Modeling Of Networks Research Articles

Broadcast Modeling and Rate Optimization for Ad Hoc Networks using Epidemic Theory

Broadcast Modeling and Rate Optimization for Ad Hoc Networks using Epidemic Theory

ІМІТАЦІЙНЕ МОДЕЛЮВАННЯ МЕРЕЖІ МАСОВОГО ОБСЛУГОВУВАННЯ

In this work, simulation modeling of mass service network is investigated. The relevance of researching the topic lies in its potential to enhance efficiency and effectiveness across various service industries. In today's increasingly interconnected and technologically driven world, understanding how to optimize service delivery processes is paramount. By employing simulation modeling techniques, researchers and practitioners can simulate real-world scenarios, identify bottlenecks, test different strategies, and ultimately improve the overall performance of mass service networks. This research can have practical applications in areas such as transportation, healthcare, telecommunications, and customer service, where efficient management of large-scale service networks is essential for meeting demand, reducing costs, and enhancing customer satisfaction. Moreover, the insights gained from such research can inform policy-making decisions aimed at improving service accessibility, equity, and resilience in the face of disruptions, thus contributing to the advancement of both academic knowledge and practical applications in the field of service operations management. It is noted that simulation modeling can be considered as a methodology and not a theory of problem solving. One of the features of simulation models is the practically significant assumption that they can serve as a tool for analyzing the behavior of the system under the conditions determined by the experimenter. The elements of a mass service network are the input flow, the mass service system, and communication. The input flow is characterized by the interval of requests to the network, which can be a random or deterministic value, as well as the number of the mass service system to which the request is received. The state of the incoming stream is determined by the moment in time when the next request arrives in the mass service network. In the case when the time interval for the arrival of requirements is a random variable with a known distribution law, a random number generator is used to calculate it.

МОДЕЛЮВАННЯ ФІНАНСОВИХ ПОТОКІВ В МЕРЕЖІ МАРШРУТІВ НА ПРИКЛАДІ ЗАЛІЗНИЧНОГО ТРАНСПОРТУ

Transport is an important element of the functioning of society and its economic condition. The problem of researching transportation systems is the cost of such research. It is impossible to conduct an experiment to study changes in passenger flows on routes when the cost of traveling on the route itself or on competitors' routes changes, because such experiments will have their own monetary impact on society and on transport enterprises. It is also difficult to conduct experiments on the state of the system when changing rolling stock, which is also very expensive given the existing scientific interest. A significant cost for the financial burden on transport enterprises or societies can significantly affect the financial flows of the manufacturer or society, and therefore such experiments and conclusions from experiments are not fully conducted, which can lead to uncertainty of actions at certain stages of the project development for the operation of a vehicle on the route. To prevent the occurrence of uncertainties, scientists have proposed modeling certain processes of the route operation. The disadvantages of this approach are that its actual implementation is limited to simulating a certain set of factors. As a result, the obtained models largely consider one route and the impact of one or two factors on it. The proposed model of the transport route network takes into account the interconnectedness of the simultaneous operation of many types of transport and their routes in the network. For the first time, an integrated approach to the study of transport processes is proposed, taking into account the dependence of economic indicators on technical ones and vice versa. The modeling of the route network of the entire region of Ukraine with simultaneous operation of water, air, road and rail transport is carried out.

Modelling of a WDM Network Using Graph Theory and Dijkstra Algorithm for Traffic Redirection

Modelling of a WDM Network Using Graph Theory and Dijkstra Algorithm for Traffic Redirection

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

Design of structured meshes of mining excavations based on variability trends of real point clouds from laser scanning for numerical airflow modeling

Various technologies are used to acquire and process 3D data from mining excavations, such as Terrestrial Laser Scanning (TLS), photogrammetry, or Mobile Mapping Systems (MMS) supported by Simultaneous Localization and Mapping (SLAM) algorithms. Due to the often difficult measurement conditions, the data obtained are often incomplete or inaccurate. There are gaps in the point cloud due to objects obscuring the tunnel. Data processing itself is also time-consuming. Point clouds must be cleaned of unnecessary noise and elements. On the other hand, accurate modeling of airflows is an ongoing challenge for the scientific community. Considering the utilization of 3D data for the numerical analysis of airflow in mining excavations using Computational Fluid Dynamics (CFD) tools, this poses a considerable problem, especially the creation of a surface mesh model, which could be further utilized for this application. This paper proposes a method to create a synthetic model based on real data. 3D data from underground mining tunnels captured by a LiDAR sensor are processed employing feature extraction. A uniformly sampled tunnel of given dimensions, point cloud resolution, and cross-sectional shape is created for which obtained features are applied, e.g. general trajectory of the tunnel, shapes of walls, and additional valuable noise for obtaining surfaces of desired roughness. This allows to adjust parameters such as resolution, dimensions, or strengths of features to obtain the best possible representation of a real underground mining excavation geometry. From a perspective of Computational Fluid Dynamics (CFD) simulations of airflow, this approach has the potential to shorten geometry preparation, increase the quality of computational meshes, reduce discretization time, and increase the accuracy of the results obtained, which is of particular importance considering airflow modeling of extensive underground ventilation networks.

Qualitative and quantitative concentration-response modelling of gene co-expression networks to unlock hepatotoxic mechanisms for next generation chemical safety assessment_Suppl7

Qualitative and quantitative concentration-response modelling of gene co-expression networks to unlock hepatotoxic mechanisms for next generation chemical safety assessment_Suppl7

Bridge percolation: electrical connectivity of discontinued conducting slabs by metallic nanowires.

The properties of nanostructured networks of conductive materials have been extensively studied under the lens of percolation theory. In this work, we introduce a novel type of local percolation phenomenon used to investigate the conduction properties of a new hybrid material that combines sparse metallic nanowire networks and fractured conducting thin films on flexible substrates. This original concept could potentially lead to the design of a novel composite transparent conducting material. Using a complementary approach including formal analytical derivations, Monte Carlo simulations and electrical circuit representation for the modelling of bridged-percolating nanowire networks, we unveil the key relations between linear crack density, nanowire length and network areal mass density that ensure electrical percolation through the hybrid. The proposed theoretical model provides key insights into the conduction mechanism associated with the original concept of bridge percolation in random nanowire networks.

Spatial Correlation Modeling and Performance Analysis of Urban UAV Networks

Spatial Correlation Modeling and Performance Analysis of Urban UAV Networks

Adaptive Synaptic Scaling in Spiking Networks for Continual Learning and Enhanced Robustness.

Synaptic plasticity plays a critical role in the expression power of brain neural networks. Among diverse plasticity rules, synaptic scaling presents indispensable effects on homeostasis maintenance and synaptic strength regulation. In the current modeling of brain-inspired spiking neural networks (SNN), backpropagation through time is widely adopted because it can achieve high performance using a small number of time steps. Nevertheless, the synaptic scaling mechanism has not yet been well touched. In this work, we propose an experience-dependent adaptive synaptic scaling mechanism (AS-SNN) for spiking neural networks. The learning process has two stages: First, in the forward path, adaptive short-term potentiation or depression is triggered for each synapse according to afferent stimuli intensity accumulated by presynaptic historical neural activities. Second, in the backward path, long-term consolidation is executed through gradient signals regulated by the corresponding scaling factor. This mechanism shapes the pattern selectivity of synapses and the information transfer they mediate. We theoretically prove that the proposed adaptive synaptic scaling function follows a contraction map and finally converges to an expected fixed point, in accordance with state-of-the-art results in three tasks on perturbation resistance, continual learning, and graph learning. Specifically, for the perturbation resistance and continual learning tasks, our approach improves the accuracy on the N-MNIST benchmark over the baseline by 44% and 25%, respectively. An expected firing rate callback and sparse coding can be observed in graph learning. Extensive experiments on ablation study and cost evaluation evidence the effectiveness and efficiency of our nonparametric adaptive scaling method, which demonstrates the great potential of SNN in continual learning and robust learning.

Research on the Synchronization Control of Fractional-Order Complex Networks Based on Switching Topology

Abstract: In the contemporary epoch, bolstered by information technology, the quintessence of networks is ubiquitously manifested, with a plethora of network types—ranging from the Internet, vehicular traffic frameworks, electrical distribution systems, cellular communication matrices, to social interconnection webs—being intricately woven into the fabric of societal functionality and quotidian existence. The domain of complex networks has burgeoned into a fervently pursued research vector, magnetizing an eclectic cohort of investigators from disciplines as variegated as mathematics, biosciences, and engineering. Notably, the fractional calculus has eclipsed its integer-order counterpart by offering enhanced precision in the depiction of real-world systems and phenomena. Consequently, the infusion of fractional calculus into the modeling of complex networks, to dissect their dynamic attributes and attendant control paradigms, has crystallized as a research nexus of burgeoning interest, eliciting scholarly discourse at both national and international echelons. [Purpose] This inquiry into the synchronization control of fractional-order complex networks, predicated on switching topology, endeavors to harness said topology as a scaffold for probing the synchronicity inherent within fractional-order complex networks. The objective is to augment the operational efficacy of these networks, broaden their sphere of applicability, and fortify the synchronal linkage amongst fractional-order complex networks and their counterparts. [Method] Predominantly, this exploration is underpinned by a synthesis of bibliographic scrutiny and analytical modeling, employing an extensive compendium of model equations to elucidate the subject matter. The spotlight is cast upon the Caputo fractional-order differential equation, with a focus on assaying the stability traits of its equilibrium junctures and formulating more expansive and pragmatic conditions for stability. In addition, to facilitate the precise estimation of elusive topologies within complex networks, a supplementary network—comprising isolated nodes and a regulatory protocol—is conceptualized. [Results] The findings suggest that the investigation into synchronization control based on switching topology propels the advancement of fractional-order complex networks and has substantial reference value. It serves to substantiate, to a certain degree, the postulations of antecedent theorists and chart a trajectory for ensuing scholarly endeavors in cognate domains, thereby perpetually amplifying the pragmatic utility of fractional-order complex networks.

Bi-Directional Delay Propagation Analysis and Modeling for High-Speed Railway Networks Under Disturbance

Bi-Directional Delay Propagation Analysis and Modeling for High-Speed Railway Networks Under Disturbance

Hierarchical assembly and modeling of DNA nanotube networks using Y-shaped DNA origami seeds.

DNA nanotechnology offers many means to synthesize custom nanostructured materials from the ground up in a hierarchical fashion. While the assembly of DNA nanostructures from small (nanometer-scale) monomeric components has been studied extensively, how the hierarchical assembly of rigid or semi-flexible units produces multi-micron scale structures is less understood. Here we demonstrate a mechanism for assembling micron-scale semi-flexible DNA nanotubes into extended structures. These nanotubes assemble from nanometer-scale tile monomers into materials via heterogeneous nucleation from rigid, Y-shaped DNA origami seeds to form Y-seeded nanotube architectures. These structures then assemble into networks via nanotube end-to-end joining. We measure the kinetics of network growth and find that the assembly of networks can be approximated by a model of hierarchical assembly that assumes a single joining rate between DNA nanotube ends. Because the number of nucleation sites on Y-seeds and their spatial arrangement can be systematically varied by design, this hierarchical assembly process could be used to form a wide variety of networks and to understand the assembly mechanisms that lead to different types of material architectures at length scales of tens to hundreds of microns.

Qualitative and quantitative concentration-response modelling of gene co-expression networks to unlock hepatotoxic mechanisms for next generation chemical safety assessment.

Next generation risk assessment of chemicals revolves around the use of mechanistic information without animal experimentation. In this regard, toxicogenomics has proven to be a useful tool to elucidate the underlying mechanisms of adverse effects of xenobiotics. In the present study, two widely used human in vitro hepatocyte culture systems, namely primary human hepatocytes (PHH) and human hepatoma HepaRG cells, were exposed to liver toxicants known to induce liver cholestasis, steatosis or necrosis. Benchmark concentration-response modelling was applied to transcriptomics gene co-expression networks (modules) to derive benchmark concentrations (BMCs) and to gain mechanistic insight into the hepatotoxic effects. BMCs derived by concentration-response modelling of gene co-expression modules recapitulated concentration-response modelling of individual genes. Although PHH and HepaRG cells showed overlap in deregulated genes and modules by the liver toxicants, PHH demonstrated a higher responsiveness, based on the lower BMCs of co-regulated gene modules. Such BMCs can be used as transcriptomics point of departure (tPOD) for assessing module-associated cellular (stress) pathways/processes. This approach identified clear tPODs of around maximum systemic concentration (Cmax) levels for the tested drugs, while for cosmetics ingredients the BMCs were 10-100-fold higher than the estimated plasma concentrations. This approach could serve next generation risk assessment practice to identify early responsive modules at low BMCs, that could be linked to key events in liver adverse outcome pathways. In turn, this can assist in delineating potential hazards of new test chemicals using in vitro systems and used in a risk assessment when BMCs are paired with chemical exposure assessment.

Comparison and modelling of neutral operating modes in 6-35 kV distribution networks

The article discusses the issues of modeling and analysis of operating modes of a distribution network with a voltage of 6–35 kV with an isolated and compensated neutral. An analysis of the influence of various types of neutral operation was carried out. It has been shown that the use of a compensated neutral can reduce overvoltage and capacitive currents, and increase system stability. Modeling of network operating modes was carried out using modern software systems. Based on the analysis, solutions were proposed to optimize networks and improve their operational characteristics.

Reduced-order modelling and operational optimization of a high-density district heating network

This study focuses on the reduced-order modeling and the operational optimization of the heating network of Université Libre de Bruxelles (ULB) to decrease its energy consumption and CO2 emissions. A reduced-order model of the network was developed, and alternatives to the current operation were investigated to minimize its operational cost. As a very first improvement, replacing the current fixed-speed pumps with optimally operated variable-speed pumps, the pumping energy can be reduced by up to 96 %. Further optimizing the network operating temperatures can lead to a reduction of the pumping energy, the heat losses and the CO2 emissions by 99 %, 19 %, and 7 %, respectively, compared to the current operation.

Bayesian inference of financial networks

Network data arises naturally in a wide variety of applications in different fields. In this article we discuss in detail the statistical modeling of financial networks. The structure of such networks red has not been studied thoroughly in the past, mainly due to limited accessible data. We explore the structure of a real trading network corresponding to transactions within the natural gas future market over a four-year period. The detection of meaningful communities of actors within networks is particularly relevant to understand the topology of a complex system like this. We explore the usage of stochastic block models in conjunction with a nonparametric Bayesian approach in order to identify clusters of traders in a flexible modeling framework. Our findings strongly indicate that the proposed models are highly reliable at detecting community structures.

Thoughtful paths of Nature Park „Ogres Zilie kalni”

In many countries around the world, urbanization has led to a disconnection from nature within major cities and this has prompted increased research into the significance of nature in the daily lives of urban residents. In the wake of COVID-19 pandemic, people began to visit the walking trails en masse and the habit has continued even after the COVID-19 pandemic. This study, which was conducted in July of 2023 is a part of a broader research initiative aimed at enhancing recreational opportunities and optimizing the physical and wellness benefits provided by walking paths. The research is centered on the "Ogres Zilie kalni" nature park, which features recreational and activity trails. For a majority of residents, these walking paths and ski tracks are vital for recreation. The research's objective is to outline the planning principles for the nature park's trail network, drawing not only from landscape architecture but also from the field of environmental psychology. This holistic approach is expected to yield better recreational outcomes and subsequently improve human health. The primary goal of this study is to investigate how different types of nature walking trails impact human attention dynamics. Using Schulte's Tables, a psychological method, the study assessed how various stimuli in trail planning and route selection influence human attention. The research results do not offer a definitive answer regarding the influence of path type on attention dynamics. However, the findings indicate that attention dynamics tend to improve after the second or third measurement, typically occurring at 20-30 minute intervals. This aligns with the theory that 20 minutes is sufficient for attention reset. Attention dynamics then diminish towards the end of the walk due to physical exhaustion. It was hypothesized that adequate rest could lead to improved dynamics. In the modelling of the walking path network and route selection, cognitive factors such as the purpose of the walk, group participation, individual walks, social interactions, and the perception of the three-dimensional spatial environment serve as the foundation for designing the layout of walking paths. The study raises intriguing and complex research questions, further complicated by the diverse groups involved, including students, families, and the elderly. Social factors play a pivotal role in determining the optimal path for both fundamental research and practical applications. Understanding the mechanisms and information sources guiding decision-making in path selection should encompass not only psychological but also social aspects. This comprehensive approach will contribute to a better understanding of path planning for psychological wellness and the cultivation of emotionally positive behavioural patterns.

Research on UAV Swarm Network Modeling and Resilience Assessment Methods.

The traditional UAV swarm assessment indicator lacks the whole process description of the performance change after the system is attacked. To meet the realistic demand of increasing resilience requirements for UAV swarm systems, in this paper, we study the modeling and resilience assessment methods of UAV swarm self-organized networks. First, based on complex network theory, a double layer coupled UAV swarm network model considering the communication layer and the structure layer is constructed. Then, three network topological indicators, namely, the average node degree, the average clustering factor, and the average network efficiency, are used to characterize the UAV swarm resilience indicators. Finally, the UAV swarm resilience assessment method, considering dynamic evolution, is designed to realize the resilience assessment of the UAV swarm under different strategies in multiple scenarios. The simulation experiments show that the UAV swarm resilience assessment, considering dynamic reconfiguration, has a strong correlation with the network structure design.

Modelling of derivational networks of emotive verbs in Romanian

The present paper aims to model derivational networks (nests) of emotive verbs in Romanian, on the basis of the semantic-structural types of derivatives. First, we define the most important notions of the nest derivatology, we introduce the notion of derivational nest modelling and of semantic-structural types of derivatives, and describe the principles of the analysis. After that, the derivational nests of selected verbs and their formal-semantic analysis are presented, with semantic and formal aspects in the commentary after each nest. At the end, the matrix of models resulting from the juxtaposition of all nest models and the conclusions from the research are presented.