Search Results (5,432)

Climate change intensifies the challenge of elevated temperatures in dense urban areas, notably in Seoul, South Korea. This study investigates the effects of land use and urban form on summer air temperatures by leveraging Seoul’s city-wide Smart Seoul Data of Things sensor network. Using spatial regression models and temperature data collected during July and August 2021, the analysis identifies key environmental factors associated with urban heat dynamics. The results show that medium- and high-density residential areas, industrial zones, and roads consistently increase temperatures, while greenery, taller buildings, and greater urban porosity contribute to cooling effects. The findings highlight the need for urban planning strategies that expand green spaces, promote vertical development with attention to ventilation, and reconfigure built environments to enhance thermal comfort. This study provides robust empirical insights and offers evidence-based recommendations for climate-responsive urban planning and policies in Seoul and similar high-density cities worldwide. Full article

From the corona virus pandemic in Japan that started with the “Diamond Princess” accident, it became clear that rapid detection, a high sensitivity, multiple diagnostic items, one-step one-base point mutation detection, a fast speed of system development, portability (small size and light weight), full automation, random access, and other conditions are required for future point-of-care testing systems. The Eprimer-SmartAmp technology that was developed possesses characteristics fully aligned with these requirements. Building upon this platform, the “GenPad” system was subsequently established. The GenPad system is widely applicable not only to emerging foreign infectious diseases, but also to cancer, lifestyle-related diseases, and other areas of healthcare through telemedicine and intraoperative nucleic acid diagnoses. In collaboration with telecommunication systems, GenPad is expected to contribute to the establishment of a smart medical city with a countermeasure against emerging foreign infectious diseases, where individuals can check their own health conditions in all healthcare areas. Full article

The Sixth Assessment Report of the IPCC highlights that global surface temperatures have risen by 1.1 °C above pre-industrial levels, with a marked increase in the frequency and intensity of extreme heat events in hot–humid regions. Buildings in these areas urgently require passive design strategies to enhance climate adaptability. Employing Zhupu Ancient Village in Chaoshan region in China as an example, this study analyzes and evaluates the wind-driven ventilation archetype and buoyancy-driven ventilation archetype of the village through integrated meteorological data analysis (ECMWF) and computational fluid dynamics (CFD) simulations. The results indicate that the traditional climate-adaptive archetype facilitates wind speeds exceeding 0.5 m/s in over 80% of outdoor areas, achieving unobstructed airflow and a discernible stack ventilation effect. Through archetype translation, the visitor center design incorporates open alleyway systems and water-evaporative cooling strategies, demonstrating that over 80% of outdoor areas attain wind speeds of 0.5 m/s during summer, thereby achieving enhanced ventilation performance. The research provides a climate-response-archetype translation-performance validation framework and practical case studies for climate-adaptive design of public buildings in hot–humid regions. Full article

Artificial intelligence (AI) soft skills have become increasingly vital in today’s technology-driven world, as they support decision-making systems, strengthen collaboration among stakeholders, and enable individuals to adapt to rapidly changing environments—factors that are fundamental for achieving the sustainability goals of smart cities. Even though AI soft skills are becoming more important, no scale specifically designed to identify and evaluate individuals’ AI soft skills has been found in the existing literature. Therefore, this paper aimed to develop a reliable and valid scale to identify the AI soft skills of individuals. A sample of 685 individuals who were employed in AI-active sectors, with a minimum of a bachelor’s degree, and at least one year of AI-related work experience, participated in the study. A sequential exploratory mixed-methods research design was utilized. Exploratory factor analysis (EFA) identified a five-factor structure that accounted for 67.37% of the total variation, including persuasion, collaboration, adaptability, emotional intelligence, and creativity. Factor loadings ranged from 0.621 to 0.893, and communalities ranged from 0.587 to 0.875. Confirmatory factor analysis (CFA) supported this structure, with strong model fit indices (GFI = 0.940, AGFI = 0.947, NFI = 0.949, PNFI = 0.833, PGFI = 0.823, TLI = 0.972, IFI = 0.975, CFI = 0.975, RMSEA = 0.052, SRMR = 0.035). Internal consistency for each factor was high, with Cronbach’s alpha values of dimensions ranging from 0.804 to 0.875, with a value of 0.921 for the overall scale. Convergent and discriminant validity analyses further confirmed the construct’s robustness. The finalized AI soft skills (AISS) scale, consisting of 24 items, offers a psychometrically valid and reliable tool for assessing essential AI soft skills in professional contexts. Ultimately, this developed scale enables the determination of the social and cognitive skills needed in the human-centered and participatory governance structures of smart cities, supporting the achievement of specific Sustainable Development Goals such as SDG 4, SDG 8, and SDG 11, and contributes to the design of policies and training programs to eliminate the deficiencies of individuals in these areas. Thus, it becomes possible to create qualified human resources that support sustainable development in smart cities, and for these individuals to take an active part in the labor market. Full article

We introduce a novel optimization method, the Bud Lifecycle Algorithm (BLA), and present a mathematical model for optimizing urban transportation systems, demonstrated through a Baltimore case study. Our approach centers on the Proximity Topology Attribute Model, which integrates topological graph properties with K-means clustering to partition city nodes and identify key activity areas via betweenness centrality. A simulated bridge collapse reveals significant impacts on insurance companies and transport users. To balance traffic efficiency with construction costs in public transport projects, we propose a multi-objective optimization model prioritizing transit hubs while minimizing expenses in congested zones. We introduce the Bud Lifecycle Algorithm (BLA) to enhance traditional Genetic Algorithm performance, achieving improvements in system coverage, cost-efficiency, and user satisfaction. Our findings suggest that expanding public transport networks and optimizing rail projects could substantially boost employment and tourism in West Baltimore. We propose the Smart Traffic Management System (STMS) and Community Traffic Safety Program (CTSP) to enhance traffic safety, reduce congestion, and improve residents’ quality of life. Full article

Meeting the European Green Deal’s goal of climate neutrality by 2050 calls for a 90 percent decrease in emissions from the transportation sector. Thus, there is need to accelerate the shift to more sustainable mobility for integrated and smarter multimodal and intermodal mobility. In European countries, more than 70% of the inhabitants live in metropolitan areas. Achieving low-carbon and more sustainable mobility is important to ensuring sustainable urban infrastructure. However, current mobility planning frameworks do not consider the key factors and strategies that encourage residents to choose sustainable transport modes. Hence, there is a need to identify the most efficient actions that should be employed either in the short or long term to achieve accessible, safe, cost-effective, and green transport systems specifically through the development of sustainable public transportation. Moreover, a paradigm shift is needed to explore the synergy between transportation and its relationship to the city. Accordingly, this article presents an action plan as an approach to assess key strategies needed to foster sustainable and smart mobility planning and design by deploying effective strategies and design solutions that support different green means of transportation for smart urban development. Qualitative data on sustainable mobility planning and design strategies was collected via secondary sources from the literature, and descriptive data analysis was carried out. Findings from this study identify internal and external factors required to promote sustainable multimodal and intermodal mobility based on the city’s transport policies and actions. Implications from this study provide a use case for the technological requirements required for electric mobility planning, design, and system operation for the actualization of sustainable public transportation to improve smart urban development. Full article

This paper explores cybersecurity risk modeling for open data in Smart City environments, with a specific case study focused on the Hradec Kralove Region. The goal is to establish a cybersecurity baseline through automated analysis using extended BPMN modeling, complemented by Business Impact Analysis (BIA). The approach identifies critical data flows and quantifies the impact of disruptions in terms of Recovery Time Objective (RTO), Maximum Tolerable Period of Disruption (MTPD), and Maximum Tolerable Data Loss (MTDL). A framework for automated risk mitigation selection is proposed. Results demonstrate the effectiveness of combining process mapping with security requirements to prioritize protections for Smart City data. As an example from the open data domain, the visualization-publishing process was found to tolerate an outage of up to one week, but required high confidentiality and integrity. The maximum tolerable data loss (MTDL) was set at 24 h, leading to the selection of measures such as encryption, access control, and regular backups. This structured methodology enhances data availability and integrity, supporting resilient urban digital infrastructure. Full article

Urban safety critically depends on effective street lighting systems; however, rapidly expanding cities, such as Astana, face considerable challenges in maintaining these systems due to the inefficiency, high labor intensity, and error-prone nature of conventional manual inspection methods. This necessitates an urgent shift toward automated, accurate, and scalable monitoring systems capable of quickly identifying malfunctioning streetlights. In response, this study introduces an advanced computer vision-based approach for automated detection and analysis of street lighting conditions. Leveraging high-resolution dashcam footage collected under diverse nighttime weather conditions, we constructed a robust dataset of 4260 carefully annotated frames highlighting streetlight poles and lamps. To significantly enhance detection accuracy, we propose the novel YOLO-CSE model, which integrates a Channel Squeeze-and-Excitation (CSE) module into the YOLO (You Only Look Once) detection architecture. The CSE module leverages the inherent symmetry of streetlight structures, such as the bilateral symmetry of poles and the radial symmetry of lamps, to dynamically recalibrate feature channels, emphasizing spatially repetitive and geometrically uniform patterns. By modifying the bottleneck layer through the addition of an extra convolutional layer and the SE block, the model learns richer, more discriminative feature representations, particularly for small or distant lamps under partial occlusion or low illumination. A comprehensive comparative analysis demonstrates that YOLO-CSE outperforms conventional YOLO variants and state-of-the-art models, achieving a mean average precision (mAP) of 0.798, recall of 0.794, precision of 0.824, and an F1 score of 0.808. The model’s symmetry-aware design enhances robustness to urban clutter (e.g., asymmetric noise from headlights or signage) while maintaining real-time efficiency. These results validate YOLO-CSE as a scalable solution for smart cities, where symmetry principles bridge geometric priors with computational efficiency in infrastructure monitoring. Full article

As cities grow, they increase in complexity, requiring the effective use of land resources. Cycling is generally regarded as an alternative transport mode to support the development of the cities of tomorrow. In response to urbanization, in many cities worldwide, a common concern associated with investing in cycling networks is the resulting use after such investment. This study uses a continuous longitudinal dataset of daily cycling counts from January 2018 to June 2024 to assess bicycle volumes across three of New Zealand’s largest cities. The results reveal that the relationship between distance and cycle count is not uniform across space, with some areas showing a negative effect between distance and cycling, and others showing a positive one. A global OLS model hides these complexities, as shown in the geographically weighted regression (GWR) model. The coefficients for distance (−0.49) and precipitation (−95.23) in the global OLS are higher, and do not reveal the non-uniformity between cities, wheras themultiple GWR coefficients for distance range between −0.57 and −0.47 and precipitation between −33.47 and −97.63. The results reveal that cycling volume demonstrates lower sensitivity to changes in distance compared to variations in weather conditions. At the city level, there are notable intercity differences in sensitivity. The variability in the coefficients across locations suggests that, although distance and precipitation have general effects, local factors, such as infrastructure quality, topography, weather adaptation measures, and cultural attitudes toward cycling, play a critical role in modulating these relationships. The findings highlight the complexity of spatial interactions and emphasize the need for localized interventions when planning cycling networks. Full article

Sustainability has an ever-increasing importance in our lives, mainly due to climate changes, finite resources, and a growing population, where each of us is called to make a change. Although climate change is a global phenomenon, our individual choices can make the difference. The transportation sector is one of the largest contributors to global carbon emissions, making the transition toward sustainable mobility a critical priority. The adoption of electric vehicles is widely recognized as a key solution to reduce the environmental impact of transportation. However, their widespread acceptance depends on various technological, behavioral, and economical factors. Within this research we use as an artifact the CO₂ Emission Management Gauge (CEMG) devices to better understand how the manufacturers, with integrated features on vehicles, could significantly enhance sales and drive the movement towards electric vehicle adoption. This study proposes an innovative new theoretical model based on Task-Technology Fit, Technology Acceptance, and the Theory of Planned Behavior to understand the main drivers that may foster electric vehicle adoption, tested in a quantitative study with structural equation modelling (SEM), and conducted in a South European country. Our findings, not without some limitations, reveal that while technological innovations like CEMG provide consumers with valuable transparency regarding emissions, its influence on the intention of adoption is dependent on the attitude towards electric vehicles and subjective norm. Our results also support the influence of task-technology fit on perceived usefulness and perceived ease-of-use, the influence of perceived usefulness on consumer attitude towards electric vehicles, and the influence of perceived ease-of-use on perceived usefulness. A challenge is also presented within our work to expand CEMG usage in the future to more intrinsic urban contexts, combined with smart city algorithms, collecting and proving CO₂ emission information to citizens in locations such as traffic lights, illumination posts, streets, and public areas, allowing the needed information to better manage the city’s quality of air and traffic. Full article

Despite significant progress in industrial heritage regeneration, limited attention to post-occupancy operation and maintenance has caused many projects to decline after initial success. This is largely due to the failure to adapt to evolving user needs, highlighting the importance of understanding user experiences. This study aims to develop a scientific and systematic method for evaluating user satisfaction in reused industrial heritage projects. Recognizing the critical role of user needs in project sustainability, the research adopts a user-centered approach to assess spatial experiences. Qualitative feedback was collected through open-ended interviews with the users of two reused buildings—an exhibition hall and a commercial space—within Bingshan Wisdom Park in China. The data were analyzed using frequency-based categorization to construct evaluation factor sets, weight sets, and satisfaction sets. The Fuzzy Comprehensive Evaluation (FCE) method was then applied to quantify overall user satisfaction. Results indicate that satisfaction with the exhibition hall is driven by four key experiential dimensions, while the commercial space is evaluated across five distinct factors. Furthermore, the method enables uniform quantification across different project types for comparative analysis. The proposed approach provides a replicable framework for identifying strengths and weaknesses in user satisfaction, thereby supporting the long-term viability of industrial heritage regeneration projects. Full article

For applications like smart cities and autonomous driving, high-precision direction-of-arrival (DOA) estimation for 5G broadband signals is essential. A primary obstacle for existing methods is the spatial incoherence caused by multi-frequency propagation. We present a sparse Bayesian learning (SBL) algorithm specifically designed to resolve this issue while also minimizing computational load. The algorithm synergistically combines three key components: first, a multiple-signal classification (MUSIC)-like focusing technique ensures a coherent sparse model; second, a real-valued transformation significantly cuts down on computational complexity; and third, an optimized variational Bayesian inference accelerates convergence via root-finding. Validation against MUSIC and rootSBL confirms our method’s marked superiority in low-SNR, limited-snapshot, and multipath conditions delivering both higher accuracy and faster convergence. This work, thus, contributes an effective and practical solution for real-time 5G DOA sensing. Full article

The proliferation of small, agile unmanned aerial vehicles (UAVs) has exposed the limits of single-sensor surveillance in cluttered airspace. We propose an Internet of Things-enabled integrated sensing and communication (IoT-ISAC) framework that converts cellular base stations into cooperative, edge-intelligent sensing nodes. Within a four-layer design—terminal, edge, IoT platform, and cloud—stations exchange raw echoes and low-level features in real time, while adaptive beam registration and cross-correlation timing mitigate spatial and temporal misalignments. A hybrid processing pipeline first produces coarse data-level estimates and then applies symbol-level refinements, sustaining rapid response without sacrificing precision. Simulation evaluations using multi-band ISAC waveforms confirm high detection reliability, sub-frame latency, and energy-aware operation in dense urban clutter, adverse weather, and multi-target scenarios. Preliminary hardware tests validate the feasibility of the proposed signal processing approach. Simulation analysis demonstrates detection accuracy of 85–90% under optimal conditions with processing latency of 15–25 ms and potential energy efficiency improvement of 10–20% through cooperative operation, pending real-world validation. By extending coverage, suppressing blind zones, and supporting dynamic surveillance of fast-moving UAVs, the proposed system provides a scalable path toward smart city air safety networks, cooperative autonomous navigation aids, and other remote-sensing applications that require agile, coordinated situational awareness. Full article

Urban Living Labs (ULLs) are pivotal for promoting socio-technical innovation in smart cities, yet their role in achieving sustainable urban development remains underexplored. This study addresses this gap by proposing a systematic literature review (SLR) to develop effective implementation strategies. Unlike previous studies focusing on individual aspects of these labs, our holistic approach emphasizes the orchestration of actors and innovative experiment design to co-create value with citizens. By addressing specific issues in current smart city practices—such as the misalignment between technology and community needs and among stakeholders, limited citizen engagement, and the lack of iterative testing environments—the study explores practical strategies for improvement. The proposed strategies illustrate how Urban Living Labs can serve as essential platforms for achieving sustainable and inclusive urban growth through effective socio-technical innovation integration. Full article