Node In Hierarchy Research Articles

Generalized strong locality inequality for multi-star-shaped quantum networks

The interest in quantum network correlations has surged, as Bell inequalities originating from a single source fall short of capturing the intricate many-body correlations within generic quantum networks. The introduction of a 3-grade m-star quantum network, a natural extension of star-shaped networks, features a hierarchy of nodes including A, m stars B 1, …, B m centered around A, and m stars C1j,…,Cmj centered around each B j , and m + m 2 independent sources. The strong locality of this network has been explored, assuming that each party possesses only two observables. In this paper, we put forward a significant extension to the strong locality of 3-grade m-star quantum networks. Specifically, each C j k performs n dichotomic measurements, while A and each B j conduct 2 n−1 dichotomic measurements. We establish a generalized strong locality inequality that holds for any value of n, and subsequently conduct a thorough analysis to determine the optimal quantum violation of established inequality. Through specific examples, we confirm the feasibility of achieving the optimal quantum violation of the generalized strong locality inequality for any n. We notice that for n > 3, a single copy of a two-qubit entangled state may not be sufficient to reveal the non-strong locality, whereas utilizing multiple copies can trigger this property.

Urban Polycentricity and Architectural Heritage: A PROMETHEE-Based Multicriteria Approach

Polycentricity is a multiscalar concept referring to the interconnections of neighboring spatial areas in search of synergies. More specifically, urban polycentricity has lately gained increasing momentum in academic research and strategic planning as urban polycentric structures could stimulate urban and regional performance in a sustainable way. Architectural heritage, with its connotative meanings, is among the indicators of incorporation in polycentric networking. The present paper investigates the challenges of implementing urban polycentricity methodological approaches to highlight new polycentric networks based on built architectural heritage. In this study, appointed architectural assets are considered as nodes of the potential networks. As a new research strand, the MCDA PROMETHEE method is applied to compile and assemble all indicators (namely, rank size and node hierarchy, accessibility, transportation, and digital promotion). The ultimate research goal is to build a new polycentricity index as an innovative methodological tool to highlight polycentric networking synergies on interurban and intraregional scales. The region of Thrace in Northern Greece is chosen to apply the proposed methodology. Research on ways to couple polycentricity and PROMETHEE methods is limited, and thus, the proposed methodological approach is expected to contribute to the field of urban and regional strategies for researchers and practitioners.

Distribution Network Reconfiguration Using Chaotic Particle Swarm Chicken Swarm Fusion Optimization Algorithm

Aiming at the problems of traditional optimization algorithms for reconfiguring distribution networks, which easily fall into a local optimum, have difficulty finding a global optimum, and suffer from low computational efficiency, the proposed algorithm named Chaotic Particle Swarm Chicken Swarm Fusion Optimization (CPSCSFO) is used to optimize the reconfiguration of the distribution network with distributed generation (DG). This article works to solve the problems mentioned above from the following three aspects: Firstly, chaotic formula is used to improve the initialization of the particles and optimize the optimal position. This increases individual randomness while avoiding local optimality for inert particles. Secondly, chicken swarm optimization (CSO) and particle swarm optimization (PSO) are combined. The multi-population nature of the CSO algorithm is used to increase the global search capability, and, at the same time, the information exchange between groups is completed to extend the particle search range, which ensures the independence and excellence of each particle group. Thirdly, the node hierarchy method is introduced to calculate the power flow. The branching loop matrix and the node hierarchy strategy are used to detect the network topology. In this way, improper solutions can be reduced, and the efficiency of the algorithm can be improved. This paper has demonstrated better performance by CPSCSFO based on simulation results. The network loss has been reduced and the voltage level of each node in the optimal reconfiguration with distributed power supply has been improved; the network loss in the optimal reconfiguration with DG is 69.59% lower than that reconfiguration before. The voltage level of each node is increased, the minimum node voltage is increased by 3.44% and a better convergence speed is presented. As a result, the quality of network operation and the distribution network is greatly improved and provides guidance for building a safer, more economical and reliable distribution network.

TSM: Temporal segmentation and modules-based computation offloading using predictive analytics and NR-V2X

Recent increases in the usage of applications dependent on distributed computing have persuaded designers and entrepreneurs to utilize these solutions for diverse purposes, especially latency-constrained computation-intensive applications. Vehicular fog computing (VFC) is the innovative paradigm of distributed computing techniques, and therefore, a number of VFC offloading frameworks have been developed using AI-based advanced optimization procedures, with support from standards maintenance organizations like IEEE, 3rd Generation Partnership Project (3GPP), and some others. However, many of these strategies have not been adapted to specific application data and types, therefore these frameworks may function poorly despite their comprehensive offloading principles. Moreover, most computation offloading frameworks ignore the use of updated V2X protocols. We designed a temporal segmentation and modules (TSM)-based method specific for computation-intensive V2X applications that uses a four-tier hierarchy of resource-rich nodes and works in discrete time periods. TSM relies on status updates from previous time periods and uses predictive analytics to address the stochastic nature of vehicular networks using the latest 3GPP 5G V2X standards. Using an online modular computation offloading structure that heuristically manages the whole process, we were able to successfully and timely execute the latency-sensitive advanced vehicular applications. TSM supports computation-deficient devices in under a hundred milliseconds, makes use of smart vehicles’ processing units as fog nodes, and solves the optimization problem in short, discrete stages. We utilized Monte Carlo analysis, which confirmed that TSM outperformed the three other baseline methods.

Two complementary AI approaches for predicting UMLS semantic group assignment: heuristic reasoning and deep learning.

Use heuristic, deep learning (DL), and hybrid AI methods to predict semantic group (SG) assignments for new UMLS Metathesaurus atoms, with target accuracy ≥95%. We used train-test datasets from successive 2020AA-2022AB UMLS Metathesaurus releases. Our heuristic "waterfall" approach employed a sequence of 7 different SG prediction methods. Atoms not qualifying for a method were passed on to the next method. The DL approach generated BioWordVec and SapBERT embeddings for atom names, BioWordVec embeddings for source vocabulary names, and BioWordVec embeddings for atom names of the second-to-top nodes of an atom's source hierarchy. We fed a concatenation of the 4 embeddings into a fully connected multilayer neural network with an output layer of 15 nodes (one for each SG). For both approaches, we developed methods to estimate the probability that their predicted SG for an atom would be correct. Based on these estimations, we developed 2 hybrid SG prediction methods combining the strengths of heuristic and DL methods. The heuristic waterfall approach accurately predicted 94.3% of SGs for 1 563 692 new unseen atoms. The DL accuracy on the same dataset was also 94.3%. The hybrid approaches achieved an average accuracy of 96.5%. Our study demonstrated that AI methods can predict SG assignments for new UMLS atoms with sufficient accuracy to be potentially useful as an intermediate step in the time-consuming task of assigning new atoms to UMLS concepts. We showed that for SG prediction, combining heuristic methods and DL methods can produce better results than either alone.

Influence and influenceability: global directionality in directed complex networks.

Knowing which nodes are influential in a complex network and whether the network can be influenced by a small subset of nodes is a key part of network analysis. However, many traditional measures of importance focus on node level information without considering the global network architecture. We use the method of trophic analysis to study directed networks and show that both 'influence' and 'influenceability' in directed networks depend on the hierarchical structure and the global directionality, as measured by the trophic levels and trophic coherence, respectively. We show that in directed networks trophic hierarchy can explain: the nodes that can reach the most others; where the eigenvector centrality localizes; which nodes shape the behaviour in opinion or oscillator dynamics; and which strategies will be successful in generalized rock-paper-scissors games. We show, moreover, that these phenomena are mediated by the global directionality. We also highlight other structural properties of real networks related to influenceability, such as the pseudospectra, which depend on trophic coherence. These results apply to any directed network and the principles highlighted-that node hierarchy is essential for understanding network influence, mediated by global directionality-are applicable to many real-world dynamics.

Low-power time synchronization algorithm for wireless sensor networks

Time synchronization technology is very significant for wireless sensor networks. A typical time synchronization algorithm is applied to the multi-hop topology network, which has the problems of poor time synchronization accuracy, slow convergence speed, and high power consumption. In this paper, a low-power time synchronization algorithm (LPTSA) is proposed. It is based on clustering in a multi-hop environment and adopts a node hierarchy strategy to reduce the synchronous communication overhead. The synchronization error compensation mechanism is adopted to reduce the impact of algorithm synchronization errors. The clustering algorithm divides the whole network into a primary network and a secondary network. By using different time synchronization algorithms in the primary and secondary networks respectively, unnecessary message overhead is reduced. Simulation shows that the algorithm effectively reduces energy consumption and improves synchronization accuracy.

The Flow of Green Exercise, Its Characteristics, Mechanism, and Pattern in Urban Green Space Networks: A Case Study of Nangchang, China

An urban green space network provides safe and green exercise routes for residents. This study selected Nanchang as the study area. Using fitness application data, we explored the mobility of people exercising in the network, i.e., the flow of green exercise. Spatial analysis based on social networks, GIS, and the gravity model was used to analyze the nodes, network characteristics, and mechanism of the flow of green exercise. The results show that there were differences in the hierarchy and attractiveness of nodes. Distance had an important influence on green exercise. It was found that walkers moved between adjacent parks, runners visited more parks and corridors, while cyclists covered longer distances and preferred to explore suburban green spaces. The length of the exercise route in green spaces had a positive effect on mobility. Because of the many combinations of patches and corridors, three flow subnetworks were formed. In addition, the green space network expanded the scope of exercise services in the central node. The management of green spaces should pay attention to the social value of urban green space networks and create a hierarchical and interconnected green space for exercisers.

Agent-based modelling of the global phonographic market

Based on empirical properties of the global phonographic market, we propose a simple epidemiological model of innovation spread on a network whose structure reflects the structure of the real-world market. The global phonographic market consists of the national markets of various size and mutual interconnections. In the model, we assume that the national markets are the subnetworks that have a scale-free structure achieved by the preferential attachment construction with a node hierarchy and binary edges, while the global market is a complete, directed, weighted network of these subnetworks treated as nodes with the edges representing the cultural and geographical proximity of the national markets. “Viruses” with a defined strength or aggressiveness occur independently at one of the nodes of a selected subnetwork and correspond to a piece of music that has been released by an artist somewhere in the world. We study the virus dynamics set by varying parameter values and observe a variety of phenomena including local and global pandemics and the existence of an epidemic threshold in the subnetworks. Apart from studying the model, we also present some results on empirical data from the European and the U.K. phonographic markets, which are then utilized in the model construction.

Trans-dimensional gravity and magnetic joint inversion for 3-D earth models

SUMMARY Studying 3-D subsurface structure based on spatial data is an important application for geophysical inversions. However, major limitations exist for conventional regularized inversion when applied to potential-field data. For example, global regularization parameters can mask model features that may be important for interpretation. In addition, 3-D inversions are typically based on data acquired in 2-D at the Earth’s surface. Such data may contain significant spatial error correlations in 2-D due to the choice of spatial sampling, acquisition geometry, ambient noise and model assumptions. These correlations can cause trade-offs with spatial resolution and should be accounted for. However, correlations are often ignored, particularly 2-D correlations in spatial data, such as potential field data recorded on the Earth’s surface. Non-linear Bayesian methods can address these shortcomings and we present a new hierarchical model for 2-D correlated errors. Nonetheless, limitations also exist. For example, non-linear Bayesian estimation requires numerical integration with a considerable computational burden to collect a posterior ensemble of models. For 3-D applications, this cost can be prohibitive. This paper presents a non-linear Bayesian inversion with trans-dimensional (trans-D) partitioning of space by a hierarchy of Voronoi nodes and planes (VP), and trans-D estimation of the data noise covariance matrix. The addition of planes permits the introduction of prior information which reduces non-uniqueness. The covariance matrix estimation uses a trans-D autoregressive (AR) noise model to quantify correlated noise on 2-D potential-field data. We address computational cost by wavelet compression in the forward problem and by basing susceptibility on an empirical relationship with density contrast. The method is applied to simulated data and field data from off-shore Abu Dhabi. With simulated data, we demonstrate that subsurface structures are well-resolved with the trans-D model that applies hierarchical VP partitioning. In addition, the model locally adapts based on data information without requiring regularization. The method is also successful in reducing 2-D error correlation via trans-D AR models in 2-D. From field data, the inversion efficiently resolves basement topography and two distinct salt diapirs with a parsimonious and data-driven parametrization. Results show a considerable reduction in 2-D spatial correlations of field data using the proposed trans-D AR model.

Static and Kinetic Friction From Nanoscale Slip—A Multiscale Approach Using a 2D Binary Hierarchy of Nodes

A local, elastic deformation model is combined with a dynamic simulation to investigate nanoscale slip between a rigid, curved pin and an elastic slab, and its influence on static and kinetic friction. The elastic deformation model utilizes a novel multiscale grid based on a binary hierarchy. To maximize accuracy, bi-quadratic functions are introduced to interpolate the stresses on the boundaries of the nodal elements. The onset of slip is based on a maximum allowable nodal shear stress to nodal pressure ratio. A nanoscale friction function is developed by translating the pin quasistatically across the slab. The effect of the nanoscale friction profile on a dynamic system is investigated by integrating the equations of motions governing the pin as it is pulled by a stage via a coupling spring. A direct connection is found between the nanoscale slip characteristics and macroscopically observed static and kinetic coefficients of friction.

Exploring the Spatiotemporal Evolution and Sustainable Driving Factors of Information Flow Network: A Public Search Attention Perspective.

The promotion of information flow reinforces the interactive cooperation and evolutionary process among cities. In the information age, public online search is a typical behavior of Internet society, which is the key to information flow generation and agglomeration. In this study, we attempt to explore the evolutionary characteristics of intercity networks driven by public online social behavior in the information age and construct an information flow network (IFN) from the perspective of public search attention. We also explore the evolution of the IFN in terms of the whole network, node hierarchy, and subgroup aggregation. Meanwhile, we also discuss the impact of the sustainable driving factors on the IFN. Finally, an empirical study was conducted in Guanzhong Plain Urban Agglomeration (GPUA). Our results show that: (1) the information flow in GPUA fluctuating upward in the early study period and gradually decreasing in the later study period. However, the agglomeration degree of information flow in the urban agglomeration continues to increase. (2) The hierarchical structure of urban nodes in GPUA presents a trend of “high in the middle and low on both sides”, and the formation of subgroups is closely related to geographic location. (3) The driving factors all impacting the IFN include public ecology, resource investment, information infrastructure, and economic foundation. This study provides theoretical and practical support for exploring the intercity network and promotes the sustainable urban development.

Tree pruning in the structural approach to vowel reduction and lenition

The article scrutinises several vowel reduction and lenition phenomena by employing a model of syntax-like structural representations, i.e. Government Phonology 2.0. In contrast to the standard GP model, whereby lenition and vowel reduction can be viewed as shortening, element suppression or status switching, the structural approach employs the procedure of tree pruning with a heavily limited role of melodic annotation. This paper will take a closer look at node removal with special attention to its trajectory. In particular, two basic directionalities are considered: top-down and bottom-up. The former has been proposed to account for vowel reduction whereby the highest positions are deleted retaining the head and potentially its sister. The acquisition of plosives and fricatives points to the latter trajectory, which disposes of nodes closer to the head. However, the choice of positions that are targeted in weak contexts might be also related to the inherently encoded hierarchy of terminal nodes within the constituents in question.

How to plug out of the networks in jeopardy of ASF, Covid-19, social media or markets toxicity

Aim: In our research, we examine universal properties of the global network whose structure represents a real-world network that might be later extended to social media, commodity market or countries under the infl uence of diseases like Covid-19 or ASF.Methods: We propose quasi-epidemiological agent-based model of virus spread on a network. Firstly, we consider countries represented by subnetworks that have a scale-free structure achieved by the preferential attachment construction with a node hierarchy and binary edges. The global network of countries is a complete, directed, weighted network of thesesubnetworks connected by their capitals and divided into cultural and geographical proximity. Viruses with a defi ned strength or aggressiveness occur independently at one of the nodes of a selected subnetwork and correspond to a piece of products or messages or diseases.Results and conclusion: We analyse dynamics set by varying parameter values and observe a variety of phenomena including local and global pandemics and the existence of an epidemic threshold in the subnetworks. These phenomena have been also shown fromindividual users points of view because the node removal from the network might have impact on its nearest neighbours differently. The selective participation in global network is proposed here to avoid side effects when the global network has been fully connected and no longer divided into clusters.

Anti-Microbiota Vaccines Modulate the Tick Microbiome in a Taxon-Specific Manner.

The lack of tools for the precise manipulation of the tick microbiome is currently a major limitation to achieve mechanistic insights into the tick microbiome. Anti-tick microbiota vaccines targeting keystone bacteria of the tick microbiota alter tick feeding, but their impact on the taxonomic and functional profiles of the tick microbiome has not been tested. In this study, we immunized a vertebrate host model (Mus musculus) with live bacteria vaccines targeting keystone (i.e., Escherichia-Shigella) or non-keystone (i.e., Leuconostoc) taxa of tick microbiota and tested the impact of bacterial-specific antibodies (Abs) on the structure and function of tick microbiota. We also investigated the effect of these anti-microbiota vaccines on mice gut microbiota composition. Our results showed that the tick microbiota of ticks fed on Escherichia coli-immunized mice had reduced Escherichia-Shigella abundance and lower species diversity compared to ticks fed on control mice immunized with a mock vaccine. Immunization against keystone bacteria restructured the hierarchy of nodes in co-occurrence networks and reduced the resistance of the bacterial network to taxa removal. High levels of E. coli-specific IgM and IgG were negatively correlated with the abundance of Escherichia-Shigella in tick microbiota. These effects were not observed when Leuconostoc was targeted with vaccination against Leuconostoc mesenteroides. Prediction of functional pathways in the tick microbiome using PICRUSt2 revealed that E. coli vaccination reduced the abundance of lysine degradation pathway in tick microbiome, a result validated by qPCR. In contrast, the gut microbiome of immunized mice showed no significant alterations in the diversity, composition and abundance of bacterial taxa. Our results demonstrated that anti-tick microbiota vaccines are a safe, specific and an easy-to-use tool for manipulation of vector microbiome. These results guide interventions for the control of tick infestations and pathogen infection/transmission.

Evaluation of the Effects of Mobile Smart Object to Boost IoT Network Synchronization.

This paper deals with the synchronization of Mobile Smart Objects (MSOs). Today, this scenario is becoming typical in Industrial IoT applications due to the plethora of MSOs available as robots, drones and wearables, equipped by sensors making them measurement instruments cooperating in distributed measurement systems. In this context, the synchronization accuracy is directly tied with the accuracy of the performed measurements. In hierarchical synchronization approaches, the presence of an MSO makes the network topology time varying, and this could prevent the synchronization of the whole network. Peer to peer approaches do not need node hierarchy to synchronize but could not converge to a common sense of time. To overcome these challenges, this paper proposes a consensus-based approach for which the convergence to a common sense of time is here demonstrated. The proposal deploys the MSO to bring the common sense of time from an SO to another, establishing new paths among SOs. The new paths are temporary and depend on the MSO’s route. In the paper, the influence of the MSO’s route on the synchronization accuracy σ and the time interval to synchronize all the SOs ∆TIS is investigated, also. The mathematical proof, the simulations and the experimental tests confirm that the MSO can reduce both the values of σ and ∆TIS, because the new connections introduced by the MSO can boost the exchange of information among SOs. Consequently, the criteria to a priori select the route ameliorating σ and ∆TIS values are proposed.

Improving discourse representations with node hierarchy attention

Long text representation for natural language processing tasks has capture researchers’ attention recently. Beyond the sentence, finding a good representation for the text turns to the bag of the words that losses sequence order. Indeed, the text does not pattern in a haphazard way; rather, in a coherent document there exist systematic connections between sentences. Rhetorical structure theory models this connection in a tree structure format. This tree models text span and their relation. The importance of each text span is distinguished by their hierarchy type in the tree named nucleus and satellite. In this paper, we try to enrich text representation by taking into account the contribution of each phrase in the text based on its hierarchy type. We employ a deep recursive neural network as the attention mechanism to improve text representation. Our hypothesis is evaluated in a sentiment analysis framework. In addition, basic recursive neural network and predefined weighting attention consider as benchmarks. Results show that reweighting span vectors via a deeper layer of recursive neural network outperforms predefined scalar and no attention methods.

Hyperbolic node embedding for signed networks

Signed network embedding methods aim to learn vector representations of nodes in signed networks. However, existing algorithms only managed to embed networks into low-dimensional Euclidean spaces whereas many intrinsic features of signed networks are reported more suitable for non-Euclidean spaces. For instance, previous works did not consider the hierarchical structures of networks, which is widely witnessed in real-world networks. In this work, we answer an open question that whether the hyperbolic space is a good choice to accommodate signed networks and learn embeddings that can preserve the corresponding special characteristics. We also propose a non-Euclidean signed network embedding method based on structural balance theory and Riemannian optimization, which embeds signed networks into a Poincaré ball in a hyperbolic space. This space enables our approach to capture underlying hierarchy of nodes in signed networks because it can be seen as a continuous tree. We empirically compare our method against six Euclidean-based baselines in three tasks on seven real-world datasets, and the results show the effectiveness of our method.

Assignment of Legal Responsibilities for Decisions by Autonomous Cars Using System Architectures

Increasing automation in road vehicles moves decision making from humans to algorithms. This gives a lack of clarity in assigning responsibility for the consequences of the resultant actions, presenting problems for ethicists, lawyers, and engineers. This article applies proven methods based on the 4D/RCS reference model architecture to derive proposed architectures for autonomous vehicles with J3016 autonomy levels 2-5. The architectures have a hierarchy of nodes, which can be either a human or an automated process, giving unambiguous allocation of authority to act and consequent responsibilities to individual nodes in the system. A concept called authorized power is introduced for every node, giving clear limits on each node's ability to act. Setting node requirements gives a firm basis for engineers and lawyers to agree where responsibilities lie, whether with the user, owner, or in the supply chain. It also provides a framework to assess engineering risk and the wider financial implications of the new technologies as they are introduced.

A re-organizing biosurveillance framework based on fog and mobile edge computing

Biological threats are becoming a serious security issue for many countries across the world. Effective biosurveillance systems can primarily support appropriate responses to biological threats and consequently save human lives. Nevertheless, biosurveillance systems are costly to implement and hard to operate. Furthermore, they rely on static infrastructures that might not cope with the evolving dynamics of the monitored environment. In this paper, we present a reorganizing biosurveillance framework for the detection and localization of biological threats with fog and mobile edge computing support. In the proposed framework, a hierarchy of fog nodes are responsible for aggregating monitoring data within their regions and detecting potential threats. Although fog nodes are deployed on a fixed base station infrastructure, the framework provides an innovative technique for reorganizing the monitored environment structure to adapt to the evolving environmental conditions and to overcome the limitations of the static base station infrastructure. Evaluation results illustrate the ability of the framework to localize biological threats and detect infected areas. Moreover, the results show the effectiveness of the reorganization mechanisms in adjusting the environment structure to cope with the highly dynamic environment.