Geometry Properties Research Articles

Utilizing Gate-Controlled Supercurrent for All-Metallic Tunable Superconducting Microwave Resonators.

Hybridizing a microwave mode with a quantum state requires precise frequency matching of a superconducting microwave resonator and the corresponding quantum object. However, fabrication always brings imperfections in geometry and material properties, causing deviations from the desired operating frequencies. An effective and universal strategy for their resonant coupling is to tune the frequency of a resonator, as quantum states like phonons are hardly tunable. Here, we demonstrate gate-tunable, titanium-nitride (TiN)-based superconducting resonators by implementing a nanowire inductor whose kinetic inductance is tuned via the gate-controlled supercurrent (GCS) effect. We investigate their responses for different gate biases and observe 4% (∼150 MHz) frequency tuning with decreasing internal quality factors. We also perform temperature-controlled experiments to support phonon-related mechanisms in the GCS effect and the resonance tuning. The GCS effect-based method proposed in this study provides an effective route for locally tunable resonators that can be employed in various hybrid quantum devices.

Synthesis of Superabsorbent Hydrogels with Predefined Geometries and Controlled Swelling Properties for Versatile 3D Cell Culture Scaffolds.

This research presents a simple but general method to prepare water-soluble-polymer-based superabsorbent hydrogels with predefined microscale geometries and controlled swelling properties. Unlike conventional hydrogel preparation methods based on bulk solution-phase cross-linking, poly(vinyl alcohol) is homogeneously mixed with polymer-based cross-linkers in the solution phase and thermally cross-linked in the solid phase after drying; the degree of cross-linking is modulated by controlling the cross-linker concentration, pH, and/or thermal annealing conditions. After the shape definition process, cross-linked films or electrospun nanofibers are treated with sulfuric acid to weaken hydrogen bonds and introduce sulfate functionality in polymer crystallites. The resultant superabsorbent hydrogels exhibit an isotropic expansion of the predefined geometry and tunable swelling properties. Particularly, hydrogel microfibers exhibit excellent optical transparency, good biocompatibility, large porosity, and controlled cell adhesion, leading to versatile 3D cell culture scaffolds that not only support immortalized cell lines and primary neurons but also enable stiffness-modulated cell adhesion studies.

Ultrasonic tool shank with multiple vibration mode for micro-nano drilling: Design, optimization and experiment

The machining effects of hole drilling is limited by merely changing the amplitude. Adjusting ultrasonic vibration frequency to expand the matching range of machining parameters optimization is necessary to improve hole drilling effects. Therefore, the design, optimization, and experiment evaluation for a novel integrated, fined, low-cost dual-frequency ultrasonic tool shank is proposed, which can work in three vibration modes for micro-nano precision drilling. The principle of the dual-frequency ultrasonic transducer is introduced. The electro-mechanical equivalent circuit model with step horn is applied to study the dual-frequency characteristics. The transducer displacement equation model based on the wave equation theory is deduced to find the shared vibration node of the first and third frequency. Sensitivity analysis by equivalent circuit model and Finite element method (FEM) simulation of the key parameter is both conducted to optimize ultrasonic transducer geometry dimension. The vibration node of mounting flange is specified in an identical position for the dual vibration modes. A prototype of ultrasonic tool shank is fabricated and evaluated in experiments. It demonstrates that the dual working frequency of the ultrasonic transducer is 34.9 kHz and 104.5 kHz in the first and third mode resonant vibration, which is benefit to the low and high frequency drilling. Three working modes of the low, high, and coupling frequency vibration is measured by a self-developed ultrasonic generator. When 40 V voltage is excited, the vibration amplitudes of the low and high frequency are 17.8 µm, and 9.3 µm, respectively, which can meet with the drilling requirement. Moreover, the coupling vibration with the low and high mode are excited simultaneously, and the motion trajectory of the coupling vibration is observed in a “M” shape, which is a novel trajectory for the precision micro-nano drilling. Micro drilling experiments show that the 35 kHz ultrasonic vibration drilling can achieve better machining effect than conventional drilling (CD). The 105 kHz ultrasonic drilling outperforms the CD, 35 kHz and coupling ultrasonic drilling at cutting force reduction. The coupling ultrasonic vibration drilling achieves the lowest chipping diameter. It is to be noted that the 105 kHz-8 µm produce the fined smoothly surface. The 35 kHz, 105 kHz and coupling ultrasonic drilling can produce better surface micro morphology with less quantity and size of craters than that of CD.

Sex-specific effects of Fat-1 transgene on bone material properties, size, and shape in mice.

Western diets are becoming increasingly common around the world. Western diets have high omega 6 (ω-6) and omega 3 (ω-3) fatty acids and are linked to bone loss in humans and animals. Dietary fats are not created equal; therefore, it is vital to understand the effects of specific dietary fats on bone. We aimed to determine how altering the endogenous ratios of ω-6:ω-3 fatty acids impacts bone accrual, strength, and fracture toughness. To accomplish this, we used the Fat-1 transgenic mice, which carry a gene responsible for encoding a ω-3 fatty acid desaturase that converts ω-6 to ω-3 fatty acids. Male and female Fat-1 positive mice (Fat-1) and Fat-1 negative littermates (WT) were given either a high-fat diet (HFD) or low-fat diet (LFD) at 4wk of age for 16wk. The Fat-1 transgene reduced fracture toughness in males. Additionally, male BMD, measured from DXA, decreased over the diet duration for HFD mice. In males, neither HFD feeding nor the presence of the Fat-1 transgene impacted cortical geometry, trabecular architecture, or whole-bone flexural properties, as detected by main group effects. In females, Fat-1-LFD mice experienced increases in BMD compared to WT-LFD mice; however, cortical area, distal femur trabecular thickness, and cortical stiffness were reduced in Fat-1 mice compared to pooled WT controls. However, reductions in stiffness were caused by a decrease in bone size and were not driven by changes in material properties. Together, these results demonstrate that the endogenous ω-6:ω-3 fatty acid ratio influences bone material properties in a sex-dependent manner. In addition, Fat-1 mediated fatty acid conversion was not able to mitigate the adverse effects of HFD on bone strength and accrual.

Reliability updating for lateral failure of historic quay walls

ABSTRACT The historic canal walls of Amsterdam, stretching 200 km in total, are constructed as a masonry wall on a timber deck supported by vertical timber piles. Understanding the resistance against lateral failure of these quays has been challenging due to uncertainties in their working principles, geometry, soil and structural properties. This paper proposes a Bayesian approach to include evidence from past loading situations and corresponding deformations into the reliability assessment. This approach enables refinement of the reliability predictions and parameter distribution uncertainties, leading to a more accurate prediction of the resistance against the lateral failure of historic quay wall. Depending on the type of evidence, an a-priori reliability prediction for a quay wall that fails to meet safety standards can be updated to any of the three consequence classes outlined in NEN8700. In a case study, a quay wall with an a-priori reliability of β = 1.5 has been increased to β = 3.2 by including evidence of an extreme survived load of 10 kN/m2 that resulted in displacements of less than 4 mm. This is a decrease in failure probability by two orders of magnitude, showing the potential impact of using observational information in combination with Bayesian updating.

High-performing neural network models of visual cortex benefit from high latent dimensionality.

Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core representational principles of computational models in neuroscience. Here we examined the geometry of DNN models of visual cortex by quantifying the latent dimensionality of their natural image representations. A popular view holds that optimal DNNs compress their representations onto low-dimensional subspaces to achieve invariance and robustness, which suggests that better models of visual cortex should have lower dimensional geometries. Surprisingly, we found a strong trend in the opposite direction-neural networks with high-dimensional image subspaces tended to have better generalization performance when predicting cortical responses to held-out stimuli in both monkey electrophysiology and human fMRI data. Moreover, we found that high dimensionality was associated with better performance when learning new categories of stimuli, suggesting that higher dimensional representations are better suited to generalize beyond their training domains. These findings suggest a general principle whereby high-dimensional geometry confers computational benefits to DNN models of visual cortex.

Direct Z-scheme MS2/Si2PAs (M = Zr, Hf) heterostructures for efficient water splitting: A first-principles study

Designing direct Z-scheme heterostructure is an effective strategy to enhance redox ability, greatly raising the attention of photocatalysis in recent years. Here, we design 24 diverse vertical MS2/Si2PAs (M = Zr, Hf) heterostructures with different stacking configurations. Four kinds of heterostructures with different interlayer contacts are taken as examples to investigate the geometry, stability, and electronic properties, as well as the photocatalytic mechanism based on the first-principles calculations. We find that the competitiveness of MS2/Si2PAs (M = Zr, Hf) heterostructures is attributed to their excellent visible light absorption (∼2 × 105 cm−1), ultrafast carrier migration (∼13 587.28 cm2 V−1 s−1), and high solar-to-hydrogen efficiency (10.02%), indicating that this kind of system can be as a promising candidate in the field of semiconductor photocatalysis.

Instabilities of an inflated and extended doubly fiber-reinforced cylindrical membrane under damage processes and different natural configurations of its constituents with application to abnormal artery dilation

This article studies extended and inflated tubes made of doubly fiber-reinforced hyperelastic material in which the constituents undergo damage processes at large deformations. Damage processes are initiated by a combination of external loads and material characteristics including volume changes in the ground substance matrix and constituents with different natural configurations. The inflation of such a cylinder is stable at relatively small deformations. Under higher inflation pressures the cylinder is likely to develop various types of instabilities that are shown to be related to loading, geometry, and (current) mechanical properties of the solid. Analyses focus on the role of material volume changes, fiber pre-stretch, and damage processes in the initiation of bulging and inflation-jump instabilities.

High-impedance arc fault modeling for distribution networks based on dynamic geometry dimension

Accurate modeling of high-impedance arc fault (HIAF) is of great significance for studying the characteristics of arc fault and suppressing its harm. In traditional arc models, the arc column is usually considered a cylindrical channel with constant length and diameter. However, the arc-burning process is susceptible to the environment. The changes in arc length and diameter present complex characteristics. Therefore, this study proposes a new HIAF model for distribution networks based on the arc's dynamic geometry dimension (DGD). First, the arc length, diameter, and field strength expressions are improved based on the classical cybernetic model. Next, an automatic parameter optimization method for the DGD model is proposed, and then this model is compared with existing advanced models. After that, the effects of the variable parameters of this model on arc characteristics are analyzed. Finally, the practical application effect of this model is tested. The experimental results show that the DGD model can accurately describe the dynamic arc development process, approximate the given arc waveform closely, and generate high-quality samples, which has certain advantages.

Thermal fluctuations (eventually) unfold nanoscale origami

Origami is a scale invariant paradigm for morphing robotics, deployable structures (e.g. satellites, disaster relief shelters, medical stents), and metamaterials with tunable thermal, mechanical, or electromagnetic properties. There has been a resurgence of interest in using origami principles – along with 2D materials or DNA – to design a wide array of nanoscale devices. In this work, we take cognizance of the fact that small-scale devices are vulnerable to entropic thermal fluctuations and thus a foundational question underlying small-scale origami pertains to its stability, i.e. the origami structure’s propensity to “unfold” due to thermal fluctuations and the rate at which the unfolding will ensue. To properly understand the behavior of these origami-based nanodevices, we must simultaneously consider the geometric mechanics of origami along with the interplay between thermal fluctuations, entropic repulsive forces, van der Waals attraction, and other molecular-scale phenomena. In this work, to elucidate the rich behavior underpinning the evolution of an origami device at the nanoscale, we develop a minimal statistical mechanics model of folded nanoscale sheets. We use the model to investigate (1) the thermodynamic multistability of nanoscale origami structures and (2) the rate at which thermal fluctuations drive its unfolding—that is, its temporal stability. We identify, for the first time, an entropic torque that is a critical driving force for the unfolding process. Both the thermodynamic multistability and temporal stability have a nontrivial dependence on the origami’s bending stiffness, the radii of curvature of its creases, the ambient temperature, its thickness, and its interfacial energy (between folded layers). Specifically, for graphene, we show that there is a critical side length below which it can no longer be folded with stability; similarly, there exists a critical crease diameter, membrane thickness (e.g. for multilayer graphene), and temperature above which a crease cannot be stably folded. To investigate the rate of thermally driven unfolding, we extend Kramers’ escape rate theory to cases where the minima of the energy well occurs at a boundary. Rates of unfolding are found to span from effectively zero to instantaneous, and there is a clear interplay between temperature, geometry, and mechanical properties on the unfolding rate.

Experimental and model analysis of the effect of pore and mineral characteristics on fluid transport in porous soil media

The fluid transport in porous media is a critical property for oil and gas exploitation, construction engineering, and environmental protection. It is profoundly influenced by pore geometry and mineral properties. Currently, the Kozeny–Carman equation serves as the permeability prediction equation for porous media, established on the circular pores model. However, it fails to fully account for the impact of pore shape and mineral properties of the soil, leading to significant deviations between predicted and measured soil permeability results. In this paper, based on scanning electron microscope image and mercury intrusion porosimetry, the pores were divided into circular pores and narrow slit pores according to the ratios of pore area and circumference. Then, the quantitative expression of the two types of pores and their connectivity and tortuosity were given, and the circular and narrow slit composite pore model was used to describe the soil pore. Subsequently, the electrostatic potential of pore water was calculated by the Poisson–Boltzmann equation to consider the adsorption effect of minerals on pore water. Combined with the Navier–Stokes equation, the permeability prediction equation considering pore geometry, pore connectivity, and tortuosity and mineral properties was established. Finally, the experimental results illustrated that the theoretical prediction results were in good agreement with the experimental results. The proposed permeability prediction equation proves valuable for assessing and predicting the fluid transport in porous media.

Exploring elliptic geometry in neural networks : The concept of elliptic neural networks (ENN)

The exploration of non-Euclidean geometries in the context of neural network architectures presents a novel avenue for enhancing the processing of complex data structures. This paper introduces the concept of Elliptic Neural Networks (ENN), a framework that integrates the intrinsic properties of elliptic geometry into the fabric of neural computation. Characterized by constant positive curvature and closed geodesic paths, elliptic geometry provides a unique perspective for representing data, especially advantageous for configurations exhibiting cyclic and dense hierarchical interconnections. We begin by delineating the fundamental aspects of elliptic geometry, focusing on its impact on conventional notions of distance and global structural interpretation within data sets. This foundational understanding paves the way for our proposed mathematical schema for ENNs. Within this framework, we articulate the methodologies for embedding data in elliptic spaces and adapt neural network operations by encompassing neuron activation, signal propagation, and learning paradigms to conform to the topological idiosyncrasies of elliptic geometry. In this paper addressing the implementation of ENNs, we discuss the computational challenges and the development of novel algorithms requisite for navigating the elliptic geometric landscape. The prospective applications of ENNs are extensive and diverse, ranging from the enhancement of cyclic pattern recognition in time-series analysis to more sophisticated representations in graph-based data and natural language processing tasks. This theoretical exposition aims to set the groundwork for subsequent empirical research to validate the proposed model and assess its practical performance relative to conventional neural network architectures. By harnessing the distinctive characteristics of elliptic geometry, ENNs mark a significant stride towards enriching the arsenal of machine learning methodologies, potentially leading to more versatile and efficacious computational tools in data analysis and artificial intelligence.

FRACTAL GEOMETRY-BASED RESOURCE ALLOCATION FOR MIMO RADAR

The netted Collocated Multiple-Input Multiple-Output (C-MIMO) radar systems have attracted widespread attention due to their high resolution and excellent target detection capabilities. The self-similarity and spatial filling properties of fractal geometry enable antenna layouts to function effectively across multiple scales and frequency bands, which helps improve the overall performance and resource utilization of systems. In this paper, a radar array element model based on the Fudgeflake fractal is used to establish a target localization performance model directly related to power bandwidth allocation. Based on the model, a three-step solving strategy was designed with the Sequence Quadratic Program (SQP) algorithm as the foundation to achieve optimized allocation of power and bandwidth while maximizing overall performance. Experimental results demonstrate that under this algorithm, the use of power bandwidth resources is optimized through fractal geometry-based design.

Optimization of Wax Model Printing using a DLP 3D Printer for Castable Wax Resin material

Wax material can be used to make craft items as models that are printed using 3D printing. Custom and mass production processes for craft items can be carried out according to customer needs. This research aims to find the best process parameters in optimising the wax printing process as a printed model using 3D Printing DLP technology to have the best dimensional accuracy, geometric accuracy, and surface roughness. The method used is a design of experiment with factors in the form of exposure time and layer thickness, each of which has three levels to produce the best response: dimensional accuracy, geometry, and surface roughness. The research results show that print parameters with an exposure time value of 16 seconds and a layer thickness of 0.06 are the best experiments in producing dimensional accuracy of the print results. This is indicated by the smallest shrinkage percentage value of 0.41%. Meanwhile, to obtain smooth print results with minimal defects, printing is carried out with an exposure time parameter of 14 seconds with a layer thickness of 0.06 mm.

Effects of geometry of soil specimens on the formation of desiccation cracks

Desiccation cracks in soils are important in engineering structures that need hydraulic integrity such as earth dams, and containment facilities. At the element test level, such cracks are a hindrance when the volume change of the soil specimen needs to be measured during the test. One such test is the volumetric shrinkage test where the volume of the soil specimen needs to be continuously determined together with its mass change as the soil specimen dries. The soil specimen may deform non-uniformly. Previously hazardous and cumbersome methods using mercury or wax are needed to determine the volume of soil specimens that deform non-uniformly, but the advent of 3D scanning and photogrammetry enables the volume of non-uniform soil specimens to be measured quite accurately. However, such techniques cannot accurately determine the volume of the deformed soil specimen once cracks start appearing in the soil specimen. Volumetric shrinkage tests are commonly conducted for cylindrical soil specimens. This paper investigates the geometry (shapes and dimensions) of soil specimens other than cylindrical on the formation of desiccation cracks. Bentonite is used as the test soil as it experiences large volume change on drying. The test results provide guidance for the geometry of a soil specimen to be used in the volumetric shrinkage test.

Investigation of coated hydrophobic granular materials by means of computed tomography and environmental scanning electron microscopy

Hydrophobic materials in geotechnical engineering and soil science can have natural or artificial origin. They can be applied, e. g., to waterproof structures in the industry. In this contribution, hydrophobic granular material was manufactured through a cold plasma polymer coating procedure. The monomer used was C4F8 (octafluorocyclobutane) and the material to be coated was Hamburg sand, a coarse grained sand. In this context, computed microtomography and environmental scanning electron microscopy were used to investigate the materials in their unsaturated state. The tools are applied to visualize unsaturated phenomena on the microscale. The hydrophobic and untreated materials were imaged by both techniques at different saturation degrees in order to understand the influence of the coating on the sample’s hydraulic behaviour. The chosen environmental scanning electron microscope is able to provide relative humidity in the sample chamber, and so water drops were condensed on the grain surface, allowing to also observe the initial contact of water and the hydrophobic coating. It was observed how the capillary menisci, their geometry and contact properties evolve at different degrees of saturation. The measurements obtained and respective analyses state qualitatively the influence of the hydrophobic coatings on the pore water dynamics at different saturation degrees, which dictates the material’s hydraulic behaviour. Contact angles were also analysed were it was physically possible.

Displacement Measurement and 3D Reconstruction of Segmental Retaining Wall Using Deep Convolutional Neural Networks and Binocular Stereovision

Computer vision techniques were employed to monitor the displacement of retaining walls using artificial markers, traditional feature detection algorithms, and photogrammetry‐based point cloud reconstruction. However, the use of artificial markers often increases both installation time and costs, whereas the performance of traditional feature matching is affected by uneven illumination, and photogrammetry techniques require multiple images for point cloud reconstruction. To overcome these limitations, a nontarget‐based displacement monitoring method for segmental retaining walls (SRWs) using a combination of deep learning and stereovision was proposed. Binocular stereovision was employed to reconstruct the geometry and surface properties of the SRW in a digital three‐dimensional (3D) model. Deep learning models were then used to extract natural features from SRW blocks, enabling displacement calculation without using artificial targets. The performance was evaluated by monitoring the behaviors of SRW experiments at both laboratory and field scales. The deep learning–based image segmentation models identified SRW block features in the experiment and real case datasets with an average F1 score from 0.910 to 0.965 under various environmental conditions. The reconstructed results of point cloud coordinates demonstrated high accuracy, ranging from 95.2% to 98.6%. Furthermore, the calculated displacement exhibited a high degree of agreement with the measured displacement. The accuracy of the calculated displacements for the laboratory and field experiments ranged from 89.5% to 99.1%. The proposed method can be used for automatic SRW displacement monitoring.

A Non-Intrusive Stochastic Phase-Field for Fatigue Fracture in Brittle Materials with Uncertainty in Geometry and Material Properties

A Non-Intrusive Stochastic Phase-Field for Fatigue Fracture in Brittle Materials with Uncertainty in Geometry and Material Properties

Structural Reliability of pressure tube of a PHWR with volumetric flaws

The volumetric flaws can get generated in the pressure tubes (PT) of PHWR (made of Zr-2.5Nb material) during the operation of the reactor. The PT material has hydrogen present during the fabrication. Additionally, the zirconium material has a tendency to pick up hydrogen from the surrounding environment and can cause initiation of a crack through delayed hydride cracking (DHC) mechanism. This crack can grow due to the DHC and has the potential to cause leakage or break of the PT. The crack formation occurs only when a set of conditions are met. The model for the crack initiation ahead of the tip of a volumetric flaws is prescribed by the CSA Standard N285.8-15. The formation of the crack is dependent of the geometry of PT, geometry of the flaw (length, depth and root radius), applied loading (due to internal pressure, residual stresses), hydrogen concentration in the material and the material tensile and fracture properties. Most of these parameters are well defined. However, some of them like flaw geometry and material properties are not known with certainty. The structural reliability based methods quantify the uncertainty using probability distributions and present results in form of probability estimates. In this work, the probability of initiation of DHC ahead of a volumetric flaw and its subsequent growth is reported. Monte Carlo based simulation method along with variance reduction techniques is used to predict the probability. This study is useful to establish the Leak-Before-Break behaviour of the PT.

Antifungal potential of mannopyranoside derivatives through computational and molecular docking studies against Candida albicans 1IYL and 1AI9 proteins

Methyl α-D-mannopyranoside (MAM) is a naturally occurring carbohydrate derivative that has gained attention in drug discovery due to its potential therapeutic applications, particularly as an antifungal agent. In this study, we employed a computational approach to investigate the interactions between MAM and two Candida albicans antifungal proteins, 1IYL and 1AI9, through molecular docking simulations. Furthermore, we performed a PASS (Prediction of Activity Spectra for Substances) analysis to predict MAM potential biological activities, explored the pharmacokinetic properties and ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles, and optimized the MAM using the density functional theory (DFT) method. The molecular docking results revealed favorable binding interactions between MAM and the active sites of the 1IYL and 1AI9 proteins, suggesting potential antifungal activity. Additionally, the ADMET profiles indicated low toxicity and suitable drug-like properties, such as moderate metabolic stability and minimal risk of adverse effects. Furthermore, DFT optimization was performed to investigate the molecular geometry and electronic properties of MAM. The optimization results provided valuable information on the stability and reactivity of MAM, enabling a better understanding of its chemical behavior and potential modifications for enhanced activity. Finally, PASS prediction was employed to evaluate MAM's potential biological activities beyond its antifungal properties. The analysis revealed several potential activities, including antibacterial, antiviral, and immunomodulatory effects, expanding the scope for future research and therapeutic applications. In conclusion, this computational study sheds light on the molecular interactions, pharmacokinetic properties, ADMET profiles, DFT optimization, and PASSES predictions of MAM. These findings highlight the potential of MAM as a promising antifungal agent with favorable pharmacological properties.