Due to rapid urbanization, a balance between resources and urban growth is required. For the achievement of this equilibrium, the use of information technologies is essential. smart cities are the answer to this requirement, as a result, they improve various aspects of urban life and address related challenges and/or mitigate them. Modern technologies, including a wide range of Internet of Things (IoT) sensors, are used in smart cities for collecting and analyzing data on different aspects of urban life to enhance their inhabitants' lives. smart cities improve the sustainability and efficiency of urban dynamics. Today, smart cities can enhance services and citizens' lives in various fields such as governance, education, healthcare, transportation, and energy. smart city applications require collaboration among individuals from various disciplines, including engineering, architecture, urban design, and economics, to plan, design, implement, and deploy a smart solution for a specific task. Therefore, a proper understanding of the applications and architecture of smart cities and the challenges they face is crucial. In this paper, we will provide background information about the applications of smart cities, describe the architecture of applications in smart cities, present security and privacy challenges to examine robustness and flexibility in smart city applications, and examine new trends in this field.

This study explores the transformative role of artificial intelligence (AI) in designing and managing sustainable urban environments. Through a comprehensive literature review, we examined topics such as AI, sustainable architecture, smart cities, urban planning, and energy efficiency, selecting articles that provide significant insights into AI's influence on the built environment. The research highlights how AI-driven innovations, including data-driven decision-making, energy optimization, and predictive maintenance, enhance urban infrastructures' efficacy, resilience, and sustainability. Case studies demonstrate AI's impact on energy-efficient building design, smart materials selection, and intelligent urban systems such as traffic management, water distribution, and waste management. AI-powered tools, such as generative design and predictive analytics, enable architects and urban planners to create adaptive, resource-efficient solutions that address global urbanization and climate challenges. However, challenges such as data interoperability, ethical concerns, and computational demands remain. Despite these limitations, AI is poised to set new benchmarks for sustainable urban development, promoting flexible, ecologically friendly ecosystems. AI holds immense potential to redefine urban design and management, fostering innovative, sustainable solutions to global urban challenges.

The smart manufacturing in the context of a smart factory is allowed through different uncertain processes which make big challenges. In this case, smart manufacturing should be applied reliably, interoperable, and consistently. Thus, it faces the requirement for orchestrating services provided by uncertain processes to satisfy the challenges. These uncertain processes are commonly managed by an uBPMS which is specifically designed to address unknown conditions. The current uBPMS architecture does not consider the business process orchestration and the objective of this paper is to achieve an extension of uBPMS architecture with business process orchestration feature to make a response in real time and satisfy the uncertainty conditions in smart factory. The proposed extension can perform autonomous orchestration of business processes inward traditional uBPMS architecture based on desired values of different objectives optimization. This new architecture operates based on a robust bi-level optimization approach. The Rousselot smart factory in Belgium as a simulated case study was studied. The results show the robustness of the new architecture for process orchestration design in this case. Also, uncertain business process management based on the process orchestration feature presents efficiency and accuracy improvement in smart manufacturing systems.