Simple Structural Model Research Articles

Single-Molecule Electrical Conductance in Z-form DNA:RNA.

Nucleic acids have emerged as new materials with promising applications in nanotechnology, molecular electronics, and biosensing, but their electronic properties, especially at the single-molecule level, are largely underexplored. The Z-form is an exotic left-handed helical oligonucleotide conformation that may be involved in critical biological processes such as the regulation of gene expression and epigenetic processes. In this work, the electrical conductance of individual Guanine Cytosine (GC)-rich DNA:RNA molecules is measured in physiological buffer and 2,2,2-Trifluoroethanol (TFE) solvent, corresponding to the natural (right-handed helix) A-form typical in DNA:RNA hybrids and the (left-handed) Z-form conformations, respectively. Single-molecule conductance measurements are performed using the Scanning Tunneling Microscopy (STM)-assisted break-junction method in the so-called "blinking" approach, recording the spontaneous formation of single-biomolecule junctions and performing statistical analysis of the signals. Circular Dichroism (CD) experiments and ab initio calculations are also done to rationalize the measured molecular conductivity with a simple structural and electronic model. These results show that the electrical conductivity of the Z-form is one order of magnitude lower than that of the more compact A-form. The longer molecular length and higher energy for the Highest Occupied Molecular Orbital (HOMO) of the Z-form account for the differences in single-molecule conductance observed experimentally.

Optimizing Vehicle Body Cross-Sections Using a Parametric Mathematical Model

This paper proposes a fast optimization method of body section at the conceptual design stage, based on the demand for body performance in body concept design. The study first establishes a geometrically simplified model of the truss body structure and uses the transfer matrix method to establish a fully parameterized model of the geometrically simplified body under bending conditions. Then, the stochastic gradient genetic algorithm is used to optimize the solution and determine the geometric parameters of each section. In the example of this paper, after the optimization of the established meshless model, the mass of the whole vehicle is reduced by 30%, and the stiffness of the whole vehicle is greater than that of the benchmark vehicle (5128 N/mm, 4386 N/mm), and at the same time, compared with the conceptual design method of the body of CAE technology, the modeling time is greatly reduced, and the computational efficiency of the analytical method is greatly improved compared with the finite element method.

Research on seismic characteristics of supercharged boiler based on measured boundary

Supercharged boilers are susceptible to seismic impact loads, which can cause severe structural responses and exceed their ultimate bearing capacity. Compared with the traditional simplified structural model for earthquake resistance research in cold state, this paper establishes a real and complete three-dimensional physical model of the supercharged boiler base, and uses the reverse calculation method to obtain the boundary conditions of the boiler based on the temperature of the experimental measurement points. The temperature field and pressure field of the boiler are reconstructed, combined with the coupling method of thermal mechanical solid multiple physical fields. Using time-domain data of peak acceleration in various directions under typical earthquakes as external input loads, this study explores the seismic characteristics of boilers in the X, Y, and Z directions using time-domain analysis methods, and clarifies the vibration response mechanism in the stress concentration area of the tube sheet. The results show that the measurement and simulation error of the temperature of the inner and outer walls of the drum is 0.10 %; The individual effects of thermal stress and mechanical stress account for 107.56 % and 66.46 % of the total stress, respectively. Thermal stress plays a dominant role, while mechanical stress produces impedance effects; Under the action of an earthquake, the tube sheet undergoes reciprocating oscillation motion, and the impedance effect generated by the flexibility of the tube bundle gradually attenuates. The local stress in the X and Y directions increases by 7.70–8.27 MPa, while the local stress concentration phenomenon occurs in the Z direction due to low damping and stiffness.

Incorporating soil‐structure interaction into simplified numerical models for fragility analysis of RC structures

AbstractSimplified building models are a valuable option for seismic assessment at the regional scale. These models often use calibrated springs to model column behaviour, and recent advances have made them suitable for capturing torsional response in Reinforced‐Concrete Moment‐Resisting‐Frames. Nevertheless, their validation is typically achieved using fixed‐base models, which do not include the influence of soil‐structure interaction (SSI). This study introduces a novel approach to quantify the accuracy of a recently developed simplified model while accounting for dynamic SSI, using a newly implemented, refined 3D Finite Element non‐linear soil model in OpenSees. The accuracy of the simplified structural model is assessed by comparing the results of non‐linear dynamic analyses with those of a refined model in terms of (i) a peak structural demand parameter such as the interstorey‐drift ratio and (ii) fragility curves computed from cloud analysis and accounting for collapse cases. The study presents details of the proposed refined approach for 3D soil modelling in OpenSees, focusing on implementing free‐field boundary conditions and structure‐to‐soil connections. Results show that the accuracy of the simplified model is maintained, even in the presence of SSI, and it successfully captures the overall structural response measured at peak demand. For the proposed case study, the difference between the simplified and refined models’ fragility curves’ medians is 4% and 2% for fixed and SSI models, respectively. The simplified structural model, combined with the refined soil model for SSI effects, presents an innovative and conservative, yet computationally efficient, alternative for seismic risk analysis, even in the presence of structural irregularity.

Design and development of a flexible-rigid Triglide sorting manipulator

Flexible manipulators have been widely applied in various tasks involving grasping and manipulation, and their unique characteristics in terms of lightweight design and adaptability make them particularly suitable for tackling complex spatial pick-and-place operations. In this work, a Triglide flexible manipulator is presented and developed, based on the parallel Biglide mechanism and dual leaf springs in parallelogram structure, which features adjustable stiffness by changing the transverse configuration of the leaf springs. To analyze the deflections of the leaf springs efficiently, a segmented approach is deployed, to calculate large deflection within the plane of the leaf-springs parallelogram structure. The adopted approach uses classical bending deformation formulas from mechanics of materials. Consequently, the static models of the manipulator are derived for analyzing bending deformation and workspace by means of numerical calculations. Finite element analysis is adopted to investigate the mechanical characteristics of the manipulator. A prototype of the variable stiffness manipulator is built, and an experimental setup is established for static testing. The obtained results align with the previously observed mechanical characteristics. The main advantages of the flexible robotic manipulator lie in its simple structure, small footprint, and simple kinematic model for control.

Monte Carlo Estimation of CoVaR

CoVaR is an important measure of financial systemic risk due to its ability to capture tail dependence between the losses of different portfolios and its capacity to predict financial crises. Estimating CoVaR is challenging because its definition involves a zero-probability event, which is unobservable in the data. The existing model-based methods address this issue by assuming simplified structural models, which introduce biases that are difficult to eliminate. In “Monte Carlo Estimation of CoVaR,” Huang, Lin, and Hong propose using Monte Carlo methods to estimate CoVaR, leveraging the modeling flexibility of Monte Carlo simulation. Specifically, they introduce a batching estimator applicable to a wide range of financial models and prove that its best rate of convergence is [Formula: see text], where n is the sample size. Under the widely used delta-gamma approximation model, they further introduce an importance sampling–inspired estimator and prove that its best rate of convergence can be improved to [Formula: see text].

The applicability of a SIF-based mechanistic model for estimating GPP at the canopy scale

Mechanistically linking gross primary productivity (GPP) and sun-induced chlorophyll fluorescence (SIF) is an essential step to unleash the full potential of SIF for remote sensing-based predictions of GPP across biomes, climates, and spatiotemporal scales. The latest SIF-based mechanistic light response model that includes the fraction of open photosystem II reaction centers as key parameter (qMLR-SIF model), can accurately reproduce leaf-scale photosynthesis under various conditions. However, it remains unclear to what extent the qMLR-SIF model is suitable for estimating GPP at larger scales such as the canopy scale. Therefore, canopy-scale data of tower-based far-red SIF, GPP and key environmental variables from 10 study sites were collected to analyze the SIF-GPP relationship and to compare the qMLR-SIF model with the widely used Farquhar, von Caemmerer, Berry (FvCB) model and with a light use efficiency (LUE) model for different plant functional types (PFTs), photosynthetic pathways (C3 and C4), and temporal scales (hourly, daily and 4-day). Results showed that the nonlinear SIF-GPP relationship existed in all PFTs and the degree of linearity increased at larger temporal scales. The qMLR-SIF model exhibited wide applicability to quantify canopy GPP for different PFTs (R2 = 0.55–0.80, RMSE = 2.72–11.03 μmol CO2 m-2 s-1), photosynthetic pathways (R2 = 0.70–0.78, RMSE = 5.29–9.05 μmol CO2 m-2 s-1) and temporal scales (R2 = 0.82–0.97, RMSE = 3.42–8.32 μmol CO2 m-2s-1). Compared with the two other models, the qMLR-SIF model performed best overall, which is mainly due to its simpler model structure and the mechanistic link between SIF and photosynthesis. Particularly, the qMLR-SIF model could more accurately estimate GPP in C4 species, with higher R2 (0.78) and lower RMSE (8.46 μmol CO2 m-2s-1). These findings highlight the advantages of the qMLR-SIF model in GPP estimation at the canopy scale, showing its potential in applications at regional and global scales.

Reinforcement Learning Control of Hypersonic Vehicles and Performance Evaluations

This work presents a new framework for model-based continuous-time reinforcement learning (CT-RL) control of hypersonic vehicles (HSVs). The predominant classes of CT-RL methods for general nonlinear systems in adaptive dynamic programming (ADP) and deep RL tend to either present substantial theoretical results but lack practical synthesis capability (ADP) or show empirical promise without offering theoretical guarantees (deep RL). Meanwhile, RL control frameworks developed directly for HSVs tend to require a simplified model and complicated control structure, and they lack the substantial numerical evaluations essential for real-world flight implementation. To directly address these challenges, we propose a new decentralized excitable integral reinforcement learning framework within which the reference input-based exploration improves persistence of excitation. Together with new insights on prescaling and established decentralized control structure for HSVs, we demonstrate the resulting controller for significant performance improvement over classical Linear Quadratic Regulator (LQR) and feedback linearization methods. Additionally, we provide convergence, optimality, and closed-loop stability guarantees of the proposed method. We demonstrate these performance guarantees over a set of substantial and systematic numerical evaluations on an unstable, nonminimum phase HSV model subject to varying modeling errors and initial conditions.

Structural literacy in architectural studio learning

ABSTRACT Architectural education encompasses a multidisciplinary curriculum including design, theory, history, technology, environmental considerations, urban planning, social aspects, and professional practice. This study aims to investigate the extent to which students can effectively integrate structural concepts into architectural design. This examination takes place within the framework of a third-year architectural design studio course, where students advance their design processes with input from architectural design instructors and experts in structural concepts. To facilitate this endeavour, a simple visual screening form was introduced to aid in the learning process. The study utilises a newly generated survey, namely ‘Structural System Control Form’, which is inspired by the rapid visual screening forms, to assess the students’ comprehension of structural concepts during a 14-week architectural design studio. Employing a descriptive and correlational research approach, the study assesses the effectiveness of this integrated strategy in enhancing the students’ understanding of structural principles. The findings revealed a notable development among students who participated in the integrated approach, highlighting the value of feedback and evaluation from instructors versed in construction. Furthermore, the use of simplified structural models in students’ projects improved their understanding of structural concepts and their ability to incorporate them into their architectural representations.

Mechanical properties of agarose hydrogels tuned by amphiphilic structures

Agarose is a biocompatible polysaccharide that is capable to form physically cross-linked hydrogels in aqueous dispersion. These materials offer a wide range of applications and are commonly used in the food industry or electrophoresis. Moreover, due to their favourable characteristics (biocompatibility, high water absorption, porosity), agarose hydrogels have recently also found applications also in drug delivery systems, wound dressing, and extracellular matrix (ECM) modelling. This paper presents a comprehensive rheological characterization of agarose hydrogels with various self-assembled amphiphilic structures (CTAB, TTAB, SDS, Tween 20) acting as simple membrane structure models. These findings demostrated that the presence of surfactants of diverse charge above their critical micellar concentration (CMC) can enhance hydrogel mechanical properties. Moreover, pronounced enhancement was observed for cationic surfactant, and even concentrations below CMC resulted in a higher stiffness of the hydrogel. Therefore, both the electrostatic effect and the amphiphilic structure effect can have a significant impact on the rheological properties on agarose hydrogels. These findings are of a significant interest in the field of polymer and surfactants interactions, since agarose is widely considered essentially uncharged. However, it is assumed, that traces of negatively charged groups on the agarose backbone can have a significant impact when interacting with oppositely charged surfactants. Moreover, these findings could be beneficial for better understanding of interaction of polysaccharides (e.g. agarose) and self-assembled amphiphilic structures and can also be considered as a crude model of ECM in which membrane structures are embedded.

Sub-Nanometer-Range Structural Effects From Mg2+ Incorporation in Na-Based Borosilicate Glasses Revealed by Heteronuclear NMR and MD Simulations.

Magic-angle-spinning (MAS) nuclear magnetic resonance (NMR) experiments and molecular dynamics (MD) simulations were employed to investigate Na2O-B2O3-SiO2 and MgO-Na2O-B2O3-SiO2 glass structures up to ≈0.3 nm. This encompassed the {Na[p]}, {Mg[p]}, and {B[3], B[4]} speciations and the {Si, B[p], M[p]}-BO and {Si, B[p], M[p]}-NBO interatomic distances to the bridging oxygen (BO) and nonbridging oxygen (NBO) species, where the superscript indicates the coordination number. The MD simulations revealed the dominance of Mg[5] coordinations, as mirrored in average Mg2+ coordination numbers in the 5.2-5.5 range, which are slightly lower than those of the larger Na+ cation but with a narrower coordination distribution stemming from the higher cation field strength (CFS) of the smaller divalent Mg2+ ion. We particularly aimed to elucidate such Na+/Mg2+ CFS effects, which primarily govern the short-range structure but also the borosilicate (BS) glass network order, where both MD simulations and heteronuclear double-resonance 11B/29Si NMR experiments revealed a reduction of B[4]-O-Si linkages relative to B[3]-O-Si upon Mg2+-for-Na+ substitution. These effects were quantified and discussed in relation to previous literature on BS glasses, encompassing the implications for simplified structural models and descriptions thereof.

Time‐Frequency Analysis of Experimental and Analytical Rotor Thrust during a Rotor Speed Change

A study was conducted examining a small-scale, two‐bladed rotor undergoing a change in rotor speed. The data acquired consists of time histories of nonstationary signals; therefore, a Stockwell transform was employed to analyze the data in lieu of traditional Fourier transform-based methods. The analysis included extraction of time‐varying frequency content of the rotor thrust and hub motion, instantaneous rotor speed, damping ratios, and instantaneous phase difference between thrust and hub motion. This work is divided into two parts. First, an analytical study was conducted to understand how rotor transient response, determined using a time‐marching solution, is affected by the inclusion of a simplified elastic support structure model, and is used to demonstrate the utility of the Stockwell transform. The second part of the study compared the results from an analytical model of the NASA Multirotor Test Bed (MTB) undergoing a rotor speed change to wind tunnel data. The current analytical model of the MTB, including an elastic support structure, properly predicted the measured trends in the frequency content of the thrust; however, it underpredicted the amplitude of these unsteady loads.

Grain boundary chemistry and increased thermoelectric performance in Bi2Se3–Fe3O4 nanocomposites

The addition of Fe3O4 nanoparticles to Bi2Se3 nanocomposites resulted in a significant increase of the thermoelectric figure of merit zT at room temperature from 0.14 in Bi2Se3 to 0.21 in Bi2Se3 with added 5 % volume of Fe3O4 nanoparticles. The main contributor to the improved zT is the large increase in carrier concentration, hence increased electrical conductivity. Electron microscopy investigation revealed chemical changes in the Bi2Se3 leading to segregation of Bi to the interface with the Fe3O4 nanoparticles. The fact that the altered Bi/Se proportion was constrained close to the grain boundaries resulted in a mild reduction of the Seebeck coefficient, less than what is expected from simple band structure models for the same increase in carrier density. The addition of Fe3O4 in relatively small volume fraction allowed to both decrease the thermal conductivity and increase the power factor in a Bi2Se3 material, the latter more intensely than usual substitution methods.

Modeling and Control of a Two-Axis Stabilized Gimbal Based on Kane Method.

A two-axis stabilizing gimbal is a device that ensures a sensor is working properly on a moving platform. When classical mechanics (Newton-Euler and Lagrange) is employed to model a two-axis stable gimbal, its limitations can complicate the modeling process. To address this issue, a method for establishing a dynamic model for a two-axis stabilizing platform based on the Kane method is proposed in this paper. The Kane method offers the advantage of a simple model structure and computational efficiency. Initially, utilizing a generalized coordinate system, expressions of the generalized velocities, deflection velocities and angular velocities are derived. Subsequently, the generalized active forces and inertial forces acting on the two-axis stabilized gimbal are analyzed. Finally, by combining force and velocity with the Kane equation, the dynamic model of the two-axis stable platform is obtained, demonstrating the validity of the Kane method for establishing the two-axis stable platform model. To ensure the pointing accuracy stability of the two-axis stabilizing platform, a Novel Particle Swarm Optimization Proportion Integration Differentiation (NPSO-PID) controller is designed using the PSO algorithm. It is then simulated in MATLAB/Simulink and compared with a classical PID controller. Simulation results demonstrate that NPSO-PID exhibits superior object tracking performance compared to classical PID controllers and better optimization of control parameters compared to traditional PSO-PID controllers.

Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach.

As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.

Using Unmanned Aerial Vehicles and Multispectral Sensors to Model Forage Yield for Grasses of Semiarid Landscapes

Forage yield estimates provide relevant information to manage and quantify ecosystem services in grasslands. We fitted and validated prediction models of forage yield for several prominent grasses used in restoration projects in semiarid areas. We used field forage harvests from three different sites in Northern Utah and Southern California, USA, in conjunction with multispectral, high-resolution UAV imagery. Different model structures were tested with simple models using a unique predictor, the forage volumetric 3D space, and more complex models, where RGB, red edge, and near-infrared spectral bands and associated vegetation indices were used as predictors. We found that for most dense canopy grasses, using a simple linear model structure could explain most (R2 0.7) of the variability of the response variable. This was not the case for sparse canopy grasses, where a full multispectral dataset and a non-parametric model approach (random forest) were required to obtain a maximum R2 of 0.53. We developed transparent protocols to model forage yield where, in most circumstances, acceptable results could be obtained with affordable RGB sensors and UAV platforms. This is important as users can obtain rapid estimates with inexpensive sensors for most of the grasses included in this study.

Residential location choices and commuting patterns considering telecommuting

The COVID-19 pandemic has initiated more telecommuting (or working from home) than ever. This paper provides an economic analysis of the impacts of telecommuting on the residential location choice equilibrium. In particular, this paper employs a simplified monocentric city model of urban spatial structure to analytically examine the impacts of telecommuting on residential location choices and the associated commuting pattern in a city, considering that telecommuting may have positive, zero, or negative impact on individual income level. We found that the income effect of telecommuting and its relative magnitude against the commuting cost govern whether telecommuting will attract more to live in the suburb or the downtown. Moreover, the impacts of housing supply and commuting costs on the residential location choice equilibrium are dependent on the level of telecommuting, where such impacts diminish when the level of telecommuting increases. Furthermore, this paper derives and examines analytical solutions for the optimal level of telecommuting, the optimal housing supply and the optimal transport investment (that reduces commuting cost) in order to improve social welfare or the housing operator’s profit in the context of telecommuting. How these optimal solutions are related to the level of telecommuting is also analyzed. This study provides understanding on how telecommuting may affect the joint equilibrium of residential location choice and commuting and how housing supply and transport investment should be adjusted in response to telecommuting.

Drivers and implications of alternative routes to fuels decarbonization in net-zero energy systems

Energy transition scenarios are characterized by increasing electrification and improving efficiency of energy end uses, rapid decarbonization of the electric power sector, and deployment of carbon dioxide removal (CDR) technologies to offset remaining emissions. Although hydrocarbon fuels typically decline in such scenarios, significant volumes remain in many scenarios even at the time of net-zero emissions. While scenarios rely on different approaches for decarbonizing remaining fuels, the underlying drivers for these differences are unclear. Here we develop several illustrative net-zero systems in a simple structural energy model and show that, for a given set of final energy demands, assumptions about the use of biomass and CO2 sequestration drive key differences in how emissions from remaining fuels are mitigated. Limiting one resource may increase reliance on another, implying that decisions about using or restricting resources in pursuit of net-zero objectives could have significant tradeoffs that will need to be evaluated and managed.

Microscopic Mechanism for Further NO Heterogeneous Reduction by Potassium-Doped Biochar: A DFT Study.

Biomass reburning is an efficient and low-cost way to control nitric oxide (NO), and the abundant potassium (K) element in biomass affects the heterogeneous reaction between NO and biochar. Due to the incomplete simulation of the NO heterogeneous reduction reaction pathway at the molecular level and the unclear catalytic effect of K element in biochar, further research is needed on the possible next reaction and the influencing mechanism of the element. After the products of the existing reaction pathways are referenced, two reasonably simplified biochar structural models are selected as the basic reactants to study the microscopic mechanism for further NO heterogeneous reduction on the biochar surface before and after doping with the K atom based on density functional theory. In studying the two further NO heterogeneous reduction reaction pathways, we find that the carbon monoxide (CO) molecule fragment protrudes from the surface of biochar models with the desorption of N2 at the TS4 transition state, and the two edge types of biochar product models obtained by simulation calculation are Klein edge and ac56 edge observed in the experiment. In studying the catalytic effect of potassium in biochar, we find that the presence of K increases the heat release of adsorption of NO molecules, reduces the energy barrier of the rate-determining step in the nitrogen (N2) generation and desorption process (by 50.88 and 69.97%), and hinders the CO molecule from desorbing from the biochar model surface. Thermodynamic and kinetic analyses also confirm its influence. The study proves that the heterogeneous reduction reaction of four NO molecules on the surface of biochar completes the whole reaction process and provides a basic theoretical basis for the emission of nitrogen oxides (NOx) during biomass reburning.

Toxicity of individual and mixture of organic compounds to P. Phosphoreum and S. Capricornutum using interpretable simple structural parameters

Human and environmental ecosystem beings are exposed to multicomponent compound mixtures but the toxicity nature of compound mixtures is not alike to the individual chemicals. This work introduces four models for the prediction of the negative logarithm of median effective concentration (pEC50) of individual chemicals to marine bacteria Photobacterium Phosphoreum (P. Phosphoreum) and algal test species Selenastrum Capricornutum (S. Capricornutum) as well as their mixtures to P. Phosphoreum, and S. Capricornutum. These models provide the simplest approaches for the forecast of pEC50 of some classes of organic compounds from their interpretable structural parameters. Due to the lack of adequate toxicity data for chemical mixtures, the largest available experimental data of individual chemicals (55 data) and their mixtures (99 data) are used to derive the new correlations. The models of individual chemicals are based on two simple structural parameters but chemical mixture models require further interaction terms. The new model's results are compared with the outputs of the best accessible quantitative structure–activity relationships (QSARs) models. Various statistical parameters are done on the new and comparative complex QSAR models, which confirm the higher reliability and simplicity of the new correlations.