Search Results (354)

Axis definition plays a key role in the establishment of animal body plans, both in normal development and regeneration. The cnidarian Hydra can re-establish its simple body plan when regenerating from a random cell aggregate or a sufficiently small tissue fragment. At the beginning of regeneration, a hollow cellular spheroid forms, which then undergoes symmetry breaking and de novo body axis definition. In the past, we have published related work in a physics journal, which is difficult to read for scientists from other disciplines. Here, we review our work for readers not so familiar with this type of approach at a level that requires very little knowledge in mathematics. At the same time, we present a few aspects of Hydra biology that we believe to be linked to our work. These biological aspects may be of interest to physicists or members of related disciplines to better understand our approach. The proposed theoretical model is based on fluctuations of gene expression that are triggered by mechanical signaling, leading to increasingly large groups of cells acting in sync. With a single free parameter, the model quantitatively reproduces the experimentally observed expression pattern of the gene ks1, a marker for ‘head forming potential’. We observed that Hydra positions its axis as a function of a weak temperature gradient, but in a non-intuitive way. Supposing that a large fluctuation including ks1 expression is locked to define the head position, the model reproduces this behavior as well—without further changes. We explain why we believe that the proposed fluctuation-based symmetry breaking process agrees well with recent experimental findings where actin filament organization or anisotropic mechanical stimulation act as axis-positioning events. The model suggests that the Hydra spheroid exhibits huge sensitivity to external perturbations that will eventually position the axis. Full article

Energy generation from renewable sources has increased exponentially worldwide, particularly wind energy, which is converted into electricity through wind turbines. The growing demand for renewable energy has driven the development of horizontal-axis wind turbines with larger dimensions, as the energy captured is proportional to the area swept by the rotor blades. In this context, the dynamic loads typically observed in wind turbine towers include vibrations caused by rotating blades at the top of the tower, wind pressure, and earthquakes (less common). In offshore wind farms, wind turbine towers are also subjected to dynamic loads from waves and ocean currents. Vortex-induced vibration can be an undesirable phenomenon, as it may lead to significant adverse effects on wind turbine structures. This study presents a two-dimensional transient model for a rigid body anchored by a torsional spring subjected to a constant velocity flow. We applied a coupling of the Fourier pseudospectral method (FPM) and immersed boundary method (IBM), referred to in this study as IMERSPEC, for a two-dimensional, incompressible, and isothermal flow with constant properties—the FPM to solve the Navier–Stokes equations, and IBM to represent the geometries. Computational simulations, solved at an aspect ratio of $\varphi =4.0$, were analyzed, considering Reynolds numbers ranging from $Re=150$ to Re = 1000 when the cylinder is stationary, and $Re=250$ when the cylinder is in motion. In addition to evaluating vortex shedding and Strouhal number, the study focuses on the characterization of space–time symmetry during the galloping response. The results show a spatial symmetry breaking in the flow patterns, while the oscillatory motion of the rigid body preserves temporal symmetry. The numerical accuracy suggested that the IMERSPEC methodology can effectively solve complex problems. Moreover, the proposed IMERSPEC approach demonstrates notable advantages over conventional techniques, particularly in terms of spectral accuracy, low numerical diffusion, and ease of implementation for moving boundaries. These features make the model especially efficient and suitable for capturing intricate fluid–structure interactions, offering a promising tool for analyzing wind turbine dynamics and other similar systems. Full article

The reliability and safety of five-axis CNC abrasive water jet machining are critical for many industries. This study employs Failure Mode and Effects Analysis (FMEA) to identify and mitigate potential failures in this machining system. Traditional FMEA, which relies on crisp numerical values, often struggles with handling uncertainty in risk assessment. To address this limitation, this paper introduces an Interval-Valued Spherical Fuzzy FMEA (IVSF-FMEA) approach, which enhances risk evaluation by incorporating membership, non-membership, and hesitancy degrees. The IVSF-FMEA method leverages the inherent rotational symmetry of interval-valued spherical fuzzy sets and the permutation symmetry among severity, occurrence, and detectability criteria, resulting in a transformation-invariant and unbiased risk assessment framework. Applying IVSF-FMEA to seven periodic failure (PF) modes in five-axis CNC water jet machining achieves a more precise prioritization of risks, leading to improved decision-making and resource allocation. The findings highlight improper fixturing of the workpiece (PF6) as the most critical failure mode, with the highest RPN value of −0.54, followed by mechanical vibrations (PF2) and tool wear and breakage (PF1). This indicates that ensuring proper fixturing stability is essential for maintaining machining accuracy and preventing defects. Comparative analysis with traditional FMEA demonstrates the superiority of the proposed fuzzy-based approach in handling subjective assessments and reducing ambiguity. The findings highlight improper fixturing, mechanical vibrations, and tool wear as the most critical failure modes, necessitating targeted risk mitigation strategies. This research contributes to advancing risk assessment methodologies in complex manufacturing environments. Full article

Web-tapered beams with I-sections, which are aesthetic and structurally efficient, have been widely used in steel structures. Web-tapered I-section beams bent about the strong axis may undergo out-of-plane buckling through lateral deflection and twisting. This primary stability failure mode in slender beams is known as lateral-torsional buckling (LTB). Unlike prismatic I-beams, the complex mode shape of web-tapered I-section beams makes it challenging or even impossible to derive a closed-form expression for the LTB load under certain transverse loading conditions. Therefore, the LTB assessment of web-tapered I-section beams is primarily performed using finite element analysis (FEA). However, this method involves multiple steps, requires specialized expertise, and demands significant computational resources, making it impractical in certain cases. This study proposes an analytical approach based on the Ritz method to evaluate the LTB of simply supported web-tapered beams with doubly or mono-symmetric I-sections. The proposed analytical method accounts for web tapering, I-section mono-symmetry, types and positions of transverse loads, and beam slenderness. The method was implemented in Mathematica to allow the rapid evaluation of the LTB capacity of web-tapered I-beams. The study validates the LTB loads computed using the developed Mathematica package against results from shell-based FEA. An excellent agreement was observed between the analytically and numerically calculated LTB loads. Full article

Medium- and large-scale heat sinks are critical for thermal load management in high-performance systems. However, their high heat flux densities and limited space complicate cooling, leading to risks of overheating, performance degradation, or failure. This study employs the Cascaded Lattice Boltzmann Method (CLBM) to enhance their thermal performance. This numerical approach is known for being stable, accurate when dealing with complex boundaries, and efficient when computing in parallel. The numerical code was validated against a benchmark configuration and an experimental setup to ensure its reliability and accuracy. While previous studies have explored mixed convection in cavities or heat sinks, few have addressed configurations involving side air injection and boundary conditions periodicity in the transition-to-turbulent regime. This gap limits the understanding of realistic cooling strategies for compact systems. Focusing on mixed convection in the transition-to-turbulent regime, where buoyancy and forced convection interact, the study investigates the impact of Rayleigh number values ($5\times {10}^7$ to $5\times {10}^8$) and Reynolds number values (${10}^3$ to $3\times {10}^3$) on heat transfer. Simulations were conducted in a rectangular cavity with periodic boundary conditions on the vertical walls. Two heat sources are located on the bottom wall ($T_h$ = 50 °C). Two openings, one on each side of the two hot sources, force a jet of fresh air in from below. An opening at the level of the cavity ceiling’s axis of symmetry evacuates the hot air. Mixed convection drives the flow, exhibiting complex multicellular structures influenced by the control parameters. Calculating the average Nusselt number (Nu) across the surfaces of the heat sink reveals significant dependencies on the Reynolds number. The proposed correlation between Nu and Re, developed specifically for this configuration, fills the current gap and provides valuable insights for optimizing heat transfer efficiency in engineering applications. Full article

This study investigates the shell hydroforming of 1.2 mm-thick AA5052 aluminum alloy sheets to produce hemispherical domes which possess inherent spatial symmetry about their central axis. Shell hydroforming is widely used in fabricating lightweight, high-strength components for aerospace, automotive, and energy applications. The forming process was driven by a spatially symmetrical internal pressure distribution applied uniformly across the blank to maintain balanced deformation and minimize geometrical distortion. Experimental trials aimed at achieving a dome depth of 50 mm revealed wrinkle formation at the blank periphery caused by circumferential compressive stresses symmetrical in nature with respect to the dome’s central axis. To better understand the forming behavior, a validated 3D finite element (FE) model was developed, capturing key phenomena such as material flow, strain rate evolution, hydrostatic stress distribution, and wrinkle development under symmetric boundary conditions. The effects of the internal pressure (IP), blank holding force (BHF), coefficient of friction (CoF), and flange radius (FR) were systematically studied. A strain rate of 0.1 s⁻¹ in the final stage improved material flow, while a symmetric tensile hydrostatic stress of 160 MPa facilitated dome expansion. Although tensile stresses can induce void growth, the elevated strain rate helped suppress it. An optimized parameter set of IP = 5.43 MPa, BHF = 140 kN, CoF = 0.04, and FR = 5.42 mm led to successful formation of the 50 mm dome with 19.38% thinning at the apex. Internal pressure was identified as the most critical factor influencing symmetric formability. A process window was established to predict symmetric failure modes such as wrinkling and bursting. Full article

The defects in zinc oxide crystals are of crucial importance for their usability in many applications and are not yet fully understood. Here, we demonstrate that dioxygen species are present as defects in the grown ZnO, resulting in a bending of the atom layers that lie perpendicular to the c-axis. In the Raman spectra, these defects cause the appearance of bands different from the known bands of perfect ZnO crystals allowed by symmetry. These additional Raman bands, which have been frequently reported for ZnO in the past, can thus be fully explained by the presence of dioxygen species, and the widespread assumption of second-order modes for the assignments of these bands is not necessary. Furthermore, the Raman spectrum belonging to perfect zinc oxide in the ideal wurtzite structure is presented, obtained from small domains in ZnO(0001) crystals exposed to pressures up to 2 GPa. The dependence of the O-O stretching modes on the applied pressure proves the presence of dioxygen species in ZnO, which is also confirmed by phonon calculations of structure models with embedded dioxygen species. The surface quality of the ZnO crystals studied is also reflected in the Raman spectra and is included in the analysis. Full article

The external appearance of strawberry fruits serves as a critical criterion for their commercial value and grading standards. However, current research primarily emphasizes ripeness and surface defects, with limited attention given to the quantitative analysis of geometric characteristics such as deformity and symmetry. To address this gap, this study proposes a comprehensive evaluation framework that integrates deep learning-based segmentation with geometric analysis for strawberry appearance quality assessment. First, an enhanced YOLOv11 segmentation model incorporating a Squeeze-and-Excitation attention mechanism was developed to enable high-precision extraction of individual fruits, achieving Precision, Recall, AP₅₀, and F1 scores of 91.11%, 87.46%, 92.90%, and 88.45%, respectively. Second, a deformity quantification method was designed based on the number of deformity points (N_d), deformity rate (R_d), and spatial distance metrics (G_min and G_max). Experimental results demonstrated significant differences in R_d and G_max between deformed and normal strawberries, indicating strong classification capability. Finally, principal component analysis (PCA) was employed to extract the primary axis direction, and morphological symmetry was quantitatively evaluated using Intersection over Union (IoU) and Area Difference Ratio (AreaD_Ratio). The results revealed that most samples fell within an IoU range of 0.6–0.8 and AreaD_Ratio below 0.4, indicating noticeable inter-individual differences in fruit symmetry. This study aims to establish a three-stage analytical framework—segmentation, deformity quantification, and symmetry evaluation—for assessing strawberry appearance quality, with the goal of supporting key applications in automated grading and precision quality inspection. Full article

The flight feather rachis is a lightweight, anisotropic structure that must withstand asymmetric aerodynamic loads generated during flapping flight—particularly under unidirectional compression during the wing downstroke. To accommodate this spatiotemporal loading regime, the rachis exhibits refined internal organization, especially along the dorsoventral axis. In this study, we used finite element modeling (FEM) to investigate how dorsoventral polarization in cortical keratin allocation modulates the mechanical performance of shaft-like structures under bending. All models were constructed with conserved second moments of area and identical material properties to isolate the effects of spatial material placement. We found that dorsal-biased reinforcement delays yield onset, enhances strain dispersion, and promotes elastic recovery, while ventral polarization leads to premature strain localization and plastic deformation. These outcomes align with the dorsally thickened rachises observed in flight-specialized birds and reflect their adaptation to asymmetric aerodynamic forces. In addition, we conducted a conceptual exploration of radial (cortex–medulla) redistribution, suggesting that even inner–outer asymmetry may contribute to directional stiffness tuning. Together, our findings highlight how the flight feather rachis integrates cortical material asymmetry to meet directional mechanical demands, offering a symmetry-informed framework for understanding biological shaft performance. Full article

A new approach for skeleton extraction has been designed to work directly with 3D point cloud data. It blends hierarchical segmentation with a multi-scale ensemble built on top of modified PointNet models. Outputs from three network variants trained at different spatial resolutions are aggregated using majority voting, unweighted averaging, and adaptive weighting, with the latter yielding the best performance. Each joint is set at the center of its part. A radius-based filter is used to remove any outliers, specifically, points that fall too far from where the joints are expected to be. When evaluated on benchmark datasets such as DFaust, CMU, Kids, and EHF, the model demonstrated strong segmentation accuracy (mIoU = 0.8938) and low joint localization error (MPJPE = 22.82 mm). The method generalizes well to an unseen dataset (DanceDB), maintaining strong performance across diverse body types and poses. Compared to benchmark methods such as L1-Medial, Pinocchio, and MediaPipe, our approach offers greater anatomical symmetry, joint completeness, and robustness in occluded or overlapping regions. Structural integrity is maintained by working directly with 3D data, without the need for 2D projections or medial-axis approximations. The visual assessment of DanceDB results indicates improved anatomical accuracy, even in the absence of quantitative comparison. The outcome supports practical applications in animation, motion tracking, and biomechanics. Full article

The segmentation of jujube tree branches and the estimation of primary branch inclination angles (IAs) are crucial for achieving intelligent pruning. This study presents a primary branch IA estimation algorithm for jujube trees based on an improved PointNet++ network. Firstly, terrestrial laser scanners (TLSs) are used to acquire jujube tree point clouds, followed by preprocessing to construct a point cloud dataset containing open center shape (OCS) and main trunk shape (MTS) jujube trees. Subsequently, the Chebyshev graph convolution module (CGCM) is integrated into PointNet++ to enhance its feature extraction capability, and the DBSCAN algorithm is optimized to perform instance segmentation of primary branch point clouds. Finally, the generalized rotational symmetry axis (ROSA) algorithm is used to extract the primary branch skeleton, from which the IAs are estimated using weighted principal component analysis (PCA) with dynamic window adjustment. The experimental results show that compared to PointNet++, the improved network achieved increases of 1.3, 1.47, and 3.33% in accuracy (Acc), class average accuracy (CAA), and mean intersection over union (mIoU), respectively. The correlation coefficients between the primary branch IAs and their estimated values for OCS and MTS jujube trees were 0.958 and 0.935, with root mean square errors of 2.38° and 4.94°, respectively. In summary, the proposed method achieves accurate jujube tree primary branch segmentation and IA measurement, providing a foundation for intelligent pruning. Full article

During the continuous casting process, the submerged entry nozzle (SEN) should be maintained at the geometric center of the mold. However, in actual production, factors such as deformation of the tundish bottom and inaccurate positioning of the traversing car occasionally cause SEN offset. SEN offset can make the molten steel flow field in the mold asymmetric, increasing the risks of slag entrainment on the surface of the casting blank and breakout accidents. To evaluate the influence of different SEN offsets on the mold flow field, this study uses a slab continuous casting mold with a cross-section of 920 mm × 200 mm from a specific factory as the research object. Mathematical simulations were used to investigate the influence of SEN offsets (including width-direction and thickness-direction offsets) on the flow behavior of molten steel in the mold. A physical water model at a 1:1 scale was established for verification. Two parameters, the symmetry index (S) and the bias flow index (N), were introduced to quantitatively evaluate the symmetry of the flow field, and the rationality of the liquid-level fluctuation under this flow field was verified using the F-number (proposed by Japanese experts for mold level fluctuation control) from the index model. The results show the following: when the SEN offset in the thickness direction increases from 0 to 50 mm, the longitudinal symmetry index (Sy) of the molten steel flow field in the mold decreases from 0.969 to 0.704—a reduction of 27.4%; the longitudinal bias flow index (Ny) of molten steel level fluctuation increases from 0.007 to 0.186, representing a 25.6-fold increase, and the F-number rises from 4.297 to 8.482; when the SEN offset in the width direction increases from 0 to 20 mm, the transverse-axis symmetry index (Sx) of the flow field decreases gradually from 0.969 to 0.753 at a 20 mm offset, which is a reduction of approximately 22.29%; the transverse-axis bias flow index (Nx) increases from 0.015 to 0.174 at a 20 mm offset—an increase of 10.6 times; and the F-number increases from 4.297 to 5.548. Considering the comprehensive evaluation of horizontal/vertical symmetry indices, bias flow indices, and F-numbers under the two working conditions, the width-direction SEN offset has the most significant impact on the symmetry of the molten steel flow field. Full article

This study introduces a computational framework for understanding the symmetry and asymmetry of human movement by integrating Laban Movement Analysis (LMA). By conceptualizing movement refinement as a structured computational process, we model the golf swing as a series of state transitions where perceptual invariants guide biomechanical optimization. The golf club’s motion is analyzed using the instantaneous screw axis (ISA) and inertia tensor revealing how expert golfers dynamically adjust movement by detecting and responding to invariant biomechanical structures. This approach extends Gibson’s ecological theory by proposing that movement execution follows an iterative optimization process analogous to a Turing machine updating its states. Furthermore, we explore the role of symmetry in motor control by aligning Laban’s X-scale with structured computational transitions, demonstrating how movement coordination emerges from dynamically balanced affordance–action couplings. This insight gained from the study suggests that AI-driven sports training and rehabilitation can leverage symmetry-based computational principles to enhance motion learning and real-time adaptation in virtual and physical environments. Full article

Chapparvoviruses (ChPVs) comprise a divergent lineage of the Parvoviridae ssDNA virus family and evolved to infect vertebrate animals independently from the Parvovirinae subfamily. Despite being pathogenic and widespread in environmental samples and metagenomic assemblies, their structural characterization has proven challenging. Here, we report the first structural analysis of a ChPV, represented by the fish pathogen, Syngnathus scovelli chapparvovirus (SsChPV). We show through the SsChPV structure that the lineage harbors a surface morphology, subunit structure, and multimer interactions that are unique among parvoviruses. The SsChPV capsid evolved a threefold-related depression of α-helices that is analogous to the β-annulus pore of denso- and hamaparvoviruses and may play a role in monomer oligomerization during assembly. As interacting β-strands are absent from the twofold symmetry axis, the viral particle lacks the typical stability and resilience of parvovirus capsids. Although all parvoviruses thus far rely on the threading of large, flexible N-terminal domains to the capsid surface for their intracellular trafficking, our results show that ChPVs completely lack any such N-terminal sequences. This led to the subsequent degradation of their fivefold channel, the site of N-terminus externalization. These findings suggest that ChPVs harbor an infectious pathway that significantly deviates from the rest of the Parvoviridae. Full article

The Gaussian Error Linear Unit (GELU), a crucial component of the transformer model, poses a significant challenge for hardware implementation. To address this issue, this paper proposes internal symmetry piecewise approximation (ISPA) and error peak search strategy (EPSS) for high-precision and high-efficiency implementation of the GELU activation function. ISPA only approximates the positive axis of the erf in GELU and then leverages its internal symmetry to calculate the negative axis part. With ISPA, the mean square error (MSE) between the fitted result and the true value can reach $4.29\times$${10}^{-9}$ with 16 parts of the approximation segment, outperforming the regular method, which achieves $1.19\times$${10}^{-6}$ with 16 parts. Furthermore, EPSS can automatically find suitable and high-precision intervals for our piecewise approximation method. To evaluate the effectiveness of ISPA and EPSS, we conducted experiments on three different ViT models and observed negligible loss of prediction accuracy. The hardware implementation is on an XCZU9EG FPGA running at 450 MHz. Experimental results indicate that ISPA outperforms existing methods. Full article