Variation In Shape Research Articles

The quantum vortices dynamics: spatio-temporal scale hierarchy and origin of turbulence

Abstract This study investigates the evolution and interaction of quantum vortex loops with a small but non-zero radius of core ${\sf a}$. The quantization scheme of the classical vortex system is based on the approach proposed by the author \cite{Tal,Tal_PhRF}. 
 We consider small perturbations in the ring-shaped loops, which include both helical-type shape variations and small excitations of the flow in the vortex core. 
The quantization of the circulation $\Gamma$ is deduced from the first principles of quantum theory. As a result of our approach, the set of quantized circulation values is wider than the standard one.
The developed theory introduces a hierarchical spatio-temporal scale in the quantum evolution of vortices. We also explore the applicability of this model for describing the origins of turbulence in quantum fluid flows. To achieve this specific objective, we employ the method of random Hamiltonians to describe the interaction of quantum vortex loops.

Few-shot Learning for Sign Language Recognition with Embedding Propagation

Sign language is a primary channel for the deaf and hard-hearing to communicate. Sign language consists of many signs with different variations in hand shapes, motion patterns, and positioning of hands, faces, and body parts. This makes sign language recognition (SLR) a challenging field in computer vision research. This paper tackles the problem of few-shot SLR, where models trained on known sign classes are utilized to recognize instances of unseen signs with only a few examples. In this approach, a transformer encoder is employed to learn the spatial and temporal features of sign gestures, and an embedding propagation technique is used to project these features into the embedding space. Subsequently, a label propagation method is applied to smooth the resulting embeddings. The obtained results demonstrate that combining embedding propagation with label propagation enhances the performance of the SLR system and achieved an accuracy of 76.6%, which surpasses the traditional few-shot prototypical network's accuracy of 72.4%.

Modeling Backlash-like Hysteresis and Feedforward Control of Tendon-Sheath Mechanism Pair with Arbitrary Shape: Excluding Distal Sensory Input

Abstract The tendon-sheath mechanism (TSM) has garnered attention for its versatile applications in soft robotics, robotic hands, and surgical robots due to its adeptness in transmitting force through tortuous and narrow channels. However, precise control of the TSM is challenging due to its shape-dependent deformation and force generation, highlighting the need to address shape variations in dynamic environments where direct measurement is impractical. This paper introduces a novel methodology leveraging the concept of the equivalent circle in TSM to model its shape under varying conditions. The equivalent circle substitutes an arbitrarily shaped TSM with a circle, whose radius is derived from TSM's deformation and forces measured at the proximal end during calibration. This approach provides a concise and effective framework for understanding and analyzing the distinct characteristics of TSM. Additionally, a control strategy is proposed, utilizing the estimated equivalent circle to adapt to dynamic environments. Experimental results demonstrate promising performance without distal end sensory feedback, with mean percentage errors (MPE) of 0.78% and 2.33% for predicting cumulated curve angle and equivalent circle radius, respectively. Moreover, by utilizing the equivalent circle to calculate and feedforward deadband, effective control is achieved. The root mean square error (RMSE) reduced from 1.31 mm to an average of 0.19 mm in two feedforward experiments, representing an average reduction of 85.5%. These findings shed light on effective control strategies for TSM, offering valuable insights for robotics and surgical applications where TSM shape configurations are uncertain and subject to dynamic change.

Improved visualisation of ACP-engineered osteoblastic spheroids: a comparative study of contrast-enhanced micro-CT and traditional imaging techniques.

This study investigates osteoblastic cell spheroid cultivation methods, exploring flat-bottom, U-bottom, and rotary flask techniques with and without amorphous calcium phosphate (ACP) supplementation to replicate the 3D bone tissue microenvironment. ACP particles derived from eggshell waste exhibit enhanced osteogenic activity in 3D models. However, representative imaging of intricate 3D tissue-engineered constructs poses challenges in conventional imaging techniques due to notable scattering and absorption effects in light microscopy, and hence limited penetration depth. We investigated contrast-enhanced micro-CT as a methodological approach for comprehensive morphological 3D-analysis of the in-vitro model and compared the technique with confocal laser scanning microscopy (CLSM), scanning electron microscopy (SEM) and classical histology. Phosphotungstic acid (PTA) and iodine-based contrast agents were employed for micro-CT imaging in laboratory and synchrotron micro-CT imaging. Results revealed spheroid shape variations and structural integrity influenced by cultivation methods and ACP particles. The study underscores the advantage of 3D spheroid models over traditional 2D cultures in mimicking bone tissue architecture and cellular interactions, emphasising the growing demand for novel imaging techniques to visualise 3D tissue-engineered models. Contrast-enhanced micro-CT emerges as a promising non-invasive imaging method for tissue-engineered constructs containing ACP particles, offering insights into sample morphology, enabling virtual histology before further analysis.

The ventricular septal defect in tetralogy of Fallot: A multi-centre morphological and statistical shape analysis study

Abstract Background Ventricular septal defects (VSDs) in Tetralogy of Fallot (TOF) are often thought to be ‘large and unrestricted’ but experience suggests they have a more heterogenous morphology. Understanding the morphological variations and dimensions is the first step in modifying surgical closure or optimising potential device design. Purpose The objective of this study was to perform a comprehensive multi-center morphological analysis of the characteristics of the VSD in TOF. Additionally, we sought to perform a preliminary 2D shape analysis and principal component analysis (PCA) on standardised photos of the VSDs. Through this multi-dimensional approach, we aim to elucidate the morphological variations and structural characteristics of the VSD in TOF as a basis for future 3D shape analysis. Methods Of 495 specimens analysed, 460 were included in the morphological analysis. Standardised protocol was applied to assess the characteristics of the VSD: basic measurements of the height, width, inferior limb, and length of the inferior fibrous rim (if perimembranous) were taken and indexed to ventricular length. The degree of aortic override and presence of aorto-mitral continuity were also documented with double outlet right ventricle (DORV) being defined as more than 50% of aorta supported by the right ventricle. Statistical analysis was performed to assess variations in TOF morphology across different age groups and disease phenotypes. Standardised photographs of 130 VSDs were taken from the right ventricle and traced for PCA, in order to assess shape variations among the VSDs. Results Both the length of the inferior limb of the PM VSD and the length of the fibrous rim increased with ventricular length but the rate of change was lower for the fibrous rim. A chi-square analysis comparing the presence of double outlet right ventricle and the presence of a fibrous continuity between the aortic and mitral valves showed no association between these features (p = 0.89). The results of PCA in the indexed datasets showed that the largest variation was in the size of the VSD and then from the shape of the VSD where two distinct morphologies emerged: a classic V-shaped VSD and wide but shallow variant. Discussion Our study challenges conventional definitions and assumptions regarding the morphological characteristics of the VSD in TOF and suggests that components may change size at differing rates. Our study conclusively shows that DORV and TOF can co-exist and the definition of DORV should be made independently of the presence of aorto-mitral continuity. Contrary to the traditional view of TOF featuring large unrestricted VSDs, our findings suggest wide shallow VSDs are common. These findings challenge paradigms and underscore a necessity for the continuing morphological re-evaluation of TOF utilising large datasets with a diverse phenotypic spread.

Neural fields for rapid aircraft aerodynamics simulations

This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The proposed models can be applied directly to unstructured domains for different flow conditions, handle non-parametric 3D geometric variations, and generalize to unseen shapes at test time. The coordinate-based formulation naturally leads to robustness with respect to discretization, allowing an excellent trade-off between computational cost (memory footprint and training time) and accuracy. The method is demonstrated on two industrially relevant applications: a RANS dataset of the two-dimensional compressible flow over a transonic airfoil and a dataset of the surface pressure distribution over 3D wings, including shape, inflow condition, and control surface deflection variations. On the considered test cases, our approach achieves a more than three times lower test error and significantly improves generalization error on unseen geometries compared to state-of-the-art Graph Neural Network architectures. Remarkably, the method can drastically accelerate high fidelity numerical solver, achieving a five order of magnitude speedup on the RANS transonic airfoil dataset.

An enhanced method for reconstruction of full SIF spectrum for near-ground measurements

Recently the applications of remotely sensed Solar-Induced chlorophyll Fluorescence (SIF) in the study of photosynthesis, stress conditions, and gross primary production have increased significantly. The full SIF spectrum spans over a spectral region of 650 ∼ 850 nm with two characteristic peaks around 685 nm and 740 nm. Over recent decades, many retrieval algorithms have been developed to estimate SIF at Top-Of-Canopy (TOC) using in-situ measurements of solar irradiance and canopy radiance spectra. Although the majority of retrieval methods retrieve SIF at a narrow spectral window, there exists a potential for retrieval of SIF in the full emission spectrum. Moreover, solar irradiance and canopy radiance spectra should ideally be measured at the same time but are usually measured sequentially with a certain time lag, raising potential errors in SIF retrieval. In this study, an enhanced retrieval algorithm of the full SIF spectrum at TOC is proposed. The proposed algorithm attempts to minimize the errors owing to time mismatch in measurements of solar irradiance and canopy radiance spectra. As an improvement to the previous algorithm (advanced Fluorescence Spectrum Reconstruction, aFSR), this proposed algorithm (aFSR-SVE) models the SIF-free contribution with principal components using the singular value decomposition technique. The optimal parameter settings in the forward model were determined for the experimental data collected by spectrometers used in the study. Firstly, the proposed algorithm was used to reconstruct full SIF spectrum for simulated data. The results were compared with known reference SIF values. After achieving satisfying results from simulated data, the proposed algorithm was compared with retrievals from established algorithms using experimental data. The results show improved SIF retrieval accuracy, without the need to simultaneously measure solar irradiance and canopy radiance spectra. The retrieval values comply with the results of previous algorithms in terms of spectral shape, diurnal trend, and temporal variations. The induced errors in SIF retrievals due to non-simultaneous measurements of solar irradiance and canopy radiance spectra were also investigated and the proposed algorithm was found to be less prone to such errors. Hence, the proposed algorithm is an improvement in reconstructing the full SIF spectrum with near-ground measurements. With the help of the proposed algorithm, field measurements using sequential systems and automated measurements of multiple targets can be performed effectively as it relaxes the requirement of concurrent measurement of solar irradiance and canopy radiance spectra. For future work, the applicability of this method can be investigated under more variable illumination conditions, like high cirrus clouds, passing clouds or persistent thin clouds.

Quantitative analysis of facial shape in children to support respirator design

The COVID-19 pandemic demonstrated the need for respiratory protection against airborne pathogens. Respirator options for children are limited, and existing designs do not consider differences in facial shape or size. We created a dataset of children's facial images from three cohorts, then used geometric morphometric analyses of dense and sparse facial landmark representations to quantify age, sex and ancestry-related variation in shape. We found facial shape and size in children vary significantly with age from ages 2 to 18, particularly in dimensions relevant to respirator design. Sex differences are small throughout most of the age range of our sample. Ancestry is associated with significant facial shape variation in dimensions that may affect respirator fit. We offer guidance on how to our results can be used for the appropriate design of devices such as respirators for pediatric populations. We also highlight the need to consider ancestry-related variation in facial morphology to promote equitable, inclusive products.

Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.

Skin cancer is one of the top three hazardous cancer types, and it is caused by the abnormal proliferation of tumor cells. Diagnosing skin cancer accurately and early is crucial for saving patients' lives. However, it is a challenging task due to various significant issues, including lesion variations in texture, shape, color, and size; artifacts (hairs); uneven lesion boundaries; and poor contrast. To solve these issues, this research proposes a novel Convolutional Swin Transformer (CSwinformer) method for segmenting and classifying skin lesions accurately. The framework involves phases such as data preprocessing, segmentation, and classification. In the first phase, Gaussian filtering, Z-score normalization, and augmentation processes are executed to remove unnecessary noise, re-organize the data, and increase data diversity. In the phase of segmentation, we design a new model "Swinformer-Net" integrating Swin Transformer and U-Net frameworks, to accurately define a region of interest. At the final phase of classification, the segmented outcome is input into the newly proposed module "Multi-Scale Dilated Convolutional Neural Network meets Transformer (MD-CNNFormer)," where the data samples are classified into respective classes. We use four benchmark datasets-HAM10000, ISBI 2016, PH2, and Skin Cancer ISIC for evaluation. The results demonstrated the designed framework's better efficiency against the traditional approaches. The proposed method provided classification accuracy of 98.72%, pixel accuracy of 98.06%, and dice coefficient of 97.67%, respectively. The proposed method offered a promising solution in skin lesion segmentation and classification, supporting clinicians to accurately diagnose skin cancer.

A novel framework for elucidating the effect of mechanical loading on the geometry of ovariectomized mouse tibiae using principal component analysis

IntroductionMurine models are used to test the effect of anti-osteoporosis treatments as they replicate some of the bone phenotypes observed in osteoporotic (OP) patients. The effect of disease and treatment is typically described as changes in bone geometry and microstructure over time. Conventional assessment of geometric changes relies on morphometric scalar parameters. However, being correlated with each other, these parameters do not describe separate fractions of variations and offer only a moderate insight into temporal changes.MethodsThe current study proposes a novel image-based framework that employs deformable image registration on in vivo longitudinal images of bones and Principal Component Analysis (PCA) for improved quantification of geometric effects of OP treatments. This PCA-based model and a novel post-processing of score changes provide orthogonal modes of shape variations temporally induced by a course of treatment (specifically in vivo mechanical loading).Results and DiscussionErrors associated with the proposed framework are rigorously quantified and it is shown that the accuracy of deformable image registration in capturing the bone shapes (∼1 voxel = 10.4 μm) is of the same order of magnitude as the relevant state-of-the-art evaluation studies. Applying the framework to longitudinal image data from the midshaft section of ovariectomized mouse tibia, two mutually orthogonal mode shapes are reliably identified to be an effect of treatment. The mode shapes captured changes of the tibia geometry due to the treatment at the anterior crest (maximum of 0.103 mm) and across the tibia midshaft section and the posterior (0.030 mm) and medial (0.024 mm) aspects. These changes agree with those reported previously but are now described in a compact fashion, as a vector field of displacements on the bone surface. The proposed framework enables a more detailed investigation of the effect of disease and treatment on bones in preclinical studies and boosts the precision of such assessments.

Evaluasi Tingkat Kesukaan Panelis Terhadap Uji Organoleptik Ravioli Ayam Woku Berdasarkan Lima Atribut

Ravioli is a dish similar to dumplings, filled and wrapped in thin pasta dough. It is a staple in traditional Italian cuisine. In this study, the author collaborated with Cafe Sanji Eatery to create a pasta menu variant using Chicken and Woku. The research aims to determine the preference levels of panelists based on five evaluation attributes: appearance, aroma, color, texture, and taste, as well as to establish standard recipes and conduct a business analysis of the Chicken Woku Ravioli product. The methodology used in this study is research and development, involving five expert panelists from the faculty and 20 moderately trained student panelists. The results of the preference evaluation for the appearance, aroma, color, texture, and taste of Chicken Woku Ravioli showed: appearance 64% liked it very much, aroma 52% liked it very much, color 64% liked it very much, texture 68% liked it very much, and taste 52% liked it very much. Based on the research findings, the standard recipe consists of 50g chicken breast, 100g flour, 80g eggs, 10g chicken powder, 10g basil, 50g tomatoes, 25g garlic, 36g shallots, 30g lemongrass, 75g red chili, 9g bird’s eye chili, 4g pandan leaves, 2g turmeric leaves, 15g turmeric, 20g ginger, 5g salt, and 5g sugar. The business analysis results indicate a food cost of Rp 9,000 per serving with a selling price of Rp 22,500 per serving. The Chicken Woku Ravioli seller will break even (BEP) after selling 287 servings. To achieve a target margin of Rp 2,000,000 per month, the author plans to sell 435 servings per month. The seller aims for a profit margin of 40%. It is recommended to add more pasta shape variations to increase consumer interest.

TSAE-UNet: A Novel Network for Multi-Scene and Multi-Temporal Water Body Detection Based on Spatiotemporal Feature Extraction

The application of remote sensing technology in water body detection has become increasingly widespread, offering significant value for environmental monitoring, hydrological research, and disaster early warning. However, the existing methods face challenges in multi-scene and multi-temporal water body detection, including the diverse variations in water body shapes and sizes that complicate detection; the complexity of land cover types, which easily leads to false positives and missed detections; the high cost of acquiring high-resolution images, limiting long-term applications; and the lack of effective handling of multi-temporal data, making it difficult to capture the dynamic changes in water bodies. To address these challenges, this study proposes a novel network for multi-scene and multi-temporal water body detection based on spatiotemporal feature extraction, named TSAE-UNet. TSAE-UNet integrates convolutional neural networks (CNN), depthwise separable convolutions, ConvLSTM, and attention mechanisms, significantly improving the accuracy and robustness of water body detection by capturing multi-scale features and establishing long-term dependencies. The Otsu method was employed to quickly process Sentinel-1A and Sentinel-2 images, generating a high-quality training dataset. In the first experiment, five rectangular areas of approximately 37.5 km2 each were selected to validate the water body detection performance of the TSAE-UNet model across different scenes. The second experiment focused on Jining City, Shandong Province, China, analyzing the monthly water body changes from 2020 to 2022 and the quarterly changes in 2022. The experimental results demonstrate that TSAE-UNet excels in multi-scene and long-term water body detection, achieving a precision of 0.989, a recall of 0.983, an F1 score of 0.986, and an IoU of 0.974, significantly outperforming FCN, PSPNet, DeepLabV3+, ADCNN, and MECNet.

Relevance of Anatomical Significance of AV Nodal Structures within Koch's Triangle and Pyramid.

The exploration of the cardiac conduction system evolved over a century, marked by groundbreaking discoveries in atrioventricular (AV) nodal physiology. Atrioventricular nodal re-entrant tachycardia (AVNRT), the most prevalent regular tachycardia in humans, remains enigmatic despite extensive research. Detailed examinations of AV nodal anatomy and histology reveal variations in location and shape, influencing electrophysiological properties. Variability in AV nodal extensions and their embryological origins contribute to the complexity of the conduction system. Physiologically, the AV node plays a crucial role in modulating AV conduction, introducing delays for ventricular filling and filtering atrial impulses. Dual-pathway physiology involving fast and slow pathways further complicates AVNRT circuitry. Integrated approaches combining pre-procedural imaging with electroanatomical mapping enhance our understanding of AV nodal structures and high-definition mapping improves precision in identifying ablation targets. Electrophysiological-anatomical correlations may unveil the specific roles of conduction axis components, aiding in the optimization of ablation strategies. This review traces the historical journey from Tawara's pioneering work to recent integrated approaches aimed at unraveling the intricacies of AV nodal structures while emphasizing the importance of a multidimensional approach, incorporating technological advancements, anatomical understanding, and clinical validation in human mapping studies.

Advances in Multiscale Modeling and Mechanical Properties Characterization of 3D‐Braided Composites

The 3D‐braided composites have been widely used in aerospace and other fields due to the excellent mechanical properties, designability, and resistance to delamination. Accurately analyzing and evaluating mechanical properties of braided composite structures is the key, which successfully designs related structural components. However, 3D‐braided composites have complex internal meso–microscopic structures, and directly establishing a solid structure model to predict mechanical behavior poses certain challenges. The multiscale research methods are a type of approach that studies the cross‐scale structural characteristics at macro–meso–microscopic scales and couples relevant scales into a whole. Its emergence provides the possibility for evaluating the overall structural performance of 3D‐braided composites. In this article, first, the multiscale analysis models of 3D‐multidirectional‐braided composites are reviewed, and then the theoretical and numerical research progress on the static and dynamic multiscale mechanical performance of regular components of 3D‐braided composites is summarized. In addition, due to the distortion and variation of geometric shapes of 3D‐braided composites on the meso–microscopic structure; therefore, also in this article, the research progress on multiscale mechanical performance characteristics of special‐shaped components is summarized. Finally, the key problems in the multiscale research of 3D‐braided composites are presented.

The issue of continuous characters in the copulatory bulb of Neodiplothele spiders: a morpho-geometric approach

ABSTRACT The definition of species boundaries in mygalomorph spiders is historically based on morphological characters, which can be problematic due to the homogeneous morphology and continuous variation across species. The male copulatory bulb of Neodiplothele, as of many mygalomorphs, is phenotypically diverse and the differences are displayed mostly by subtle shape variations. The purpose of this research was to evaluate the traditional morphology-based delimitation of species informal groups and assess the significance of morphological characters in the systematics of the highly variable and widespread mygalomorph spiders of the barychelid genus Neodiplothele. We conducted a geometric morphometric analysis on the bulb of specimens of Neodiplothele using Fourier descriptors. The results show that the bulb is rather difficult to apply as a discrete character in a taxonomic context and show the proximity of a group of undescribed species of Neodiplothele to both groups proposed in a previous revision, indicating a continuous variation. As a taxonomic character, the use of the copulatory bulb shape as a discrete character may be uninformative in the case of species that present one or more intermediate states, when the incorrect treatment of the character can result in a misrepresentation of shape diversity and equivocate inference of evolutionary relationships.https://zoobank.org/urn:lsid:zoobank.org:author:471C6BE5-D24C-4805-B2DE-EB89B6D1D8FA https://zoobank.org/urn:lsid:zoobank.org:author:D4955FF5-FE7F-4E68-AB2F-4A89593F9850

Welding Defect Monitoring Based on Multi-Scale Feature Fusion of Molten Pool Videos.

Real-time quality monitoring through molten pool images is a critical focus in researching high-quality, intelligent automated welding. However, challenges such as the dynamic nature of the molten pool, changes in camera perspective, and variations in pool shape make defect detection using single-frame images difficult. We propose a multi-scale fusion method for defect monitoring based on molten pool videos to address these issues. This method analyzes the temporal changes in light spots on the molten pool surface, transferring features between frames to capture dynamic behavior. Our approach employs multi-scale feature fusion using row and column convolutions along with a gated fusion module to accommodate variations in pool size and position, enabling the detection of light spot changes of different sizes and directions from coarse to fine. Additionally, incorporating mixed attention with row and column features enables the model to capture the characteristics of the molten pool more efficiently. Our method achieves an accuracy of 97.416% on a molten pool video dataset, with a processing time of 16 ms per sample. Experimental results on the UCF101-24 and JHMDB datasets also demonstrate the method's generalization capability.

Vegetative traits, floral biology, and mutualistic interactions in the tropical mountain shrub Ribes ciliatum (Grossulariaceae)

Plant species in tropical mountain ecosystems experience significant environmental variation across short spatial distances. These systems offer an opportunity to assess geographic variation in plant traits in relation to biotic and abiotic environments. We examined plant vegetative and reproductive traits in the shrub R. ciliatum across two native conifer forests in central Mexico. We measured plant height, number of branches, leaf size, flower production, floral morphology, floral longevity, and plant reproductive success in R. ciliatum populations found in fir forests (3100 m) and Hartweg's pine forests (subalpine habitat, 3700 m). We also quantified the level of pollinator dependence, pollinator visitation rates, and fruit consumption rates by frugivorous birds in both populations. Plants were shorter and had smaller leaves in the subalpine forest than in the fir forest. Floral longevity decreased in the subalpine forest, but flower production, flower size, and fruit weight remained similar across populations. Fruit set and pollinator visitation rates were higher in the subalpine forest; however, seed weight and fruit consumption rates did not differ between sites. Hummingbirds and bumblebees were the primary pollinators of R. ciliatum, and the bird Ptiliogonys cinereus was the most important fruit consumer at both sites. Fruit set was entirely dependent on pollinators. Reduced plant size in the subalpine forest coincides with a general pattern associated with high mountain environments. In contrast, reproductive characters had a closer relationship to the pollination environment. The absence of variation in flower size and shape was consistent with the similarity in the composition of the pollinator community. Meanwhile, compared to lower-elevation forests, the subalpine forest exhibited shorter-lived flowers and increased fruit set, associated with higher pollinator visitation rates. Ribes ciliatum is a key resource for pollinators and frugivores at a time of year when few other floral resources are available in these high-elevation mountain forests.

LMVSegRNN and Poseidon3D: Addressing Challenging Teeth Segmentation Cases in 3D Dental Surface Orthodontic Scans.

The segmentation of teeth in 3D dental scans is difficult due to variations in teeth shapes, misalignments, occlusions, or the present dental appliances. Existing methods consistently adhere to geometric representations, omitting the perceptual aspects of the inputs. In addition, current works often lack evaluation on anatomically complex cases due to the unavailability of such datasets. We present a projection-based approach towards accurate teeth segmentation that operates in a detect-and-segment manner locally on each tooth in a multi-view fashion. Information is spatially correlated via recurrent units. We show that a projection-based framework can precisely segment teeth in cases with anatomical anomalies with negligible information loss. It outperforms point-based, edge-based, and Graph Cut-based geometric approaches, achieving an average weighted IoU score of 0.97122±0.038 and a Hausdorff distance at 95 percentile of 0.49012±0.571 mm. We also release Poseidon's Teeth 3D (Poseidon3D), a novel dataset of real orthodontic cases with various dental anomalies like teeth crowding and missing teeth.

Assessing shell morphology in freshwater mussels (Mollusca: Bivalvia: Unionidae) from the Maâden River, Tunisia: Insights from geometric morphometrics and shape descriptors

Identifying freshwater mussel species within the Unionidae family can be challenging due to systematic confusion. This study investigates shell shapes to identify four sympatric species of freshwater mussels (Potamida littoralis, Unio durieui, U. gibbus, and U. ravoisieri) in the Maâden river (northern Tunisia) using two morphological approaches: global shape descriptors and geometric morphometrics of shell contours. The results revealed significant differences in global shape descriptors among the species. More in-depth analyses showed distinct shape variations, with P. littoralis and U. gibbus significantly differing from U. durieui and U. ravoisieri across most descriptors. Notably, U. durieui and U. ravoisieri exhibited similar shape traits, differing primarily in roundness, characterized by low circularity and high ellipticity. Principal Component Analyses depicted substantial morphological variance, with Procrustes data requiring the first two components to fully elucidate dispersion. Linear Discriminant Analysis revealed effective discrimination among species, with an accuracy rate of over 74 % in classifying individuals into their respective populations across both approaches. Geospatial analyses indicated varying influences of latitude, longitude, and altitude on shell morphology among species. Latitude had noteworthy impacts on P. littoralis, approaching significance for shape descriptors and Procrustes data. Conversely, U. durieui and U. gibbus displayed minimal geographic influence on shell morphology, while U. ravoisieri showed significant variations across these parameters. This study highlights the distinct morphological differences among sympatric mussel species in northern Tunisia, providing valuable insights for conservation efforts, especially for species vulnerable to climate change.

Combination Pattern Method Using Deep Learning for Pill Classification

The accurate identification of pills is essential for their safe administration in the medical field. Despite technological advancements, pill classification encounters hurdles such as ambiguous images, pattern similarities, mixed pills, and variations in pill shapes. A significant factor is the inability of 2D imaging to capture a pill’s 3D structure efficiently. Additionally, the scarcity of diverse datasets reflecting various pill shapes and colors hampers accurate prediction. Our experimental investigation shows that while color-based classification obtains a 95% accuracy rate, shape-based classification only reaches 66%, underscoring the inherent difficulty distinguishing between pills with similar patterns. In response to these challenges, we propose a novel system integrating Multi Combination Pattern Labeling (MCPL), a new method designed to accurately extract feature points and pill patterns. MCPL extracts feature points invariant to rotation and scale and effectively identifies unique edges, thereby emphasizing pills’ contour and structural features. This innovative approach enables the robust extraction of information regarding various shapes, sizes, and complex pill patterns, considering even the 3D structure of the pills. Experimental results show that the proposed method improves the existing recognition performance by about 1.2 times. By improving the accuracy and reliability of pill classification and recognition, MCPL can significantly enhance patient safety and medical efficiency. By overcoming the limitations inherent in existing classification methods, MCPL provides high-accuracy pill classification, even with constrained datasets. It substantially enhances the reliability of pill classification and recognition, contributing to improved patient safety and medical efficiency.