Complex Shapes Research Articles

Adaptive Opto-Thermal-Hydrodynamic Manipulation and Polymerization (AOTHMAP) for 4D Colloidal Patterning.

Precision colloidal patterning holds great promise in constructing customizable micro/nanostructures and functional frameworks, which showcases significant application values across various fields, from intelligent manufacturing to optoelectronic integration and biofabrication. Here, a direct 4D patterning method via adaptive opto-thermal-hydrodynamic manipulation and polymerization (AOTHMAP) with single-particle resolution is reported. This approach utilizes a single laser beam to automatically transport, position, and immobilize colloidal particles through the adaptive utilization of light-induced hydrodynamic force, optical force, and photothermal polymerization. The AOTHMAP enables precise 1D, 2D, and 3D patterning of colloidal particles of varying sizes and materials, facilitating the construction of customizable microstructures with complex shapes. Furthermore, by harnessing the pH-responsive properties of hydrogel adhesives, the AOTHMAP further enables 4D patterning by dynamic alteration of patterned structures through shrinkage, restructuring, and cloaking. Notably, the AOTHMAP also enables biological patterning of functional bio-structures such as bio-micromotors. The AOTHMAP offers a simple and efficient strategy for colloidal patterning with high versatility and flexibility, which holds great promises for the construction of functional colloidal microstructures in intelligent manufacturing, as well as optoelectronic integration and biofabrication.

Modeling and Estimation of Adaptive Optics-Corrected long-exposure Point Spread Function Using Gaussian Sum Approximation

Abstract Adaptive optics (AO) enhances astronomical images by correcting perturbations in the optical system, resulting in complex point spread function (PSF) shapes, which provide critical information for optical design, calibration, and diagnostics. This paper presents a parametric model for an AO-corrected long-exposure PSF that adapts to complex shapes and various seeing conditions. A complementary estimation method is also introduced, addressing the inverse problem of estimating the atmospheric PSF or power spectral density (PSD) from observed PSF data. The model, based on Gaussian sum approximation (GSA), was tested using simulated PSF data from the OOMAO toolbox for Fried parameter (r0) values ranging from 0.10 to 0.20 m and on-sky data from the Very Large Telescope’s MUSE instrument. Two approaches were analyzed: (i) direct PSF estimation using GSA, and (ii) PSF estimation derived from atmospheric PSD estimation. Both methods were evaluated using root mean square error (RMSE). While approach (i) provided the best performance, approach (ii) produced more accurate atmospheric PSF estimates compared to the Moffat approximation and the Fetick method. Additionally, approach (ii) enabled the estimation of key physical parameters, such as the Fried parameter (r0) and residual AO variance ($\sigma _{AO}^2$). The proposed methods effectively estimate atmospheric PSF and PSD, even for complex PSF shapes influenced by spiders, central obstructions, or static aberrations.

Shrinkable Hydrogels through Host–Guest Interactions: A Robust Approach to Obtain Tubular Cell‐Laden Scaffolds with Small Diameters

AbstractThe availability of realistic in vitro models is crucial for tissue engineering, disease modeling, and drug screening assays. However, reproducing the complex shapes and intricate structures of naturally occurring tissues or organs in the presence of functional cells remains a challenge. For example, it is still not trivial to obtain cell‐laden tubular structures on a micrometer scale present in the nephrons of the human kidney. Here, a unique hydrogel‐based shrinking approach making use of host–guest interactions to decrease the diameters of the preformed hydrogel tubules seeded with cells is proposed as a tool to overcome the abovementioned challenge. The hydrogels are composed of covalently crosslinked methacrylated hyaluronic acid and methacrylated dextran modified with either cyclodextrin or adamantane groups that can form dynamic bonds. The hydrogels are initially formed in the presence of small‐molecule competitors that block any interpolymer host–guest interactions, and the shrinking process is triggered by the release of these competitor molecules. The high shrinking efficiency with a shrinking factor up to eight times in volume and robust cytocompatibility make the host‐guest‐based shrinking approach an appealing tool to obtain hydrogel tubular in vitro models with the desired dimensions on demand.

Mesh Simplification for Unfolding*

AbstractWe present a computational approach for unfolding 3D shapes isometrically into the plane as a single patch without overlapping triangles. This is a hard, sometimes impossible, problem, which existing methods are forced to soften by allowing for map distortions or multiple patches. Instead, we propose a geometric relaxation of the problem: We modify the input shape until it admits an overlap‐free unfolding. We achieve this by locally displacing vertices and collapsing edges, guided by the unfolding process. We validate our algorithm quantitatively and qualitatively on a large dataset of complex shapes and show its proficiency by fabricating real shapes from paper.

Exploring alternative manufacturing techniques for high-quality solar cell interconnectors: a comparative study of laser cutting and electric discharge machining

Abstract Solar cell interconnectors are typically crafted from thin foils to establish an electrical connection between two components. For limited production of interconnectors with non-standard dimensions, common production methods (such as stamping and sheet metal processes) cannot be utilized. This article examines alternative techniques, such as Fiber Laser Cutting Machine and Electric Discharge Machining (wire cutting). The laser cutting method did not achieve the desired quality standards, leading to the rejection of the interconnectors. Issues such as burning, local melting, and damage to the silver coating were noted at the edges. The melting of the coating layers formed brittle intermetallic compounds, which can cause cracking and reduce weldability and solderability. In contrast, the Electric Discharge Machining method successfully produces interconnectors with complex shapes by stacking layers of foil. This approach results in high-quality interconnectors that showed no signs of burning or local melting on their edges.

Three-Dimensional Printing of Metallic Parts by Means of Fused Filament Fabrication (FFF)

Obtaining metallic parts via Additive Manufacturing can yield several advantages over using other traditional manufacturing methods such as machining. Material extrusion (MEX) can handle complex shapes with porous structures and, at the present time, much low-end and desktop equipment is available. In the present work, different industrial and medical applications of metallic Fused Filament Fabrication (FFF) parts are presented. First, an overview of the process, equipment, and of the metal-filled filaments currently available is provided. Then, the properties of parts and different applications are shown. For example, metal-filled filaments with a low metal content that can be used to obtain plastic parts with metallic appearance (with either steel, copper, or bronze), and filaments with a high metallic content allow obtaining metallic parts with high mechanical strength after a sintering operation. The present contribution aims to be an up-to-date panorama for current industrial and medical results and lessons learnt from the application of FFF to obtain metallic parts.

Single motor driven soft actuator design based on nonuniform sheering auxetic structure

Abstract Grasping functionality is one of the core functionalities that a manipulator needs to possess. Traditional rigid manipulators are typically composed of rigid materials. However, when dealing with objects of complex shapes, irregular surfaces, or soft materials, rigid manipulators often face challenges in effectively grasping and stably holding objects due to their rigidity, which can lead to damage or failure. To address these issues, constructing manipulators using soft actuators can be highly advantageous. In most current research, soft actuators are typically composed of flexible materials and fluids, which can result in highly complex dynamic behaviors and nonlinear characteristics. This complexity makes precise modeling and control of soft actuators more challenging, necessitating the use of advanced control algorithms and techniques to mitigate nonlinear effects. In this study, we first establish a mathematical model for the torsional and bending deformations of non-uniform shearing-auxetic structures. Subsequently, we validate the reliability of the mathematical model through simulation. Finally, we design a soft actuator that requires only single-motor control, which is based on the structural design of non-uniform shearing-auxetic structures. This type of soft actuator not only simplifies control aspects but also facilitates practical modeling and manufacturing processes. Moreover, it’s capable of achieving spiral grasping functionality.

Polydopamine-modified MXene-integrated photothermal responsive hydrogel for constructing rapid photothermal and self-sensing gradi- ent hydrogel actuators

Abstract PNIPAm-based stimuli-responsive hydrogel actuators have made significant progress. By introducing responsive modules such as photothermal nanoparticles, the hydrogel can respond to environmental changes and construct anisotropic structures, allowing the hydrogel actuators to quickly bend and realize complex shape deformation. How-ever, there remains a need to enhance the mechanical properties of these hydrogels to accommodate multiple deformation actions and different application scenarios, and enhance the compatibility between nanoparticles and the hydrogels to improve the comprehensive performance of materials are still issues that need to be solved. In this study, we modified two-dimensional MXene nanosheets with polydopamine (PDA) to obtain P-MXene, which served as a photothermal agent and can be stably dispersed within the hydrogel system. We copolymerized thermosensitive N-isopropylacrylamide (NIPAm) and 2-(dimethylamino)ethyl methacrylate (DMAEMA) in situ and used a direct current (DC) electric field to induce a concentration gradient distribution of P-MXene nanosheets within the hydrogel, resulting in the preparation of anisotropic gradient hydrogel actuators with good stretchability and conductivity. The hydrogel structure was characterized using scanning electron microscopy (SEM) and the Fourier transform infrared (FTIR) spectra. The swelling properties, mechani-cal properties and conductivity of the hydrogel were studied. The actuation behavior of P(NIPAm-DMAEMA)/P-MXene gradient hydrogel actuators under different thickness and electric field intensity was investigated. Additionally, the sensitivity and stability of the conductive hydrogel actuators were tested. and the functions of monitoring human health and information transmission were realized. It integrated with rapid photo responsiveness and self-sensing capabilities to monitor the defor-mation process of the actuators through real-time relative resistance changes during the actuations. This work is expected to expand the application field of soft hydrogel actuators and provide new insights into the design of a new generation of soft robots.

The prognostic importance of glioblastoma size and shape.

Extent of resection, MGMT promoter methylation status, age, functional level, and residual tumor volume are established prognostic factors for overall survival in glioblastoma patients. Preoperative tumor volume has also been investigated, but the results have been inconclusive. We hypothesized that the surface area and the shape were more representative of the tumor's infiltrative capacities, and thus, the purpose of this study was to assess the prognostic value of tumor size and shape in patients with glioblastoma. In total, 271 patients with primary, unifocal glioblastoma were included from two centers in Norway and Sweden, respectively. All tumors were automatically segmented on preoperative MRI scans and manually validated. Tumor volume was used as a measurement of size, whereas sphericity index and area-to-volume ratio defined the shape complexity of the tumor. Contact surface area of the tumor was considered a measurement of both size and shape. Multivariable Cox proportional hazards models were used to assess the prognostic value of the respective tumor measurements, with previously established prognostic factors as covariates. There were no associations between preoperative tumor volume and overall survival. Contact surface area (HR = 1.013, p = 0.002) and sphericity index (HR = 2.223, p = 0.001) were both significant independent prognostic factors for survival in the multivariable Cox models. Contact surface area was also associated with MGMT promoter methylation (p = 0.039) and extent of resection (p = 0.017). Tumor shape complexity appears to be an independent prognostic factor in glioblastoma patients and may also be associated with MGMT promoter methylation status and extent of surgical resection.

Bayesian mesh optimization for graph neural networks to enhance engineering performance prediction

Abstract In engineering design, surrogate models are widely employed to reduce the computational costs of simulations by approximating design variables and geometric parameters from computer-aided design (CAD) models. However, traditional surrogate models often lose critical information when simplified to lower dimensions, and face challenges in handling the complexity of 3D shapes, especially in industrial datasets. To address these limitations, we propose a Bayesian graph neural network (GNN) framework that directly learns geometric features from CAD mesh representations for accurate engineering performance prediction. Our framework leverages Bayesian optimization (BO) to dynamically determine the optimal mesh element size, significantly improving model accuracy while balancing computational efficiency. This approach addresses the challenge of mesh quality by optimizing mesh resolution, ensuring that critical geometric features are preserved in 3D deep-learning-based surrogate models. Unlike existing models that rely on static or predefined mesh resolutions, our method adapts mesh size based on the task, making it highly flexible for a wide range of engineering applications. Experimental results demonstrate that mesh quality directly impacts prediction accuracy, and the proposed BO-EI GNN model outperforms state-of-the-art models such as 3D CNN, SubdivNet, GCN, and GNN in predicting mass, rim stiffness, and disk stiffness. Our method also significantly reduces the computational costs compared to traditional optimization techniques. The proposed framework shows promising potential for application in finite element analysis (FEA) and other mesh-based simulations, enhancing the utility of surrogate models across various engineering fields.

Combining finite element analysis and reinforcement learning for optimal grip point planning of flexible components

Abstract Handling large flexible components is still a challenge in many industries. Examples include the handling of fibre-reinforced plastics or the assembly of industrial-scale electrolytic cells. Difficulties often arise in the design of suitable endeffectors. Inefficient gripping point design, i.e. the total number and positioning of grippers, can lead to increased stress and deflection of the component being handled. To counteract this, endeffectors are often oversized resulting in the use of more grippers than needed. Correspondingly, heavier moving masses imply longer handling times as well as higher energy consumption. This paper presents a process for planning and optimising gripping points for large flexible components. In addition to the shape of the component, actual dynamic loads of the handling path are also taken into account. The key element to the process is an optimisation algorithm based on reinforcement learning and trained using an finite element method (FEM) simulation. After computing a desirable starting configuration, the algorithm optimises the placement of gripper positions while aiming for a reduced total number. In addition, the optimisation has prescribed handling limits, such as physical and geometric constraints, that must not be exceeded. It was shown that the algorithm satisfactorily optimises the gripping points for dynamic loads for different materials and shapes. Furthermore, it has been shown that the computation of an initial configuration yields preferable results for simple components, yet requiring optimisation in the case of more complex shapes.

Improving the Accuracy of RPA Image Classification in Vegetation Mapping of Magsaysay, Occidental Mindoro using RGB Indices, Canopy Heights, and Feature Importance Weighting

Abstract. Mapping vegetation cover is an important step in generating baseline data for various purposes such as smart agriculture, disaster and hazard monitoring, as well as risk assessment and planning. For this purpose, the use of Remotely Piloted Aircraft (RPA) has become prevalent in recent years as an efficient and cost-effective way of obtaining very-high-resolution images. However, it is limited by its lack of spectral bands used for discriminating between land cover classes, especially vegetation. An object-based approach was used due to its suitability with high-resolution input datasets, as it can recognize complex shapes and patterns aside from spectral characteristics. The drone images were segmented using optimal parameters to produce image objects which were subsequently classified through supervised learning using the Random Forest (RF) algorithm. This study incorporated non-conventional spectral indices that use RGB bands only, such as Triangular Greenness Index (TGI), Excess Green (ExG), and Tree-Grass Differentiation Index (TGDI), as well as the canopy height data derived from RPA photogrammetry to improve classification accuracy. To further improve model performance, appropriate band weights for segmentation were determined by running a RF classifier to obtain band importance values. Accuracy assessments reveal that using additional indices and heights improved the accuracy resulting in a 20% increase in the average f1-score, with the vegetation classes improving by a 25% increase in their f1-scores (8–41% improvement per class). Using the integration of band importance values as weights to the object-based segmentation slightly decreased accuracy values for the vegetation classes by an average of 0.04 in the f-1 score. The methods developed to improve the accuracy of RPA image classification make it more suitable for mapping vegetation.

Urban placemaking and the commercial property investor/developer

AbstractReal estate investment companies direct substantial capital flows into built environments and are significant urban place‐makers. Geographers have long been interested in the ‘urbanisation of capital’ but more recently attention has been directed towards the activities of specific property actors. Employing a firm‐level analysis, this paper presents a case study of Kiwi Property, a large‐scale property company with an investment portfolio exceeding $3 billion. The study charts its corporate and financial dynamics and examines examples of its speculative real estate developments. Employing a firm‐level analysis offers insights into complex and contingent investment processes shaping urban placemaking.

Three-Dimensional Multi-Morphology TPMS-based Microchannel Design Method for Conformal Cooling in Injection Molding

Abstract Triply Periodic Minimal Surface (TPMS) possesses diverse morphological characteristics, such as pore sizes, porosity, and structural types. Integrating TPMS-based microchannels into micro-cellular cooling structures is advantageous for designing and controlling fluid characteristics within mold cooling channels. However, it is still difficult to design multi-morphology TPMS-based cooling microchannels that conform to the external shapes of injection molds. This work proposes a three-dimensional (3D) multi-morphology TPMS-based design method that transforms 3D constraints into a combination of two-dimensional (2D) constraints. Firstly, a beta growth algorithm based on closed-loop constraints is proposed to transition different morphologies smoothly on the plane. Subsequently, a transition optimization algorithm along the normal direction of the plane is introduced to smoothly transition multi-morphology TPMS-based microchannels in all directions. With this layered approach, multi-morphology microchannels can be obtained with first-order geometric continuity under complex shape constraints. Finally, several TPMS-based conformal cooling structures are designed for an automotive hood cover. The results of finite element simulations show that the cooling structures generated by the proposed method have a better cooling effect than the conformal channels. It can be concluded that multi-morphology TPMS-based structures perform better by contrast with conformal channels as the average temperature of the cooling surface decreases by 8.48K, and the standard deviation of temperature distribution decreases by 24.65%.

Direct Three-Dimensional Observation of the Plasmonic Near-Fields of a Nanoparticle with Circular Dichroism.

Characterizing the spatial distribution of the electromagnetic fields of a plasmonic nanoparticle is crucial for exploiting its strong light-matter interaction for optoelectronic and catalytic applications. However, observing the near-fields in three dimensions with a high spatial resolution is still challenging. To realize efficient three-dimensional (3D) nanoscale mapping of the plasmonic fields of nanoparticles with complex shapes, this work established autoencoder-embedded electron energy loss spectroscopy (EELS) tomography. A 432-symmetric chiral gold nanoparticle, a nanoparticle with a high optical dissymmetry factor, was analyzed to relate its geometrical features to its exotic optical properties. Our deep-learning-based feature extraction method discriminated plasmons with different energies in the EEL spectra of the nanoparticle in which signals from multiple plasmons were intermixed; this component was key for acceptable 3D visualization of each plasmonic field separately using EELS tomography. With this methodology, the electric field of the plasmon that induces far-field circular dichroism was observed in 3D. The field linked to this chiroptical property was strong along the swirling edges of the particle, as predicted by a numerical calculation. This study provides insight into the correlation between structural and optical chiralities through direct 3D observation of the plasmonic fields. Furthermore, the strategy of implementing an autoencoder for EELS tomography can be generalized to achieve competent 3D analysis of other features, including the optical properties of the dielectrics and chemical states.

Complex shape markers can detect alterations in the spatial distribution of cell nuclei in human lung squamous cell carcinoma: a useful tool for automatic analysis?

Lung cancer is the leading cause of cancer-related death. The use of computational methods to quantify changes that are not perceptible to the human eye is increasing in digital pathology imaging and has quickly improved detection rates at a low cost. Therefore, the present study aims to use complex computational shape markers as tools for automated analysis of the spatial distribution of cells in microscopy images of squamous cell lung carcinoma (SqCC). Photomicrographs from pathology glass slides in the LC25000 dataset were used in this study. Compared with those of the control, the fractal dimension (28%) and lacunarity (41%) of the cell nuclei changed in SqCC. The multifractal analysis revealed a significant difference in parameters Dq, α, and f(α) for all values of q (-10 to + 10), with a greater increase for more positive q values. The values at q + 10 increased by 34% for Dq, 36% for α, and 53% for f(α) in the SqCC images. The circularity, area, and perimeter also changed in the SqCC images. However, the parameters of aspect ratio, roundness, and solidity did not significantly differ between SqCC and benign tissue. The complex shape markers with the greatest changes in this study were the f(α) values for multifractality (53%) and lacunarity (41%). In conclusion, automated quantification of the spatial distribution of cell nuclei can be a fast, low-cost tool for evaluating the microscopic characteristics of SqCC; therefore, complex shape markers could be useful tools for software and artificial intelligence to detect lung carcinoma.

Experimental investigation on optimizing wire-cut EDM process parameters of hybrid MMC(AA7475/ZrO2/Gr) using artificial neural network

Abstract Wire cut Electrical discharge machining (WCEDM) is a widely used method for machining complex shapes in advanced materials like metal matrix composites (MMCs) and hybrid metal matrix composites (HMMCs). To address these challenges, this study focusses on the wire-cut EDM (WCEDM) process of a workpiece made from zirconium dioxide and graphite-reinforced aluminium alloy 7475 with a molybdenum electrode. The effects of input process variables such as peak current (IP), pulse-on-time (TON), and flushing pressure (PF) on the output response features are investigated. These output responses include material removal rate (MRR), surface roughness (SR), and wire wear ratio (WWR). To optimise the process parameters, the Taguchi design technique is used. An artificial neural network (ANN) with a feed-forward back propagation architecture is utilised to find the best fit for the optimisation challenges. ANN predicted the results with an accuracy of 97.81% for MRR, 97.95% for SR and 95.865% for WWR. The results reveal that the WCEDM of AA7475/ZrO2/Gr with a molybdenum electrode achieved minimal WWR and SR, while maximizing the MRR.

Vegetation traits and biogeomorphic complexity shape the resilience of salt marshes to sea-level rise

The adaptive capacity of ecosystems, or their ability to function despite altered environmental conditions, is crucial for resilience to climate change. However, the role of landscape complexity or species traits on adaptive capacity remains unclear. Here, we combine field experiments and morphodynamic modelling to investigate how ecosystem complexity shapes the adaptive capacity of intertidal salt marshes. We focus on the importance of tidal channel network complexity for sediment accumulation, allowing vertical accretion to keep pace with sea-level rise. The model showed that landscape-scale ecosystem complexity, more than species traits, explained higher sediment accumulation rates, despite complexity arising from these traits. Landscape complexity, reflected in creek network morphology, also improved resilience to rising water levels. Comparing model outcomes with real-world tidal networks confirmed that flow concentration, sediment transport and deposition increase with drainage complexity. These findings emphasize that natural pattern development and persistence are crucial to preserve resilience to climate change.

Machine Learning-Enhanced Fabrication of Three-Dimensional Co-Pt Microstructures via Localized Electrochemical Deposition

This paper presents a novel method for fabricating three-dimensional (3D) microstructures of cobalt–platinum (Co-Pt) permanent magnets using a localized electrochemical deposition (LECD) technique. The method involves the use of an electrolyte and a micro-nozzle to control the deposition process. However, traditional methods face significant challenges in controlling the thickness and uniformity of deposition layers, particularly in the manufacturing of magnetic materials. To address these challenges, this paper proposes a method that integrates machine learning algorithms to optimize the electrochemical deposition parameters, achieving a Co:Pt atomic ratio of 50:50. This optimized ratio is crucial for enhancing the material’s magnetic properties. The Co-Pt microstructures fabricated exhibit high coercivity and remanence magnetization comparable to those of bulk Co-Pt magnets. Our machine learning framework provides a robust approach for optimizing complex material synthesis processes, enhancing control over deposition conditions, and achieving superior material properties. This method opens up new possibilities for the fabrication of 3D microstructures with complex shapes and structures, which could be useful in a variety of applications, including micro-electromechanical systems (MEMSs), micro-robots, and data storage devices.

Microstructural analysis of AA8079 alloy sheets formed by friction stir incremental forming

Friction Stir Incremental Forming (FSIF) is an innovative solid-state metal-forming technique that uses frictional heat and mechanical deformation to create complex shapes without dyes. It has found applications in industries like aerospace, automotive, and consumer goods. In this study, FSIF was applied to AA8079 alloy sheets to form cone and pyramid geometries. A detailed investigation of process parameters such as spindle speed, table feed, step size, and coatings was conducted to assess their effects on surface roughness and forming forces. Optimal parameters were identified using the Taguchi L27 orthogonal array and the TOPSIS method, which determined the best settings as a wall angle of 20°, a step size of 1 mm, a spindle speed of 1500 rpm, and a table feed of 2400 mm/min. Scanning electron microscopy was employed to study surface roughness, texture, and defects, revealing the influence of these parameters on surface quality. Additionally, Finite Element Method (FEM) analysis, performed using ABAQUS software, was used to predict formability and stress distribution during the forming process. Key results showed successful formation of cone and triangular pyramid shapes on AA8079 sheets, with FEM accurately predicting stress levels. This comprehensive study provides valuable insights into optimizing FSIF for improved material performance and structural integrity. The findings emphasize the importance of fine-tuning FSIF parameters to enhance surface finish and reduce defects, offering potential improvements for various industrial applications.