Search Results (4,672)

This study uses four dimensions, namely social resilience, economic resilience, infrastructure resilience, and ecological resilience, to construct an index system for urban resilience. The subject data came from the panel data of 282 prefecture-level cities in China from 2006 to 2020. We selected a multiperiod double-difference model to study the effects of smart city pilot policies on macro-urban resilience. By conducting parallel-trend tests and selecting appropriate robustness tests, this study drew the following relevant conclusion: smart city pilot policies can have a positive effect on the urban resilience level. These policies exert their influence by facilitating industrial structure upgrading, which plays a partial mediating role. Considering different city area distributions and city scales, smart city pilot policies can have significant heterogeneity in their enhancement of urban resilience. The effect is pronounced in the “east > central > west” states and is more likely to have a significant impact on small- and medium-sized cities. Therefore, promoting the scope of smart city pilots, strengthening the intermediary role of industrial structure upgrading, and implementing differentiated policies for different regions and city sizes are important for sustainable urban development. Full article

The development of geographic information systems has grown significantly over the past decade. Simultaneously, the concept of smart cities based on the management of large volumes of data has also spread worldwide. The digital twin concept has recently been incorporated into the technological domain of urban management. However, currently, phases such as technological integration, standardization, data and process interconnection, the development of tools and utilities, professional training, and the application of digital urban development in real-world situations are converging. This paper presents the experience developed on a university campus, detailing each of the phases carried out from the initial design to a fully operational pilot phase model. The article highlights the importance of certain aspects to consider in each phase, demonstrating that there are barriers and limitations and at the same time, great strengths and opportunities in applying the digital twin model in urban management, considering aspects such as mobility, accessibility, energy management, and involving students and university administrators in the process. Full article

The advancement to Urban 4.0 requires urban digitization and predictive maintenance of infrastructure to improve efficiency, durability, and quality of life. This study aims to integrate intelligent technologies for the predictive maintenance of photovoltaic panel systems, which serve as essential smart city renewable energy sources. In addition, we employ vision transformers (ViT), a deep learning architecture devoted to evolving image analysis, to detect anomalies in PV systems. The ViT model is pre-trained on ImageNet to exploit a comprehensive set of relevant visual features from the PV images and classify the input PV panel. Furthermore, the developed system was integrated into a web application that allows users to upload PV images, automatically detect anomalies, and provide detailed panel information, such as PV panel type, defect probability, and anomaly status. A comparative study using several convolutional neural network architectures (VGG, ResNet, and AlexNet) and the ViT transformer was conducted. Therefore, the adopted ViT model performs excellently in anomaly detection, where the ViT achieves an AUC of 0.96. Finally, the proposed approach excels at the prompt identification of potential defects detection, reducing maintenance costs, advancing equipment lifetime, and optimizing PV system implementation. Full article

In an efficient aerial package delivery scenario carried out by multiple Unmanned Aerial Vehicles (UAVs), a task allocation problem has to be formulated and solved in order to select the most suitable assignment for each delivery task. This paper presents the development methodology of an evolutionary-based optimization framework designed to tackle a specific formulation of a Drone Delivery Problem (DDP) with charging hubs. The proposed evolutionary-based optimization framework is based on a double-chromosome task encoding logic. The goal of the algorithm is to find optimal (and feasible) UAV task assignments such that (i) the tasks’ due dates are met, (ii) an energy consumption model is minimized, (iii) re-charge tasks are allocated to ensure service persistency, (iv) risk-aware flyable paths are included in the paradigm. Hard and soft constraints are defined such that the optimizer can also tackle very demanding instances of the DDP, such as tens of package delivery tasks with random temporal deadlines. Simulation results show how the algorithm’s development methodology influences the capability of the UAVs to be assigned to different tasks with different temporal constraints. Monte Carlo simulations corroborate the results for two different realistic scenarios in the city of Turin, Italy. Full article

Worldwide, the increase in impervious surfaces due to urbanization has led to significant water cycle issues such as groundwater depletion, urban heat islands, and flooding. To address these challenges, Low-Impact Development (LID) techniques are increasingly being applied in stormwater management. This study focuses on Ulsan, designated as a water cycle model city in South Korea, with a particular emphasis on the highly urbanized Okgyo drainage watershed. Using the Stormwater Management Model (SWMM) version 5.1, long-term runoff simulations were conducted to evaluate the effects of LID implementation on water cycle change rates and recovery rates. The model incorporates detailed hydrological and hydraulic parameters, including inflow, runoff, infiltration, and evapotranspiration for six subcatchments within the watershed. The SWMM was calibrated and validated using 30 years of historical rainfall data (1987–2016) from the Ulsan weather station. Calibration and validation processes used the NRCS-CN (Curve Number) method to ensure accuracy in simulating runoff patterns and water balance. The study specifically evaluated the effectiveness of two LID techniques: bioretention and permeable pavements. Three scenarios were modeled: bioretention applied to 5% of the area, permeable pavements applied to 5% of the area, and a combined application of both techniques. The results showed that the combined scenario provided the best outcome, with a 7.80% reduction in surface runoff and a 14.56% improvement in water cycle health. The LID application scenario confirmed the potential to achieve the water cycle management target of handling 25.5 mm of rainfall. These findings demonstrate that the introduction of LID techniques in public spaces can significantly enhance water management. This research provides insights into effective water cycle management methods tailored to specific urban land uses, laying a foundation for future urban planning and sustainable development. Full article

In this research, we investigate the integration of an Intrusion Detection System (IDS) with a Digital Twin (DT) to enhance the cybersecurity of physical devices in cyber–physical systems. Using Eclipse Ditto as the DT platform and Snort as the IDS, we developed a near-realistic test environment that included a Raspberry Pi as the physical device and a Kali Linux virtual machine to perform common cyberattacks such as Hping3 flood attacks and NMAP reconnaissance scans. The results demonstrated that the IDS effectively detected Hping3-based flood attacks but showed limitations in identifying NMAP scans, suggesting areas for IDS configuration improvements. Furthermore, the study uncovered significant system resource impacts, including high Central Processing Unit (CPU) usage during SYN and ACK flood attacks and persistent memory usage after Network Mapper (NMAP) scans, highlighting the need for enhanced recovery mechanisms. This research presents a novel approach by coupling a Digital Twin with an IDS, enabling real-time monitoring and providing a dual perspective on both system performance and security. The integration offers a holistic method for identifying vulnerabilities and understanding resource impacts during cyberattacks. The work contributes new insights into the use of Digital Twins for cybersecurity and paves the way for further research into automated defense mechanisms, real-world validation of the proposed model, and the incorporation of additional attack scenarios. The results suggest that this combined approach holds significant promise for enhancing the security and resilience of IoT devices and other cyber–physical systems. Full article

The use of green roofs, a low-impact development practice, can be an effective means of reducing direct runoff in urban centers. Green roof modeling can enable efficient design by preliminarily grasping the behavior of the green roof system according to specific configurations. In this study, we aimed to find appropriate evapotranspiration and parameter optimization schemes for HYDRUS-1D, a commonly used modeling tool for green roofs. Comparative studies of this sort in the context of green roof runoff modeling have not been conducted previously and are important in guiding users to overcome the difficulties of choosing the right numerical schemes for an accurate prediction of runoff from a green roof. As a study site, the Portland Building Ecoroof in Portland, Oregon, USA, was chosen, as green roof configurations and observed data for climate and runoff were available. From the simulation results of the runoff volume, the Blaney–Criddle method, which was considered an alternative, was found to be appropriate for calculating evapotranspiration from a green roof (R² = 0.82) relative to the Hargreaves method built in HYDRUS-1D (R² = 0.46). In addition, this study showed that the optimization method using the harmony search algorithm, which was proposed as an alternative optimizer, was better (R² = 0.95) than that of the HYDRUS-1D’s own optimization module (R² = 0.82) in calibrating HYDRUS-1D for green roof runoff. The findings are thought to be useful in guiding modelers who are considering using HYDRUS-1D for green roof runoff simulations. Full article

Quality improvement is essential for a nation’s economy to transition from large to strong. In the 21st century, a new wave of quality development has emerged globally, and upgrading the quality of enterprise export products is a key measure for driving exports and supporting high-quality economic development. The development of artificial intelligence, as the new core engine driving technological revolution and industrial transformation, will profoundly alter various aspects of economic activities, including production, distribution, exchange, and consumption. Exploring and cultivating new artificial intelligence-driven momentum to enhance the quality of enterprise export products is inevitably a major theoretical and practical issue of common interest to governments, enterprises, and academia. This paper uses China, a major developing and export-oriented economy, as a case study to explore the policy measures for stimulating new momentum in artificial intelligence development and their effects and transmission mechanisms on improving the quality of enterprise export products. Specifically, it constructs a theoretical model to examine the relationship between smart city construction, artificial intelligence development, and the quality of enterprise export products. By considering the smart city construction projects launched by the Chinese government as a quasi-natural experiment to facilitate artificial intelligence development, the study employs matched city-enterprise data from 2007 to 2015 and utilizes a difference-in-differences (DID) methodology to empirically test the impact of smart city construction on enhancing the quality of enterprise export products. According to the study, the policy-driven nature of smart city construction significantly enhances the quality of enterprise export products. This beneficial impact is particularly evident in the eastern regions, as well as in labor-intensive and capital-intensive industries, and among foreign-invested and private enterprises. Mechanism tests and additional analyses indicate that artificial intelligence development is significantly more advanced in smart cities than in non-smart cities, with the gap between them steadily widening. The construction of smart cities significantly advances artificial intelligence development, which subsequently enhances the quality of enterprise export products. Furthermore, smart cities can substantially contribute to this improvement by facilitating a more efficient, market-oriented allocation of resources. Full article

Today’s increasingly complex energy systems require innovative approaches to integrate and optimize different energy sources and technologies. In this paper, we explore the system of systems (SoS) approach, which provides a comprehensive framework for improving energy systems’ interoperability, efficiency, and resilience. By examining recent advances in various sectors, including photovoltaic systems, electric vehicles, energy storage, renewable energy, smart cities, and rural communities, this study highlights the essential role of SoSs in addressing the challenges of the energy transition. The principal areas of interest include the integration of advanced control algorithms and machine learning techniques and the development of robust communication networks to manage interactions between interconnected subsystems. This study also identifies significant challenges associated with large-scale SoS implementation, such as real-time data processing, decision-making complexity, and the need for harmonized regulatory frameworks. This study outlines future directions for improving the intelligence and autonomy of energy subsystems, which are essential for achieving a sustainable, resilient, and adaptive energy infrastructure. Full article

The rapid urbanization trend underscores the need for effective management of city resources and services, making the concept of smart cities increasingly important. This study leverages the IMD Smart City Index (SCI) dataset to analyze and rank smart cities worldwide. Our research has a dual objective: first, we aim to apply a set of unsupervised learning models to cluster cities based on their smartness indices. Second, we aim to employ supervised learning models such as random forest, support vector machines (SVMs), and others to determine the importance of various features that contribute to a city’s smartness. Our findings reveal that while smart living was the most critical factor, with an importance of 0.259014. Smart mobility and smart environment also played significant roles, with the importance of 0.170147 and 0.163159, respectively, in determining a city’s smartness. While the clustering provides insights into the similarities and groupings among cities, the feature importance analysis elucidates the critical factors that drive these classifications. The integration of these two approaches aims to demonstrate that understanding the similarities between smart cities is of limited utility without a clear comprehension of the importance of the underlying features. This holistic approach provides a comprehensive understanding of what makes a city ’smart’ and offers a robust framework for policymakers to enhance urban living standards. Full article

The “Metaverse” is evaluated as having significant potential in a “Smart city” design and operation. Despite growing interest, there is still a lack of comprehensive quantitative analysis on the “Metaverse”, particularly in the context of smart cities. This study conducts a bibliometric analysis of 604 articles selected from the “WoS” database and employs three dimensions of technology, people, and institutions as a balanced perspective on smart cities, providing a comprehensive understanding of research trends on the “Metaverse” in the context of smart cities. This study identifies the “Metaverse” as a Virtual reality technology, popular since 2021, and provides information on the active years, countries, fields, journals, authors, and institutions involved in “Metaverse” research on smart cities. This study also identifies three stages of research development as follows: Stage 1 (2007–2013) to Stage 2 (2014–2020) and Stage 3 (2021–20 October 2023), revealing the research focus evolution from basic “urban planning” to complex “urban governance” and “Smart city” construction with consideration of multi-stakeholders’ perspectives. Additionally, this study reveals that “Metaverse” research studies on the “technology” dimension have consistently outnumbered that on “institutions” and “people” across all stages in the “Smart city” domain. These findings address current theoretical gaps and offer a foundation for future research. Full article

The evaluation of smart affordable cities considering sustainable subsystems improves urban quality of life through efficient resource usage, reduced environmental impacts, and improved living conditions for residents. This study aims to develop an interactive and dynamic Web Geographic Information System (GIS) framework to facilitate decision-making processes during the design phase while including third parties and stakeholders using a spatial interactive impact assessment approach. The methodology follows a quantitative research method based on delivering a tool that could be replicated in other contexts. This tool assesses the impact of smart scenarios to support affordable city planning through selecting Key Performance Indicators (KPIs). This tool was applied to Brazilian large-scale affordable housing within a smart city project. Based on this study, the conclusion reports some research limitations and the possibility of creating a beta version of the tool for future development. The findings show that this Web-GIS framework enhances stakeholder engagement and the effectiveness of decision making in developing sustainable urban planning. Full article

The influence of mobility modes within Positive Energy Districts (PEDs) has gained limited attention, despite their crucial role in reducing energy consumption and greenhouse gas emissions. Buildings in the European Union (EU) account for 40% of energy consumption and 36% of greenhouse gas emissions. In comparison, transport contributes 28% of energy use and 25% of emissions, with road transport responsible for 72% of these emissions. This study aims to design and optimize a synthetic PED in Istanbul that integrates renewable energy sources and public mobility systems to address these challenges. The renewable energy sources integrated into the synthetic PED model include solar energy, hydrogen energy, and regenerative braking energy from a tram system. Solar panels provided a substantial portion of the energy, while hydrogen energy contributed to additional electricity generation. Regenerative braking energy from the tram system was also utilized to further optimize energy production within the district. This system powers a middle school, 10 houses, a supermarket, and the tram itself. Optimization techniques, including Linear Programming (LP) for economic purposes and the Weighted Sum Method (WSM) for environmental goals, were applied to balance cost and CO₂ emissions. The LP method identified that the PED model can achieve cost competitiveness with conventional energy grids when hydrogen costs are below $93.16/MWh. Meanwhile, the WSM approach demonstrated that achieving a minimal CO₂ emission level of 5.74 tons requires hydrogen costs to be $32.55/MWh or lower. Compared to a conventional grid producing 97 tons of CO₂ annually, the PED model achieved reductions of up to 91.26 tons. This study contributes to the ongoing discourse on sustainable urban energy systems by addressing key research gaps related to the integration of mobility modes within PEDs and offering insights into the optimization of renewable energy sources for reducing emissions and energy consumption. Full article

As a consequence of climate change and its negative impacts on the environment and on human health, the topic of sustainability has become an integral part of urban policy. Smart city initiatives around the world are focusing on different aspects of sustainability in order to provide better living conditions for their residents. The aim of this study is to investigate the impact of selected smart city indicators on the average life expectancy as a variable for quality of life and well-being. Based on a Common Correlated Effects (CCE) model, Instrumental Variable Estimator with Common Factors (2SIV), and clustering regression model, EU countries were divided into three distinct clusters indicating common elements but also specificities of each group. The analysis confirmed the positive impact of GDP growth, renewable energy consumption, and the proportion of the population with a tertiary level of education on life expectancy. On the other hand, CO₂ emissions and transport pollution have an adverse effect. The analysis provides valuable insights into the complex relationship between smart city variables and quality of life, and it may serve as a basis for informed and responsible decision-making by relevant urban stakeholders aimed at designing more sustainable, resilient, and healthier cities. Full article

Background: With an aging population and the continuous advancement of smart technology, the Chinese government is exploring smart elderly care models to address the challenges posed by aging. Although smart home systems are viewed as a promising solution, their adoption rate among older people remains low. Objectives: This study aimed to investigate the factors influencing the behavioral intention to use smart home systems among older people in Linyi City, Shandong Province, China. Methods: A literature review revealed a lack of quantitative research on older people’s behavioral intention toward smart home systems based on the Innovation Diffusion Theory. This study developed an extended model based on the Innovation Diffusion Theory, Technology Acceptance Model, and external variables, incorporating eight variables: intergenerational technical support, perceived cost, self-reported health conditions, compatibility, observability, trialability, perceived usefulness, perceived ease of use, and behavioral intention. Results: Analysis of 387 valid questionnaires showed that compatibility and trialability significantly and positively affect perceived ease of use, while self-reported health conditions, perceived ease of use, and perceived usefulness have significant effects on behavioral intention. In addition, perceived cost had a negative influence on behavioral intention. Contributions/Significance: These findings highlight the importance of considering these factors in the design of smart home systems to improve user experience and provide valuable practical guidance to smart home system developers, R&D institutions, and policymakers. Full article