Search Results (896)

In a situation where cities are increasingly being developed vertically and complexly, a novel approach for analyzing vertical urban functional patterns is proposed. For this purpose, a multi-dimensional GDC (Geo Data Cube) consisting of spatial and temporal data x, y, z, t, and f dimensions containing layer information was created. At this time, the size of the GDC cell (interval in x, y, z dimensions) is calculated by cell point data using the three-dimensional (3D) Moran’s I index value calculated with the 3D Diversity Factor (DF) based on information entropy proposed to reduce the uncertainty of information for each cell. In other words, the cell with the smallest index value was chosen to minimize the influence of Modifiable Areal Unit Problem (MAUP) that occurs when mapping. The 3D land use index (3D LUI) is calculated as a linearly weighted sum of the spatial accessibility of uses between cells (3D KDF) and the enrichment of uses (3D EF), taking into account the first law of geography. Finally, the 3D LUI value for each use was calculated for each cell of the GDC, and the use with the highest value was determined as the urban function of the cell. As a result of applying this to Seocho-gu, Seoul, Republic of Korea (ROK) in June 2024 and visually evaluating it using the street view provided by Kakao Map, it was confirmed that commercial and residential functions were vertically separated in buildings with residential–commercial complexes or shops on the ground floor. It was also confirmed that such characteristics did not appear in the two-dimensional (2D) urban functional patter analysis. Full article

Historic city centers host dense ensembles of heritage buildings where conservation goals must coexist with sustainable and smart urban development, yet the semi-outdoor “liminal” spaces of these complexes, such as cloisters, loggias and courtyards, are rarely included in microclimate monitoring networks. This study develops and tests the Liminal Environmental Monitoring System (LEMS), a flexible environmental data acquisition architecture designed for long-term monitoring in such spaces. The LEMS is based on a custom, low-cost data acquisition board able to handle multiple analogue and digital sensors, combined with a daisy-chain communication layout using the MODBUS RS485 protocol and a commercial datalogger as master, in order to meet the technical and visual constraints of historic buildings. Board calibration and sensor characterisation are reported, and the system is deployed in the cloister of Palazzo Costabili, a renaissance complex in the historic city center of Ferrara (Italy). This case study illustrates how the LEMS captures spatial and temporal variation in air temperature, relative humidity and solar irradiance and how an annual solar-shading indicator derived from 3D ray-tracing simulations supports the interpretation of irradiance measurements. The results indicate that the LEMS is a viable tool for heritage-compatible microclimate monitoring and can be adapted to other historic courtyards and loggias. Full article

Rapid urbanization and increasingly frequent extreme rainfall events have intensified stormwater challenges, underscoring the need for watershed-scale strategies that integrate blue-green infrastructure (BGI). This study evaluates the stormwater control performance of combined initial reservoir storage level regulation, river water level adjustment, and green infrastructure (GI) implementation in the 42.4 km² Baihuayong watershed of Guangzhou, China. A coupled stormwater model (SWMM) was developed, calibrated, and coupled with TELEMAC-2D to simulate schemes varying initial reservoir storage levels (30.6 m to 27.6 m), river water levels (11 m to 8 m), and GI proportions (0–45%) under 2- to 100-year rainfall events. Results show that lowering initial reservoir storage levels from 30.6 m to 27.6 m enhanced runoff reduction by ~40% and reduced discharged water volume by ~30%, though overflow mitigation remained limited. Decreasing river water levels from 11 m to 8 m reduced flooded areas by up to 8.3%, with diminishing benefits below 9 m. Increasing GI coverage from 0% to 45% reduced overflow nodes from 236 to 192 and flood extent from 10.76 ha to 9.20 ha under moderate storms, but improvements were modest during extreme events. A synergistic configuration, combining a low initial reservoir storage level (27.6 m), low river water level (8 m), and a high GI proportion (35–45%), yielded the most comprehensive improvements. These findings demonstrate the strong potential of integrated BGI for watershed-scale flood resilience and provide quantitative guidance for sponge city planning. Full article

This study develops and evaluates a regionally calibrated magnitude scaling and early warning framework based on the ElarmS–EPIC algorithm using the 23 April 2025 Silivri (Istanbul) Earthquake (Mw = 6.2) scenario. A comprehensive dataset comprising the mainshock and its aftershocks was used to derive local regression relationships between earthquake magnitude (Mw) and the peak displacement amplitude ($Pd$) and predominant period ($T_p^{max}$) parameters. Replay simulations were conducted to assess real-time performance, and the results of the regional models were compared with those of the default EPIC configuration. The results indicate that the $Pd$-based magnitude estimation model produces faster and more stable results than the $T_p^{max}$-based approach, significantly improving accuracy and operational reliability. The region-specific $Pd$–Mw scaling provided higher consistency with catalog magnitudes compared to the default EPIC relationships. The calculated distribution of warning times shows that the system can provide actionable warning times of 5–9 s in districts near the epicenter (e.g., Silivri, Avcılar, Beylikdüzü) and 20–50 s in more distant districts and city centers (e.g., Kadıköy, Pendik, Bursa, Sakarya). These values demonstrate that a regionally optimized early warning system can provide critical decision-making time for automatic safety systems and emergency responses in the densely populated Marmara Region. Overall, this study emphasizes the importance of regional calibration in improving earthquake early warning (EEW) performance in Türkiye. The findings show that the success of EEW systems depends on station density, network latency, data transmission speed, processing capacity, and algorithmic optimization. The proposed $Pd$-based regional framework provides a scientifically robust and operationally applicable foundation for future EEW implementations in Istanbul and the Marmara Region. Full article

Infestations of Dendrolimus punctatus Walker (D. punctatus) pose significant threats to forest ecosystem health, necessitating accurate and efficient monitoring for sustainable forest management. A monthly monitoring framework integrating spectral bands, vegetation indices, time-series features, meteorological variables, and topographic characteristics was developed. First, cloud-free Sentinel-2 composites were generated via median synthesis, and training samples were selected by integrating GF-1/2 data. Subsequently, a Weighted Composite Index (WCI) was constructed through logistic regression to quantitatively classify infestation severity levels. Meanwhile, time-series features extracted from vegetation indices were incorporated to characterize temporal damage dynamics. Finally, Random Forest (RF) models were then trained for monthly monitoring, achieving overall accuracies exceeding 86.9% with Kappa coefficients ranging from 0.825 to 0.858. The Inverted Red Edge Chlorophyll Index (IRECI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) exhibited the highest sensitivity to D. punctatus damage and thus received the greatest weights in the WCI. Time-series features ranked second in importance after vegetation indices, substantially enhancing model performance. Monitoring results from 2019 to 2024 revealed that D. punctatus infestation in Qianshan City exhibited an occurrence pattern progressing from mild to severe and from scattered to aggregated distributions, with major outbreak periods in 2019, 2021, and 2023 reflecting characteristic cyclical dynamics. This study advances existing quantitative monitoring methodologies for D. punctatus and provides technical support and a scientific foundation for precision pest monitoring and forest health management. Full article

Background/Objectives: Microalgae are recognized as prolific producers of bioactive metabolites with pharmaceutical potential. This study aimed to isolate and characterize cytotoxic constituents from selected cytotoxic microalgae, collected in Hue city, Vietnam. Methods: Microalgal samples were collected from freshwater bodies, morphologically identified, and maintained in laboratory culture. Thirteen strains were successfully isolated and cultivated in BG11, Z8, and BBM media to determine optimal growth conditions. Cytotoxic effects of extracts/compounds were determined using the sulforhodamine B assay on human lung cancer (SK-LU-1) and human liver cancer (HepG2) cell lines. The methanol extract was partitioned with n-hexane and CH₂Cl₂, followed by extensive chromatographic separation and HPLC purification to afford twelve compounds, including two new and ten known compounds. The structures were elucidated by HR-ESI-MS and NMR spectra, chemical methods, and comparing compounds in the literature. Results: From the phytoplankton samples collected across six freshwater bodies in Hue city, Vietnam, thirteen microalgal strains were successfully isolated and purified under laboratory conditions. These strains were morphologically and taxonomically identified to be Microcystis aeruginosa HU05, Microcystis viridis HU13, Anabaena circinalis HU08, Aphanizomenon flos-aquae HU02, Dolichospermum smithii HU04, Calothrix braunii HU14, Nostoc muscorum HU12, Nostoc punctiforme HU11, Raphidiopsis raciborskii HU03, Lyngbya spiralis HU15, Planktothrix stagnina HU16, Phormidium subtilis HU06, and Scenedesmus quadricauda HU07. All methanol extracts of those microalgae were evaluated for cytotoxic activity. The MeOH extracts of M. viridis (HU13) and D. smithii (HU04) exhibited significant cytotoxic effects, with IC₅₀ values of 6.19 ± 0.80 and 4.89 ± 0.76 µg/mL for M. viridis, and 9.51 ± 0.84 and 8.32 ± 0.94 µg/mL for D. smithii against SK-LU-1 and HepG2 cell lines, respectively. Furthermore, chemical studies of D. smithii HU04 led to the isolation of two new compounds, smithioside A (1) and smithioside B (2) and ten known ones, 3,4,5-trimethoxyphenyl-1-O-β-D-glucopyranoside (3), 4′-hydroxy-3′-methoxyphenol-β-D-[6-O-(4″-hydroxy-3″,5″-dimethoxylbenzoate)]-glucopyranoside (4), 4′-hydroxy-2′,6′-dimethoxyphenol 1-O-β-D-(6-O-syringoyl)glucopyranoside (5), mallophenol B (6), pisoninol II (7), guaiacylglycerol (8), (E)-asarone (9), deacetylsarmentamide B (10), (E)-2-hexenyl-β-D-glucopyranoside (11), and 5,6-dihydropyridin-2(1H)-one (12). The cytotoxic activity of all isolated compounds was also evaluated against SK-LU-1 and HepG2 cancer cell lines. Compound 12 showed the strongest activity, with IC₅₀ values of 9.13 ± 0.89 µM (SK-LU-1) and 7.64 ± 0.46 µM (HepG2). Compounds 5 and 6 exhibited moderate cytotoxic activity on both human cancer cell lines with IC₅₀ values ranging from 25.99 to 51.47 µM. Conclusions: These results highlight the potential of Dolichospermum smithii HU04 as a source of bioactive compounds, particularly in anticancer applications. These findings suggest that D. smithii HU04 extracts could be developed for therapeutic purposes targeting cancer. Full article

With the accelerating pace of urbanization, cities are increasingly affected by rainstorm and flood disasters, which pose severe threats to the safety of residents’ lives and property. Existing models are increasingly inadequate in meeting the accuracy requirements for flood simulation in highly urbanized regions. Thus, it is urgent to develop a new method for flood inundation simulation based on high-resolution urban hydrological units. The novelty of the model lies in the novel structure of the high-resolution Urban Hydrological Units model (HRGM), which replaces coarse sub-catchments with a fine-grained network of urban hydrological units. The primary innovation is the node-based coupling strategy, in which the HRGM provides precise overflow hydrographs at drainage inlets as point sources for LISFLOOD-FP, rather than relying on diffuse runoff inputs from larger areas. In this paper, a high-resolution hydraulic model (HRGM) based on urban hydrological units coupled with a 2D hydrodynamic model (LISFLOOD-FP) was constructed and successfully applied in the Chebeichong watershed. Results show that the model’s simulations align well with observed data, achieving a Nash efficiency coefficient above 0.8 under typical rainfall events. Compared with the SWMM model, the simulation results of HRGM were significantly improved and more consistent with measured results. Taking the rainstorm event on 10 August 2021 as an example, the Nash coefficient increased from 0.7 to 0.85, while the peak flow error decreased markedly from 15.8% to 3.1%. It should be emphasized that urban waterlogging distribution is not continuous but appears as patchy, discontinuous, and fragmented patterns due to the segmentation and blocking effects of roads and buildings in urban areas. The framework presented in this study shows potential for application in other regions requiring flood risk assessment at urban agglomeration scales, offering a valuable reference for advancing flood prediction methodologies and disaster mitigation strategies. Full article

Current assessments of urban green spaces (UGS) rely largely on two-dimensional (2D) indicators, which fail to capture the three-dimensional (3D) structure necessary for evaluating ecological functions and human exposure. Among these, the Normalized Difference Vegetation Index (NDVI) describes top-down canopy greenness from a nadir perspective, whereas the Green View Index (GVI) quantifies vegetation visibility at street level from a pedestrian perspective. Because the relationship between NDVI and GVI remains unclear, multi-indicator assessments become difficult to interpret, limiting their ability to jointly characterize urban greenery. To address these gaps, we develop a synergy framework that integrates remote sensing with street-view images. First, we aligned the observation scales through street-view depth estimation and converted NDVI into fractional vegetation cover (FVC) through nonlinear mapping to unify measurement units. Correlation experiments revealed that the consistency between GVI and FVC was weak across the city (R² = 0.27) but substantially stronger along arterial roads with continuous vegetation (R² = 0.61). On this basis, we design a Green Synergy Index (GSI) that combines FVC and GVI using fractional power-law adjustments and an interaction term to capture their joint effects. Robustness tests indicate that GSI effectively handles extreme or mismatched cases, differentiates greening patterns, and integrates complementary information from nadir and street views without numerical instability. Furthermore, we assess the consistency between GSI and land surface temperature (LST), showing that the proposed index improves explanatory power compared with FVC and GVI alone (by 5.6% and 8.8%, respectively). Application to the study area yields a mean GSI value of 0.44 on a 0–1 scale, with spatial variations closely associated with road geometry and functional zoning. This enables the identification of mismatched canopy and visibility segments and supports targeted, climate-sensitive green infrastructure planning. Full article

The shipping service industry plays a pivotal role in enhancing port competitiveness and fostering urban economic growth, yet limited studies systematically integrate its spatial temporal dynamics with the processes driving port–city synergy. This study constructs a three-dimensional analytical framework encompassing port operations, urban economic development, and shipping service industry agglomeration. Using data from 13 port cities in Jiangsu Province (2015–2023), we apply the entropy weight method, coupling coordination degree model, relative development model, and panel Tobit regression to evaluate interaction intensity, coordination patterns, and influencing factors. Results reveal a clear spatial gradient in coupling coordination, higher in southern Jiangsu and lower in the north, driven by disparities in economic foundations, port capacities, and service industry structures. In most cities, port operations and urban economies lag behind shipping service industry agglomeration, reflecting the predominance of low- and mid-end services. Port construction level, cargo and container throughput, economic development, openness, fixed asset investment, and population density significantly promote coordination, whereas R&D capacity shows no significant effect. The findings advance understanding of port–city service interlinkages and provide targeted policy recommendations for differentiated regional development, infrastructure enhancement, and upgrading toward high-end shipping services, with implications for maritime regions worldwide. Full article

Digitalization has reshaped economic systems worldwide, yet its distributional consequences remain uneven and raise new challenges for sustainable development. China, where digital infrastructure has expanded rapidly, provides a critical setting to examine these effects and their implications for sustainable and inclusive growth. Using a balanced panel of 285 prefecture-level cities from 2007 to 2023, this study constructs a text-based index of digital infrastructure from government work reports and applies two-way fixed effects, instrumental variables, nonlinear models, placebo tests, heterogeneity analysis, and spatial Durbin models. The results show that digital infrastructure significantly widens the urban–rural income gap, with the effect becoming increasingly convex as digital development deepens. Two mechanisms drive this pattern: the concentration of innovation resources in urban areas, which crowds out rural R&D, and a modest degree of wage-structure polarization. Spatial spillovers also matter; digital development in neighboring cities partially offsets local inequality by enhancing interregional connectivity and knowledge diffusion. These findings provide city-level causal evidence on the unequal distributional impacts of digitalization in large emerging economies and highlight the need for sustainability-oriented digital governance, inclusive innovation systems, and regionally coordinated strategies to prevent digital infrastructure from reinforcing structural disparities. Strengthening these policies is essential for achieving more sustainable urban–rural integration in the digital era. Full article

In China’s rapidly urbanizing coastal areas, inclusive green development (IGD) has become an important way to achieve a reduction in economic development disparities, environmental sustainability, and social equity. This study investigates the spatiotemporal dynamics and structural drivers of IGD across 54 coastal cities within three marine economic zones (MEZs) using a hybrid analytical framework that integrates evaluation techniques, inequality decomposition, spatial factor detection, and spatial econometrics. The result shows that a distinctive “four-pillar” spatial structure has emerged, centered on the Shandong Peninsula, Yangtze River Delta (YRD), West Coast of the Taiwan Strait, and Pearl River Delta (PRD). Spatial autocorrelation has intensified since 2020, indicating the cumulative effect of China’s post-2020 regional integration policies and digital infrastructure investments, which accelerated resource flows between cities. Spatial econometric analysis further reveals that economic development and equitable public service provision are the most influential drivers, while public investment in R&D and digital transformation exhibit significant cross-city spillover effects. The findings highlight the importance of regionally adaptive and digitally integrated strategies to promote inclusive and sustainable urban development in coastal economies. Therefore, efforts should be intensified to strengthen the role of core cities as diffusion engines for neighboring areas, with a strategic focus on regional digital transformation and R&D investment, to advance inclusive and sustainable development in coastal economies. Full article

The conservation of cultural heritage increasingly requires a transition from emergency restoration to preventive and planned strategies supported by systematic data management. Within this context, this paper, conceived as a project report, presents the methodological premises, operational framework, and preliminary outcomes of the Fountains and Monuments in the Public Space of the City of Turin project, developed within the CHANGES Project—National Recovery and Resilience Plan (NRRP), funded by the European Union—NextGenerationEU. The project explores the integration of preventive and planned conservation methodologies with digital tools for the sustainable management of outdoor cultural heritage. Five case studies in Turin, identified in collaboration with local authorities, provided the basis for developing a protocol for planned conservation. A digital platform was designed as the operational tool of this protocol, integrating georeferenced data, 3D models, interactive dashboards, and modules for inspection, planning, and monitoring. The platform enables data-driven prioritisation of interventions, traceability of conservation activities, and long-term documentation management. Although still at the demonstrator stage, it shows potential for scalability and transferability. The study concludes that the integration of interdisciplinary expertise and digital innovation can effectively support preventive and planned conservation, strengthening the systematic management of outdoor cultural heritage. Full article

Many important tasks in smart city development and management are solved by systems of monitoring and control installed on-board of unmanned aerial vehicles (UAVs). UAV sensors can be imperfect or they can operate in unfavorable conditions, which can then result in obtaining images or video sequences that are noisy. Noise can degrade the performance of methods of vehicle and human localization and classification. Therefore, specific techniques to improve performance have to be applied. In this paper, we consider YOLO family neural networks as tools for solving the aforementioned tasks. This family of networks is rapidly developing; however, the input data may still require pre-processing. One option is to apply denoising before object localization and classification. In addition, approaches based on augmentation and training can be used as well. We consider the performance of these approaches for various noise intensities. We identify the noise levels at which network performance starts to degrade and analyze possibilities of performance improvement for two filters–BM3D and DRUNet. Both improve such performance criteria as the F1 score, the Intersection over Union and the mean Average Precision. Datasets of urban areas are used in the network training and verification. Full article

This study addresses the technical gaps in current flood simulation for regulated lakes, such as insufficient accuracy in simulating complex gate and dam operation processes and low computational efficiency that fails to meet practical engineering needs. By employing an improved two-dimensional (2D) hydrodynamic model, it systematically analyzes flood control strategies for large regulated lakes. Using the August 2018 flood event for model validation, the final simulation results indicate that the current flood control capacity meets standards for 50-year floods (Nanyang 36.79 m, Weishan 35.99 m) but fails for 100-year floods, exceeding limits by 0.23 m (Nanyang 37.22 m) and 0.15 m (Weishan 36.64 m). The designed conditions reduce 100-year flood levels to 36.98 m and 36.47 m, respectively, achieving the required flood defense standard for 100-year events. The findings provide a quantitative framework for evaluating flood control capacity across different planning scenarios, which advances flood risk management and offers implementable insights for achieving sustainable water resource management in regulated lake basins globally. This, in turn, contributes directly to two United Nations Sustainable Development Goals (SDGs): enhancing human community safety and resilience (SDG 11: Sustainable Cities and Communities) through improved flood control engineering and operations, and strengthening climate adaptation (SDG 13: Climate Action) by boosting basin-wide resilience to extreme rainfall and flooding. Full article

Urban Digital Twins (UDTs) demand both simplified geometry and rich semantic information from Building Information Models (BIM) to be effectively integrated into Geospatial Information Systems (GIS). However, current BIM-to-GIS conversion methods struggle with geometric complexity and semantic loss, particularly at scale. This paper proposes a novel, scalable methodology for comprehensive BIM/GIS integration, addressing both geometric and semantic challenges. The approach introduces a geometry conversion workflow that transforms solid BIMs into valid, simplified CityGML representations through a level-by-level detection of building elements and outer surface extraction. To preserve semantic richness, all entities, attributes, and relationships—including implicit connections—are automatically extracted and stored in a Labeled Property Graph (LPG) database. The method is further extended with a new CityGML Application Domain Extension (ADE) that supports Multi-LoD4 representations, enabling selective interior visualization and efficient rendering. A web-based urban digital twin platform demonstrates the integration, allowing dynamic semantic querying and scalable 3D visualization. Results show a significant reduction in geometric complexity, full semantic retention, and robust performance in visualization and querying, offering a practical pathway for advanced UDT development. Full article