Search Results (196)

Emerging transportation challenges necessitate cutting-edge technologies for real-time infrastructure and traffic monitoring. To create a dynamic digital twin for intersection monitoring, data gathering, performance assessment, and calibration of microsimulation software, this study presents a state-of-the-art platform that combines high-resolution LiDAR sensor data with VISSIM simulation software. Intending to track traffic flow and evaluate important factors, including congestion, delays, and lane configurations, the platform gathers and analyzes real-time data. The technology allows proactive actions to improve safety and reduce interruptions by utilizing the comprehensive information that LiDAR provides, such as vehicle trajectories, speed profiles, and lane changes. The digital twin technique offers unparalleled precision in traffic and infrastructure state monitoring by fusing real data streams with simulation-based performance analysis. The results show how the platform can transform real-time monitoring and open the door to data-driven decision-making, safer intersections, and more intelligent traffic data collection methods. Using the proposed platform, this study calibrated a VISSIM simulation network to optimize the driving behavior parameters in the software. This study addresses current issues in urban traffic management with real-time solutions, demonstrating the revolutionary impact of emerging technology in intelligent infrastructure monitoring. Full article

Rural regions face significant barriers to adopting IoT technologies, due to limited connectivity, energy constraints, and poor technical infrastructure. While urban environments benefit from advanced digital systems and cloud services, rural areas often lack the necessary conditions to deploy and evaluate secure and autonomous IoT solutions. To help overcome this gap, this paper presents the Smart Rural IoT Lab, a modular and reproducible testbed designed to replicate the deployment conditions in rural areas using open-source tools and affordable hardware. The laboratory integrates long-range and short-range communication technologies in six experimental scenarios, implementing protocols such as MQTT, HTTP, UDP, and CoAP. These scenarios simulate realistic rural use cases, including environmental monitoring, livestock tracking, infrastructure access control, and heritage site protection. Local data processing is achieved through containerized services like Node-RED, InfluxDB, MongoDB, and Grafana, ensuring complete autonomy, without dependence on cloud services. A key contribution of the laboratory is the generation of structured datasets from real network traffic captured with Tcpdump and preprocessed using Zeek. Unlike simulated datasets, the collected data reflect communication patterns generated from real devices. Although the current dataset only includes benign traffic, the platform is prepared for future incorporation of adversarial scenarios (spoofing, DoS) to support AI-based cybersecurity research. While experiments were conducted in an indoor controlled environment, the testbed architecture is portable and suitable for future outdoor deployment. The Smart Rural IoT Lab addresses a critical gap in current research infrastructure, providing a realistic and flexible foundation for developing secure, cloud-independent IoT solutions, contributing to the digital transformation of rural regions. Full article

Plant growth monitoring is a complex and challenging task, which depends on a variety of environmental variables, such as temperature, humidity, nutrient availability, and solar radiation. Advances in optical sensors have significantly enhanced data collection on plant growth. These developments enable the optimization of agricultural practices and crop management through the integration of artificial vision techniques. Despite advances in the application of these technologies, limitations and challenges persist. This review aims to analyze the current state-of-the-art methodologies for using artificial vision and optical sensors in plant growth assessment. The systematic review was conducted following the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Relevant studies were analyzed from the Scopus and Web of Science databases. The main findings indicate that data collection in agricultural environments is challenging. This is due to the variability of climatic conditions, the heterogeneity of crops, and the difficulty in obtaining accurately and homogeneously labeled datasets. Additionally, the integration of artificial vision models and advanced sensors would enable the assessment of plant responses to these environmental factors. The advantages and limitations were examined, as well as proposed research areas to further contribute to the improvement and expansion of these emerging technologies for plant growth assessment. Finally, a relevant research line focuses on evaluating AI-based models on low-power embedded platforms to develop accessible and efficient decision-making solutions in both agricultural and urban environments. This systematic review was registered in the Open Science Framework (OSF). Full article

Background: Osteogenesis imperfecta (OI) is a rare genetic connective tissue disorder characterized by bone fragility, most often caused by pathogenic variants in type I collagen genes. In this context, we aimed to describe the clinical and epidemiological characteristics of patients with OI who were hospitalized for coronavirus disease (COVID)-19 in Brazil between 2020 and 2024. Methods: We conducted a retrospective descriptive analysis using data from the Brazilian Unified Health System (SUS, which stands for the Portuguese Sistema Único de Saúde) through the Open-Data-SUS platform. Patients with a confirmed diagnosis of OI and hospitalization due to COVID-19 were included. Descriptive statistical analysis was performed to evaluate demographic, clinical, and outcome-related variables. We included all hospitalized COVID-19 cases with a confirmed diagnosis of OI between 2020 and 2024. Results: Thirteen hospitalized patients with OI and COVID-19 were identified. Most were adults (9; 69.2%), male (7; 53.8%), self-identified as White (9; 69.2%), and all were residents of urban areas (13; 100.0%). The most frequent symptoms were fever (10; 76.9%), cough (9; 69.2%), oxygen desaturation (9; 69.2%), dyspnea (8; 61.5%), and respiratory distress (7; 53.8%). Two patients had heart disease, one had chronic lung disease, and one was obese. As for vaccination status, five patients (38.5%) had been vaccinated against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Four patients (30.8%) required admission to an intensive care unit (ICU), and six (46.2%) required noninvasive ventilatory support. Among those admitted to the ICU, only two required invasive mechanical ventilation. The clinical outcome was death in two cases (15.4%). Both patients were male, White, and had not been vaccinated against SARS-CoV-2. One was 47 years old, was not admitted to the ICU, but required noninvasive ventilation. Despite the underlying condition most patients had favorable outcomes, consistent with an international report. Conclusions: This is the first report to describe the clinical and epidemiological profile of patients with OI hospitalized for COVID-19 in Brazil, providing initial insights into how a rare bone disorder intersects with an acute respiratory infection. The generally favorable outcomes observed—despite the underlying skeletal fragility—suggest that individuals with OI are not necessarily at disproportionate risk of severe COVID-19, particularly when appropriately monitored. The occurrence of deaths only among unvaccinated patients underscores the critical role of SARS-CoV-2 vaccination in this population. Although pharmacological treatment data were unavailable, the potential protective effects of bisphosphonates and vitamin D merit further exploration. These findings support the need for early preventive strategies, systematic vaccination efforts, and dedicated clinical protocols for rare disease populations during infectious disease outbreaks. Full article

Efficient detection of road surface defects is vital for timely maintenance and traffic safety. This study introduces a novel AI-powered web framework, TriRoad AI, that integrates multiple versions of the You Only Look Once (YOLO) object detection algorithms—specifically YOLOv8 and YOLOv11—for automated detection of potholes and cracks. A user-friendly browser interface was developed to enable real-time image analysis, confidence-based prediction filtering, and severity-based geolocation mapping using OpenStreetMap. Experimental evaluation was conducted using two datasets: one from online sources and another from field-collected images in Ankara, Turkey. YOLOv8 achieved a mean accuracy of 88.43% on internet-sourced images, while YOLOv11-B demonstrated higher robustness in challenging field environments with a detection accuracy of 46.15%, and YOLOv8 followed closely with 44.92% on mixed field images. The Gemini AI model, although highly effective in controlled environments (97.64% detection accuracy), exhibited a significant performance drop of up to 80% in complex field scenarios, with its accuracy falling to 18.50%. The proposed platform’s uniqueness lies in its fully integrated, browser-based design, requiring no device-specific installation, and its incorporation of severity classification with interactive geospatial visualization. These contributions address current gaps in generalization, accessibility, and practical deployment, offering a scalable solution for smart infrastructure monitoring and preventive maintenance planning in urban environments. Full article

Mangroves, as a key component of the blue-carbon ecosystem, have exceptional carbon sequestration capacity and are mainly distributed in tropical coastal regions. In the Solomon Islands, ongoing degradation of mangrove forests, primarily due to land conversion and timber exploitation, highlights an urgent need for high-resolution spatial data to inform effective conservation strategies. The present study introduces an efficient and accurate methodology for mapping mangrove habitats and prioritizing protection areas utilizing open-source satellite imagery and datasets available through the Google Earth Engine platform in conjunction with a U-Net deep learning algorithm. The model demonstrates high performance, achieving an F1-score of 0.834 and an overall accuracy of 0.96, in identifying mangrove distributions. The total mangrove area in the Solomon Islands is estimated to be approximately 71,348.27 hectares, accounting for about 2.47% of the national territory. Furthermore, based on the mapped mangrove habitats, an optimized hotspot analysis is performed to identify regions characterized by high-density mangrove distribution. By incorporating spatial variables such as distance from roads and urban centers, along with mangrove area, this study proposes priority mangrove protection areas. These results underscore the potential for using openly accessible satellite data to enhance the precision of mangrove conservation strategies in data-limited settings. This approach can effectively support coastal resource management and contribute to broader climate change mitigation strategies. Full article

This paper presents a systematic, narrative review of 62 academic publications (2015–2025) that explore the integration of spatial data infrastructures (SDIs) with emerging smart city technologies to improve local governance. SDIs provide a structured framework for managing geospatial data and, in combination with IoT sensors, geospatial and 3D platforms, cloud computing and AI-powered analytics, enable real-time data-driven decision-making. The review identifies four key technology areas: IoT and sensor technologies, geospatial and 3D mapping platforms, cloud-based data infrastructures, and AI analytics that uniquely contribute to smart governance through improved monitoring, prediction, visualization, and automation. Opportunities include improved urban resilience, public service delivery, environmental monitoring and citizen engagement. However, challenges remain in terms of interoperability, data protection, institutional barriers and unequal access to technologies. To fully realize the potential of integrated SDIs in smart government, the report highlights the need for open standards, ethical frameworks, cross-sector collaboration and citizen-centric design. Ultimately, this synthesis provides a comprehensive basis for promoting inclusive, adaptive and accountable local governance systems through spatially enabled smart technologies. Full article

Background/Objectives: The INTegRated InterveNtion of pSychogerIatric Care (INTRINSIC) network introduces an innovative model of psychogeriatric care, combining tertiary mental healthcare with primary care for older adults in low-resource settings in Greece via telemedicine. This study explores viewpoints of healthcare professionals on care delivery within the frames of old-age mental telehealth services in low-resource settings. Methods: All healthcare professionals, including 13 medical and 11 non-medical professionals from diverse healthcare units in urban, rural, and insular areas, participated in a semi-structured survey. Thematic analysis identified key insights. Results: Most participants (N = 19) highlighted the high usability of the INTRINSIC services and their high satisfaction for being members of the network (N = 17) was attributed to the collaborative delivery of integrated, specialized healthcare services in primary healthcare (N = 17). Further identified advantages of the services included the positive impact on timely care delivery (N = 6), cost effectiveness, and alleviation of hospital strain. Healthcare professionals valued the holistic approach of the INTRINSIC services to psychogeriatric care (N = 8) and their role in the improvement of it in communities in low-resource settings (N = 13). However, challenges were also reported, including the low openness and reluctance of service users (N = 7), difficulties in using the INTRINSIC digital platform (N = 5), and increased workload (N = 5). Conclusions: Despite these issues, the INTRINSIC services embody an innovative telehealth model for delivering high-quality, tertiary, mental, and cognitive healthcare services to older adults in underserved areas. Full article

This paper explores the innovative integration of digitalization and gamification in urban design to address social sustainability challenges in rapidly evolving cities. Using Gensen, a metaverse platform launched in 2024, as a case study, the research investigates how the convergence of virtual environments and real urban spaces can foster new forms of social interaction, spatial usage, and community engagement. The motivation behind this study is to assess the potential of gamification in enhancing social sustainability within digitalized urban contexts. By introducing the opening event, “Treasure Hunting”, established by Gensen, the study examines how users engage with existing urban settings through a gamified approach. The research investigates how gameful design, facilitated by digitalization, can transform public spaces into more dynamic environments that encourage diverse participation while also addressing emerging risks such as spatial inequality resulting from varying levels of access to digital tools and literacy. The research questions focus on how gamification can bridge these gaps and contribute to creating more inclusive urban environments. In conclusion, the study argues that current gamified design approaches, based on digitalization, often overlook the playfulness inherent in physical environments and human interaction. However, a bottom-up approach that emphasizes individuals’ understanding of the inherent playfulness in existing urban spaces is still lacking. This aspect needs further exploration to inform and enhance gameful design strategies aimed at promoting social sustainability in urban development. Bridging this gap is essential for integrating digital interventions into everyday life, ultimately achieving a more effective gameful design in urban contexts. Full article

This paper presents an original solution for the archiving, preliminary analysis, cleaning, and publishing of data recorded by an IoT platform in an open format accessible to any interested researcher. The implementation of the presented mechanism started from an IoT platform already in use that manages and records data from IoT sensors monitoring air quality in private spaces in the urban area of the city of Bucharest, Romania. Publishing in an open format the data recorded over a period of years from IoT sensors allows any researcher to access and carry out studies that improve the quality of life in the urban environment. The presentation of the publication method aims to inspire a good practice method for all platforms for monitoring environmental parameters. The results of the scientific research in this paper consist of a perfectly functional automatic archiving and publishing mechanism. Full article

The increasing frequency and intensity of heat waves, combined with urban heat islands (UHIs), pose significant public health challenges. Implementing low-cost, real-time monitoring networks with distributed stations within the smart city framework faces obstacles in transforming urban spaces. Accurate data are essential for assessing these effects. This paper compares different network types in a medium-sized city in western Spain and their implications for UHI identification quality. The study first presents a purpose-built monitoring network using Open-Source platforms, IoT technology, and LoRaWAN communications, adhering to World Meteorological Organization guidelines. Additionally, it evaluates two citizen weather observer networks (CWONs): one from a commercial smart device company and another from a global community connecting environmental sensor data. The findings highlight several advantages of bespoke monitoring networks over CWON, including enhanced data accessibility and greater flexibility to meet specific requirements, facilitating adaptability and scalability for future upgrades. However, specialization is crucial for effective deployment and maintenance. Conversely, CWONs face limitations in network uniformity, data shadow zones, and insufficient knowledge of real sensor situations or component characteristics. Furthermore, CWONs exhibit some data inconsistencies in probability distribution and scatter plots during extreme heat periods, as well as improbable UHI temperature values. Full article

Digital tools, especially games, are increasingly important for enabling citizen participation in urban planning. Among these, Minecraft has been widely utilized to engage children, leveraging its virtual environment to represent geospatial data. However, systematic methods for collecting and analyzing player-generated data within Minecraft remain underexplored. Playful Participatory Planning System (P-PPS) framework that transforms player actions (e.g., building, removing, planting) within Minecraft, using OpenStreetMap (OSM) data to create game environments, back into geospatial data for analysis. The framework’s applicability was demonstrated through two case studies, one with 58 schoolchildren and 18 adults in Ireland. The results reveal that schoolchildren, while highly engaged, demonstrated a high density of actions within limited areas, suggesting a need for guidance on spatial distribution and ecological considerations. In contrast, adults prioritized the urban context and exhibited greater spatial consistency in their actions. Challenges emerged in managing online interactions, emphasizing the need for clear guidelines and moderation strategies. This research demonstrates the potential of Minecraft as a platform for participatory urban planning, exploring its use as a collaborative immersive mapping tool. Full article

In the era of digital economy, the open sharing of public data elements has become a key engine driving the digital transformation of enterprises. This article is based on a quasi-natural experiment using government data platforms launched from 2011 to 2022, and it uses the asymptotic difference-in-differences method to empirically test the mechanism and impact of public data elements on enterprise digital transformation. It has been discovered that making data items publicly available dramatically increases the degree of enterprise digital transformation. According to mechanism analysis, corporate digital transformation is primarily impacted by releasing public data elements through two channels: information resource sharing and removing financial restraints. According to heterogeneity analysis, public data elements significantly impact enterprise digital transformation when they are located in regions with high data openness quality, when they are located in regions with substantial levels of digital economic vitality, and when they are available during businesses’ growth and maturity stages. The enabling influence of public data elements on organizational digital transformation was positively mitigated via a fully functional digital infrastructure and cutting-edge digital products. This study has confirmed the driving value of the market-oriented allocation of data elements in the digital transformation of enterprises, providing a theoretical basis for improving the government’s data openness system and unleashing the productivity of data elements. It also provides practical inspiration for enterprises to seize digital opportunities. Full article

Smart cities are urban areas that use contemporary technology to improve citizens’ overall quality of life. These modern digital civil hubs aim to manage environmental conditions, traffic flow, and infrastructure through interconnected and data-driven decision-making systems. Today, many applications employ intelligent sensors for real-time data acquisition, leveraging visualization to derive actionable insights. However, despite the proliferation of such platforms, challenges like high data volume, noise, and incompleteness continue to hinder practical visual analysis. As missing data is a frequent issue in visualizing those urban sensing systems, our approach prioritizes their correction as a fundamental step. We deploy a hybrid imputation strategy combining SARIMAX, k-nearest neighbors, and random forest regression to address this. Building on this foundation, we propose an interactive web-based pipeline that processes, analyzes, and presents the sensor data provided by Basel’s “Smarte Strasse”. Our platform receives and projects environmental measurements, i.e., NO₂, O₃, PM_2.5, and traffic noise, as well as mobility indicators such as vehicle speed and type, parking occupancy, and electric vehicle charging behavior. By resolving gaps in the data, we provide a solid foundation for high-fidelity and quality visual analytics. Built on the Flask web framework, the platform incorporates performance optimizations through Flask-Caching. Concerning the user’s dashboard, it supports interactive exploration via dynamic charts and spatial maps. This way, we demonstrate how future internet technologies permit the accessibility of complex urban sensor data for research, planning, and public engagement. Lastly, our open-source web-based application keeps reproducible, privacy-aware urban analytics. Full article

Streetscapes in old urban areas are not only an important carrier to show regional economies and city style, but also closely correlate to urban residents’ everyday life and the hustle and bustle in which they live. Nevertheless, previous studies have either focused on a few examples with low-throughput surveys or have lacked a specific consideration of spontaneous features in the data-driven explorations. Furthermore, the impact of spontaneous streetscape features on diversified social sensing has rarely been examined. This paper combined the mobile collection of street view images (SVIs) and a machine learning algorithm to calculate eight types of spontaneous streetscape elements and integrated two online platforms (Dianping and Sina Weibo) to map the distribution of economic vitality and social media perception, respectively. Then, through comparing multiple regression models, the impacts of the spontaneous streetscape characteristics on social sensing were revealed. The results include the following two aspects: (1) overall, the spontaneous streetscape features have a certain similarity in the impact on both dimensions of social sensing in Quanzhou, with significant clustering and transitional trends and strong spatial heterogeneity; and (2) specifically, the spontaneous streetscape elements can be divided into three categories, given the differentiated roles of significantly positive, negative, and polarizing impacts on the social sensing results. For example, proper use of open-interface storefronts, ads, and banners is consistent with the common suggestions, while the excessive pursuit of interface diversity and the use of cultural elements may bring an ambiguous effect. This paper provides a transferable analytical framework for mixed and data-driven sensing of streetscape regeneration and can potentially inspire related decisionmakers to adopt a more refined and low-cost approach to enhance urban vitality and sustainability. Full article