Search Results (10,120)

Background: Accurate femoral localization is a critical factor influencing graft length-change behavior in medial patellofemoral ligament reconstruction (MPFLR). However, the commonly used Schöttle point is derived from static radiographs and does not account for subject-specific patellofemoral kinematics during active knee motion. In this study, we integrated four-dimensional computed tomography (4D-CT) with constrained optimization to establish a motion-informed, patient-specific femoral localization framework. Methods: A total of 1382 4D-CT knee datasets were screened, and 58 knees were selected for detailed kinematic modeling. Subject-specific femoral and patellar point clouds were reconstructed from time-resolved CT data acquired during voluntary knee flexion. Within a predefined 5–15 mm neighborhood of the Schöttle point, a constrained sequential quadratic programming (SQP) approach was applied to identify an individualized femoral point (I-point) that minimized MPFL length variability while enforcing a femoral-surface constraint. Results: Compared with the Schöttle point, the I-point demonstrated a distinct spatial distribution, characterized primarily by a proximal shift along the femoral axis (PERMANOVA pseudo-F = 4.457, p = 0.006). Across 0−90° of knee flexion, the I-point was associated with reduced MPFL length variation and approached a relatively stable length-change profile near mid-flexion. Conclusions: These findings indicate that integrating 4D-CT-derived kinematics with constrained optimization can provide quantitative, imaging-based, motion-informed guidance for patient-specific femoral localization. This imaging-based framework may serve as a preoperative decision-support tool for personalized MPFLR planning. Full article

Resilience to climate change is a complex concept, especially in metropolitan areas where diverse services and stakeholders interact. Promoting sustainable climate adaptation, a resilience assessment method focused on regional areas and nature-based solutions is presented, along with its open-access, web-based platform, supporting resilience assessment, planning, and monitoring. Floods, droughts, heat or cold waves, windstorms, and forest fires can be assessed. A framework for holistic assessment and other framework, addressing critical infrastructure, are integrated. Four resilience dimensions are assessed: organizational (governance, social aspects, finance); spatial (exposure, impacts, and mapping); functional (service management, interdependencies); and physical (infrastructure robustness, redundancy). Strategic services comprise, e.g., water, waste, and natural areas. Resilience capacities, e.g., to prevent, respond, and recover from disruptions, are also assessed. The paper emphasizes new developments and assessment. Practical step-by-step guidance aligned with assessment purposes is included, aiming to address observed limitations (e.g., fragmented service provision, communication silos, data constraints). Overall results of a Spanish metropolitan area (AMB) and an exploratory application to an Austrian rural case (SLR) are also presented. Following the guidelines, AMB progressed from an essential to a comprehensive assessment. Overall, almost 1/3 of the metrics are advanced or progressing. SLR assessed its resilience capabilities regarding electrical infrastructure. Full article

Objective: We aimed to evaluate the morphometric characteristics of the internal acoustic meatus (IAM) using high-resolution computed tomography (CT), with emphasis on sex- and age-related differences, with particular emphasis on the IAM orientation angle as a less-studied spatial parameter and its potential clinical and forensic relevance. Methods: Temporal bone CT scans of 162 patients (94 females, 68 males; age 1–77 years) were retrospectively analyzed. Measurements included the IAM inlet diameter, length, mid-diameter, lateral angle (LA), and orientation angle. Inter-observer agreement was assessed in 30 randomly selected cases. Morphometric parameters were compared by sex and age using t-tests and Mann–Whitney U tests. Results: Mean IAM lengths were 11.0 mm (right) and 11.1 mm (left), and the mean mid-diameter was 4.2 mm bilaterally. IAM lengths and diameters showed no significant sex- or age-related differences (p > 0.05). In contrast, LA and orientation angle differed significantly by sex (p < 0.05), with females showing higher LA values, which may influence posterior fossa surgical exposure. Conclusions: IAM size parameters are largely independent of sex and age, whereas lateral and orientation angles exhibit sex-related variation. Preoperative evaluation of IAM orientation on CT can support skull base surgical planning, and LA may provide supportive morphometric information in forensic contexts, although it should not be considered a standalone sex classification parameter. Full article

This study presents the results of comprehensive functional-spatial analyses conducted using cellular models in relation to the cities of the GZM Metropolis and its surroundings. The Abbreviation “GZM” stands for Górnośląsko-Zagłębiowska Metropolia, due to its location, which in English has been recognized as the GZM Metropolis. The GZM Metropolis, the largest metropolitan area in Poland, has a complex administrative and spatial structure that includes 41 very diverse municipalities, which poses a significant challenge in interpreting data and understanding its complexity. The research was conducted by a multi-person and interdisciplinary team using various tools, including geographic information systems (GIS) and statistical data. The spatial models built on the basis of the collected data were visualized using augmented reality tools to facilitate data interpretation. Special attention was paid to environmental aspects, especially blue-green infrastructure, which plays a key role in maintaining this heavily urbanized area. Furthermore, the authors developed urbanization scenario models for the GZM Metropolis based on their own approaches to cellular modeling and examined the integration of artificial intelligence techniques to further refine these forecasts. Full article

Surface subsidence has grown to be a major geological problem for big and medium-sized cities in the context of urbanization and climate change. Changchun, a city of moderate size and rapid development, was chosen as the study region for this project. The Enhanced Permanent Scatterer Interferometric Synthetic Aperture Radar (E-PS-InSAR) technique was used based on Sentinel-1A imagery to gather time-series surface deformation information in order to perform long-term, high-precision monitoring and a mechanistic study of surface deformation in urban–rural integration areas. Subsequently, temperature and land-use type data were then integrated for a thorough investigation using techniques including correlation analysis and functional fitting. The following are the primary conclusions: (1) The E-PS-InSAR technique integrating both PS and DS targets can significantly improve the density of monitoring points compared to traditional methods, providing the complete spatial coverage. (2) Changchun has an average annual subsidence rate of −0.14 mm and an average cumulative subsidence of −0.08 mm. The highest cumulative subsidence is up to −41.31 mm, and the maximum subsidence rate is −17.27 mm/yr. (3) Surface subsidence was correlated with land use types, and cultivated land was the primary contributor to subsidence. (4) Surface subsidence exhibits distinct seasonal fluctuations, and climatic factors exhibit a lagged influence on surface subsidence. These results are crucial for safe infrastructure operation, urban planning, and promptly preventing geological dangers in mid-sized cities. Full article

The Incompatible Insect Technique (IIT) is a species-specific, eco-friendly mosquito control method that relies on releasing Wolbachia-infected males, which induce cytoplasmic incompatibility (CI), rendering eggs inviable when mating with wild females. Aiming at optimizing IIT protocols in terms of cost-effectiveness, data on incompatible male dispersal and survival and the distance- and time-related impact of induced sterility are fundamental. This study plans to fill this gap and reports findings from a two-year field trial (2022–2023) at the ENEA-Casaccia Research Center, based on single-spot releases of incompatible Aedes albopictus males (ARwP strain). Male releases were carried out in late September 2022 (~15,000 released males) and the early Ae. albopictus season (at the end of June 2023; ~24,000 released males). Fifty-eight ovitraps were located at a 20–900 m distance from the ARwP release spot and were monitored weekly from May to November to assess egg hatching rates and measure CI effects in relation to both distance and time. Following the 2023 release, samples of adults were collected at increasing distances from the release site and at multiple post-release time points to assess, individually, wild female fertility and ARwP male dispersal and survival using Wolbachia as a genetic marker. Statistical analyses revealed that: (a) the highest reduction in the egg hatching was found within 100 m from the release spot (46.5% and 19.9%, respectively, in 2022 and 2023) but remained significant even at greater distances (29.9% and 7.7% at 300 m, respectively, in 2022 and 2023); (b) accordingly, the highest reduction in the wild female fertility occurred within 100 m from the release spot (47.3%), but similar effects were recognizable up to 600 m; (c) the overflooding ratio of the ARwP males did not significantly differ between 3 and 11 days after the release, with ARwP males remaining active up to 18 days and dispersing as far as 400 m. These results demonstrate the potential of localized, non-inundative IIT trials to furnish clues for the setup of spatially optimized release strategies, especially in scaled-up applications. The study also emphasizes the need for standardized assessment tools and further research regarding environmental and behavioral factors influencing long-term suppression outcomes. Full article

Accurate building footprint is a fundamental element of large-scale base maps, which serve as critical inputs for urban planning, infrastructure development, environmental monitoring, and disaster management. While building footprint extraction and geometric regularization have been widely studied, their combined application for automated, large-scale basemap generation using very-high-resolution satellite imagery has received limited attention. To address this gap, this study proposes an integrated framework that leverages deep learning and geometric regularization to efficiently extract and refine building footprints for large-scale base maps. The framework first enhances spectral, spatial, and textural features of very-high-resolution satellite imagery through pan-sharpening, NDVI computation, GLCM-based texture analysis, and PCA. A Mask R-CNN model is then trained on multi-band imagery to segment building footprints, followed by geometric regularization to simplify and align polygons along dominant structural orientations. Object-based evaluation on ground-truth buildings demonstrates high performance, with 97.6% precision, 91.6% recall, and a 94.5% F1-score. The proposed systematic framework substantially reduces production time compared to manual stereo-plotting, requiring less than an hour per 5.29 km² map sheet in operational production, representing a more than 35-fold efficiency gain. While minor geometric inaccuracies and merged adjacent buildings persist, the methodology offers a robust, scalable, and efficient approach to support large-scale base map production. Full article

Urban areas worldwide face significant pressure from population growth and urban expansion, resulting in habitat loss. Urban planners need to develop a comprehensive strategy for protecting, restoring and enhancing natural heritage (such as natural features and assets), at the municipal and regional levels. Here, we propose an approach to design a Natural Heritage System (NHS) that interconnects natural features and areas. This resulting NHS aims to guide and prioritize the protection, restoration, and enhancement of ecological areas and their functions. The NHS integrates terrestrial and aquatic ecosystem functions for conservation planning. We leverage the Marxan optimization tool to identify target areas using 36 ecological features. We compare three spatial scenarios: regional-scale, watershed-scale, and a hybrid approach. We found that the hybrid scenario proved to be the most effective, covering 52% of the jurisdiction. Then, we classified the target areas into three tiers of the NHS: (1) existing natural cover (23.4%), (2) potential natural cover (12.3%), and (3) contributing areas (16.3%). Contributing areas represent additional parts of the NHS within developed or partly developed landscapes to support overall NHS health and ecological function. These tiers allow for tailored management actions: protection of existing natural cover and restoration of potential natural cover. Altogether, the areas identified for the NHS by Marxan provide a strong, science-based framework to address urbanization impacts and support long-term implementation of biodiversity and urban sustainability solutions. It also provides enhancement opportunities through green infrastructure in contributing areas using nature-based solutions aiming to conserve biodiversity in urban areas. Full article

This study developed an integrated vision–actuation system for precision weeding in indoor soil bin environments, with cabbage as a case example. The system integrates lightweight object detection, 3D co-ordinate mapping, path planning, and a three-axis synchronized conveyor-type actuator to enable precise weed identification and automated removal. By integrating ECA and CBAM attention mechanisms into YOLO11, we developed the YOLO11-WeedNet model. This integration significantly enhanced the detection performance for small-scale weeds under complex lighting and cluttered backgrounds. Based on the optimal model performance achieved during experimental evaluation, the model achieved 96.25% precision, 86.49% recall, 91.10% F1-score, and a mean Average Precision (mAP@0.5) of 91.50% calculated across two categories (crop and weed). An RGB-D fusion localization method combined with a protected-area constraint enabled accurate mapping of weed spatial positions. Furthermore, an enhanced Artificial Hummingbird Algorithm (AHA⁺) was proposed to optimize the execution path and reduce the operating trajectory while maintaining real-time performance. Indoor soil bin tests showed positioning errors of less than 8 mm on the X/Y axes, depth control within ±1 mm on the Z-axis, and an average weeding rate of 88.14%. The system achieved zero contact with cabbage seedlings, with a processing time of 6.88 s per weed. These results demonstrate the feasibility of the proposed system for precise and automated weeding at the cabbage seedling stage. Full article

Progressive climate change, intensified urbanization, and deteriorating urban environmental quality pose significant challenges for compact mid-sized city centers, where limited land availability and strong investment pressure hinder the development of green spaces. In this context, green and blue infrastructure (GBI) is increasingly seen as a key element of climate change adaptation strategies and strengthening the resilience of cities. This study aims to assess the state of GBI in the city center of Rzeszów and identify the opportunities for its integration into a coherent and multifunctional public space system. The research was conducted using a case study method combining GIS spatial analyses, remote sensing data (NDVI index), an assessment of the accessibility of green spaces according to the 3–30–300 rule, an expert assessment of the quality of public spaces, and field visits to the selected areas. An analysis of changes in vegetation cover between 2016 and 2024 showed a systematic decline in the proportion of green areas and insufficient tree cover and continuity in the GBI system. The results indicate that, despite the relatively good accessibility of larger green areas within a 300 m radius, the city center does not meet the key criteria for tree visibility, tree canopy coverage, and the creation of a coherent GBI system. The areas with the greatest integration potential were identified as the Wisłok River valley, marginal spaces, interiors between blocks, and green microforms, such as pocket parks, rain gardens, and linear greenery. The results obtained form the basis for formulating planning recommendations to support the development of GBI in densely built-up city centers. Full article

Conventional infrastructure appraisal in Africa prioritizes short-term economic performance while insufficiently accounting for the environmental conditions that govern long-term sustainability, spatial equity, and development resilience. To address this gap, this study develops an explicitly SDG-oriented spatial–ecological framework to examine how environmental quality conditions the economic returns of large-scale infrastructure investments under corridor-based development. The primary objective is to quantify infrastructure–environment complementarity and identify ecological thresholds regulating spatial spillovers and investment effectiveness along Tanzania’s Belt and Road Initiative (BRI) corridors. High-resolution remote sensing and spatially explicit socioeconomic data for 2012–2023 are integrated within a spatial econometric design. A Spatial Durbin Model (SDM) incorporating the Normalized Difference Vegetation Index (NDVI) is estimated to capture non-linear interaction effects, with economic activity proxied by Night-Time Light (NTL) intensity across 2680 corridor grid cells. The results identify a statistically robust ecological threshold at NDVI = −0.8σ, beyond which infrastructure investments shift from low to high economic effectiveness. A strong positive infrastructure–environment interaction (β = 6.44, p < 0.001) indicates that environmental quality functions as a productive modulating factor rather than a passive constraint. Spatial classification shows that 63% of corridor areas are investment-ready, while 15% require ecological restoration prior to effective infrastructure deployment. Although institutional quality and long-term post-construction dynamics are not explicitly modeled, the framework provides a replicable and policy-relevant decision-support tool, offering actionable guidance for aligning corridor development with SDGs 9, 11, and 13 and advancing sustainable infrastructure planning in the Global South. Full article

This article examines the everyday dynamics of informal activities in touristified urban environments through a qualitative case study of Porto, Portugal. Drawing on an urban ethnography combining observation and semi-structured interviews, we analyse how individuals providing tourism-related services perceive their role within informality, how they articulate their agency, and how their practices contribute to the everyday production of the tourist experience. The study shows that engagement in informal tourism work is structured by intersecting legal, economic and institutional constraints that channel professional trajectories into unregulated or semi-recognised forms of labor. Individuals display significant agency through adaptive strategies, craft-based skills and relational networks that enable them to navigate surveillance, seasonality and spatial exclusion. We argue that these practices operate as a form of urban tourism scaffolding, to conceptualise informal tourism practices as a contingent support structure that sustains tourist experiences beyond formal planning and infrastructure. Although situated in precarity and vulnerability, these practices produce structural effects on the urban tourism offer by filling gaps, organizing encounters and animating public space. By conceptualising informal tourism work as a processual and relational support structure rather than as marginal spontaneity or residual activity, the article highlights the need to reconsider informal labour as a constitutive dimension of tourist cities. Full article

Integrating artificial intelligence (AI) into residential architectural design faces challenges due to fragmented workflows and the lack of localized datasets. This study proposes the CorbuAI framework, hypothesizing that a multimodal AI system integrating Pix2pix-GAN and Stable Diffusion (SD) can streamline the transition from floor plan generation to elevation and interior design within a specific regional context. We developed a custom dataset featuring 2335 manually refined Chinese residential floor plans and 1570 elevation images. The methodology employs a specialized U-Net V2.0 generator for functional layout synthesis and an SD-based model for stylistic transfer and elevation rendering. Evaluation was conducted through both subjective professional scoring and objective metrics, including the Perceptual Hash Algorithm (pHash). Results demonstrate that CorbuAI achieves high accuracy in spatial allocation (scoring 0.88/1.0) and high structural consistency in elevation generation (mean pHash similarity of 0.82). The framework significantly reduces design iteration time while maintaining professional aesthetic standards. This research provides a scalable AI-driven methodology for automated residential design, bridging the gap between schematic layouts and visual representation in the Chinese architectural context. Full article

Rapid urban expansion in tropical coastal cities has intensified vegetation moisture stress, compromising urban resilience and ecological stability. This study investigates the spatial drivers of the Moisture Stress Index (MSI) in Makassar City, Indonesia, by integrating biophysical indicators and land-use characteristics through multi-scale regression analyses. Utilizing dry-season satellite composites (May–August 2025), the research derived Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Built-up Index (NDBI). MSI was modeled using Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) across 240 m, 480 m, and 960 m grids. Results indicate that MSI is highly sensitive to urban morphology and land-use configuration. High moisture stress was concentrated in commercial–industrial and dense residential zones characterized by extreme population densities exceeding 28,000 people/km² and elevated NDBI. In contrast, agricultural zones and open/green spaces provided significant cooling and moisture retention. Comparative performance analysis reveals that the local GWR model significantly outperformed the global OLS model, achieving a substantial reduction in AICc (−10,475.81) and resolving significant spatial autocorrelation to achieve random residuals (z-score = 1.55). The study further confirms that NDBI is the most robust biophysical predictor of MSI. Spatial heterogeneity analysis demonstrated that land-use influences are geographically contingent, with institutional areas showing varied effects based on campus design and canopy presence. These findings emphasize the necessity of scale-aware, climate-adaptive urban planning and demonstrate that GWR provides a high-fidelity tool for identifying neighborhood-level “micro-hotspots” overlooked by global modeling frameworks. Full article

The emergence of the Smart Cities paradigm and the rapid expansion and integration of Internet of Things (IoT) technologies within this context have created unprecedented opportunities for high-resolution behavioral analytics, urban optimization, and context-aware services. However, this same proliferation intensifies privacy risks, particularly those arising from cross-modal data linkage across heterogeneous sensing platforms. To address these challenges, this paper introduces a comprehensive, statistically grounded framework for generating synthetic, multimodal IoT datasets tailored to Smart City research. The framework produces behaviorally plausible synthetic data suitable for preliminary privacy risk assessment and as a benchmark for future re-identification studies, as well as for evaluating algorithms in mobility modeling, urban informatics, and privacy-enhancing technologies. As part of our approach, we formalize probabilistic methods for synthesizing three heterogeneous and operationally relevant data streams—cellular mobility traces, payment terminal transaction logs, and Smart Retail nutrition records—capturing the behaviors of a large number of synthetically generated urban residents over a 12-week period. The framework integrates spatially explicit merchant selection using K-Dimensional (KD)-tree nearest-neighbor algorithms, temporally correlated anchor-based mobility simulation reflective of daily urban rhythms, and dietary-constraint filtering to preserve ecological validity in consumption patterns. In total, the system generates approximately 116 million mobility pings, 5.4 million transactions, and 1.9 million itemized purchases, yielding a reproducible benchmark for evaluating multimodal analytics, privacy-preserving computation, and secure IoT data-sharing protocols. To show the validity of this dataset, the underlying distributions of these residents were successfully validated against reported distributions in published research. We present preliminary uniqueness and cross-modal linkage indicators; comprehensive re-identification benchmarking against specific attack algorithms is planned as future work. This framework can be easily adapted to various scenarios of interest in Smart Cities and other IoT applications. By aligning methodological rigor with the operational needs of Smart City ecosystems, this work fills critical gaps in synthetic data generation for privacy-sensitive domains, including intelligent transportation systems, urban health informatics, and next-generation digital commerce infrastructures. Full article