Arbitrary Structures Research Articles

General Mapping Between Complex Spatial and Temporal Frequencies by Analytical Continuation

This article introduces a general technique for intermapping the complex spatial frequency (or propagation constant) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma =\alpha +j\beta $ </tex-math></inline-formula> and the temporal frequency <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega = \omega _{\text {r}}+j\omega _{\text {i}}$ </tex-math></inline-formula> of arbitrary electromagnetic media and structures. This technique is based on the analytic property of complex functions that describe physical phenomena; it invokes the analytic continuity theorem to assert the unicity of the mapping function and builds this function from known data within a restricted domain of its analyticity by curve fitting to a generic polynomial expansion. It is not only applicable to canonical problems admitting an analytical solution but also to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">general</i> medium or structure problems, from eigen- or driven-mode full-wave simulation results. The proposed technique is demonstrated for several systems, namely, an unbounded lossy medium, a dielectric-filled rectangular waveguide, a periodically loaded transmission line, a 1-D photonic crystal, and a series-fed patch (SFP) leaky-wave antenna (LWA), and it is validated either by analytical results or by full-wave simulated results.

Mapping the Magnetic Field Intensity of Light with the Nonlinear Optical Emission of a Silicon Nanoparticle.

To detect the magnetic component of arbitrary unknown optical fields, a candidate probe must meet a list of demanding requirements, including a spatially isotropic magnetic response, suppressed electric effect, and wide operating bandwidth. Here, we show that a silicon nanoparticle satisfies all these requirements, and its optical magnetism driven multiphoton luminescence enables direct mapping of the magnetic field intensity distribution of a tightly focused femtosecond laser beam with varied polarization orientation and spatially overlapped electric and magnetic components. Our work establishes a powerful nonlinear optics paradigm for probing unknown optical magnetic fields of arbitrary electromagnetic structures, which is not only essential for realizing subwavelength-scale optical magnetometry but also facilitates nanophotonic research in the magnetic light-matter interaction regime.

Donaldson Co Inc, USA

A semi-analytical method is developed for vibration and acoustic analysis of a submerged ring-stiffened cylindrical shell coupled with arbitrary inner structures. The whole structure is firstly disassembled into the stiffened cylindrical shell and inner structures, which are respectively analyzed through analytical substructure method and numerical approach. For the analytical substructure method, the cylindrical shell is decomposed into several shell segments according to positions of stiffeners and external forces, including exciting forces and coupling forces. These shell segments are described by Flügge shell theory and the ribs are governed by the annular plate equations. The solutions for cylindrical shell segments and rings are expanded as wave functions and Bessel functions. For the inner structures with arbitrary irregular shapes, finite element method (FEM) is utilized to establish their motion equations. At each coupling node, an artificial spring is adopted to connect the shell and inner structures, so rigid and elastic coupling conditions can be easily simulated by setting different stiffenesses to these springs. For external fluid field, an axisymmetric boundary element method (BEM) is employed to calculate the acoustic pressure. Combining the displacement continuity and force balance conditions at the joints of adjacent shell segments and introducing the coupling forces derived from inner structures and the fluid loading, the vibro-acoustic equations of the coupled system are obtained. To verify the validity of the present approach, vibro-acoustic responses of the semi-analytical method are compared with the ones calculated through FEM/BEM. Furthermore, effects of the coupling stiffness between the shell and inner structures and external fluid loading on vibro-acoustic responses are also evaluated.

Complex Eigenmodes and Eigenfrequencies in Electromagnetics

A comprehensive treatment on complex eigenmodes is presented for general lossy traveling-wave electromagnetic structures. The per unit length propagation phase shift ( β)-dependent complex eigenfrequencies Ω(β) are mapped to frequency-dependent complex propagation constant γ(ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) for a variety of electromagnetic structures. Rigorous procedures are presented to compute the complex eigenmodes of both uniform and periodic electromagnetic structures, confirmed using full-wave simulations and known analytical results. We further present two mapping procedures for arbitrary uniform and periodic structures, where the known {Ω-β} relationship is expressed using rational polynomial expansions. Consequently, replacing {Ω, jβ} with {ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> , γ} in the known {Ω-β} relation, a characteristic equation is formed, which is then numerically solved for the propagation constant γ, representing the physical dispersion relation ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> (β) and the frequency-dependent attenuation relation α(ω <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) of the structure. The mapping procedure is demonstrated for a variety of cases, including an unbounded uniform media, a rectangular waveguide, a Drude dispersive metamaterial, a biased ferrite medium, a periodic dielectric stack, and a periodic rectangular waveguide. The exact propagation characteristics have been successfully retrieved in all cases across both passbands and stopbands across frequency.

A model for screen utility to predict the future of printed solar cell metallization

Fine line screen printing for solar cell metallization is one of the most critical steps in the entire production chain of solar cells, facing the challenge of providing a conductive grid with a minimum amount of resource consumption at an ever increasing demand for higher production speeds. The continuous effort of the industrial and scientific community has led to tremendous progress over the last 20 years, demonstrating an average reduction rate for the finger width of approximately 7 µm per year with the latest highlight of achieving widths of 19 µm. However, further reductions will become a major challenge because commonly used metal pastes are not able to penetrate arbitrary small screen opening structures. Therefore, this study introduces the novel dimensionless parameter screen utility index SUI which quantifies the expected printability of any 2-dimensional screen architecture in reference to a given paste. Further, we present a full theoretical derivation of the SUI, a correlation to experimental results and an in-depth simulation over a broad range of screen manufacturing parameters. The analysis of the SUI predicts the point when commonly used wire materials will fail to provide sufficient meshes for future solar cell metallization tasks. Therefore, novel wire materials (e.g. the use of carbon nanotubes) with very high ultimate tensile strengths are discussed and suggested in order to fulfill the SUI requirements for printing contact fingers with widths below 10 µm. We further analyze economic aspects of design choices for screen angles by presenting an analytical solution for the calculation of mesh cutting losses in industrial screen production. Finally, we combine all aspects by presenting a generalized approach for designing a 2-dimensional screen architecture which fulfills the task of printing at a desired finger width.

Novel Scattering Operator for Arbitrary Finite Element Models in Optical Waveguides

An efficient finite-element-method-based scattering operator (FEM-SO) is proposed. Utilizing field based propagation operators as boundary conditions, arbitrary light waves including radiation and evanescent waves can be treated at input and output ports. In contrast to conventional scattering operator techniques, the FEM-SO is applicable to arbitrary structures by using finite element models. In addition, considering structural discontinuities at the connecting boundary of scattering operators, an interface matrix to satisfy the boundary conditions between unit structures is introduced. To verify the present approach, numerical examples are shown for propagation characteristics of high-index-contrast waveguide facet and power spectrum of a photonic-crystal Fabry–Perot (FP) cavity filter.

Accountable Attribute-based Encryption with Public Auditing and User Revocation in the Personal Health Record System

In the system of ciphertext policy attribute-based encryption (CP-ABE), only when the attributes of data user meets the access structure established by the encrypter, the data user can perform decryption operation. So CP-ABE has been widely used in personal health record system (PHR). However, the problem of key abuse consists in the CP-ABE system. The semi-trusted authority or the authorized user to access the system may disclose the key because of personal interests, resulting in illegal users accessing the system. Consequently, aiming at two kinds of existing key abuse problems: (1) semi-trusted authority redistributes keys to unauthorized users, (2) authorized users disclose keys to unauthorized users, we put forward a CP-ABE scheme that has authority accountability, user traceability and supports arbitrary monotonous access structures. Specifically, we employ an auditor to make a fair ruling on the malicious behavior of users. Besides, to solve the problem of user leaving from the system, we use an indirect revocation method based on trust tree to implement user revocation. Compared with other existing schemes, we found that our solution achieved user revocation at an acceptable time cost. In addition, our scheme is proved to be fully secure in the standard model.

Performance Study of Grass-Derived Nano-Cellulose and Polycaprolactone Composites for 3D Printing

In recent years, 3D printing has received increasing attention from researchers. This technology overcomes the limitations of traditional technologies by printing precise and personalized scaffold with arbitrary shapes, pore structures, and porosities for the applications in various tissues. The cellulose nanocrystal (CNC) is extracted from Humulus Japonicus (HJS) and mixed with poly(ε-caprolactone) (PCL) to prepare a series of CNC/PCL composites for printing. Based on the analysis of the physical and chemical properties of the series of the CNC/PCL composites, an optimal mass ratio of CNC to PCL was obtained. The Solidworks was used to simulate the stretching and compression process of the scaffolds with three different patterns under an external force. The flow of nutrient solution in the scaffolds with different patterns was simulated by ANSYS FLUENT, and then a new optimization scaffold pattern with a concave hexagon shape was advised based on the simulation results. Collectively, the mechanical test results of the material and scaffold confirmed that the optimal filling amount of the CNC was 5%, and the scaffold pattern with concave hexagon shape exhibited better mechanical properties and suitable for the transport of cells and nutrients, which is expected to be more widely used in 3D printing.

Bypassing the computational bottleneck of quantum-embedding theories for strong electron correlations with machine learning

A cardinal obstacle to performing quantum-mechanical simulations of strongly-correlated matter is that, with the theoretical tools presently available, sufficiently-accurate computations are often too expensive to be ever feasible. Here we design a computational framework combining quantum-embedding (QE) methods with machine learning. This allows us to bypass altogether the most computationally-expensive components of QE algorithms, making their overall cost comparable to bare Density Functional Theory (DFT). We perform benchmark calculations of a series of actinide systems, where our method describes accurately the correlation effects, reducing by orders of magnitude the computational cost. We argue that, by producing a larger-scale set of training data, it will be possible to apply our method to systems with arbitrary stoichiometries and crystal structures, paving the way to virtually infinite applications in condensed matter physics, chemistry and materials science.

Tensor Decomposition Learning for Compression of Multidimensional Signals

Multidimensional signals like multispectral images and color videos are becoming ubiquitous in modern times, constantly introducing challenges in data storage and transfer, and therefore demanding efficient compression strategies. Such high dimensional observations can be naturally encoded as tensors, exhibiting significant redundancies across dimensions. This property is exploited by tensor decomposition techniques that are being increasingly used for compactly encoding large multidimensional arrays. While efficient, these methods are incapable of utilizing prior information present in training data. In this paper, a novel tensor decomposition learning method is proposed for the compression of high dimensional signals. Specifically, instead of extracting independent bases for each example, our method learns an appropriate basis for each dimension from a set of training samples by solving a constrained optimization problem. As such, each sample is quantized and encoded into a reduced-size core tensor of coefficients that corresponds to the multilinear combination of the learned basis matrices. Furthermore, the proposed method employs a symbol encoding dictionary for binarizing the decomposition outputs. Experimental results on synthetic data and real satellite multispectral image sequences demonstrate the efficacy of our method, surpassing competing compression methods while offering the flexibility to handle arbitrary high dimensional data structures.

Investigations on the aero-thermo-elastic characteristics of arbitrary polygon built-up structures in supersonic airflow

The polygon built-up structures are widely used in engineering applications, which consist of a few flat plates. This paper develops a unified solution for the vibration and flutter problems of heated arbitrary polygon built-up structures with general boundary conditions and in supersonic airflow. By using the first-order shear deformation theory (FSDT), thermo-elastic theory and supersonic piston theory, the dynamic model of heated arbitrary polygon built-up structures in supersonic airflow is established based on the energy method. The unified solution is obtained by means of a variational approach and several numerical cases are performed to validate the proposed model, showing the high efficiency and excellent accuracy. Finally, to reveal the effects of the constraint, thermal load, numbers of cross-section edge and cross-section shape of the arbitrary polygon built-up structure on the vibration and flutter characteristics of the structures, the parametric studies are carried out. The present study can be used as the reference for the dynamic design of the arbitrary polygon built-up structure to improve its aero-elastic stability characteristic.

Estimating FST and kinship for arbitrary population structures.

FST and kinship are key parameters often estimated in modern population genetics studies in order to quantitatively characterize structure and relatedness. Kinship matrices have also become a fundamental quantity used in genome-wide association studies and heritability estimation. The most frequently-used estimators of FST and kinship are method-of-moments estimators whose accuracies depend strongly on the existence of simple underlying forms of structure, such as the independent subpopulations model of non-overlapping, independently evolving subpopulations. However, modern data sets have revealed that these simple models of structure likely do not hold in many populations, including humans. In this work, we analyze the behavior of these estimators in the presence of arbitrarily-complex population structures, which results in an improved estimation framework specifically designed for arbitrary population structures. After generalizing the definition of FST to arbitrary population structures and establishing a framework for assessing bias and consistency of genome-wide estimators, we calculate the accuracy of existing FST and kinship estimators under arbitrary population structures, characterizing biases and estimation challenges unobserved under their originally-assumed models of structure. We then present our new approach, which consistently estimates kinship and FST when the minimum kinship value in the dataset is estimated consistently. We illustrate our results using simulated genotypes from an admixture model, constructing a one-dimensional geographic scenario that departs nontrivially from the independent subpopulations model. Our simulations reveal the potential for severe biases in estimates of existing approaches that are overcome by our new framework. This work may significantly improve future analyses that rely on accurate kinship and FST estimates.

3D nonlinear photolithography of Tin oxide ceramics via femtosecond laser

As a wide band gap semiconductor material, tin oxide (SnO2) has been widely used in gas sensing, optoelectronics and catalysis. The complex micro and nanoscale three-dimensional (3D) geometric structures endow the conventional SnO2 ceramics with novel properties and functionalities. Nevertheless, ceramics cannot be cast or machined easily due to their high mechanical toughness and resistance. The additive manufacturing opens a great opportunity for flexibly geometrical shaping, while the arbitrary shaping of SnO2 ceramics at micro and nanoscale is always a challenge. Herein, preceramic monomers which can be polymerized under ultrafast laser irradiation, were utilized to form complex and arbitrary 3D preceramic polymer structures. After calcination treatment, these green-body structures could be converted into pure high-dense SnO2 ceramics with uniform shrinkage, and the feature size was down to submicron. Transmission electron microscopy (TEM) analysis displays that the printed SnO2 ceramic nanostructures can be nanocrystallized with grain sizes of 2.5 ± 0.4 nm. This work provides the possibility of manufacturing 3D SnO2 ceramic nanostructures arbitrarily with sub-100 nm resolution, thus making it promising for the applications of SnO2 in different fields.

Optical near-field imaging and nanostructuring by means of laser ablation

In this review we consider the development of optical near-field imaging and nanostructuring by means of laser ablation since its early stages around the turn of the century. The interaction of short, intense laser pulses with nanoparticles on a surface leads to laterally tightly confined, strongly enhanced electromagnetic fields below and around the nano-objects, which can easily give rise to nanoablation. This effect can be exploited for structuring substrate surfaces on a length scale well below the diffraction limit, one to two orders smaller than the incident laser wavelength. We report on structure formation by the optical near field of both dielectric and metallic nano-objects, the latter allowing even stronger and more localized enhancement of the electromagnetic field due to the excitation of plasmon modes. Structuring with this method enables one to nanopattern large areas in a one-step parallel process with just one laser pulse irradiation, and in the course of time various improvements have been added to this technique, so that also more complex and even arbitrary structures can be produced by means of nanoablation. The near-field patterns generated on the surface can be read out with high resolution techniques like scanning electron microscopy and atomic force microscopy and provide thus a valuable tool—in conjunction with numerical calculations like finite difference time domain (FDTD) simulations—for a deeper understanding of the optical and plasmonic properties of nanostructures and their applications.

DNA origami: an outstanding platform for functions in nanophotonics and cancer therapy.

Due to the proposal and evolution of the DNA origami technique over the past decade, DNA molecules have been utilized as building blocks for the precise construction of nanoscale architectures. Benefiting from the superior programmability of DNA molecules, the sequence-dependent recognition mechanism and robust complementation among DNA strands make it possible to customize almost arbitrary structures. Such an assembly strategy bypasses some of the limits of conventional fabrication methods; the fabrication accuracy and complexity of the target product are unprecedentedly promoted as well. Furthermore, due to the spatial addressability of the final products, nanostructures assembled through the DNA origami technique can also serve as a versatile platform for the spatial positioning of functional elements, represented by colloidal nanoparticles (NPs). The subsequent fabrication of heterogeneous functional nanoarchitectures is realized via modifying colloidal NPs with DNA strands and manipulating them to anchor into DNA origami templates. This has given rise to investigations of their novel properties in nanophotonics and therapeutic effects towards some diseases. In this review, we survey the crucial progress in the development of DNA origami design, assembly and structural analysis and summarize available applications in nanophotonics and cancer therapy based on the object-dressed DNA origami complex. Moreover, we elucidate the development of this field and discuss the potential directions of this kind of application-oriented nanomanufacturing.

Qualification of 3-D Printed Mortar With Electrical Conductivity Measurements

Additive manufacturing (AM) or 3-D printing of concrete allows for construction of arbitrary shape structures without a mold. Since reproducibility of 3-D printed concrete is lower than that of conventional fabrication, each 3-D printed structure should be monitored for proper curing. Conventional qualification of concrete is based on several tests, including destructive compressive strength measurements. Because a structure is 3-D printed layer-by-layer, the surfaces of AM concrete structures have significant surface roughness. This limits the applicability of conventional nondestructive testing methods. We investigated qualification of 3-D printed mortar by monitoring curing with nondestructive electrical conductivity measurements. Bulk resistance of concrete was extracted from electrochemical impedance spectroscopy (EIS) measurements made using custom flexible self-adhesive electrodes, which contour to rough surfaces. We show that bulk resistivity of concrete increases linearly with time. This allows for developing a calibration curve for compressive strength lookup from nondestructive electrical conductivity measurements. Conductivity measurements also allow for estimation of formation factor, which is an indicator of mortar permeability.

FlexTileSource: An OpenSeadragon Extension for Efficient Whole-Slide Image Visualization

Background: Web-based digital slide viewers for pathology commonly use OpenSlide and OpenSeadragon (OSD) to access, visualize, and navigate whole-slide images (WSI). Their standard settings represent WSI as deep zoom images (DZI), a generic image pyramid structure that differs from the proprietary pyramid structure in the WSI files. The transformation from WSI to DZI is an additional, time-consuming step when rendering digital slides in the viewer, and inefficiency of digital slide viewers is a major criticism for digital pathology. Aims: To increase efficiency of digital slide visualization by serving tiles directly from the native WSI pyramid, making the transformation from WSI to DZI obsolete. Methods: We implemented a new flexible tile source for OSD that accepts arbitrary native pyramid structures instead of DZI levels. We measured its performance on a data set of 8104 WSI reviewed by 207 pathologists over 40 days in a web-based digital slide viewer used for routine diagnostics. Results: The new FlexTileSource accelerates the display of a field of view in general by 67 ms and even by 117 ms if the block size of the WSI and the tile size of the viewer is increased to 1024 px. We provide the code of our open-source library freely on https://github.com/schuefflerlab/openseadragon. Conclusions: This is the first study to quantify visualization performance on a web-based slide viewer at scale, taking block size and tile size of digital slides into account. Quantifying performance will enable to compare and improve web-based viewers and therewith facilitate the adoption of digital pathology.

Predicting chemical shifts with graph neural networks.

Inferring molecular structure from Nuclear Magnetic Resonance (NMR) measurements requires an accurate forward model that can predict chemical shifts from 3D structure. Current forward models are limited to specific molecules like proteins and state-of-the-art models are not differentiable. Thus they cannot be used with gradient methods like biased molecular dynamics. Here we use graph neural networks (GNNs) for NMR chemical shift prediction. Our GNN can model chemical shifts accurately and capture important phenomena like hydrogen bonding induced downfield shift between multiple proteins, secondary structure effects, and predict shifts of organic molecules. Previous empirical NMR models of protein NMR have relied on careful feature engineering with domain expertise. These GNNs are trained from data alone with no feature engineering yet are as accurate and can work on arbitrary molecular structures. The models are also efficient, able to compute one million chemical shifts in about 5 seconds. This work enables a new category of NMR models that have multiple interacting types of macromolecules.

Infinite Approaching Superlubricity by Three-Dimensional Printed Structures.

The rapid development of three-dimensional (3D) printing technology opens great opportunities for the design of various multiscale lubrication structures. 3D printing allows high customization of arbitrary complex structures and rapid prototyping of objects, which provides an avenue to achieve effective lubrication. Current experimental observations on superlubricity are limited to atomically smooth clean surfaces, extreme operating conditions, and nano- or microscales. With the in-depth exploration of 3D printed lubrication, construction of multifunctional 3D structures with refined dimensions spanning from micronanoscale to macroscale is increasingly regarded as an important means to approach superlubricity and has aroused great scientific interest. To document recent advances in 3D printing for structural lubrication, a detailed literature review is provided. Emphasis is given on the design and lubrication performance of geometric and bioinspired lubrication structures with characteristic dimensions. The material requirements, merits, drawbacks, and representative applications of various 3D printing techniques are summarized. Potential future research trends aiming at the design strategy and manufacturing process of 3D printed lubrication structures are also highlighted.

Finite Sample Change Point Inference and Identification for High-Dimensional Mean Vectors

AbstractCumulative sum (CUSUM) statistics are widely used in the change point inference and identification. For the problem of testing for existence of a change point in an independent sample generated from the mean-shift model, we introduce a Gaussian multiplier bootstrap to calibrate critical values of the CUSUM test statistics in high dimensions. The proposed bootstrap CUSUM test is fully data dependent and it has strong theoretical guarantees under arbitrary dependence structures and mild moment conditions. Specifically, we show that with a boundary removal parameter the bootstrap CUSUM test enjoys the uniform validity in size under the null and it achieves the minimax separation rate under the sparse alternatives when the dimension p can be larger than the sample size n.Once a change point is detected, we estimate the change point location by maximising the ℓ∞-norm of the generalised CUSUM statistics at two different weighting scales corresponding to covariance stationary and non-stationary CUSUM statistics. For both estimators, we derive their rates of convergence and show that dimension impacts the rates only through logarithmic factors, which implies that consistency of the CUSUM estimators is possible when p is much larger than n. In the presence of multiple change points, we propose a principled bootstrap-assisted binary segmentation (BABS) algorithm to dynamically adjust the change point detection rule and recursively estimate their locations. We derive its rate of convergence under suitable signal separation and strength conditions.The results derived in this paper are non-asymptotic and we provide extensive simulation studies to assess the finite sample performance. The empirical evidence shows an encouraging agreement with our theoretical results.