Simple Structural Model Research Articles

Favoritism and Firms: Micro Evidence and Macro Implications

We study the economic implications of regional favoritism, a form of distributive politics that redistributes resources spatially within countries. We use a large sample of enterprise surveys spanning across many low and middle income countries, and utilize transitions of national political leaders for identification. We document strong evidence of regional favoritism among firms located in close vicinity to leader's birthplaces but not in other regions, nor in home regions before leader's rise to power. Firms in favored regions become substantially larger in sales and employment, and also produce more output per worker, pay higher wages and, more generally, have higher total factor productivity. Furthermore, evidence from several mechanisms suggests that leaders divert public resources into their home regions by generating higher demand for firms operating in non-tradable sectors. A simple structural model of resource misallocation that is calibrated to match our empirical estimates implies that favoritism generates aggregat eoutput loss of 0.5% annually.

From graphene to graphene oxide: the importance of extended topological defects.

Graphene oxide (GO) represents a complex family of materials related to graphene: easy to produce in large quantities, easy to process, and convenient to use as a basis for further functionalization, with the potential for wide-ranging applications such as in nanocomposites, electronic inks, biosensors and more. Despite their importance, the key structural traits of GO, and the impact of these traits on properties, are still poorly understood due to the inherently berthollide character of GO which complicates the establishment of clear structure/property relationships. Widely accepted structural models of GO frequently neglect the presence of extended topological defects, structural changes to the graphene basal plane that are not removed by reduction methods. Here, a combination of experimental approaches and molecular simulations demonstrate that extended topological defects are a common feature across GO and that the presence of these defects strongly influences the properties of GO. We show that these extended topological defects are produced following even controlled 'gentle' functionalization by atomic oxygen and are comparable to those obtained by a conventional modified Hummers' method. The presence of the extended topological defects is shown to play an important role in the retention of oxygen functional groups after reduction. As an exemplar of their effect on the physical properties, we show that the GO sheets display a dramatic decrease in strength and stiffness relative to graphene and, due to the presence of extended structural defects, no improvement is seen in the mechanical properties after reduction. These findings indicate the importance of extended topological defects to the structure and properties of functionalized graphene, which merits their inclusion as a key trait in simple structural models of GO.

Case Study of Fatigue Life Analysis of Heat Exchanger Tube Subjected to Thermomechanical Loads

The aim of this work is to predict the fatigue life of a heat exchanger tube subjected to variable thermo-mechanical loading. In order to achieve this objective, this study has been based on two fracture mechanics models, i.e. the global approach and the continuous damage one. These models take into account the geometrical non-linearity introduced by the presence of a semi-elliptic crack and also the thermo-mechanical cyclic loading type. The component investigated in this study is a 316L steel tube with a semi-elliptical crack. Then the 14 mm thick tube is induction heated to 300-550 °C. The experimental results have been compared with the numerical computations of these models carried out using a finite element code. From these results, the number of cycles before the damage to the simplified structural model of the heat exchanger can be predicted.

First-Principles Study on Structure and Anisotropy of High N-atom Density Layer in 4H-SiC

A nitridation annealing process is well employed to reduce interface trap states that degrade the channel mobility of 4H-SiC/SiO${}_2$ metal-oxide-semiconductor field-effect transistor. In recent experiments, the existence of high N-atom density layers at the annealed interface is reported and their concentrations are known to be anisotropic in the crystal planes. Until now, the role of atomic structure and the electronic states surrounding the N atoms incorporated by the nitridation annealing process on the origin of anisotropy is not well understood. In this work, we propose a simplified atomic-scale model structure of 4H-SiC with the a high N-atom density layer ($\sim 10^{15}~\mathrm{atom}/\mathrm{cm}^2$), which is of the order of the experimental observation. We use bulk 4H-SiC as host crystal and consider several sets of the atomic configurations of the N-atom incorporated structure at the quasi cubic-($k$-) and hexagonal-($h$-)sites on $a$-, $m$-, and Si-(C-)planes. Based on the density functional theory calculations, we investigate the influence of the energy stability on the distribution directions. Although our bulk model is simplified compared to the realistic interface structures, we confirm significant difference among models and observe that the incorporation of N atoms on the $a$-face is stable. Furthermore, from the analysis of the electronic states, we suggest that this anisotropy of the formation energy originates from the change of the coordinating number due to the difference in geometric configurations of the N-atom incorporated structures.

Optical properties of two-dimensional materials with tilted anisotropic Dirac cones: theoretical modeling with application to doped 8-Pmmn borophene

The optical reflection, transmission and absorption properties of borophene, a newly discovered two-dimensional material with tilted anisotropic Dirac cones, are explored within a simple electronic band structure model of 8-Pmmn borophene, proposed by Zabolotskiy and Lozovik (2016 Phys. Rev. B 94 165403). It is assumed that the borophene layer is deposited on a dielectric substrate, such as Al2O3, and that the borophene’s electron density is controlled by an external gate voltage. The reflectance, transmittance and absorbance of the borophene layer, the conduction band of which is filled with electrons up to the Fermi level, are calculated against the frequency of the incident radiation, as well as on the angle of its incidence on the layer. Considered are the two principal cases of the incident radiation polarization either parallel to or normal to the plane of incidence. We reveal that the optical characteristics of 8-Pmmn borophene are distinctly different for the above two cases at all angles of radiation incidence, excepting the grazing incidence, for which the borophene layer is found to behave like a mirror regardless of the wave polarization. The results obtained indicate the possibility of visualizing the borophene layer deposited on a dielectric substrate by observing the minimum reflectivity of this layer at a certain angle incidence (called the quasi-Brewster angle) of the p-polarized radiation, which may differ by a value of about ten degrees from the Brewster angle of the substrate.

Belief-driven growth slowdowns and zero-bounded risk-free rate

The literature presents mounting evidence that global long-run growth has been declining since the recession of 2008. Thus, to explore possible reasons for the growth slowdown and its risk-free rate and risk premium implications, this study constructs an endogenous growth model to examine the US economy before the 2008 recession and during the subsequent recovery period. The model features technological externalities that imply multiple perfect foresight balanced growth paths. In this setup, a change in agents’ beliefs may trigger persistent slumps, low interest rates, and elevated risk premium, consistent with the recent US experience. Numerically, using the Epstein and Zin preferences, the model calibration suggests that the historical data moments can be accommodated by persistent regimes and high intertemporal elasticity of substitution. Notably, the study’s simple-structured model with multiplicity and a regime-switching structure is a preliminary contribution to the field of economics, paving the way for future empirical studies.

Lower Bound Uncertainty and Long‐Term Interest Rates

AbstractNominal interest rates are constrained by an effective lower bound, but the level of the lower bound is uncertain. This paper uses a simple shadow rate term structure model to study how lower bound uncertainty affects long‐term interest rates. A decline in lower bound uncertainty, in the sense of a mean‐preserving contraction, is associated with a drop in expected short rates. The effect on the variance of short rates, and hence the term premium, is ambiguous. A calibration to Canadian data suggests that a decline in lower bound uncertainty is associated with a modest drop in interest rates.

Suggestion of advanced regression model on friction angle of fault gouge in South Korea

Although friction characteristics of fault gouge are important to understand reactivation of fault, behavior of earthquake, and mechanism of slope failure, analysis results of fault gouge have low accuracy mostly than those of soils or rocks due to its high heterogeneity and low strength. Therefore, to improve the accuracy of analysis results, we conducted simple regression analysis and structural equation model analysis and selected major influential factors of friction characteristics among many factors, and then we deduced advanced regression model with the highest coefficient of determination ( R 2 ) via multiple regression analysis. Whereas most coefficients of determination in simple regression analysis are below 0.3–0.4, coefficient of determination in multiple regression analysis is remarkably large as 0.657.

The dynamic stiffness matrix method for seismograms synthesis for layered transversely isotropic half-space

Natural rocks and soils show significant anisotropic behavior owing to sedimentation and weathering, particularly apparent in the crust. However, seismograms synthesis for the anisotropic medium is scarce, especially bottlenecks for large-scale and broadband seismic wave propagation. In this paper, seismogram synthesis for a multi-layered transversely isotropic half-space due to seismic point sources is addressed by using a dynamic stiffness matrix method. The critical step of the proposed method lies in the construction of the global dynamic stiffness matrix in frequency-wavenumber domain, which avoids the accumulation of errors that appeared in the reflection and transmission matrix method. Exponential divergent terms related to wavenumber and layer thickness in dynamic stiffness matrix are extracted for fast simulating large-scale and high-frequency wavefield. The seismic point sources are equated to the external loads imposed at the introduced auxiliary surfaces, through which the dynamic stiffness matrix can be utilized to obtain the solutions. The accuracy is verified by comparing the proposed method results with three examples in the publications, involving different source types and depth scales. Two numerical examples regarding Thomsen parameters are given to illustrate the mechanism of wave propagation and ground motion in an anisotropic media. Numerical results show that the anisotropic characteristics affect the wave propagation time, waveform and wave-front energy distribution, further leading to shear wave splitting phenomena, which remarkably differ from the isotropic case. A case study using a simplified three-layered crustal structure model in Tibet elucidates that peak ground displacement and its distribution difference within the epicenter of 10 km by comparing with the equivalent isotropic model without considering anisotropic properties. The results show a 10.91% deviation in the location of the peaks, with amplitude differences of up to 120% due to a 45° dip-slip source at different frequencies (from 1 Hz to 15 Hz).

Design and Assessment of ADRC-Based Autopilot for Energy-Efficient Ship Steering

This paper looks at a typical problem encountered in the process of designing an automatic ship’s course stabilisation system with the use of a relatively new methodology referred to as the Active Disturbance Rejection Control (ADRC). The main advantage of this approach over classic autopilots based on PID algorithms, still in the majority, is that it eliminates the tuning problem and, thus, ensures a much better average performance of the ship in various speed, loading, nautical and weather conditions during a voyage. All of these factors call for different and often dynamically variable autopilot parameters, which are difficult to assess, especially by the ship’s crew or owner. The original result of this article is that the required controller parameters are approximated based on some canonical model structure and analysis of the hydrodynamic properties of a wide class of ships. Another novelty is the use of a fully verified, realistic numerical hydrodynamic model of the ship as a simulation model as well as a basis for deriving a simplified model structure suitable for controller design. The preliminary results obtained indicate good performance of the proposed ADRC autopilot and provide prospects for its successful implementation on a real ship.

Buckling Analysis of a Large Shelter with Composites.

We present here linear and nonlinear finite element analyses of a newly designed deployable rapid assembly shelter (DRASH J) manufactured by DHS Systems. The structural analysis is carried out in three stages. Firstly, single composite tubes (struts) under three-point bending are modeled with five layers of orthotropic materials in three different orientations and the simulation results are compared with the actual test data for validation. Secondly, a comprehensive structural model for the entire shelter is constructed with the consideration of two types of strut scissor points, namely natural and forced scissor (crossing) points, as well as partial-fixed hub joints, which allow rotations along individual hub slots (grooves). Finally, a simplified structural model is created by introducing fixed joints for the scissor points as well as rigid links for the hubs. With sufficient verifications with experiments and different modeling methods, linear and nonlinear finite element analyses are then carried out for both the comprehensive and simplified shelter models. Based on the simulation results, we are able to identify a few critical issues pertaining to proper design and modifications of such shelter systems, such as various end wall supports pertaining to the overall structural stability.

Interference Signal Identification of Sensor Array Based on Convolutional Neural Network and FPGA Implementation

The pulse carries important physiological and pathological information about the human body. The piezoresistive sensor used to capture vascular pulsation information has transitioned from a single-point to a sensor array. However, the interference signal between channels has become a key bottleneck restricting the development of the sensor array pulse diagnosis equipment. The sensor in contact with vascular pulsation obtains the pulse signal. When some sensors are displaced due to vascular pulsation, other sensors will be driven to move, which will produce interference signals. Signal interference is a common problem for sensor arrays, but few people have analyzed this problem from the perspective of the algorithm. In this paper, an interference signal recognition algorithm of the sensor array based on a convolutional neural network (CNN) is proposed. Firstly, a simple mechanical structure model was established to analyze the generation mechanism of interference signals in one MEMS sensor array acquisition system. Then, a CNN model with fewer parameters was designed for identifying interference signals. Finally, the CNN model was implemented on a field-programmable gate array (FPGA). The results show that the CNN algorithm could identify interference signals well, and the accuracy of the algorithm was 99.3%. The power consumption of the CNN accelerator was 0.673 W at a working frequency of 100 MHz. The interference signal identification algorithm is proposed to ensure the accurate analysis of array signals. FPGA implementation lays the foundation for the miniaturization and portability of the equipment.

Study of Building Safety Monitoring by Using Cost-Effective MEMS Accelerometers for Rapid After-Earthquake Assessment with Missing Data.

Suffering from structural deterioration and natural disasters, the resilience of civil structures in the face of extreme loadings inevitably drops, which may lead to catastrophic structural failure and presents great threats to public safety. Earthquake-induced extreme loading is one of the major reasons behind the structural failure of buildings. However, many buildings in earthquake-prone areas of China lack safety monitoring, and prevalent structural health monitoring systems are generally very expensive and complicated for extensive applications. To facilitate cost-effective building-safety monitoring, this study investigates a method using cost-effective MEMS accelerometers for buildings’ rapid after-earthquake assessment. First, a parameter analysis of a cost-effective MEMS sensor is conducted to confirm its suitability for building-safety monitoring. Second, different from the existing investigations that tend to use a simplified building model or small-scaled frame structure excited by strong motions in laboratories, this study selects an in-service public building located in a typical earthquake-prone area after an analysis of earthquake risk in China. The building is instrumented with the selected cost-effective MEMS accelerometers, characterized by a low noise level and the capability to capture low-frequency small-amplitude dynamic responses. Furthermore, a rapid after-earthquake assessment scheme is proposed, which systematically includes fast missing data reconstruction, displacement response estimation based on an acceleration response integral, and safety assessment based on the maximum displacement and maximum inter-story drift ratio. Finally, the proposed method is successfully applied to a building-safety assessment by using earthquake-induced building responses suffering from missing data. This study is conducive to the extensive engineering application of MEMS-based cost-effective building monitoring and rapid after-earthquake assessment.

Dual Attention Convolutional Neural Network Based on Adaptive Parametric ReLU for Denoising ECG Signals with Strong Noise.

Electrocardiogram (ECG) signal is one of the most important methods for diagnosing cardiovascular diseases but is usually affected by noises. Denoising is therefore necessary before further analysis. Deep learning-related methods have been applied to image processing and other domains with great success but are rarely used for denoising ECG signals. This paper proposes an effective and simple model of encoder-decoder structure for denoising ECG signals (APR-CNN). Specifically, Adaptive Parametric ReLU (APReLU) and Dual Attention Module (DAM) are introduced in the model. Rectified Linear Unit (ReLU) is replaced with the APReLU for better negative information retainment. The DAM is an attention-based module consisting of a channel attention module and spatial attention module, through which the inter-spatial and inter-channel relationship of the input data are exploited. We tested our model on the MIT-BIH dataset, and the results show that the APR-CNN can handle ECG signals with a different signal-to-noise ratio (SNR). The comparative experiment proves our model is better than other deep learning and traditional methods.Clinical Relevance- This paper proposed a method capable of denoising ECG signals with strong noise to alleviate difficulties for further medical analysis.

Estimating Treatment-Switching Bias in a Randomized Clinical Trial of Ovarian Cancer Treatment: Combining Causal Inference with Decision-Analytic Modeling.

Bevacizumab is efficacious in delaying ovarian cancer progression and controlling ascites. The ICON7 trial showed a significant benefit in overall survival for bevacizumab, whereas the GOG-218 trial did not. GOG-218 allowed control group patients to switch to bevacizumab upon progression, which may have biased the results. Lack of data on switching behavior prevented the application of g-methods to adjust for switching. The objective of this study was to apply decision-analytic modeling to estimate the impact of switching bias on causal treatment-effect estimates. We developed a causal decision-analytic Markov model (CDAMM) to emulate the GOG-218 trial and estimate overall survival. CDAMM input parameters were based on data from randomized clinical trials and the published literature. Overall switching proportion was based on GOG-218 trial information, whereas the proportion switching with and without ascites was estimated using calibration. We estimated the counterfactual treatment effect that would have been observed had no switching occurred by denying switching in the CDAMM. The survival curves generated by the CDAMM matched well with the ones reported in the GOG-218 trial. The survival curve correcting for switching showed an estimated bias such that 79% of the true treatment effect could not be observed in the GOG-218 trial. Results were most sensitive to changes in the proportion progressing with severe ascites and mortality. We used a simplified model structure and based model parameters on published data and assumptions. Robustness of the CDAMM was tested and model assumptions transparently reported. Medical-decision science methods may be merged with empirical methods of causal inference to integrate data from other sources where empirical data are not sufficient. We recommend collecting sufficient information on switching behavior when switching cannot be avoided.

ARRHYTHMIA CLASSIFICATION USING DEEP RESIDUAL NEURAL NETWORKS

Arrhythmia classification with electrocardiogram (ECG) is of great importance for the identification of arrhythmia diseases. However, since the variance of ECG signal in wave appears frequently, it is still a very challenging task to obtain a very good classification result. In this paper, an arrhythmia classification with ECG based on deep residual networks is proposed, of which two improved residual blocks are used to combine soft and hard subsampling. With such blocks, the network can well hold spatial information and improve the classification performance with a simple model structure. Experiments on the MIT-BIH arrhythmia database show that the proposed method obtained an average classification accuracy of 99.59% and an average classification specificity 99.63%, which are 0.26% and 0.57% higher than that of the most state-of-art method based on deep learning.

Structural full-field responses reconstruction by the SVD and pseudo-inverse operator-estimated force with two-degree multi-scale models

The measured structural response currently is in a deficient state for structural health monitoring (SHM). To obtain complete structural responses, a good solution is first estimating the external input, then applying the estimated input to the finite element model (FEM) of a structure, and calculating the complete structural responses by the FEM. The key content is estimating the unknown structural input from the limited measurements, which indicates that an under-determined differential equation should be solved. In the previous literature, the dynamic equilibrium equation is usually converted into the state space and solved as a discrete-time linear system. In this paper, the external input time history is estimated from the difference of the state space vectors at different moments combining with the singular value decomposition. The complete structural responses are then obtained by applying the estimated load vectors to the FEM. Adopting a single degree of freedom system, the robustness of the approach is theoretically deduced. The modeling error can merely change the estimated external force, while the reconstructed structural responses retain accuracy. To improve computational efficiency, the two-degree multi-scale FEM is proposed. The first model is a simplified model of the original structure, only adopting interior elements such as the beam elements. This model is to quickly estimate the external inputs and to reconstruct the displacement responses. The second model is a FEM adopting both interior and superior elements such as the beam and shell elements, and it can reconstruct both the displacement and detailed strain–stress responses. Finally, numerical simulations of a complex bridge FEM are carried out demonstrating that the proposed approach has high accuracy under different loading conditions, including the vehicle load, impact load, and random load. An in-field experiment has also validated the proposed framework applies to the quasi-static plus dynamic loading condition.

Establishing spatially elastic hydrogen-bonding interaction in electrochemical process for selective CO2-to-CH4 conversion

In a CO2 electroreduction reaction, accurately acquiring the variations of coordination microenvironment around the catalytic sites in the electrochemical process is extremely important to promote product (especially hydrocarbons) selectivity and explain the related catalytic reaction mechanism. In this work, we elaborately design and construct a simple and stable crystalline Cu-based supramolecular structure model system (including Cu-TPP, Cu-2TPyP, Cu-3TPyP, and Cu-4TPyP), in which the spatially elastic hydrogen-bonding interaction between pyridine nitrogen atom and hydrogen-donating substances (∗COOH) can be established in the electrochemical process by adjusting the position of pyridine nitrogen atoms in molecules. Based on this well-defined catalyst model system, we systematically studied the influences of variations of a spatially elastic hydrogen-bonding microenvironment on improving the CO2-to-CH4 electroreduction performance. Experimental and theoretical calculation results show that subtle changes of the hydrogen-bonding interaction in the electrochemical process can affect the adsorption energy of ∗COOH, thus the faradic efficiency for CH4 (FECH4) can be improved from 32.3% to 62.4%.

The macro-structure of quartz flocs

In this study, the macro-structure of quartz flocs was assessed from X-ray computed tomography images. An image segmentation workflow was established to identify the floc sub-units that comprised each macro-floc, which permitted the macro-structure to be defined in terms of the number, size, and connectivity of the sub-units. A simplified structural model of non-penetrating spheres was used to understand the scaling relationships of the macro-floc properties. It was found that although sub-unit size varied widely, there was a limiting volume (2.8 × 106μm3), above which floc growth occurs only by sub-unit aggregation. This approach permits the quantification of macro-floc structure and composition in 3D on the scale of tens of μm to the mm scale. Knowledge of macro-floc structure allows a deeper understanding of the role of macro-floc breakage and re-arrangement in subsequent dewatering treatments such as thickening or filtration.

The Most Influential Factor to Pass Rate of Modified OSCE Exam on Student Batch 2 during Pandemic Period

The Objective structure clinical examination is an assessment tool to evaluate clinical skills. There are many factors that influence to pass rate of modified OSCE. Moreover, during online learning implementation, there are some barriers. The aim of the study is to determine the most influential factor that contributing to achieving the learning outcome in terms of passing rate modified online OSCE. An observational study was chosen involving 87 medical students batch 2. Sample size calculation used the formulation of an estimated proportion population with a simple random strategy. This study employed a questionnaire of online skill learning that was made by the researcher through analysis of factor exploratory. The participants were asked to score their agreement on the five-Likert scale. Using SPSS version 24 and software of AMOS 26, we analyzed the data for simple linear regression and structural equation modeling (SEM). Results were summarized following 2 factors that influencing pass rate of modified OSCE online, in terms of transactional distance (p value=0.001, r=0.235) and learning platform availability ((p value=0.013, r=0.087). Our finding highlights that self-reflection and student access to resources have significant contributions toward independent learner characteristics. This study has contributed to solving the problem regarding the foundational concept for the requirement of online learning. The theoretical concept of a learning platform also enhances online teaching. Foremost and utmost that self-reflection and student access to resources have become two important factors to autonomy learning. The major strength of this study is the systematic manner in which it was conducted.