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Crowd network systems have been deemed as a promising mode of modern service industry and future economic society, and taking crowd network as the research object and exploring its operation mechanism and laws is of great significance for realizing the effective governance of the government and the rapid development of economy, avoiding social chaos and mutation. Because crowd network is a large-scale, dynamic and diversified online deep interconnection, its most results cannot be observed in real world, and it cannot be carried out in accordance with traditional way, simulation is of great importance to put forward related research. To solve above problems, this paper aims to propose a simulation architecture based on the characteristics of crowd network and to verify the feasibility of this architecture through a simulation example.

This paper adopts a data-driven architecture by deeply analyzing existing large-scale simulation architectures and proposes a novel reflective memory-based architecture for crowd network simulations. In this paper, the architecture is analyzed from three aspects: implementation framework, functional architecture and implementation architecture. The proposed architecture adopts a general structure to decouple related work in a harmonious way and gets support for reflection storage by connecting to different devices via reflection memory card. Several toolkits for system implementation are designed and connected by data-driven files (DDF), and these XML files constitute a persistent storage layer. To improve the credibility of simulations, VV&A (verification, validation and accreditation) is introduced into the architecture to verify the accuracy of simulation system executions.

Implementation framework introduces the scenes, methods and toolkits involved in the whole simulation architecture construction process. Functional architecture adopts a general structure to decouple related work in a harmonious way. In the implementation architecture, several toolkits for system implementation are designed, which are connected by DDF, and these XML files constitute a persistent storage layer. Crowd network simulations obtain the support of reflective memory by connecting the reflective memory cards on different devices and connect the interfaces of relevant simulation software to complete the corresponding function call. Meanwhile, to improve the credibility of simulations, VV&A is introduced into the architecture to verify the accuracy of simulation system executions.

This paper proposes a novel reflective memory-based architecture for crowd network simulations. Reflective memory is adopted as share memory within given simulation execution in this architecture; communication efficiency and capability have greatly improved by this share memory-based architecture. This paper adopts a data-driven architecture; the architecture mainly relies on XML files to drive the entire simulation process, and XML files have strong readability and do not need special software to read.

As main mode of modern service industry and future economy society, the research on crowd network can greatly facilitate governances of economy society and make it more efficient, humane, sustainable and at the same time avoid disorders. However, because most results cannot be observed in real world, the research of crowd network cannot follow a traditional way. Simulation is the main means to put forward related research studies. Compared with other large-scale interactive simulations, simulation for crowd network has challenges of dynamic, diversification and massive participants. Fortunately, known as the most famous and widely accepted standard, high level architecture (HLA) has been widely used in large-scale simulations. But when it comes to crowd network, HLA has shortcomings like fixed federation, limited scale and agreement outside the software system.

This paper proposes a novel reflective memory-based framework for crowd network simulations. The proposed framework adopts a two-level federation-based architecture, which separates simulation-related environments into physical and logical aspect to enhance the flexibility of simulations. Simulation definition is introduced in this architecture to resolve the problem of outside agreements and share resources pool (constructed by reflective memory) is used to address the systemic emergence and scale problem.

With reference to HLA, this paper proposes a novel reflective memory-based framework toward crowd network simulations. The proposed framework adopts a two-level federation-based architecture, system-level simulation (system federation) and application-level simulation (application federations), which separates simulation-related environments into physical and logical aspect to enhance the flexibility of simulations. Simulation definition is introduced in this architecture to resolve the problem of outside agreements and share resources pool (constructed by reflective memory) is used to address the systemic emergence and scale problem.

Simulation syntax and semantic are all settled under this framework by templates, especially interface templates, as simulations are separated by two-level federations, physical and logical simulation environment are considered separately; the definition of simulation execution is flexible. When developing new simulations, recompile is not necessary, which can acquire much more reusability, because reflective memory is adopted as share memory within given simulation execution in this framework; population can be perceived by all federates, which greatly enhances the scalability of this kind of simulations; communication efficiency and capability has greatly improved by this share memory-based framework.

Logistic strategies represent a key factor to increase supply chain (SC) effectiveness, as the optimization of logistics networks enables transport and storage costs reduction as well as quick response leading to higher customer satisfaction. A useful approach for SC performance improvement may be pursued by resorting to advanced software tools able to analyze complex production scenarios, performing concurrent, synchronized and distributed simulations. Support to the logistics planning phase is offered by geographical information system technology, included in the simulation environment in order to manage related geographical data (i.e. warehouse location, choice of vehicle routeings, etc.). In the paper the main characteristics of the proposed hardware and software architecture have been illustrated, focusing attention on the logic phases for implementation by the transport federate. Furthermore, a preliminary functionality validation of the developed tool is presented with reference to simplified test cases.

This study intends to review the existing studies on the application of immersive learning technology (ILT) in architecture education field. A systematic literature review (SLR) was conducted on the characteristics and implementation of ILT, research purpose, approach and outcome of research.

The PICO concept (Population, Intervention, Comparison, Outcome measures) was used to form the research keywords. The Scopus database was searched and supported by supplementary search on Google Scholar, ProQuest, Emerald Insight and Springer Link. Based on the inclusion and exclusion criteria, 19 peer-reviewed journal articles published between 2013 and 2019 were identified.

Virtual reality was found to be the most prevalent ILT applied in architecture education and commonly used as simulation. Most of the studies were applied in year-two architecture study and were mainly implemented for architecture design subject. Very few studies have associated the use of ILT with learning theories, and most of the existing studies have examined the effect of ILT on learning performance. Motivation was found to be the dominant emotional state, and most of the interventions deal with a higher cognitive level.

Only a small number of articles were selected due to the limited number of studies on the subject. Nevertheless, analysis of the selected few has provided valuable insight into the current scenario of the research topic.

This study adds to the existing literature by examining the existing empirical evidence on ILT intervention in the architecture education field. The findings will contribute towards innovating the learning process among architecture students and encouraging the use the ILT as part of architecture education system in higher education institutions.

Conventionally, various levels of decisions with regard to production are made in a number of sequential stages such as system design, production/process planning, production scheduling, system reconfiguration and system restructure. This paper aims to present an integrated approach for modelling, restructuring and simulating manufacturing systems to suit changing manufacturing situations as quickly as possible.

An agent‐based approach is employed where each manufacturing resource is represented by an agent. Simply speaking, the approach enables the machines in a manufacturing system to manage themselves efficiently and effectively.

The agent‐based modelling and interaction approach enables manufacturing resources to be allocated dynamically in an optimal manner. The modelling approach also enables alternative system configurations to be identified and evaluated using distributed discrete event simulation system. Resource allocation and manufacturing system control can take place simultaneously with simulation.

This paper presents a successful integration of agent‐based modelling, process planning, scheduling, control, identification of alternative system configurations, simulation and analysis of the configurations and PLC reprogramming within a coherent framework for improved manufacturing responsiveness.

Describes the development of an adaptive simulation model for a keyboard assembly cell for real‐time decision support. Discusses the architecture of the modelling and control system, including the movement of entities and conveyors, describing how up to four different keyboard types may be modelled, with a PC cell controller continually monitoring the state changes of the assembly line, passing the data captured to the simulation model created in ARENA.

In the grid‐based simulation, the resource application needed is distributed in the grid environment as grid service, and time management is a key problem in the simulation system. Grid workflow provides convenience for grid user to management and executes grid services. But it emphasizes process and no time‐management, so a temporally constrained grid workflow model is pointed out based on grid flow with temporally constraint to schedule resources and manage time.

The temporally constrained grid workflow model is distributed model: the federate has local temporal constraints and interactive temporal constraints among federates. The problem to manage time is a temporally distributed constraint satisfaction problem given deadline time and duration time of grid services. Multi‐asynchronous weak‐commitment search (AWS) algorithm is an approach to resolve DCSP, so a practical example of a simulation project‐based grid system was presented to introduce application of Multi‐AWS algorithm.

The temporally constrained grid workflow is based temporal reasoning and grid workflow description about grid services.

The new problem about scheduling resources and managing time in the grid‐based simulation is pointed out; and the approach to resolve the problem is applied into a practical example.

This paper aims to propose a theoretical decision support framework, which integrates artificial intelligence (AI), discrete-event simulation (DES) and database management technologies so as to determine the steady state flow of items (e.g. fixtures, jigs, tools, etc.) in manufacturing.

The existing literature was carefully reviewed to address the state of the arts in decision support systems (DSS), the shortcomings of pure simulation-based and pure AI-based DSS. A conceptual example is illustrated to show the integrated application of AI, simulation and database components of the proposed DSS framework.

Recent DSS studies have revealed the limitations of pure simulation-based and pure AI-based DSS. A new DSS framework is required in manufacturing to address these limitations, taking into account the problems of flowing items.

The theoretical DSS framework is proposed using simple rules and equations. This implies that it is not complex for software development and implementation. Practical data are not presented in this paper. A real DSS will be developed using the proposed theoretical framework and realistic results will be presented in the near future.

The proposed theoretical framework reveals how the integrated components of DSS can work together in manufacturing in order to determine the stable flow of items in a specific production period. Especially, the integrated performance of case-based reasoning (CBR) and DES is conceptually illustrated.

The purpose of this paper is to analyze and compare the performance of several different UAV trajectory tracking algorithms in normal and abnormal flight conditions to investigate the fault‐tolerant capabilities of a novel immunity‐based adaptive mechanism.

The evaluation of these algorithms is performed using the West Virginia University (WVU) UAV simulation environment. Three types of fixed‐parameter algorithms are considered as well as their adaptive versions obtained by adding an immunity‐based mechanism. The types of control laws investigated are: position proportional, integral, and derivative control, outer‐loop nonlinear dynamic inversion (NLDI), and extended NLDI. Actuator failures on the three channels and increased turbulence conditions are considered for several different flight paths. Specific and global performance metrics are defined based on trajectory tracking errors and control surface activity.

The performance of all of the adaptive controllers proves to be better than their fixed parameter counterparts during the presence of a failure in all cases considered.

The immunity inspired adaptation mechanism has promising potential to enhance the fault‐tolerant capabilities of autonomous flight control algorithms and the extension of its use at all levels within the control laws considered and in conjunction with other control architectures is worth investigating.

The WVU UAV simulation environment has been proved to be a valuable tool for autonomous flight algorithm development, testing, and evaluation in normal and abnormal flight conditions.

A novel adaptation mechanism is investigated for UAV control algorithms with fault‐tolerant capabilities. The issue of fault tolerance of UAV control laws has only been addressed in a limited manner in the literature, although it becomes critical in the context of imminent integration of UAVs within the commercial airspace.

The purpose of this paper is to develop a generic framework for hybrid (integrated deployment of system dynamics and discrete event simulation) simulation which can be applied in the healthcare domain.

As hybrid simulation in an organisational context is a new topic with limited available data on deployment of hybrid simulation in organisational context, an inductive approach has been applied. On the basis of knowledge induced from literature, a generic conceptual framework for hybrid simulation has been developed. The proposed framework is demonstrated using an explanatory case study comprising an accident and emergency (A&E) department.

The framework provided detailed guidance for the development of a hybrid model of an A&E case study. Findings of this case study suggest that the hybrid model was more efficient in capturing behavioural impact on operational performances.

The framework is limited to only SD and DES; as agent‐based is another simulation method which is emerging as a promising tool for analysing problems such as spread of infectious diseases in healthcare context, inclusion of this into the framework will enhance the utility of the framework.

This framework will aid in the development of hybrid models capable of comprehending both detail as well as dynamic complexity, which will contribute towards a deeper understanding of the problems, resulting in more effective decision making.

It is expected that this research will encourage those engaged in simulation (e.g. researchers, practitioners, decision makers) to realise the potential of cross‐fertilisation of the two simulation paradigms.

Currently, there is no conceptual framework which provides guidance for developing hybrid models. In order to address this gap, this paper contributes by proposing a conceptual framework for hybrid simulation for the healthcare domain.