Abstract

Growing urban areas are major consumers of natural resources, energy and raw materials. Understanding cities´ urban metabolism is salient when developing sustainable and resilient cities. This paper addresses concepts of smart city and digital twin technology as means to foster more sustainable urban development. Smart city has globally been well adopted concept in urban development. With smart city development cities aim to optimize overall performance of the city, its infrastructures, processes and services, but also to improve socio-economic wellbeing. Dynamic digital twins are constituted to form real-time connectivity between virtual and physical objects. Digital twin combines virtual objects to its physical counterparts. This conceptual paper provides additionally examples from dynamic digital twin platforms and digital twin of Helsinki, Finland.

Highlights

  • The effective transportation management systems should provide an optimal route with recommended optimized non - work stops (Nimchuk & Mckinney, 2018)

  • Because of the different priorities of key stakeholders involved in transport management, different technologies are needed for a specific business area

  • Not so much is known about MSN, the pathological characteristics of death upper and lower motor neurons and the presence of numerous protein inclusions in the remaining motor neurons resulting in impaired transactive responses (Neumann et al 2006,Wright et al 2016) culminates in problems that can lead to poor gait and memory loss (Olivier et al 2016)

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Summary

Introduction

The effective transportation management systems should provide an optimal route with recommended optimized non - work stops (Nimchuk & Mckinney, 2018). In the framework proposed by Goodfellow et al (Goodfellow et al, 2014) the model passes noise through a multilayer perceptron (a type of artificial neural network) (Pal & Mitra, 1992) to create randomness, which allows it to generate a new image based on the real world examples it has been taught. This method can be described as an adversarial network, using deep generative models. The synthesis of these methods created Generative Adversarial Networks, which is the technology used to generate the faces for this research

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