Layer Model Research Articles

Characterization of Optical Depth for Laser Produced Plasma Extreme Ultraviolet Source

This study investigates the emission mechanism of laser-produced plasma extreme ultraviolet source (LPP-EUV). In the experiment, the EUV radiation is generated by a 1064 nm laser interacting with slab Sn target. The EUV emission is characterized by EUV energy monitors and an EUV spectrometer. The dependency of CE on laser intensity and plasma relative optical depth is explored by a plasma emission-absorption layer model. The dependency is confirmed by an optical interferometry measurement of plasma electron density for the first time. Our work provides a method for characterizing and optimizing the optical depth of laser driven EUV source.

Study on Shear Strength of Soil–Root Systems of Different Vegetation Types

The root systems of vegetation significantly contribute to enhancing slope stability. The shear strength of soil–root systems is a crucial parameter for assessing slope stability. This study focuses on six types of vegetation in the Yellow River Basin of China (woodland: Populus przewalskii and Broussonetia papyrifera; shrubland: Periploca sepium and Ziziphus jujuba; grassland: Artemisia hedinii and Setaria viridis), employing in situ shear tests and the Wu–Waldron model (Wu model) to investigate the shear strength of soil–root systems. The results show that the shear stress–displacement curves for P. przewalskii, B. papyrifera, and Z. jujuba are higher and steeper, with clear inflection points. The tensile strength of the roots from the six vegetation types decreases as the root diameter increases. According to the Wu model, the additional root cohesion is ranked as follows: A. hedinii > B. papyrifera > P. przewalskii > Z. jujuba > P. sepium > S. viridis. Based on the in situ shear tests, the shear strength increments are ranked as follows: Z. jujuba > B. papyrifera > P. przewalskii > A. hedinii > P. sepium > S. viridis. Overall, the additional root cohesion obtained by the Wu model in each soil layer is greater than the shear strength increment measured from the in situ shear tests. In the 0–30 cm soil layers, the soil–root systems of Z. jujuba, B. papyrifera, P. przewalskii, and A. hedinii exhibit a better shear strength, whereas P. sepium and S. viridis perform poorly. A principal component analysis reveals that the shear strength of the soil–root systems of different vegetation types is primarily influenced by the soil moisture content and root mass density. Z. jujuba, B. papyrifera, P. przewalskii, and A. hedinii are recommended for ecological restoration projects in the Yellow River Basin of China.

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Reconstruction and Modeling of Porous Transport Layers Based on X-ray Computed Tomography Imaging

Reconstruction and Modeling of Porous Transport Layers Based on X-ray Computed Tomography Imaging

Comparison of passive and semi-active piezoelectric transducer damping of cantilever oscillation

The aim of this work is the analysis of passive and semi-active damping methods on the model woven beam with a piezoelectric (PZ) layer. The mathematical model of the PZ layer on cantilever beam is derived. For modeling, real product parameters of PZ layer are used, and through simulation find the optimal values to dampen it. The resistive-inductance passive model, and State-Switch Damping SSD semi-active model are used in this work. The models are processed in the MATLAB Simulink environment and the results of the behavior of the models compared to each other. The program involving passive and semi-active systems, where analyze the behavior of the model for different parameters and the damping capabilities of individual methods are compared. The simulation results show the effectiveness of the semi-active system in comparison with the passive systems, also it shows the passive method is not so effective at low frequency as it in high frequency, and the semi-active method is not optimal for high frequency vibration.

Estimating Models and Evaluating their Efficiency under Multicollinearity in Multiple Linear Regression: A Comparative Study

Multicollinearity between independent variables occurs in multiple linear regression analysis characterized by high correlations, which complicates discerning individual variable effect, impacting model accuracy, stability, and interpretation of relationships. The research aims to diagnose the multicollinearity problem between explanatory variables in the linear regression model and identifying the variables causing this problem based on the variance inflation factor (VIF), then estimation and evaluate the performance of three alternative methods, which are Ridge regression, principal components analysis, and Feedforward Neural Networks (FFNN) models with one and two hidden layers and application of the models to compressive strength data for high-performance concrete. The results showed that Ridge regression and PCA effectively addressed multicollinearity problem, but the single hidden layer model FFNN showed superior predictive accuracy in estimating the compressive strength of high-performance concrete when comparing RMSE, MAE, and R2 values.

Pest and Disease Video Classification with Convolutional Neural Network and Transfer Learning

The important field crops of agriculture are affected due to attack of various pests and diseases which leads to reduction in crop production. Early classification and identification of pests and diseases in plant helps farmers to take mitigation steps. To address this issue with computer vision based techniques, convolutional neural network (CNN) based deep learning models were studied for classification of pests and diseases videos. Six different CNN models were developed. Two approaches namely from scratch learning and transfer learning were used. Data augmentation techniques such as reflection, scaling, rotation, and translation were also applied to prevent the network from overfitting. The classification accuracy of 99.19%, 99.08% and 98.80% was attained in VGG19, DENSENET201 and CNN 5 Layer model. The results demonstrated that CNN models with good architecture can classify pests and diseases with good performance.

A deep autoencoder for electric double layer capacitance prediction in electrochemical sensors

This study explores the application of a deep autoencoder neural network to accurately predict the electric double layer capacitance from real-world parameters in binary, asymmetric electrolytes under low concentration conditions. By utilizing a modest simulation-based dataset of just 250 samples, the deep autoencoder neural network model developed in this study effectively predicted the capacitance by learning the critical features and relationships of the electric double layer model and encoding this learned representation into a low-dimensional latent space. From the latent variables, the decoder block of the neural network learned to effectively recreate the high-dimensional input. To enhance the model's robustness, prevent overfitting, and better simulate real-world conditions, noise was incorporated into the training and test data. The model demonstrated strong performance across various conditions, such as ionic size, ionic charge, and surface potential, yielding satisfactory results on both clean and noisy test datasets. A key feature of this approach was the mapping of real-world electric double layer parameters to the latent variables of the model, allowing for direct input of physical parameters to predict the electric double layer capacitance. This research highlights the potential of machine learning techniques to expedite the design and analysis of complex multi-physics systems such as electrochemical sensors by reducing the dependence on extensive domain expertise throughout the design process.

Modeling of Melting Layer in Cross‐Platforms Radar Observation Operator ZJU‐AERO: Multi‐Stage Melting Particle Model, Scattering Computation, and Bulk Parameterization

AbstractThis study presents an implementation of a new melting layer model in the ZJU‐AERO radar observation operator (Accurate and Efficient Radar Operator designed by ZheJiang University). The proposed model utilizes a coated spheroid to represent melting snow and graupel. It consists of three stages–coating, soaking, and melting–to account for the dielectric and density effects of melting particles. The scattering properties of the melting particles are computed with the Invariant‐Imbedding T‐Matrix (IITM) method, and the results are tabulated as look‐up tables for the radar operator. Regarding the parameterization of bulk optical properties, a flux‐conservation scheme is employed to estimate the size distribution of melting particles. To demonstrate its flexibility and superiority, the single and bulk scattering properties of our multi‐stage melting model are compared against the traditional homogeneous model, which uses the effective medium approximation (EMA). The effectiveness of the multi‐stage melting model has also been assessed by mapping model states in the regional mesoscale model of the China Meteorology Administration (CMA‐MESO) to radar observations. In the microphysics package of CMA‐MESO, the melting process is not explicitly represented, and we assume that melting hydrometeors occur where solid and liquid phases overlap. When compared with observations, the present multi‐stage melting model successfully reproduces melting layer signatures, highlighting its potential for microphysic validation, quantitative precipitation estimations, and data assimilation studies.

Acoustic radiation characteristics of shark skin inspired surface modified plates

This paper aims to evaluate the acoustic radiation characteristics of thin plates featuring a layer of small-scale biomimetic shark skin type additive surface treatment. The shark skin dermal denticles are modelled as point masses arranged in a bi-directional pattern on both the upper and lower surfaces of the plate. The governing equations are obtained through a variational approach, incorporating the Dirac Delta function in the derivation of the proposed semi-analytical model for the shark skin layer. A semi-analytical method based on the Rayleigh–Ritz formulation is utilized to analyze the vibrations of these plates with surface modification. The sound radiation characteristics are then derived from the solution of the Rayleigh integral. A comprehensive investigation is performed on the influence of surface modification on different vibro-acoustic characteristics, using a continuous structural mode and power transfer matrix-based approach. Notable observations include a reduction in peak vibro-acoustic responses with dense denticle arrangements, especially at resonance, demonstrating a direct relationship with mass ratios, i.e., the ratio of denticle mass to plate mass. The study further reveals a shift of vibro-acoustic responses towards low frequencies with an increase in mass ratios. A thorough comparative study indicates that while additive surface modifications inspired by shark skin may weaken sound radiation characteristics at resonance frequencies, a reverse effect can be observed at intermittent operational frequencies.

The Effect of Ratio of Fiber Bundles in the Textile Composite Layer

Abstract The article shows how the orthotropic material properties of the textile composite layer change if the ratio of longitudinal and transversal fiber bundles in the textile is modified in the case of a composite reinforced with plain weave textile. I determine the material properties using model-cell and finite element simulation. The density of the longitudinal fiber bundles is the same in each of the five examined layer models. I change the ratio of the fiber bundles of the textile by varying the distance of the transversal roving’s in each model.

A novel pretreatment approach on titanium alloys for electroless nickel

To augment the wear resistance of the titanium alloy, electroless nickel (EN) is an effective method to strengthen the surface properties. However, the oxide layer on the surface of titanium alloy will inhibit the adhesion of EN. Meanwhile, the traditional pretreatment methods are cumbersome steps and time-consuming. To overcome these limitations, this study systematically explores a one-step pretreatment approach through amino tris(methylenephosphonic acid) (ATMPA) and HF, which is designed to improve the binding strength of EN. The results exhibit that the compact composite membrane with Ti-ATMPA and TiF3 are successfully prepared under pH 3.0, which has provided a pretreatment layer to replace the oxidation of titanium alloy during EN process, and the adhesion of EN coating (EN-3.0) based on the pretreatment layer reaches 22.46 MPa. The wear resistance of titanium alloy and EN plating system studied via a friction measurement, and the lowest wear rate exhibit in EN-3.0 plating (880.9 × 10-10mm3/Nm) that is lower than EN-traditional pretreatment with a decrease of 32 %. Moreover, the formation and catalytic model of medial layer are also discussed.

Study on construction technology and bearing mechanism of pressure cast-in-situ pile with spray-expanded frustum

The pressure cast-in-situ pile with spray-expanded frustum (PCPSF) is a new type of pile developed by the last author, which is composed of pile body, ribbed plate and expanded body with double-frustum. This paper presents the equipment used to produce the PCPSF and the spray-expansion extrusion vibration and pressure method used for construction of the PCPSF. The PCPSF model of cement soil transition layer around pile is built with finite element software. The accuracy of the numerical calculations is verified through a comparison with the results of static load tests conducted on PCPSF. Furthermore, the impact of the expanding ratio, expanded body location, and ribbed plate length on the response of the PCPSF is investigated. The results show that: (1) the ultimate bearing capacity (UBC) of PCPSF single pile is proportional to the diameter expanding ratio and the length of the ribbed plate; (2) the improvement of the bearing capacity of single cubic concrete of PCPSF decreases with the increase of expanding ratio; (3) the bearing capacity of expanded body can be activated ahead of time as it advances up in position, and there is a trend toward a steady increase in the combined bearing capacity of the expanded body and pile end; (4) as the expanded body moves up, the vertical stress of the soil below pile tip decreases, the rate of decay of additional stress is accelerated; (5) under the identical circumstances, the PCPSF's bearing capacity is 1.5 times greater than the traditional cast-in-place pile's, and the presence of cement soil transition layer contributes 15 % of the lateral resistance of the PCPSF.

A statistical physics-based methodology for evaluating the efficiency of an adsorption refrigeration cycle using C3H2F4/Maxsorb III

Recently, the statistical physics modeling found a great success in the description of various aspects of adsorption phenomena such as olfaction, depollution, and photovoltaic processes. Therefore, we attempted here to apply this treatment in the domain of refrigeration by an adsorption/ desorption process cycle, using the grand canonical formalism of the statistical physics as a working tool. So, for this study, four advanced equations based on statistical physics treatment are used to clarify the adsorption process at molecular level and define its physico-chemical parameters for refrigeration applications. Our results showed that the double layer model with two energies provides useful details concerning this phenomenon by determining the number of captured HFO-1234ze (E) (C3H2F4) molecules per site nAM, the density of receptor site Drs, the adsorption quantity at saturation N a sat and two energetic parameters Phs1 and Phs2. The steric results revealed that the adsorbed C3H2F4 molecules tended towards a parallel position when the temperature increased. Evaluations of the adsorption energies and enthalpy values indicated that the anchoring process is exothermic and of a physisorption nature. In order to improve the thermodynamic efficiency, the coefficient of performance was calculated by analyzing changes in thermodynamic functions, giving a value of 0.58 for the C3H2F4 / Maxsorb III. Also, the COP value can be enhanced by using the same adsorbate with high compressibility to minimize the machine size but changing the present adsorbent by activated carbon derived from biomass such as: Waste Palm Trunk (WPT-AC) and Mangrove wood (M−AC).

Prediction of residual stress in precision injection molding of plastic optical lenses based on an improved solidification layer model

Precision injection molding technology (PIM) is the primary technique used for large scale manufacture of plastic optical lenses. However, the injection molding of plastic optical lenses results in a problem of residual stress, which affects the imaging quality of the optical system. The conventional injection molding process uses an orthogonal approach for adjusting the process parameters, requiring a large number of experiments. Therefore, this article proposes an improved residual stress model for precision injection molding of plastic optical lenses based on the solidification layer theory. The residual stress of precision injection molded aspherical plastic optical lens used for imaging optical systems was analyzed through simulation and experimentation. The results demonstrate that the proposed method achieves residual stress prediction for plastic optical lenses after injection molding.

Understanding the effect of physical aging on slip dynamics in soft-glassy materials using pure elongation flow

Slip over a solid surface is a very common occurrence in industrial scale transport and the processing of complex fluids. The knowledge of slip also plays a huge role in the correct estimation of rheological properties. In this work, we have studied the slip dynamics in a model soft-glassy material that exhibits physical aging, wherein the structure evolves gradually toward a more solid-like character via rearrangement of constituents. More precisely, we have investigated the impact of physical aging on slip associated with pure elongation flow of the material. We have allowed the sample to age over different waiting times, followed by the sample being deformed slowly in elongation mode by pulling the top plate to achieve a pure elongation flow. Normal force as a function of gap has been recorded during such pure elongation. These normal force–gap curves demonstrated a remarkable gap-waiting time superposition, manifesting the strong signature of self-similarity in the pure elongation flow of the soft-glassy system. We have adopted a slip layer model, which predicted these normal force–gap flow curves remarkably well. Such prediction also rendered slip layer thickness as a function of waiting time, using which we have explained the intriguing self-similar nature of normal force–gap dependence. Finally, we have established a relationship between the slip layer thickness and the age-dependent bulk rheological properties. We have provided a possible physical reasoning to explain this link between the physical aging-driven state of material and the slip dynamics.

Deep Ring-Block-Wise Network for Hyperspectral Image Classification.

Deep learning has achieved many successes in the field of the hyperspectral image (HSI) classification. Most of existing deep learning-based methods have no consideration of feature distribution, which may yield lowly separable and discriminative features. From the perspective of spatial geometry, one excellent feature distribution form requires to satisfy both properties, i.e., block and ring. The block means that in a feature space, the distance of intraclass samples is close and the one of interclass samples is far. The ring represents that all class samples are overall distributed in a ring topology. Accordingly, in this article, we propose a novel deep ring-block-wise network (DRN) for the HSI classification, which takes full consideration of feature distribution. To obtain the good distribution used for high classification performance, in this DRN, a ring-block perception (RBP) layer is built by integrating the self-representation and ring loss into a perception model. By such way, the exported features are imposed to follow the requirements of both block and ring, so as to be more separably and discriminatively distributed compared with traditional deep networks. Besides, we also design an optimization strategy with alternating update to obtain the solution of this RBP layer model. Extensive results on the Salinas, Pavia Centre, Indian Pines, and Houston datasets have demonstrated that the proposed DRN method achieves the better classification performance in contrast to the state-of-the-art approaches.

Simulation of plaque formation in a realistic geometry of a human aorta: effects of endothelial layer properties, heart rate, and hypertension.

Nowadays, cardiovascular diseases are the most common cause of death worldwide. Besides, atherosclerosis is a cardiovascular disease that occurs with persistent narrowing of arteries, especially medium and large-sized arteries. Atherosclerosis begins with a local elevation in the permeability of the arterial wall as a result of endothelial inflammation. Subsequently, excess LDL permeates into the arterial wall. Then, through several chemical responses and reactions, foam cells are produced. These foam cells serve as a crucial indicator for assessing the development of atherosclerosis within the arteries. In this study, the effect of endothelial layer modeling, heart rate (HR) and hypertension on the foam cell accumulation is numerically investigated in a patient-specific geometry of the human thoracic aorta. Navier-Stokes, Darcy, and mass transfer equations are used to obtain the velocity and concentration field within the domain. Regarding the dependence of endothelial cell properties on time-averaged wall shear stress, it is observed that foam cells are mainly concentrated in the outer curvature of the aortic arch, downstream of the left subclavian artery. However, considering oscillatory-shear-rate as the determinant of endothelial cell properties leads to the accumulation of foam cells in the inner curvature of the descending aorta. Regarding the HR, with the increase of HR, the volume average concentration of the foam cell decreases. However, there is no substantial difference between the cases of different HRs. Moreover, foam cell concentration significantly increases in the hypertension case. This result implies that a slight increase in the blood pressure may induce irreparable problems in the circulatory system.

The role of secondary voids in the mechanism of ductile fracture at a crack tip

The mechanics and mechanisms of ductile fracture, ahead of a blunting crack tip, have been studied extensively. Several computational studies analyzing the effect of initial void volume fraction, void shape, void spatial distribution and the mode of loading (KI,KII etc.) on crack-void interaction and its consequence on the mechanism of fracture have been reported. The influence of small size secondary voids on the failure of ligament between the large primary voids and, hence, on fracture toughness has been analyzed using the Gurson-type homogenized models of ductile fracture. In the present work, the two populations of voids ahead of a crack tip are modeled discretely. A plane strain, central line cracked boundary layer model under small-scale yielding is considered. The role of initial shape and spatial distribution of secondary voids, matrix strain hardening and mode of imposed loading in the mechanism of ductile crack growth initiation and advance is analyzed in detail. For completeness sake, numerical calculations are also performed using a homogenized representation of the secondary voids. The results so obtained are then compared with the predictions based on discrete modeling of the secondary voids. Our numerical studies revealed that plastic flow localization resulting from a small initial volume fraction of favorably distributed secondary voids may alter the path of crack growth initiation and advance, thus, influencing the ductile fracture toughness.

Mathematical Nonlinear Graph Theory Topology Layer Model for Photoelectric Tracking System

The performance of the photoelectric tracking system mainly depends on the tracking accuracy. In order to achieve the purpose of high precision tracking, controlling the power dragging device, the main component of the photoelectric tracking system, is the main means to achieve this purpose. In order to improve the tracking speed and accuracy of photoelectric tracking systems, the author proposed a mathematical nonlinear graph theory topology layer model for photoelectric tracking systems. The topology layer model and the motion node servo mechanism model of the photoelectric tracking system are studied, and the two-stage disturbance sources that affect the tracking stability are analyzed. The results show that the interference estimation error range designed by the author reaches 0.003 through comparison, it can be seen that the estimation error designed by the author is significantly smaller than the ESO estimation error, and the buffeting is small. Through comparison, it can be concluded that the estimation error of the algorithm and the disturbance observer designed by the author are significantly smaller than the estimation error of ESO, and the buffeting is small, and they have strong compensation ability for the disturbance of different frequencies. The interference observer can quickly and accurately estimate the interference and prove its stability. The effectiveness of the proposed observer and disturbance observer is fully demonstrated. The finite time integrated site selection mode disturbance observer (F-ISMDOB) is designed to quickly estimate and compensate for equivalent interference, and effectively improves the anti-interference performance of the system structure layer.