Search Results (1,604)

This research examines the emerging ecosystem of models that are developed and run across a distributed network of computers called decentralized artificial intelligence. The focus is to understand these models in the healthcare context and with a focus on their core components: technologies, governance frameworks, and real-world applications. A systematic literature review was conducted, analyzing peer-reviewed studies from PubMed, Scopus, and Web of Science to map the current landscape of the field. The primary objective was to synthesize the current research on decentralized approaches in healthcare, including core approaches like federated learning and blockchain-based AI models, as well as emerging concepts such as agentic AI blockchain-based AI models and DAOs, to comprehend their application in clinical and operational settings. The research assesses the maturity of these implementations, ranging from pilot programs to large-scale organizational settings. It also identified the key computational and technical methods and platforms used and the key benefits and challenges influencing their adoption. The findings underscore the pivotal role of the decentralized paradigm in addressing the fundamental limitations of traditional AI, including data privacy, trust, institutional silos, and regulatory complexity. Insights are also offered for healthcare providers, technology developers, researchers, and policymakers aiming to navigate and leverage decentralized AI to build more equitable, efficient, and collaborative healthcare systems. Full article

A continuous integration framework and methodology for hydrological modeling is proposed that integrates model sensitivity analysis with real-time sensor tasking to prioritize data collection in regions and periods of high hydrological variability and drive model refinement. The Cahaba River Watershed in central Alabama serves as a case study to develop this approach. To this end, a benchmark Soil and Water Assessment Tool (SWAT) model (30 m DEM) was refined with high-resolution spatial datasets in QGIS, including 1 m DEMs, NLCD land cover, and SSURGO soil data. The refined model significantly enhanced subbasin delineation, increasing granularity from 8 to 99 subbasins, thereby improving representation of slope, runoff, and storage variability across heterogeneous landscapes. Sensitivity analyses were performed to evaluate the influence of DEM resolution and curve number (CN) perturbations on hydrologic responses, including retention, flow partitioning, and dominant flow direction. High-resolution DEMs (≤5 m) captured microtopographic features that strongly affect infiltration and surface runoff, while coarser DEMs (≥20 m) systematically underestimated retention and smoothed hydrologic gradients. The higher-resolution DEMs can be used to selectively improve the model at certain hotspots/areas of higher sensitivity. Localized flow simulations demonstrated that fine-scale terrain data substantially improve model realism, with up to 58% greater retention captured in 10 m DEMs compared to 30 m DEMs. The results confirm that aligning sensor placement and model refinement with spatially explicit sensitivity zones enhances both predictive accuracy and computational efficiency. The proposed continuous integration approach provides a scalable pathway for coupling high-resolution modeling with adaptive sensing in watershed management and supports future integration of real-time data assimilation for continuous model improvement. Full article

Highway landscape quality is important for visual comfort, environmental coordination, and infrastructure management. However, conventional assessment methods rely heavily on manual inspection and qualitative judgment, which are subjective and inefficient for large-scale applications. To address this issue, this study proposes an AI-based quantitative evaluation framework for highway landscape quality using an improved Panoptic-DeepLab model for panoramic image segmentation. The model identifies major landscape elements in highway scenes, including vegetation, sky, roads, buildings, and billboards. Based on the segmentation results, the proportions of natural elements, spatial openness, and artificial interference are integrated into a landscape quality score (LQS) model for quantitative assessment. Experimental results demonstrate that the proposed method achieves reliable segmentation performance and stable convergence in complex highway environments. Comparative analysis further shows that the method provides competitive accuracy with good computational efficiency. The proposed framework offers an effective tool for highway landscape evaluation and can support highway planning, landscape optimization, and visual environment management. Full article

In the era of urban stock development, the regeneration of historic districts must abandon the misguided approach of large-scale, sweeping transformations and shift toward a micro-regeneration model characterized by small-scale, precise, and incremental interventions. However, as urban renewal enters this stock-based phase, the issues of “physical dissonance” and “cultural discontinuity” in the heritage landscapes of historic districts are becoming increasingly pronounced. To solve this problem, this paper aims to identify the heritage landscape genes of historical districts, explore the characteristics of historical districts, provide operational targets for the micro-renewal of historical districts, guide the implementation of micro-regeneration policies of historical districts, and then improve the quality of historical district heritage landscapes. Taking the Chinese Baroque Block in Harbin as an example, this paper proposes a genetic recognition method for the heritage landscape of historical districts based on the spatial translation of historical information, spatial topology analysis, an improved U-Net deep learning model, and text mining theme analysis. The micro-regeneration path of historical blocks of “gene identification-feature mining-targeted operation-quality improvement” is proposed. The micro-regeneration countermeasures of “gene replacement and texture repair in open space, gene repair and targeted acupuncture in street and alley, gene embedding and catalyst adjustment in courtyard layout, gene recombination and embroidery treatment of architectural style, and retrospective and contextual narrative of intangible genes” are formulated. The heritage landscape gene of historical districts is conducive to the refined control of the characteristics and quality of historical districts and provides new ideas for the micro-regeneration of historical districts in the stock era. Full article

Metaheuristic optimization algorithms have attracted considerable research interest for solving complex optimization problems, yet many existing algorithms suffer from premature convergence and an inadequate balance between exploration and exploitation. The Coati Optimization Algorithm (COA) is a recently proposed nature-inspired metaheuristic that models the hunting and escape behaviors of coatis; however, it exhibits limited search diversity and tends to stagnate in local optima on high-dimensional, multimodal landscapes. This paper proposes an Improved Coati Optimization Algorithm (ICOA) that integrates four complementary enhancement strategies: (1) a Dynamic Adaptive Step-Size strategy that combines Lévy flights with Student’s t-distribution perturbations for heavy-tailed exploration; (2) a Population-Adaptive Dynamic Perturbation strategy that incorporates differential evolution operators with fitness-proportional scaling; (3) an Iterative-Cyclic Differential Perturbation strategy that employs sinusoidal scheduling and population-differential guidance; and (4) a Cosine-Adaptive Gaussian Perturbation strategy for refined exploitation with time-decaying intensity. ICOA is evaluated on 29 CEC2017, 10 CEC2020, and 12 CEC2022 benchmark functions across dimensions ranging from 10 to 100, compared against seven state-of-the-art algorithms in each benchmark suite. A statistical analysis using the Friedman test and the Wilcoxon rank-sum test confirms that ICOA achieves overall rank 1 on all three benchmark suites, with Friedman mean ranks of 1.207 (CEC2017, $D=100$), 1.000 (CEC2020, $D=10$), and 2.208 (CEC2022, $D=10$); the CEC2020 result should be interpreted in the context of its low dimensionality. A scalability analysis across four dimensionalities (10D, 30D, 50D, 100D) demonstrates consistent first-place rankings with mean ranks between 1.000 and 1.207. An ablation study and a sensitivity analysis of the strategy activation probability validate the contribution of each individual strategy and the optimality of the 50% activation setting. Furthermore, ICOA achieves the best results on all six constrained engineering design problems tested, with all improvements confirmed as statistically significant ($p<0.05$). Full article

With accelerating global urbanization and increasingly diverse demands for public spaces, promoting urban low-carbon transitions and enhancing residents’ quality of life have become central missions of modern urban development. As one of the city’s primary arteries, streets—through their green landscapes, slow-moving transportation systems, and public facilities—play an indispensable role in reducing carbon emissions, promoting healthy living, and improving residents’ well-being. In this study, the Yubei District of Chongqing was selected as the research area, and an automated evaluation framework was proposed for street visual quality, based on multi-source street view data and ensemble learning. PSP-Net semantic segmentation model was employed to extract eight key visual indicators from street view images, including green view index, Visual Entropy (Entropy), sky view factor (SVF), drivable space, sidewalk, safety facilities, buildings, and enclosure. Based on these features, a Stacking-based ensemble learning model was constructed, integrating multiple base models such as Random Forest, XGBoost, and LightGBM, with Linear Regression as the meta-learner, to predict street visual quality. The results demonstrate that the ensemble model significantly outperforms any single model, achieving a correlation coefficient (r) of 0.77 and effectively capturing the complex perceptual features of street environments. This study provides a reliable, intelligent, and quantitative method for large-scale evaluation of urban street visual quality, while supplying data support and decision-making references for street renewal and spatial optimization. Full article

Although urban streetscape visual esthetic quality (VAQ) assessment has progressed markedly, its findings are rarely operationalized in urban planning policy-making. The resulting discontinuity in the assessment–policy linkage is a critical impediment to streetscape VAQ enhancement. We propose an eye-tracking-enabled, end-to-end decision support framework that links evidence acquisition, intervention prioritization, design strategy formulation, and outcome feedback. Eye tracking is integrated to establish a three-dimensional assessment system spanning spatial, psychological, and physiological dimensions. Within this integrated system, we construct a three-level eye-tracking-based visual characteristics (ET-VC) framework across streetscape elements, formal characteristics, and public esthetic perception (PAP). Together, the three-dimensional system provides a theoretical basis for acquiring the multi-modal data required for VAQ enhancement. Building on this integrated assessment, we embed scenario planning theory to construct a planning facing decision model with PAP as the core outcome. The model combines importance-performance analysis (IPA) with the coupling coordination degree model (CCDM) to guide resource allocation decisions and intervention prioritization, and further uses eye-tracking evidence to support the development of refined, actionable enhancement strategies. A case study in Wudadao validates the framework’s robustness and feasibility. The ET-VC results provide additional evidence for interpreting esthetic perception: (1) ET-VC indicators differ significantly across streetscape elements, and “being viewed more” does not necessarily correspond to higher esthetic ratings; (2) four groups of key formal characteristic indicators—color configuration, naturalness, historicity and planning/regulatory control, and visual scale—systematically reshape fixation onset and maintenance patterns; and (3) PAP appears to involve partially nonlinear relationships between material landscape features and additional top-down influences (e.g., historical narratives and individual experience), rather than being fully explained by linear associations alone. Overall, this study provides both a theoretical basis and an applied demonstration for evidence-based streetscape VAQ enhancement. Full article

Patterns of resource selection are driven by the decision-making processes of animals occurring at multiple scales from where to establish a home range (i.e., second order selection) to which resource patches to use within the home range (i.e., third order selection). Elk (Cervus canadensis) were reintroduced to southwestern Virginia, USA, from 2012 to 2014 following successful translocations onto reclaimed surface coal mines in the region. We sought to understand how elk have acclimated following their translocation using location data from GPS-collared adult female elk (n = 33) collected from 2019 to 2022 along with remotely sensed terrain and land cover data. We utilized continuous-time movement models paired with generalized linear mixed-effects modeling to describe seasonal resource selection at second and third orders. At both scales of selection and throughout the year, female elk selected reclaimed surface mines, conifer forests, ridgetops, and areas with lower terrain roughness, while avoiding mixed hardwood and oak (Quercus spp.) forests. Unmined open land was only selected at the third order during periods of forage scarcity (i.e., winter) and increased metabolic requirements (i.e., late gestation). Although surface coal mining leaves legacy environmental impacts on the landscape, management of these sites provides benefits to elk and maintains open habitat that is otherwise limited. Full article

This study focuses on the Dabie Mountain Comprehensive Station in Anhui Province, constructing a multi-scale analytical framework and integrating remote sensing and socio-economic data to systematically assess the spatiotemporal evolution of ecosystem service bundles (ESBs) and landscape ecological risks using SOFM, XGBoost, and SHAP models. The research categorizes ecosystem service functions into four types: water conservation core areas, carbon storage–habitat optimization areas, carbon storage–water production composite areas, and multifunctional synergy areas. From 2013 to 2023, the proportion of multifunctional synergy areas increased from 39.85% to 42.86%, while carbon storage-habitat optimization areas and water conservation core areas decreased by 28,035.47 hm² and 2118.8 hm², respectively, indicating significant spatial restructuring of regional ecosystem service functions. The landscape ecological risk exhibits a pattern of “medium risk dominance with high-low polarization,” where high-risk areas overlap with urban expansion zones, and low-risk areas are concentrated in ecological conservation zones. Quantitative analysis reveals that climatic factors (e.g., annual precipitation) dominate the risk patterns in water conservation core areas and ecological conservation zones, topographic factors (e.g., elevation) influence regional spatial differentiation, and socio-economic factors (e.g., nighttime light index) significantly affect agricultural production core areas. The findings elucidate the evolutionary patterns of ecosystem service functions and the mechanisms of risk formation in the Dabie Mountain region, providing a scientific basis and technical support for regional land use optimization, ecosystem function enhancement, and ecological security assurance. Full article

Live Fuel Moisture Content (LFMC) is a key determinant of vegetation flammability and fire behaviour, yet LFMC products have traditionally relied on coarse-resolution sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS, 500 m), limiting their utility for fine-scale fire management. This study introduces the first continental-scale operational LFMC product for Australia derived from Sentinel-2 imagery at 20 m resolution. We developed a Random Forest regression model trained on approximately 680,000 paired Sentinel-2 reflectance and MODIS-LFMC samples (2015–2022) to emulate outputs from the Australian Flammability Monitoring System (AFMS), a MODIS-based pre-operational LFMC product. Model evaluation against AFMS showed strong agreement for grasslands (R² = 0.83, RMSE = 32.45%) and moderate performance for forests (R² = 0.43, RMSE = 20.84%) and shrublands (R² = 0.21, RMSE = 10.28%). Validation using 2279 in situ LFMC measurements from Globe-LFMC 2.0 indicated improved accuracy at homogeneous sites (NDVI CV ≤ 20th percentile: R² = 0.42, RMSE = 31.39%). Additionally, when validating with a dedicated field campaign specifically designed for Sentinel-2 LFMC assessment, the model achieved its highest accuracy (R² = 0.53, RMSE = 32.14%), highlighting the importance of tailored ground protocols for satellite product validation. Predicted LFMC also reproduced observed seasonal dynamics at sites with frequent field monitoring. Despite variability across vegetation types, the Sentinel-2 LFMC product effectively captured spatial patterns and seasonal dynamics, providing a step change in monitoring vegetation moisture at landscape scales. This high-resolution dataset offers actionable intelligence for prescribed burning, fuel treatment planning, and fire behaviour modelling in fire-prone environments. Full article

This study developed and tested a neighborhood-scale framework that integrates unmanned aerial vehicle (UAV)-based multispectral mapping and georeferenced socioeconomic data to identify inequities in urban green infrastructure and translate them into an operational prioritization tool for inclusive planning. Using object-based image analysis, impervious surfaces, low vegetation, and woody vegetation (trees and shrubs) were mapped across 33 Neighborhood Units in Temuco, Chile, and landscape metrics describing dominance, edge, isolation/connectivity, and diversity were derived. Socioeconomic conditions were summarized through Principal Component Analysis, and their relationships with vegetation metrics were evaluated using Generalized Additive Models. The results revealed strongly nonlinear and metric-specific associations, with the most robust relationships observed for woody-structure metrics, particularly total woody edge and built-environment isolation, whereas landscape diversity showed weaker but still significant dependence on resource-access gradients. To support inclusive planning, a dimensionless Green Infrastructure Prioritization Index (GIPI) was computed by combining standardized green deficit and standardized social vulnerability with equal weights. GIPI values ranged from 0.318 to 0.740 (median = 0.528), identifying 11 high-priority units characterized by higher social vulnerability and less favorable woody structure, including lower largest-patch dominance and greater isolation. Sensitivity analyses varying the deficit weight from 0.30 to 0.70 showed that 10 of the 11 high-priority units remained in the same class in at least 80% of weighting scenarios, indicating a stable priority set. Further classification of high-priority units according to dominant deficit type supported a staged intervention strategy, in which woody canopy is first increased in deficit nodes and subsequently reinforced through corridor-oriented greening to improve structural connectivity. These findings demonstrate the value of coupling fine-scale vegetation mapping with socioeconomic gradients to support more equitable urban green infrastructure planning. Full article

Forest structure is fundamental for linking ecological processes with management outcomes, and it influences key ecosystem services. However, the high cost and complexity of field data collection often limit the application of structural indices to small-scale studies, constraining operational assessments of forest multifunctionality. This study develops and tests an operational indicator of forest multifunctionality based on the structural heterogeneity index derived from forest management plans (FMPs). We analyzed the dendrometric data from 134 management units across 15 FMPs in the Lombardy region (Italy). Horizontal diversity was quantified using a Gini-based index, calculated from tree diameter-class distributions and combined with stand age, timber stock, and tree density using principal component analysis. Two orthogonal gradients emerged: a productivity gradient and a maturity–structural heterogeneity gradient. Generalized linear mixed models were used to assess their effects on carbon sequestration, timber yield, and touristic–recreational value. Structural heterogeneity was positively associated with all three functions, while productivity showed contrasting effects, particularly a negative relationship with recreational value. These results demonstrate that structural complexity and productivity are not necessarily in conflict and highlight the potential of FMPs as cost-effective data sources for operational, landscape-scale assessments of forest multifunctionality. Full article

Measuring the service levels and spatial equity of urban parks constitutes a core research topic within the field of environmental justice. Against the backdrop of low-carbon urban transformation and sustainable development, this study constructs an ecological supply indicator calculation model for parks based on landscape ecology theory. Leveraging spatio-temporal big data such as Points of Interest (POI) and second-hand property transactions, it establishes a demand evaluation indicator system centered on human activity intensity. The study employs the Gini coefficient and location entropy to gauge the spatial equity of park supply–demand balance, utilizing the Z-score method to classify supply–demand matching types. An empirical case study is conducted in Shenzhen. Findings indicate that despite Shenzhen possessing abundant global-scale park resources, a Gini coefficient of 0.489 reveals significant deficiencies in the equitable provision of park services, with spatial distribution exhibiting pronounced social stratification. Specifically: (1) location entropy values exhibit an east-high, west-low spatial pattern; (2) areas with high location entropy are predominantly concentrated in Dapeng New District, rich in green space resources, where supply exceeds demand, creating an imbalance; and (3) areas with low locational entropy values are predominantly distributed in industrial clusters such as western Bao’an and western Longgang, exhibiting contradictory characteristics of low supply and high demand. Overall, the distribution of park and green space resources exhibits a polarized pattern. Full article

In road planning and landscape design, data collection emphasizes existing site conditions, particularly in projects involving modifications rather than new construction, as such data directly inform subsequent planning decisions. Beyond conventional surveying techniques, large-scale street-region digital elevation models can be generated using aerial imagery acquired from unmanned aerial vehicles. The point clouds derived from these aerial photographs provide a basis for constructing spatial models applicable to street landscape and road planning. In this study, aerial data were processed using Pix4D software 4.8.4 to generate the initial spatial model, which was subsequently integrated into a building information modeling-based design framework in Autodesk Revit 2022. This approach enabled rapid and precise design outputs, while the resulting BIM model was further applied to mapping applications to establish a foundational database for regional public works. Full article

A major limitation in studying gastroesophageal adenocarcinoma (GEA) has been the lack of reliable models that represent the disease’s complexity. We present lessons learned from a comprehensive large-scale biobanking effort combining traditional sample collection with several in vitro models, including 3-dimensional patient-derived organoids (PDOs), 2-dimensional cancer-associated fibroblasts (CAFs), tumor-infiltrating lymphocytes (TILs), and/or in vivo xenografts. This initiative started in 2018, integrating multiple advanced ex vivo models such as PDOs, patient-derived xenografts (PDXs), and organoids (PDXOs). This unique resource now includes tumor avatars from over 380 consented patients, making it the world’s largest living GEA biobank. We achieved a >90% success rate in creating per-patient models, including 227 tumor-derived and 203 neighboring normal PDOs. These organoids accurately mirror key features of the original tumors, such as their histology (e.g., microsatellite instability), mutations, and drug response across treatment points. Notably, PDOs can predict individual patient responses to chemotherapy within five weeks, underscoring their clinical relevance. Furthermore, high-throughput drug screening on PDO subsets with known genetic landscapes generates personalized chemosensitivity profiles for 22 drugs. Through a process of continued refinement of culture techniques and tumor sampling approach, our large-scale comprehensive collection of GEA avatars represents a unique and valuable preclinical experimental resource for precision oncology. Full article