Search Results (115)

This study introduces an innovative and scalable approach for automated road surface assessment by integrating Mobile Mapping System (MMS)-based LiDAR data analysis with an open-source WebGIS platform. In a U.S.-based case study, over 20 datasets were collected along Interstate I-65 in West Lafayette, Indiana, using the Purdue Wheel-based Mobile Mapping System—Ultra High Accuracy (PWMMS-UHA), following Indiana Department of Transportation (INDOT) guidelines. Preprocessing included noise removal, resolution reduction to 2 cm, and ground/non-ground separation using the Cloth Simulation Filter (CSF), resulting in Bare Earth (BE), Digital Terrain Model (DTM), and Above Ground (AG) point clouds. The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. DBSCAN parameter tuning was guided by silhouette scores, while model performance was evaluated using precision, recall, F1-score, and the Jaccard Index, benchmarked against reference data. Results demonstrate that RF consistently outperformed DBSCAN, particularly under intensity normalization, achieving Jaccard Index values of 94% for longitudinal and 88% for transverse cracks. A key contribution of this work is the integration of geospatial analytics into an interactive, open-source WebGIS environment—developed using Blender, QGIS, and Lizmap—to support predictive maintenance planning. Moreover, intervention thresholds were defined based on crack surface area, aligned with the Pavement Condition Index (PCI) and FHWA standards, offering a data-driven framework for infrastructure monitoring. This study emphasizes the practical advantages of comparing clustering and machine learning techniques on 3D LiDAR point clouds, both with and without intensity normalization, and proposes a replicable, computationally efficient alternative to deep learning methods, which often require extensive training datasets and high computational resources. Full article

This study analyzes the spatial aggregation and activity of the urban population in Almaty using anonymized population density data provided by a telecommunications operator and geographic data from OpenStreetMap. The study focuses on identifying stable zones of high population activity, which facilitates the optimization of transport routes, urban infrastructure planning, and the efficient allocation of city resources. The novelty of this work lies in the integration of aggregated spatiotemporal data with advanced clustering methods, including DBSCAN, KMeans++, and agglomerative clustering. The research methodology involves dividing the city into 500 × 500 m quadrants, calculating normalized population density metrics, and identifying high-activity clusters. Based on a comparative analysis of clustering algorithms, DBSCAN exhibited the highest clustering quality according to the silhouette coefficient and the Davies–Bouldin index, allowing for the identification of key zones of urban activity. The identified clusters were utilized to assess transport load, analyze disparities in the distribution of public transport stops, and develop recommendations to improve public transport accessibility in the most congested areas. The study’s findings are applicable not only to optimizing the transport network but also to addressing a broader range of urban planning challenges, including the strategic placement of infrastructure facilities and the management of population flows. The proposed methodology is scalable and can be adapted to other cities requiring effective tools for analyzing the spatiotemporal activity of urban populations. Full article

The vertical heterogeneous structures formed during the evolution of urban agglomerations, driven by globalization, pose challenges to traditional two-dimensional spatial analysis methods. This study addresses the vertical heterogeneity and spatial multiscale problem in three-dimensional urban space and proposes an adaptive framework that takes into account building height for multiscale clustering in urban areas. Firstly, we established a macro-, meso- and micro-level analysis system for the characteristics of urban spatial structures. Subsequently, we developed a parameter-adaptive model through a dynamic coupling mechanism of height thresholds and average elevations. Finally, we proposed a density-based clustering method that integrates the multiscale urban analysis with parameter adaptation to distinguish urban spatial features at different scales, thereby achieving multiscale urban regional delineation. The experimental results demonstrate that the proposed clustering framework outperforms traditional density-based and hierarchical clustering algorithms in terms of both the Silhouette Coefficient and the Davies–Bouldin Index, effectively resolving the problem of vertical density variation in urban clustering. Full article

Background/Objectives: Buffalo populations exhibit distinct genetic variations influenced by domestication history, geographic distribution, and selection pressures. This study investigates the genetic structure and differentiation of 11 buffalo populations, focusing on five loci related to milk protein (CSN1S1 and CSN3) and fat metabolism (LPL, DGAT1 and SCD). The aim is to assess genetic variation between river, swamp, and wild-type buffaloes and identify key loci contributing to population differentiation. Methods: Genetic diversity was analyzed through allele frequency distribution, the Hardy−Weinberg equilibrium testing, and observed (Ho) and expected heterozygosity (He) calculations. Population structure was assessed using principal component analysis (PCA), F_ST statistics, and phylogenetic clustering (k-means and UPGMA tree). The silhouette score (SS) and the Davies−Bouldin index (DBI) were applied to determine optimal population clustering. Results: Significant genetic differentiation was observed between river and swamp buffaloes (p < 0.001). DGAT1 and CSN3 emerged as key markers distinguishing buffalo types. The Italian Mediterranean buffalo exhibited the highest genetic diversity (Ho = 0.464; He = 0.454), while the Indonesian, Chinese, and Vietnamese populations showed low heterozygosity, likely due to selection pressures and geographic isolation. The global F_ST (0.2143; p = 0.001) confirmed moderate differentiation, with closely related populations (e.g., Nepal and Pakistan) exhibiting minimal genetic divergence, while distant populations (e.g., Egypt and Indonesia) showed marked differences, and the Romanian population showed a unique genetic position. Conclusions: These findings contribute to a deeper understanding of buffalo genetic diversity and provide a valuable basis for exploiting the potential of this species in the light of future breeding and conservation strategies specific for each buffalo type. Full article

The construction and operation of dams or weirs has been demonstrated to induce alterations in riparian vegetation, a critical factor in evaluating and sustaining ecosystem health and resilience. A notable instance of this phenomenon is evidenced by the implementation of multifunctional large weirs along the major rivers of South Korea from 2008 to 2012. This study examined the successional changes in riparian vegetation caused by weir construction and operation using multi-year data from a combination of remote sensing, based on the spectra of satellite images, and field surveys on vegetation and geomorphology in the Geumgang River. The exposure duration of the sandbars and the colonization time of riparian vegetation were estimated using the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI) from multispectral satellite imagery. The study found that the duration of exposure and the vegetation successional ages varied according to the construction and operation of the weirs. The Geumgang River vegetation was classified into ten plant communities using the optimal partitioning and optimal silhouette algorithms. The in situ changes in the vegetation were traced, and the successional ages of the classified vegetations were determined. Based on these findings, three successional pathways could be proposed: The first pathway is characterized by a transition from pioneer herbaceous plants and then tall perennial grasses to willow trees on the exposed sandbar. The second pathway involves direct colonization by willow shrubs starting on the sandbar. The third pathway is marked by hydric succession, starting from aquatic vegetation in stagnant waters and lasting to willow trees. The observed vegetation succession was found to be contingent on the initial hydrogeomorphic characteristics of the environment, as well as the introduction of willow trees within the sandbar that was exposed by the operation of the weir. These findings emphasize the need for adaptive river management that integrates ecological and geomorphological processes. Controlled weir operations should mimic natural flow to support habitat diversity and vegetation succession, while targeted sediment management maintains sandbars. Long-term monitoring using field surveys and remote sensing is crucial for refining restoration efforts. A holistic approach considering hydrology, sediment dynamics, and vegetation succession is essential for sustainable river restoration. Full article

Urbanization has intensified traffic loads, posing significant challenges to the efficiency and stability of urban road networks. Overloaded nodes risk congestion, thus making accurate intersection importance classification essential for resource optimization. This study proposes a hybrid clustering method that combines Self-Organizing Maps (SOMs), K-Means, and the Gaussian Mixture Model (GMM), which is supported by the Traffic Flow–Network Topology–Social Economy (TNS) evaluation framework. This framework integrates three dimensions—traffic flow, road network topology, and socio-economic features—capturing six key indicators: intersection saturation, traffic flow balance, mileage coverage, capacity, betweenness efficiency, and node activity. The SOMs method determines the optimal k value and centroids for K-Means, while GMM validates the cluster membership probabilities. The proposed model achieved a silhouette coefficient of 0.737, a Davies–Bouldin index of 1.003, and a Calinski–Harabasz index of 57.688, with the silhouette coefficient improving by 78.1% over SOMs alone, 65.2% over K-Means, and 11.5% over SOM-K-Means, thus demonstrating high robustness. The intersection importance ranking was conducted using the Mahalanobis distance method, and it was validated on 40 intersections within the road network of Zibo City. By comparing the importance rankings across static, off-peak, morning peak, and evening peak periods, a dynamic ranking approach is proposed. This method provides a robust basis for optimizing resource allocation and traffic management at urban intersections. Full article

Graphimages, which represent data structures through nodes and edges, present significant challenges for clustering due to their intricate topological properties. Traditional clustering algorithms, such as K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), often struggle to effectively capture both spatial and structural relationships within graph images. To overcome these limitations, we propose a novel two-stage clustering approach that integrates conventional clustering techniques with graph-based methodologies to enhance both accuracy and efficiency. In the first stage, a distance- or density-based clustering algorithm (e.g., K-means or DBSCAN) is applied to generate initial cluster formations. In the second stage, these clusters are refined using spectral clustering or community detection techniques to better preserve and exploit topological features. We evaluate our approach using a dataset of 8118 graph images derived from depth measurements taken at various angles. The experimental results demonstrate that our method surpasses single-method clustering approaches in terms of the silhouette score, Calinski-Harabasz index (CHI), and modularity. The silhouette score measures how similar an object is to its own cluster compared to other clusters, while the CHI, also known as the Variance Ratio Criterion, evaluates cluster quality based on the ratio of between-cluster dispersion to within-cluster dispersion. Modularity, a metric commonly used in graph-based clustering, assesses the strength of division of a network into communities. Furthermore, qualitative analysis through visualization confirms that the proposed two-stage clustering approach more effectively differentiates structural similarities within graph images. These findings underscore the potential of hybrid clustering techniques for various applications, including three-dimensional (3D) measurement analysis, medical imaging, and social network analysis. Full article

Background: The harmonious development of gross motor skills (GMSs) is vital for children, fostering their physical, cognitive, and socio-emotional growth. This study aimed to achieve three primary objectives: (1) to establish standardized reference values for all GMS tests conducted; (2) to examine the impact of overweight and obesity on factors influencing the development of GMSs and cardiorespiratory fitness (CRF); and (3) to investigate the relationship between GMSs and CRF levels and body image dissatisfaction among Canadian children from the province of Québec. Methods: The study encompassed 3144 children aged 6 to 12 years (1535 boys and 1609 girls) recruited from 24 elementary schools situated in five urban areas. Anthropometric measurements included body mass, body height, and body mass index (BMI). Physical performance was assessed using a maximal aerobic power test and 12 GMS tests, which comprised two segmental speed tests, four agility tests, two static balance tests, one simple reaction time test, and three coordination tests. Body perception and body image dissatisfaction were evaluated using a silhouette scale featuring two sets of nine drawings depicting a spectrum of body shapes ranging from very thin to obese. Results: Standardized normative values were established for each GMS test. GMSs demonstrated continuous improvement throughout childhood, albeit with a deceleration in progress during later developmental stages. At comparable age, boys generally outperformed girls on tests demanding greater strength, speed, or endurance, whereas girls exhibited superior performance in balance and hand–foot coordination tasks (p ≤ 0.05). However, segmental speed remained equivalent between sexes (p > 0.05). GMS and CRF were significantly influenced by obesity status. Children with a normal BMI demonstrated superior performance compared to their overweight or obese counterparts, particularly in tests requiring body mass displacement (p ≤ 0.05). Conversely, socioeconomic status exhibited no significant impact on body perception in boys (p = 0.106), but it was a notable factor among 6–8-year-old girls from lower socioeconomic backgrounds (p = 0.045). Conclusions: Obesity status is linked to diminished GMS performance, especially in tasks involving body mass movement. These findings underscore the importance of early intervention strategies to encourage an active lifestyle and promote a healthy body composition in children. Full article

Resolving the genetic relationships between cultivars is one of the objectives of research in viticulture. To this end, both DNA markers and morphological analysis help to identify synonyms and homonyms and to determine the degree of relatedness between cultivars. Results of genetic analysis using single sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) point to Hebén as the female progenitor of many of the cultivars currently used in viticulture. Here, seed shape is compared between Hebén and genetically related cultivars. An average silhouette derived from seeds of Hebén was used as a model, and the comparisons were made visually and quantitatively by calculation of J-index values (percent similarity of the seeds and the model). Quantification of seed shape by J-index confirms the data of DNA markers supporting different levels of conservation of maternal seed shape in the varieties. Other seed morphological measurements help to explain the basis of the differences in shape between Hebén, genetically related groups and the external group of unrelated cultivars. Curvature analysis in seeds silhouettes confirms the relationship between Hebén and other cultivars and supports the utility of this technique in the analysis of parental relationships. Full article

Clustering algorithms are widely used in statistical data analysis as a form of unsupervised machine learning, playing a crucial role in big data mining research for Maritime Intelligent Transportation Systems. While numerous studies have explored methods for optimizing ship trajectory clustering, such as narrowing dynamic time windows to prevent errors in time warp calculations or employing the Mahalanobis distance, these methods enhance DBSCAN (Density-Based Spatial Clustering of Applications with Noise) by leveraging trajectory similarity features for clustering. In recent years, machine learning research has rapidly accumulated, and multiple studies have shown that HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) outperforms DBSCAN in achieving accurate and efficient clustering results due to its hierarchical density-based clustering processing technique, particularly in big data mining. This study focuses on the area near Taichung Port in central Taiwan, a crucial maritime shipping route where ship trajectories naturally exhibit a complex and intertwined distribution. Using ship coordinates and heading, the experiment normalized and transformed them into three-dimensional spatial features, employing the HDBSCAN algorithm to obtain optimal clustering results. These results provided a more nuanced analysis compared to human visual observation. This study also utilized O notation and execution time to represent the performance of various methods, with the literature review indicating that HDBSCAN has the same time complexity as DBSCAN but outperforms K-means and other methods. This research involved approximately 293,000 real historical data points and further employed the Silhouette Coefficient and Davies–Bouldin Index to objectively analyze the clustering results. The experiment generated eight clusters with a noise ratio of 12.7%, and the evaluation results consistently demonstrate that HDBSCAN outperforms other methods for big data analysis of ship trajectory clustering. Full article

In the era of increasing digitalisation, organisations face the critical challenge of detecting anomalies in large volumes of data, which may indicate suspicious activities. To address this challenge, audit engagements are conducted regularly, and internal auditors and purchasing specialists seek innovative methods to streamline these processes. This study introduces a methodology to prioritise the investigation of anomalies identified in two large real-world purchase datasets. The primary objective is to enhance the effectiveness of companies’ control efforts and improve the efficiency of anomaly detection tasks. The approach begins with a comprehensive exploratory data analysis, followed by the application of unsupervised machine learning techniques to identify anomalies. A univariate analysis is performed using the z-Score index and the DBSCAN algorithm, while multivariate analysis employs k-Means clustering and Isolation Forest algorithms. Additionally, the Silhouette index is used to evaluate the quality of the clustering, ensuring each method produces a prioritised list of candidate transactions for further review. To refine this process, an ensemble prioritisation framework is developed, integrating multiple methods. Furthermore, explainability tools such as SHAP are utilised to provide actionable insights and support specialists in interpreting the results. This methodology aims to empower organisations to detect anomalies more effectively and streamline the audit process. Full article

To address the issue of excessive grid-connected power fluctuations in wind farms, this paper proposes a capacity optimization method for a hybrid energy storage system (HESS) based on wind power two-stage decomposition. First, considering the susceptibility of traditional k-means results to initial cluster center positions, the k-means++ algorithm was used to cluster the annual wind power, with the optimal number of clusters determined by silhouette coefficient and Davies–Bouldin Index. The overall characteristics of each cluster and the cumulative fluctuations were considered to determine typical daily data. Subsequently, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) was used to decompose the original wind power data for typical days, yielding both the grid-connected power and the HESS power. To leverage the advantages of power-type and energy-type storage while avoiding mode aliasing, the improved pelican optimization algorithm—variational mode decomposition (IPOA-VMD) was applied to decompose the HESS power, enabling accurate distribution of power for different storage types. Finally, a capacity optimization model for a HESS composed of lithium batteries and supercapacitors was developed. Case studies showed that the two-stage decomposition strategy proposed in this paper could effectively reduce grid-connected power fluctuations, better utilize the advantages of different energy storage types, and reduce HESS costs. Full article

The phenotype (P) of a crop is determined by the genotype (G), environment (E), and genotype-by-environment (G × E) interaction, expressed as P = G + E + G × E. Thus, studying G × E interactions is essential for phenotypic research. Traditional methods of crop phenotypes and adaptability based on G × E interaction analysis, based on large ecological regions, fail to account for year-to-year environmental changes and the blurring of region boundaries, leading to inaccurate insights into the relationship between genotypes and environmental factors. To address these issues, this study divided the research area into small ecological regions through the clustering of meteorological data, providing a more accurate framework for studying G × E interactions in maize. To ascertain the optimal method for ecological region delineation, the yield variance (SYV), the Davies–Bouldin Index (DBI), and the Silhouette Index (SI) were used to evaluate and compare the performance of the K-Means, Autoencoder K-Means (Ae-KM), and Deep K-Means Clustering Neural Network (DKMCNN) methodologies. The DKMCNN surpassed other methodologies and was selected for delineation. Based on this delineation result, the interactions between genotypes and the environment on maize were investigated and clarified using genome-wide association analysis (GWAS) and analysis of variance (ANOVA). Ultimately, through the analysis of maize field trial data from 2020 to 2021, we identified up to 108 single-nucleotide polymorphisms (SNPs) in 2020 and 153 SNPs in 2021 that exerted significant effects on maize yield and exhibited strong correlations with environmental factors, including temperature, cumulative precipitation, and cumulative sunshine duration. Full article

Background: Chest X-rays are among the most frequently used imaging tests in medical practice. We aimed to assess the prognostic value of the cardio–thoracic ratio (CTR) and transverse cardiac diameter (TCD) and compare them with novel chest X-ray parameters used in screening for cardiac enlargement. Methods: CTR, TCD, and five other non-standard new radiographic indexes, including basic spherical index (BSI), assessing changes in cardiac silhouette in chest radiographs in posterior–anterior projection were related to increased left ventricular end-diastolic volume (LVEDV) and left ventricular hypertrophy (LVH) assessed in cardiac magnetic resonance imaging (CMR). Results: TCD, CTR, and BSI were the best predictors of both LVH and increased LVEDV diagnosed in CMR. The best sensitivity, along with good specificity in LVH prediction, defined as left ventricular mass/body surface area (BSA) > 72 g/m² in men or >55 g/m² in women, was observed when TCD and BSI parameters were used jointly (69.2%, 95% confidence interval [CI]: 52.4–83.0% and 80.0%, 95% CI: 51.9–95.7%, respectively). In the prediction of cardiac enlargement defined as LVEDV/BSA > 117 mL/m² in men or >101 mL/m² in women, BSI > 137.5 had the best sensitivity and specificity (85.0%, 95% CI: 62.1–96.8% and 82.4%, 95% CI: 65.5–93.2%, respectively). Conclusions: TCD may be valuable in the assessment of patients suspected of having cardiac enlargement. CTR and BSI serve as complementary tools for a more precise approach. TCD appears particularly useful for the prediction of LVH, while BSI demonstrates greater utility as an indicator of increased LVEDV. Full article

Background: The obesity epidemic among adolescents significantly impacts not only their physical health but also various psychological factors, including their perception of body image. Thus, this study pursued three main objectives: (1) to update the reference standard values for all the physical fitness tests performed; (2) to examine the impact of overweight and obesity on factors influencing physical fitness in adolescents; and (3) to determine the relationship between the physical fitness level and the body image dissatisfaction among a population of French-Canadian adolescents. Methods: A total of 1862 adolescents aged 12 to 17 (1008 boys and 854 girls) participated in this study. Data were collected from 12 French-language high-schools from different socioeconomic backgrounds and spread across four regions of the province of Québec, Canada. Anthropometric measures (body mass, body height, body mass index (BMI), waist circumference, waist-to-height ratio) and fitness tests (aerobic power, anaerobic power, muscle endurance, muscular power, flexibility) were conducted. To assess adolescents’ body perception, a silhouette scale was used. Results: Standardized normative values were established for each fitness test (Lambda Mu Sigma; LMS method). In boys, performance generally improved with age, except for the V-test and sit-ups, which remained stable, and VO₂peak, which declined during adolescence in both genders (unpaired t-test and Cohen’s d effect size). In girls, only the vertical jump and 30 m sprint improved with age, while the other tests stabilized by age 13. Fitness level was significantly influenced by obesity status. Boys and girls with a normal BMI performed better than those who were overweight or obese (ANOVA = p < 0.001 and effect size F). Girls appeared to be less affected by obesity status, with differences between overweight and obese groups rarely being significant (p > 0.05). Fitness level was also linked to body satisfaction, with satisfied adolescents generally achieving better scores than dissatisfied ones, even among those with a typical BMI. Socioeconomic status did not impact body image perception in boys (p = 0.351). In contrast, girls from lower socioeconomic backgrounds exhibited significantly more negative perceptions (p = 0.002) than their peers from more affluent families. Conclusions: Obesity status is strongly associated with poorer performance on fitness tests. Conversely, higher levels of physical fitness are linked to improved body image satisfaction. This positive relationship between fitness and body image holds true even for individuals with a healthy body weight (typical BMI). Full article