Multilayer Type Research Articles

Synthesis, thermal and magnetic properties of nanoceramics with a multilayer Aurivillius phase type

We report the co-precipitation synthesis and properties of nanomaterials with multilayerAurivillius phase structures Bim+1Fem-3Ti3O3m+3 (BFTO). This paper discusses the thermal behavior of materials based on seven-layerand eight-layercompounds and presents their magnetic characteristics. The structure and morphology were characterized using PXRD, helium pycnometry, and SEM/EDX. Thermal analyses were conducted using DSC/TG. The sintering behavior was investigated through dilatometry. Mössbauer spectroscopy revealed that varying the synthesis conditions allows control over the iron distribution within the Aurivillius phase structure. Spin-phonon coupling effects were examined using Raman spectroscopy. The magnetic characteristics were assessed using vibrating-sample magnetometry. The magnetic propertieswere analyzed by measuring the temperature dependence of magnetization and magnetic hysteresis loops. The magnetic experiments demonstrated that the composition has a more significant impact on the BFTO magnetic responsethan the size effect. The results of this study suggest that the obtained materials have promising functional applications.

Effective particles in a multishell nanostructure with hardcore

In-medium effective carriers with position-dependent mass in a multishell heterostructure are analytically studied. We obtain the exact spectrum of three-dimensional bound eigenstates and the scattering wave-functions for several von Roos ordering classes. Ascribing a continuously varying mass to the carriers in a multilayer type spherical system we use our solutions to compute optical properties such as the absorption coefficients and refraction indices of a nanometric heterostructure. We analyze in detail the case of a GaAs/AℓGaAs alloy and show how these results depend on the ordering class of the kinetic Hamiltonian.

Solute segregation in a moving grain boundary: a phase-field approach

We present a phase-field approach for investigating monolayer and multilayer type solute segregation in a moving Grain boundary (GB). In this model, we introduce an expression for the GB solute interaction potential which allows for easy modification of the shape of the solute segregation profile at the GB. As a consequence, our phase-field simulations capture various segregation profiles in both stationary and migrating GB that agree with Cahn’s solute drag theory. Furthermore, we explore how different segregation profiles evolve at varying GB velocities owing to the inequality of the atomic flux of solute between the front and back faces of the moving GB. At a low-velocity regime, we observe that multilayer segregation results in significantly increased drag force compared to monolayer segregation. At a high-velocity regime, the opposite holds. Our simulation results also provide valuable insights for predicting grain growth in polycrystalline materials in the presence of solute segregation.

Suppression of inactive sites in NiMo@amorphous alumina catalysts for hydrodesulfurization reaction: Effect of Ga doping

Hydrodesulfurization (HDS) reaction activity depends on the formation of active NiMoS (completely sulfided multilayer type 2 sites) and inactive NiAl2O4/NixSy phases on the catalyst surface. These inactive phases not only itself inactive for the reaction but also hinder the formation of active NiMoS phases. In addition, the formation of strong Mo-O-Al bonds also lowers the reducibility of the catalyst, suggesting lesser catalytic activity (type 1 sites, partially sulfided single-layer sites). To this end, we report the formation of NiMo/Ga doped amorphous alumina (A-Al2O3) catalysts prepared by the colloidal synthesis method. A-Al2O3 was chosen as a support because it has lower surface energy, better physicochemical properties, and enhanced acidic sites than the crystalline alumina phase, which favours the formation of type 2 sites or weak metal-support interaction (completely sulfided multilayer NiMoS sites). Further, the Ga doping in A-Al2O3 support substitute the tetrahedral Al sites and form GaAl2O4 phases by reducing NiAl2O4 inactive sites, and the free Ni decorate the edges of MoS2 to form HDS active NiMoS sites. Therefore, NiMo/Ga doped A-Al2O3 catalyst (at 1 wt% Ga loading) reported enhanced HDS catalytic activity (25%), HDS reaction rate constant (kHDS-2.15 times) and turn over frequency (TOF-1.3 times) than the conventional NiMo/γ-Al2O3 catalysts.

Estimation of monthly sunshine duration using satellite derived cloud data

Sunshine duration (SD) is one of the critical meteorological parameters used in different fields of application such as climate, renewable energy and agriculture. In this respect, determination and/or estimation of the temporal and spatial variability of SD is critical. Meteorological satellite data/products can be used for estimating SD and in constructing their maps due to their frequent observation of large areas at once. In this study, a multilayer perceptron type artificial neural network model was built to estimate the monthly mean SD for Türkiye using the EUMETSAT CM SAF (Satellite Application Facility on Climate Monitoring) CFC (Cloud Fractional Coverage) and CTY (Cloud Type) data, GMTED2010 (Global Multi-resolution Terrain Elevation Data) data, month number and daylength. The datasets of 45 stations, spanning nine years (2005–2013), were used for training the model and 12 stations for testing and validating the simulated values. We have compared the results of our model with the ground-measured values for the whole period under consideration and the root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE) and the coefficient of determination (R2) were found as 0.7803 h, 0.6206 h, 0.1751 h and 0.9387, respectively. It has been shown that using the new generation cloud products such as CFC and CTY, elevation data such as GMTED2010 and daylength, it is possible to predict the SD for regions under the coverage of the satellite, in case no measurement is possible or may be unreliable, without needing any measured meteorological data.

Combined use of radiomics and artificial neural networks for the three‐dimensional automatic segmentation of glioblastoma multiforme

AbstractGlioblastoma multiforme (GBM) is the most prevalent and aggressive primary brain tumour that has the worst prognosis in adults. Currently, the automatic segmentation of this kind of tumour is being intensively studied. Here, the automatic three‐dimensional segmentation of the GBM is achieved with its related subzones (active tumour, inner necrosis, and peripheral oedema). Preliminary segmentations were first defined based on the four basic magnetic resonance imaging modalities and classic image processing methods (multithreshold Otsu, Chan–Vese active contours, and morphological erosion). After an automatic gap‐filling post processing step, these preliminary segmentations were combined and corrected by a supervised artificial neural network of multilayer perceptron type with a hidden layer of 80 neurons, fed by 30 selected radiomic features of gray intensity and texture. Network classification has an overall accuracy of 83.9%, while the complete combined algorithm achieves average Dice similarity coefficients of 89.3%, 80.7%, 79.7%, and 66.4% for the entire region of interest, active tumour, oedema, and necrosis segmentations, respectively. These values are in the range of the best reported in the present bibliography, but even with better Hausdorff distances and lower computational costs. Results presented here evidence that it is possible to achieve the automatic segmentation of this kind of tumour by traditional radiomics. This has relevant clinical potential at the time of diagnosis, precision radiotherapy planning, or post‐treatment response evaluation.

Application of Artificial Neural Networks to Numerical Homogenization of the Precast Hollow-Core Concrete Slabs

The main goal of this work is to combine the usage of the numerical homogenization technique for determining the effective properties of representative volume elements with artificial neural networks. The effective properties are defined according to the classical laminate theory. The purpose is to create and train a rapid surrogate model for the quick calculation of the mechanical properties of hollow concrete slabs. First, the homogenization algorithm was implemented, which determines membrane, bending and transverse shearing properties of a given parametrized hollow-core precast slab reinforced with steel bars. The algorithm uses the finite element mesh but does not require a formal solution of the finite element method problem. Second, the learning and training artificial intelligence framework was created and fed with a dataset obtained by optimal Latin hypercube sampling. In the study, a multilayer perceptron type of artificial neural network was used. This allows for obtaining rapid calculations of the effective properties of a particular hollow-core precast slab by using a surrogate model. In the paper, it has been proven that such a model, obtained via complex numerical calculations, gives a very accurate estimation of the properties and can be used in many practical tasks, such as optimization problems or computer-aided design decisions. Above all, the efficient setup of the artificial neural network has been sought and presented.

The modulation of perpendicular magnetic anisotropy and spin–orbit toque in Pt/Co/Pt multilayers with interfacial decoration by insertion of a Bi layer

Bismuth is a well-known semimetal material with strong spin–orbit coupling and has been confirmed to exhibit a larger spin Hall angle in CuBi and PtBi alloys with low Bi-doping concentration. In this study, we investigated perpendicular magnetic anisotropy (PMA) and spin–orbit torques (SOTs) in Pt/Co/Pt multilayers by inserting a Bi layer with a thickness of 2 nm at the Co/Pt interface. Two types of PMA multilayers, Pt (3 nm)/Co (1 nm)/Pt (5 nm) and Pt (3 nm)/Co (1 nm)/Pt (1 nm), with different spin accumulations, were designed and prepared by varying the top Pt thickness. A significant enhancement of PMA and SOT efficiency is observed in the Pt (3 nm)/Co (1 nm)/Bi (2 nm)/Pt (5 nm) multilayer via a Bi-layer interfacial decoration. However, for the Pt (3 nm)/Co (1 nm)/Bi (2 nm)/Pt (1 nm) multilayers, both PMA and SOT efficiency decrease with decoration of the Bi layer at the Co/Pt interface. Meanwhile, the sign of SOTs changes in the Pt (3 nm)/Co (1 nm)/Pt (5 nm) and Pt (3 nm)/Co (1 nm)/Pt (1 nm) multilayers when introducing a Bi layer. This completely opposite behavior illustrates that the Bi/Pt interface plays an important role in modulating the PMA and SOT efficiency of Pt/Co/Bi/Pt multilayers. Optimizing the alloying of Bi/Pt could be an effective approach to increase the PMA and SOT efficiency of Pt/Co/Bi/Pt multilayers.

Cybervictim vs. cyberaggressor. profile determination and comparison through artificial neural networks

ABSTRACT This research aims to determine the magnitude to which aspects of the sociodemographic profile of students (gender, age, religion, ethnicity and race) influence both the acts of harassment received as a cybervictim and those carried out as a cyberaggressor. Additionally, aiming to discover which of these acts suffered as cybervictim or carried out as cyberaggressor increase or decrease to a greater extent, comparing a profile of potential cyberaggressor with another of potential cybervictim. For this purpose, an artificial neural network of the multilayer perceptron type is employed, generating an estimation model of these cybervictim and cyberaggressor facts based on the students’ profile using data obtained through a five-points’ Likert questionnaire. That is especially useful for detecting specific behaviours based on a certain profile, being able to determine which of these attitudes are most at risk of occurring to put the relevant measures in place to prevent them.

Numerical Modeling for Collapse Analysis of Cable-Stayed Bridges using the Improved Applied Element Method

This work presents a novel numerical simulation of the collapse process of cable-stayed bridges based on the improved applied element method, which was originally developed as an effective numerical tool for large-scale framed structures under severe loading circumstances. For that, a straight spring element type is utilized for modeling the stay-cable with an equivalent modulus of elasticity that combines the effects of material and geometric deformation of the bridge cables. Moreover, the advantage of the multi-layered element type, which allows for the modeling of various rectangular or non-rectangular RC and composite sections without any complications, is utilized for modeling the pylon and the deck. The pretension forces in the cables are applied by adding an initial loading procedure in which the stay-cable is exposed to an initial strain to consider the effect of prestressing. The mass matrix is modified to include the stay-cable mass, which is lumped at the centroid of the elements connected by the stay-cable. The proposed modeling approach takes material and geometric nonlinearities into account for both the multi-layered element and the stay-cable. Verification examples are provided to examine the capability of the model. The comparison between the results of the proposed modeling technique and the finite element results has shown good agreement, highlighting the reliability of the proposed modeling technique. In addition, the developed tool is used to carry out collapse analysis of a cable-stayed bridge under a cable-loss scenario to examine the capability of the proposed technique.

Predicting Rheological Properties of Wheat Dough from Flour Properties Using NIR Coupled with Artificial Neural Network

Highlights A multi-layered perceptron type artificial neural network (ANN) was developed to predict the farinograph properties of wheat dough. ANN models with two to four hidden layers were developed for each Farinograph response, i.e., water absorption, dough development time, and dough stability. Permutation importance and Shapley values analysis emphasized the significance of protein content in model prediction. The developed model would serve as a decision support tool for flour mill and bakery managers and would assist in adapting flour mill settings and modifying processing parameters in bakeries. Abstract. Farinograph analysis serves as the standard for determining the baking quality of wheat flour. It measures the water absorption (WA), dough development time (DDT), and stability (DS) of wheat dough. These characteristics depend on wheat flour properties such as protein content, moisture, ash, and falling number. Additionally, farinograph analysis is costly, time-consuming, and requires skilled personnel. Therefore, an artificial neural network (ANN) model was developed to predict the farinograph properties of wheat dough as a function of flour properties. The models were developed using data from 192 wheat samples. Multi-layer perceptron-type feed-forward ANN models with increasing complexity were developed for each response variable, i.e., ANN-WA, ANN-DDT, and ANN-DS, and model success was evaluated via mean squared error (MSE) and correlation coefficient (r). The optimal models had two to four hidden layers, each with five to sixty neurons, and exhibited the lowest MSE and highest r values. In terms of predictive performance, the models ANN-WA and ANN-DDT (r = 0.79) demonstrated superior performance when compared with ANN-DS (r = 0.63). A feature importance analysis was conducted to provide insight on variable contributions, underscoring the significance of flour protein content in the model’s prediction. The study explored the applicability of data-driven ANN models in predicting the rheological characteristics of dough. The developed models could serve as a decision support tool and aid millers in adjusting mill settings and bakers in modifying dough mixing based on dough rheology. Keywords: Artificial neural network, Dough rheology, Farinograph analysis, Milling, NIR.

Recycling of enamelled copper wire from end-of-life electric motor via room temperature methanolysis

Polyester enamelled copper wire plays an important role in the manufacturing of electric motors. In line with the electrification of transport, the demand for electric motors and the future waste generated from their end-of-life cannot be ignored. The waste from the polyester enamelled copper wire is expected to increase steadily. Methods proposed by researchers are mainly focused on thermal treatment to either pyrolyse or burn off the polyester enamel. However, thermal treatments fail to consider the potential risk of air pollution and to recover the polyester enamel.In this manuscript, we propose two-stage processes comprised of methanol washing and room temperature methanolysis with dichloromethane as co-solvent and K2CO3 as catalyst to delaminate multilayered type enamelled copper wire. The methanol washing recovers polyvinyl butyral as it is, via dissolution. Whereas the methanolysis products are dimethyl terephthalate (DMT) and dimethyl isophthalate (DMI) which are precursors to the polyester and could be used to make new polyester. At room temperature, the parameters of solid to liquid, DCM to methanol, and K2CO3 to Cuwire ratio, of 500 g/L, 1.00 mol/mol, and 0.10 wt%, respectively, allow complete removal of polyester enamel in 24 h. The methanolysis parameters described manage to give a modest DMT and DMI yield of 86.0% and 92.2%, respectively. The reaction time can be sped up by increasing the temperature by 10 °C, leading to complete depolymerisation in 4 h. Compared to thermal treatment, the proposed method requires 80.7% lower energy with the products contained within the solution.

Interlayer coupling of the Kittel mode and the perpendicular standing spin wave in magnetic multilayers

Magnetic/nonmagnetic multilayers are complex and fascinating systems with immense potential for various technological applications. To meet the requirements of spintronic devices, it is essential to comprehend their magneto-dynamic behaviour and interlayer coupling mechanisms. Here, we systematically study the static and dynamic coupling of different spin precession modes, including the Kittel mode and the perpendicular standing spin wave (PSSW) mode, between permalloy (Py) layers with variable thicknesses. Our main focus centers around three distinct types of magnetic multilayers featuring different spacers (Ta, Pt, Ru). According to the ferromagnetic resonance results, we find that the nonmagnetic layers inside these structures display distinct magnetic static and dynamic characteristics over varying ranges of magnetic layer thickness. When the thickness of the Py layer is 10 nm, it demonstrates a ferromagnetic exchange coupling between the Py layers via the Ta and Pt layers. Conversely, when the spacer layer is composed of Ru, a clear antiferromagnetic coupling is detected. All of the multilayers, however, exhibit increased ferromagnetic exchange coupling as the thickness of the Py layer increases. Furthermore, it is found that the Pt spacer layer still plays a crucial role in dynamic interlayer coupling in the uniform precession mode, while the Py/Ru multilayer exhibits a stronger coupling in the non-uniform precession mode.

지역 연계 노동인권교육의 방향성

This study aims to explore localized, contextually-situated labor rights education in schools based on the perceptions and experiences of in-service teachers in the Seoul metropolitan area. This study, adopting FGI (Focus Group Interviews) with 9 elementary, middle, and high public school teachers who have expertise in labor rights education, sought to portray the direction of school labor rights education that reflects the specificity of Seoul's regional context and the local curriculum while also being universal, integrative, and inclusive in nature. The research results led to specific and practical suggestions from teachers in three dimensions: curricular contents, classroom practices, and policy support. First, the need for labor rights education in schools was emphasized, which reflects the multi-layered and variable industrial structure and types of work in Seoul, while considering regional diversity and polarization, and also focusing on timely and future-oriented social changes. Second, the effectiveness of real-life integrated, participatory, and issue-based inquiry methods was highlighted, emphasizing the importance of collaboration with the local communities and the integration of regional curriculum policies. Third, it was suggested that labor rights education content be included in professional development programs as mandatory while also supporting voluntary learning communities of educators to facilitate long-term vision and continuous practice. The research findings provide empirical evidence for labor rights education at the regional education levels in the future and offer insights into future implications.

Neural Network Predictions of Atomic Form Factors and Incoherent Scattering Functions

In order to predict atomic form factors and incoherent scattering functions which are used to calculate the coherent and incoherent total scattering cross sections, a technique based on artificial neural networks of the multilayer type was implemented. In this context, two neural models have been developed and compared with those in the literature. This study revealed both the accuracy of the results obtained and the effectiveness of the designed model. The mean relative error for the least estimated property does not exceed 16.5 %. The software realized in this way give a prediction of the above parameters for the input variables Z: Atomic number, x: sin(ϑ/2)/λ and E: Photon energy, and it provides users with flexibility for prediction. The advantages of this technique lie in its very fast handling, due to its ease of use, and in the two integrated networks, which it guarantees for a variety of input parameters such as atomic number, photon energy, and momentum transfer variable.

Correlation between Cone Penetration Test parameters, soil type, and soil liquidity index using long short-term memory neural network

Abstract Accuracy and quality of recognizing soil properties are crucial for optimal building design and for ensuring safety in the construction and exploitation stages. This article proposes use of long short-term memory (LSTM) neural network to establish a correlation between Cone Penetration Test (CPTU) results, the soil type, and the soil liquidity index IL . LSTM artificial neural network belongs to the class of networks requiring deep machine learning and is qualitatively different from artificial neural networks of the multilayer perceptron type, which have long been widely used to interpret the results of geotechnical experiments. The article outlines the methodology of CPTU testing and laboratory testing of the liquidity index, as well as construction and preparation of data for the network. The proposed network achieved good results when considering a database consisting of the parameters of eight CPTU soundings, soil stratifications, and laboratory test results.

Bottom-Up Short-Term Load Forecasting Considering Macro-Region and Weighting by Meteorological Region

Activities related to the planning and operation of power systems use premise load forecasting, which is responsible for providing a load estimative for a given horizon that assists mainly in the operation of an electrical system. Hierarchical short-term load forecasting (STLF) becomes an approach used for this purpose, where the overall forecast is performed through system partition in smaller macro-regions and, soon after, is aggregated to compose a global forecast. In this context, this paper presents a bottom-up STLF approach for macro-regions. The main innovation is the Average Consumption per Meteorological Region (CERM) index, used to weigh the importance of each station meteorological (EM) in total load demand. Another index, the Variation of Load and Temperature (IVCT), based on historical temperature and demand changes, is proposed. These indexes are incorporated into an ANN model of the multi-layer perceptron type (MLP). The results showed a higher average performance of the index CERM and variable IVCT in relation to the other combinations performed, and the best results were used to compose the prediction of the MTR. Finally, the proposed model presented a Mean Absolute Percentage Error lower than 1%, presenting superior performance compared to an aggregate model for MTR, which shows the efficiency and contribution of the proposed methodology.

Guiding ablation strategies for ventricular tachycardia in patients with structural heart disease by analyzing links and conversion patterns of traceable abnormal late potential zone.

Substrate-based ablation can treat uninducible or hemodynamically instability scar-related ventricular tachycardia (VT). However, whether a correlation exists between the critical VT isthmus and late activation zone (LAZ) during sinus rhythm (SR) is unknown. To demonstrate the structural and functional properties of abnormal substrates and analyze the link between the VT circuit and abnormal activity during SR. Thirty-six patients with scar-related VT (age, 50.0 ± 13.7 years and 86.1% men) who underwent VT ablation were reviewed. The automatic rhythmia ultrahigh resolution mapping system was used for electroanatomic substrate mapping. The clinical characteristics and mapping findings, particularly the LAZ characteristics during SR and VT, were analyzed. To determine the association between the LAZ during the SR and VT circuits, the LAZ was defined as five activation patterns: entrance, exit, core, blind alley, and conduction barrier. Forty-five VTs were induced in 36 patients, 91.1% of which were monomorphic. The LAZ of all patients was mapped during the SR and VT circuits, and the consistency of the anatomical locations of the LAZ and VT circuits was analyzed. Using the ultrahigh resolution mapping system, interconversion patterns, including the bridge, T, puzzle, maze, and multilayer types, were identified. VT ablation enabled precise ablation of abnormal late potential conduction channels. Five interconversion patterns of the LAZ during the SR and VT circuits were summarized. These findings may help formulate more precise substrate-based ablation strategies for scar-related VT and shorter procedure times.

Development of a modified Marshall mix design for Hot-mix asphalt concrete mixed with recycled plastic based on dry mixing processes

Plastic waste, particularly single-use plastic products, is becoming a more significant environmental problem that must be mitigated effectively. Applying thermoplastic waste in the hot-mix asphalt concrete would create an alternative to recycling plastic waste before going to landfills. This study aims to introduce a modified Marshall mix design framework developed for hot-mix asphalt concrete mixed with recycled plastic (ACP). The Marshall mix design method was used as the baseline procedure for the ACP mix design development of the dry mixing process in which plastic was mixed directly with the hot aggregate instead. Two types of recycled plastic, multilayer, and mixed plastics, were employed in the ACP mix design development process. A new parameter of the plastic-to-binder ratio (PBR) and a replacement concept were introduced as add-on steps to the conventional Marshall mix design to appropriately determine an optimum amount of additional plastic and asphalt binder according to the developed mix design. A full series of laboratory tests were also conducted to compare conventional asphalt concrete (AC) and ACP performances. The laboratory test results indicated the superior performance of ACP over AC.

Estrogen receptor alpha regulates uterine epithelial lineage specification and homeostasis

Postnatal development of the uterus involves specification of undifferentiated epithelium into uterine-type epithelium. That specification is regulated by stromal-epithelial interactions as well as intrinsic cell-specific transcription factors and gene regulatory networks. This study utilized mouse genetic models of Esr1 deletion, endometrial epithelial organoids (EEO), and organoid-stromal co-cultures to decipher the role of Esr1 in uterine epithelial development. Organoids derived from wild-type (WT) mice developed a normal single layer of columnar epithelium. In contrast, EEO from Esr1 null mice developed a multilayered stratified squamous type of epithelium with basal cells. Co-culturing Esr1 null epithelium with WT uterine stromal fibroblasts inhibited basal cell development. Of note, estrogen treatment of EEO-stromal co-cultures and Esr1 conditional knockout mice increased basal epithelial cell markers. Collectively, these findings suggest that Esr1 regulates uterine epithelium lineage plasticity and homeostasis and loss of ESR1 promotes altered luminal-to-basal differentiation driven by ESR1-mediated paracrine factors from the stroma.