Search Results (86)

The principles of fractal geometry have revolutionized the characterization of complex geometric objects since Benoit Mandelbrot’s groundbreaking work. Richardson’s method for determining the fractal dimension of boundaries laid the groundwork for Mandelbrot’s later developments in fractal theory. Despite extensive research, challenges remain in accurately calculating fractal dimensions, particularly when dealing with digital images and their inherent limitations. This study examines the numerical artifacts introduced by Richardson’s method when applied to the Von Koch Island, a classic fractal curve, and proposes a novel approach for computing fractal dimensions in image analysis. The Koch snowflake serves as a key example in this analysis; it serves to assess the algorithm of fractal dimension calculation as his theoretical one is known. However, there is a fundamental difference between the theoretical calculation of fractal dimension and the actual calculation of the fractal dimension from digital images with a given resolution undergoing discretization. We propose eight different calculation methods based on Richardson’s area–perimeter relationship: the Self-Convolution Patterns Research (SCPR) method accurately estimates the fractal dimension, as the 95% confidence interval includes the theoretical dimension. Full article

The integration of traditional geomorphological approaches with advanced artificial intelligence techniques represents a promising frontier in flood risk assessment for arid regions. This study presents a comprehensive analysis of the Wadi Ranuna basin in Medina, Saudi Arabia, combining detailed morphometric parameters with advanced Geospatial Artificial Intelligence (GeoAI) algorithms to enhance flood susceptibility modeling. Using digital elevation models (DEMs) and geographic information systems (GISs), we extracted 23 morphometric parameters across 67 sub-basins and applied XGBoost, Random Forest, and Gradient Boosting (GB) models to predict both continuous flood susceptibility indices and binary flood occurrences. The machine learning models utilize morphometric parameters as input features to capture complex non-linear interactions, including threshold-dependent relationships where the stream frequency impact intensifies above 3.0 streams/km², and the compound effects between the drainage density and relief ratio. The analysis revealed that the basin covers an area of 188.18 km² with a perimeter of 101.71 km and contains 610 streams across six orders. The basin exhibits an elongated shape with a form factor of 0.17 and circularity ratio of 0.23, indicating natural flood-moderating characteristics. GB emerged as the best-performing model, achieving an RMSE of 6.50 and an R² value of 0.9212. Model validation through multi-source approaches, including field verification at 35 locations, achieved 78% spatial correspondence with documented flood events and 94% accuracy for very high susceptibility areas. SHAP analysis identified the stream frequency, overland flow length, and drainage texture as the most influential predictors of flood susceptibility. K-Means clustering uncovered three morphometrically distinct zones, with Cluster 1 exhibiting the highest flood risk potential. Spatial analysis revealed 67% of existing infrastructure was located within high-risk zones, with 23 km of major roads and eight critical facilities positioned in flood-prone areas. The spatial distribution of GBM-predicted flood susceptibility identified high-risk zones predominantly in the central and southern parts of the basin, covering 12.3% (23.1 km²) of the total area. This integrated approach provides quantitative evidence for informed watershed management decisions and demonstrates the effectiveness of combining traditional morphometric analysis with advanced machine learning techniques for enhanced flood risk assessment in arid regions. Full article

Urban green spaces (UGS) serve as critical mitigators of urban heat islands (UHIs), yet the scale-dependent mechanisms through which UGS morphology regulates thermal effects remain insufficiently understood. This study investigates the multi-scale relationships between UGS spatial patterns and cooling effects in Macao, employing morphological spatial pattern analysis (MSPA) to characterize UGS configurations and geographically weighted regression (GWR) to examine city-scale thermal interactions, complemented by patch-scale buffer analyses of area, perimeter, and landscape shape index effects. Results demonstrate that high-UGS-integrity areas significantly enhance cooling capacity (area with proportion of core ≥35% showing optimal performance), while fragmented elements (branches, edges) exacerbate UHIs, with patch-scale analyses revealing nonlinear threshold effects in cooling efficiency. A tripartite classification of UGS by cooling capacity identifies strong mitigation types with optimal shape metrics and cooling extents. These findings establish a tripartite UGS classification system based on cooling performance and identify optimal morphological parameters, advancing understanding of thermal regulation mechanisms in urban environments. This research provides empirical evidence for UGS planning strategies prioritizing core area conservation, morphological optimization, and seasonal adaptation to improve urban climate resilience, offering practical insights for sustainable development in high-density coastal cities. Full article

Many researchers focus on improving reproductive health in sows and ensuring successful breeding by accurately identifying the optimal time of ovulation through estrus detection. One promising non-contact technique involves using computer vision to analyze temperature variations in thermal images of the sow’s vulva. However, variations in camera distance during dataset collection can significantly affect the accuracy of this method, as different distances alter the resolution of the region of interest, causing pixel intensity values to represent varying areas and temperatures. This inconsistency hinders the detection of the subtle temperature differences required to distinguish between estrus and non-estrus states. Moreover, failure to maintain a consistent camera distance, along with external factors such as atmospheric conditions and improper calibration, can distort temperature readings, further compromising data accuracy and reliability. Furthermore, without addressing distance variations, the model’s generalizability diminishes, increasing the likelihood of false positives and negatives and ultimately reducing the effectiveness of estrus detection. In our previously proposed methodology for estrus detection in sows, we utilized YOLOv8 for segmentation and keypoint detection, while monocular depth estimation was used for camera calibration. This calibration helps establish a functional relationship between the measurements in the image (such as distances between labia, the clitoris-to-perineum distance, and vulva perimeter) and the depth distance to the camera, enabling accurate adjustments and calibration for our analysis. Estrus classification is performed by comparing new data points with reference datasets using a three-nearest-neighbor voting system. In this paper, we aim to enhance our previous method by incorporating the mean pixel intensity of the region of interest as an additional factor. We propose a detailed four-step methodology coupled with two stages of evaluation. First, we carefully annotate masks around the vulva to calculate its perimeter precisely. Leveraging the advantages of deep learning, we train a model on these annotated images, enabling segmentation using the cutting-edge YOLOv9 algorithm. This segmentation enables the detection of the sow’s vulva, allowing for analysis of its shape and facilitating the calculation of the mean pixel intensity in the region. Crucially, we use monocular depth estimation from the previous method, establishing a functional link between pixel intensity and the distance to the camera, ensuring accuracy in our analysis. We then introduce a classification approach that differentiates between estrus and non-estrus regions based on the mean pixel intensity of the vulva. This classification method involves calculating Euclidean distances between new data points and reference points from two datasets: one for “estrus” and the other for “non-estrus”. The classification process identifies the five closest neighbors from the datasets and applies a majority voting system to determine the label. A new point is classified as “estrus” if the majority of its nearest neighbors are labeled as estrus; otherwise, it is classified as “non-estrus”. This automated approach offers a robust solution for accurate estrus detection. To validate our method, we propose two evaluation stages: first, a quantitative analysis comparing the performance of our new YOLOv9 segmentation model with the older U-Net and YOLOv8 models. Secondly, we assess the classification process by defining a confusion matrix and comparing the results of our previous method, which used the three nearest points, with those of our new model that utilizes five nearest points. This comparison allows us to evaluate the improvements in accuracy and performance achieved with the updated model. The automation of this vital process holds the potential to revolutionize reproductive health management in agriculture, boosting breeding success rates. Through thorough evaluation and experimentation, our research highlights the transformative power of computer vision, pushing forward more advanced practices in the field. Full article

Background/Objectives: Percutaneous electrolysis is a physiotherapeutic technique based on the application of galvanic current to induce structural and biochemical changes in musculoskeletal tissues. Although widely used in tendinopathies, its application in muscle tissue, particularly regarding optimal dosage, remains poorly understood. This study aimed to evaluate the dose-dependent effects of galvanic current on cadaveric muscle tissue (medial gastrocnemius) using quantitative ultrasound analysis, and to identify objective biomarkers to guide dosage. Methods: An experimental model was employed, applying galvanic current at varying intensities (0–10.0 mA) to 29 samples of cadaveric medial gastrocnemius. Quantitative ultrasound parameters were measured, including geometric and textural features. Correlation analyses and simple and multiple linear regressions were performed to assess the relationship between current intensity and ultrasound variables. Additionally, dose segmentation into three groups (low: 0–1.0 mA, medium: 1.0–4.0 mA, high: >4.0 mA) allowed for comparative statistical analysis using Kruskal–Wallis and post hoc Mann–Whitney U tests. Results: Significant dose–response relationships were observed in key ultrasound parameters, including A_Number, A_Area, A_Perimeter, and A_Contrast (p < 0.001). Regression analysis revealed that a combination of A_Area, A_Number, and A_Perimeter accounted for 66.7% of the variance in applied dose (R² = 0.667, p < 0.001), leading to the creation of the predictive variable Muscle_Electrolysis_Dose. Comparative analysis confirmed significant differences between low-, medium-, and high-dose groups, particularly between lower and higher doses. Conclusions: Quantitative ultrasound effectively detects structural changes in muscle tissue following percutaneous electrolysis. The results support the development of objective, image-based criteria for optimizing galvanic current dosage, enhancing the precision and personalization of treatment. Full article

In this research, different hydrological and hydraulic methods were employed to estimate the environmental flow demands of the Sofi Chay River, Iran. In total, 50 years (1969–2018) of flow data exhibited high variability with a mean annual flow of 9.37 m³/s and standard deviation of 42.15 m³/s. Hydrological techniques included Tennant, Flow Duration Curve, and Range of Variability Approach; recommended minimum flows ranged from 0.53 to 2.66 m³/s, respectively, or in other words, 10–50% of mean annual flow. In contrast, hydraulic techniques such as Wetted Perimeter, R2CROSS, and Hydraulic Habitat Simulation suggested higher flows of 1.60–5.38 m³/s, or 30–101% of mean annual flow. The Hydraulic Habitat Simulation Method provided a maximum Weighted Usable Area for target species at the flow of 5.38 m³/s. Sediment analysis showed that there was a power relationship between discharge and SSC, where SSC = 14.23 × Q1^.68 and R² = 0.99. Integration of methods yielded a proposed environmental flow regime of base flows of 1.5–2.5 m³/s during the dry season and 3.0–5.0 m³/s during the wet season, with small floods contributing 15.0–20.0 m³/s and large floods > 35.0 m³/s to maintain channel morphology and ecosystem functions. After realizing the need to incorporate all the approaches in the environmental flow assessment, the hydraulic methods consistently recommended higher flows than the hydrologic methods. An adaptive management framework has been put forward for implementing and refining these recommendations to ensure long-term ecosystem health, coupled with meeting human water needs within the Sofi Chay River basin. Full article

Urban greenspace is an effective strategy to mitigate the urban heat island (UHI) effect. While its cooling effects are well-established, uncertainties remain regarding the combined impact of internal and external landscape patterns, particularly the role of morphological spatial patterns. Taking 40 urban greenspaces in Wuhan as the sample, this study quantified cooling effects from maximum and accumulative perspectives and investigated the impacts of internal and external landscape patterns. First, using land surface temperature (LST) data, four cooling indexes—greenspace cooling area (GCA), cooling efficiency (GCE), cooling intensity (GCI), and cooling gradient (GCG)—were quantified. Then, the relationships between these indexes and landscape patterns, including scale and landscape composition, morphological spatial pattern, and surrounding environmental characteristics, were investigated by correlation analysis and multiple stepwise regression. The results showed that the cooling effects of greenspace varied across different perspectives. Both greenspace area and perimeter exerted non-linear impacts on cooling effects, and morphological spatial pattern significantly influenced cooling effects. Core proportion was positively correlated with cooling effects, with an optimal threshold of 55%, whereas bridge and branch proportions had negative impacts. External landscape patterns, particularly the proportion of impervious surfaces and building coverage, also affected cooling effects. Additionally, cluster analysis using Ward’s system clustering method revealed five cooling bundles, indicating that urban greenspaces with diverse cooling needs exhibited different cooling effects. This study offers valuable insights for optimizing urban greenspace design to enhance cooling effects and mitigate UHI. Full article

This paper explores the application of automated reasoning tools, specifically those implemented in GeoGebra Discovery, to the perimeter-area inequality in triangles. Focusing on the computational complex and real algebraic geometry methods behind these tools, this study analyzes a geometric construction involving a triangle with arbitrary side lengths and area, investigating the automated derivation of the relationship between the area and perimeter of a triangle, and showing that only equilateral triangles satisfy the exact perimeter-area equality. The main contribution of this work is to describe the challenges, and potential ways to approach their solutions, still posed by the use of such automated, symbolic computation, methods in dynamic geometry, in particular concerning the discovery of loci of points that satisfy specific geometric conditions, suggesting relevant improvements for the future development of these symbolic AI-based educational tools in geometry. Full article

The intertidal ecosystem serves as a critical transitional zone between terrestrial and marine environments, supporting diverse biodiversity and essential ecological functions. However, these systems are increasingly threatened by climate change, rising sea levels, and anthropogenic impacts. Accurately mapping intertidal ecosystems and differentiating mangroves, salt marshes, and tidal flats remains a challenge due to inconsistencies in classification frameworks. Here, we present a high-precision mapping approach for intertidal ecosystems using multi-source satellite data, including Sentinel-1, Sentinel-2, and Landsat 8/9, integrated with the Google Earth Engine (GEE) platform, to enable the detailed mapping of intertidal zones across China–ASEAN. Our findings indicate a total intertidal area of 73,461 km² in China–ASEAN, with an average width of 1.16 km. Analyses of patch area, abundance, and perimeter relationships reveal a power-law distribution with a scaling exponent of 1.52, suggesting self-organizing characteristics shaped by both natural and human pressures. Our findings offer foundational data to guide conservation and management strategies in the region’s intertidal zones and present a novel perspective to propel research on global coastal ecosystems. Full article

The positive relationship between species richness and area is a fundamental principle in ecology. However, this pattern deviates on small islands, where species richness either changes independently of area or increases at a slower rate—a phenomenon known as the Small-Island Effect (SIE). While the SIE has been well documented in natural ecosystem, its presence in highly fragmented and disturbed urban ecosystem remains unexplored, posing challenges for urban vegetation conservation. Urban remnant vegetation, isolated by surrounding infrastructures, preserves intact zonal vegetation characteristics, serves as a benchmark for restoring near-natural habitats and offers ideal conditions to test the existence of the SIE in urban area landscapes. In this study, we surveyed 17 remnant vegetation patches in Qingdao City, China. A total of 331 plants attributed to 255 genera in 81 families have been recorded. Firstly, by using six species–area relationship regression models testing the SIE for remnant vegetation with different plant life forms, we found the SIE in only woody plants, with the land surface area threshold ranging from 6.38 ha (tree) to 11.91 ha (shrub). Our finding revealed that the drivers of the SIE in shrubs were landscape shape index, perimeter–area ratio, and the proportion of sealed surfaces within the patch. For trees, the SIE was influenced by the distance to the source of species, GDP, night light intensity, and perimeter–area ratio. This finding justifies that conservation in urban planning, construction, and development should focus not only on protecting large areas but also on maintaining and promoting diverse habitats within these areas. At the same time, reducing anthropogenic disturbance and enhancing the connectivity of green spaces are important for the persistence of metacommunities and can contribute to the local species pool, thus potentially improving the ecological resilience of urban environments. Full article

To address the challenges of multi-energy coupling decision-making caused by the complex interactions and significant conflicts of interest among multiple entities in integrated energy systems, an energy management strategy for integrated energy systems with electricity, heat, and hydrogen multi-energy storage is proposed. First, based on the coupling relationship of electricity, heat, and hydrogen multi-energy flows, the architecture of the integrated energy system is designed, and the mathematical model of the main components of the system is established. Second, evaluation indexes in three dimensions, including energy storage device life, load satisfaction rate, and new energy utilization rate, are designed to fully characterize the economy, stability, and environmental protection of the system during operation. Then, an improved radar chart model considering multi-evaluation index comprehensive optimization is established, and an adaptability function is constructed based on the sector area and perimeter. Combined with the operation requirements of the electric–thermal–hydrogen integrated energy system, constraint conditions are determined. Finally, the effectiveness and adaptability of the strategy are verified by examples. The proposed strategy can obtain the optimal decision instructions under different operation objectives by changing the weight of evaluation indexes, while avoiding the huge decision space and secondary optimization problems caused by multiple non-inferior solutions in conventional optimization, and has multi-scenario adaptability. Full article

S. sphyraena is a widely distributed species with low commercial value and no sufficient scientific knowledge of its biology. In the present study, the age, growth, weight–length relationship, otolith morphometry, and reproduction of the species were investigated in the Eastern Ionian Sea for the first time. The von Bertalanffy growth function parameters were L_∞ = 63.65 cm, k = 0.14 year⁻¹ and t₀ = −2.01 years and Φ′ = 2.75. The negative allometric growth in weight was found. Slope b of the weight–length relationship was 2.634. For the otolith moprhometry, the variables radius, length, width, area, perimeter, roundness, circularity, form factor, rectangularity, and ellipticity were examined, which showed that the otolith shape is elongated with an elliptical and rectangular form. Six otolith variables (radius, length, width, area, perimeter, and ellipticity) showed a significant relationship with size. The sex ratio (females/males) was 1:0.74 (no statistically significant difference from 1:1), and the spawning season extended from April to June with peak values of GSI in May for females and April for males. The results of this work improve our knowledge of the species life cycle and provide basic information for species stock identification and fisheries management. Full article

Particulate pollutants, particularly PM_2.5 and PM₁₀, pose serious threats to human health and environmental quality. Therefore, effectively mitigating and reducing the concentrations of these pollutants is crucial for human survival and development. In this study, we analyzed the distribution characteristics of air particulate pollutants in a typical high-latitude city, extracted urban forest areas from high-resolution remote sensing images, and examined the changing characteristics of PM concentration and the relationship between landscape pattern indexes and PM at different scales. The results showed that the concentrations of PM_2.5 and PM₁₀ were highest in winter and lowest in summer. At the small scales of 0.5 km × 0.5 km to 1.5 km × 1.5 km, PM concentration decreased with the decrease in PARA (Perimeter–Area Ratio). At the mesoscales of 2 km × 2 km to 2.5 km × 2.5 km, both PARA and CIRCLE (Related Circumscribing Circle) were highly significant (p < 0.001) correlated with PM concentration. At the large scales of 3 km × 3 km to 4 km × 4 km, PARA and PAFRAC (Perimeter–Area Fractal Dimension) were positively correlated with PM concentration. Our study indicates that reducing the complexity of forest patches in small-scale planning can help mitigate particulate air pollution. In the medium scale of urban forest planning, the more regular the forest patch shape and the more similar the patch shape to the strip, the better PM can be alleviated, while in large-scale planning, increasing the forest area and making the patches more normalized and simplified can reduce PM concentration. Moreover, reducing the complexity of forest patches can significantly mitigate PM pollution at all scales. The results of this research provide theoretical support and guidance for improving air quality in urban forest planning at different scales. Full article

Otoliths are important structures for balance and hearing of fish and constitute a useful tool in fisheries science. This study provides, for the first time in the Mediterranean, information on the otolith morphometrics of Dentex maroccanus, collected from the South Aegean Sea, and enriches the existing information on its age and growth by sex. The otolith shape variables examined showed a more circular to square otolith shape, related to the body size. Significant differences between sexes were detected for the otolith Area, Diameter, Perimeter, and Radius. Exponential regressions were used to examine the relationship of the otolith variables with total body length, from which five showed a strong correlation (Diameter, Width, Radius, Area, and Perimeter). The eviscerated weight–length relationship exhibited an isometric growth for both sexes, whereas when total weight was applied, a positive allometric growth was found for females. Sagittal otolith readings revealed four age groups for females and five for males. A Bhattacharya method was used for age validation. Von Bertalanffy growth parameters were as follows: L∞ = 23.08, k = 0.27, t₀ = −1.93 for females and L∞ = 24.07, k = 0.24, t₀ = −2.26 for males. This research offers valuable biological information for Dentex maroccanus useful in fisheries science. Full article

Background/Objectives: Studies using optical coherence tomography angiography (OCTA) have revealed that individuals recovering from COVID-19 have a reduced retinal vascular density (VD) and larger foveal avascular zones (FAZs) than healthy individuals, with more severe cases showing greater reductions. We aimed to examine aspects of the retinal microvascularization in patients with post-COVID-19 condition (PCC) classified by COVID-19 severity and how these aspects relate to cognitive performance. Methods: This observational cross-sectional study included 104 PCC participants from the NAUTILUS Project, divided into severe (n = 59) and mild (n = 45) COVID-19 groups. Participants underwent cognitive assessments and OCTA to measure VD and perfusion density (PD) in the superficial capillary plexus (SVP) and FAZ. Analysis of covariance and partial Pearson and Spearman correlations were used to study intergroup differences and the relationships between cognitive and OCTA variables. Results: Severe PCC participants had significantly lower central (p = 0.03) and total (p = 0.03) VD, lower central (p = 0.02) PD measurements, and larger FAZ areas (p = 0.02) and perimeters (p = 0.02) than mild cases. Severe cases showed more cognitive impairment, particularly in speed processing (p = 0.003) and executive functions (p = 0.03). Lower central VD, lower central PD, and larger FAZ areas and perimeters were associated with worse executive function performance in the entire PCC sample and in the mild COVID-19 group. Conclusions: Retinal microvascular alterations, characterized by reduced VD and PD in the SVP and larger FAZ areas, were associated with cognitive impairments in PCC individuals. These findings suggest that severe COVID-19 leads to long-lasting microvascular damage, impacting retinal and cognitive health. Full article