Search Results (80)

Catastrophic disturbances pose significant challenges to remote sensing because landscapes can change rapidly, while access for field validation is limited, making it difficult to consistently track the spatiotemporal dynamics of discrete land-surface types. Building on the metaphor of the “ecosystem as an organism” and the individualistic perspective on ecosystems, each surface type is treated as a spectrally coherent entity whose identity must remain comparable over time despite changing conditions. To achieve this comparability, a Procrustes-based framework is introduced to align multi-index feature spaces from different dates to a common archetype, enabling cross-date classification within a commensurable coordinate system. Since Procrustes alignment requires a stable reference, the concept of core pixels (centroid-typical samples in feature space) is extended to spatially grounded anchor pixels that are invariant in both spectral and geographic space, thereby representing the persistent “organismal” structure of the landscape. Regression-based evaluation indicates that the Procrustes–anchor workflow improves classification fidelity and produces a clearer, more interpretable transition matrix of type changes, facilitating the separation of systematic transient dynamics from noisy reassignments. The resulting discrete habitat maps are independently validated using field geobotanical vegetation types, providing an ecological basis for the classified surface-type dynamics under catastrophic conditions. Full article

This study aimed to develop a robust morphometric-based framework for classifying Apis cerana populations using deep learning and machine learning approaches. Previous studies on Apis cerana population differentiation have primarily relied on manual morphometrics or genetic markers, which are labor-intensive and often lack scalability for large image-based datasets. Forewing landmarks were automatically detected through a deep learning model employing a heatmap regression and Hourglass Network architecture. The extracted coordinates were processed by Principal Component Analysis (PCA) for dimensionality reduction, and shape alignment was further refined through Procrustes ANOVA to minimize non-biological variation. Nine machine learning algorithms were trained and compared under identical preprocessing and validation settings. Among them, the Extra Trees classifier achieved the highest accuracy (99.7%) in distinguishing the three populations—A. cerana cerana from China and A. cerana indica from Thailand, the northern and southern populations. After applying error-based data filtering and retraining, classification accuracy improved further, with almost perfect population separation. The Procrustes ANOVA confirmed that individual variation significantly exceeded residual error (Pillai’s trace = 1.13, p < 0.0001), validating the biological basis of shape differences. Mahalanobis distance and permutation tests (10,000 rounds) revealed significant morphological divergence among populations (p < 0.0001). The integration of geometric alignment and ensemble learning demonstrated a highly reliable strategy for population identification, supporting morphometric and evolutionary studies in Apis cerana. Full article

Apple breeding programs focus on enhancing yield, quality, and disease resistance, with a strong emphasis on evaluating phenological traits like flowering time and pomological traits such as fruit size and flavour, which are crucial for commercial success and consumer preference. Twenty-four families were obtained by crossing six apple varieties selected as pollen receptors and four apple genotypes resistant to scab selected as pollen donors. Data related to bud burst date, flowering date, harvest date, lengths of the periods between bud burst and flowering and from flowering to harvest (developmental period), fruit equatorial and polar diameter, fruit polar/diameter ratio, soluble solid content (SSC) and flesh firmness were analysed as a genetic partial diallel design. The study’s ANOVA on 24 fruit families across two years revealed significant genotype–environment interactions affecting flowering date, harvest date, and developmental periods, with some variables like fruit weight and soluble solids showing consistent variation. During each year, temperature influenced phenological phases, with earlier budbreak and flowering in warmer, less variable conditions in 2019. Analysis of genetic effects indicated high heritability for phenological traits and moderate heritability for fruit morphology and quality, with parental genetic contributions varying over years. Principal component and Procrustes analyses identified key variable groupings and parent profiles, highlighting genotypes such as ‘Granny Smith’, ‘McIntosh’, and ‘HM100’ with consistent additive effects, and certain families with notable heterotic performance. Overall, genetic and environmental interactions significantly shape phenological and fruit quality traits, guiding breeding strategies. Full article

The skull provides essential diagnostic features for species identification and sex determination. Cranial sexual dimorphism in guinea pigs (Cavia porcellus) is particularly important to understand, as these animals are frequently used in experimental research and veterinary practice, yet detailed morphometric evaluations remain limited. This study aimed to assess cranial size and shape variation between sexes using three-dimensional geometric morphometric (GM) methods. Computed tomography scans of 30 clinically healthy guinea pigs were used to reconstruct three-dimensional skull models. Twenty-one anatomical landmarks were digitized, and the dataset was subjected to generalized Procrustes analysis, followed by principal component analysis, Procrustes ANOVA, and regression-based allometric assessments. The results revealed pronounced sexual dimorphism. Males exhibited significantly larger centroid sizes and more robust cranial morphologies, whereas females presented smaller and more gracile skull forms. Procrustes ANOVA confirmed significant shape differences between sexes (p < 0.01), with size being the primary driver of morphological divergence. Body weight had a weaker but detectable influence on cranial variation, although its effect diminished after accounting for centroid size (R²: 0.085). These findings demonstrate that three-dimensional geometric morphometrics can effectively detect subtle patterns of cranial dimorphism in guinea pigs. Beyond their anatomical relevance, the results provide a valuable reference for veterinary sciences, taxonomy, and future biomedical research requiring precise morphological characterization. These findings show that three-dimensional geometric morphometrics can effectively detect subtle patterns of sexual dimorphism in cranial shape, which is particularly relevant for sex identification in skeletal collections and for the development of comparative anatomical databases in veterinary and experimental research. Full article

Despite growing awareness of the emotional challenges faced by engineering students and their impact on academic performance and retention, the field lacks validated tools to systematically assess affective states in theoretically grounded ways. First-year students are particularly vulnerable during the transition to university-level engineering education, experiencing stress, anxiety, and disengagement that contribute to attrition. This study aimed to develop and validate a psychometrically sound scale based on Russell’s Circumplex Model of Affect to assess first-year engineering students’ emotional experiences and provide educators with a theoretically grounded assessment instrument. A 12-item circumplex-based affective-state scale was administered to 176 first-year engineering students. Validation combined exploratory factor analysis on raw and ipsatized data, Procrustes alignment to check how closely the items formed a circle, and structural summary modeling to test circumplex geometry. Internal consistency was assessed using Cronbach’s α and McDonald’s ω. Exploratory factor analysis confirmed a robust two-dimensional Valence × Arousal structure explaining 51% of total variance. Procrustes rotation yielded excellent item-level congruence coefficients (0.929–1.000), while Structural Summary Modeling revealed strong sinusoidal patterns (R² = 0.94), indicating a near-circular configuration consistent with circumplex theory. Internal consistency was high across both dimensions (Cronbach’s α ≥ 0.76; McDonald’s ω ≥ 0.84). The validated scale provides a reliable, theoretically coherent instrument for assessing engineering student emotions along pleasant–unpleasant and activation–deactivation dimensions, enabling systematic emotional assessment and targeted interventions while addressing critical gaps in affective assessment tools. Full article

Common swifts are extreme aerial specialists that spend most of their lives in flight and use their legs mainly for clinging rather than locomotion. Because functional load is therefore expected to be concentrated on the wing, we hypothesized that the forelimb would exhibit stronger left–right differentiation than the hindlimb. In addition to testing this hypothesis, we used landmark-based geometric morphometric methods to describe humeral and femoral shape in common swifts and to test the effects of sex and body size on bone morphology. The humerus showed clear directional asymmetry: the effect of side explained 13.8% of total shape variance (F = 42.0, p = 0.001), indicating a consistent left–right shift across individuals. The femur also exhibited significant but weaker directional asymmetry, with side accounting for 5.4% of variance (F = 19.3, p = 0.001). In both bones, the individual term explained the largest proportion of variation, whereas residual variance (containing fluctuating asymmetry and measurement error) was moderate (≈27% in the humerus, ≈23% in the femur). Allometric regressions showed a weak but significant size–shape relationship for the humerus and only a marginal effect for the femur, and males and females showed almost complete overlap in the distribution of humeral and femoral shapes. Sex had no detectable effect on humeral or femoral shape or asymmetry, and body size explained only a modest proportion of shape variation in both elements. Overall, our results support the functional expectation: the more intensively used forelimb element is also the more directionally asymmetric one, whereas the femur of this largely aerial bird remains comparatively more symmetrical. Full article

The mandible of domestic dogs represents a key structure in veterinary anatomy. This study tested the hypothesis that mandibular shape variation in adult mesocephalic dogs follows a non-random modular pattern with limited allometric influence. A total of 168 dry mandibles from academic osteological collections were analyzed using geometric morphometrics. Four anatomical landmarks and two curves of sliding semilandmarks were digitized and processed through Generalized Procrustes Analysis. Principal component analysis revealed that 62.7% of total variance was concentrated in the first two axes, associated with the coronoid height, ramus robustness, and curvature of the mandibular body. Cluster and Canonical Variate Analyses identified two overlapping but statistically distinct configurations, reflecting the intrinsic morphological diversity of mesocephalic dogs. Procrustes regression confirmed a significant yet low allometric effect (2.34%), while modularity tests based on RV coefficients supported a structured organization involving the ramus, coronoid, and angular processes (processus angularis mandibulae) as relatively independent modules. These results indicate that mandibular shape variation is hierarchically organized rather than random, highlighting the coexistence of integration and modular independence within the masticatory apparatus. Beyond its morphometric contribution, this study provides a reproducible anatomical baseline for veterinary and comparative research, facilitating future analyses of sexual dimorphism, functional adaptation, and surgical applications. Full article

In Russia, Western Siberia, Anopheles from maculipennis subgroup comprises three vector species: An. messeae, An. daciae, An. beklemishevi, and the hybrid between An. messeae and An. daciae (Anopheles m-d), which exhibit complex cryptic morphological traits. Traditional morphological methods, such as egg morphology and exochorion coloration, have proven insufficient for reliably distinguishing these closely related species due to overlapping characteristics and high intra-species variability. To overcome these limitations, geometric morphometrics (GM) has emerged as a powerful tool for analyzing cryptic morphology. This article focuses on wing venation patterns, where GM provides precise, quantitative data based on defined anatomical landmarks, enabling detailed assessment of size and shape variation among species. Procrustes ANOVA, principal component analysis (PCA), and canonical variate analysis (CVA) were employed to assess shape variation and species differentiation. Centroid size and its relationship to shape variation were examined using multivariate regression. Despite significant morphological differences, the overlap observed in hybrids (An. m-d) reflects their intermediate position between the parental species. Our analyses revealed significant differences in wing shape and size among An. messeae, An. daciae, An. beklemishevi, and their hybrids, with hybrids showing intermediate morphologies. Landmarks on radial and medial veins were the most consistent contributors to species separation. No evidence of static allometry was detected, and wing shape differences were not explained by size. These findings demonstrate that wing morphometrics, combined with molecular identification, provides a reliable framework for species delimitation and surveillance of malaria vectors in temperate regions. Full article

Background: The anatomical variability of the nasal cavity affects intranasal drug delivery, especially to the olfactory region for nose-to-brain treatments. While previous studies used average models or 2D measurements to account for inter-individual variability, 3D shape variation of the region crossed by drug particles that target the olfactory area, namely the region of interest (ROI), remains unexplored to our knowledge. Methods: A geometric morphometric analysis was performed on the ROI of 151 unilateral nasal cavities from the CT scans of 78 patients. Ten fixed landmarks and 200 sliding semi-landmarks were digitized, using Viewbox 4.0, and standardized via Generalized Procrustes Analysis. Shape variability was analyzed through Principal Component Analysis. Morphological clusters were identified using Hierarchical Clustering on Principal Components, and characterized with MANOVA, ANOVA, and Tukey tests. Results: Validation tests confirmed the method’s reliability. Three morphological clusters were identified. Variations were significant in the X and Y axes, and minimal in Z. Cluster 1 had a broader anterior cavity with shallower turbinate onset, likely improving olfactory accessibility. Cluster 3 was narrower with deeper turbinates, potentially limiting olfactory accessibility. Cluster 2 was intermediate. Notably, 31.5% of patients had at least one cavity in cluster 1. Conclusions: Three distinct morphotypes of the region of the nasal cavity that potentially influence accessibility were identified. These findings will guide future computational fluid dynamics studies for optimizing nasal drug targeting and represent a practical step toward tailoring nose-to-brain drug delivery strategies in alignment with the principles of personalized medicine. Full article

Understanding morphological variation and asymmetry in avian limbs provides essential insights into functional anatomy, locomotor behavior, and developmental stability. In this study, we investigated shape and size variation in the feet of quails (Coturnix coturnix) using two-dimensional geometric morphometric methods. A total of 233 animals were analyzed, representing both the left and right feet of male and female individuals. Nine homologous fixed landmarks were digitized on each foot, and configurations were subjected to Generalized Procrustes Analysis, followed by mirroring of right-side landmarks to ensure consistent orientation. Statistical analyses revealed no significant sexual dimorphism in either foot shape or centroid size. Principal Component Analysis indicated that the main shape variation was distributed individually rather than by sex and primarily affected the relative positions of toes and claws. Procrustes ANOVA confirmed that differences between sexes were not greater than expected by chance. Directional and fluctuating asymmetry were evaluated using a bilateral symmetry model to assess bilateral asymmetry. Directional asymmetry indicated consistent left–right differences, while fluctuating asymmetry reflected individual-level developmental instability and comprised the main source of variation. These findings provide a detailed morphological baseline for quail foot structure and highlight the importance of considering asymmetry in studies of avian functional morphology. The approach may also be a reference for future research into developmental stress, locomotor adaptation, or species-specific anatomical patterns. Full article

Sensory evaluation is essential to understand consumer perception. This study compared three descriptive methods (Check-All-That-Apply (CATA), Flash Profile, and Pivot Profile) to characterize muffins formulated with alternative flours (purple corn and amaranth) in comparison to a wheat-based control. Six formulations (T0–T5) were evaluated: CATA and Pivot Profile were applied with 100 consumers, while Flash Profile was conducted with 15 panelists. Multivariate statistical analyses were used: correspondence analysis for CATA and Pivot, and Generalized Procrustes Analysis for Flash Profile. All three methods showed high discriminative power: CATA explained 94.36% of the variance, identifying three main groups; Flash Profile explained 63.88%, highlighting differences in texture and aroma; and Pivot Profile explained 81.10%, revealing complex interactions among sensory attributes. Sample T1 (100% purple corn) showed a distinctive sensory profile (bitter and dry), while samples T2 to T5 presented intermediate characteristics. The RV coefficient confirmed significant congruence between the methods. CATA effectively identified relevant sensory differences, Pivot Profile generated descriptors in relation to a control sample, and Flash Profile enabled exploratory analysis. The choice of method depends on the study objective, with each approach offering complementary sensory information. Full article

Disease caused by Plasmodiophora brassicae severely disrupts cruciferous crops by altering root physiology and rhizosphere ecology. While pathogen-induced shifts in rhizosphere microbiomes are documented, the mechanisms linking root exudate reprogramming to microbial community remodeling remain poorly understood. Here, we integrated untargeted metabolomics and 16S rRNA sequencing to investigate how root exudates reshape the rhizosphere microbiome of tumorous stem mustard (Brassica juncea var. tumida) through P. brassicae infection. Metabolomic profiling identified 1718 root exudate metabolites, with flavones (e.g., apigenin 7-O-β-D-rutinoside, VIP > 1.5) and phenolic derivatives (e.g., gastrodin) being selectively enriched in infected plants. P. brassicae infection significantly increased rhizobacterial richness (ACE index, p < 0.05) and restructured the community composition, marked by enrichment of Paenibacillus (LDA score > 3.0). Procrustes analysis revealed tight coupling between microbial community shifts and metabolic reprogramming (M² = 0.446, p = 0.005), while Spearman correlations implicated pathogen-induced metabolites like geniposidic acid in recruiting beneficial Paenibacillus. Our results reveal that plant hosts dynamically secrete defense-related root metabolites to remodel the rhizosphere microbiome in response to P. brassicae infection. This dual-omics approach elucidates a chemical dialogue mediating plant–microbe–pathogen interactions, offering novel insights for engineering disease-suppressive microbiomes through root exudate manipulation. Full article

With continuous advancements in autonomous driving technology, systematic and reliable safety verification is becoming increasingly important. However, despite the active development of various X-in-the-loop simulation (XILS) platforms to validate autonomous driving systems (ADSs), standardized evaluation frameworks for assessing the credibility of the simulation platforms themselves remain lacking. Therefore, we propose a novel integrated credibility-assessment methodology that combines dynamics-based fidelity assessment, parameter-based reliability assessment, and scenario-based reliability assessment. These three techniques evaluate the similarity and consistency between XILS and real-world test data based on statistical and mathematical comparisons. The three consistency measures are then utilized to derive a dynamics-based correlation metric for fidelity, along with parameter-based and scenario-based correlation and applicability metrics for reliability. The novel contribution of this paper lies in a geometric similarity analysis methodology that significantly enhances the efficiency of credibility assessment. We propose a methodology that enables geometric similarity assessment through spider chart visualization of metrics derived from the credibility-assessment process and shape comparison, based on Procrustes, Fréchet, and Hausdorff distances. As a result, speed is not a dominant factor for credibility evaluation, enabling assessment with a single representative speed test; the framework simplifies the XILS evaluation and enhances ADS validation efficiency. Full article

The patella plays a vital role in stifle joint function by contributing to limb extension and joint stabilization. While its clinical significance in small animal orthopedic surgery is well established, detailed comparative data on patellar morphology in domestic cats and dogs remain limited. This study aimed to investigate interspecific and intraspecific variation in patellar shape and size using three-dimensional geometric morphometric techniques. Computed tomography images of 18 cats and 55 dogs were used to construct 3D models, and a total of 14 anatomical landmarks were manually placed on each patella. Generalized Procrustes Analysis was applied, followed by principal component analysis to explore patterns of shape variation. The results revealed a significant difference in shape between cats and dogs, with dogs exhibiting broader variation and larger centroid sizes. Regression analysis indicated that 12.2% of the observed shape variation could be attributed to centroid size, reflecting the influence of patellar size. This strong link suggests that size has a significant influence on how the patella’s shape varies, especially in dogs. These findings highlight the importance of individual variation in patellar morphology, particularly in dogs. A single standardized implant or surgical technique may not be appropriate for all cases. Integrating shape and size evaluations into preoperative imaging can help improve implant fit, joint stability, and overall surgical success. In the absence of breed-specific data, individualized planning remains the most reliable approach. Full article

The cooperative localization of Unmanned Aerial Vehicles (UAVs) has emerged as a pivotal application in Internet of Things (IoT) tasks. However, the frequent exchange of localization data among UAVs leads to significant energy consumption and escalates the computational complexity involved in multi-UAV cooperative localization tasks. To address these challenges, this paper proposes a cooperative localization algorithm that integrates a biogeography optimization-based cluster networking and adaptive sampling-improved Nystrom super-multidimensional scaling (BOCN-ASNSMS). The proposed method leverages biogeography optimization (BO), prioritizing nodes with higher residual energy and density to serve as cluster heads, thereby optimizing energy usage. Subsequently, an improved adaptive sampling Nystrom super-multidimensional scaling algorithm is employed to dynamically select the kernel matrix row vectors. This selection process not only reduces data processing requirements but also enhances the accuracy of the similarity matrix approximation, thus diminishing computational complexity and achieving precise relative positioning of UAVs. Furthermore, Procrustes analysis and least squares methods are utilized to fuse coordinates across UAV clusters, aligning them into a unified coordinate system and converting them into absolute coordinates, which facilitates high-precision global localization. Theoretical analysis and simulation results underscore that the proposed algorithm substantially reduces computational complexity and energy consumption while enhancing localization accuracy, compared to conventional multi-UAV cooperative localization approaches. Full article