Structural Parameters Research Articles

Design and Analysis of Surface-Mounted Permanent-Magnet Field-Modulation Machine for Achieving High Power Factor

Along with the establishment of general air-gap field modulation theory, a new type of field-modulation machine has attracted increasing attention. However, the low power factor always plagued its popularization. This paper is to focus on the simplest structure surface-mounted permanent-magnet field-modulation (SMPMFM) machine, aiming at exploring the implementation of high power-factor design. The key is that the power factor calculation formula is derived parametrically, hence the originally vague causal relationship between the structural parameters and power factor become clear, guided by which the initial structure design for high power factor achievement is determined, meanwhile the power factor adjustment evaluation and the electromagnetic performance comparison with the help of finite element analysis (FEA) are conducted to verify the validity of the proposed power factor calculation formula. Finally, a feasible high-power-factor SMPMFM prototype is fabricated and the experimental testing is performed, which has a good agreement with the formular calculation and FEA results. Hence, the validity of the proposed analysis for achieving high power-factor design of field-modulation machines is verified.

Nanostructure and dynamics of N-truncated copper amyloid-β peptides from advanced X-ray absorption fine structure.

An X-ray absorption spectroscopy (XAS) electrochemical cell was used to collect high-quality XAS measurements of N-truncated Cu:amyloid-β (Cu:Aβ) samples under near-physiological conditions. N-truncated Cu:Aβ peptide complexes contribute to oxidative stress and neurotoxicity in Alzheimer's patients' brains. However, the redox properties of copper in different Aβ peptide sequences are inconsistent. Therefore, the geometry of binding sites for the copper binding in Aβ4-8/12/16 was determined using novel advanced extended X-ray absorption fine structure (EXAFS) analysis. This enables these peptides to perform redox cycles in a manner that might produce toxicity in human brains. Fluorescence XAS measurements were corrected for systematic errors including defective-pixel data, monochromator glitches and dispersion of pixel spectra. Experimental uncertainties at each data point were measured explicitly from the point-wise variance of corrected pixel measurements. The copper-binding environments of Aβ4-8/12/16 were precisely determined by fitting XAS measurements with propagated experimental uncertainties, advanced analysis and hypothesis testing, providing a mechanism to pursue many similarly complex questions in bioscience. The low-temperature XAS measurements here determine that CuII is bound to the first amino acids in the high-affinity amino-terminal copper and nickel (ATCUN) binding motif with an oxygen in a tetragonal pyramid geometry in the Aβ4-8/12/16 peptides. Room-temperature XAS electrochemical-cell measurements observe metal reduction in the Aβ4-16 peptide. Robust investigations of XAS provide structural details of CuII binding with a very different bis-His motif and a water oxygen in a quasi-tetrahedral geometry. Oxidized XAS measurements of Aβ4-12/16 imply that both CuII and CuIII are accommodated in an ATCUN-like binding site. Hypotheses for these CuI, CuII and CuIII geometries were proven and disproven using the novel data and statistical analysis including F tests. Structural parameters were determined with an accuracy some tenfold better than literature claims of past work. A new protocol was also developed using EXAFS data analysis for monitoring radiation damage. This gives a template for advanced analysis of complex biosystems.

Butea monosperma aided green synthesis of α-MoO3 nanoparticles: Biosensing and photocatalytic activity towards hazardous dyes and rangoli colorants

Increasing environmental pollutants and the need for efficient photocatalytic and biosensing materials have led to the development of novel green synthesis methods. In this study, MoO3 nanoparticles (NPs) were successfully synthesized by using green chemistry approach. In that respect, Butea monosperma leaf powder was used as a novel fuel. Structural parameters, optical properties, shape and morphology of MoO3 NPs are examined using XRD, FTIR, UV–Vis spectrophotometer, Photoluminescence (PL), SEM - EDAX and TEM analysis. The use of MoO3 NPs as a dusting agent in fingerprint applications results in improved fingerprint visibility, which is useful in forensic investigation. In this study, we modified the GCE electrode surface with MoO3 NPs to develop a highly sensitive dopamine (DA) sensor. Electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and differential pulse voltammetry (DPV) were used to investigate the electrochemical behaviour and electrocatalytic property of the MoO3 NPs. The synthesized NPs have excellent performance for detection of highly sensitive biologically active DA. The sensor exhibits a low detection limit of 0.45 μM. Additionally, it shows good stability, simplicity, and selectivity for the rapid detection of DA. MoO3 NPs shows good photocatalytic activity under UV light for the degradation of important textile industries dye such as methylene blue (MB) and harmful rangoli colors which are available in local shops of Tumkur, Karnataka. The photocatalyst exhibits a remarkable 97 % degradation efficiency for MB dye. Coumarin studies ensure the effective generation of OH radicals on the surface of the photocatalyst, leading to the degradation of organic dyes. Additionally, the MoO3 NPs reusability was examined, and even after four cycles, the level of photocatalytic activity did not drop much. Therefore, it is a potential photocatalyst for the processing of organic colours.

New Relations Between Zagreb Indices and Omega Invariant.

In this work, we studied the problem of determining the values of the Zagreb indices of all the realizations of a given degree sequence. We first obtained some new relations between the first and second Zagreb indices and the forgotten index sometimes called the third Zagreb index. These relations also include the triangular numbers, order, size, and the biggest vertex degree of a given graph. As the first Zagreb index and the forgotten index of all the realizations of a given degree sequence are fixed, we concentrated on the values of the second Zagreb index and studied several properties including the effect of vertex addition. In our calculations, we make use of a new graph invariant, called omega invariant, to reach numerical and topological values claimed in the theorems. This invariant is closely related to Euler characteristic and the cyclomatic number of graphs. Therefore this invariant is used in the calculation of some parameters of the molecular structure under review in terms of vertex degrees, eccentricity, and distance.

Unraveling thermodynamic anomalies of water: A molecular simulation approach to probe the two-state theory with atomistic and coarse-grained water models.

Thermodynamic and dynamic anomalies of water play a crucial role in supporting life on our planet. The two-state theory attributes these anomalies to a dynamic equilibrium between locally favored tetrahedral structures (LFTSs) and disordered normal liquid structures. This theory provides a straightforward, phenomenological explanation for water's unique thermodynamic and dynamic characteristics. To validate this two-state feature, it is critical to unequivocally identify these structural motifs in a dynamically fluctuating disordered liquid. In this study, we employ a recently introduced structural parameter (θavg) that characterizes the local angular order within the first coordination shell to identify these LFTSs through molecular dynamics simulations. We employ both realistic water models with a liquid-liquid critical point (LLCP) and a coarse-grained water model without an LLCP to study water's anomalies in low-pressure regions below 2 kbar. The two-state theory consistently describes water's thermodynamic anomalies in these models, both with and without an LLCP. This suggests that the anomalies predominantly result from the two-state features rather than criticality, particularly within experimentally accessible temperature-pressure regions.

Scissor Arm for Cambered Snow: Mechanical Theory

In this study, a novel cambered snow removal device is designed to achieve automatic snow removal in large curved areas, such as the south roof of a Chinese solar greenhouse. The theory of structural parameters and shear force is ambiguous. People are not based on the greenhouse structure parameters for the selection of snow removal devices. Therefore, the quantitative relationship between the structure of the greenhouse span and the number of scissor arm-length knots is analysed, and the relationship between the material strength and application distance is determined. This study’s objectives are (1) to establish a theoretical model of scissor arm motion and (2) to analyse the force distribution of the scissor arm using multi-body dynamics. The results show that the scissor arm of a round-arch greenhouse has fewer sections but a larger arm length, whereas the scissor arm of a traditional solar greenhouse has more sections but a smaller arm length. Based on the shear force of the scissor structure, the optimised wall thickness reduces the force of the node by 17%.

Probing rotated Weyl points on one-dimensional sonic crystals

Recently, researchers have devoted their intense efforts to investigating Weyl physics in synthetic space. In this Letter, we study the intriguing topological rotated Weyl physics in a three-dimensional parameter space, which consists of two extra structural parameters and the wave vector of a simple one-dimensional sonic crystal. In our ultrasonic experiments, we observe that the topological interface states propagate along the interface formed by two sonic crystals of distinct chirality caused by the rotated Weyl points. We detect the rotated synthetic Weyl points and measure the singularities of the reflection phase, which results in the robustness of the interface states. At the same time, it also shows the advantages of synthetic dimensions in exploring high-dimensional physics problems in low-dimensional systems.

Compliance and Kinetostatics of a Novel 2PRS-2PSS Compliant Parallel Micromanipulator: Modeling and Analysis

A novel 2PRS-2PSS (P represents the prismatic pair, R represents the revolute hinge, S represents the spherical hinge) compliant parallel micromanipulator with two translational DOFs and two rotational DOFs is presented, and its compliance model and kinetostatic model are sequentially developed and analyzed. Initially, an analytical model used to describe the compliance of this micromanipulator was developed using the compliance matrix method (CMM). Through a comparison with finite element analysis, the accuracy of this analytical model is confirmed, and the influence of various dimensional and structural parameters on the compliance behavior is investigated. Subsequently, the micromanipulator is treated as an equivalent spring system, allowing for the derivation of its governing equation based on the established compliance model. From this equation, a kinetostatic model relating input forces to output displacements is derived. Validation of this model is performed by comparing analytical results with finite element simulations under specific motion trajectories, revealing a maximum relative error of 6.18%. This close agreement verifies the accuracy of the kinetostatic model. Finally, the impact of the parameters of the flexure hinge on the mapping matrix is examined to offer insights into minimizing undesired displacements, providing valuable guidance for optimizing the micromanipulator’s performance.

Temperature-Controlled and Adjustable Terahertz Device Based on Vanadium Dioxide

We propose a simple multifunctional terahertz absorber based on the simulation. The device consists of a gold layer, a SiO2 dielectric layer, and a VO2 top layer. The modulation mechanism of this device is to utilize the thermally induced phase transition characteristics of vanadium dioxide material. The simulation results show that when the temperature is 312 K, the device has the effect of complete reflection of terahertz waves. When the temperature is 345 K, the device has almost perfect absorption of terahertz wave in the range of 4.7–9.7 THz, and the spectral absorptivity is modulated in the range of 0~0.999. The electric field conditions at different temperatures were plotted to further explain the reasons for the performance transition of the device. The terahertz device was explained using impedance matching theory. In addition, the influence of different structural parameters on absorption rate was studied, providing reference for practical applications. At the same time, the device is polarization-insensitive and insensitive to the incident angle. When the incident angle changes from 0°to 45°, the device still has a stable absorption effect. The device has great application prospects in terahertz stealth, modulation, and other fields and provides ideas for the design of related devices.

Erosion Wear Analysis On Valve Cage of Cage-Typed Sleeve Control Valve for Coal Liquefaction

Abstract Cage-typed sleeve control valve (CSCV) is the key basic equipment in direct coal liquefaction project. The working condition of CSCV has the characteristics of high pressure difference, high velocity and high solid content. There is a general problem of liquid-solid two-phase erosion wear in CSCV, which is easy to lead to the failure of internal structure in the valve cage. Therefore, it is necessary to study erosion wear characteristics of internal structure in the valve cage. Based on the actual situation of erosion wear in the valve cage, the simplified T-shaped flow path is designed, and the accuracy of the liquid-solid two-phase flow model and the erosion prediction model are verified; The flow characteristics and erosion wear characteristics of T-shaped flow path under different working conditions are studied; Based on the simulation results of different structural parameters and boundary conditions, the erosion wear of T-shaped flow path is predicted and calculated by response surface method, and the prediction formula of maximum erosion rate is obtained. The formula allows for the rapid determination of optimized flow path structural parameters to reduce damage to the valve from erosion and wear. This work can quickly predict the erosion wear rate of the key areas in the valve cage, which can provide a certain reference value for the life prediction and structural optimization of CSCV, and it can also benefit safety and maintenance of coal liquefaction system.

Inverse Design of Two-Function Transmission-type Reconfigurable Polarization Control Metasurfaces Based on Deep Learning

Abstract Compared with single-function metasurfaces, the design difficulty of multi-function metasurfaces increases significantly. This paper introduces an inverse design method based on deep learning to address this challenge. By this method, a transmission-type reconfigurable polarization control metasurface (TRPCM) with two functions is rapidly designed. The network model used in the method consists of an electromagnetic parameter reconstruction network model (EMPRNM) and an inverse prediction network model (IPNM). The combination of the two models can solve the problem of difficulty in defining high-dimensional inputs in traditional inverse design, and achieve accurate prediction of metasurface structure parameters under given design targets. To optimize the hyperparameters of the neural network model, a genetic algorithm (GA) was introduced. To solve the non-uniqueness problem of inverse design, a method for eliminating similar data by calculating Euclidean Distance (ED) was introduced. Both schemes further improve the predictive performance of the proposed network model. Finally, six design targets were set based on the TRPCM. The structural parameters of the metasurface were successfully predicted using two neural network models and achieved the required performance. On this basis, a set of parameters was selected for experimental validation. By controlling the ON or OFF of the PIN diodes, the fabricated metasurface achieves two functions: linear-to-circular polarization conversion (LTCPC) and linear polarization maintenance (LPM) in the range of 2-3.6 GHz. Study results show that the inverse design scheme proposed in the paper is feasible and practical for solving the rapid optimization design of complex multi-function metasurfaces.

Hydrodesulfurization of Dibenzothiophene: A Machine Learning Approach.

The hydrodesulfurization (HDS) process is widely used in the industry to eliminate sulfur compounds from fuels. However, removing dibenzothiophene (DBT) and its derivatives is a challenge. Here, the key aspects that affect the efficiency of catalysts in the HDS of DBT were investigated using machine learning (ML) algorithms. The conversion of DBT and selectivity was estimated by applying Lasso, Ridge, and Random Forest regression techniques. For the estimation of conversion of DBT, Random Forest and Lasso offer adequate predictions. At the same time, regularized regressions have similar outcomes, which are suitable for selectivity estimations. According to the regression coefficient, the structural parameters are essential predictors for selectivity, highlighting the pore size, and slab length. These properties can connect with aspects like the availability of active sites. The insights gained through ML techniques about the HDS catalysts agree with the interpretations of previous experimental reports.

Study of the kurtoses transmission of linear structures under stationary non-Gaussian random loadings

Recent research has discovered that the kurtoses of non-Gaussian stress loadings can significantly impact the fatigue life of in-service structures. To predict potential damage, a fatigue life estimation method based on Gaussian damage and a kurtosis-related corrective coefficient has been developed. However, the transmission of kurtoses from excitations to responses has not been well studied, particularly in multi-input cases. This hinders efforts to accelerate fatigue damage by controlling input kurtoses. Therefore, this paper aims to establish a kurtoses transmission model for a linear structure under multiple uncorrelated stationary non-Gaussian excitations generated by zero-memory non-linear method. Firstly, a single-input kurtosis transmission equation is proposed to establish the relationship between excitation kurtosis and response kurtosis. Using this equation, the response kurtosis is formulated with input kurtosis and the system parameters of a linear structure. Secondly, the response kurtosis of multi-input cases is deduced, and the study shows that the response kurtosis is the weighted sum of the kurtosis induced by each excitation. Finally, numerical validation and experimental studies are conducted to verify the accuracy of both the single-input and multi-input kurtoses transmission models. The validations demonstrate that the proposed models can predict the response kurtoses with satisfactory precision, regardless of the input spectral shape and the number of uncorrelated input forces.

Interpersonal Problem Profiles of Personality and Psychopathology Constructs in Chinese Undergraduates and Offenders.

The interpersonal problem circumplex is extensively used in the field as an assessment framework for understanding the interpersonal implications of a range of personality and psychopathology constructs. The vast majority of this large literature has been conducted in Western convenience and clinical samples. We computed interpersonal problem structural summary parameters for a range of personality and psychopathology variables in two Chinese offender samples (N = 424 and N = 555) and one undergraduate sample (N = 511) to test how well findings from Western samples generalize to Chinese undergraduates and offenders. The results showed that findings in Western samples generalized reasonably well to Chinese young adult and forensic contexts, although the interpersonal profiles of external variables were less specific in Chinese samples. Compared with undergraduates, interpersonal distress has stronger associations with the mental health of offenders. This study further elaborates the interpersonal correlates of individual differences in personality and psychopathology across cultures and assessment contexts, and it also extends the literature examining interpersonal problems in forensic settings.

Advancing blast fragmentation simulation of RC slabs: A graph neural network approach

Accurate prediction of blast-induced fragmentation in reinforced concrete (RC) structures is pivotal for structural debris hazard assessment in an explosion event. This assessment is essential for designing effective measures for protection of people and structure in the vicinity. While conventional computational techniques are proficient, they are computationally very demanding and may not be readily adaptable in practical applications. This study proposes a novel approach, the Fragment Graph Network (FGN), a specialized variant of graph neural networks (GNNs), to simulate complex interactions between macro-level particles of RC structures subjected to close-in blast loads. The RC structure is discretised into particles, with the model predicting structural responses and fragmentation based on states of these particles. Utilizing an autoregressive prediction mechanism, our model can accurately simulate spatiotemporal responses of RC slabs against blast loads. The efficacy of the model was tested on a dataset consisting of 149 simulated cases, encompassing around 180,000 states each, covering a wide range of blasting scenarios and structural design parameters including TNT charge weight, standoff distance, slab dimension, concrete strength, and reinforcement ratio and layout. Notably, the proposed model delivered accurate predictions, achieving relative errors under 10% and R2 values exceeding 0.93 for both standard and extrapolated test scenarios as compared to the SPH simulations. Remarkably, our model dramatically outpaced traditional numerical approaches, reducing simulation times from hours to minutes on CPUs and seconds on GPUs. This pioneering effort paves the way for efficient and accurate simulations of RC blast fragmentations, offering a rapid alternative to standard numerical approaches.

Linewidth characterization of a self-traceable grating by SEM

To achieve high-precision nanometrology, a self-traceable grating reference material has been reported and prepared using atom lithography and soft x-ray interference techniques (Liu et al 2021 Nanotechnology 32 175 301). In this work, we employ a Monte Carlo simulation method to investigate the scanning electron microscopy (SEM) image contrast and linewidth characterization of the grating linewidth. The 3D structure of mushroom-shaped grating lines made of multilayers (Pt, SiO2 and Si) is modeled according to transmission electron microscopy (TEM) images, enabling the SEM linescan profiles of secondary electron signals to be obtained for different values of structural linewidth parameters from Monte Carlo simulations. Using the principle of the model-based library method, a model database of Monte Carlo-simulated SEM linescan profiles is thus constructed by varying the incident electron beam conditions and the grating linewidths; then, the grating linewidth is successfully characterized using experimental SEM images. The comparison with the TEM measurement reveals that the measurement accuracy is verified to within 0.3% for the linewidth of ∼25 nm.

Influence of Permeability and Shear-Thinning Behavior on the Hydrodynamics Flow Features Around Porous Square Cylinders

Abstract This article investigates the laminar flow of power-law fluids through two porous square cylinders in a side-by-side configuration. The effects of power-law index (n), Darcy number (Da), and gap ratio (g/W) are examined within ranges of g/W = 0.5–5, n = 0.4–0.8, and Da = 10−6–10−2, respectively. Two flow conditions are considered: first, for a creeping flow (unseparated flow) at Re = 1 where Darcy's law is applicable; second, for a viscous dominant flow at Re = 100, where Darcy–Forchheimer-extended model is exercised. Flow patterns behind the porous cylinders are analyzed using streamlines, velocity profiles, pressure distribution curves, and vorticity structural parameters (Г). In low permeability levels, the flow exhibits an irregular nonperiodic vortex shedding characterized by a single large vortex street far off the downstream for g/W = 0.5. However, synchronized wake patterns were observed in either antiphase or in-phase modes for higher gap ratios. Leading-edge separation with two-side recirculation induces quasi-periodicity in the flow for all g/W. It was found that increasing the permeability can prevent the leading edge separation. Additionally, a transition from antiphase to in-phase mode occurs when the permeability is altered while maintaining constant flow-time. The presence of a jet-like flow between cylinders significantly impacts unsteady wake patterns. The impact of g/W, power-law index, and permeability on drag is also examined. A jump in some flow parameters was observed at higher Re for the midrange Darcy number, but no such increase was noted for the high shear-thinning behavior. These findings provide a potential approach for improving the design of fluidic systems involving porous cylinders.

Design of miniaturized wide band-pass plasmonic filters in MIM waveguides with tailored spectral filtering

This paper reports the design and numerical results of three new extremely compact and efficient flat-top band-pass plasmonic filters operating in the near-infrared region. The proposed structures are realized in metal–insulator-metal plasmonic waveguides based on stub, tilted T-junction and right-angle trapezoid configurations. A built-in parameterized genetic algorithm is applied to maximize the transmission efficiency, while at the same time contributing to shrinking down the size of the device structures. It is shown that the tunability of the optical filters can be realized by modulating their structural parameters to gain control over the band-pass filtering wavelengths. Numerical calculations are conducted based on the finite element method of CST Microwave Studio and demonstrate that the suggested ultra-compact plasmonic waveguide filters offer wide bandwidths of more than 270 nm, 424 nm, and 289 nm, with transmission efficiencies of higher than 80%, 74.2%, and 74.3%, respectively. The sizes of the proposed wavelength filters are 490 nm × 575 nm, 350 nm × 180 nm, and 420 nm × 150 nm, respectively, which make them attractive candidates for applications in high density photonic integrated circuits (PICs). As a result, because of the promising characteristics of the proposed topologies such as their high efficiency, compact size, tunability, and simple structure they may find applications in on-chip integration, laser technology, and multi-photon fluorescence.

Adaptive Residual Convolutional Neural Network for Compressive Strength Prediction of Energetic Materials Using SEM Images

Abstract The microstructure of materials has a great influence on the macroscopic mechanical properties of materials, and the relationship between them is of great significance to the design of materials. With the development of artificial intelligence and deep learning, many researchers have used convolutional neural networks(CNN) to correlate microscopic images with material properties, and achieved good results. However, for different types of material performance prediction tasks and datasets of different sizes, researchers need to design a special CNN network structure, and the adjustment steps of network structure parameters are cumbersome and time consuming. In this paper, an adaptive residual convolutional neural network with variable depth and width is proposed. We take the prediction error of the test dataset of the specific material performance prediction task as the evaluation index, and adaptively adjusts the network structure based on simulated annealing algorithm to find the best network structure. Finally, it is verified on the dataset composed of SEM images and compressive strength of polymer explosive materials. MAPE of our model is 1.3% and the goodness of fit is 0.943. Further, we use Grad-CAM to explore the correlation between material microstructure and material properties.

Research on the mechanism and control methods of mechanical drift in linear ultrasonic motors

Linear ultrasonic motors (LUMs) have advantages such as de-energized self-locking and micro-nano displacement resolution. However, their positioning and control accuracy are negatively affected by mechanical drift, which limits their application in ultra-precision fields. To date, the quantitative mechanism of LUM mechanical drift under power-off conditions remains unreported. To solve the problem, we employ the creep theory to identify the clamp stiffness parameters and consider the internal friction and stick-slip effects of the slider, thereby establishing a non-autonomous dynamic model of the LUM mechanical drift in the power-off state. Subsequently, we utilize this model to investigate how the LUM’s structural parameters influence mechanical drift and explore methods to mitigate this undesirable phenomenon. Finally, we validate the model’s validity through experimental research. Our findings reveal that structural creep is the primary cause of mechanical drift in LUMs. Increasing the tangential stiffness of the clamp component and slider internal friction proves to be an effective approach to reducing mechanical drift. This study holds substantial theoretical and practical significance as it deepens understanding of the mechanisms of mechanical drift in LUMs and offers a pathway to achieve effective mechanical drift control.