Variation In Shape Research Articles

Geometric Morphometric Study on Distinguishing Metopic Craniosynostosis from Metopic Ridging.

Craniosynostosis, a common congenital anomaly, results from premature fusion of the cranial sutures. One of the forms of craniosynostosis is premature fusion of the metopic suture, referred to as trigonocephaly, but the diagnosis of metopic suture synostosis remains controversial. The purpose of this study was to clarify, using geometric morphometric analysis, if a metopic ridge alone observed in cases of mild trigonocephaly represents a pathological phenomenon. Three different cranial morphologies were compared among patients up to 2 years old who were categorized into the true group, the mild group, and the normal group, based on the presence or absence of specific symptoms, history of cranioplasty for trigonocephaly, or lack of any abnormality on computed tomography. Using the obtained computed tomography images, 235 anatomical landmarks and semi-landmarks were plotted on the entire cranial surface for analysis of neurocranial morphology, and the cranial shapes represented by landmarks were analyzed using geometric morphometrics. Principal components of shape variations among specimens were then computed, based on the variance-covariance matrix of the Procrustes residuals of all specimens, and statistically analyzed. The principal component analyses of the variations in endocranial shape, frontal bone shape, and occipital bone shape did not show any significant differences in cranial morphology between mild trigonocephaly and normal skulls; however, true trigonocephaly was found to differ significantly from mild trigonocephaly and normal skulls. These findings suggest that in assessments of cranial morphology, the presence of a ridge alone cannot be diagnosed as fundamentally pathological, and may represent normal morphology.

The different subtypes of cam morphology as defined by statistical shape modeling and their relationship with the development of hip osteoarthritis: A nationwide prospective cohort study (CHECK) with 10 years follow-up

ObjectiveTo determine if subtypes of cam morphology on anteroposterior (AP) radiographs exist using statistical shape modelling (SSM), and to assess their association with incident radiographic hip osteoarthritis (RHOA) within 10 years. DesignThe nationwide prospective Cohort Hip and Cohort Knee (CHECK) study included 1,002 participants aged 45-65 years with 10-year follow-up. Subtypes of cam morphology were defined as SSM-based shape variations of femoral head-neck junction that are associated with baseline cam morphology (alpha angle ≥60°). The association between each subtype in hips free of osteoarthritis at baseline (Kellgren & Lawrence (KL) grade <2) and incident RHOA (KL grade≥2, or a total hip replacement) was estimated using logistic regression at 10-year follow-up and stratified by sex. ResultsIn sex-combined group, but also for males and females separately, cam morphology subtypes were captured in modes 1, 3, 4, and 5 with odds ratios (ORs) ranging from 0.39(0.27-0.58) to 2.25(1.64-3.10). For sex-combined group, only mode 3, a flattened head-neck junction, was associated with incident RHOA (OR:1.14, 1.02-1.27). Males’ modes 1 and 3 and females’ modes 3 and 4 were associated with RHOA. Notably, the female mode 4, a slightly flattened neck but with subtle curvature, was significantly protective for RHOA (OR:0.88, 0.80-0.98). ConclusionsWe identified four distinct morphological subtypes of cam morphology defined by alpha angle. Only some subtypes were found acting as risk factors for RHOA at 10-year follow-up, which differed between males and females. This highlights the need to study cam morphology beyond the alpha angle alone.

Automated contouring of CTV and OARs in planning CT scans using novel hybrid convolution-transformer networks for prostate cancer radiotherapy

Purpose objective(s)Manual contouring of the prostate region in planning computed tomography (CT) images is a challenging task due to factors such as low contrast in soft tissues, inter- and intra-observer variability, and variations in organ size and shape. Consequently, the use of automated contouring methods can offer significant advantages. In this study, we aimed to investigate automated male pelvic multi-organ contouring in multi-center planning CT images using a hybrid convolutional neural network-vision transformer (CNN-ViT) that combines convolutional and ViT techniques.Materials/methodsWe used retrospective data from 104 localized prostate cancer patients, with delineations of the clinical target volume (CTV) and critical organs at risk (OAR) for external beam radiotherapy. We introduced a novel attention-based fusion module that merges detailed features extracted through convolution with the global features obtained through the ViT.ResultsThe average dice similarity coefficients (DSCs) achieved by VGG16-UNet-ViT for the prostate, bladder, rectum, right femoral head (RFH), and left femoral head (LFH) were 91.75%, 95.32%, 87.00%, 96.30%, and 96.34%, respectively. Experiments conducted on multi-center planning CT images indicate that combining the ViT structure with the CNN network resulted in superior performance for all organs compared to pure CNN and transformer architectures. Furthermore, the proposed method achieves more precise contours compared to state-of-the-art techniques.ConclusionResults demonstrate that integrating ViT into CNN architectures significantly improves segmentation performance. These results show promise as a reliable and efficient tool to facilitate prostate radiotherapy treatment planning.

Living in a predictable environment: Intraspecific variation in tadpole size and shape of the Sierra's treefrog

AbstractWithin the breeding sites of anurans, factors at the microhabitat scale can exhibit variations over the years, resulting in temporal fluctuations within these sites. These fluctuations may lead to modifications in “normal larval development” and observable phenotypic changes. These developmental alterations appear to have evolved as shared strategies for achieving reproductive success and ensuring the survival of offspring. In a population of Boana cordobae inhabiting the Central Pampean Sierras (Córdoba, Argentina), the coexistence of pre‐ and prometamorphic tadpoles throughout the year, coupled with variations in size and shape within the same developmental stage, has led us to question about the duration of the larval period, variations in the timing of developmental events, and the possible existence of distinct larval morphs for this species. Utilizing data collected over four consecutive years from various locations, we described the development, growth, and larval morphology using both linear and geometric morphometrics during the pre‐ and prometamorphic stages. Our aim was to comprehend how anuran larvae in predictable environments synchronize the timing of their phenotypic transformations with external conditions. We identified two distinct cohorts: spring–summer and autumn–spring, each exhibiting distinct rates of growth and development until metamorphosis, resulting in size discrepancies. Moreover, the physical attributes of the breeding sites influenced the size of the tadpoles. However, the observed variation in shape was not related to the ontogenetic period, cohort, or developmental site. Ultimately, the observed variations had no impact on the morphology at metamorphosis. The comprehensive data gathered from our observations in B. cordobae yield valuable insights that contribute to a deeper comprehension of anuran larval development under natural conditions where changes are predictable and follow a regular pattern throughout the year, ultimately maximizing reproductive success.

Resolution of cryptic mosquito species through wing morphometrics

Mosquitoes are medically important insects, and accurate species identification is crucial to understanding vector biology, forming the cornerstone of successful vector control programs. Identification is difficult owing to morphologically similar species. Wing morphometrics can provide a simple, fast, and accurate way to classify species, and using it as a method to differentiate vector species among its cryptic congeners has been underexplored. Using a total of 227 mosquitoes and 20 landmarks per specimen, we demonstrated the utility of wing morphometrics in differentiating species two groups occurring in sympatry – Culex (Culex) vishnui group and Culex (Lophoceraomyia) subgenus, as well as explored population-level variation in the wing shape of Aedes albopictus across habitats. Cytochrome oxidase subunit I (COI) gene region was sequenced to validate the morphological and morphometric identification. Procrustes ANOVA regression and CVA based on wing shape reflected that the wing landmarks across all species differed significantly, and leave-one-out cross validation revealed an overall high accuracy of >97% for the two Culex groups. Wing morphometrics uncovered population-level variation within Aedes albopictus, but cross validation accuracy was low. Overall, we show that wing geomorphometric analysis is able to resolve cryptic Culex species (including vectors) occurring sympatrically, and is a robust tool for identifying mosquitoes reliably.

Long term measurements of aerosol mass concentration with optical particle counters: Discrepancies with plausible reasons

Gravimetry-based direct measurements of mass concentration require offline analysis which is not suited for field campaigns. Hence such campaigns rely on the estimation of mass concentration by indirect methods mostly calibrated in controlled laboratory conditions. Optical particle counter (OPC) employs algorithms converting the measured number concentration to mass concentration using appropriate conversion factors. The accuracy of such conversion has not been validated for widely varying atmospheric conditions. This study compares the mass concentration estimated by OPC with those directly obtained from gravimetry-based instruments for outdoor samples collected in Bathinda City, Punjab, India from January 2022 to November 2023. The difference in the gravimetrically measured and OPC predicted values quantified in terms of ratios (gravimetric to optically estimated mass concentration), came out to be 1.42 ± 0.77, 0.99 ± 0.51, and 1.17 ± 0.58 for PM10, PM2.5 and PM1, respectively. This difference when estimated with the back-up filter of OPC itself (C Factor), was 1.37 ± 0.66. More than half of the samples showed ratios outside the 0.8–1.2 range thus indicating under or over-estimation in the OPC predicted values. The probable role of variation in density, shape, and refractive index of atmospheric aerosol particles towards the observed inaccuracy of estimated mass concentration has been highlighted. In the absence of clear guidelines and protocols, the study suggests ways to improve the accuracy via periodic measurement of the C Factor and/or incorporating calibration factors in such measurements.

Mechanical characteristics of biomimetic twisted honeycomb structures fabricated by powder bed fusion

Honeycomb structures have been widely used in aerospace and automotive applications due to their high efficiency, high strength and high specific stiffness. However, most of the reported publications have focused on the surface shape variations of honeycomb structures, and there is a lack of studies on the variations of honeycomb structures with respect to the internal cell walls (Z-direction). To address these issues, this study proposes to combine the torsional design of honeycomb and Bouligand structures to fabricate twisted honeycomb structures (THS) with cell wall (z-direction) variations using powder bed fusion technology. The THS (30°) exhibits excellent specific energy absorption (SEA) performance, and the SEA of THS (0°), THS (60°), and THS (90°) are reduced by 8.5%, 19.7%, and 37.3%, respectively, compared with that of THS (30°) structure, and the THS (30°) has better repeatability. The effects of structural parameters on the energy absorption performance were analyzed. At THS (30°), changing the number of unit cells, wall length and wall thickness has a large effect on the compression performance. The results show that the specific energy absorption performance of THS reaches a maximum value of 74.6 kJ/kg for the number unit cells n = 3, wall length l = 5 mm and wall thickness t = 1 mm.

Sella turcica and facial bones: Morphological integration in the human fetal cranium.

The cranial base plays a significant role in facial growth, and closer analyses of the morphological relationship between these two regions are needed to understand the morphogenesis of the face. Here, we aimed to study morphological integration between the sella turcica (ST) and facial bones during the fetal period using geometric morphometrics. Magnetic resonance images of 47 human fetuses in the Kyoto Collection, with crown-rump lengths of 29.8-225 mm, were included in this study. Anatomical homologous landmarks and semilandmarks were registered on the facial bones and the midsagittal contour of the ST, respectively. The shape variations in the craniofacial skeleton and the ST were statistically investigated by reducing dimensionality using principal component analysis (PCA). Subsequently, the morphological integration between the facial bones and ST was investigated using two-block partial least squares (2B-PLS) analysis. PCA showed that small specimens represented the concave facial profile, including the mandibular protrusion and maxillary retrusion. The 2B-PLS showed a strong integration (RV coefficient = 0.523, r = .79, p < .01) between the facial bones and ST. The curvature of the anterior wall of the ST was highly associated with immature facial morphology characterized by a concave profile. The strong integration between the two regions suggested that the anterior ST may be associated with facial morphology. This result quantitatively confirms previous studies reporting ST deformities in facial anomalies and induces further research using postnatal subjects.

Studying seriality in material culture by geometric morphometrics–gold wild boars from the Arzhan-2 barrow, Tuva

In the field of material culture, seriality refers to the serial production of nearly the same object in terms of shape and size, yielding visually identical artefacts. Subtle variations may nevertheless occur, depending on the technologies used, or the number and reliability of moulds, for example. Geometric morphometrics based on landmark analysis, along with accompanying statistical techniques, provides methods well-suited for identifying small but archaeologically significant variations in shape and size within such datasets. In this study, we exemplify the efficiency of geometric morphometrics in a context of seriality, using a large series of centimetric-sized gold wild boars decorating a case for bow and arrows, discovered in the Arzhan-2 barrow of the early Scythian time. A total of twenty-seven 2D landmarks was collected for each specimen to assess the level of similarity between individuals with high precision, and to investigate the presence of subgroups, possibly indicating the use of several models. However, due to the homogeneous nature of the dataset, notable measurement errors may obscure the sought-after archaeological signal. To mitigate this, each specimen was measured twice by three different operators. Boas coordinates of the six replicates were then averaged, resulting in a reduction of the effect of measurement errors. Two distinct shape groups are identified, consisting of an approximately equal number of individuals. These findings suggest that the entire set of wild boars could have been produced via two separate manufacturing chains, possibly running in parallel, where two distinct, albeit very similar, solid models were involved. Within each group, discreet variations in size were observed. They are probably due to variable shrinkage during casting. These observations would have been difficult for the naked eye, even for an expert in the field, because the striking similarity within the series and the post-processing by the goldsmith obscure the shape signal originating from the moulds. Besides the original information provided here about the gold wild boars of Arzhan-2, it is worth emphasizing that the use of these techniques should be encouraged, particularly when applied to the study of seriality. The workflow described can easily be reproduced and adapted for almost any serially produced archaeological assemblage.

Penetration state recognition for tungsten inert gas welding via an alternating cusp-shaped magnetic field-assisted molten pool-oscillation

To ensure optimal penetration in direct current (DC) argon tungsten arc welding, a method was proposed to recognize the molten pool penetration during molten pool oscillation under alternating cusp-shaped magnetic field (ACSMF). An arc discharge model is established to analyze the oscillation mechanism of the molten pool under ACSMF. The periodic variation of arc shape induces the periodic oscillation to establish a mapping relationship with the fluctuation of arc voltage waveform. At the excitation current of 3 A and frequency of 10 Hz, the arc voltage waveform undergoes abrupt changes at 1.26 V and 1.37 V, corresponding to the critical and the full penetration states, respectively. The errors in identifying moving penetration are 0.8 % and 0.7 %, with a full penetration weld, demonstrating a penetration rate of 100 %, indicating effective penetration recognition. Arc sensing technology for tungsten inert gas (TIG) welding under ACSMF is poised for gradual application in single-side welding and double-side forming of medium and thick plates. It is anticipated to evolve towards real-time identification technology for molten pool penetration monitoring.

Geometric morphometric analysis of beak shape of Columbimorphae (Columbas, Van, Mardin and Dönek).

Selective breeding over centuries has led to the emergence of numerous pigeon breeds from the single species Columba livia, showcasing unparalleled phenotypic diversity. In Eastern Turkey, Van pigeons originate, while Tumbler pigeon varieties thrive countrywide, reflecting local traditions. The avian beak, vital for survival, offers insights into domestication and traits influenced by natural selection. Geometric morphometrics, a shape analysis method, allows for a comprehensive examination of beak shapes among domestic pigeons, providing a nuanced understanding of their complexity. Understanding beak diversity in pigeons, especially those found in Turkey, enhances our knowledge of avian evolution and adaptation processes. The study utilized 48 skulls from 4 pigeon breeds, including wild rock pigeons and domestic pigeons of Tumbler, Mardin and Van breeds, all free from pathological lesions and adults. Geometric morphometric analyses of beak shape were conducted using dorsal and lateral photographs, with landmarks placed using tpsDig software. MorphoJ software facilitated procrustes analysis and principal component analysis (PCA) to assess morphological variability and differentiate pigeon breeds based on shape patterns, with significance set at p < 0.05. PCA revealed significant shape variations among pigeon breeds, with 47 principal components identified for lateral beak views and 36 principal components for dorsal views. Canonical variates analysis further distinguished morphological patterns among breeds, indicating distinct shape variations in both dorsal and lateral views, elucidating the unique characteristics of each breed's beak morphology. Our results demonstrate statistically significant differences in pigeon beak shape, particularly in the lateral view, confirming the importance of these variations (p < 0.05) and suggesting that such differences occur less than 5% of the time under the null hypothesis.

Morphology of hemipenes and its taxonomic implication in the fused eyelids species of the genus Ablepharus (Squamata: Scincidae)

The representatives of the genus Ablepharus Fitzinger, 1824 have not been studied in detail from a morphological and taxonomic perspective. The morphology of hemipenes of 14 taxa of the fused eyelids species of the genus was studied. Manual eversion and inflation with absolute ethanol was used for preparation of the organs. The results indicate that the Ablepharus hemipenes show little variation in general shape and main structures: all hemipenes are deeply bifurcated, without large external calcified or keratinous spines or similar structures. Based on hemipenial morphology, the studied taxa can be separated into two clades: 1) with long, slender and 2) with short, robust lobes of the hemipenis. The morphology of this structure demonstrates close relationships between the fused eyelids ablepharine skinks, points out unresolved taxonomic problems, and shows general congruence with available molecular phylogenies. Hemipenial characteristics appear to be a useful tool for the identification of the species within the genus. Problems associated with the real taxonomic status of the Ablepharus budaki anatolicus paratypes and the distribution of other taxa are discussed. The morphology of the hemipenis of the family Scincidae and the related, inconsistent use of terminology are also commented on.

Adaptive radiotherapy for muscle invasive bladder cancer: a retrospective audit of two bladder filling protocols

BackgroundRadical radiotherapy for muscle-invasive bladder cancer (MIBC) is challenging due to large variations in bladder shape, size and volume during treatment, with drinking protocols often employed to mitigate geometric uncertainties. Utilising adaptive radiotherapy together with CBCT imaging to select a treatment plan that best fits the bladder target and reduce normal tissue irradiation is an attractive option to compensate for anatomical changes. The aim of this retrospective study was to compare a bladder empty (BE) protocol to a bladder filling (BF) protocol with regards to variations in target volumes, plan of the day (PoD) selection and plan dosimetry throughout treatment.MethodsForty patients were included in the study; twenty were treated with a BE protocol and twenty with a BF protocol to a total prescribed dose of 55 Gy in 20 fractions. Small, medium and large bladder plans were generated using three different CTV to PTV margins. Bladder (CTV) volumes were delineated on planning CTs and online pre-treatment CBCTs. Differences in CTV volumes throughout treatment, plan selection, PTV volumes and resulting dose metrics were compared for both protocols.ResultsMean bladder volume differed significantly on both the planning CTs and online pre-treatment CBCTs between the protocols (p < 0.05). Significant differences in bladder volumes were observed between the planning CT and pre-treatment CBCTs for BF (p < 0.05) but not for BE (p = 0.11). Both protocols saw a significant decrease in bladder volume between first and final treatment fractions (p < 0.05). Medium plans were preferentially selected for BE whilst when using the BF protocol the small plan was chosen most frequently. With no significant change to PTV coverage between the protocols, the volume of body receiving 25.0–45.8 Gy was found to be significantly smaller for BE patients (p < 0.05).ConclusionsThis work provides evidence in favour of a BE protocol compared to a BF protocol for radical radiotherapy for MIBC. The smaller treatment volumes observed in the BE protocol led to reduced OAR and total body doses and were also observed to be more consistent throughout the treatment course. These results highlight improvements in dosimetry for patients who undergo a BE protocol for MIBC.

Multi-planar dual adversarial network based on dynamic 3D features for MRI-CT head and neck image synthesis

Objective. Head and neck radiotherapy planning requires electron densities from different tissues for dose calculation. Dose calculation from imaging modalities such as MRI remains an unsolved problem since this imaging modality does not provide information about the density of electrons. Approach. We propose a generative adversarial network (GAN) approach that synthesizes CT (sCT) images from T1-weighted MRI acquisitions in head and neck cancer patients. Our contribution is to exploit new features that are relevant for improving multimodal image synthesis, and thus improving the quality of the generated CT images. More precisely, we propose a Dual branch generator based on the U-Net architecture and on an augmented multi-planar branch. The augmented branch learns specific 3D dynamic features, which describe the dynamic image shape variations and are extracted from different view-points of the volumetric input MRI. The architecture of the proposed model relies on an end-to-end convolutional U-Net embedding network. Results. The proposed model achieves a mean absolute error (MAE) of 18.76±5.167 in the target Hounsfield unit (HU) space on sagittal head and neck patients, with a mean structural similarity (MSSIM) of 0.95±0.09 and a Frechet inception distance (FID) of 145.60±8.38 . The model yields a MAE of 26.83±8.27 to generate specific primary tumor regions on axial patient acquisitions, with a Dice score of 0.73±0.06 and a FID distance equal to 122.58±7.55 . The improvement of our model over other state-of-the-art GAN approaches is of 3.8%, on a tumor test set. On both sagittal and axial acquisitions, the model yields the best peak signal-to-noise ratio of 27.89±2.22 and 26.08±2.95 to synthesize MRI from CT input. Significance. The proposed model synthesizes both sagittal and axial CT tumor images, used for radiotherapy treatment planning in head and neck cancer cases. The performance analysis across different imaging metrics and under different evaluation strategies demonstrates the effectiveness of our dual CT synthesis model to produce high quality sCT images compared to other state-of-the-art approaches. Our model could improve clinical tumor analysis, in which a further clinical validation remains to be explored.

LA-Net: An End-to-End Category-Level Object Attitude Estimation Network Based on Multi-Scale Feature Fusion and an Attention Mechanism

In category-level object pose estimation tasks, determining how to mitigate intra-class shape variations and improve pose estimation accuracy for complex objects remains a challenging problem to solve. To address this issue, this paper proposes a new network architecture, LA-Net, to efficiently ascertain object poses from features. Firstly, we extend the 3D graph convolution network architecture by introducing the LS-Layer (Linear Connection Layer), which enables the network to acquire features from different layers and perform multi-scale feature fusion. Secondly, LA-Net employs a novel attention mechanism (PSA) and a Max-Pooling layer to extract local and global geometric information, which enhances the network’s ability to perceive object poses. Finally, the proposed LA-Net recovers the rotation information of an object by decoupling the rotation mechanism. The experimental results show that LA-Net can has much better accuracy in object pose estimation compared to the baseline method (HS-Pose). Especially for objects with complex shapes, its performance is 8.2% better for the 10°5 cm metric and 5% better for the 10°2 cm metric.

Label-aware Attention Network with Multi-scale Boosting for Medical Image Segmentation

Deep medical image segmentation calls for features with strong discrimination and rich scales due to ambiguous background distraction and large variations in object sizes and shapes. In this paper, we propose two modules to obtain these features. First, existing encoders tend to extract similar foreground/background features at blurry boundaries due to mixed-label feature aggregation. To enhance the discrimination of these features, a Label-Aware Attention (LAA) module is presented to reconstruct them by fusing same-label local features. The fusion is guided by local attention maps based on label-aware affinity learning. Second, instead of relying on a single encoder for scale context mining, we propose a Multi-scale Feature Boosting (MFB) module that applies parallel convolution with different receptive fields for scale embedding and integrates an additional backbone for cross-encoder scale reference. Combining LAA and MFB, a new encoder–decoder based framework is presented, where MFBs act as encoder blocks to recursively extract features with rich scale context, while LAA operates in the decoder layer to enhance the label-aware discriminativeness of features. Extensive experiments on three standard medical segmentation datasets demonstrate the effectiveness of the proposed framework.

Segmentation and visualization of Retinal Detachment lesions through Retinal fundus images

Medical image segmentation is a prominent task in medical image processing and a fundamental aspect of medical image analysis. However, accurate segmentation is exceedingly challenging due to the variations in size, shape, and location of the lesions. U-Net is well-linked in the domain of medical image segmentation, but it fails to fully exploit the advantageous characteristics of the channel or leverage contextual information. This article presents a novel, improved MultiResU-Net framework called DCA-MultiResU-Net to minimize the loss of crucial features and also to enhance the efficiency of end-to-end image segmentation by utilizing the full advantages of U-Net, Res2Net, Channel attention mechanism, Dilated convolution layers, and MultiResUNet models. In contrast to the typical convolutions of U-Net in the encoder path, the dilated convolution modules are inserted into the MultiRes blocks to extract and concatenate the multi-scale features. Furthermore, a channel attention mechanism is employed in the bottom portion of the “U” shaped network and the decoder path to fuse the information from receptive fields of varying sizes. Afterward, the virtual reality visualization technology was implemented for the 3-D visualization of segmented RD lesions. The proposed segmentation model was evaluated on four online databases: Retinal Image Bank, RIADD, Kaggle, and Cataract Image Dataset or GitHub. The performance outcomes of this work were measured using standard evaluation indexes such as Dice similarity coefficient, Intersection over the union, F1-score, and Loss function score of 90.76%, 88.42%, 93.28%, and 0.1641, respectively. The experimental findings indicate that the DA-MultiResU-Net framework achieved superior performance and generalization capabilities while utilizing 6.76% fewer parameters than the U-Net model.

Floquet valley Hall edge solitons

We introduce traveling Floquet valley Hall edge solitons in a genuinely continuous system consisting of a waveguide array with a dynamically varying domain wall between two honeycomb structures exhibiting broken inversion symmetry. Inversion symmetry in our system is broken due to periodic and out-of-phase longitudinal modulation of the refractive index applied to the constituent sublattices of the honeycomb structure. By combining two honeycomb arrays with different initial phases of refractive index modulation we create a dynamically changing domain wall that supports localized linear Floquet edge states despite the fact that on average two sublattices in each honeycomb structure forming the domain wall have the same refractive index. In the presence of focusing nonlinearity, bright or dark Floquet edge solitons may bifurcate from such linear Floquet edge states. We numerically identified family of these solitons and compared them with the results obtained from the analytical approach, which involved averaging over one longitudinal period in the evolution coordinate. These solitons exhibit localization in both spatial directions – along the interface due to nonlinear self-action and across the interface as the edge states – that allows them to travel along the domain wall over long distances without noticeable shape variations.

BFT‐Net: A transformer‐based boundary feedback network for kidney tumour segmentation

AbstractKidney tumours are among the top ten most common tumours, the automatic segmentation of medical images can help locate tumour locations. However, the segmentation of kidney tumour images still faces several challenges: firstly, there is a lack of renal tumour endoscopic datasets and no segmentation techniques for renal tumour endoscopic images; secondly, the intra‐class inconsistency of tumours caused by variations in size, location, and shape of renal tumours; thirdly, difficulty in semantic fusion during decoding; and finally, the issue of boundary blurring in the localization of lesions. To address the aforementioned issues, a new dataset called Re‐TMRS is proposed, and for this dataset, the transformer‐based boundary feedback network for kidney tumour segmentation (BFT‐Net) is proposed. This network incorporates an adaptive context extract module (ACE) to emphasize local contextual information, reduces the semantic gap through the mixed feature capture module (MFC), and ultimately improves boundary extraction capability through end‐to‐end optimization learning in the boundary assist module (BA). Through numerous experiments, it is demonstrated that the proposed model exhibits excellent segmentation ability and generalization performance. The mDice and mIoU on the Re‐TMRS dataset reach 91.1% and 91.8%, respectively.

Estimating hydrodynamic performances of the designed leisure boats: a study considering hull shape and dimension variations

Abundant marine natural resources make Indonesia have potential in the field of marine tourism. Karimunjawa is one of the regions that still relies on marine tourism as the main sector in supporting the economy. Unfortunately, the existing modes of marine tourism transportation are dominated by traditional ships that have low performance, safety, and comfort. Therefore, infrastructure development is needed by developing a leisure boat design that has stability and good ship movement so that tourists are comfortable and safe on the trip. This study focuses on optimizing the hydrodynamic characteristics of leisure boats with variations in hull shape and hull dimensions adjusted to the characteristics of waters in Karimunjawa. The research was conducted by designing and analyzing 16 variations of hull models based on 5 reference ships. The regression approach is carried out on the dimensions of the reference ship to obtain variations in ship dimensions, then variations in hull shape include deep v, shallow v, flat bottom and round bottom. Each model variation is tested for resistance, stability, and seakeeping to determine the hydrodynamic performance of each model. Multi Attribute Decision Making (MADM) calculations are carried out afterwards to get the best tourist hull design. Then a sensitivity analysis was carried out on each variation to determine the influence of variations in dimensions and hull shape. The results showed the Deep V D model as the best model based on MADM calculations and hull shape variations had a significant influence on the hydrodynamic performance of the ship. It is hoped that this model can help the development of safe and comfortable tourist boats in accordance with the characteristics of the waters in Karimunjawa.