45, P < 0.001). Overall, we elucidated the ecological significance of QSB and provided support for QS-based regulation of activated sludge microbial communities.

Reinforcement Learning-Based Joint Beamwidth and Beam Alignment Interval Optimization in V2I Communications

The directional antenna combined with beamforming is one of the attractive solutions to accommodate high data rate applications in 5G vehicle communications. However, the directional nature of beamforming requires beam alignment between the transmitter and the receiver, which incurs significant signaling overhead. Hence, we need to find the optimal parameters for directional beamforming, i.e., the antenna beamwidth and beam alignment interval, that maximize the throughput, taking the beam alignment overhead into consideration. In this paper, we propose a reinforcement learning (RL)-based beamforming scheme in a vehicle-to-infrastructure system, where we jointly determine the antenna beamwidth and the beam alignment interval, taking into account the past and future rewards. The simulation results show that the proposed RL-based joint beamforming scheme outperforms conventional beamforming schemes in terms of the average throughput and the average link stability ratio.

Comparative assessment of projection and clustering method combinations in the analysis of biomedical data

BackgroundClustering on projected data is common in biomedical research analysis. Principal component analysis (PCA) is widely used for projection, focusing on data dispersion (variance), while clustering identifies data concentrations (neighborhood). These are conflicting aims. This study re-evaluates combinations of PCA and other projection methods with common clustering algorithms. MethodsSix projection methods (PCA, ICA, isomap, MDS, t-SNE, UMAP) were combined with five clustering algorithms (k-means, k-medoids, single link, Ward's method, average link). Projections and clusterings were evaluated using a numerical criterion for evaluating clustering performance and a visual criterion based on plotting the projected data on a Voronoi tessellation plane with class-wise coloring. Nine artificial and five real biomedical datasets were analyzed. ResultsNo combination consistently captured prior classifications in projections and clusters. Visual inspection proved essential. PCA was often but not always outperformed or equaled by neighborhood-based methods (UMAP, t-SNE) and manifold learning techniques (isomap). ConclusionsThe results dissaprove PCA as a standard projection method prior to clustering. Therefore, method selection should be data specific as a tailored approach to data projection and clustering in biomedical analysis. To aid this process, we propose a novel visualization technique that combines Voronoi tessellation with color coding.

Generalization of Split DC Link Voltages Behavior in Three-Phase-Level Converters Operating with Arbitrary Power Factor with Restricted Zero-Sequence Component

This article examines the impact of a power factor on the behavior of partial DC link voltages in three-phase three-level AC/DC (or DC/AC) converters operating without additional balancing hardware. We consider the case in which the controller utilizes a bandwidth-restricted (DC in steady state) zero-sequence component to achieve average partial DC link voltage equalization since the injection of high-order zero-sequence components is impossible or forbidden. An assessment of partial split DC-link capacitor voltage behavior (particularly that of ripple magnitudes and phases) is necessary for, e.g., minimizing the values of DC link capacitances and selecting reference voltage values. Previous studies assessed the abovementioned behavior analytically for operation under a unity power factor based on third-harmonic-dominated split partial voltages’ ripple nature. However, it is shown here that deviation from the unity power factor introduces additional (to the third harmonic) non-negligible harmonic content, increasing partial voltage ripple magnitudes and shifting their phase (relative to the mains voltages). As a result, the third-harmonic-only assumption is no longer valid, and it is then nearly impossible to derive corresponding analytical expressions. Consequently, a numerical approach is used in this work to derive a generalized expression of normalized ripple energy as a function of the power factor, which can then easily be utilized for assessments of split DC link voltage behaviors for certain DC link capacitances and reference voltages. Simulations and experimental results validate the proposed methodology applied to a 10 kVA T-type converter prototype.

Data conversion control of virtual network devices in cloud computing: A deep reinforcement learning approach

As the response time of virtual space task requests is affected by different data conversion processes, it becomes more serious to solve the load imbalance problem between the underlying nodes and the underlying data links. The traditional least square method needs to distinguish each node for the remapping of the original virtual space. After the trajectories in different spaces and times are mapped again, it is easier to cause new resource bottlenecks. Aiming at the necessary virtual space remapping cycle of cloud service nodes, this paper selects the nodes with the highest upper and lower limits of load rate according to the dynamic critical value of each node's load rate. It migrates part of the virtual network space on the overloaded service nodes to the non-important nodes with a lower load rate, which is to avoid the gradual formation of new resource bottlenecks after remapping. The data transformation model based on reinforcement learning can further improve the acceptance rate of virtual space requests and reduce the computational overhead caused by space remapping. The experiments compare the performance evaluation of different algorithm models on the simulation platform data conversion tasks, including complex instructions test, virtual network performance test, average remapping link overhead, and root mean square error of the underlying node load rate test. The test results indicate that the calculation accuracy of the proposed method is 7.25% higher than other comparison methods, and the computational overhead is reduced by 15.22%. The overall performance is improved by 10.74%. It is verified that the technology in this paper improves the data conversion control method of virtual network devices in the cloud computing environment, and has practical significance for virtual network remapping and data services in cloud computing.

Outlier detection of vital sign trajectories from COVID-19 patients.

In this work, we present a novel trajectory comparison algorithm to identify abnormal vital sign trends, with the aim of improving recognition of deteriorating health.There is growing interest in continuous wearable vital sign sensors for monitoring patients remotely at home. These monitors are usually coupled to an alerting system, which is triggered when vital sign measurements fall outside a predefined normal range. Trends in vital signs, such as increasing heart rate, are often indicative of deteriorating health, but are rarely incorporated into alerting systems.We introduce a dynamic time warp distance-based measure to compare time series trajectories. We split each multi-variable sign time series into 180 minute, non-overlapping epochs. We then calculate the distance between all pairs of epochs. Each epoch is characterized by its mean pairwise distance (average link distance) to all other epochs, with clusters forming with nearby epochs.We demonstrate in synthetically generated data that this method can identify abnormal epochs and cluster epochs with similar trajectories. We then apply this method to a real-world data set of vital signs from 8 patients who had recently been discharged from hospital after contracting COVID-19. We show how outlier epochs correspond well with the abnormal vital signs and identify patients who were subsequently readmitted to hospital.

Connectivity-Aware Contract for Incentivizing IoT Devices in Complex Wireless Blockchain

Blockchain is considered the critical backbone technology for secure and trusted Internet of Things (IoT) in the future 6G network. However, deploying a blockchain system in a complex wireless IoT network is challenging due to the limited resources, complex wireless environment, and the property of self-interested IoT devices. The existing incentive mechanism of blockchain is not compatible with the wireless IoT network. In this paper, to incentivize IoT devices to join the construction of the wireless blockchain network, we propose a multi-dimensional contract to optimize the blockchain utility while addressing the issues of adverse selection and moral hazard. Specifically, the proposed contract considers the IoT device’s hash power and communication cost and especially explores the connectivity of devices from the perspective of complex network theory. We investigate the energy consumption and the block confirmation probability of the wireless blockchain network via simulations under varied network sizes and average link probability. Numerical results demonstrate that our proposed contract mechanism is feasible, achieves 35% more utility than existing approaches, and increases utility by 4 times compared with the original PoW-based incentive mechanism.

A hybrid estimation of carbon footprints for urban commuting transportation via path reconstruction

The transportation sector is a major source of carbon emissions, and it is of great significance to study the estimation method of carbon emissions from urban commuting traffic for energy conservation and emission reduction. In view of the difficulty of collecting detailed trip trajectory data, this paper first reconstructs the trip paths via an improved modal choice model and a modified path planning model based on the O-D trip matrix, taking seven single traffic modals and two combined modals into account. In order to estimate the carbon footprints with theoretical accuracy, the bottom-up method is adopted considering the trip modal, vehicle type, power source, vehicle occupancy, operation characteristics and traffic conditions. Meanwhile, faced with the converted carbon emissions from electric vehicles, factors like charging efficiency, vehicular load, regional power structure and transmission loss are further considered in the estimation function. A case study of Changzhou City has been performed to verify the feasibility of the proposed models, where the volume distribution of commuting trips is predicted upon a modified network traffic assignment by TransCAD, and the spatial distribution of carbon emission intensity has further expanded to the adjacent areas via ArcMap analysis tools. The total carbon emission and the average link emission intensity of daily commuting in the study area are about 14.7 × 105 kg/day and 870 kg/km respectively. The discussion results indicate that the CO2 emission of fuel-driven vehicles accounts for over 86%, and the equivalent carbon emission of electric vehicles accounts for about 14% under given modal choices. The correlations of carbon emissions to road levels and zone attributes get further revealed and discussed based on the estimation results.

A minimal model for adaptive SIS epidemics

The interplay between disease spreading and personal risk perception is of key importance for modelling the spread of infectious diseases. We propose a planar system of ordinary differential equations (ODEs) to describe the co-evolution of a spreading phenomenon and the average link density in the personal contact network. Contrary to standard epidemic models, we assume that the contact network changes based on the current prevalence of the disease in the population, i.e. the network adapts to the current state of the epidemic. We assume that personal risk perception is described using two functional responses: one for link-breaking and one for link-creation. The focus is on applying the model to epidemics, but we also highlight other possible fields of application. We derive an explicit form for the basic reproduction number and guarantee the existence of at least one endemic equilibrium, for all possible functional responses. Moreover, we show that for all functional responses, limit cycles do not exist. This means that our minimal model is not able to reproduce consequent waves of an epidemic, and more complex disease or behavioural dynamics are required to reproduce epidemic waves.

Optimizing incremental SDN upgrades for load balancing in ISP networks

Software defined network (SDN) can dynamically and timely reply to the changes of network states, thus enabling advance traffic engineering mechanisms. To enhance the management ability of the network, Internet Service Providers (ISPs) are upgrading traditional network devices to SDN devices incrementally. In this paper, we study the k-LB problem, i.e., upgrading at most k legacy switches to SDN switches to achieve load balance. We prove that k-LB problem is NP-hard and there is no polynomial time (N+M)1−ϵ-approximation algorithm for any constant ϵ>0 unless P=NP, where N (M) is the number of switches (links) in the network. Nevertheless, we propose an effective greedy algorithm and prove that it reaches an approximation guarantee of cavgcminM, where cavg (cmin) is the average (minimum) link capacity. Furthermore, we show that the greedy algorithm touches the tight lower bound of approximation ratio by extending the inapproximability result. The simulation results from large-scale ISP network topologies illustrate the effectiveness of our algorithm and show that the maximum link utilization can be decreased by 30% on average compared with the SOTA.

Bacterial co-occurrence patterns are more complex but less stable than archaea in enhanced oil recovery applied oil reservoirs

A complex microbial community exists in oil reservoirs after the application of chemical enhanced oil recovery (EOR) technologies, where the dynamic interactions of microorganisms are thought to be critical to petroleum composition, recovery, and production technologies but are poorly understood. Here, we used the 16S rRNA gene to construct co-occurrence networks for bacterial and archaeal communities associated with water-, polymer-, and alkaline surfactant polymer (ASP)-flooded oil reservoirs. Nonmetric multidimensional scaling analysis showed that bacterial and archaeal community structures differed significantly (p < 0.01) among the oil reservoirs with different EOR compositions. The microbial co-occurrence pattern in water-flooded reservoirs was more complex, with more nodes, edges, and average degrees (i.e., average links per node in the network) than in the polymer- and ASP-flooded reservoirs. However, the archaeal co-occurrence pattern maintained a more robust structure than that of bacteria under the application of polymer and ASP flooding technologies, suggesting that the application of different enhanced oil recovery techniques has diverse effects on the bacterial and archaeal co-occurrence patterns in reservoirs. The pH and [HCO3−] were significantly correlated with the variations in bacterial and archaeal network structures. Our findings provide new insights into the response of microbes to diverse EOR processes by regulating their interactions in deep oil reservoirs. Revealing the effects of water, polymer, and ASP flooding on the co-occurrence patterns of bacteria and archaea in oil reservoirs, which may facilitate the implementation of microbial enhanced oil recovery technology in the future.

Throughput properties and optimal locations for limited deployment of Max-pressure controls

Max-pressure (MP) control is one of the few traffic signal controllers proven to maximize network throughput or maximally stabilize the network. According to the theoretical results published so far, it can stabilize a network if all intersections are equipped with MP control for all stabilizable demands. However, budget constraints may not allow the installation of MP control on all intersections. Previous work did not consider a limited number of MP controlled intersections while proving the stability properties. Therefore, it is not clear whether a network can still be stabilized with a limited deployment of MP control. Using Lyapunov drift techniques this paper proves that even with a limited deployment, MP control can stabilize a network within feasible demand. Then we present an optimization formulation to find the optimal intersections to install MP control given a limited budget. We also present a greedy efficient algorithm to solve that optimization problem and prove that the algorithm solves the problem to optimality. Numerical results from simulations conducted on the downtown Austin network using an in-house custom simulator called AVDTA are then presented. The change in theoretical maximum servable demands for different amounts of deployments obtained from the optimization problem seemed to match with simulation results most of the times. We found that limited deployment of MP control almost always performed better than random deployment of MP control in terms of servable stable demand. Average total queue length and link density were observed to decrease as the number of MP controls increased which indicates better network performance. Average travel times per vehicle also decreased with installations of MP controls which shows how the travelers would benefit from more MP controls.