Search Results (1,104)

The importance of GIS models to monitor the spread of infectious diseases and support emergency response has been underlined by a large body of literature and strengthened by the COVID-19 pandemic to identify possible solutions able to recognise spatio-temporal clusters and patterns, evaluate the presence of acceleration factors and define specific actions. In the field of applied research on health geography and geography of safety, this work briefly displays the main aims of the project “Integrated revealing GIS models to monitor, understand and foresee the spread of diseases and support emergency response” and shows some illustrative applications. The basic assumption of the project is to test revealing models regarding key objectives of social utility, and one of its main aims is to elaborate GIS applications able to understand the spread of COVID-19, relating the geocalisations of the cases with specific variables. In order to provide targeted evidence able to better highlight local differences, a number of elaborations derived from (Arc)GIS models and based on data regarding COVID-19 according to sex, age and healthcare facilities in the Rome municipality (Italy) are presented and contextualised as examples, also replicable for precision preparedness. Full article

The Iraqi coastline in the northern Persian Gulf is highly vulnerable to the impacts of future sea level rise. This study introduces a novel approach in the Arc Geographic Information System (ArcGIS) for inundation risk of the 58 km Iraqi coast of the northern Persian Gulf through a combination of multi-data sources, machine-learning predictions, and hydrological connectivity by Landsat. The Prophet/Neural Prophet time-series framework was used to extrapolate future sea level rise with 11 satellite altimetry missions that span 1993–2023. The coastline was obtained by using the Landsat-8 Operational Land Imager (OLI) imagery based on the Normalised Difference Water Index (NDWI), and topography was obtained by using the ALOS World 3D 30 m DEM. Global Land Use and Land Cover (LULC) projections (2020–2100) and population projections (2020–2100) were used as future inundation values. Two scenarios were compared, one based on an altimeter-based projection of sea level rise (SLR) and the other based on the National Aeronautics and Space Administration (NASA) high-emission scenario, Representative Concentration Pathway 8.5 (RCP8.5). It is found that, by the IPCC AR6 end-of-century projection horizon (relative to 1995–2014), 154,000 people under the altimeter case and 181,000 people under RCP8.5 will have a risk of being inundated. The highest flooded area is the barren area (25,523–46,489 hectares), then the urban land (5303–5743 hectares), and finally the cropland land (434–561 hectares). Critical infrastructure includes 275–406 km of road, 71–99 km of electricity lines, and 73–82 km of pipelines. The study provides the first hydrologically verified Digital Elevation Model (DEM)-refined inundation maps of Iraq that offer a baseline, in the form of a comprehensive and quantitative base, to the coastal adaptation and climate resilience planning. Full article

Ghost imaging (GI) offers a robust framework for remote sensing under degraded visibility conditions. However, atmospheric scattering in phenomena such as fog introduces significant noise and signal attenuation, thereby limiting its efficacy. Inspired by the selective attention mechanisms of biological visual systems, this study introduces a novel deep learning (DL) architecture that embeds a self-attention mechanism to enhance GI reconstruction in foggy environments. The proposed approach mimics neural processes by modeling both local and global dependencies within one-dimensional bucket measurements, enabling superior recovery of image details and structural coherence even at reduced sampling rates. Extensive simulations on the Modified National Institute of Standards and Technology (MNIST) and a custom Human-Horse dataset demonstrate that our bio-inspired model outperforms conventional GI and convolutional neural network-based methods. Specifically, it achieves Peak Signal-to-Noise Ratio (PSNR) values between 24.5–25.5$\mathrm{dB}/\mathrm{m}$ and Structural Similarity Index Measure (SSIM) values of approximately 0.8 under high scattering conditions ($\beta \ge 3.0 \mathrm{dB}/\mathrm{m}$) and moderate sampling ratios ($N \ge 50\%$). A comparative analysis confirms the critical role of the self-attention module, providing high-quality image reconstruction over baseline techniques. The model also maintains computational efficiency, with inference times under $0.12 \mathrm{s}$, supporting real-time applications. This work establishes a new benchmark for bio-inspired computational imaging, with significant potential for environmental monitoring, autonomous navigation and defense systems operating in adverse weather. Full article

Out-of-hospital cardiac arrest (OHCA) is a major cause of death during mountain activities in the Alpine regions. Due to the time-critical nature of these emergencies and the logistical challenges of remote terrain, emergency medical services (EMS) are investigating the use of unmanned aerial vehicles (UAVs) to deliver automated external defibrillators (AEDs). This study presents a geospatial strategy for optimising AED delivery by UAVs in mountainous environments, using the Province of South Tyrol, Italy, as a model region. A Geographic Information System (GIS) framework was developed to identify suitable sites for vertical drone ports based on terrain, infrastructure, and regulatory constraints. A Low-Altitude-Flight Elevation Model (LAFEM) was implemented to generate obstacle-avoiding, regulation-compliant 3D flight paths using least-cost path analysis. The results identified 542 potential vertical-port locations, covering approximately 49% of South Tyrol within ten minutes of flight, and demonstrated significant time savings for AED delivery in field tests compared with manual and Euclidean routing. These findings show that integrating GIS-based vertical-port placement and terrain-adaptive UAV routing can substantially improve AED accessibility and response times in mountainous regions. The LAFEM model aligns with U-space airspace regulations and supports safe, automated AED deployment for improved outcomes in OHCA emergencies. Full article

Establishing digital twin scenes facilitates the understanding of geospatial phenomena, representing a significant research focus for GIS scientists and engineers. However, current research on digital twin scenes modeling relies on manual intervention or the overlay of static models, resulting in low modeling efficiency and poor standardization. To address these challenges, this paper proposes a knowledge graph-guided and multimodal data fusion-driven rapid modeling method for digital twin scenes, using bridge tower construction as an illustrative example. We first constructed a knowledge graph linking the three domains of “event-object-data” in bridge tower construction. Guided by this graph, we designed a knowledge graph-guided multimodal data association and fusion algorithm. Then a rapid modeling method for bridge tower construction scenes based on dynamic data was established. Finally, a prototype system was developed, and a case study area was selected for analysis. Experimental results show that the knowledge graph we built clearly captures all elements and their relationships in bridge tower construction scenes. Our method enables precise fusion of 5 types of multimodal data: BIM, DEM, images, videos, and point clouds. It improves spatial registration accuracy by 21.83%, increases temporal fusion efficiency by 65.6%, and reduces feature fusion error rates by 70.9%. Local updates of the 3D geographic scene take less than 30 ms, supporting millisecond-level digital twin modeling. This provides a practical reference for building geographic digital twin scenes. Full article

The ultra-high frequency (UHF)-based partial discharge (PD) detection technology for gas-insulated switchgear (GIS) has achieved large-scale applications due to its high sensitivity and real-time monitoring capabilities. However, long-term service-induced antenna corrosion in UHF sensors may lead to degraded reception characteristics. To ensure the credibility of monitoring data, on-site sensor calibration under ambient noise conditions is required. This study first analyzes the time–frequency domain characteristics of white noise received by UHF sensors in GIS environments. Leveraging the transceiver reciprocity principle of sensors, a noise reconstruction method based on external sensors is proposed to simulate on-site white noise. Subsequently, CST simulation models are established for both standard and degraded sensors, quantifying the impact of factors like antenna corrosion on performance parameters such as echo impedance S11 and voltage standing wave ratio (VSWR). Finally, the two sensor models are coupled into GIS handholes for comparative simulation analysis. Results show that antenna corrosion causes resonant frequency shifts in sensors, reducing PD signal power by 55.27% and increasing noise power by 64.11%. The signal-to-noise ratio (SNR) decreases from −9.70 dB to −15.34 dB, with evident waveform distortion in the double-exponential PD pulses. These conclusions provide theoretical references for on-site UHF sensor calibration in noisy environments. Full article

In this study we employed nanopore whole genome sequencing to analyze the resistance genes, genomic islands and prophage DNA in two multidrug resistant E. rhusiopathiae strains, i.e., 670 and 147, isolated from domestic geese. MLST profiles and core-genome phylogeny were determined to assess strain relatedness. In strain 670 (serotype 8, ST 113), a novel 53 kb prophage φEr670 carrying the lnuB and lsaE resistance genes was identified. Regions highly homologous to the φEr670 prophage were detected in 36 of 586 (6.14%) publicly available E. rhusiopathiae genomes, as well as in some other Gram-positive bacteria, and usually contained resistance genes. E. rhusiopathiae strain 147 (serotype 5, ST 243) was found to contain a composite 98 kb genomic island (GI_Er147) carrying the ant(6)-Ia and spw genes, as well as gene encoding a putative lincosamide nucleotidyltransferase designated lnu(J) and a vat family gene encoding a putative streptogramin A O-acetyltransferase. The lnu(J) gene exhibited 83.6% homology to the lnu(D) gene, and lnu(J)-positive E. rhusiopathiae strains displayed intermediate susceptibility to lincomycin. Vat-positive strain 147 and vat-negative E. rhusiopathiae strains showed similar susceptibility to quinupristin/dalfopristin. The presence of the Tn916 transposon carrying the tetM gene was confirmed in the genomes of both E. rhusiopathiae strains; in strain 147, however, Tn916 was located within ICEEr1012. Based on analyses of additional E. rhusiopathiae genomes, the integration sites of Tn916, ICEEr1012, and GI_Er147 were identified as genomic “hot spots,” contributing to the genome plasticity of E. rhusiopathiae. Prophage φEr670 and GI_Er147 as well as the Tn916 transposon and ICEEr1012 are most likely responsible for the dissemination of resistance genes in E. rhusiopathiae. Prophages highly homologous to φEr670 act as carriers of resistance genes in various Gram-positive bacteria. However, the transferability of the identified genetic elements and the functional role of the lnu(J) gene require further investigation. This study provides new insights into the diversity of MGEs in E. rhusiopathiae and advances understanding of the genomic mechanisms driving antimicrobial resistance in Gram-positive bacteria. Full article

Simulating what-if scenarios in 3D environments has become popular due to its potential to improve planning and operations for large-scale, complex events, such as the annual Hajj. Despite the extensive availability of spatial data through Geographic Information Systems (GIS)—information systems specialized in collecting, organizing, and analyzing geospatial information—these resources remain underutilized for such use cases. Three-dimensional modelers create simulations for these use cases and build them from scratch despite the availability of GIS data, which can be tedious and error-prone. We raise the question of whether it is reasonable to use machine learning (ML) algorithms to learn coordinate conversion systems, laying the foundations for accelerating the construction of 3D environments and achieving more accurate results. In this paper, we introduce a simple yet novel framework that facilitates learning coordinate conversion systems. Using our framework, we evaluate 35 ML models and provide a detailed analysis of their prediction performance, overfitting characteristics, and trade-offs in terms of time and model size. Notably, 14 of the ML models demonstrate near-perfect learning (i.e., R-squared very close to 1.0). Furthermore, we use grid search to find better parameter settings for underperforming models. Our results demonstrate that ML can help 3D modelers automate manual tasks and improve the efficiency of 3D modeling from GIS data. Full article

Current urban green volume quantity and equity evaluations primarily rely on two-dimensional (2D) indicators that capture the planar distribution characteristics but overlook vertical structure variations. This study constructed a three-dimensional (3D) evaluation system for green volume quantity and equity by introducing Lorenz curves and Gini coefficients. Using multi-source data, including a 10 m global vegetation canopy height dataset, land cover, and population distribution data, an automated calculation workflow was established in ArcGIS Model Builder. Focusing on regional and neighborhood scales, this study calculates and analyzes two-dimensional green volume (2DGV) and three-dimensional green volume (3DGV) indicators, along with the spatial equity for 413 Chinese cities and residential and commercial areas of Wuhan, Suzhou, and Bazhong. Meanwhile, a green volume quantity and equity type classification method was established. The results indicated that 3DGV exhibits regional variations, while Low 2DGV–Low 3DGV cities have the highest proportion. Green volume in built-up areas showed a balanced distribution, while park green spaces exhibited 2DGV Equitable Only. At the neighborhood scale, residential areas demonstrated higher green volume equity than commercial areas, but most neighborhood areas’ indicators showed low and imbalanced distribution. The proposed 2DGV and 3DGV evaluation method could provide a reference framework for optimizing urban space. Full article

This study proposes a novel two-dimensional Empirical Mode Decomposition (2D EMD)-based deep learning framework to enhance model performance in multi-class image classification tasks and potential early detection of diseases in healthcare using medical imaging. To validate this approach, we apply it to gastrointestinal (GI) endoscopic image classification using the publicly available Kvasir dataset, which contains eight GI image classes with 1000 images each. The proposed 2D EMD-based design procedure decomposes images into a full set of intrinsic mode functions (IMFs) to enhance image features beneficial for AI model development. Integrating 2D EMD into a deep learning pipeline, we evaluate its impact on four popular models (ResNet152, VGG19bn, MobileNetV3L, and SwinTransformerV2S). The results demonstrate that subtracting IMFs from the original image consistently improves accuracy, F1-score, and AUC for all models. The study reveals a notable enhancement in model performance, with an approximately 9% increase in accuracy compared to counterparts without EMD integration for ResNet152. Similarly, there is an increase of around 18% for VGG19L, 3% for MobileNetV3L, and 8% for SwinTransformerV2. Additionally, explainable AI (XAI) techniques, such as Grad-CAM, illustrate that the model focuses on GI regions for predictions. This study highlights the efficacy of 2D EMD in enhancing deep learning model performance for GI image classification, with potential applications in other domains. Full article

The risk of urban flooding has escalated with increasing rainfall intensity and the expansion of impervious surfaces. While commercial models such as XP-SWMM provide reliable hydraulic analyses, their closed-source structure limits transparency and integration with external tools. In contrast, the Grid-Based Urban Drainage System Analysis Model (GUDS), developed on the Weighted Cellular Automata 2D (WCA2D) framework, offers greater flexibility for process verification and coupling with platforms such as GIS and spreadsheets. This study presents a comparative assessment of numerical stability and velocity estimation schemes between XP-SWMM and GUDS. Moving beyond previous validation-focused studies, it quantitatively examines how algorithmic formulations—particularly in flow velocity computation and numerical treatment—affect inundation propagation and model stability under varying topographic conditions. Results demonstrate that XP-SWMM yields higher analytical precision but is prone to numerical instability on steep slopes, whereas GUDS maintains stable simulations due to its simplified water-level-difference approach, albeit with reduced responsiveness to rapidly changing flows. The differences in maximum inundation depth, inundation area, and propagation speed were relatively minor—approximately 11.6%, 10.7%, and 9.2% on average, respectively. This work provides a novel quantitative perspective on the trade-offs between precision and stability in urban flood modeling, highlighting GUDS’s robustness and practical applicability as an open and extensible alternative to conventional equation-based models. Full article

Multiscale 3D geological characterization and joint analysis are increasingly important topics in spatial information science. However, the non-uniform spatial distribution of objects and scale heterogeneity in geological surveys lead to dispersed storage, long access paths, and limited query performance in managing multiscale 3D geological model data. This study presents a W-Hilbert curve-based multiscale data model (WH-MSDM) that improves data indexing and management through a unified data structure (UDS) for multi-scale blocks and a bidirectional mapping model (BMM) linking spatial coordinates to memory locations. It supports spatial, attribute, hybrid, and cross-scale queries for diverse retrieval tasks. By exploiting the space-filling properties of the W-Hilbert curve to linearize multidimensional geological data into a one-dimensional index, it preserves locality and increases query efficiency across scales. Experimental results on a real 3D geological model demonstrate that WH-MSDM outperforms three mainstream baselines in both unified data organization and diverse query workloads. It thus provides a data-model foundation for Digital Earth-oriented multiscale geological analysis. Full article

Japanese encephalitis virus (JEV) is a mosquito-borne zoonotic flavivirus causing severe neurological disease across Asia, and genotype V (GV) is now predominant in Korea. Despite frequent detection of GV in mosquitoes, human-derived complete genome data remain scarce. To elucidate the molecular and antigenic characteristics of human GV infections, cerebrospinal fluid samples from unvaccinated patients positive for JEV RNA during 2018–2023 were subjected to virus isolation in LLC-MK2 cells (rhesus monkey kidney-derived epithelial cell line). Three human GV isolates (K18P80, K23P84, K23P88) were successfully obtained and their complete open reading frames (~10.3 kb) sequenced. Phylogenetic analysis with representative JEV strains (GI–GV) revealed that these isolates form a distinct lineage, clustering into two domestic clades (Clade I and II), suggesting endemic circulation and local evolution in Korea. Sequence identities with GIII-based vaccine strains were low (79% nucleotide, 91.1% amino acid), with notable divergence in nonstructural regions. Three consistent E protein substitutions (Q52E, S156T, D292E) near antigenic epitopes indicate possible immune escape. Additional clade-defining substitutions in NS3 (L31F) and NS5 (K269R, M330I) were shared with mosquito isolates, supporting human–vector molecular continuity. These findings provide fundamental genomic evidence of human JEV GV in Korea and highlight the need for genotype-specific surveillance and next-generation vaccine evaluation. Full article

Fuchsia standishii J.Harrison is a species widely used in traditional medicine in southern Ecuador for treating various ailments, including high blood pressure, as an antacid and a relaxant. The pharmacological basis for these traditional uses is unknown. Given the reported anti-inflammatory and cytotoxic properties of the Onagraceae family, we investigated the plant’s potential for addressing chronic conditions. This study explored the bioactive potential of polar extracts from the aerial parts of F. standishii, focusing on antiproliferative activity against a panel of human tumor cell lines (A549, HBL-100, HeLa, SW1573, T-47D). The plant material was sequentially extracted and partitioned into nine fractions. All fractions were screened for antiproliferative activity, and the most active fractions were further evaluated for their mechanism of cell death (apoptosis/necrosis), genotoxicity, and induction of oxidative stress. Specialized metabolites in the fractions were characterized using UHPLC-DAD-MS³ analysis. F. standishii extracts showed potent antiproliferative activity. The dichloromethane fraction (MW_D) was the most active (GI₅₀ range: 8.5–39 µg/mL), demonstrating the ability to induce apoptosis in tumor cells and cause genotoxic damage linked to oxidative stress. The UHPLC-DAD-MS³ analysis successfully characterized the specialized metabolites present in the active fractions. The initial aqueous extract yielded a total of 47 secondary metabolites, 15 of which remained unassigned. F. standishii possesses a promising pharmacological profile that extends beyond its documented traditional uses. The MW_D fraction represents a plausible source of novel anti-cancer agents due to its ability to induce apoptosis, supporting further bioguided investigation of this ethnobotanically relevant species. Full article

As digital and intelligent technologies become increasingly intertwined. Digital and Intelligent Transformation (DIT) emerges as a key catalyst for advancing high-quality economic and social development. Against this backdrop, as the core entities in green and low-carbon transition, corporate green innovation (GI) capabilities have garnered increasing attention. To evaluate the effects of DIT on corporate GI, the study employs the establishment of the “National New Generation Artificial Intelligence Innovation and Development Pilot Zones” (NAIPZ) as a quasi-natural experimental. This paper analyzes the impact and transmission channels of DIT on GI, using panel data from Chinese A-share listed companies (2011–2022). Employing a multi-period DID approach, the results indicate that the policy promotes GI. Additionally, the findings are supported by extensive robustness checks. Heterogeneity analysis reveals that the policy impact is moderated by firm size, and industry characteristics. Mechanism analysis reveals that urban DIT promotes corporate GI by enhancing government governance capacity, accelerating corporate digital transformation, and optimizing human capital structures. Based on these findings, we recommend tailored policy frameworks, strengthened innovation infrastructure, increased R&D support, and effective performance-tracking mechanisms. These measures can help maximize the potential of artificial-intelligence technology in advancing corporate GI. Full article