Node-based Model Research Articles

Two-stage heuristic algorithm with pseudo node-based model for electric vehicle routing problem

Electric vehicle routing problem (EVRP) is a special vehicle routing problem, which owns several specific characteristics of EV technology including the limited charging stations and the limited cruising range of EVs. This paper proposes a two-stage heuristic algorithm for EVRP with a kind of pseudo node, termed EVRPPN-TSH, which not only contributes to the model innovation but also to the algorithm design. In model innovation, the pseudo node is introduced into the EVRP model, forming EVRPPN, to reduce the search space and time complexity for obtaining service orders of customers from O(n) to O(1). Moreover, different from other research works which only use total distance as the fitness function, an improved fitness function is proposed, which takes all the distance, the electricity and capacity constraints compliance into consideration. In algorithm design, we first adopt the two-stage idea, which divides EVRP into the capacitated vehicle routing problem (CVRP) and a fixed route vehicle charging problem (FRVCP). Then we design a two-stage heuristic algorithm, termed as TSH. Specifically, for CVRP, a route division heuristic algorithm and a depot move heuristic algorithm are designed to divide routes. Further, an adjustment strategy with three operators is designed for adjusting the customer order. For FRVCP, an existing heuristic algorithm is adopted and enhanced by a variable extent shaking algorithm to avoid local trapped. Experimental results show that EVRPPN-TSH generally outperforms other state-of-the-art algorithms. Moreover, EVRPPN-TSH can obtain the best-known solution in all the 7 test cases, and update the best-known solution in test case E101 from 834.84 to 834.22.

Towards transferable metamodels for water distribution systems with edge-based graph neural networks

Data-driven metamodels reproduce the input-output mapping of physics-based models while significantly reducing simulation times. Such techniques are widely used in the design, control, and optimization of water distribution systems. Recent research highlights the potential of metamodels based on Graph Neural Networks as they efficiently leverage graph-structured characteristics of water distribution systems. Furthermore, these metamodels possess inductive biases that facilitate generalization to unseen topologies. Transferable metamodels are particularly advantageous for problems that require an efficient evaluation of many alternative layouts or when training data is scarce. However, the transferability of metamodels based on GNNs remains limited, due to the lack of representation of physical processes that occur on edge level, i.e. pipes. To address this limitation, our work introduces Edge-Based Graph Neural Networks, which extend the set of inductive biases and represent link-level processes in more detail than traditional Graph Neural Networks. Such an architecture is theoretically related to the constraints of mass conservation at the junctions. To verify our approach, we test the suitability of the edge-based network to estimate pipe flowrates and nodal pressures emulating steady-state EPANET simulations. We first compare the effectiveness of the metamodels on several benchmark water distribution systems against Graph Neural Networks. Then, we explore transferability by evaluating the performance on unseen systems. For each configuration, we calculate model performance metrics, such as coefficient of determination and speed-up with respect to the original numerical model. Our results show that the proposed method captures the pipe-level physical processes more accurately than node-based models. When tested on unseen water networks with a similar distribution of demands, our model retains a good generalization performance with a coefficient of determination of up to 0.98 for flowrates and up to 0.95 for predicted heads. Further developments could include simultaneous derivation of pressures and flowrates.

A post-disaster load supply restoration model for urban integrated energy systems based on multi-energy coordination

Urban integrated energy systems (UIES) have emerged as a promising solution to address the challenges of urban energy supply and consumption. However, UIES are vulnerable to extreme disasters, such as earthquakes, hurricanes, or floods, which can disrupt the energy infrastructure and lead to power outages and energy shortages. This study introduces a novel approach aimed at enhancing the resilience and load recovery capabilities of UIES in the face of extreme events. The proposed approach encompasses several key innovations, including the comprehensive coordination of local energy sources, prioritized restoration of critical loads, and a node-based modeling approach for gas and heat networks. Firstly, by fully integrating electricity, gas, and heat sources, the overall resilience of UIES is significantly improved, ensuring higher levels of load recovery even in the event of energy supply shortages. Secondly, the critical load recovery is prioritized by load classification, and the weights set can fully consider the connection status and load level of nodes to ensure priority provisioning of important node loads. In addition, the node-based modeling approach allows for accurate consideration of the flow of gas and heat in the pipe during modeling of the pipe energy storage. Finally, the case study section conducts simulation experiments with the UIES E33-G20-H6 test system to verify the effectiveness of the proposed approach and to demonstrate its potential in enhancing the post-disaster load recovery capability of UIES.

A unified framework for simplicial Kuramoto models

Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified framework to describe different variants of these models, categorized into three main groups: “simple” models, “Hodge-coupled” models, and “order-coupled” (Dirac) models. Our framework is based on topology and discrete differential geometry, as well as gradient systems and frustrations, and permits a systematic analysis of their properties. We establish an equivalence between the simple simplicial Kuramoto model and the standard Kuramoto model on pairwise networks under the condition of manifoldness of the simplicial complex. Then, starting from simple models, we describe the notion of simplicial synchronization and derive bounds on the coupling strength necessary or sufficient for achieving it. For some variants, we generalize these results and provide new ones, such as the controllability of equilibrium solutions. Finally, we explore a potential application in the reconstruction of brain functional connectivity from structural connectomes and find that simple edge-based Kuramoto models perform competitively or even outperform complex extensions of node-based models.

An Economic Model Predictive Control for Knowledge Transmission Processes in Multilayer Complex Networks.

In this article, we study the optimal feedback control problems of knowledge dissemination processes in multilayer complex networks. First, a node-based model is established in multilayer complex networks and two collaborative control strategies are exerted to increase the scope and speed of knowledge dissemination, forming a closed-loop control system. Then, we develop a two-layer optimal control framework. At the upper level, the optimal solution of the control system is solved and sent to the lower layer. At the lower level, a model predictive controller (MPC) receives input information from the upper level and is formulated to decide on the network and then transmits it to its heterogeneous networks which can reduce control resources and computation complexity. Finally, numerical simulations are conducted to confirm the theoretical results.

BONE REMODELLING PREDICTION FOR IMPROVED POROUS IMPLANT DESIGN

AbstractObjectivesYoung patients receiving metallic bone implants after surgical resection of bone cancer require implants that last into adulthood, and ideally life-long. Porous implants with similar stiffness to bone can promote bone ingrowth and thus beneficial clinical outcomes. A mechanical remodelling stimulus, strain energy density (SED), is thought to be the primary control variable of the process of bone growth into porous implants. The sequential process of bone growth needs to be taken into account to develop an accurate and validated bone remodelling algorithm, which can be employed to improve porous implant design and achieve better clinical outcomes.MethodsA bone remodelling algorithm was developed, incorporating the concept of bone connectivity (sequential growth of bone from existing bone) to make the algorithm more physiologically relevant. The algorithm includes adaptive elastic modulus based on apparent bone density, using a node-based model to simulate local remodelling variations while alleviating numerical checkerboard problems. Strain energy density (SED) incorporating stress and strain effects in all directions was used as the primary stimulus for bone remodelling. The simulations were developed to run in MATLAB interfacing with the commercial FEA software ABAQUS and Python. The algorithm was applied to predict bone ingrowth into a porous implant for comparison against data from a sheep model.ResultsThe accuracy of the predicted bone remodelling was verified for standard loading cases (bending, torsion) against analytical calculations. Good convergence was achieved. The algorithm predicted good bone remodelling and growth into the investigated porous implant. Using the standard algorithm without connectivity, bone started to remodel at locations unconnected to any bone, which is physiologically implausible. The implementation of bone connectivity ensures the gradual process of bone growth into the implant pores from the sides. The bone connectivity algorithm predicted that the full remodelling required more time (approximately 50% longer), which should be considered when developing post-surgical rehabilitation strategies for patients. Both algorithms with and without bone connectivity implementation converged to same final stiffness (less than 0.01% difference). Almost all nodes reached the same density value, with only a limited number of nodes (less than 1%) in transition areas with a strong density gradient having noticeable differences.ConclusionsAn improved bone remodelling algorithm based on strain energy density that modelled the sequential process of bone growth has been developed and tested. For a porous metallic bone implant the same final bone density distribution as for the original adaptive elasticity theory was predicted, with a slower and more fidelic process of growth from existing surrounding bone into the porous implant.Declaration of Interest(b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project.

Exploring the tradeoff between energy dissipation, delay, and the number of backbones for broadcasting in wireless sensor networks through goal programming

Broadcasting, which is an essential mode of operation in wireless sensor networks (WSNs), dissipates a non-negligible portion of the energy budget of a sensor node. Broadcasting is achieved by the dissemination of broadcast packets (originated at the BS) by a set of relay nodes, which constitute a backbone so that all sensor nodes receive broadcast packets. Utilization of multiple backbones is necessary to achieve balanced energy dissipation of sensor nodes in broadcasting. In this study, we propose two mixed integer programming (MIP) models (i.e., the flow-based model and the node-based model), which minimize the energy dissipation of the highest energy-consuming node for broadcasting by utilizing multiple backbones. Balanced energy minimization and delay minimization objectives are integrated through a goal programming (GP) framework built upon the foundation provided by the scalable node-based model. Performance evaluations based on the optimal solutions of our models reveal that maximum energy dissipation and delay in WSN broadcasting can be significantly reduced, simultaneously, by utilizing multiple backbones (e.g., with two backbones maximum energy dissipation and delay can be, concurrently, reduced by more than 2% and 22%, respectively, in comparison to the single backbone case, likewise, it is also possible to simultaneously reduce maximum energy dissipation and delay more than 8% and 13%, respectively, depending on the priorities assigned to the objectives). Nevertheless, employing more than two backbones does not provide any significant performance improvements.

Merak: An Efficient Distributed DNN Training Framework With Automated 3D Parallelism for Giant Foundation Models

Foundation models are in the process of becoming the dominant deep learning technology. Pretraining a foundation model is always time-consuming due to the large scale of both the model parameter and training dataset. Besides being computing-intensive, the pretraining process is extremely memory- and communication-intensive. These challenges make it necessary to apply 3D parallelism, which integrates data parallelism, pipeline model parallelism, and tensor model parallelism, to achieve high training efficiency. However, current 3D parallelism frameworks still encounter two issues: i) they are not transparent to model developers, requiring manual model modification to parallelize training, and ii) their utilization of computation resources, GPU memory, and network bandwidth is insufficient. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Merak</i> , an automated 3D parallelism deep learning training framework with high resource utilization. Merak automatically deploys 3D parallelism with an automatic model partitioner, which includes a graph-sharding algorithm and proxy node-based model graph. Merak also offers a non-intrusive API to scale out foundation model training with minimal code modification. In addition, we design a high-performance 3D parallel runtime engine that employs several techniques to exploit available training resources, including a shifted critical path pipeline schedule that increases computation utilization, stage-aware recomputation that makes use of idle worker memory, and sub-pipelined tensor model parallelism that overlaps communication and computation. Experiments on 64 GPUs demonstrate Merak's capability to speed up training performance over state-of-the-art 3D parallelism frameworks of models with 1.5, 2.5, 8.3, and 20 billion parameters by up to 1.42, 1.39, 1.43, and 1.61×, respectively.

SMolBoxes: Dataflow Model for Molecular Dynamics Exploration.

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.

Adjusting cyber insurance premiums based on frequency in a communication network

This study compares cyber insurance premiums with and without a communication network effect frequency. As a cybersecurity factor, the frequency in a communication network influences the speed of cyberattack transmission. It means that a network or a high activity node is more vulnerable than a network with low activity. Traditionally, cyber insurance pricing considers historical data to set premiums or rates. Conversely, the network security level can evaluate using the Monte Carlo simulation based on the epidemic model. This simulation requires spreading parameters, such as infection rate, recovery rate, and self-infection rate. Our idea is to modify the infection rate as a function of the frequency in a communication network. The node-based model uses probability distributions for the communication mechanism to generate the data. It adopts the co-purchase network formation in market basket analysis for building weighted edges and nodes. Simulations are used to compare the initial and modified infection rates. This paper considered prism and Petersen graph topology as case studies. The relative difference is a metric to compare the significance of premium adjustment. The results show that the premium for a node with a low level in a communication network can reach 28.28% lower than the initial premium. The premium can reach 20.99% lower than the initial network premium for a network. Based on these results, insurance companies can adjust cyber insurance premiums based on computer usage to offer a more appropriate price.

Emergence of a smooth interface from growth of a dendritic network against a mechanosensitive contractile material.

Structures and machines require smoothening of raw materials. Self-organized smoothening guides cell and tissue morphogenesis and is relevant to advanced manufacturing. Across the syncytial Drosophila embryo surface, smooth interfaces form between expanding Arp2/3-based actin caps and surrounding actomyosin networks, demarcating the circumferences of nascent dome-like compartments used for pseudocleavage. We found that forming a smooth and circular boundary of the surrounding actomyosin domain requires Arp2/3 in vivo. To dissect the physical basis of this requirement, we reconstituted the interacting networks using node-based models. In simulations of actomyosin networks with local clearances in place of Arp2/3 domains, rough boundaries persisted when myosin contractility was low. With addition of expanding Arp2/3 network domains, myosin domain boundaries failed to smoothen, but accumulated myosin nodes and tension. After incorporating actomyosin mechanosensitivity, Arp2/3 network growth locally induced a surrounding contractile actomyosin ring that smoothened the interface between the cytoskeletal domains, an effect also evident in vivo. In this way, a smooth structure can emerge from the lateral interaction of irregular active materials.

An optimal scheduling method for integrated power and heat system with multiple thermal flexibilities

Integrated power and heat system (IPHS) is a typical multi-energy system which involves power system and district heating system (DHS). The utilization of thermal flexibilities in the joint operation of power system and district heating system has shown the potential to improve energy efficiency and economic performance. In this paper, an optimal scheduling method for IPHS with multiple thermal flexibilities is proposed. Thermal flexibilities of electric boilers and district heating network (DHN) are modelled and analysed, in which the flexibility of DHN is described by a detailed node-based quasi-dynamic model. The proposed scheduling method is to minimize the operation cost of IPHS. Case study is carried out on a typical IPHS with a 6-node power system and a 12-node DHS. Simulation results show that the scheduling method can reduce the operation cost of IPHS and improve wind accommodation.

Node-based vs. path-based location models for urban hydrogen refueling stations: Comparing convenience and coverage abilities

For optimizing locations of hydrogen refueling stations, two popular approaches represent fuel demands as either nodes or paths, which imply different refueling behavior and definitions of convenience. This paper compares path-based vs. node-based models from the perspective of minimizing total additional travel time and feasibly covering all demands with the same number of stations. For this comparison, two new station location models are introduced that extend the Flow Capturing Location Model (FCLM) and p-Median Problem (PMP) by consistently defining upper limits on vehicle driving range and maximum inconvenience on refueling trips. Results for an idealized metropolitan area and Orlando, Florida show that path-based refueling substantially reduces wasteful travel time for refueling and covers more demand feasibly and more equitably in most scenarios. Path-based models incorporate the fact that residents of a zone regularly interact with other zones; therefore, individual stations can cover flows originating both near and far from their locations. This study suggests that path-based approaches to planning hydrogen refueling infrastructure enable more people in more neighborhoods to refuel fuel-cell vehicles without wasting excessive time or running out of fuel.

Tensorial and bipartite block models for link prediction in layered networks and temporal networks.

Many real-world complex systems are well represented as multilayer networks; predicting interactions in those systems is one of the most pressing problems in predictive network science. To address this challenge, we introduce two stochastic block models for multilayer and temporal networks; one of them uses nodes as its fundamental unit, whereas the other focuses on links. We also develop scalable algorithms for inferring the parameters of these models. Because our models describe all layers simultaneously, our approach takes full advantage of the information contained in the whole network when making predictions about any particular layer. We illustrate the potential of our approach by analyzing two empirical data sets: a temporal network of e-mail communications, and a network of drug interactions for treating different cancer types. We find that multilayer models consistently outperform their single-layer counterparts, but that the most predictive model depends on the data set under consideration; whereas the node-based model is more appropriate for predicting drug interactions, the link-based model is more appropriate for predicting e-mail communication.

Modeling Ransomware Spreading by a Dynamic Node-Level Method

Ransomware attacks are becoming increasingly sophisticated, more damaging to victims and more challenging to prevent. In this paper, a dynamic modelling method is used to study the spreading behaviors of ransomware. The dynamic state of each network node is assumed to be statistically dependent of the states of its neighboring nodes. By incorporating the full topology of the propagating network via its adjacent matrix, a novel node-based model is developed which is also suitable over generic connected networks. The dynamics of the model is theoretically analyzed. Especially, the global stability of its trivial equilibrium is theoretically confirmed depending on a sufficient condition including the leading eigenvalue of the adjacent matrix. We also present results from extensive numerical simulations designed for some specific networks, exhibiting different dynamic properties over distinct networks. Consequently, we suggest that ransomware spreading can be availably contained by properly adjusting the network structure so that its largest eigenvalue satisfies the desired requirement.

On computer virus spreading using node-based model with time-delayed intervention strategies

On computer virus spreading using node-based model with time-delayed intervention strategies

Agent-Based Modelling of Locating Public Transport Facilities for Conventional and Electric Vehicles

This paper proposes an agent-based transport facility development model for both Conventional Vehicles (CVs) and Electric Vehicles (EVs), as a key component of an agent-based land use-transport model, SelfSim. The model attempts to simultaneously locate public parking lots, refuelling stations, charging stations and charging posts at parking lots with the consideration of competitions and interactions between the facilities. The facility development model is composed of a link-based model and node-based model that are used to simulate the development of link-based (e.g., replenishing stations) and node-based facilities (e.g., parking lots), respectively, based on the spatial and temporal disaggregate demand. The demand is extracted from the activity-based simulation with MATSim-EV that is an EV extension of MATSim (Multi-Agent Transport Simulation). In the model, facility agents are defined with several specific attributes and behavioural rules, and act the role of locating transport facilities to accommodate the demand. Finally, both global and local sensitivity analyses are applied to fully test the model in several experiments set up based on a Chinese medium-sized city, Baoding. The global SA that is based on Elementary Effect Method is firstly applied to quantify the extent to which the twelve model outputs of interest are sensitive to forty key model parameters, resulting in nine significantly important parameters; Then the Once-At-A-Time (OAT)-based local SA is used to provide further insight into how these important parameters influence the model outputs of interest over years. The SA results are expected to be useful for model calibration, and how the SA results can be used to calibrate the model is discussed.

Modeling and analyzing the dynamic spreading of epidemic malware by a network eigenvalue method

• The influence of network characteristics on malware spreading is addressed. • A node-level dynamic model is newly developed. • Some sufficient conditions for the stability of equilibrium are established. • Numerical simulations suggest effective strategies for inhibiting malware prevalence. This paper mainly focuses on studying the influence of network characteristics on malware spreading. Firstly, a generalized model with weakly-protected and strongly-protected susceptible nodes is developed by considering the possibility of an intruded node converting back to a weakly-protected susceptible one. The dynamics of the generalized compartmental model is intensively discussed and analyzed, deriving several sufficient conditions for its global stability. Following this work, a novel node-based model is newly proposed to describe malware propagation over an arbitrary connected network including synthesized and real networks. From a microscopic perspective, we establish the novel model by introducing several different variables for each node which describe the probabilities of a node locating at respective states. Our theoretical analysis shows that the largest eigenvalue of the propagating network is a key factor determining malware prevalence. Specifically, the range of the leading eigenvalue can be split into three subintervals in which malware approaches extinction very quickly, or tends to extinction, or persists, depending on into which subinterval the largest eigenvalue of the propagating network falls. Theoretically, the trivial equilibrium of our new node-based model is clearly proved to be exponentially globally stable when the maximum eigenvalue is less than a threshold. We also illustrate the predictive effectiveness of our model by designing some numerical simulations on some regular and scale-free networks. Consequently, we conclude that malware prevalence can be effectively prevented by properly adjusting the spreading network, e.g., reducing the number of nodes and deleting some edges, so that its maximum eigenvalue falls into the appropriate subinterval.

The multi-route location-routing problem and zone price decision-making using a tabu and variable neighborhood search algorithm

Zone pricing is one of the most important pricing policies that are prevalently used by very companies. This paper considers integrating zone pricing and location-routing problems for profit maximization. Hence, a node-based model is proposed as mixed integer nonlinear programming to solve the problem. In order to handle the nonlinearity elements and change the model to a mixed integer linear programming, a piecewise linearization method is employed to approximate the problem. Moreover, we introduce a new heuristic variable neighborhood search (VNS) and tabu search algorithm for large-scale problems. The proposed algorithms consist of four intra-tour and seven inter-tour operators in its local search. Finally, some test instances with 20, 200, 400, and 600 nodes are solved and the results of them confirm the efficiency of the proposed algorithms and comparative superiority of the VNS algorithm over the tabu search algorithm.

A method for assessing infrastructure for [formula omitted] utilization: A case study of Finland

Synthetic hydrocarbons can be produced sustainably with power-to-gas processes, resulting in a net reduction of greenhouse gas emissions due to the substitution of conventional natural gas and other fossil fuels with carbon-neutral alternatives. Acquisition of the CO2 for the synthetic fuel production can be implemented in multiple ways. This work introduces a node-based model to assess different implementation strategies of CO2 utilization systems, taking into account temporal effects, regional variation, and economies of scale for CO2 capture. Intermediate storage volumes, capture costs, transport quantities, and other relevant infrastructural aspects of the CCU system can be estimated with the model. Finland is used as a case study, focusing specifically on the national and regional scale. CO2 capture costs are significant, being nearly four times larger than the cost of storage in the baseline scenario (354M€, 85M€). CO2 sources with smaller annual emissions increases capture costs by 14% compared to baseline. This increase in cost is comparable to the cost of transporting over a quarter of all captured CO2 to off-site processing (varying distance, 100–400km). Seasonal storage of CO2 is found to be beneficial for the cost-efficient production of synthetic fuels, owing to the temporal disparity between CO2 emissions and utilization, as well as the overall cost structure of the components. Five key decision categories are proposed for a carbon utilization system: scale, type, units, location, and technological decisions. These may be applied to describe any carbon utilization system, helping to form a more comprehensive picture of a future energy system, where carbon is widely used as a raw material.