Layer Parameters Research Articles

Optimizing lead-free Cs3Bi2I9 perovskite solar cells: exploring absorber and charge transport layers parameters for improved efficiency

Optimizing lead-free Cs3Bi2I9 perovskite solar cells: exploring absorber and charge transport layers parameters for improved efficiency

Conceptual design and simulation of a thermal matrix-type SFCL

Simultaneous quench is a prerequisite for achieving high-rated voltage and current in superconducting fault current limiters (SFCLs) based on superconducting thin films. In this article, we propose the T-MSFCLs, i.e. the matrix-type SFCLs (MSFCLs) with thermally-assisted simultaneous quenches, to mitigate hotspot and dielectric breakdown problems. We present the typology of the non-isolated and isolated T-MSFCLs and develop a computational model for evaluating their quench performances. The short-circuit simulation results demonstrates that the quench propagation between SFCL elements is easy to establish and is self-sustaining in the non-isolated and the isolated T-MSFCLs due to an electric-thermal coupling. The effects of Jc distribution and shunt layers on quench properties are studied. We find that T-MSFCLs always produce global quench despite having poor global uniformity ΔJc/Jc . The quench activation, quench development and the current-limiting properties of T-MSFCL are relevant to the parameters of shunt layers and the weakest SFCL element. The design parameters of the shunt layers are the main conditions for the maximum tolerance voltage and the maximum response rate of the T-MSFCL. By generalizing these findings, a method for capacity design of T-MSFCL is presented.

Prediction of In-Class Performance Based on MFO-ATTENTION-LSTM

In this paper, we present a novel approach to predicting in-class performance using log data from course learning, which is important in the field of personalized education and classroom management. Specifically, a set of fine-grained features is extracted from unit learning log data to train a prediction model based on long short-term memory (LSTM). However, to enhance the accuracy of the model, we introduce moth flame optimization-attention-LSTM (MFO-Attention-LSTM) as an improvement to the conventional LSTM-attention model. The MFO algorithm is utilized instead of the traditional backward propagation method to calculate attention layer parameters, thereby allowing the model to jump out of local optima. The proposed model outperforms the SVM, CNN, RNN, LSTM, and LSTM-Attention models in terms of the F1 score. Empirical results demonstrate that the optimization of the MFO algorithm contributes significantly to the improved performance of the prediction model. In conclusion, the proposed MFO-Attention-LSTM model offers a promising solution for predicting in-class performance using log data from course learning and could provide valuable insights for personalized education and classroom management.

Fast imaging of local perturbations in a unknown bi-periodic layered medium

We discuss a novel approach for imaging local faults inside an infinite bi-periodic layered medium in R3 using acoustic measurements of scattered fields at the bottom or the top of the layer. The faulted area is represented by compactly supported perturbations with erroneous material properties. Our method reconstructs the support of perturbations without knowing or reconstructing the constitutive material parameters of healthy or faulty bi-period layer; only the size of the period is needed. This approach falls under the class of non-iterative imaging methods, known as the generalized linear sampling method with differential measurements, first introduced in [2] and adapted to periodic layers in [25]. The advantage of applying differential measurements to our inverse problem is that instead of comparing the measured data against measurements due to healthy structures, one makes use of periodicity of the layer where the data operator restricted to single Floquet-Bloch modes plays the role of the one corresponding to healthy material. This leads to a computationally efficient and mathematically rigorous reconstruction algorithm. We present numerical experiments that confirm the viability of the approach for various configurations of defects.

Deep learning model for dynamic color design of all-dielectric metasurfaces.

Silicon nanostructure colors have rapidly developed in recent years, offering high resolution and a broad color gamut that traditional pigments cannot achieve. The reflected colors of metasurfaces are determined by the geometric structure of the unit cell and the refractive index matching layer parameters. It is evident that the design of specific colors involves numerous parameters, making it challenging to achieve through conventional calculations. Therefore, the tandem network instead of conventional electromagnetic simulation is natural. The forward part of the network incorporates feature cross terms to improve accuracy, enabling high-precision predictions of structural colors based on structural parameters. The average color difference between the predicted and actual color values in the L,a,b color space is 1.38. The network has been proven to accurately predict the refractive index and height of the refractive index matching layer during the dynamic tuning process. Additionally, the issue of the inverse network converging to incorrect solutions was addressed by leveraging the characteristics of the activation function. The results show that the color difference between the colors designed by the inverse network compared to the actual colors in the L,a,b color spaces is only 2.22, which meets the requirements for commercial applications.

Enhancing formation and retention of hollow structural integrity through intermediate and outermost layer design in seeded emulsion polymerization of hollow latex particles

AbstractThe synthesis of hollow latex particles through seeded emulsion polymerization involves a series of intricate steps, including the formation of distinct polymer layers with specific properties. Despite extensive research, preserving the desired hollow structure remains challenging due to the unclear role of the encapsulating polymer layers. This study systematically adjusts the glass transition temperature (Tg) of the intermediate layer by varying the butyl acrylate (BA) ratio in the monomer feed mixture. By controlling the reaction temperature during alkali swelling, we explore the critical influence of Tg on hollow latex particle formation from carboxylated core latex particles. To ensure long‐term hollow structure retention after drying, a rigid outer layer is polymerized onto the intermediate layer. Surprisingly, higher divinylbenzene (DVB) mass ratios (5.0 and 10.0 wt%) do not result in a highly crosslinked hollow shell due to DVB self‐nucleation. This paper emphasizes the importance of precise design parameters for both intermediate and outermost layers in achieving and maintaining hollow latex particle structures. Understanding each layer's role and optimizing their compositions contribute to advancing hollow latex particle synthesis through seeded emulsion polymerization.

Ensemble of Deep Features for Breast Cancer Histopathological Image Classification

Abstract Analysis of histopathological images (HIs) is crucial for detecting breast cancer (BR). However, because they vary, it is still very difficult to extract well-designed elements. Deep learning (DL) is a recent development that is used to extract high-level features. However, DL techniques continue to confront several difficult problems, such as the need for sufficient training data for DL models, which reduces the classification findings. In this study, an ensemble deep transfer convolutional neural network is presented to address this problem. The pre-trained models (ResNet50 and MobileNet) are employed to extract high-level features by freezing the front layer parameters while fine-tuning the last layers. In the proposed ensemble framework, KNN, SVM, logistic regression and neural networks are used as base classifiers. The majority vote and product approaches are used to integrate the predictions of each separate classifier. In the benchmark BreaKHis dataset, the suggested ensemble model is compared to some current approaches. It demonstrates that while the ensemble model obtains a considerable accuracy of 97.72% for the multiclass classification test, it achieves an accuracy of 99.2% for the binary task. The suggested ensemble model’s effectiveness in extracting useful features for BR images is demonstrated by comparison with existing cutting-edge models.

Artificial intelligence generates novel 3D printing formulations

Formulation development is a critical step in the development of medicines. The process requires human creativity, ingenuity and in-depth knowledge of formulation development and processing optimization, which can be time-consuming. Herein, we tested the ability of artificial intelligence (AI) to create de novo formulations for three-dimensional (3D) printing. Specifically, conditional generative adversarial networks (cGANs), which are generative models known for their creativity, were trained on a dataset consisting of 1437 fused deposition modelling (FDM) printed formulations that were extracted from both the literature and in-house data. In total, 27 different cGANs architectures were explored with varying learning rate, batch size and number of hidden layers parameters to generate 270 formulations. After a comparison between the characteristics of AI-generated and human-generated formulations, it was discovered that cGANs with a medium learning rate (10−4) could strike a balance in generating formulations that are both novel and realistic. Four of these formulations were fabricated using an FDM printer, of which the first AI-generated formulation was successfully printed. Our study represents a milestone, highlighting the capacity of AI to undertake creative tasks and its potential to revolutionize the drug development process.

Calibration and Testing of Parameters for the Discrete Element Simulation of Soil Particles in Paddy Fields

The parameters of the discrete element simulation model for rice field soils serve as valuable data references for investigating the dynamic characteristics of the walking wheel of high-speed precision seeding machinery in paddy fields. The research specifically targets clay loam soil from a paddy field in South China. Calibration of essential soil parameters was achieved using EDEM_2022 software (and subsequent versions) discrete element simulation software, employing the Edinburgh Elasto-Plastic Adhesion (EEPA) nonlinear elastic-plastic contact model. The tillage layer and plough sub-base layer underwent calibration through slump and uniaxial compression tests, respectively. Influential contact parameters affecting slump and axial pressure were identified through a Plackett–Burman test. The optimal contact parameter combinations for the discrete element model of the tillage layer and plough sub-base layer were determined via a quadratic rotational orthogonal test. The accuracy of the discrete element simulation model’s parameters for paddy field soils was further validated through a comparative analysis of the simulation test’s cone penetration and the field soil trench test. Results indicate that the Coefficient of Restitution, surface energy, Contact Plasticity Ratio, and Tensile Exp significantly influence slump (p < 0.05). Additionally, the Coefficient of Restitution, Contact Plasticity Ratio, coefficient of rolling friction, and Tangential Stiff Multiplier significantly impact axial pressure (p < 0.05). Optimal contact parameters for the plough layer were achieved with a particle recovery coefficient of 0.49, a surface energy of 18.52 J/m2, a plastic deformation ratio of 0.45, and a tensile strength of 3.74. For the plough subsoil layer, optimal contact parameters were a particle recovery coefficient of 0.47, a coefficient of interparticle kinetic friction of 0.32, a plastic deformation ratio of 0.49, and a tangential stiffness factor of 0.31. Results from the cone penetration test reveal no significant disparity in compactness between the actual experiment and the simulation test. The calibrated discrete element model’s contact parameters have been verified as accurate and reliable. The findings of this study offer valuable data references for understanding the dynamic characteristics of the walking wheel of the entire machinery in high-speed precision seeding in paddy fields.

Electromagnetic guided waves in composite liquid crystal-based interfaces

We study an air–crown glass planar interface that includes a thin layer of a cholesteric liquid crystal doped with silver spheres of nanometer size. We propose a new theoretical model for the propagation of electromagnetic waves through the liquid crystal part and use the Marcuvitz–Schwinger form of the Maxwell equations to compute guided surface wave profiles. The results suggest the presence of anisotropic surface modes with negligible attenuation. The dependence of the surface wave parameters on the liquid crystal layer parameters can be used in liquid crystal-based sensors.

The transverse dynamic behavior study of a beam system coupled through a local nonlinear coupling layer

In engineering, some complex systems can be reduced to the beam system connected by serval coupling elements. To explore the potential application of local coupling nonlinearity on the transverse dynamic behavior of beams, this work establishes the physical model of a beam system coupled by a local nonlinear coupling layer. On the basis of ensuring the validity of the results, studies the effect of the nonlinear coupling layer on the transverse forced vibration of the beam system. Exploring the characters and reason for complex responses of the beam system coupled through a local nonlinear coupling layer. After discussing the numerical results, it can be found that the beam system presents complex responses within some parameters of the local nonlinear coupling layer, including amplitude jumping, quasi-periodic state, and targeted energy transfer, in which the change of the nonlinear restoring force is the reason for the appearance or disappearance of complex responses. For single-frequency responses, complex responses of the beam system can be motivated by changing the parameters of the local nonlinear coupling layer. With the simultaneous variation of the nonlinear stiffness and viscous damping, responses of the beam system contain both amplitude-sensitive regions and amplitude-jumping regions, in which the amplitude-sensitive region provides an effective parameter range for controlling the vibration by adjusting the local nonlinear coupling layer. On the whole, a suitable combination of nonlinear stiffness and viscous damping of the local nonlinear coupling layer can effectively reduce the vibration at the boundaries of the beam system.

One-step fabrication of asymmetric alumina ceramic membranes with tunable pore parameters for oil-water separation

Porous ceramic membranes with low-cost and high performance are desirable in industrial filtration field. To improve the performance while reducing the production cost, various measures were developed, however, pre-sintering of supports, preparation of membrane-forming suspensions and adoption of extensive organic additives that involved were adverse to cost reduction. Herein, cost-effective one-step co-pressing and co-sintering technique was proposed to prepare asymmetric ceramic membranes. The results revealed that pore parameters and performance of the fabricated membranes could be adjusted by altering mole ratio of Al2O3 to SiO2 (Al2O3/SiO2) in the support and sintering temperature. The influence of supports on the permeation flux of membranes surpassed that of the parameters of membrane layer. Additionally, the oil rejection rate of the membranes exceeded 96 % within 2 min and even reached 100 % (~60 min), the water flux and steady-state feed flux were 1330–1730 L·m−2·h−1 and 470–620 L·m−2·h−1 at 1 bar, respectively. Moreover, cake filtration was the main fouling mechanism throughout the separation process. This research provides a feasible and simple method for preparing low-cost high-performance ceramic membrane for wastewater treatment.

A layerwise geometric error compensation procedure for additive manufacturing

PurposeThe purpose of this paper is to provide a new procedure for in-plane compensation of geometric errors that often appear in the layers deposited by an additive manufacturing (AM) process when building a part, regardless of the complexity of the layer geometry.Design/methodology/approachThe procedure is based on comparing the real layer contours to the nominal ones extracted from the STL model of the part. Considering alignment and form deviations, the compensation algorithm generates new compensated contours that match the nominal ones as closely as possible. To assess the compensation effectiveness, two case studies were analysed. In the first case, the parts were not manufactured, but the distortions were simulated using a predictive model. In the second example, the test part was actually manufactured, and the distortions were measured on a coordinate measuring machine.FindingsThe geometric deviations detected in both case studies, as evaluated by various quality indicators, reduced significantly after applying the compensation procedure, meaning that the compensated and nominal contours were better matched both in shape and size.Research limitations/implicationsAlthough large contours showed deviations close to zero, dimensional overcompensation was observed when applied to small contours. The compensation procedure could be enhanced if the applied compensation factor took into account the contour size of the analysed layer and other geometric parameters that could have an influence.Originality/valueThe presented method of compensation is applicable to layers of any shape obtained in any AM process.

Experimental analysis of efficacy of carrier aggregation

Abstract3GPP introduced carrier aggregation (CA) in Release‐10 to mitigate the increasing bandwidth demand of users. The efficacy of CA in meeting user demand for higher throughput in real‐life scenarios is an important consideration. This letter undertakes a simulation route using NS3 to understand the impact of variations in specific parameter values on the performance of CA in 3GPP‐specified scenarios. This work further establishes an empirical relation between throughput and some varying parameters. The work also shows that maintaining steady cell throughput in a densely populated cell comes at the cost of an individual user throughput. Simulation results exhibit that optimal CA design by tuning physical layer parameters alone cannot provide sustained throughput to the users.

Low frequency inversion method based on multi-layer holizontal variation shallow seafloor model

Sound propagation in shallow water is significantly influenced by geoacoustic properties. Estimating these geoacoustic parameters is essential for sound field analysis and sonar performance assessment. As a common practice, the seafloor is often treated as a single-layer or two-layer range-independent geoacoustic model to reduce the number of involved parameters. However, acoustic parameters inverted through these two geoacoustic models are typically limited in their applicability to a specific frequency range, thus posing challenges when applied across a broader frequency range. A range-dependent multi-layer geoacoustic model based on experimental measurements obtained with a sub-bottom profiler is proposed in this study. The inversion scheme combines three inversion methods to estimate geoacoustic parameters, considering the different sensitivities of geoacoustic parameters to different physical parameters within the acoustic field. Firstly, modal dispersion is used to invert the geoacoustic parameters of each layer, with the dispersion curve obtained through warping transform and the Wigner-Ville distribution. After that, both the localization using matched field processing and the dispersion curve fitting demonstrate the effectiveness of the inversion results for each layer, although the peak of the probability distribution of sound speed in the first layer is broader than in others. Secondly, matched field processing is employed to invert the geoacoustic parameters of the first layer. This method is based on the theory that as frequency increases, the depth of sound rays penetrating the seabed decreases, revealing changes in the first layer's sound speed with the seabed depth. Lastly, bottom attenuation coefficients at different frequencies are inverted by the transmission loss (TL), and a fitting relationship between the attenuation coefficient and the frequency is derived. The inversion results obtained by using the range-dependent multi-layer geoacoustic model are compared with results estimated by the single-layer geoacoustic model. The findings indicate that the transmission loss (TL) error from the range-dependent multi-layer geoacoustic model in this study is smaller than that from the single-layer geoacoustic model, especially in the lower frequency band. The range-dependent multi-layer geoacoustic model proves to be suitable for a broader frequency range, providing better precision in explaining various acoustic phenomena.

Unraveling the effects of geometrical parameters on dynamic impact responses of graphene reinforced polymer nanocomposites using coarse-grained molecular dynamics simulations.

Nacre plays an important role in bionic design due to its light weight, high strength, and structure-function integration. The key to elucidate its reinforcing and toughening mechanisms is to truly characterize its multi-layer structure and properties. In this work, the dynamic impact responses of graphene reinforced polymer nanocomposites with a unique brick-and-mortar structure are investigated using coarse-grained molecular dynamics simulations, in which the interfacial coarse-grained force field between graphene and the polymer matrix is derived by the energy matching approach. The influences of various geometrical parameters on dynamic impact responses of the nanocomposites are studied, including the interlayer distance, lateral distance, and number of graphene layers. The results demonstrate that the impact resistance of the nacre-like structure can be significantly improved by tuning the geometrical parameters of graphene layers. It is also found that the chain scission and interchain disentanglement of polymer chains are the main failure mechanisms during the perforation failure process as compared to the stretching and breaking of bonds. In addition, the microstructure analysis is performed to deeply interpret the deformation and damage mechanisms of the nanocomposites during impact. This study could be helpful for the rational design and preparation of graphene reinforced nacre-like nanocomposites with high impact resistance.

Effect of dietary supplementation of Arjun bark powder on production and egg quality parameters in Uttara layers

Effect of dietary supplementation of Arjun bark powder on production and egg quality parameters in Uttara layers

Enhanced photonic spin Hall effect and its sensing application via optical Tamm state based on nanocomposites

In this contribution, by combining the nanocomposite with distributed Bragg reflector, a simple multilayer structure to enhance the photonic spin Hall effect (PSHE) via optical Tamm state (OTS) for both horizontal (H) and vertical (V) polarized lights is presented. The structural parameters of nanocomposite layer (e.g., thickness and filling factor) have a significant influence on PSHE. The maximal spin shifts of 16.2 and 17.8 μm can be achieved for H and V polarized lights, respectively, which are attributed to strong excitation of OTS. Moreover, the spin shift is extremely sensitive to the refractive index changes of sensing medium, and a gas sensor with superior sensing performance is theoretically proposed for both H and V polarized beams. These findings provide an effective method to enhance the PSHE for both H and V polarized lights and therefore open an opportunity for developing PSHE refractive index sensors.

Echographic Optic Nerve Evaluation: A Novel Diagnostic Modality in Glaucoma

Introduction: Primary Open-Angle Glaucoma (POAG) is considered a leading cause of blindness among all others. Different technologies such as Scanning Laser Polarimetry (SLP) and Optical Coherence Tomography (OCT) closely correlate in measuring structural parameters of the Retinal Nerve Fiber Layer (RNFL) and Optic Nerve Head (ONH). Visual impairment related to glaucomatous damage is attributed to the RNFL. Earlier studies have shown that retro-bulbar optic nerve thickness is reduced in glaucoma and have suggested that this is also the result of RNFL destruction. Aim: To investigate the correlation between the orbital and intraocular portions of the optic nerve among POAG patients. Materials and Methods: This was a hospital-based crosssectional study done in a tertiary care ophthalmic institute from October 2019 to February 2021. One eye of 32 volunteers with newly diagnosed POAG underwent optic disc analysis using OCT and echographic measurements of the retrobulbar optic nerve. For statistical calculations, Statistical Package for Social Sciences (SPSS) Statistics version 20.0 software (IBM Corp., Armonk, NY, USA) was used. Spearman’s rho (rs) was used as the index of correlation between retrobulbar optic nerve dimensions and ONH topographical data. A correlation between OCT-based RNFL and optic disc parameters was compared with retrobulbar optic nerve dimensions measured with the help of Ultrasonography- Brightness (USG B) Scan. Results: Orbital Optic Nerve Diameter (OND) and Optic Nerve Cross-sectional Area (ONCSA) significantly and positively correlated with Neuro-retinal Rim (NR) area (OND: p-value=0.00001; ONCSA: p-value=0.00001) and average nerve fiber layer thickness (OND: p-value=0.0001; ONCSA: p-value=0.00002). The Retrobulbar ONCSA-to-disc area ratio (ONCSA/D) was found to have a statistically demonstrable positive correlation with Neuro-retinal Rim Area/Disc area ratio (NR/D) (p=0.00003). Conclusion: This study showed that retrobulbar optic nerve dimensions correlate well with SD-OCT-based ONH parameters. Echographic measurements of the retrobulbar optic nerve add a new biomarker in the diagnosis of glaucoma.

Going Deeper, Generalizing Better: An Information-Theoretic View for Deep Learning.

Deep learning has transformed computer vision, natural language processing, and speech recognition. However, two critical questions remain obscure: 1) why do deep neural networks (DNNs) generalize better than shallow networks and 2) does it always hold that a deeper network leads to better performance? In this article, we first show that the expected generalization error of neural networks (NNs) can be upper bounded by the mutual information between the learned features in the last hidden layer and the parameters of the output layer. This bound further implies that as the number of layers increases in the network, the expected generalization error will decrease under mild conditions. Layers with strict information loss, such as the convolutional or pooling layers, reduce the generalization error for the whole network; this answers the first question. However, algorithms with zero expected generalization error do not imply a small test error. This is because the expected training error is large when the information for fitting the data is lost as the number of layers increases. This suggests that the claim "the deeper the better" is conditioned on a small training error. Finally, we show that deep learning satisfies a weak notion of stability and provides some generalization error bounds for noisy stochastic gradient decent (SGD) and binary classification in DNNs.