High repetition granular Co/Pt multilayers with improved perpendicular remanent magnetization for high-density magnetic recording

Thanks to the strong perpendicular magnetic anisotropy(PMA), excellent processing compatibility as well as novel spintronic phenomenon, Co/Pt multilayers have been attracting massive attention and widely used in magnetic storage.However, reversed magnetic domains come into being with the increasing layer repetition ‘N’ to reduce magneto-static energy, resulting in the remarkable diminishment of the remanent magnetization(Mr). As a result, the product of Mrand thickness(i.e., the remanent moment-thickness product, Mrt), a key parameter in magnetic recording for reliable data storing and reading, also decreases dramatically. To overcome this issue, we deposit an ultra-thick granular [Co/Pt]80multilayer with a total thickness of 68 nm on granular SiNxbuffer layer. The Mrt value, Mrto saturation magnetization(Ms) ratio as well as out of plane(OOP) coercivity(Hcoop) are high up to 2.97 memu/cm^(2), 67%, and 1940 Oe(1 Oe = 79.5775 A·m^(-1)),respectively, which is remarkably improved compared with that of continuous [Co/Pt]80multilayers. That is because large amounts of grain boundaries in the granular multilayers can efficiently impede the propagation and expansion of reversed magnetic domains, which is verified by experimental investigations and micromagnetic simulation results. The simulation results also indicate that the value of Mrt, Mr/Msratio, and Hcoopcan be further improved through optimizing the granule size, which can be experimentally realized by manipulating the process parameter of SiNxbuffer layer. This work provides an alternative solution for achieving high Mrt value in ultra-thick Co/Pt multilayers, which is of unneglectable potential in applications of high-density magnetic recording.

Ferromagnetic resonance in Co/Pt multilayers

Out-of-plane angular dependence of ferromagnetic resonance spectra was measured in sputtered Co/Pt multilayers and analyzed with the Landau-Lifshitz-Gilbert equation. The effective demagnetizing field 4πMeff is found to increase with decreasing tPt and decreasing 1/tco, which can be considered as a result of interplay between the interlayer coupling and a lowdimensional effect. The g factor increases with increasing tpt and decreasing tCo, indicating contribution of spin-polarization of Pt atoms and additional contribution of orbital moment of Co atoms. The in-plane resonance line-width increases with decreasing too and increases with increasing tPt.

Magnetization and coercivity in Co/Pt multilayers with constant total Co layer thickness

Co/Pt multilayers with perpendicular anisotropy were deposited using a dc magnetron sputtering system under high vacuum.Magnetization process was investigated by the measurement of magnetic components parallel and perpendicular to the applied field.A dependence of the coercivity of Co/Pt multilayers on the Co layer thickness was reported,in which the total thickness of Co layers kept constant.It is observed that the coercivity increases with the increment of Co layer thickness.For the samples with the same Co layer thickness while different total Co layer thickness,the coercivity first increases and then decreases with the increase of the total thickness of Co layers.This effect could be attributed to the competition between the reduction of HC related to incoherent reversal and the step-up of HC contributed by the magnetic polarization of Pt atoms at the interface of Co and Pt layers during magnetization reversal.The results show that the change of the coercivity is strongly related to the Co layer thickness,but not the total thickness of Co layers.The dependence of the coercivity on the angle between an applied field and the easy axis shows that the nucleation mode is dominant in magnetization reversal process of the samples.

OptoGPT: A foundation model for inverse design in optical multilayer thin film structures

Optical multilayer thin film structures have been widely used in numerous photonic applications.However,existing inverse design methods have many drawbacks because they either fail to quickly adapt to different design targets,or are difficult to suit for different types of structures,e.g.,designing for different materials at each layer.These methods also cannot accommodate versatile design situations under different angles and polarizations.In addition,how to benefit practical fabrications and manufacturing has not been extensively considered yet.In this work,we introduce OptoGPT(Opto Generative Pretrained Transformer),a decoder-only transformer,to solve all these drawbacks and issues simultaneously.

Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.

Adaptable and Dynamic Access Control Decision-Enforcement Approach Based on Multilayer Hybrid Deep Learning Techniques in BYOD Environment

Organizations are adopting the Bring Your Own Device(BYOD)concept to enhance productivity and reduce expenses.However,this trend introduces security challenges,such as unauthorized access.Traditional access control systems,such as Attribute-Based Access Control(ABAC)and Role-Based Access Control(RBAC),are limited in their ability to enforce access decisions due to the variability and dynamism of attributes related to users and resources.This paper proposes a method for enforcing access decisions that is adaptable and dynamic,based on multilayer hybrid deep learning techniques,particularly the Tabular Deep Neural Network Tabular DNN method.This technique transforms all input attributes in an access request into a binary classification(allow or deny)using multiple layers,ensuring accurate and efficient access decision-making.The proposed solution was evaluated using the Kaggle Amazon access control policy dataset and demonstrated its effectiveness by achieving a 94%accuracy rate.Additionally,the proposed solution enhances the implementation of access decisions based on a variety of resource and user attributes while ensuring privacy through indirect communication with the Policy Administration Point(PAP).This solution significantly improves the flexibility of access control systems,making themmore dynamic and adaptable to the evolving needs ofmodern organizations.Furthermore,it offers a scalable approach to manage the complexities associated with the BYOD environment,providing a robust framework for secure and efficient access management.

A Multilayer Perceptron Artificial Neural Network Study of Fatal Road Traffic Crashes

This paper examines the relationship between fatal road traffic accidents and potential predictors using multilayer perceptron artificial neural network (MLANN) models. The initial analysis employed twelve potential predictors, including traffic volume, prevailing weather conditions, roadway characteristics and features, drivers’ age and gender, and number of lanes. Based on the output of the model and the variables’ importance factors, seven significant variables are identified and used for further analysis to improve the performance of models. The model is optimized by systematically changing the parameters, including the number of hidden layers and the activation function of both the hidden and output layers. The performances of the MLANN models are evaluated using the percentage of the achieved accuracy, R-squared, and Sum of Square Error (SSE) functions.

Chitosan/Sodium Alginate Multilayer pH-Sensitive Films Based on Layer-by-Layer Self-Assembly for Intelligent Packaging

The abuse of plastic food packaging has brought about severe white pollution issues around the world.Developing green and sustainable biomass packaging is an effective way to solve this problem.Hence,a chitosan/sodium alginate-based multilayer film is fabricated via a layer-by-layer(LBL)self-assembly method.With the help of superior interaction between the layers,the multilayer film possesses excellent mechanical properties(with a tensile strength of 50 MPa).Besides,the film displays outstanding water retention property(blocking moisture of 97.56%)and ultraviolet blocking property.Anthocyanin is introduced into the film to detect the food quality since it is one natural plant polyphenol that is sensitive to the pH changes ranging from 1 to 13 in food when spoilage occurs.It is noted that the film is also bacteriostatic which is desired for food packaging.This study describes a simple technique for the development of advanced multifunctional and fully biodegradable food packaging film and it is a sustainable alternative to plastic packaging.

A multilayer network diffusion-based model for reviewer recommendation

With the rapid growth of manuscript submissions,finding eligible reviewers for every submission has become a heavy task.Recommender systems are powerful tools developed in computer science and information science to deal with this problem.However,most existing approaches resort to text mining techniques to match manuscripts with potential reviewers,which require high-quality textual information to perform well.In this paper,we propose a reviewer recommendation algorithm based on a network diffusion process on a scholar-paper multilayer network,with no requirement for textual information.The network incorporates the relationship of scholar-paper pairs,the collaboration among scholars,and the bibliographic coupling among papers.Experimental results show that our proposed algorithm outperforms other state-of-the-art recommendation methods that use graph random walk and matrix factorization and methods that use machine learning and natural language processing,with improvements of over 7.62%in recall,5.66%in hit rate,and 47.53%in ranking score.Our work sheds light on the effectiveness of multilayer network diffusion-based methods in the reviewer recommendation problem,which will help to facilitate the peer-review process and promote information retrieval research in other practical scenes.

Studying the co-evolution of information diffusion,vaccination behavior and disease transmission in multilayer networks with local and global effects

Today,with the rapid development of the internet,a large amount of information often accompanies the rapid transmission of disease outbreaks,and increasing numbers of scholars are studying the relationship between information and the disease transmission process using complex networks.In fact,the disease transmission process is very complex.Besides this information,there will often be individual behavioral measures and other factors to consider.Most of the previous research has aimed to establish a two-layer network model to consider the impact of information on the transmission process of disease,rarely divided into information and behavior,respectively.To carry out a more in-depth analysis of the disease transmission process and the intrinsic influencing mechanism,this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamic co-evolution of information diffusion,vaccination behavior,and disease transmission.This is achieved by considering four influential relationships between adjacent layers in multilayer networks.In the information layer,the diffusion process of negative information is described,and the feedback effects of local and global vaccination are considered.In the behavioral layer,an individual's vaccination behavior is described,and the probability of an individual receiving a vaccination is influenced by two factors:the influence of negative information,and the influence of local and global disease severity.In the disease layer,individual susceptibility is considered to be influenced by vaccination behavior.The state transition equations are derived using the micro Markov chain approach(MMCA),and disease prevalence thresholds are obtained.It is demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior,and is mainly influenced by global vaccination behavior;vaccination behavior is mainly influenced by local disease conditions,and is less influenced by global disease conditions;the disease transmission threshold increases with the increasing vaccination rate;and the scale of disease transmission increases with the increasing negative information diffusion rate and decreases with the increasing vaccination rate.Finally,it is found that when individual vaccination behavior considers both the influence of negative information and disease,it can increase the disease transmission threshold and reduce the scale of disease transmission.Therefore,we should resist the diffusion of negative information,increase vaccination proportions,and take appropriate protective measures in time.

Love wave propagation in one-dimensional piezoelectric quasicrystal multilayered nanoplates with surface effects

The exact solutions for the propagation of Love waves in one-dimensional(1D)hexagonal piezoelectric quasicrystal(PQC)nanoplates with surface effects are derived.An electro-elastic model is developed to investigate the anti-plane strain problem of Love wave propagation.By introducing three shape functions,the wave equations and electric balance equations are decoupled into three uncorrelated problems.Satisfying the boundary conditions of the top surface on the covering layer,the interlayer interface,and the matrix,a dispersive equation with the influence of multi-physical field coupling is provided.A surface PQC model is developed to investigate the surface effects on the propagation behaviors of Love waves in quasicrystal(QC)multilayered structures with nanoscale thicknesses.A novel dispersion relation for the PQC structure is derived in an explicit closed form according to the non-classical mechanical and electric boundary conditions.Numerical examples are given to reveal the effects of the boundary conditions,stacking sequence,characteristic scale,and phason fluctuation characteristics on the dispersion curves of Love waves propagating in PQC nanoplates with surface effects.

Improving the spaceborne GNSS-R altimetric precision based on the novel multilayer feedforward neural network weighted joint prediction model

Global navigation satellite system-reflection(GNSS-R)sea surface altimetry based on satellite constellation platforms has become a new research direction and inevitable trend,which can meet the altimetric precision at the global scale required for underwater navigation.At present,there are still research gaps for GNSS-R altimetry under this mode,and its altimetric capability cannot be specifically assessed.Therefore,GNSS-R satellite constellations that meet the global altimetry needs to be designed.Meanwhile,the matching precision prediction model needs to be established to quantitatively predict the GNSS-R constellation altimetric capability.Firstly,the GNSS-R constellations altimetric precision under different configuration parameters is calculated,and the mechanism of the influence of orbital altitude,orbital inclination,number of satellites and simulation period on the precision is analyzed,and a new multilayer feedforward neural network weighted joint prediction model is established.Secondly,the fit of the prediction model is verified and the performance capability of the model is tested by calculating the R2 value of the model as 0.9972 and the root mean square error(RMSE)as 0.0022,which indicates that the prediction capability of the model is excellent.Finally,using the novel multilayer feedforward neural network weighted joint prediction model,and considering the research results and realistic costs,it is proposed that when the constellation is set to an orbital altitude of 500 km,orbital inclination of 75and the number of satellites is 6,the altimetry precision can reach 0.0732 m within one year simulation period,which can meet the requirements of underwater navigation precision,and thus can provide a reference basis for subsequent research on spaceborne GNSS-R sea surface altimetry.

Study on theoretical model for electrical explosion resistivity of Al/Ni reactive multilayer foil

Al/Ni reactive multilayer foil(RMF)possesses excellent comprehensive properties as a promising substitute for traditional Cu bridge.A theoretical resistivity model of Al/Ni RMF was developed to guide the optimization of EFIs.Al/Ni RMF with different bilayer thicknesses and bridge dimensions were prepared by MEMS technology and electrical explosion tests were carried out.According to physical and chemical reactions in bridge,the electrical explosion process was divided into 5 stages:heating of condensed bridge,vaporization and diffusion of Al layers,intermetallic combination reaction,intrinsic explosion,ionization of metal gases,which are obviously shown in measured voltage curve.Effects of interface and grain boundary scattering on the resistivity of film metal were considered.Focusing on variations of substance and state,the resistivity was developed as a function of temperature at each stage.Electrical explosion curves were calculated by this model at different bilayer thicknesses,bridge dimensions and capacitor voltages,which showed an excellent agreement with experimental ones.

Mass-spring model for elastic wave propagation in multilayered van der Waals metamaterials

Multilayered van der Waals(vdW)materials have attracted increasing interest because of the manipulability of their superior optical,electrical,thermal,and mechanical properties.A mass-spring model(MSM)for elastic wave propagation in multilayered vdW metamaterials is reported in this paper.Molecular dynamics(MD)simulations are adopted to simulate the propagation of elastic waves in multilayered vdW metamaterials.The results show that the graphene/MoS_(2)metamaterials have an elastic wave bandgap in the terahertz range.The MSM for the multilayered vdW metamaterials is proposed,and the numerical simulation results show that this model can well describe the dispersion and transmission characteristics of the multilayered vdW metamaterials.The MSM can predict elastic wave transmission characteristics in multilayered vdW metamaterials stacked with different two-dimensional(2D)materials.The results presented in this paper offer theoretical help for the vibration reduction of multilayered vdW semiconductors.

Identifying the enhancement mechanism of Al/MoO_(3) reactive multilayered films on the ignition ability of semiconductor bridge using a one-dimensional gas-solid two-phase flow model

Energetic Semiconductor bridge(ESCB)based on reactive multilayered films(RMFs)has a promising application in the miniature and intelligence of initiator and pyrotechnics device.Understanding the ignition enhancement mechanism of RMFs on semiconductor bridge(SCB)during the ignition process is crucial for the engineering and practical application of advanced initiator and pyrotechnics devices.In this study,a one-dimensional(1D)gas-solid two-phase flow ignition model was established to study the ignition process of ESCB to charge particles based on the reactivity of Al/MoO_(3) RMFs.In order to fully consider the coupled exothermic between the RMFs and the SCB plasma during the ignition process,the heat release of chemical reaction in RMFs was used as an internal heat source in this model.It is found that the exothermal reaction in RMFs improved the ignition performance of SCB.In the process of plasma rapid condensation with heat release,the product of RMFs enhanced the heat transfer process between the gas phase and the solid charge particle,which accelerated the expansion of hot plasma,and heated the solid charge particle as well as gas phase region with low temperature.In addition,it made up for pressure loss in the gas phase.During the plasma dissipation process,the exothermal chemical reaction in RMFs acted as the main heating source to heat the charge particle,making the surface temperature of the charge particle,gas pressure,and gas temperature rise continuously.This result may yield significant advantages in providing a universal ignition model for miniaturized ignition devices.

Least Square Finite Element Model for Analysis of Multilayered Composite Plates under Arbitrary Boundary Conditions

Laminated composites are widely used in many engineering industries such as aircraft, spacecraft, boat hulls, racing car bodies, and storage tanks. We analyze the 3D deformations of a multilayered, linear elastic, anisotropic rectangular plate subjected to arbitrary boundary conditions on one edge and simply supported on other edge. The rectangular laminate consists of anisotropic and homogeneous laminae of arbitrary thicknesses. This study presents the elastic analysis of laminated composite plates subjected to sinusoidal mechanical loading under arbitrary boundary conditions. Least square finite element solutions for displacements and stresses are investigated using a mathematical model, called a state-space model, which allows us to simultaneously solve for these field variables in the composite structure’s domain and ensure that continuity conditions are satisfied at layer interfaces. The governing equations are derived from this model using a numerical technique called the least-squares finite element method (LSFEM). These LSFEMs seek to minimize the squares of the governing equations and the associated side conditions residuals over the computational domain. The model is comprised of layerwise variables such as displacements, out-of-plane stresses, and in- plane strains, treated as independent variables. Numerical results are presented to demonstrate the response of the laminated composite plates under various arbitrary boundary conditions using LSFEM and compared with the 3D elasticity solution available in the literature.

Co/Pt multilayer-based pseudo spin valves with perpendicular magnetic anisotropy

Pseudo spin valves（SVs） exhibiting perpendicular magnetic anisotropy were prepared by magnetron sputtering. Magnetization measurements of the Co/Pt multilayers were performed to select the reference and free layers. The selection criteria are square magnetic hysteresis loops, weaker current shunting effect, and proper coercivity. The optimal reference layer and free layer are Pt（5.0 nm）/[Co（0.4 nm）/Pt（0.6 nm）]3/Co（0.4 nm）/Cu（3.0 nm）and Cu（3.0 nm）/[Co（0.4 nm）/Pt（1.5 nm）]4, respectively.The resulting pseudo SV exhibits two well-separated hysteresis loops when the field is applied perpendicular to the film plane. The minor hysteresis loop corresponding to the free layer shifts toward negative direction of the magnetic field axis, indicating ferromagnetic interlayer exchange coupling between the two magnetic layers. The coupling also enhances the coercivity（HC） of both layers. The perpendicular giant magnetoresistance（GMR） of 2.7 % is achieved with current in plane measurement. The GMR first increases when Pt seed layer is thickened, reaches a maximum of 3.0 % at 4 nm and then decreases with the further increase of thickness. But thicker Cu spacer layer always lowers the GMR of the SV.

Tailoring perpendicular magnetic anisotropy in Co/Pt multilayers by interface doping with ultrathin Fe layer

The effect of ultrathin Fe layer on perpendicular magnetic anisotropy(PMA) in magnetron-sputtered Co/Pt multilayers was investigated by magnetic measurements.Magnetization hysteresis(M-H) loops and microstructures of the samples were measured by vibrating sample magnetometer(VSM) and X-ray diffraction(XRD) and highresolution transmission electron microscopy(HRTEM),respectively.It is found that the PMA is strongly dependent on the interface where Fe layer was doped.When Fe layer was doped at Co/Pt interface where Pt was deposited on Co,the PMA decreases monotonically with Fe layer thickness(tFe) increasing.However,when Fe layer was doped at Pt/Co interface where Co was deposited on Pt,the PMA shows a peak at t_(Fe)=0.1 nm.It is considered that the PMA variation is mainly due to the tuning in the electron occupation states of 3 d orbits at Fe-doped Pt/Co interface.Furthermore,the annealing stability of PMA can also be improved when Fe layer was doped at Pt/Co interface.HRTEM results demonstrate that the magnetic anisotropy evolution is mainly caused by anneal-induced interdiffusion.

四川盆地PT1井区海相地层工程地质特征研究

PT1井区超深海相地层钻井过程中气侵和井漏频发,严重制约该区块的安全高效开发。文章基于有效应力法,利用最优化方法开展了海相地层孔隙压力评价;结合断层和裂缝发育程度探讨了PT1井区气侵和井漏纵横向分布特征。结果表明,受异常高压和裂缝发育程度的双重影响,气侵主要发生在龙潭组,占比达40%;井漏主要发生在筇竹寺组和灯影组,占总漏失量的80%以上。气侵与异常压力、裂缝发育程度、断裂情况等因素有关,异常高压是气侵内在的原因,而断裂带和发育的构造裂缝为气侵提供了必要条件。井漏层位和漏失量主要受一、二级大型断裂带控制,不同断裂带的影响具有显著的叠加效应,断裂带附近发育的构造裂缝为漏失提供了通道,距离F I7断裂越近越易出现漏失,且漏失量越大。研究成果为PT1井区钻井过程井下复杂预防和处理提供了科学支撑。

Pt NPs增强ZnO@ZnS核-壳异质纳米棒光催化产氢性能研究

采用原位光沉积方法将铂纳米颗粒(Pt NPs)负载在核-壳异质纳米棒ZnO@ZnS表面,制备出ZnO@ZnS/Pt复合光催化剂。调控Pt NPs在ZnO@ZnS表面的负载量,探究了不同质量的Pt NPs对光催化产氢气性能的影响。结果表明,当贵金属Pt的负载量为2%(ZnO@ZnS/Pt-2)时,复合光催化产H_(2)性能最好,产氢速率为4.52 mmol/(g·h),是ZnO@ZnS的1.8倍,催化剂表现出优异的催化稳定性。在此复合材料中,贵金属Pt纳米颗粒作为助催化剂,Pt对H~+具有较低的吸附能,优先吸附H~+,光生电子转移到Pt纳米粒子表面,H^(+)在其表面得到光生电子,被还原为氢气,成功地抑制了光生电子和空穴的复合,有效地提升了催化剂的催化性能。