Layer Parameters Research Articles

Variability in the Occurrence of Sporadic E in the Geminid Meteor Shower using Inosonde BPAA Agam Data

Variability in the appearance of sporadic E in the Geminid Meteor Shower. The appearance of sporadic E does not always exist and suddenly appears, so sporadic E appears as a result of the Geminid Meteor Shower. Therefore, this research aimed to determine the variability in the appearance of sporadic E during the Geminid Meteor Shower. This type of research was descriptive analysis research. This research includes data collection, data processing, and data analysis on sporadic E layers. The data was linked to the Geminid Meteor Shower events on December 12, 13, and 14 in 2014, 2019, and 2020. The results of this research show that there were significant differences in the appearance of the Sporadic E layer at the peak of the Geminid Meteor shower. Where it had increased foEs and h'Es as Sporadic E layer parameters. foEs and h'Es before the peak of the Geminid Meteor Shower were known to be normal with foEs values between 2-4.2 MHz and h'Es at an altitude of 102-147 km. When the Geminid Meteor Shower peaked, foEs increased to 11.67 MHz and h'Es at an altitude of 147 km. After the peak of the Geminid Meteor Shower, there was a decline until it returned to normal with foEs ranging between 2-4.4 MHz and h'Es at an altitude of 99-147 km. An increase in foEs and h'Es was associated with the Geminid Meteor Shower phenomenon, so the Geminid Meteor Shower was variability in the appearance of Sporadic E during the Geminid meteor shower

Design and performance optimization of the Sb2S3 based broadband photodetector

Photodetectors are one of the most critical components in visible light communication systems. Herein, a broadband photodetector with a structure of FTO/TiO2/Sb2S3/MoO3/Au was proposed. The device performance and its influencing factors were simulated and analyzed using wxAMPS software. By optimizing the material parameters of the absorber layer and the interface characteristics at the TiO2/Sb2S3 and Sb2S3/MoO3 heterointerfaces, the device achieved a responsivity of 0.545 AW-1 and a detectivity as high as 1.116×1014 Jones. These simulation analyses and results provide some reference for the preparation of high performance visible light photodetectors.

SCAPS study on the effect of various hole transport layer on highly efficient 31.86% eco-friendly CZTS based solar cell

Copper Zinc Tin Sulphide (CZTS) is a propitious semiconductor for active absorber material in thin-film solar cells (SCs). Here, SC architecture comprising FTO/ZnS/CZTS/variable HTLs/Au is discussed. Fluorine-doped tin oxide (FTO) and gold (Au) are used as front and back contacts, respectively. Zinc sulphide (ZnS) is used as an active electron transport layer (ETL), while different Cu-based materials (Cu2O, CuO, CuI, and CuSCN) are used as hole transport layers (HTL). A one-dimensional solar cell capacitance simulator (SCAPS-1D) is utilized to simulate the SC structure. Among different Cu-based HTLs, Cu2O is preferred as a potential candidate for high cell performance of CZTS-based SC. The effects of various layer parameters such as thickness, doping density, and carrier concentrations, electron affinity of HTL and absorber, respectively, are also discussed. After optimization of the device, variation of operating temperature and the effect of series and shunt resistance are also taken into consideration. The optimized results of thickness and acceptor concentration (NA) of absorber material are 1.5 µm and approx. 1.0 × 1019 cm−3, respectively. In addition, the function of HTL (with and without) in the designed SC structure is also studied. Capacitance–voltage (C–V) characteristics are also discussed to get an insight of built-in potential. We have achieved cell performances viz. efficiency = 31.86%, short circuit current density = 32.05 mA/cm2, open circuit voltage = 1.19 V, and fill factor = 83.37%.

Airfield pavement resiliency assessment against extreme dynamic events by explicit axisymmetric finite element models

Infrastructure resiliency has become an essential element in ensuring community recovery after unexpected events. In airfield pavement, resilience can be achieved through an improved design approach by understanding the fundamental elements of damage mitigation and thus allowing rapid recovery. The objective of this study was to numerically evaluate easy-to-implement alternatives for mitigating damage in rigid airfield layered pavement systems when subjected to dynamic events such as blasts or high-velocity impacts. This was accomplished by developing an efficient axisymmetric explicit finite element model, which served as an analytical tool for the study. The model was developed in LS-DYNA and the methodology consisted of a comprehensive parametric analysis. The results showed that the concrete pavement layer parameters—mass density and compressive strength—have the most significant effect on the damage to the pavement when subjected to extreme dynamic events. The material parameters for the pavement layers were optimized to minimize the damage, caused by dynamic loads, achieving more than 70% damage mitigation. Furthermore, adding a granular base layer to the rigid airfield pavement design reduces the concrete pavement layer damage by almost 40%. An asphalt concrete overlay reduces the concrete pavement layer damage by about 55%. Moreover, adding an asphalt concrete overlay to the optimized rigid airfield pavement design reduces the damage by 10%. In contrast, adding a granular base layer to the optimized rigid airfield pavement design is ineffective.

Optimizing the Performance of Tin‐Based Perovskite Solar Cells Employing 2D Tungsten Disulfide as an HTL by Numerical Simulation

Perovskite solar cells (PSC) have emerged as a prominent research area in solar cell development owing to their high‐efficiency and low‐cost photovoltaic technology. However, the transport layers typically employed in PSCs are organic materials, which may lead to unstable performance. In this work, a PSC with an indium tin oxide/WS2/FASnI3/Cd0.5Zn0.5S/Al configuration is proposed, and the parameters are optimized using the solar cell capacitance simulator‐1D program. In the present study, the impact of the WS2 layer thickness, electron‐transport layer material selection, absorber layer parameters, and interfacial defect states on the performance of the proposed solar cell is investigated. The optimized structure yields a power conversion efficiency of 23.1%, which is comparable to that of state‐of‐the‐art PSCs. The combination of an inorganic hole‐transport layer of WS2 presented in this work offers a novel approach to the development of PSCs.

Investigations on the Effects of Rail Longitudinal Forces in Train-Track Dynamic Interaction

Nowadays, attention to railways has been increased as one of the important transportation methods. Various factors can cause longitudinal forces on the railway tracks, so the most important factors are the temperature changes due to the rail contraction and expansion (thermal forces), train braking system forces and acceleration, etc. Increasing longitudinal force can lead to buckling phenomena in railway tracks. In this paper, the effects of tensile and compressive longitudinal forces on the parameters of rail, sleeper, and ballast layers under vertical moving loads are investigated by using the finite element method. In this regard, by performing sensitivity analyses for different values of longitudinal forces and train speeds dynamic responses (displacement, velocity, and acceleration) of railway track components like rail, sleeper, and ballast have been studied. The results show that increasing the values of longitudinal axial force from –2000 [kN] up to 2000 [kN] as well as increasing the train speed from 10 [m/s] (36 [km/h]) up to 100 [m/s] (360 [km/h]) increases the rail displacement and velocity in the range of 25% up to 37% also the rail acceleration in the range of 9% up to 14%. The velocity and acceleration of the sleeper also the ballast velocity increase in the range of 24% up to 30% and 7% up to 9%, respectively, by increasing the speed of the train from 10 [m/s] (36 [km/h]) to 100 [m/s] (360 [km/h]) in all three modes without applying axial force, considering compressive and tensile one in the train-track interaction system.

Multi-material and parameter-controllable stereolithography 3D printing of graded permittivity composites for high voltage insulators

ABSTRACT Graded permittivity materials have gained significant attention due to their exceptional ability to regulate electric fields. Multi-material stereolithography (SLA) 3D printing has opened up new possibilities for creating such materials. However, conventional SLA printers typically generate graded material using fixed printing parameters and multiple feedstocks with limited differences, resulting in a constrained capacity for modulating the electric field distribution. To address this limitation, we have developed a multi-material, parameter-controllable SLA strategy, enabling us to assign varying printing parameters for each building layer and switch between feedstocks with significant differences. Solid insulators with graded permittivity are optimised through electric field distribution simulations and subsequently manufactured using our innovative multi-material SLA approach. A 4-layered graded insulator effectively decreases the maximum electric field strength from 82.5–30.8 kV/mm. Both flashover tests and partial discharge signals confirm that graded insulators outperform homogeneous ones in electrical insulation.

CoSeNet: A novel approach for optimal segmentation of correlation matrices

In this paper, we propose a novel approach for the optimal identification of correlated segments in noisy correlation matrices. The proposed model is known as CoSeNet (Correlation Segmentation Network) and is based on a four-layer algorithmic architecture that includes several processing layers: input, formatting, re-scaling, and segmentation layer. The proposed model can effectively identify correlated segments in such matrices, better than previous approaches for similar problems. Internally, the proposed model utilizes an overlapping technique and uses pre-trained Machine Learning (ML) algorithms, which makes it robust and generalizable. CoSeNet approach also includes a method that optimizes the parameters of the re-scaling layer using a heuristic algorithm and fitness based on a Window Difference-based metric. The output of the model is a binary noise-free matrix representing optimal segmentation as well as its segmentation points and can be used in a variety of applications, obtaining compromise solutions between efficiency, memory, and speed of the proposed deployment model.

Evaluation of retinal angioarchitectonics by optical coherence angiography and its diagnostic value in functional amblyopia

AIM: This study aimed to evaluate the density parameters of the superficial and deep plexuses of the retina, choriocapillary layer, and avascular zone in eyes with amblyopia of various origins and paired fellow eyes.
 MATERIAL AND METHODS: The study included 40 patients aged 616 (mean, 8.723.04) years. All patients were divided into 2 groups: group 1 included amblyopic, dysbinocular, and anisometropic eyes (n=48), and group 2 (control group) included paired fellow eyes without amblyopia (n=32). The density of the superficial and deep vascular plexuses of the retina, choriocapillary layer, and avascular zone parameters (area, perimeter, and circumference) were evaluated using spectral optical coherence tomography (RS-3000 Advance 2, Nidek, Japan). Correlation analysis was performed using Pearsons linear correlation coefficient (r).
 RESULTS: No significant differences were found in the density of the superficial and deep retinal vessels, choriocapillary layer, and avascular zone parameters of the retina in eyes amblyopia of various origins compared with paired fellow eyes (p 0.05). No correlation was found between retinal perfusion data and functional and anatomical parameters of amblyopic eyes.
 CONCLUSION: No relationship was noted between the vascular parameters of the posterior pole of the eye and the maximally corrected visual acuity, which confirms the absence of changes in retinal perfusion in amblyopia and excludes its role in the pathogenesis.

Effect of Dietary Supplementation of Herbal Feed Additives in Combination with Linseed Oil on Nutrient Utilization and Haemato-biochemical Parameters in White Leghorn Layers

Effect of Dietary Supplementation of Herbal Feed Additives in Combination with Linseed Oil on Nutrient Utilization and Haemato-biochemical Parameters in White Leghorn Layers

Local Differential Privacy Based Membership-Privacy-Preserving Federated Learning for Deep-Learning-Driven Remote Sensing

With the development of deep learning, image recognition based on deep learning is now widely used in remote sensing. As we know, the effectiveness of deep learning models significantly benefits from the size and quality of the dataset. However, remote sensing data are often distributed in different parts. They cannot be shared directly for privacy and security reasons, and this has motivated some scholars to apply federated learning (FL) to remote sensing. However, research has found that federated learning is usually vulnerable to white-box membership inference attacks (MIAs), which aim to infer whether a piece of data was participating in model training. In remote sensing, the MIA can lead to the disclosure of sensitive information about the model trainers, such as their location and type, as well as time information about the remote sensing equipment. To solve this issue, we consider embedding local differential privacy (LDP) into FL and propose LDP-Fed. LDP-Fed performs local differential privacy perturbation after properly pruning the uploaded parameters, preventing the central server from obtaining the original local models from the participants. To achieve a trade-off between privacy and model performance, LDP-Fed adds different noise levels to the parameters for various layers of the local models. This paper conducted comprehensive experiments to evaluate the framework’s effectiveness on two remote sensing image datasets and two machine learning benchmark datasets. The results demonstrate that remote sensing image classification models are susceptible to MIAs, and our framework can successfully defend against white-box MIA while achieving an excellent global model.

Identification of concrete surface damage based on probabilistic deep learning of images

Crack is common damage that can reduce the durability of concrete structures and accelerate structural degradation. With intent to improve the accuracy and efficiency of image-based crack detection, deep learning has been extensively used. However, the current deep learning paradigms for this purpose are deterministic machine learning approaches that cannot evaluate the uncertainty of diagnosis results. To bridge this gap, this paper proposes a hybrid probabilistic deep learning method for concrete surface damage classification. In the proposed method, Bayesian inference is embedded in a deep convolutional neural network, and the parameters in probability layers are modified from deterministic values to Gaussian distributions. Experimental results showed that it can identify cracks with an accuracy of 0.9909. The quantitative indicator of uncertainty in the identification results increases with the noise level and the degree of occlusion, which can reflect the ability of the model to capture uncertainty well. In addition, the hybrid probability method based on transfer learning can significantly improve the recognition accuracy, which can reach 0.9249 in complex crack images. Even if the image contains more noise, it can be recognized accurately but the uncertainty indicator is greater than 0.07.

High-frequency ultrasound in anti-aging skin therapy monitoring

Over the last few decades, high-frequency ultrasound has found multiple applications in various diagnostic fields. The fast development of this imaging technique opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. In this paper, being the first in this area, we discuss the usability of HFUS in anti-aging skin therapy assessment. The fully automated algorithm combining high-quality image selection and entry echo layer segmentation steps followed by the dermal parameters estimation enables qualitative and quantitative evaluation of the effectiveness of anti-aging products. Considering the parameters of subcutaneous layers, the proposed framework provides a reliable tool for TCA-peel therapy assessment; however, it can be successfully applied to other skin-condition-related problems. In this randomized controlled clinical trial, forty-six postmenopausal women were randomly assigned to the experimental and control groups. Women were treated four times at one-week intervals and applied skin cream daily between visits. The three month follow-up study enables measurement of the long-term effect of the therapy. According to the results, the TCA-based therapy increased epidermal (entry echo layer) thickness, indicating that the thinning process has slowed down and the skin’s condition has improved. An interesting outcome is the obtained growth in the intensity of the upper dermis in the experimental group, which might suggest a reduced photo-aging effect of TCA-peel and increased water content. The same conclusions connected with the anti-aging effect of TCA-peel can be drawn by observing the parameters describing the contribution of low and medium-intensity pixels in the upper dermis. The decreased share of low-intensity pixels and increased share of medium-intensity pixels in the upper dermis suggest a significant increase in local protein synthesis.

Research on flow and heat transfer at different positions in CDPF channel based on three-dimensional thermal LBM

To explore the flow, heat transfer and oxidative regeneration mechanism of the porous media of the catalytic diesel particulate filter at the pore scale, catalytic diesel particulate filter and particulate layer structure were built using three-dimensional reconstruction technology of Micro-CT and the Quartet Structure Generation Set. A heat source term was added to the mathematical model to simulate the temperature field during the oxidative regeneration of the particulate layer. The effects of different wall structures at five different positions, air inlet velocities and particulate layer parameters on the porous media were analyzed. The results show that in the clean cordierite porous medium structures, the larger the structural porosity, the greater the average velocity at the inlet of the porous medium wall. In the porous medium wall structures loaded with particulate layers, the particulate layer has a weakening effect on the velocity field above the particulate layer porous media. As the porosity of the particulate layer decreases, the temperature rise increases. When the particulate layers are oxidized, a temperature peak is generated at the inlet of the wall porous media. As the porosity of the particulate layer decreases, the temperature peak increases during oxidative regeneration.

Maximizing the solar output power using hybrid (CdS/ZnSe) buffer for CIGS thin film solar cell

In this work, a hybrid buffer layer (CdS & ZnSe) is placed between the absorber and the window layer in a CIGS (Copper Indium Gallium Selenide) solar cell replacing the commonly used single-layer CdS. The buffer layer plays a crucial role in interface electricity to enhance the performance of the solar cell. Although, CdS is used as a buffer layer in CIGS solar cells, the toxic nature and parasitic absorption due to the narrowband gap (2.4 eV) become issues during its application in devices. To reduce Cd concentration and ameliorate the bandgap issue, the hybrid buffer layer (CdS & ZnSe) is a perfect supplant. In this study, numerical simulation is performed on CIGS-based solar cells comprised of a hybrid buffer layer to enhance the performance by optimizing the physical parameters (thickness, carrier concentration, and defect density) of both absorber and buffer layer using SCAPS-1D software. In the process of optimizing physical parameters, the effect of the mentioned parameters is observed on the solar cell performance. Later, the impact of working temperature is shown in the solar cell performance. Finally, with all those optimized parameters, a comparison has been done between the reference and the proposed structure. The comparison asserts that the proposed structure with a conversion efficiency of 24.55% is outdistancing the reference structure as well as some relative structures from the recent study. The simulation results in this study can be a didactic one in the fabrication of high-efficiency CIGS solar cells

Performance of Unslotted ALOHA With Capture and Multiple Collisions in LoRaWAN

This paper explores the capacity limits and the tradeoff between the antagonistic means of enabling reliable data delivery in a loaded LoRaWAN cell. In fact, channel attenuation and variability call for robust transmission settings but the associated load increase causes more collisions between frames. In addition to the physical layer parameters of the LoRa modulation, this paper considers the benefits and tuning of inter-packet Error Correction Codes (ECC), which also trades transmission redundancy for reliability. We thus start by proposing a refined Packet Delivery Ratio (PDR) model that improves the ones found in the literature in that, first, it takes into account the dependency between overcoming ambient noise and dominating colliding frames, and second, it considers the sum of interference powers when multiple colliding frames are present, even if the interference preexists. Moreover, the model extends to the case of a gateway with receiver diversity. In a second step, the model allows to set out the level of redundancy at which ECC is the most effective without hindering capacity: a coding rate of one third. When this is fixed, it allows to define the transmission parameters allocation within a cell and thus the size of the cell. We finally develop an ad-hoc Python discrete-event simulator, freely accessible, to complement the model for assessing the effect of inter-SF and near-far interference and to show the benefits of power control in this respect.

Chemical-thermal treatment technology for manufacturing parts of electrical equipment for automobile transport

The state of technology and equipment for thermal and chemical-thermal treatment used for manufacturing of the main parts of starters, ignition distributors, generators has been analyzed. It is noted that the technical level of these technologies and equipment does not meet modern requirements. Technologies of carburization in solid carburizer and cyanidation in liquid medium often lead to formation of unstable parameters of diffusion layer on the surface of parts and finally to “spot” hardness and intensive wear of parts during operation.In order to modernize the processes of cementation and cyanidation, environmentally friendly technologies of chemical-thermal treatment have been developed, which provide stable parameters of the diffusion layer, thereby contributing to the improvement of the operational characteristics of automotive transport electrical equipment products.

Deep transfer learning for groundwater flow in heterogeneous aquifers using a simple analytical model

Deep learning models exhibit good interpolating ability, but their performance is often hindered by the scarcity of data in groundwater problems. Analytical models (solutions) provide a first-order physical principle for groundwater flow, but they are only applicable under specific conditions, such as when the aquifer is homogeneous. This study introduces a novel framework for deep transfer learning that integrates the strengths of both methods and overcomes their limitations. Specifically, we propose a deep learning model guided by a simple analytical model to predict groundwater flow in heterogeneous aquifers. It differs from previous deep learning model by incorporating the knowledge from the simple analytical model and utilizing transfer learning technique to improve the prediction in relatively complicated problems where the analytical model is not applicable. The model is tested against the traditional deep learning model Deep Back Propagation Neural Network (DBPNN) in the scenarios with unknown hydraulic conductivity fields. The results show that the proposed model significantly improve the accuracy of hydraulic head predictions by fusing analytical knowledge with neural networks. The hydraulic conductivity mainly affects the parameters of the shallow layers in the neural network, which enables the use of transfer learning techniques in more complex problems. In all test scenarios, the prediction errors of the proposed model are much smaller than those of the DBPNN. Additionally, the proposed model performs satisfactorily even with limited training data.

Research on sentiment analysis method of opinion mining based on multi-model fusion transfer learning

With the popularity of social media, opinion mining has gradually become a popular research field. Among these fields, sentiment analysis mining is an important research direction in the field of opinion mining. It aims to reveal the public's sentiment tendency, and attitude towards specific topics or events by analyzing text data generated by users on online platforms and digital media. However, the large amount of opinion data usually lacks effective annotation, which limits the learning and construction of opinion models. Therefore, focusing on the problem of the scarcity of labeled data in opinion analysis, this paper proposes a mining method for public opinion sentiment analysis based on multi-model fusion transfer learning, that can make full use of the limited labeled data to improve the learning efficiency of sentiment features by integrating the advantages of different models. Additionally, it introduces a transfer learning strategy to enable the models of the target domains to perform better in the absence of labeled data. Furthermore, the attention mechanism is combined to strengthen the acquisition of key features of the emotional colors and improve the accuracy of sentiment analysis. Specifically, the paper uses the ERNIE model to generate dynamic representations of the text word vectors in the dataset. It also uses TextCNN and BiGRU to construct a joint model for extracting local and overall features of the text word vectors. The parameters of the feature layer of the trained model are migrated to the target domain through transfer learning. The attention mechanism is combined with the model to identify the extreme elements of the sentiment. Finally, the local and overall features are fused to achieve comprehensive mining of public opinion and emotional information. This method can effectively improve the accuracy and generalization of public opinion analysis in cases of data scarcity. In the experimental part, the paper conducts comparisons and analyses in eight aspects: word embedding model, model combination, attention mechanism, transfer learning, source domain dataset, target domain dataset, model training, and baseline model. The four indicators, namely accuracy, precision, recall, and F1-measure are used to evaluate the performance of the method. The experiments are thorough and detailed, demonstrating the effective improvement of opinion mining performance.

The joint application of a metaheuristic algorithm and a Bayesian statistics approach for uncertainty and stability assessment of nonlinear magnetotelluric data

Abstract. In this paper, we have developed three algorithms, namely hybrid weighted particle swarm optimization (wPSO) with the gravitational search algorithm (GSA), known as wPSOGSA; GSA; and PSO in MATLAB to interpret one-dimensional magnetotelluric (MT) data for some corrupted and non-corrupted synthetic data, as well as two examples of MT field data over different geological terrains: (i) geothermally rich area, island of Milos, Greece, and (ii) southern Scotland due to the occurrence of a significantly high electrical conductivity anomaly under crust and upper mantle, extending from the Midland Valley across the Southern Uplands into northern England. Even though the fact that many models provide a good fit in a large predefined search space, specific models do not fit well. As a result, we used a Bayesian statistical technique to construct and assess the posterior probability density function (PDF) rather than picking the global model based on the lowest misfit error. The study proceeds using a 68.27 % confidence interval for selecting a region where the PDF is more prevalent to estimate the mean model which is more accurate and close to the true model. For illustration, correlation matrices show a significant relationship among layer parameters. The findings indicate that wPSOGSA is less sensitive to model parameters and produces more stable and reliable results with the least uncertainty in the model, compatible with existing borehole samples. Furthermore, the present methods resolve two additional geologically significant layers, one highly conductive (less than 1.0 Ωm) and another resistive (300.0 Ωm), over the island of Milos, Greece, characterized by alluvium and volcanic deposits, respectively, as corroborated by borehole stratigraphy.