Multilayer Domains Research Articles

Design and analysis of single-electrode integrated lithium niobate optical phased array for two-dimensional beam steering

The realization of high-speed, low-power optical phased array (OPA) on thin-film lithium niobate on insulator (LNOI) is considered an ideal solution for the next generation of solid-state beam steering. Most reported on-chip two-dimensional optical phased arrays suffer from issues such as large antenna spacing, high power consumption and complex wiring due to independent control of array elements. To address these challenges while fully utilizing the benefits of the LNOI platform, we propose a two-dimensional beam-scanning OPA based on lithium niobate (LN) waveguides. We design a multi-layer cascaded domain engineering structure inside the LN waveguide, combined with wavelength tuning, to enable two-dimensional beam scanning with single electrode controlling the OPA. Through simulation, we achieve a 42°×9.2° two-dimensional beam steering. Compared to existing on-chip integrated OPAs, this work offers significant advantages in increasing integration, simplifying control units and reducing power consumption.

Electromagnetic modeling and analysis based on multilayer domain structure for GMI behavior in high frequency

Existing theoretical models on the frequency dependence of the magnetoimpedance (MI) in ferromagnetic microwires primarily describe the MI phenomenon at the limiting cases of lower MHz (<several hundred MHz) or higher GHz (>several GHz) ranges. However, in the intermediate region between these two ranges, known as the transition region, MI curves undergo complex transformations. These transformations have been documented in the literature, but their underlying causes remain poorly understood. Unambiguous knowledge of the domain structure and its correlation with MI properties is essential for elucidating this behavior. In this study, we have, for the first time, observed the inner core magnetic structure of Co-based microwires and revealed its relationship with the high-frequency MI effect. A distinct magnetic structure comprising longitudinal domains in the inner core (IC), circular domains in the outer shell (OS), and a transition region (TR) has been identified. This structure originates from compositional gradients and residual stresses during microwire fabrication. The IC/TR/OS structure manifests in the complex transformations of the MI behavior, exhibiting a turning point at GHz frequencies before the characteristic double MI peak. We developed a multilayer planar model that considers this more realistic magnetic structure, including the TR layer. This model successfully reproduces the key features of the MI curves and provides deeper insights into the high-frequency MI phenomenon. Our findings pave the way for optimizing the sensing capabilities of Co-based ferromagnetic microwires and demonstrate the potential of using high-frequency MI measurements to map their magnetic microstructures.

Modifying Polymer-Based Hard Carbons for Enhanced Understanding of Sodium Storage Properties

Numerous theories on the storage of sodium in hard carbons can be found in the literature. In particular, understanding the contributions of (heteroatom) doping, porosity and graphitic layers is of great importance. Much of the current literature on hard carbons uses different biomaterials as precursors in order to improve the sustainability of the synthesis. However, large differences in the composition of the starting materials and the presence of foreign atoms make a systematic investigation of the storage process extremely difficult.Therefore, a hard carbon material based on furan resins was used in this work to obtain a well-defined starting material that can be modified systematically. This allows a detailed investigation of the effects caused by those modifications. Modifications can be achieved by directly adding various template materials to the polymerization process. Both doping with heteroatoms by adding template chemicals such as boric acid and the introduction of graphitic layers by adding high-purity graphite during polymerization were investigated.Addition of boron and phosphorus heteroatoms led to an increase in reversible capacity up to 240 mAh g-1 from 150 mAh g-1 for carbonization temperatures as low as 650°C. Formation of a potential plateau was observed for boron doping, which is usually found at higher carbonization temperature surpassing 1000°C. Utilizing Raman and XRD spectroscopy, crystallite size was found to be decreasing with phosphorus doping, while boron doping led to an increase. This increase could be a possible explanation for the observed capacity plateau.The addition of high-purity carbon samples, such as KS6L graphite or graphene, allowed for a targeted introduction of single- and multi-layer graphitic domains without alteration of additional properties. Increasing graphite content up to 5 wt-% during polymerization resulted in enhanced electrochemical characteristics, including increased sodium intercalation into the lattice and shift of the sloping proportion to lower potentials. Further increase up to 10 wt-% led to a vanishing of the plateau potential and a lowering of the reversible capacity. The results indicate that the interlayer spacings has now decreased below a threshold, which prohibits the sodium from intercalation between the graphitic layers. Additionally, doping Hard Carbons with graphene revealed a capacity decrease at content ratios as low as 2 wt-%, emphasizing the significance of both the stacking and interlayer spacing of the graphitic lattice, as well as the chemical composition, for efficient sodium storage.In summary the results presented provide an enhanced understanding into the sodium storage properties of Hard Carbons, which help improve the design of synthesis processes for more cost-efficient and high-performance carbon materials. Figure 1

Interpretable domain adaptation transformer: a transfer learning method for fault diagnosis of rotating machinery

Domain adaptation-based transfer learning methods have been widely investigated in fault diagnosis of rotating machinery, but their basic convolution or recurrent structure is subject to poor global feature representation ability, which hinders the learning of domain-irrelevant modulation information. In addition, the “black box” nature of deep learning models limits their applications in high risk-sensitive scenarios. In this paper, an interpretable domain adaptation transformer (IDAT) is proposed for the transferable fault diagnosis of rotating machinery. First, a multi-layer domain adaptation transformer framework is proposed, which can capture the global information that is crucial for learning the modulation information of different domains, and meanwhile reduce the feature distribution discrepancy. Second, an ensemble attention weight is applied to enable the transfer learning framework to be interpretable, which is implemented by averaging the integral values of the multi-head attention maps along the key direction. In addition, the raw vibration signals are embedded as the input of the model, which provides an end-to-end fault diagnosis. The proposed IDAT is tested by various cross-condition and cross-machine bearing fault diagnosis tasks, and results confirm the advantages of the method.

FEATURES OF CHRONOTOPE IN “THE WORDS” BY JEAN-PAUL SARTRE

The article analyzes the features of the chronotope in the autobiographical novel “The Words” by the famous French writer Jean-Paul Sartre. The center of the analysis is the ways in which Sartre creates a complex and multi-layered domain of actions for his heroes, where time and space become not only physical but also philosophical realities. Particular attention is paid to how Sartre uses the chronotope to develop ideas of freedom, responsibility and existence. Through the analysis of everyday situations and the inner monologue of the main characters, the article reveals how the chronotope becomes an integral part of the concept of human existence in Sartre’s world. The article also analyzes the transformation of the classic chronotope in an autobiography, new manifestations of the spatiotemporal concept, as well as the chronotope of retrospective depiction through the author’s personal memories.

Characterization of heterogeneity of CVD graphene on copper foil by scanning probe microscopy and Raman spectroscopy

The comparative analysis of the structural, morphological, and local electrical properties of CVD graphene films synthesized at atmospheric and low pressure CVD growth conditions was carried out. The use of electrical techniques of scanning probe microscopy, such as Kelvin probe force microscopy, scanning capacitance microscopy, electrostatic force microscopy, and conductive atomic-force microscopy for investigation of heterogeneous graphene coverage was discussed. A significant change in the surface potential values was observed indicating the formation of p-type multilayered graphene domains on single layer graphene at low pressure and n-type multilayered graphene films at atmospheric pressure CVD. The effect of Ar flow rate increasing during cooling stage in the temperature range of 700–200 °C causing graphite formation was described. The employment of surface potential spectroscopy allows to indicate the presence of nonlinear phenomena in heterogeneous graphene.

A few-shot based phase-batch multi-layer domain adaptation pattern recognition method

Deep transfer learning has been widely applied in the field of intelligent fault diagnosis. However, existing deep transfer learning-based diagnostic methods struggle to train reliable diagnostic models when there is a lack of data and significant distribution differences between the two domains. To address this issue, a few-shot based phase-batch multi-layer domain adaptation pattern recognition method is proposed. This method simultaneously measures the feature distribution differences of both the fully connected layers and the classification layers, thus better correcting the data domain bias. Additionally, a phase-batch training strategy and pseudo-label learning are employed to improve the convergence speed and stability of the training process. The proposed method is validated on two public datasets, Jiang Nan and Paderborn University, as well as a dataset obtained through independent experiments. It is compared with traditional feature-based transfer learning methods, the results show that the proposed method achieves higher diagnostic accuracy, faster convergence, and greater stability. Furthermore, its superior diagnostic performance in the few-shot scenario is demonstrated through experiments on a self-collected dataset.

Finite integral transform with homogenized boundary conditions solution of transient heat conduction applied to thermal plug and abandonment of oil wells

After the end of productive life of a geological oil reservoir, a set of operations for well Plug and Abandonment (P&A) is performed to recover the soil layers to its natural state. As traditional P&A techniques are expensive activities, alternative technologies are gaining renewed interest for capital expenditure reduction and leak risks mitigation. Hence, the Thermal Plug and Abandonment (TP&A) procedure is here investigated by focusing on the fulfillment of two intermediate milestones, combining new technology with conventional P&A practices. The first milestone consists in applying a thermite exothermic reaction to generate enough heat for melting the entire thickness of the production tube. The second milestone aims to determine if the resulting molten section has sufficient dimensions to allow for the passage of the cement pumped downhole, thus ensuring the complete sealing of the oil well's cross-section. The thermal investigation was conducted by applying a homogenized form of the Finite Integral Transform (FIT) analytical framework to solve the transient heat conduction equation in 2-D polar coordinates. The well assembly was geometrically discretized as a multilayered circular domain. The thermite reaction was modeled as a theoretical volumetric heat source profile dependent on both time and space, placed in the innermost layer. While a pure FIT approach may only be used to solve Neumann boundary conditions, the combined scheme applied here copes with any type of boundary condition. Hence, the integration of FIT with homogenization satisfies the Dirichlet condition requirement of the TP&A procedure. The methodology was verified through an equivalent Finite Volume Method (FVM) solution obtained using a commercial code. The results evidenced that within the 5 min duration of thermite reaction, the entire circumferential section of the production tube exceeds the steel melting temperature by at least 227 °C, thus fulfilling both milestones set. The research outcomes are part of a series of investigation steps to thoroughly analyze the new TP&A technology with regard to its compliance with the regulatory norms established to P&A operations.

Cascaded domain engineering optical phased array for 2D beam steering

Abstract The current approach to 2D optical phased array (OPA) encounters challenges, such as the requirement for a highly tunable laser that is incompatible with certain 2D beam-steering applications or significant power consumption, large antenna spacing and complex wiring resulting from independent control of array elements. To address these challenges, we propose an OPA architecture based on cascaded periodically poled LiNbO3 sequences, a multi-layered domains engineered structure within the LiNbO3 electro-optic crystal, only two control electronics to program the 2D beam-steering trajectory with a range of approximately θ y = ±20° and θ z = ±16° through simulations. This structure enables the uniform distribution of phase differences between adjacent array elements (adjacent domains) upon beam exit from the crystal, ensuring optimal performance. The aim of this study is to develop a methodology that employs domain engineering techniques for designing high-performance phase-controlled devices with customized functional units and sequences in electro-optical crystals. Our research has implications for emerging optoelectronic applications, such as customizable optical interconnects and integrated LiDAR systems.

Disk Failure Prediction based on Multi-layer Domain Adaptive Learning

Large scale data storage is susceptible to failure. As disks are damaged and replaced, traditional machine learning models, which rely on historical data to make predictions, struggle to accurately predict disk failures. This paper presents a novel method for predicting disk failures by leveraging multi-layer domain adaptive learning techniques. First, disk data with numerous faults is selected as the source domain, and disk data with fewer faults is selected as the target domain. A training of the feature extraction network is performed with the selected origin and destination domains. The contrast between the two domains facilitates the transfer of diagnostic knowledge from the domain of source and target. According to the experimental findings, it has been demonstrated that the proposed technique can generate a reliable prediction model and improve the ability to predict failures on disk data with few failure samples.

Controllable Synthesis and Growth Mechanism of Interlayer-Coupled Multilayer Graphene.

The potential applications of multilayer graphene in many fields, such as superconductivity and thermal conductivity, continue to emerge. However, there are still many problems in the growth mechanism of multilayer graphene. In this paper, a simple control strategy for the preparation of interlayer-coupled multilayer graphene on a liquid Cu substrate was developed. By adjusting the flow rate of a carrier gas in the CVD system, the effect for finely controlling the carbon source supply was achieved. Therefore, the carbon could diffuse from the edge of the single-layer graphene to underneath the layer of graphene and then interlayer-coupled multilayer graphene with different shapes were prepared. Through a variety of characterization methods, it was determined that the stacked mode of interlayer-coupled multilayer graphene conformed to AB-stacking structure. The small multilayer graphene domains stacked under single-layer graphene was first found, and the growth process and growth mechanism of interlayer-coupled multilayer graphene with winged and umbrella shapes were studied, respectively. This study reveals the growth mechanism of multilayer graphene grown by using a carbon source through edge diffusion, paving the way for the controllable preparation of multilayer graphene on a liquid Cu surface.

Cross-attention induced multilayer domain adaptation network for extraction of sub-kilometer craters from HiRIC images

Deep learning-based crater detection algorithms have made remarkable progress in the fields of space science and engineering, establishing themselves as the leading solutions for Mars geology exploration. However, achieving outstanding performance with deep networks requires extensive annotated data, resulting in time-consuming and labor-intensive labeling processes. In response to this challenge, we propose a novel approach, the Cross-Attention-Induced Multilayer Domain Adaptation Network (CAMDA-Net), specifically designed for crater detection. CAMDA-Net leverages domain adaptation techniques to transfer valuable crater knowledge from the Moon to Mars, effectively bypassing the need for manual labeling. In our network, we integrate CenterNet*, an anchor-free detector, to expedite and enhance the accuracy of crater detection. Furthermore, we implement crater feature alignment through adversarial training at both the image and instance levels. The dual cross-attention mechanism at the image level directs the network’s focus towards transferable crater features while suppressing noise effects. At the instance level, the foreground and background alignment model streamlines feature alignment and eliminates negative proposals. To assess the efficacy of our proposed method, we build the interplanetary domain-adaptive crater dataset (IDCD), which encompasses three subsets: the LRO WAC crater dataset, THEMIS crater dataset, and HiRIC crater dataset. This diverse dataset incorporates various shapes, lighting conditions, scales, and resolutions, making it an ideal resource for conducting transfer learning studies. We have made the IDCD publicly accessible on Github at https://github.com/PlanetaryScience3510. The experimental results on the IDCD subset from the LRO WAC to HiRIC demonstrate that our CAMDA-Net achieves precision and recall values of 0.77 and 0.79, respectively, slightly surpassing classic domain-adaptive detectors, DAF and SCDA. With encouraging performance, CAMDA-Net shows great promise for advancing crater detection and analysis on Mars, presenting exciting possibilities for future research and applications.

Intelligent fault diagnosis for variable working conditions of rotor-bearing system based on vibration image and domain adaptation

In recent years, intelligent condition monitoring and diagnosis based on deep learning have made great progress. However, traditional diagnostic methods mostly perform vibration analysis based on accelerometer signals, ignoring the influence of sensors on the mass load of the measured object. On the other hand, conventional transfer learning (TL) methods are mostly based on global distribution alignment to achieve intelligent diagnosis under variable working conditions. In this paper, a deep global subdomain adaptation network (DGSAN) is proposed to solve the intelligent diagnosis problem under variable working conditions based on vibration image and TL. First, visual measurement is introduced in vibration extraction. Based on the phase vibration extraction method, the vibration feature information is obtained from the visual vibration image to construct the vibration dataset. Then, the proposed DGSAN establishes a multi-layer domain adaptive network to minimize the difference in feature distribution and realize fine-grained feature distribution alignment of fault data under variable working conditions. Comparative experiments are carried out on the vibration image datasets of rotor-bearing systems, and the results show that the proposed method achieves high-precision transfer intelligent diagnosis.

CVD Synthesis of MoS2 Using a Direct MoO2 Precursor: A Study on the Effects of Growth Temperature on Precursor Diffusion and Morphology Evolutions.

In this study, the influence of growth temperature variation on the synthesis of MoS2 using a direct MoO2 precursor was investigated. The research showed that the growth temperature had a strong impact on the resulting morphologies. Below 650 °C, no nucleation or growth of MoS2 occurred. The optimal growth temperature for producing continuous MoS2 films without intermediate-state formation was approximately 760 °C. However, when the growth temperatures exceeded 800 °C, a transition from pure MoS2 to predominantly intermediate states was observed. This was attributed to enhanced diffusion of the precursor at higher temperatures, which reduced the local S:Mo ratio. The diffusion equation was analyzed, showing how the diffusion coefficient, diffusion length, and concentration gradients varied with temperature, consistent with the experimental observations. This study also investigated the impact of increasing the MoO2 precursor amount, resulting in the formation of multilayer MoS2 domains at the outermost growth zones. These findings provide valuable insights into the growth criteria for the effective synthesis of clean and large-area MoS2, thereby facilitating its application in semiconductors and related industries.

Domain Hybrid Day-Ahead Solar Radiation Forecasting Scheme

Recently, energy procurement by renewable energy sources has increased. In particular, as solar power generation has a high penetration rate among them, solar radiation predictions at the site are attracting much attention for efficient operation. Various approaches have been proposed to forecast solar radiation accurately. Recently, hybrid models have been proposed to improve performance through forecasting in the frequency domain using past solar radiation. Since solar radiation data have a pattern, forecasting in the frequency domain can be effective. However, forecasting performance deteriorates on days when the weather suddenly changes. In this paper, we propose a domain hybrid forecasting model that can respond to weather changes and exhibit improved performance. The proposed model consists of two stages. In the first stage, forecasting is performed in the frequency domain using wavelet transform, complete ensemble empirical mode decomposition, and multilayer perceptron, while forecasting in the sequence domain is accomplished using light gradient boosting machine. In the second stage, a multilayer perceptron-based domain hybrid model is constructed using the forecast values of the first stage as the input. Compared with the frequency-domain model, our proposed model exhibits an improvement of up to 36.38% in the normalized root-mean-square error.

Exploring the essence of compound fault diagnosis: A novel multi-label domain adaptation method and its application to bearings

Compound fault diagnosis in essence is a fundamental but difficult problem to be solved. The separation and extraction of compound fault features remain great challenges in industrial applications due to the lack of labeled fault data. This paper proposes a novel multi-label domain adaptation method applicable to compound fault diagnosis of bearings. Firstly, multi-layer domain adaptation is designed based on a fault feature extractor with customized residual blocks. In that way, features from discrepant domain can be transformed into domain-invariant features. Furthermore, a multi-label classifier is applied to decompose compound fault features into corresponding single fault feature, and diagnoses them separately. The application on bearing datasets demonstrates that the proposed method could enhance the detachable degree of compound faults and achieve greater diagnostic performance than other existing methods.

Lymphatic uptake of biotherapeutics through a 3D hybrid discrete-continuum vessel network in the skin tissue.

Subcutaneous administration is a common approach for the delivery of biotherapeutics, which is achieved mainly through the absorption across lymphatic vessels. In this paper, the drug transport and lymphatic uptake through a three-dimensional hybrid discrete-continuum vessel network in the skin tissue are investigated through high-fidelity numerical simulations. We find that the local lymphatic uptake through the explicit vessels significantly affects macroscopic drug absorption. The diffusion of drug solute through the explicit vessel network affects the lymphatic uptake after the injection. This effect, however, cannot be captured using previously developed continuum models. The lymphatic uptake is dominated by the convection due to lymphatic drainage driven by the pressure difference, which is rarely studied in experiments and simulations. Furthermore, the effects of injection volume and depth on the lymphatic uptake are investigated in a multi-layered domain. We find that the injection volume significantly affects the rate of lymphatic uptake through the heterogeneous vessel network, while the injection depth has little influence, which is consistent with the experimental results. At last, the binding and metabolism of drug molecules are studied to bridge the simulation to the experimentally measured drug clearance. We provide a new approach to study the diffusion and convection of drug molecules into the lymphatic system through the hybrid vessel network.

Healthcare 4.0: A Review of Phishing Attacks in Cyber Security

The rapidly developing scene of Healthcare 4.0, has turned into a fundamental concern. This paper dives into the multi-layered domain of medical care network safety, with specific emphasis on the persistent phishing assaults. We examine the motivation for cyberattacks and the challenges involved. In this paper we review the phishing attacks in medical care, talking about preventive measures to handle this danger. By embracing this through methodology, Healthcare organizations can sustain their protections, shield patient information, and maintain the trustworthiness of medical care frameworks, all while encouraging a culture of network safety status in the computerized age.

Research on coal gangue recognition based on multi-layer time domain feature processing and recognition features cross-optimal fusion

Accurate and rapid recognition of coal gangue is an important link to realize automatic mining, which is helpful to improve the efficiency of coal mining and the safety of mining workers. Relevant studies have confirmed the effectiveness of coal gangue recognition based on tail beam vibration signal, but the recognition accuracy of a single point source vibration signal of tail beam is slightly low. In order to improve the practicability of coal gangue recognition method based on tail beam vibration signal, this paper takes the single source tail beam vibration signal as the object, improves the coverage of tail beam vibration characteristics by conducting experiments and extracting multiple vibration signals at different positions, and conducts the research on coal gangue recognition technology by multi-layer time domain feature processing and cross-optimal fusion of multiple signals. Firstly, through analysis of classification algorithm ability and coal gangue recognition principle, the coal gangue recognition process is formulated and the classification model is constructed. Then, based on the two data samples of a single signal eigenvector and multi-signal eigenmatrix obtained by Direct Data Statistics (DDS), the recognition research is carried out, and the recognition effectiveness of the eigenmatrix is verified. Based on this conclusion, the three-layers eigenmatrix data samples of DDS, EMD Data Statistics (EDS) and HHT Data Statistics (HDS) are obtained by multi-layer time domain feature processing, and the coal gangue recognition based on multi-layer eigenmatrix is carried out respectively. DDS is the most accurate signal processing method. On this basis, the construction method of DDS + EDS and DDS + HDS recognition eigenmatrix based on array series is studied, and the recognition ability of DDS + HDS recognition eigenmatrix construction method and Logic Regression algorithm is proved. Recognition accuracy based on DDS + HDS recognition eigenmatrix and Logic Regression algorithm is up to 95.25 %. Finally, in order to further improve the recognition accuracy and response speed, this paper proposes a method of feature fusion based on cross-optimal selection (FFCOS). Features of DDS and HDS are sorted by recognition sensitivity, and then cross-selected and fused according to this sort. The results show that the recognition accuracy of the serial construction method of DDS + HDS recognition eigenmatriX array and the Logistic Regression algorithm can reach 97 %, which proves the effectiveness of the FFCOS method and realizes the accurate recognition of coal gangue based on the vibration signal of single source tail beam.

The influence of confining stress and pore pressure on effective stress coefficient for permeability: A novel Discretized Clay Shell Model for clayey sandstone

The effective stress coefficient α determines the effective stress and dominates the permeability of rocks. This study proposes a novel theoretical model called the Discretized Clay Shell Model (DCSM) that modifies the Clay Shell Model (CSM) by incorporating the stress-dependent elastic modulus of clay into a discretized multi-layer clay domain to depict the relationship between α, pore pressure, and confining stress. The proposed model was successfully used to provide insights into the stress-dependent α of clayey sandstones. We found that α always decreased with increasing pore pressure and could decrease or increase with increasing confining stress. The modelling trends, which are also observed in the previous laboratory tests, can be well explained using two effects due to the heterogeneity of radial stress within clay domain, that is, the bulk and differential clay hardening effects. The bulk clay hardening effect generates a decreasing trend in α with increasing pore pressure, while the differential clay hardening effect competes with the bulk clay hardening effect and yields a reverse trend, that is, α first decreases with increasing confining stress and then decreases when the confining stress reaches a certain critical value. This study provides synthetic cases to quantify the influence of stress-dependent α evaluated by the DCSM on the evaluation of the permeability-depth relation. Based on the laboratory testing data, the calibrated parameters of the stress-dependent permeability model assuming α=1 are significantly overestimated, and the prediction model yields overestimated permeability up to three to four orders of magnitude. Meanwhile, the synthetic in-situ case shows that the predicted permeability could be underestimated by up to one third, and the errors due to laboratory analysis that neglect the stress dependency of α will propagate to and amplify the errors of prediction for the in-situ permeability-depth relation.