Layer Parameters Research Articles

Design and optimization of a long-range surface plasmon resonance-based plasmonic SERS biosensor

In this paper, we designed and studied a novel type of hybrid chip based on long-range surface plasmon resonance (LRSPR) and surface-enhanced Raman scattering (SERS). The substrate has periodic arrangement gold nanoring cavity arrays structure, which can obtain a uniform and stable electromagnetic field distribution. In addition, due to the clever design of the long-range configuration (a “sandwich” structure with symmetric refractive index), the chip has a significant extended electromagnetic field enhancement ability. In numerous existing studies focusing on SERS performance, the consideration of plasmonic structure is limited to its shape and size, while neglecting the significance of spacer layer parameters. Thus, we use the Finite Difference Time Domain (FDTD) software to design and optimize the chip substrate configuration by investigating the impact of various parameters such as refractive index, spacer layer thickness, plasmonic structure's inner and outer radius, and period of array on the long-range effect. The presented geometrically-tunable long-range plasmonic chip provides a way to identify the governing factors for SERS and presents a design principle for optimizing SERS substrates.

Airfoil Self-Noise Prediction Using Artificial Neural Networks for CFD Boundary Layer Parameter

Airfoil Self-Noise Prediction Using Artificial Neural Networks for CFD Boundary Layer Parameter

Технологическое обеспечение эксплуатационных свойств сопрягаемых поверхностей элементов сборной формообразующей оснастки с учетом установленной долговечности пресс-форм

Design and technological concepts are viewed at the stages of designing, manufacturing and joining process providing for the required reliability (durability) of mold elements. Recommendations are given for rationing and achieving the parameters of accuracy and quality of mating surfaces, taking into account the formation of the necessary operational properties at the main stages of the product life cycle. The factors that have the greatest impact on ensuring the correct and accurate location of forming tool elements have been elicited. The concept of determining the actual contact area and calculating the scale parameter, which provides a contact interface degree specification for conjugated surfaces, is presented. The hypothesis of the formation of reliability indicators is found, taking into account the manufacturing capability and technical capacity of production with the introduction of correction of the upper and lower dimensional deviations included in the dimension chains and ensuring the pre-set accuracy parameters of the master link. The analysis of the operating conditions of the studied type of pressmold elements is carried out. The main operational properties that provide the required operating time for shaping part failures are identified and analyzed. Special attention is paid to the study of the structural features of such parts, a comparison of the integral and prefabricated structures is made, and also, contact stresses under the joining operation for the most promising of the latter are calculated for contact interface degree simulation. This will allow developing practical recommendations for any type of compound forming tool for the benefit of basic mechanical properties rate making, accuracy indicators, including deviation allowances in the correct geometric shape, including quality parameters of the surface layer at the designing stage,processing methods dispatch and modes setting at the manufacturing and testing stages and also suggesting the amount of static loading when assembling.

Investigation of GeSe Photovoltaic Device Performance via 1-Dimensional Computational Modelling

Germanium selenide (GeSe) is a potential absorber material for thin film solar cells. However, many physical, electronic parameters and practical defect configurations that result in different effects on the performance of GeSe solar cells are not fully understood. In this study, a baseline of a GeSe thin film solar cell was designed and simulated using SCAPS-1D simulator. The physical and electronic parameters of the absorber layer is varied to investigate their effect on the performance of the solar cell. The simulation uses absorption files extracted from Xue et al. 2016 and the SCAPS-1D absorption model. Practical defect configurations are also introduced in GeSe thin film solar cells to optimize solar cell performance. Simulation results show that baseline GeSe solar cells had obtained Voc 0.62 V, Jsc 39.52 mA/cm<sup>2</sup>, FF 79.34 and ƞ 19.48%. Simulation using the SCAPS-1D absorption model achieved a more accurate JSC contour graph compared to simulation using absorption files extracted from Xue et al. 2016. The highest efficiency of 26.13% was achieved at 1.40 eV bandgap, 4.27 eV electron affinity, 10 cm2/Vs hole mobility, 1E+18 1/cm<sup>3</sup> hole concentration and 2000 nm GeSe layer thickness. For bulk defect, an increase in defect concentrations or capture cross section hole and electron (σ) reduce efficiency. For interfacial defect GeSe/CdS, total density of 1E+12 1/cm<sup>2</sup> with σ of 1E-13 cm<sup>2</sup>, total density of 1E+18 1/cm2 with σ of 1E-19 cm<sup>2</sup>, total density of 1E+16 1/cm<sup>2</sup> and 1E+18 1/cm<sup>2</sup> with σ of 1E-16 cm<sup>2</sup> have critical impact to solar cell.

A lubrication contact stiffness model considering asperity interactions

In practical engineering applications, most connection interfaces require lubrication. The contact stiffness of the lubrication interface can affect the static and dynamic characteristics of the interface, thus affecting the performance of the mechanical system, such as vibration, fatigue, and wear characteristics. Therefore, the measurement of lubrication contact stiffness is very important for the analysis and optimization of mechanical system performance. However, measuring the stiffness of the lubrication interface is a significant challenge. In this paper, a mixed lubrication contact stiffness model considering asperity interactions is proposed, which assumes the lubrication rough interface as an equivalent thin layer. The proposed model was compared with the lubrication contact model that does not consider the asperity interactions. Based on the microscopic contact behavior of the contact interface, the relationship between the mechanical parameters of the virtual thin layer and the contact pressure was analyzed. Finally, the predicted results of interface stiffness are verified by published theoretical and experimental results. The results show that the proposed model can more reasonably describe the actual contact behavior of the rough lubrication interface and give the variation law of the mechanical parameters of the virtual thin layer.

Threshold Voltage Modulation and Gate Leakage Suppression of Enhancement‐Mode GaN HEMT by Metal/Insulator/p‐GaN Gate Structure

Herein, a metal/insulator/p‐GaN gate HEMT (MIP‐HEMT) with Si3N4 gate dielectric layer is fabricated. Compared to the conventional p‐GaN HEMT, the MIP‐HEMT has a higher threshold voltage (Vth) of 4.8 V and better gate leakage suppression. The mechanism of threshold voltage increase in MIP‐HEMT is elucidated through an analysis of the electric field distribution in the gate region and the proposed gate capacitance model. Furthermore, MIP‐HEMTs with gate dielectric layers of varying dielectric constants and thicknesses are designed and simulated. The obtained results lead to the derivation of an empirical formula for the threshold voltage of the MIP‐HEMT under ideal dielectric/p‐GaN interface conditions, in relation to the dielectric constant and thickness of the gate dielectric layer. Additionally, simulations are conducted on the MIP‐HEMT incorporating fixed charges at the dielectric/p‐GaN interface, and the impact of interface charges is investigated. This refinement enables a more accurate prediction of the MIP‐HEMT's threshold voltage. Conclusively, MIP‐HEMTs can effectively modulate the threshold voltage by manipulating the parameters of the gate dielectric layer, thereby extending the threshold voltage range of conventional enhancement‐mode devices.

Influence of Growth Process on Suppression of Surface Morphological Defects in 4H-SiC Homoepitaxial Layers.

To address surface morphological defects that have a destructive effect on the epitaxial wafer from the aspect of 4H-SiC epitaxial growth, this study thoroughly examined many key factors that affect the density of defects in 4H-SiC epitaxial wafer, including the ratio of carbon to silicon, growth time, application of a buffer layer, hydrogen etching and other process parameters. Through systematic experimental verification and data analysis, it was verified that when the carbon-silicon ratio was accurately controlled at 0.72, the density of defects in the epitaxial wafer was the lowest, and its surface flatness showed the best state. In addition, it was found that the growth of the buffer layer under specific conditions could effectively reduce defects, especially surface morphology defects. This provides a new idea and method for improving the surface quality of epitaxial wafers. At the same time, we also studied the influence of hydrogen etching on the quality of epitaxial wafers. The experimental results show that proper hydrogen etching can optimize surface quality, but excessive etching may lead to the exposure of substrate defects. Therefore, it is necessary to carefully control the conditions of hydrogen etching in practical applications to avoid adverse effects. These findings have important guiding significance for optimizing the quality of epitaxial wafers.

Traffic Sign Recognition Using Multi-Task Deep Learning for Self-Driving Vehicles.

Over the coming years, the advancement of driverless transport systems for people and goods that are designed to be used on fixed routes will revolutionize the transportation system. Therefore, for a safe transportation system, detecting and recognizing traffic signals based on computer vision has become increasingly important. Deep learning approaches, particularly convolutional neural networks, have shown exceptional performance in various computer vision applications. The goal of this research is to precisely detect and recognize traffic signs that are present on the streets using computer vision and deep learning techniques. Previous work has focused on symbol-based traffic signals, where popular single-task learning models have been trained and tested. Therefore, several comparisons have been conducted to select accurate single-task learning models. For further improvement, these models are employed in a multi-task learning approach. Indeed, multi-task learning algorithms are built by sharing the convolutional layer parameters between the different tasks. Hence, for the multi-task learning approach, different experiments have been carried out using pre-trained architectures like, for instance, InceptionResNetV2 and DenseNet201. A range of traffic signs and traffic lights are employed to validate the designed model. An accuracy of 99.07% is achieved when the entire network has been trained. To further enhance the accuracy of the model for traffic signs obtained from the street, a region of interest module is added to the multi-task learning module to accurately extract the traffic signs available in the image. To check the effectiveness of the adopted methodology, the designed model has been successfully tested in real-time on a few Riyadh highways.

Effects of granite stratum with different weathering levels on water discharge in metro tunnels

This paper proposes a method for calculating the water discharge in metro tunnels considering limited drainage. With this method, groundwater resources and vegetation can be protected on the premise of ensuring the structural safety of tunnel linings. The water discharge in drainage-limited tunnels constructed through weathered granite was investigated. The water discharge characteristics were explored under different parameters of the shotcrete layer and grouting ring. The effects of the thickness and permeability coefficient of the weathered granite strata on the water discharge in the tunnel were discussed and the results were validated by in-situ monitoring of the water discharge. The results showed that the water discharge decreases with the increase in the thickness ratio of the grouting ring and shotcrete layer and with the decrease in the permeability coefficient ratio of the shotcrete layer and grouting ring. The water discharge can be effectively reduced by increasing the thickness or decreasing the permeability coefficient of the grouting ring. Once the ratio of the grouting ring to the thickness of the shotcrete layer exceeds 20, the water discharge no longer decreases significantly, and by tuning the parameters of the grouting ring, tunnel construction costs can be reduced without affecting the groundwater resource or vegetation around the tunnel. The proposed method can serve as a basis for regulating the water discharge in tunnels located in ecologically fragile regions.

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield

Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.

Tropical cyclone low-level wind speed, shear, and veer: sensitivity to the boundary layer parametrization in the Weather Research and Forecasting model

Abstract. Mesoscale modeling can be used to analyze key parameters for wind turbine load assessment in a large variety of tropical cyclones. However, the modeled wind structure of tropical cyclones is known to be sensitive to the boundary layer scheme. We analyze modeled wind speed, shear, and wind veer across a wind turbine rotor plane in the eyewall and outer cyclone. We further assess the sensitivity of wind speed, shear, and veer to the boundary layer parametrization. Three model realizations of Typhoon Megi are analyzed over the open ocean using three frequently used boundary layer schemes in the Weather Research and Forecasting (WRF) model. All three typhoon simulations reasonably reproduce the cyclone track and structure. The boundary layer parametrization causes up to 15 % differences in median wind speed at hub height between the simulations. The simulated wind speed variability also depends on the boundary layer scheme. The modeled median wind shear is smaller than or equal to 0.11 used in the current IEC (International Electrotechnical Commission) standard regardless of the boundary layer scheme for the eyewall and outer cyclone region. However, up to 43.6 % of the simulated wind profiles in the eyewall region exceed 0.11. While the surface inflow angle is sensitive to the boundary layer scheme, wind veer in the lowest 400 m of the atmospheric boundary layer is less affected by the boundary layer scheme. Simulated median wind veer reaches values up to 1.7×10-2° m−1 (1.2×10-2° m−1) in the eyewall region (outer cyclone region) and is relatively small compared to moderate-wind-speed regimes. On average, simulated wind speed shear and wind veer are highest in the eyewall region. Yet strong spatial organization of wind shear and veer along the rainbands may increase wind turbine loads due to rapid changes in the wind profile at the turbine location.

Electrical properties of MAH-g-PP modified PP/SEBS matrix semi-conductive composite materials

Polypropylene (PP) becomes a superior candidate material for new environmental-friendly cable insulation layer with its excellent heat resistance, electrical properties and degradability. At present, most of studies focus on the insulation layer of PP cable. However, there are few studies on semi-conductive shielding layer of PP cable which match its insulation layer. In this paper, polypropylene (PP)/styrene ethylene butylene styrene (SEBS) is adopted as matrix of PP cable semi-conductive layer to matching its insulation layer. Meanwhile, Maleic anhydride grafted polypropylene (MAH-g-PP) is used to cooperatively improve the compatibility between PP and SEBS. The physicochemical properties, space charge injection properties and compatibility of semi-conductive composites with different CB and MAH-g-PP contents is studied by combining experiment and simulation methods. The experimental results show that the surface roughness of the semi-conductive layer and the space charge inside the insulation layer decrease first and then increase with the increase of CB addition. Among them, the two parameters of 25 phr CB semi-conductive layer is the lowest, respectively 13.98 nm and 2.25 × 10−8C. These two parameters are significantly reduced after the addition of compatibilizer MAH-g-PP, which decreased by 29.47 % and 62.22 %, respectively. The simulation results show that the Flory-Huggins interaction parameters (χ) of MAH-g-PP with PP and SEBS are −73.3 and −45.2, respectively, which are 236.24 % and 107.34 % lower than those of PP/SEBS. The χ value of MAH-g-PP/CB is 8.20, which is 33.27 % and 9.59 % lower than CB/PP and CB/SEBS, respectively. It shows that MAH-g-PP can effectively improve the compatibility of matrix and filler. This work has important guiding significance for the research of semi-conductive layer formulation of PP cable.

Cross-domain heterogeneous metasurface inverse design based on a transfer learning method.

In this Letter, a transfer learning method is proposed to complete design tasks on heterogeneous metasurface datasets with distinct functionalities. Through fine-tuning the inverse design network and freezing the parameters of hidden layers, we successfully transfer the metasurface inverse design knowledge from the electromagnetic-induced transparency (EIT) domain to the three target domains of EIT (different design), absorption, and phase-controlled metasurface. Remarkably, in comparison to the source domain dataset, a minimum of only 700 target domain samples is required to complete the training process. This work presents a significant solution to lower the data threshold for the inverse design process and provides the possibility of knowledge transfer between different domain metasurface datasets.

Investigations of optical and thermal properties of surface layers of Cd1−xBexTe samples by the photoacoustic method

The work presents the results of a research on the photoacoustic spectra of thin surface layers of Cd1−xBexTe crystals formed by grinding and polishing their surfaces. As a result of matching the theoretical and experimental photoacoustic spectra, thermal and optical parameters of these layers were determined. Thermal parameters of the surface layers, such as thermal conductivity and thermal diffusivity, turned out to be much worse than the analogous parameters of the substrate. The increase in the optical absorption of surface layers for photon energies below Eg was also determined. Eg was also determined.

Numerical Simulation and Deformation Prediction of Deep Pit Based on PSO-BP Neural Network Inversion of Soil Parameters.

The finite element numerical simulation results of deep pit deformation are greatly influenced by soil layer parameters, which are crucial in determining the accuracy of deformation prediction results. This study employs the orthogonal experimental design to determine the combinations of various soil layer parameters in deep pits. Displacement values at specific measurement points were calculated using PLAXIS 3D under these varying parameter combinations to generate training samples. The nonlinear mapping ability of the Back Propagation (BP) neural network and Particle Swarm Optimization (PSO) were used for sample global optimization. Combining these with actual onsite measurements, we inversely calculate soil layer parameter values to update the input parameters for PLAXIS 3D. This allows us to conduct dynamic deformation prediction studies throughout the entire excavation process of deep pits. The results indicate that the use of the PSO-BP neural network for inverting soil layer parameters effectively enhances the convergence speed of the BP neural network model and avoids the issue of easily falling into local optimal solutions. The use of PLAXIS 3D to simulate the excavation process of the pit accurately reflects the dynamic changes in the displacement of the retaining structure, and the numerical simulation results show good agreement with the measured values. By updating the model parameters in real-time and calculating the pile displacement under different working conditions, the absolute errors between the measured and simulated values of pile top vertical displacement and pile body maximum horizontal displacement can be effectively reduced. This suggests that inverting soil layer parameters using measured values from working conditions is a feasible method for dynamically predicting the excavation process of the pit. The research results have some reference value for the selection of soil layer parameters in similar areas.

Racial-ethnic disparities in concurrent rates of peripapillary & macular OCT parameters among a large glaucomatous clinical population

ObjectivesTo compare rates of change in peripapillary retinal nerve fibre layer (pRNFL) and macular ganglion cell-inner plexiform layer (mGCIPL) parameters among different race-ethnicities from a large electronic health record database of subjects with or suspected of glaucoma.MethodsIn this retrospective cohort study, rates of change were obtained using joint longitudinal linear mixed models for eyes with ≥3 visits and ≥1 year of follow-up, adjusting for age, sex, intraocular pressure, central corneal thickness, and baseline pRNFL and mGCIPL thickness. Best linear unbiased predictor estimates of various parameters were stratified by baseline glaucoma severity and analysed by racial-ethnic group.ResultsA total of 21,472 spectral domain optical coherence tomography (OCT) pRNFL scans and 14,431 mGCIPL scans from 2002 eyes were evaluated. A total of 200 (15.6%) and 601 (46.8%) subjects identified as non-Hispanic Black (NHB) and Hispanic, respectively. NHB eyes exhibited faster rates of change in pRNFL among glaucoma suspect (global pRNFL −0.57 ± 0.55 µm/year vs. −0.37 ± 0.62 µm/year among Hispanics, p < 0.001), mild glaucoma (superior pRNFL quadrant −1.20 ± 1.06 µm/year vs. −0.75 ± 1.51 µm/year among non-Hispanic Whites (NHW), p = 0.043), and moderate glaucoma eyes (superior pRNFL quadrant −1.31 ± 1.49 µm/year vs. −0.52 ± 1.26 µm/year among Hispanics, p = 0.003). NHB eyes exhibited faster rates of mGCIPL loss corresponding to pRNFL rates. Global pRNFL and mGCIPL rates were strongly correlated (R2 = 0.70).ConclusionsAdjusted rates of pRNFL and mGCIPL loss significantly differed between racial-ethnic groups when stratified by glaucoma severity, with faster rates among NHB patients. These differences highlight key racial-ethnic disparities in adjusted rates of glaucoma OCT parameters.

Radiation attenuation properties of yttrium lithium borate glasses

As the glass is used in a variety of fields, it is vital to test it on radiation shielding purposes. In this article, the radiation attenuation properties of examined glasses by replacing Li2O for Y2O3 have been investigated. The samples formulated as 72B2O3–(28-x)Li2O–xY2O3 (where x = 0,2,4,8,12 mol%). The parameters of linear absorption coefficients (LAC) and other parameters of half value layer (HVL), mean free path (MFP), tenth value layer (TVL) and effective atomic numbers were calculated. It was found that when Li2O is replaced to Y2O3, the radiation shielding properties of glasses increment.

Multimodal Moore-Penrose Inverse-Based Recomputation Framework for Big Data Analysis.

Most multilayer Moore-Penrose inverse (MPI)-based neural networks, such as deep random vector functional link (RVFL), are structured with two separate stages: unsupervised feature encoding and supervised pattern classification. Once the unsupervised learning is finished, the latent encoding is fixed without supervised fine-tuning. However, in complex tasks such as handling the ImageNet dataset, there are often many more clues that can be directly encoded, while unsupervised learning, by definition, cannot know exactly what is useful for a certain task. There is a need to retrain the latent space representations in the supervised pattern classification stage to learn some clues that unsupervised learning has not yet been learned. In particular, the residual error in the output layer is pulled back to each hidden layer, and the parameters of the hidden layers are recalculated with MPI for more robust representations. In this article, a recomputation-based multilayer network using Moore-Penrose inverse (RML-MP) is developed. A sparse RML-MP (SRML-MP) model to boost the performance of RML-MP is then proposed. The experimental results with varying training samples (from 3k to 1.8 million) show that the proposed models provide higher Top-1 testing accuracy than most representation learning algorithms. For reproducibility, the source codes are available at https://github.com/W1AE/Retraining.

Evaluation of the Quality Parameters of the Surface Layer of Parts After Machining with Plasma Heating

Evaluation of the Quality Parameters of the Surface Layer of Parts After Machining with Plasma Heating

Optic nerve head neurovascular assessments in patients with schizophrenia: A cross‐sectional study

AbstractObjectiveThe retina is a protrusion of the brain, so researchers have recently proposed retinal changes as a new marker for studying central nervous system diseases. To investigate optic nerve head neurovascular structure assessed by optical coherence tomography angiography (OCTA) in schizophrenia compared to healthy subjects.MethodsThe study was conducted from 2019 to 2021 at the Ibn Sina Psychiatric Hospital in Mashhad, Iran. We enrolled 22 hospitalized known cases of schizophrenia, treated with risperidone as an antipsychotic drug, and 22 healthy subjects. The two groups were matched in age and gender. In the schizophrenic group, the positive and negative syndrome scale test was used to assess the illness severity. All subjects underwent complete ophthalmic evaluations and OCTA imaging.ResultsWe found that the cup/disc area ratio, vertical cup/disc ratio, and horizontal cup/disc ratio are significantly higher in patients with schizophrenia than in healthy subjects (with p‐values of 0.019, 0.015, and 0.022, respectively). No statistically significant difference in the peripapillary retinal nerve fiber layer and vascular parameters of the optic nerve head was observed between schizophrenia and healthy groups.ConclusionWe found evidence regarding the difference in the optic nerve head tomographic properties in schizophrenia compared to healthy subjects. However, ONH vascular parameters showed no significant difference. More studies are needed for a definite conclusion.