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Abstract: Recycling activities have demonstrated a remarkable increase over the last decade due to the economic and environmental dimensions of sustainability. In particular, capacity planning in production facilities has become a strategic issue of key importance affecting the profitability of the recycling industry. By integrating the simulation discipline and the feedback control theory into a dynamic consideration of recycling networks, this paper proposes a System Dynamics (SD) model for strategic capacity planning in the recycling industry. The decision-making process is based on a balanced tradeoff between profit and capacity utilization for a single producer with closed-loop recycling activities. The SD model captures physical stocks and flows apparent in real-world recycling networks and includes the feedback mechanisms which regulate these flows. When used as an “experimental tool”, the model tests alternative capacity planning policies and demonstrates policy suggestions for the forward and reverse channels, which maximize profitability over a strategic planning horizon. This experimentation is illustrated by using data from a paper producer with recycling activities, as a real-world test case. Extensive simulation runs, investigate the efficiency of a wide range capacity acquisition decisions, using total company profit as the measure of performance. Although such an analysis may differ from one recycling network to another, it has been kept as generic as possible to facilitate its applicability to a wide-spectrum of real-world local, regional or global networks. [Copyright &y& Elsevier]

The pervasive uncertainties in multiple port equipment scheduling frequently result in container handling delays or ineffective plans. To address the complexities and uncertainties of port multiple equipment integrated scheduling problem, this paper introduces a Digital Twin-driven (DT-driven) proactive-reactive scheduling framework for the first time. This framework is designed to promptly respond to uncertainties in the scheduling process and provide a transparent visualization of operational information. It specifically tackles the integrated scheduling problem of port quay cranes, Intelligent Guided Vehicles (IGVs), and yard cranes, considering uncertainties such as fluctuations in operating time, equipment failures, and IGV route conflicts. By developing a virtual container port simulation, which features a U-shaped port layout and double-cycling mode drawn from real-world scenarios, the paper evaluates the proposed framework's effectiveness. The experimental results demonstrate that the digital twin framework method significantly improves efficiency and conserves energy. Additionally, in large-scale conditions, the makespan difference between the DT-driven approach and the non-DT-driven approach is as much as 19.56 %. In terms of energy consumption savings, the DT-driven approach's scheduling plan can save 3.67 % of energy consumption under large-scale conditions. Moreover, as the fluctuation index increases, the energy consumption savings become even more significant. This paper also discusses the potential implications of adopting this framework for port companies, highlighting its benefits in enhancing operational and energy efficiency and its incorporation into port management systems. The sensitivity analysis can offer guidance to port companies on optimal equipment allocation strategies. [ABSTRACT FROM AUTHOR]

In this paper, we focus on some aspects of the finite element simulations of robotic paper folding and the reconstruction of models from the origami crease patterns by the robot arms. The paper highlights the simulation problems, which should be solved in developing our recent study in mechanical and geometrical design of the origami-performing robot. The basic premise underlying the study is that folding operations with the origami crease patterns are considered as the functions of the mechanical systems such as a robot. Manipulations with the foldable objects, such as a sheet of paper (the origami crease pattern), by the robot arms in the simulation environment lead to understanding the design of the origami-performing robot without testing physical prototypes at each design stage. In this case, dynamic and kinematic behavior of the robot arms in forming the 3D origami objects is modelled by using the finite element method (FEM) in LS-DYNA solver. For simulating, two forms of origami are considered: flexible, if bending is used for paper folding, and rigid, if origami patterns are considered as the kinematic systems. Results of the simulation are presented and provided by the illustrations. [ABSTRACT FROM AUTHOR]

The increase in the number of vehicles on roads has compounded the difficulty in parking them when people go out for movies, shopping, theatre, etc. Thus, this paper proposes smart parking system. The reservation of the parking slots can be made using this proposed smart parking system. Unlike the methods proposed in the existing research, the system proposed in this paper divides the parking area into 3 parts. One for conventional parking, one for vehicles with reservation and the other for the vehicles with and without reservation. Learning automata is used in the proposed system to determine the percentage of the parking area for conventional parking. In general, the proportionate of the slots for reserved parking, conventional parking and common slots need to be assumed. Learning Automata helps in determining this proportionate optimally. The AES-256 encryption algorithm is used to provide security for the details provided by the user during reservation process. Goodput value is maintained for each vehicle which increases or decreases the chances of getting a reservation. The time limit for the reservation of the parking slot is also maintained after which the reservation gets cancelled automatically. Markov Model is used to represent the system. The performance of the proposed algorithm, Learning Automata and Reservation based Secure Smart Parking System (LA-RSSPS) is simulated and evaluated in terms of average waiting time, search time, the probability with which the vehicles do not get parking slot when they do not have reservation and the probability with which the vehicles do not get reservation and is compared with ProNet and iERS. Results have shown that our scheme preforms better than the ProNet and iERS competing schemes. The comparison of analytical and simulations results are also presented. [ABSTRACT FROM AUTHOR]

Clustering routing protocols currently have problems such as Single point of failure of cluster head nodes, poor network dynamics, uneven data transmission, etc., which are critical to the optimization of energy efficiency, network lifespan and network topology control. However, this optimization problem is an NP hard problem that conventional algorithms are difficult to solve. This paper proposes a new multi-objective cluster routing protocol (CHEABC-QCRP) aimed at optimizing network energy consumption, system lifespan, and quality of services (QoS). The protocol is based on a new chaotic hybrid elite artificial bee colony algorithm (CHEABC) proposed in this paper, which has strong search ability and greatly reduces convergence time. At the same time, a new chaotic strategy was designed to effectively prevent falling into local optima and premature convergence. In simulation experiments, compared with multiple routing protocols, a large number of test results show that this protocol significantly reduces network energy consumption, greatly improves system lifespan, and effectively improves QoS in IWSN. [ABSTRACT FROM AUTHOR]

• The importance of discrete event simulation for training and testing DRL techniques is discussed. • The ineffectiveness of DRL implemented without a simulation environment is demonstrated. • An object-oriented agent-based discrete event simulator of the job shop is presented. • The simulator is designed to be integrated with DRL agents. • It is possible to define customized features, dispatching rules, reward function. The job shop scheduling problem, which involves the routing and sequencing of jobs in a job shop context, is a relevant subject in industrial engineering. Approaches based on Deep Reinforcement Learning (DRL) are very promising for dealing with the variability of real working conditions due to dynamic events such as the arrival of new jobs and machine failures. Discrete Event Simulation (DES) is essential for training and testing DRL approaches, which are based on the interaction of an intelligent agent and the production system. Nonetheless, there are numerous papers in the literature in which DRL techniques, developed to solve the Dynamic Flexible Job Shop Problem (DFJSP), have been implemented and evaluated in the absence of a simulation environment. In the paper, the limitations of these techniques are highlighted, and a numerical experiment that demonstrates their ineffectiveness is presented. Furthermore, in order to provide the scientific community with a simulation tool designed to be used in conjunction with DRL techniques, an agent-based discrete event simulator is also presented. [ABSTRACT FROM AUTHOR]

This paper presents a model formulation for balanced twin lip vane pumps and an experimental activity to validate the model. The simulation model begins with a geometrical module that preprocesses the CAD drawings of a given unit. The model then performs a fluid dynamic analysis using a lumped-parameter formulation to solve for the pressures inside properly defined control volumes within the unit. The fluid dynamic model is solved simultaneously with a motion module that evaluates the planar motions of the vanes using Newton's law of motion and with a lubricating interface solver based on the Reynolds equation. Contact dynamics formulations and elastohydrodynamic relations are applied at the vane locations in contact with the cam ring. The comparison with experimental results highlights a good match in volumetric and hydromechanical efficiencies. The measured outlet pressure ripple matches the simulated one for all tested speeds and pressures. The paper also shows a breakdown of the distribution of volumetric and power losses arising from various components of the machine. The proposed methodology is computationally inexpensive, so it can be used in future design and optimization studies aimed at improving the performance of such units. • A novel simulation approach for high-pressure pressure balanced vane pumps. • Multiphysics approach coupling mixed lubrication, vane motions, and fluid dynamics. • Capturing details in cam ring profile, fluid properties, clearances, vane designs. • Experimental activity performed to validate the modeling approach. • Good accuracy for pressure pulsations, volumetric and hydromechanical performance. [ABSTRACT FROM AUTHOR]

Emergency response environments in which the crisis occurs include many homogenous and heterogeneous entities, e.g., organizations and persons. These entities need to collaborate to address a crisis. However, most of the time, the organizations that are involved in a crisis suffer from communication and coordination problems. This may cause unfair resource distribution. To mitigate the problem, there is a pressing need to construct models of emergency response environments and simulate the coordination strategies proactively, before the onset of any crisis. This paper highlights the complexity of the system formed by interacting organizations during a crisis and the difficulty in programming for the simulation of such complex systems. To cope with this issue, this paper presents an executable domain-specific modeling language called CoorERE. The language facilitates modeling emergency response environments for the simulation of one of the most common coordination strategies, i.e., auction-based coordination strategies in crisis response. CoorERE is used for the graphical modeling of emergency response environments and simulation of both inter and intra-organizational auction-based coordination strategies. The paper presents two case studies with different complexities to show the applicability of CoorERE. An empirical study is conducted to compare CoorERE with MDD4ABMS, which is one of the most recent tools for modeling and simulation of multi-agent systems. The evaluation includes both objective and subjective assessments. The objective assessment considers the development effort required for modeling a complete case study, while the subjective assessment compares the tools in terms of satisfying modeling method requirements and quality metrics such as usability, productivity, and expressiveness. The results of the evaluation indicate that CoorERE reduces the development effort by about 47%. In terms of quality metrics, CoorERE obtained higher scores than MDD4ABMS for all the measurements considered, indicating its superior usability, productivity, and expressiveness. [ABSTRACT FROM AUTHOR]

In this paper, we address modelling and simulation of dynamics involved in docking of large marine vessels with aid of tugboats, with the aim of providing a tool for improved design of control algorithms for autonomous docking operations. Mathematical models of the docking vessel and tugboats in 3DOF are derived, with a special focus on realistic interactions through towline, contact and friction forces with stick–slip dynamics and time delays, which are typically not considered in previous publications on the topic. We also present the implementation of the developed models in the first step towards a Simulink simulator in which considers the special case of tugboat-assisted docking operations in the Narvik harbour. The developed simulator is available on GitHub (see Ørke et al. (2023)) and encompasses model dynamics, tugboat control and vessel control and guidance, although the scope of this paper is model dynamics. We present simulation results that show that we are indeed able to capture interaction dynamics in a realistic way. Finally, we include a discussion of our results and some ideas for further development, as well as some concluding remarks. [Display omitted] • Mathematical modelling of docking vessel and tugboats in 3DOF. • Modelling of interaction forces in pushing/pulling operations. • Model implementation in Simulink-developed simulator. • Simulation and discussion of interaction forces, and ideas for future work. [ABSTRACT FROM AUTHOR]

• A universal vehicle-wheel-ground coupling modeling method of all-terrain DDUGV with variable structure is proposed. • Based on Bekker-Wong-Janosi theoretical model and co-calibration of geotechnics experiment and DEM simulation, the mathematical model of wheel- soil interaction is verified and optimized, which is much more efficiency than Discrete Element Method (DEM) simulation. • The coupling model can simulate the real vehicle system by considering strong nonlinear factors and time delaying of hydraulic system and electric driving system. • Newton-PSO algorithm is established to simplify the problem of solving theoretical mathematical model of wheel-soil interaction and prevent chattering and sawtooth phenomenon during simulation. • The accuracy and computation efficiency of the coupling model are verified by co-simulation results. The establishment of a dynamic model of distributed driving unmanned ground vehicle (DDUGV) is the premise of vehicle state identification and high-quality control. However, due to the strong nonlinearity of the DDUGV system itself, the various external influence and the difficulties of real-time calculating, the simulation results based on existing conditions are far from the actual vehicle test results, resulting in the huge gap between the simulation test and the actual engineering practice. Regarding the problem mentioned above, this paper takes the 3-axles DDUGV with variable structure as the research object, and proposes a universal dynamic modeling method for DDUGV, which considers the uncertainty of terrain and variable structural parameters. Firstly, based on Newtonian mechanics, mathematical models of the vehicle's variable structure, body posture, static/dynamic suspension, hydraulic transmission, wheel and in-wheel motors are established. Secondly, taking the wheel as the coupling node between vehicle dynamics and ground mechanics, 3 tire models in typical driving environments are established, which are the magic tire model used in paved road, and the Bekker-Wong-Janosi theoretical model used in sandy and clay soil, respectively. Thirdly, based on the established mathematical model, a universal DDUGV modeling method is proposed. Then, based on the co-calibration of geotechnics and discrete element method (DEM), the particle parameters of sandy soil are obtained and the theoretical mathematical model of wheel-soil interaction is verified and optimized. Finally, based on the established vehicle-wheel-ground coupling model of the 3-axles DDUGV with variable structures, simulation tests are carried out under typical working conditions, which are compared with the results of existing vehicle dynamics software to verify the accuracy, computation efficiency and real-time property. The vehicle-wheel-ground coupling dynamic model established in this paper can provide a reference for the construction of the vehicle dynamic models, the analysis of driving characteristics, and the research of control strategies in complex environment. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) is a set of several sensor nodes that are used for monitoring heterogeneous physical objects. In WSNs, irregular and bursty traffic Leads to the congestion problem, which incites a decrease in Packet Delivery Ratio (PDR) and increases packet loss as well as end-to-end delay. In the recent era, manifold efforts have been carried out to reduce network congestion however, these solutions have slow and premature optimization. To address optimization issues, this paper presents a self-adaptive source-sending rate optimization algorithm, which is a hybrid version of Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Bifold-objective Proportional Integral Derivative (BPID) called N3-BPID. These techniques play a significant role in optimizing source rates to reduce network congestion. NSGA-III is a reference-based evolutionary approach, which dynamically configures the PID coefficients to get an optimal response. Furthermore, a novel bifold-objective fitness function is designed that balances the trade-offs between two PIDs performance indexes such as the Integral of Absolute Error and the Integral of Square Error. Due to simplicity and efficiency, an identically weighted aggregation mechanism is applied to ensemble both objectives into a single one. The proposed work is implemented to demonstrate a smart border surveillance application using Network Simulator v3 and compared with the state-of-the-art congestion control model Cuckoo Fuzzy PID (CFPID). The experimental result reveals that the proposed algorithm has significantly outperformed existing schemes in terms of PDR by 6.82%, packet loss by 24.52%, end-to-end delay by 15.31%, and queue length deviation by 8.93%. [ABSTRACT FROM AUTHOR]

Cycling as a mode of transport is on an upward trend as a low-emission alternative to driving in urbanized areas nowadays. With the increasing number of cyclists, it is of great importance to assess the capacity of cycling infrastructure in practice. Simulation models are useful tools to investigate bicycle flow performance considering cyclists' distinct moving behaviors. However, existing bicycle simulation models are restricted by either space discretization, lane-based setup, adaptation from models for car traffic, or complicated calibration requirement in a force-based environment. In addition, cyclists' decision-making ability in the operational-level cycling behavior are not well-captured in these models. This paper proposes a comprehensible microscopic bicycle simulation model which includes a detailed decision-making process and the ability to simulate continuous-space lateral movement. The model consists of three levels, maneuver decision, movement planning, and physical acceleration. It is able to simulate bicycle flow dynamics in undersaturated traffic conditions on an exclusive bike path. As we do not intend to show the empirical validity of the proposed model, the simulation experiment aims at verifying the model and exploring bicycle flow performance in various scenarios by estimating the fundamental diagrams (FDs). The effect of different path widths on bicycle flow capacity is first explored. Other behavioral factors, including desired speed heterogeneity, overtaking incentive, and safety region size perceived by cyclists, which can potentially influence the shape of the FD are also tested. The model can be further extended to simulate relatively complex cycling behavior with cooperative and anticipative strategies and investigate bicycle flow characteristics in congested traffic conditions. [ABSTRACT FROM AUTHOR]

The long-term freeze-thaw effect leads to the development of rock fractures and strength degradation in cold region engineering construction, which poses a serious challenge to the stability of the project. In this paper, the microscopic model of sandstone freeze-thaw cycles was established using a particle flow code (PFC2D). Through numerical simulation, the variation law of mechanical properties of rock mass with micro-cracks is systematically studied from the aspects of displacement, crack development, strain and strength. The results show that: (i) The freeze-thaw loading displacement is concentrated on both sides of the initial micro-cracks, and the crack development is not controlled by it. When the number of freeze-thaw cycles is greater than 17, the displacement and crack development are significant. The crack increases with the increase of the crack distance ratio. (ii) When the number of freeze-thaw cycles is less than 15, the compressive crack develops at the end of the initial micro-crack, and the development is controlled by it. When the number of freeze-thaw cycles is greater than 21, the crack increases sharply, and the development is no longer controlled by the initial micro-cracks. When the number of freeze-thaw cycles is less than 15, the damage strain shows minimal variation but decreases sharply with the increase of freeze-thaw cycles. (iii) Under each crack distance ratio, the strength changes in three stages: when the number of freeze-thaw cycles is less than 15, the strength changes little, and the strength decreases sharply with the increase of freeze-thaw cycles. With the increase of the crack distance ratio, it increases first and then decreases. And it is more significant when the number of freeze-thaw cycles is greater than 17. The research results can provide theoretical support for the influence and evaluation of rock freeze-thaw action in cold regions. [ABSTRACT FROM AUTHOR]

Mixed elastohydrodynamic lubrication is the main lubrication style of planetary gear mechanism. In this paper, the dynamic characteristics of planetary gear mechanism under mixed elastohydrodynamic lubrication are investigated using a computational methodology. First, the mathematic model of mixed elastohydrodynamic lubrication is proposed, in which the scaling factors are introduced to depict the balance between the asperities and the oil film. A stiffness formula of the oil film is presented to describe the time varying stiffness of the teeth pair. Then, the stiffness of the oil film and the general stiffness are obtained. Finally, dynamic model of the planetary gear mechanisms is established and the dynamic responses of planetary gear mechanism are analyzed. The influences of operational parameters on stiffnesses and dynamic characteristics of the planetary gear mechanisms are investigated. The simulation results indicate that the nonlinear dynamics characteristics are significantly influenced by the lubrication conditions. [ABSTRACT FROM AUTHOR]

As collaborative simulations gain prominence, there is a pressing need for methodologies that seamlessly integrate interoperability while safeguarding intellectual property. This paper presents a novel approach rooted in Model-Driven Architecture and combined with the High-Level Architecture standard. This approach expedites the simulation process by automating the generation of Federation Object Model files and federate codes. Distinctly, we produce two federates: one exclusively in Java and another integrating Java with the Discrete Event System Specification, addressing diverse simulation paradigms. Utilizing the Unified Modeling Language and the Business Process Model and Notation standards, we devise a systematic procedure for modeling business operations while maintaining confidentiality. While our automation framework is robust, certain intricacies of federate behavior necessitate manual adjustments to ensure secure data transmission and protection of proprietary knowledge. The efficacy of our approach, striking a balance between interoperability and confidentiality in High-Level Architecture-based simulations, is demonstrated through a comprehensive experiment. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) technology has emerged as a promising solution to guarantee high reliability in the Industrial Internet of Things (IIoT) ecosystem. Through SDN, both fault tolerance-based Route Protection (RP) and fault tolerance-based Route Restoration (RR) are available to provide traffic rerouting when a network failure occurs in IIoT. RR redefines routes dynamically based on the current network status. However, it increases significantly the recovery time, which is not suitable for the Quality-of-Service (QoS) requirements of IIoT. In contrary, RP ensures fast failover, but it cannot be updated when the network status changes until the timeout interval expires. To deal with these issues, we propose a Dynamic Route Protection (DRP) mechanism that recalculates and reinstalls new optimal link-disjoint routes in accordance with the change rather than awaiting the controller to retransmit new flow instructions. Moreover, DRP responds speedily to forward the data packets from the secondary route to the main route if it is repaired. To recover rapidly when the connection failure affects both a link on the main route and the link on the secondary route simultaneously, the DRP mechanism utilizes the strategy of caching the third route in the controller memory using the Dynamic Hash Table (DHT) structure. DRP considers the heterogeneous traffic flows such as either delay-sensitive or both delay-sensitive and loss-sensitive. Again, this paper introduces a candidate fault tolerance architecture for Software-Defined IIoT (SDIIoT) that decouples IIoT networks into three functional layers. The results from the simulation network and the experimental hardware testbed illustrated that the DRP mechanism outperforms the FT-RP, RR, LFR, Pro-VLAN, and SDNRMbw mechanisms by minimizing the failure recovery time, end-to-end delay, packet violation rate, packet loss rate, and the time required to reuse the main route when it is repaired, while maximizing the packet delivery ratio. [ABSTRACT FROM AUTHOR]

In the realm of cloud computing, effective resource allocation can significantly enhance the energy efficiency of datacenters. Task scheduling and Virtual Machine Placement (VMP) are two pivotal aspects of resource allocation. However, in current research, they are often treated separately, overlooking the potential for integrated optimization. In this paper, we propose an integrated solution for task scheduling and VMP in energy-efficient datacenters, based on queueing theory and Deep Reinforcement Learning (DRL) methods. This novel and comprehensive approach provides an alternative perspective for resource scheduling strategies in datacenters. We construct a queueing theory model for task scheduling, aiming to minimize the number of VMs that need to be instantiated, while ensuring that Service Level Agreement (SLA) violation remains at a low level. Furthermore, we design a VMP algorithm based on DRL for real-time selection of Physical Hosts (PHs) for deploying VMs. Finally, we conduct a simulation evaluation using a small-scale datacenter. The experimental results demonstrate that our method consistently ensures a lower rate of SLA violation. Compared to existing algorithms, the DRL-based VMP algorithm enables a more balanced utilization of the various resources in the PHs and reduces the total power consumption of the datacenter by more than 10% on average. • Optimizing datacenter efficiency by integrating task scheduling and VM Placement. • Introducing a dynamic task scheduling strategy based on an M/M/1 queue framework. • Employing a deep reinforcement learning (DRL)-based algorithm for efficient VMP. • Our proposed method is validated through a simulation-based evaluation. [ABSTRACT FROM AUTHOR]

• FEM simulations and modeling for a magnetic bearing's loss approximation. • Calculation and comparison of losses and load capacity of each iron cobalt alloy. • Rotational speed and control current influence on the aforementioned alloys. Iron losses appear in Active Magnetic Bearings (AMBs) mostly because of the rotor's movement, but also because of the fluctuation of the control current in the stator's coils. They can be divided into three categories: the hysteresis losses, the eddy current losses and the excess losses and while they depend significantly on the rotating speed and the magnetic flux density applied on the poles, the most contributing factor is the magnetic material used for the core. In this paper, a 2-D Finite Element Method transient model is used to simulate the rotational motion of the shaft inside the AMB and calculate the iron losses that occur due to the alternating magnetic flux inside the rotor, as well as the mechanical load capacity on the vertical direction of the AMB for each case. A simulation is carried out, at first, for a constant control current value and a speed range of 0–30,000 rpm, followed by a second one, for constant rotational speed and control current values 0–0.5 A. Geometry remains the same for all simulations. When it comes to the materials selected for the stator and the rotor, the cases of Hiperco 27, Hiperco 50 and Hiperco 50 HS laminations are tested. The iron losses of the three alloys are compared to the losses of 3 % silicon-iron. The results show that the three iron cobalt alloys have significantly lower losses than the silicon iron for the same AMB size and rotor's speeds. Hiperco 50 has the lowest loss among the three Hiperco alloys, while Hiperco 50 HS provides slightly higher mechanical load capacity under the same operating conditions. [ABSTRACT FROM AUTHOR]

• Simulation of human errors and vehicle crashes on existing microsimulation packages. • Simulation of potential conflicts with road side barriers and obstacles. • New conflict indicators based on human errors consider severity of potential crash. • Safety evaluations considering heavy vehicles and non-conflicting trajectories. This paper presents a methodology that could support researchers and engineers in the important and original issue of the simulation of crashes and the evaluation of consequences on existing microsimulation packages. The proposed approach, by introducing potential driver errors, specifically allows consideration of the potential conflicts with road side barriers and obstacles and between vehicles that are moving on trajectories (close in space) that do not intersect. The severity of resulting crashes can, then, be evaluated in terms of accurate crash dynamic variables. Moreover, the methodology presented in the paper allows researchers to develop new conflict indicators that overcome many limitations of traditional traffic safety conflict indicators which, unfortunately, do not consider: the types of vehicle that are involved in a conflict, roadside obstacles or barriers, vehicles which are travelling on non-conflicting trajectories and the severity of a potential resulting crash. The paper applies a specific family of new simple and reproducible conflict indicators and provides the information necessary to develop new algorithms and simulation methodologies which, introducing human error in traffic simulation, may revolutionize the way traffic microsimulation can be used to establish a traffic safety for different scenarios. Feasibility tests on simple networks are presented and the results show the feasibility of making safety evaluations for different traffic scenarios by introducing driver errors. [ABSTRACT FROM AUTHOR]

Accuracy and reliability of the numerical results is one of the most important challenges in the scientific community for finite element modeling (FEM) of cutting processes. It becomes more essential in machining of difficult-to-cut materials such as nickel-based alloys where high thermo-mechanical loads are induced into the workpiece. In this paper, new strategies were adopted to improve accuracy of FE modeling of cutting process of Inconel718 superalloy. Firstly, a novel hybrid strategy was established to simultaneously calibrate controllable simulation parameters. It was implemented based on the design of experiment, intelligent systems and FEM of cutting process. Using validation with experimental results, a great improvement was obtained for prediction of cutting forces, maximum temperature, and chip geometry compared with numerical results obtained previously by conventional calibration procedure. At the second stage of the paper, a grain size-based flow stress was developed by implementation of advanced FORTRAN user-subroutine in FE code to take into account the effect of changes in material properties during the chip formation. According to the results, implementation of grain size based flow stress has a significant effect on enhancement of simulation accuracy. Finally, it can be concluded that, the innovated strategies presented in this paper provide fundamental and useful approaches to improve precision in modeling of other manufacturing processes. [ABSTRACT FROM AUTHOR]

Highlights • This paper studies the driving force of additional data for ABC. • An improved artificial bee colony algorithm with data-driven is proposed. • The searching process is driven by directional guiding data and local data. • Our work is compared with some ABC-based and nature-inspired algorithms. Abstract To balance the exploration and exploitation and to enhance the convergence rate of an artificial bee colony (ABC) algorithm, the driving force of using additional data during searching process is studied in this paper, and an improved ABC algorithm with data-driven optimization (DDABC) is proposed. First, to speed up convergence rate, the searching process is driven by directional guiding data. Therefore, a bee colony would learn from the directional guiding data, instead of picking up a random direction. Second, to enhance the exploitation capability of the onlooker bees, the searching process is driven by local data of onlooker bees. Every onlooker bee would search independently for multiple times to generate local data applied into optimization. Comparisons are made with a number of other ABC-based and nature-inspired algorithms. The results show that the proposed DDABC achieves improvements in both exploitation capability and convergence rates. [ABSTRACT FROM AUTHOR]

Abstract Cities strive to invent and deploy smart solutions in the domain of urban mobility, so as to offer innovative services to citizens and visitors and improve the overall quality of life. The focus of this paper is on Autonomous Vehicular Safety (AVS) management systems. Such systems exploit vehicular and road side infrastructures to increase the effectiveness of vehicular communications and the relevant services and applications offered in urban environments. AVS management systems combine physical with digital components, each with different capabilities, and involve many different stakeholders, with often competing goals. Sophisticated development of algorithms for simulating the behaviour of the AVS system is required to avoid wasted resources if, after it is implemented, shortcomings with respect to the originally stated goals are found. To achieve this, a capability-oriented modelling approach is introduced and applied to AVS using the CORE framework. This paper argues that the development of an AVS system entails a coherent approach that gradually progresses from vague statements of requirements to the algorithm implementation. To this end, the paper demonstrates how one may proceed from requirements to implementation in a systematic and model-driven manner, while simulation results prove the applicability of the approach. [ABSTRACT FROM AUTHOR]

Highlights • A cooperative trajectory planning strategy is proposed to pass through an intersection with V2X communication. • A manager center is established to collect and distributes usage occupancy information in conflict zone. • The proposed strategy has the smallest evacuation-time among that in fixed lights and adaptive lights. Abstract This paper proposes a cooperative scheduling mechanism for autonomous vehicles passing through an intersection, called TP-AIM. The main objective of this research is to ensure safe driving while minimizing delay in an intersection without traffic lights. Firstly, an intersection management system, used as an info-collecting-organizing center, assigns reasonable priorities for all present vehicles and hence plans their trajectories. Secondly, a window searching algorithm is performed to find an entering window, which can produce a collision-free trajectory with minimal delay, besides backup windows. Finally, vehicles can arrange their trajectory individually, by applying dynamic programming to compute velocity profile, in order to pass through intersection. MATLAB/Simulink and SUMO based simulations are established among three types of traffic mechanisms with different traffic flows. The results show that the proposed TP-AIM mechanism significantly reduces the average evacuation time and increases throughput by over 20%. Moreover, the paper investigates intersection delay, which can be reduced to less than 10% compared to classical light management systems. Both safety and efficiency can be guaranteed in our proposed mechanism. [ABSTRACT FROM AUTHOR]

Highlights • The paper proposes a methodology to design urban delivery bay system. • Freight operation scenarios are assessed through a simulation-based approach. • The architecture of an app-oriented tool to support transport operators is presented. • Tool enables real-time bay reservation and monitoring. • The results were validated on Rome's limited traffic zone. Abstract Delivery bay availability for loading and unloading operations is critical to reduce times and costs of logistics operations, as well as for improving city sustainability and liveability. Beyond defining the appropriate number and location of such bays, urban sustainability and liveability can be improved with telematics tools which enable real-time bay reservation and monitoring, integrating such information within freight routing solutions. This paper presents an architecture for such tools along with methodology integrating delivery bay planning with transportation demand modelling through simulation, allowing computation of performance indicators to be used for ex-ante assessment in planning delivery scenarios. The results were validated through a simulation-based approach on Rome's limited traffic zone. [ABSTRACT FROM AUTHOR]

This paper introduces Indoor Navigation Framework for Fire Evacuation Dynamics (INFED), a novel indoor navigation framework that combines dynamic fire constraints and path congestion management. INFED considers the three-dimensional (3D) attributes of both the agents (speed, volume, location, count) and the environment (3D volume, congestion, corridor height, and corridor length) to estimate navigation routes that avoid fire-affected evacuation paths. It achieves this by integrating various proposed algorithms as modules: Environment Establisher, Fired/Safe Node Identifier, Pre-processor, Weighted Graph Generator, and Path Generator. The 3D features of the agent and environment are used to effectively estimate the capacity of the corridors in an indoor environment for the estimation of path congestion. The path congestion so computed is used during evacuation to identify the safest and congestion-free path. We discuss the performance of INFED by implementing it on various realistic scenarios in a commercial floor setup. We found that the incorporation of safety constraints results in longer evacuation routes, ranging from a 6% increase under mild fire and congestion conditions to a 40% increase under severe fire and congestion conditions. In the event of a worst-case scenario where fire-free paths are scarce, INFED utilizes congestion to reduce agent speed along the recommended evacuation route. This mechanism is activated when congestion surpasses a threshold of 0.3. The system can be used by stakeholders to test various evacuation hypotheses, which can lead to better preparedness and rescue operations, ultimately saving lives in the event of a fire. [ABSTRACT FROM AUTHOR]

Healthcare systems face critical issues worldwide such as data breaches, lack of interoperability and inefficiencies in patient data management. These challenges hinder the quality of care and patient outcomes. The increasing adoption of Electric Vehicles (EVs) in Smart Healthcare Systems (SHSs) has brought about new security and privacy challenges. EVs, including electric ambulances, rely on communication networks to exchange critical information and perform energy trading. However, the open nature of these networks makes them vulnerable to various attacks, such as false information dissemination and collusion attacks. In the recent years, Blockchain (BC) technology has emerged as a transformative solution for various industries, including healthcare. The integration of BC in healthcare systems offers enhanced security, transparency and efficiency in managing patient data and other critical information. The paper introduces a data-oriented trust paradigm that is facilitated by revocation transparency. In order to enable the present EVs operating in a SHS to realize their full potential, the model aims to successfully manage security, privacy, storage and other issues. The electric ambulance, an integral part of an SHS, is a special type of EV, which is considered in the study. The proposed approach employs the Password Authenticated Key Exchange by Juggling (J-PAKE) mechanism to provide mutual authentication across distinct entities inside a SHS. Moreover, the Real-time Message Content Validation (RMCV) approach precludes collusion attacks by performing a message credibility check. Moving ahead, anonymization of reputation data is performed via K-anonymity algorithm. Restrictions on the identification of the consistent patterns seen in the reputation data serve to avoid privacy leaks. Additionally, a Proof of Revocation (PoR) technique helps to provide revocation transparency. The Inter Planetary File System (IPFS), a decentralized storage system, houses the vehicle data in order to lessen the BC storage problem. Hashes of the data recorded in IPFS are also uploaded to the immutable BC ledger to prevent disputes. Moreover, IPFS and Cuckoo Filters (CFs) are used to enhance the efficiency of the system. In terms of execution time, data size and storage overhead, the performance evaluation is carried out to assess the proposed model's efficiency. The simulation results show the execution time for a vast number of messages to be less than 0.6 s. Moreover, K-anonymity ensures storage overhead reduction of almost 35%–40%. Finally, Oyente is used to identify bugs in the smart contract. Overall, it is determined that the proposed approach is effective in establishing mutual authentication, revocation transparency and trust. [ABSTRACT FROM AUTHOR]

Existing fluid simulation techniques mainly process single-phase fluids, and they have difficulties in accurately simulating and visualizing multiphase fluid dynamics. This paper proposes a new method for the real-time rendering of multiphase fluid simulations, which uses smoothed particle hydrodynamics in screen space. Meanwhile, the method employs phase fraction textures to differentiate various materials in multiphase fluid simulations, thereby portraying mixing and separation effects more realistically. Besides, efficient texture computation allows it to be integrated seamlessly into real-time simulation rendering workflows. Extensive testing confirms the effectiveness of the proposed method in rendering multiphase fluid behaviors with high visual fidelity and demonstrates its capability to process frames within 0.01 s, even in cases with up to 300,000 particles. This study enhances the fluid dynamics simulation field and provides a more accurate and efficient method for visualizing complex multiphase fluids in simulations. [Display omitted] • Novel method for real-time rendering of multiphase flows, addressing miscible fluid challenges. • Achieves rapid rendering of complex fluid dynamics with high computational efficiency. • Outperforms existing methods for rendering immiscible multiphase fluids. [ABSTRACT FROM AUTHOR]

• A model of the selective laser sintering process is developed. • The model is based on the lattice Boltzmann method. • Relation viscosity – surface tension is decided for material flow. • Simulation results are presented for bioactive glass 45S5. • Modeling shows the ability to simulate and analyze a very complex technology. Additive manufacturing processes, including selective laser sintering (SLS) and selective laser melting (SLM), are rapidly developing industrial fields that require scientific support. Although SLS and SLM are very similar, the level of modeling and simulation of SLM is much higher than that of SLS. This results in the number of publications before 2024 according to Web of Science with SLM simulation approximately five times more than with SLS. To test the possibility of adequate SLS simulations, a platform based on the lattice Boltzmann method (LBM), previously developed and applied to model the SLM process, was used. In addition, the possibility of modeling similar processes (SLM, SLS, and SLS/SLM) using the same modeling tool on the same modeling platform is important. The objective of this paper is to present a model of the SLS process and confirmation of the possibility of using LBM for simulation of the SLS process. A simulation of SLS and SLM with the use of LBM, and qualitative comparison of the results of these simulation for bioactive glass 45S5 is the basis of the methodology used for the research. The simulation presented in this study confirmed the possibility of simulating SLM, SLS processes using common principles, approaches, and models. The results of SLS process simulations can be treated as qualitative and require further verification, whereas SLM simulations have been previously verified. The application of the lattice Boltzmann method, which is a computational fluid dynamics (CFD) method, opens the possibility of using almost every CFD method for the simulation of several kinds of SLS, and can accelerate research in this field. [ABSTRACT FROM AUTHOR]

The evolution of simulation and implementation of P systems has been intense since the theoretical model of computation was created. In the field of software simulation of P systems, the proposals made so far have taken advantage mainly of the parallelism of GPUs, but not of the parallelism of existing multi-core processors. This paper proposes a new model for simulating P systems using a multi-threaded approach in a multi-core processor. This simulation approach establishes a new paradigm that is entirely in line with the philosophy of P-systems: since objects must react in parallel, asynchronously and autonomously with other objects, simulation using multiple synchronized threads completely mimics the behavior of objects within a membrane. This proposal has been implemented and tested using a simulator programmed in C#, and its correct operation has been tested for confluent and non-confluent systems. The experimental results confirm that the simulator scales well with the number of hardware threads of a multiprocessor. The obtained results show that the new model is correct and that it can be extended to other more complex types of P systems, in order to discover which are the limit of this multi-threaded approach when running it in multi-core processors. • A pioneering approach for membrane simulation based on CPU multi-threading. • The approach can be straightforwardly used to simulate generic transitive P systems. • A multi-threaded implementation of the P system simulator has been tested. • Performance speedup results directly proportional to the number of hardware threads. [ABSTRACT FROM AUTHOR]

• Proposes a comprehensive cellular automaton model for diverse bicycle behaviors. • Develops interaction mechanism for group and retrograde bicycle behaviors. • Employs field experiments to calibrate the proposed model. • Identifies key variables affecting bicycle flow self-organization and efficiency. The rise of shared bicycles has increased the demand for group riding, integrating bicycles into social groups. Additionally, retrograde riding, where cyclists travel against the designated direction, is a common behavior observed in bicycle flows. The interaction and self-organization phenomenon of group and retrograde behaviors are complex, significantly impacting traffic efficiency. This paper develops a two-dimensional Extended Moore Neighborhood and constructs state-updating rules for regular riding, group riding and retrograde riding. Each rule comprises a psychological decision layer and a physical execution layer, forming a cellular automaton model for group and retrograde bicycles. Field experiments are conducted to calibrate the model parameters and verify the behavioral characteristics. Finally, we execute numerical simulations at a signalized intersection to explore the coupling effects of group and retrograde behaviors on self-organization within the bicycle flow and the traffic capacity. The results indicate that group behavior increases queue length while reducing start wave speed and expansion degree. Retrograde behavior intensifies the negative effects on bicycle flow. These findings provide insights for managing both forward and retrograde bicycle flows. [ABSTRACT FROM AUTHOR]

The rapid evolution of vehicular communication has led to numerous new algorithms and applications based on this technology. Neglecting issues arising from wireless communication, such as the loss of information and delays, can result in problems such as reduced performance or compromised safety. However, while simulating V2X demands significant computational resources, it proves unsuitable for complex testing setups, including mixed-reality testing. This paper enhances V2X simulation by relying on an ecosystem based on SUMO, OMNeT++, Veins, and INET simulation tools. The proposed novel method introduces mesoscopic simulation in Vehicular Ad-hoc Networks to increase simulation performance to a level where real-time behavior is achievable. Meanwhile, it can also be beneficial in the acceleration of regular simulations. The presented solution introduces Meso nodes that are capable of aggregating communication across an entire traffic area, facilitated by a neural network function approximator. Results showed substantial performance gain while simulation accuracy was preserved. [Display omitted] • Mesoscopic V2X simulation was introduced to boost simulation performance. • Communication of vehicles far from the focus area was aggregated into a single node. • A neural network approximated the transmission frequency of meso nodes. • Simulation speed improved by 95.89% on a city-scale scenario. [ABSTRACT FROM AUTHOR]

• The model simulates car-following and lane-changing behaviors in a unified framework. • The risk field-based behavior model has the potential to diverse simulation scenarios. • The model's parameters have transparent, interpretable effects on simulations. • The underlying mechanisms of drivers' path and trajectory planning are elucidated. The modeling of driving behavior is pivotal for the accurate simulation of traffic scenarios and for providing human-like decision-making of autonomous driving systems. Car-following (CF) and lane-changing (LC) behaviors are continuous maneuvers within traffic flow, generally modeled separately in the literature. The coherence between these two behaviors may be ignored, leading to unrealistic behavioral simulations. Therefore, this paper establishes a risk field-based driving behavior model for two-dimensional motion, ensuring coherent modeling of CF and LC behaviors under a unified framework. First, a risk quantification method is developed to calculate the risk in two-dimensional scenarios, accounting for risk over the preview time. A cubic polynomial is applied to generate path curves that align with vehicle dynamics. Second, the enhanced behavior model primarily comprises two integral components: path and trajectory planning. These two components aim to identify the path or trajectory that maximizes the benefit while meeting the desired risk. Third, the maximum acceptable risk, representing a higher risk than the desired risk, is defined to facilitate path adjustment and avoid frequent path adjustment. Finally, the proposed model is proved through comparisons with existing models using driving data. Several cases are employed for further analysis to show the model's rationality and potential in various aspects. This study develops the previous risk field-based behavior model from one-dimensional to two-dimensional scenarios, furnishes a unified framework for elucidating driving behavior in various scenarios, and contributes to the progress of behavior modeling. [ABSTRACT FROM AUTHOR]

Fifth generation communication networks (5G) has received a great deal of attention from academia and industry alike, which will enable a wide variety of vertical applications by connecting heterogeneous devices and machines. Assessing availability and reliability in many circumstances and environments is critical. Researchers have recently focused on investigating and analyzing new multimedia networks with artificial intelligence (AI) technologies to achieve higher data rates and secure communication traffic between parties. User information privacy and security are of vital importance and of growing concerns that present evolving challenges to overcome in preventing attacks. Man-in-the-middle (MITM) attack is considered one of the most common attacks, where an attacker can impersonate one of the parties in a communication system to steal user data or forge the malicious data. Due to the limitation of using conventional cryptographic techniques for mobile networks and similar systems, new methods have been introduced to validate and authenticate transmitted signals dynamically, depending on the physical layer. In this paper, we present the distance-time directional delay (DTDD) model to detect the MITM attack in a variety of contexts and scenario. Indoor hotspots (InH) and urban micro-cellular (UMi) propagation environments were investigated to verify the reliability of the proposed approaches using realistic 5G millimeter-wave configurations and system setups. Simulations have been constructed based on the mmWave 5G channel simulator tool NYUSIM, in conjunction with a collection of machine learning algorithms (ML) including the extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM) as the core of the presented models and methodologies. Numerical simulations results produced a detection accuracy approaching 100% in the InH environment scenario, whereas for UMi environment scenario, a detection accuracy approaching 99% was attained. [ABSTRACT FROM AUTHOR]

As a device characterized by multiple degrees of freedom in one driving unit, analytical electromagnetic torque modeling is needed for the rotor position tracking control of a Permanent Magnet Spherical Motor (PMSpM). In this paper, Extreme Gradient Boosting (XGBoost) was proposed to be employed for establishing the output relationship between the rotor position and the electromagnetic torque of PMSpM. The Finite Element Method (FEM) was applied to obtain train data and test data concerning the rotor position and electromagnetic torque of PMSpM. Particle Swarm Optimization (PSO) was applied to optimize partial parameters of XGBoost, which serves to enhance the modeling accuracy of electromagnetic torque via XGBoost. The predictive results of algorithms, including Random Forest (RF), Gradient Boosting Regression Tree (GBRT), Multi-task Gaussian Process (MTGP), and XGBoost, were compared with FEM results and experimental results over multiple indicators. The capability of XGBoost has been validated not only to perform modeling tasks within an abbreviated time span but also to generate models that display amplified accuracy and efficiency. [ABSTRACT FROM AUTHOR]

We present a novel blockchain-based Federated Learning (FL) system that introduces a Byzantine-resilient consensus protocol that performs well in the presence of adversarial participants. Unlike existing state-of-the-art, this system can be deployed in a fully decentralized manner, meaning it does not rely on any single actor to function correctly. Using a Smart Contract-driven workflow coupled with a commitment scheme and a differential privacy-based solution, we ensure training integrity, prevent plagiarism, and protect against leakage of sensitive data while performing effective federated training. We demonstrate the system's effectiveness by performing simulation and implementation of an end-to-end proof of concept. Our practical implementation showcases the system's efficiency on a single computer with multiple trainers, revealing low memory demands and manageable network and block I/O, which suggest scalability to larger, more complex networks. The paper concludes by exploring future enhancements, including advanced cryptographic methods for enhanced privacy and potential applications extending the system's utility to broader domains within FL. Our work lays the groundwork for a new generation of decentralized learning systems, promising increased adoption in real-world scenarios where data privacy and security are of paramount concern. [ABSTRACT FROM AUTHOR]

The design of generic models, or generic building blocks, for health care operations has been discussed in depth and identified as one of the grand challenges for researchers in the field of Modeling and Simulation. In this paper we provide an investigative basis for answering recently raised research questions for a specific sub-domain of health care facilities and a specific Modeling and Simulation paradigm, i.e. Discrete Event Simulation Models of Emergency Departments. We perform a detailed analysis of a large set of published Emergency Department models. This analysis is executed following a structured conceptual modeling framework to ensure the coverage of key conceptual modeling aspects: objectives; input scenarios; output definitions, i.e. commonly used performance indicators; structural components such as physical resources and human resources (including expertise and skill levels); patient classifications (arrival modes, triage grades, complaint categories); patient pathways including triage, treatment (which possibly includes resuscitation or special care) and admission phases; and organizational structures including control policies. The resulting collection and classification of modeling archetypes are used as the basis for an initial generic modeling toolkit for Emergency Department operations. Thereby, the modeler is guided through the main steps of conceptual modeling of Emergency Departments and generic building blocks for each step are provided. The paper concludes with a discussion on the applicability and benefits of the resulting GEDMod framework along with the potential to combine the toolkit into a single generic Emergency Department model. [ABSTRACT FROM AUTHOR]

In a superscalar processor, instructions of various types flow through an execution pipeline, traversing hardware resources which are mostly shared among many different instruction types. A notable exception to shared pipeline resources is the collection of functional units, the hardware that performs specific computations. In a trade-off of cost versus performance, a pipeline designer must decide how many of each type of functional unit to place in a processor’s pipeline. In this paper, we model a superscalar processor’s issue queue and functional units as a novel queuing network. We treat the issue queue as a finite-sized waiting area and the functional units as servers. In addition to common queuing problems, customers of the network share the queue but wait for specific servers to become ready (e.g., addition instructions wait for adders). Furthermore, the customers in this queue are not necessary ready for service, since instructions may be waiting for operands. In this paper we model a novel queuing network that provides a solution to the expected queue length of each type of instruction. This network and its solution can also be generalized to other problems, notably other resource-allocation issues that arise in superscalar pipelines. [ABSTRACT FROM AUTHOR]

This article proposes an approach to modelling and analysis of distributed probabilistic timed actors. The approach rests on a lightweight infrastructure of actors - Theatre - whose design aims to favoring the development of predictable time-dependent applications. Adopted actors are thread-less and their evolution is transparently regulated by a customizable control layer which has a reflective link with the application. A Theatre system consists of a collection of interacting computing nodes (theatres) each one hosting a sub system of local actors. The control layers of the various theatre components coordinate each other so as to enforce a common global time notion (real or simulated time). The abstract Theatre modelling language can be reduced in a case to Uppaal , which opens to the analysis of the functional/non-functional aspects of a distributed system. A key factor of the reduction process concerns the possibility of making both a non-deterministic analysis of an actor model (checking that something, e.g., an event, can occur), and a quantitative evaluation of system behavior by statistical model checking of the same model (e.g., estimating the probability for an event to occur). The paper describes the Theatre architecture and introduces a real-time case study which is used as a running example throughout the paper. The operational semantics of Theatre is provided and the proposed reduction of Theatre actors on top of Uppaal detailed through the chosen example. Some experimental results are reported about qualitative and quantitative analysis of the case study. Finally, conclusions are presented with an indication of further work. [ABSTRACT FROM AUTHOR]

Deployment of a simulation-based dynamic traffic assignment (DTA) model requires calibration of a large number of demand and supply input parameters. In this paper, we present a data-intensive framework for deployment, calibration, and validation of a simulation-based DTA model of Melbourne, Australia as a large-scale congested network. The model consists of 55,719 links and 24,502 nodes and simulates almost 2.1 million commuters in a 4-h morning peak period. We propose a machine learning based technique to classify and calibrate the traffic flow fundamental diagrams against empirical data obtained from a large number of freeway loop detectors across the network. An optimization framework for estimating the time-dependent origin-destination (TDOD) demand is also presented. To enhance the quality of the TDOD demand estimation, we apply a departure time profiling technique to account for the spatial differences between OD pairs. The paper also demonstrates the impact of adaptive driving on the quality of the DTA calibration. A comparison between the simulated and observed link volumes over 1,250 locations across the entire network shows that the calibration procedure generates an approximately 30% improvement in the root mean square error (RMSE). The impact of pedestrians and cyclists on the vehicular traffic is also implicitly considered in the central business district (CBD) area to improve underestimation of the simulated travel times. Validation results suggest that the calibrated DTA model successfully replicates traffic patterns in the network and support the future applications of the model for various transportation operations and planning purposes. [ABSTRACT FROM AUTHOR]

This paper reports a review of the extensions and application of the Cell Transmission Model (CTM). Those extensions are models able to simulate complex urban traffic dynamics with all the advantages of macroscopic and microscopic urban traffic model. Over the past few years researchers have been trying to increase the level of detail by extending CTM and introducing new formulations to improve the application of the model in urban traffic. The authors classified the papers while taking into consideration all those factors characterizing the urban traffic, arterial and intersection traffic flow in particular. One of the primary goals of transport research is to develop a general framework of urban traffic networks that might be applied from a realistic point of view. Recent studies about traffic simulations have shown that, among various macroscopic simulation models, the CTM has the potential to achieve this objective. We have also reported our model the CTM_UT that improves the CTM for Urban Traffic. We believe that it is possible to apply this model to ITS application, hence increase the accuracy of the macroscopic model while maintaining the computational advantages and provide an accurate prediction of travel time approach. [ABSTRACT FROM AUTHOR]

One of the most important research fields of network sciences is the robustness of networks. A recently answered important question was the following: Which network topologies are more resistant to random malfunctions and/or direct attacks? Nevertheless, until now, “which system topology can be maintained and how to manage maintenance more efficiently and effectively” have been open questions. However, these questions are the keys both to designing large-scale systems and to scheduling maintenance tasks. This paper proposes a new means to analyze the maintainability of a large system by combining two kinds of networks, i.e., the reliability diagram of the system (1) and the network of scheduled maintenance tasks (2). This paper shows how to assign maintenance task(s) to a system component to increase the reliability of the system. With the proposed method, the maintainability of large-scale systems can be analyzed. [ABSTRACT FROM AUTHOR]

Due to the spatial–temporal spectrum mobility in cognitive radio ad-hoc networks (CRAHNs), multi-hop communication paths among secondary users have to detour or even be disconnected to avoid interfering with primary users. Consequently, the hop count in CRAHNs shows distinguishing characteristics compared with those in conventional ad-hoc networks (AHNs). Although the influences of beamforming on the connectivity of CRAHNs have been studied in the literature, no research works on investigating the hop count in cognitive environment under the joined effect of beamforming have been conducted. In this paper, we model CRAHNs where SUs are equipped with directional antennas as geometric random graphs and then propose a framework for simulation analysis of the hop count distribution. Our proposed framework comprises of two components. The first one is an algorithm which finds all possible paths between two random secondary users selected as source node and destination node then returns the hop count of the shortest path between them by using the random values of node location and active state, antenna gain, and channel fading as input data. The second one is a methodology which returns the hop count distribution and connection probability of these two nodes from vast number of examined random network topology trials. We show that the hop count and the connectivity between two secondary users greatly depend on the number of antenna elements of directional antenna. Moreover, only a specific combination of direction antenna and beamforming scheme gives better performance than omnidirectional antenna. The observed results in this paper provide useful guidelines on designing and evaluating hop count based applications in CRAHNs with beamforming. [ABSTRACT FROM AUTHOR]

With more and more high-rise building springing into the cities, the movement of pedestrians on stairs is of great importance for the evacuation of these facilities; the topic of human performance on stairs has attracted a lot of researcher to study. However, there is still a lack of analysis of specific situations, such as mid-landing and merging behaviors. In this paper we employ a modified version of the Optimal Steps Model, modified by taking block-based floor field into consideration under open boundary conditions, to reproduce movement at mid-landing and study performance of occupants in stairwell. Movements on this kind of stairs are simulated and studied with the help of extended model: achieved results are in accord with previous research in respect of specific flow and fundamental diagram. Lane formations on stairs come into being before mid-landing area through simulations. Furthermore most people reach at the mid-landing by the outside stairs at higher density due to more space and less force from other agents, which is induced that outside of the stairs is good for evacuation. At last the model is implemented to study the effect of stairs geometry on merging behaviors, and it is found when corridor is connected to the landing opposite to the incoming stairs, this structure is biased in favor of occupants from stairs. The work in this paper is intended to better understand movement during stair evacuations and develop a technical foundation for codes and standards requirements as well as egress modeling techniques. [ABSTRACT FROM AUTHOR]

Road network microsimulation is computationally expensive, and existing state of the art commercial tools use task parallelism and coarse-grained data-parallelism for multi-core processors to achieve improved levels of performance. An alternative is to use Graphics Processing Units (GPUs) and fine-grained data parallelism. This paper describes a GPU accelerated agent based microsimulation model of a road network transport system. The performance for a procedurally generated grid network is evaluated against that of an equivalent multi-core CPU simulation. In order to utilise GPU architectures effectively the paper describes an approach for graph traversal of neighbouring information which is vital to providing high levels of computational performance. The graph traversal approach has been integrated within a GPU agent based simulation framework as a generalised message traversal technique for graph-based communication. Speed-ups of up to 43 ×  are demonstrated with increased performance scaling behaviour. Simulation of over half a million vehicles and nearly two million detectors at a rate of 25 ×  faster than real-time is obtained on a single GPU. [ABSTRACT FROM AUTHOR]

In this paper, we present an agent based simulation from a mathematical formulation for Motivation and Ability axis formation, threshold line and triggers positioning of the Fogg Behavior Model (FBM). This model is primarily used for the evaluation of persuasive technology based process. A dynamic behavior for the threshold line is proposed in order to identify trigger location based on the dataset Kolmogorov mean. Results shown, with the multiagent environment implemented, that the action line proposed on the FBM must not be fixed, but dynamically allocated in order to act as a feature for identification of trigger events zones. The data acquired by agent based simulation, helped the validation process for all mathematical equations and triggers defined before only on a conceptual level. Given the lack of mathematical information for the FBM, this paper tends to contribute with a theoretical support on the persuasive technology evaluation process in order to provide reasoning for further trigger implementation with more efficiency. [ABSTRACT FROM AUTHOR]

A Cyber Physical System (CPS) is given by the integration of cyber and physical components, usually with feedback loops, where physical processes affect computations and vice versa. Design and implementation of complex CPSs is a multidisciplinary and demanding task. Challenges arise especially for the exploitation of heterogeneous and different models during the various phases of system life cycle. This paper proposes an agent-based and control-centric methodology which is well suited for the development of complex CPSs. The approach is novel and supports model continuity which enables the use of a unique model along all the development stages of a system ranging from analysis, by simulation, down to real-time implementation and execution. In the paper, basic concepts of the methodology are provided together with implementation details. Effectivenesses of the approach is demonstrated through a case study concerning a prototyped CPS devoted to the optimization of power consumption in a smart micro-grid automation system. [ABSTRACT FROM AUTHOR]