scholarly journals Urban Boundary Demarcation—An iCN Model Approach

2021 ◽  
Vol 10 (12) ◽  
pp. 448
Author(s):  
Amila Jayasinghe ◽  
Lindamullage Don Charls Hasintha Nawod Kalpana ◽  
Charithmali Chethika Abenayake ◽  
Pelpola Kankanamge Seneviratne Mahanama
Keyword(s):  
Transportation Network ◽  
Population Projections ◽  
Complex Nature ◽  
Urban And Regional Planning ◽  
Gis Applications ◽  
Proposed Model ◽  
Open Source Gis ◽  
Clustered Network ◽  
Administrative Boundary ◽  
Model Approach

During the last two decades, determining the urban boundaries of cities has become one of the major concerns in the urban and regional planning subject domains. Many scholars have tried to model the change of urban boundaries as it helps with sustainable development, population projections and social policy making, but such efforts have been futile, owing to the complex nature of urbanization and the theoretical and technical limitations of the proposed applications. Hence, many countries continue to rely on the administrative boundary demarcation, which rarely represent the actual urbanizing pattern. In such context, this study utilized the “Intersection-Based Clustered Network Model—(iCN Model)” to determine the urban boundaries of cities and selected Sri Lanka as the study area and considered few cities to test the model empirically, with satellite imagery classified urban boundaries. The findings of the study depict that the iCN Model is capable of capturing the complex and dynamic socioeconomic interdependencies of cities via the transportation network configurations. Therefore, the proposed approach is an excellent proxy to derive the urban boundaries of cities, which correspond with the same, derived by the satellite imageries. The proposed model is entirely based on open-source GIS applications and is free to implement and modify using the methods described in this paper.

scholarly journals System of Time Fractional Models for COVID-19: Modeling, Analysis and Solutions

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 787
Author(s):  
Olaniyi Iyiola ◽  
Bismark Oduro ◽  
Trevor Zabilowicz ◽  
Bose Iyiola ◽  
Daniel Kenes
Keyword(s):  
Fractional Order ◽  
Recovery Rate ◽  
Death Rate ◽  
Numerical Solutions ◽  
Complex Nature ◽  
Uniqueness Of Solutions ◽  
Fractional Equations ◽  
Effective Contact ◽  
Proposed Model ◽  
Fractional Models

The emergence of the COVID-19 outbreak has caused a pandemic situation in over 210 countries. Controlling the spread of this disease has proven difficult despite several resources employed. Millions of hospitalizations and deaths have been observed, with thousands of cases occurring daily with many measures in place. Due to the complex nature of COVID-19, we proposed a system of time-fractional equations to better understand the transmission of the disease. Non-locality in the model has made fractional differential equations appropriate for modeling. Solving these types of models is computationally demanding. Our proposed generalized compartmental COVID-19 model incorporates effective contact rate, transition rate, quarantine rate, disease-induced death rate, natural death rate, natural recovery rate, and recovery rate of quarantine infected for a holistic study of the coronavirus disease. A detailed analysis of the proposed model is carried out, including the existence and uniqueness of solutions, local and global stability analysis of the disease-free equilibrium (symmetry), and sensitivity analysis. Furthermore, numerical solutions of the proposed model are obtained with the generalized Adam–Bashforth–Moulton method developed for the fractional-order model. Our analysis and solutions profile show that each of these incorporated parameters is very important in controlling the spread of COVID-19. Based on the results with different fractional-order, we observe that there seems to be a third or even fourth wave of the spike in cases of COVID-19, which is currently occurring in many countries.

Use of geographical information systems in multiple sclerosis research: A systematic scoping review

2021 ◽  
Author(s):  
Soheil Hashtarkhani ◽  
Hossein Tabatabaei-Jafari ◽  
Behzad Kiani ◽  
MaryAnne Furst ◽  
Luis Salvador-Carulla ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Scoping Review ◽  
Geographical Information Systems ◽  
Geographical Information ◽  
Complex Nature ◽  
Gis Applications ◽  
Thematic Mapping ◽  
Health Related ◽  
Comprehensive Search ◽  
Comprehensive Search Strategy

Abstract Introduction: Geographical Information System (GIS) and spatial analysis have an emerging role in the understanding and management of health-related outcomes. However, there is a knowledge gap about the extent to which GIS has supported Multiple Sclerosis (MS) research. Therefore, this review aimed to explore the types of GIS applications and the complexity of their visualisation in MS research. Methods: A systematic scoping review was conducted based on York’s five-stage framework. PubMed, Scopus and Web of Science were searched for relevant studies published between 2000 and 2020 using a comprehensive search strategy based on the main concepts related to GIS and MS. Grounded, inductive analysis was conducted to organize studies into meaningful application areas. Further, we developed a tool to assess the visualisation complexity of the selected papers.Results: Of 3,723 identified unique citations, 42 papers met our inclusion criteria for the final review. One or more of the following types of GIS applications were reported by these studies: (a) thematic mapping (37 papers); (b) spatial cluster detection (16 papers); (c) risk factors detection (16 papers); and (d) health access and planning (two papers). In the majority of studies (88%), the score of visualisation complexity was relatively low: three or less from the range of zero to six. Conclusions: Although the number of studies using GIS techniques has dramatically increased in the last decade, the use of GIS in the areas of MS access and planning is still under-researched. Additionally, the capacity of GIS in visualising complex nature of MS care system is not yet fully investigated.

scholarly journals Modeling the Multicommodity Multimodal Routing Problem with Schedule-Based Services and Carbon Dioxide Emission Costs

2015 ◽  
Vol 2015 ◽  
pp. 1-21 ◽  
Author(s):  
Yan Sun ◽  
Maoxiang Lang
Keyword(s):  
Carbon Dioxide ◽  
Carbon Dioxide Emissions ◽  
Transportation Network ◽  
Linearization Method ◽  
Mixed Integer ◽  
Multicommodity Flow ◽  
Routing Problem ◽  
Multimodal Transportation ◽  
Proposed Model ◽  
Freight Routing

We explore a freight routing problem wherein the aim is to assign optimal routes to move commodities through a multimodal transportation network. This problem belongs to the operational level of service network planning. The following formulation characteristics will be comprehensively considered: (1) multicommodity flow routing; (2) a capacitated multimodal transportation network with schedule-based rail services and time-flexible road services; (3) carbon dioxide emissions consideration; and (4) a generalized costs optimum oriented to customer demands. The specific planning of freight routing is thus defined as a capacitated time-sensitive multicommodity multimodal generalized shortest path problem. To solve this problem systematically, we first establish a node-arc-based mixed integer nonlinear programming model that combines the above formulation characteristics in a comprehensive manner. Then, we develop a linearization method to transform the proposed model into a linear one. Finally, a computational experiment from the Chinese inland container export business is presented to demonstrate the feasibility of the model and linearization method. The computational results indicate that implementing the proposed model and linearization method in the mathematical programming software Lingo can effectively solve the large-scale practical multicommodity multimodal transportation routing problem.

Information-As-System in Information Systems

2010 ◽  
pp. 99-118
Author(s):  
Tuan M. Nguyen ◽  
Huy V. Vo
Keyword(s):  
Information Systems ◽  
Structure Function ◽  
Conceptual Model ◽  
Systems Thinking ◽  
Systems Approach ◽  
Complex Nature ◽  
Holistic Thinking ◽  
Proposed Model ◽  
Nature Of Information

This article investigates the complex nature of information in information systems (IS). Based on the systems thinking framework, this study argues that information in IS is a system in its own right. A conceptual model of information-as-system is built on the systems thinking perspective adopted from Gharajedaghi’s holistic thinking rooted from Ackoff systems approach, which is developed through Peirce’s semiotics with the validity support of Metcalfe and Powell’s perspective of information perception, Mingers and Brocklesby’s schema of situational actions, Toulmin’s theory of argumentation and Ulrich’s theory of systems boundary. The proposed model of information-as-systems is described in terms of triads–on the structure, function, and process, all interdependent–in a context of information-as-system in IS.

Simulation of consumers decision-making process using agent-based model approach

2019 ◽  
Vol 10 (06) ◽  
pp. 1950037
Author(s):  
Daniel Soto Forero ◽  
Yony F. Ceballos ◽  
German Sànchez Torres
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Real Data ◽  
World Market ◽  
Decision Making Process ◽  
Agent Based ◽  
Dynamic Social Network ◽  
Proposed Model ◽  
The World ◽  
Model Approach

This paper describes a model to simulate the decision-making process of consumers that adopts technology within a dynamic social network. The proposed model use theories and tools from the psychology of consumer behavior, social networks and complex dynamical systems like the Consumat framework and fuzzy logic. The model has been adjusted using real data, tested with the automobile market and it can recreate trends like those described in the world market.

scholarly journals Coupling Quasi-Two-Dimensional Friction Model and Discrete Vapor Cavity Model for Simulation of Transient Cavitating Flows in Pipeline Systems

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Qiang Sun ◽  
Yuebin Wu ◽  
Ying Xu ◽  
Liang Chen ◽  
Tae Uk Jang
Keyword(s):  
Cost Effective ◽  
Friction Model ◽  
Experimental Results ◽  
Complex Nature ◽  
Two Dimensional ◽  
Cavity Model ◽  
Cavitating Flows ◽  
Vapor Cavity ◽  
Proposed Model ◽  
Pipeline Systems

Accurate simulation of cavitating flows in pipeline systems is important for cost-effective surge protection. However, this is still a challenge due to the complex nature of the problem. This paper presents a numerical model that combines the discrete vapor cavity model (DVCM) with the quasi-two-dimensional (quasi-2D) friction model to simulate transient cavitating flows in pipeline systems. The proposed model is solved by the method of characteristics (MOC), and the performance is investigated through a numerical case study formulated based on a laboratory pipeline reported in the literature. The results obtained by the proposed model are compared with those calculated by the classic one-dimensional (1D) friction model with the DVCM and the corresponding experimental results provided by the literature, respectively. The comparison shows that the pressure peak, waveform, and phase of pressure pulsations predicted by the proposed model are closer to the experimental results than those obtained by the classic 1D model. This demonstrates that the proposed model that combines the quasi-2D friction model with the DVCM has provided a solution to more accurately simulate transient cavitating flows in pipeline systems.

Network Design for Shipping Service of Large-Scale Intermodal Liners

2012 ◽  
Vol 2269 (1) ◽  
pp. 42-50 ◽  
Author(s):  
Qiang Meng ◽  
Shuaian Wang ◽  
Zhiyuan Liu
Keyword(s):  
Network Design ◽  
Large Scale ◽  
Programming Model ◽  
Transportation Network ◽  
Liner Shipping ◽  
Maritime Transportation ◽  
Mixed Integer ◽  
Hub And Spoke ◽  
Shipping Company ◽  
Proposed Model

A model was developed for network design of a shipping service for large-scale intermodal liners that captured essential practical issues, including consistency with current services, slot purchasing, inland and maritime transportation, multiple-type containers, and origin-to-destination transit time. The model used a liner shipping hub-and-spoke network to facilitate laden container routing from one port to another. Laden container routing in the inland transportation network was combined with the maritime network by defining a set of candidate export and import ports. Empty container flow is described on the basis of path flow and leg flow in the inland and maritime networks, respectively. The problem of network design for shipping service of an intermodal liner was formulated as a mixed-integer linear programming model. The proposed model was used to design the shipping services for a global liner shipping company.

Research on Optimal Vehicle Path in Intelligent Transportation

2014 ◽  
Vol 644-650 ◽  
pp. 2615-2618
Author(s):  
Wen Ming Yu
Keyword(s):  
Data Storage ◽  
Traffic Congestion ◽  
Transportation Network ◽  
Rapid Development ◽  
Optimal Path ◽  
Intelligent Transportation ◽  
Proposed Model ◽  
Planning Algorithm ◽  
Single Vehicle ◽  
Vehicle Path

The rapid development of economy and science and technology is quite obvious, which makes cars becoming more and more widely into the general family life. With the rapid development of economics and technology; the number of vehicles has largely increased. In this paper, there is the basic organizational framework for intelligent transportation, intelligent transportation network proposed model and its data storage structure, and the important influence on optimal path trajectory intelligent transportation planning. This paper analyzes intersection road network in the distribution based on computer graphics language, type and grade roads. The dynamic mathematical model of single vehicle is used vehicle planning algorithm based on period, effectively avoided the traffic road, reduce vehicle travel cost, improve the real-time effect and accuracy of the vehicle dynamic path. We hope the results and researches could combine with reality in order to reduce traffic congestion.

scholarly journals Unsupervised learning and clustered connectivity enhance reinforcement learning in spiking neural networks

2020 ◽  
Author(s):  
Philipp Weidel ◽  
Renato Duarte ◽  
Abigail Morrison
Keyword(s):  
Reinforcement Learning ◽  
Unsupervised Learning ◽  
Activity Patterns ◽  
Receptive Fields ◽  
Place Cells ◽  
Spiking Neural Networks ◽  
Complex Environments ◽  
Proposed Model ◽  
Clustered Network ◽  
Better Than

ABSTRACTReinforcement learning is a learning paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. However, implementations in spiking neuronal networks typically rely on input architectures involving place cells or receptive fields. This is problematic, as such approaches either scale badly as the environment grows in size or complexity, or presuppose knowledge on how the environment should be partitioned. Here, we propose a learning architecture that combines unsupervised learning on the input projections with clustered connectivity within the representation layer. This combination allows input features to be mapped to clusters; thus the network self-organizes to produce task-relevant activity patterns that can serve as the basis for reinforcement learning on the output projections. On the basis of the MNIST and Mountain Car tasks, we show that our proposed model performs better than either a comparable unclustered network or a clustered network with static input projections. We conclude that the combination of unsupervised learning and clustered connectivity provides a generic representational substrate suitable for further computation.

scholarly journals Unsupervised Learning and Clustered Connectivity Enhance Reinforcement Learning in Spiking Neural Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Philipp Weidel ◽  
Renato Duarte ◽  
Abigail Morrison
Keyword(s):  
Reinforcement Learning ◽  
Unsupervised Learning ◽  
Ad Hoc ◽  
Activity Patterns ◽  
Receptive Fields ◽  
Complex Environments ◽  
Self Organized ◽  
Proposed Model ◽  
The Brain ◽  
Clustered Network

Reinforcement learning is a paradigm that can account for how organisms learn to adapt their behavior in complex environments with sparse rewards. To partition an environment into discrete states, implementations in spiking neuronal networks typically rely on input architectures involving place cells or receptive fields specified ad hoc by the researcher. This is problematic as a model for how an organism can learn appropriate behavioral sequences in unknown environments, as it fails to account for the unsupervised and self-organized nature of the required representations. Additionally, this approach presupposes knowledge on the part of the researcher on how the environment should be partitioned and represented and scales poorly with the size or complexity of the environment. To address these issues and gain insights into how the brain generates its own task-relevant mappings, we propose a learning architecture that combines unsupervised learning on the input projections with biologically motivated clustered connectivity within the representation layer. This combination allows input features to be mapped to clusters; thus the network self-organizes to produce clearly distinguishable activity patterns that can serve as the basis for reinforcement learning on the output projections. On the basis of the MNIST and Mountain Car tasks, we show that our proposed model performs better than either a comparable unclustered network or a clustered network with static input projections. We conclude that the combination of unsupervised learning and clustered connectivity provides a generic representational substrate suitable for further computation.

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