Search Results (91)

The dielectric relaxation behavior of polymeric materials is critical to their performance in electronic, insulating, and energy storage applications. This study presents an electrical fractional model (EFM) based on fractional calculus and the complex electric modulus ($M^{*}\mathit{=}{M}^{\prime }\mathit{+}i{M}^{″}$) formalism to simultaneously describe two key relaxation phenomena: $\alpha$-relaxation and interfacial polarization (Maxwell–Wagner–Sillars effect). The model incorporates fractional elements (cap-resistors) into a modified Debye equivalent circuit to capture polymer dynamics and energy dissipation. Fractional differential equations are derived, with fractional orders taking values between 0 and 1; the frequency and temperature responses are analyzed using Fourier transform. Two temperature-dependent behaviors are considered: the Matsuoka model, applied to $\alpha$-relaxation near the glass transition, and an Arrhenius-type equation, used to describe interfacial polarization associated with thermally activated charge transport. The proposed model is validated using literature data for amorphous polymers, polyetherimide (PEI), polyvinyl chloride (PVC), and polyvinyl butyral (PVB), successfully fitting dielectric spectra and extracting meaningful physical parameters. The results demonstrate that the EFM is a robust and versatile tool for modeling complex dielectric relaxation in polymeric systems, offering improved interpretability over classical integer-order models. This approach enhances understanding of coupled relaxation mechanisms and may support the design of advanced polymer-based materials with tailored dielectric properties. Full article

This study presents an integrated analysis of the dielectric characteristics of nitrile–butadiene rubber (NBR), ethylene–propylene–diene monomer (EPDM), and fluoroelastomer (FKM) polymers. Dispersion spectra were obtained over a wide range of frequencies and temperatures, and, via our self-developed “Dispersion Analysis” program, the obtained dielectric spectra were precisely deconvoluted. Notably, α, α’, β, and γ relaxation phenomena, including the DC conduction process, were identified in NBR, whereas three relaxation processes, namely, α, β, and the Maxwell‒Wagner‒Sillars (MWS) process, as well as DC conduction, were observed in EPDM and FKM copolymers. The activation energies (${\mathrm{E}}_a$) for secondary relaxation—namely, β, γ, and MWS—and the DC conduction process, which are observed in NBR, EPDM, and FKM, were determined via the Arrhenius temperature dependence model, and these values were compared with previously published results. Furthermore, the glass transition temperature (${\mathrm{T}}_g$), extrapolated from the relaxation rate of the α process, was estimated via the Vogel–Fulcher–Tamman–Hesse (VFTH) law. The values of ${\mathrm{T}}_g$ obtained using dielectric spectroscopy for NBR, EPDM, and FKM agreed well with the differential scanning calorimetry (DSC) measurements. This study provides foundational insights into the dielectric properties of widely used rubber polymers, offering a comprehensive reference for future research. Full article

To improve the robustness of intrusion detection systems constructed using deep learning models, a method based on an auxiliary adversarial training WGAN (AuxAtWGAN) is proposed from the defender’s perspective. First, one-dimensional traffic data are downscaled and processed into two-dimensional image data via a stacked autoencoder (SAE), and mixed adversarial samples are generated using the fast gradient sign method (FGSM), Projected Gradient Descent (PGD) and Carlini and Wagner (C&W) adversarial attacks. Second, the improved WGAN with an integrated perceptual network module is trained with mixed training samples composed of mixed adversarial samples and normal samples. Finally, the adversary-trained AuxAtWGAN model is attached to the original model for adversary sample detection, and the detected adversary samples are removed and input into the original model to improve the robustness of the original model. The average attack success rate of the original convolutional neural network (CNN) model against multiple adversarial samples is 75.17%, and after using AuxAtWGAN, the average attack success rate of the adversarial attacks decreases to 27.56%; moreover, the detection accuracy of the original CNN model against normal samples is still 93.57%. The experiment proves that AuxAtWGAN improves the robustness of the original model. In addition, validation experiments are conducted by attaching the AuxAtWGAN model to the Long Short-Term Memory Network (LSTM) and Residual Network34 (ResNet) models, which prove that the proposed method has high generalization performance. Full article

The viscoelasticity of fluids have a significant impact on the process of heat and mass transfer, which directly affects the efficiency and quality, especially for highly viscous functional polymer materials. In this work, the effect of elasticity on hydrodynamic behavior of pipe flow for highly viscous non-Newtonian fluids was studied using viscoelastic polyolefin elastomer (POE). Two constitutive rheological equations, the Cross model and Wagner model, were applied to describe the rheological behavior of typical POE melts, which have been embedded with computational fluid dynamics (CFD) simulation of the laminar pipe flow through the user-defined function (UDF) method. The influence of both viscosity and elasticity of a polymer melt on the flow mixing and heat transfer behavior has been systematically studied. The results show that the elastic effect makes a relative larger velocity gradient in the radial direction and the thicker boundary layer near pipe wall under the same feed flow rate. That leads to the higher pressure drop and more complex residence time distribution with the longer residence time near the wall but shorter residence time in the center. Under the same conditionals, the pipeline pressure drop of the viscoelastic fluid is several times or even tens of times greater than that of the viscous fluid. When the inlet velocity increases from 0.0001 m/s to 0.01 m/s, the difference in boundary layer thickness between the viscoelastic fluid and viscous fluid increases from 3% to 12%. Similarly, the radial temperature gradient of viscoelastic fluids is also relatively high. When the inlet velocity is 0.0001 m/s, the radial temperature difference of the viscoelastic fluid is about 40% higher than that of viscous fluid. Besides that, the influence of elasticity deteriorates the mixing effect of the SK type static mixer on the laminar pipe flow of highly viscous non-Newtonian fluids. Correspondingly, the accuracy of the simulation results was verified by comparing the pressure drop data from pipeline hydrodynamic experiments. Full article

Compared with traditional immunoassay methods, microfluidic immunoassay restricts the immune response in confined microchannels, significantly reducing sample consumption and improving reaction efficiency, making it worthy of widespread application. This paper proposes an exciting multi-frequency electrothermal flow (MET) technique by applying combined standing-wave and traveling-wave voltage signals with different oscillation frequencies to a three-period quadra-phase discrete electrode array, achieving rapid immunoreaction on functionalized electrode surfaces within straight microchannels, by virtue of horizontal pumping streamlines and transverse stirring vortices induced by nonlinear electrothermal convection. Under the approximation of a small temperature rise, a linear model describing the phenomenon of MET is derived. Although the time-averaged electrothermal volume force is a simple superposition of the electrostatic body force components at the two frequencies, the electro-thermal-flow field undergoes strong mutual coupling through the dual-component time-averaged Joule heat source term, further enhancing the intensity of Maxwell–Wagner smeared structural polarization and leading to mutual influence between the standing-wave electrothermal (SWET) and traveling-wave electrothermal (TWET) effects. Through thorough numerical simulation, the optimal working frequencies for SWET and TWET are determined, and the resulting synthetic MET flow field is directly utilized for microfluidic immunoassay. MET significantly promotes the binding kinetics on functionalized electrode surface by simultaneous global electrokinetic transport along channel length direction and local chaotic stirring of antigen samples near the reaction site, compared to the situation without flow activation. The MET investigated herein satisfies the requirements for early, rapid, and precise immunoassay of test samples on-site, showing great application prospects in remote areas with limited resources. Full article

Ni-based superalloys with enhanced environmental resistance at high temperatures are crucial for advanced gas turbine engines. The new polycrystalline nickel-based superalloy has excellent mechanical properties, but as a low-Cr, high-alloying superalloy, its environmental resistance has never been investigated. The hot corrosion behavior of the nickel-based superalloy under molten salt conditions and its effect on its tensile properties were investigated in this paper. The results showed the following: The diffusion of the Cr, Al, and Ni elements governs the majority of the corrosion process, resulting in the production of an environmentally damaged organization with internal sulfidation and surface oxidation. The Wagner model predicts the inability to form a dense Al oxide scale on the surface because the crucial generation condition of external Al oxides is not met. In addition, the growth stress in the damage scales is the main cause of cracking and spalling in the isothermal corrosion process. Due to the increased local stress concentration brought on by this environmental degradation, the sulfide scale acts as a fracture source, guiding the matrix cracking and influencing the tensile properties of the alloy. Full article

The rapid proliferation of ransomware variants necessitates more effective detection mechanisms, as traditional signature-based methods are increasingly inadequate. These conventional methods rely on manual feature extraction and matching, which are time-consuming and limited to known threats. This study addresses the escalating challenge of ransomware threats in cybersecurity by proposing a novel deep learning model, LSTM-EDadver, which leverages Generative Adversarial Networks (GANs) and Carlini and Wagner (CW) attacks to enhance malware detection capabilities. LSTM-EDadver innovatively generates adversarial examples (AEs) using sequential features derived from ransomware behaviors, thus training deep learning models to improve their robustness and accuracy. The methodology combines Cuckoo sandbox analysis with conceptual lattice ontology to capture a wide range of ransomware families and their variants. This approach not only addresses the shortcomings of existing models but also simulates real-world adversarial conditions during the validation phase by subjecting the models to CW attacks. The experimental results demonstrate that LSTM-EDadver achieves a classification accuracy of 96.59%. This performance was achieved using a dataset of 1328 ransomware samples (across 32 ransomware families) and 519 normal instances, outperforming traditional RNN, LSTM, and GCU models, which recorded accuracies of 90.01%, 93.95%, and 94.53%, respectively. The proposed model also shows significant improvements in F1-score, ranging from 2.49% to 6.64% compared to existing models without adversarial training. This advancement underscores the effectiveness of integrating GAN-generated attack command sequences into model training. Full article

The classic Lifshitz–Slyozov–Wagner (LSW) theory of ripening assumes a constant volume. In comparison, we present here a model of ripening assuming a constant surface area, which has occurred in the microstructure changes in intermetallic compounds in micro-bump for 3D integrated-circuit (IC) technology in consumer electronic products. However, to keep a constant surface area requires the growth of the volume. Furthermore, in 3D IC technology, the kinetics is affected by electrical charges flowing in and out of the system. Due to Joule heating and electromigration, heat flux and atomic flux can occur together. The kinetic modes of failure changes are given here, as well as the mean-time-to-failure equations based on entropy production. Full article

Background: Algometry is a validated and reliable measurement tool, but there are still no reliable data for the different algometers used by different raters in the same participant. Objective: The aim of this study was to determine the intra-reliability of pressure pain thresholds (PPTs) measured using a digital algometer with and without a digital screen by different raters at the same time in a pain-free population. Methods: Participants were healthy adults. PPTs were assessed using two different algometers: a digital algometer with a digital screen for a feedback of the pressure curve rate (SpTech Digital Algometer); and a digital algometer without a screen (Wagner Instruments FDX-25, Greenwich, CT, USA). Four PPT points were used: upper trapezius, lumbar spine, extensor carpi ulnaris, and tibialis anterior. The Copenhagen Psychosocial Questionnaire II was used to assess burnout, stress, sleeping problems, depressive symptoms, somatic stress, and cognitive stress. The intraclass coefficients (ICCs) for intra-rater reliability was calculated using a two-way mixed effects model, single measurement type, and absolute agreement definition. Results: A total of 47 healthy participants with a mean age of 30.51 (11.35) years were included. The upper trapezius and extensor carpi ulnaris had the lowest PPT values, and the tibialis anterior had the highest PPT value. Females had the lowest PPT values when compared with males with p < 0.05 in the upper trapezius and extensor carpi ulnaris regions. The intra-rater reliability ranged from good to excellent reliability, with the ICC values of rater 1 being higher when compared with rater 2. The PPT in tibialis anterior had the highest mean ICC scores. Conclusions: The intra-rater reliability of PPTs measured by different digital algometers ranged from good to excellent reliability. The rater with more experience demonstrated higher reliability. Full article

This paper addresses the author’s current understanding of the physics of interactions in polymers under a voltage field excitation. The effect of a voltage field coupled with temperature to induce space charges and dipolar activity in dielectric materials can be measured by very sensitive electrometers. The resulting characterization methods, thermally stimulated depolarization (TSD) and thermal-windowing deconvolution (TWD), provide a powerful way to study local and cooperative relaxations in the amorphous state of matter that are, arguably, essential to understanding the glass transition, molecular motions in the rubbery and molten states and even the processes leading to crystallization. Specifically, this paper describes and tries to explain ‘interactive coupling’ between molecular motions in polymers by their dielectric relaxation characteristics when polymeric samples have been submitted to thermally induced polarization by a voltage field followed by depolarization at a constant heating rate. Interactive coupling results from the modulation of the local interactions by the collective aspect of those interactions, a recursive process pursuant to the dynamics of the interplay between the free volume and the conformation of dual-conformers, two fundamental basic units of the macromolecules introduced by this author in the “dual-phase” model of interactions. This model reconsiders the fundamentals of the TSD and TWD results in a different way: the origin of the dipoles formation, induced or permanent dipoles; the origin of the Wagner space charges and the T_g,ρ transition; the origin of the T_LL manifestation; the origin of the Debye elementary relaxations’ compensation or parallelism in a relaxation map; and finally, the dual-phase origin of their super-compensations. In other words, this paper is an attempt to link the fundamentals of TSD and TWD activation and deactivation of dipoles that produce a current signal with the statistical parameters of the “dual-phase” model of interactions underlying the Grain-Field Statistics. Full article

This study investigates the magnetic and dielectric properties of cobalt–zirconium co-doped iron oxide nanoparticles synthesized via the hydrothermal method. The synthesis was conducted at 150 °C, with reaction times of 4, 6, 8, 10, and 12 h. Co-doping with cobalt and zirconium significantly influenced the magnetic phase formation of iron oxide. Magnetic properties were characterized using a Vibrating Sample Magnetometer (VSM), revealing ferromagnetic behavior with a maximum saturation magnetization of 45 emu/g for the 8 h sample. The dielectric properties were analyzed through impedance spectroscopy across a wide frequency range, and the results were interpreted using Maxwell–Wagner’s model and Koop’s theory. The dielectric constant reached its maximum value of approximately 58 at a logarithmic frequency of 1.5 Hz for the sample synthesized for 8 h. This study highlights the importance of synthesis time in optimizing both the magnetic and dielectric properties of (Co, Zr) co-doped iron oxide nanoparticles. Full article

The precipitation behavior of a novel Ni-Fe-based superalloy developed for advanced ultra-supercritical (A-USC) coal-fired power plant applications during high-temperature aging treatment was investigated. The results showed that the major precipitates in the novel alloy were randomly distributed MC carbides, M₂₃C₆ carbides at grain boundaries, and the γ′-Ni₃ (Al, Ti) phase in grain interiors after aging. MC remained relatively stable during both short-term and long-term aging. M₂₃C₆ quickly precipitated and exhibited a discrete distribution at grain boundaries during short-term aging, and partly developed into continuous films during long-term aging. After uniform precipitation, the shape of γ′ remained spherical, and the size kept increasing with aging time according to the Lifshitz–Slyozov–Wagner (LSW) model. The hardness of the novel alloy was mainly associated with the precipitation behavior of γ′; as γ′ gradually precipitated, the hardness steadily increased; after complete precipitation, as the size of γ′ increased, the hardness first increased and then decreased, reaching the peak hardness when the average radius of γ′ achieved the critical size. In addition, the novel alloy exhibited abnormal coarsening behavior at grain boundaries during both short-term and long-term aging. The coarsened grain boundaries were actually precipitate-free zones (PFZs) and the coarsened and elongated rod-like particles inside were identified as γ′ precipitates. The mechanism of strain-induced grain boundary migration and the discontinuous coarsening reaction is proposed for the formation of PFZs. Furthermore, PFZs were considered to be potential crack sources during the creep rupture test, leading to earlier failure of the material. Full article

Polymer and ceramic-based composites offer a unique blend of desirable traits for improving dielectric permittivity. This study employs an empirical approach to estimate the dielectric permittivity of composite materials and uses a finite element model to understand the effects of permittivity and filler concentration on mechanical and electrical properties. The empirical model combines the Maxwell-Wagner-Sillars (MWS) and Bruggeman models to estimate the effective permittivity using Barium Titanate (BT) and Calcium Copper Titanate Oxide (CCTO) as ceramic fillers dispersed in a Polydimethylsiloxane (PDMS) polymer matrix. Results indicate that the permittivity of the composite improves with increased filler content, with CCTO/PDMS emerging as the superior combination for capacitive applications. Capacitance and energy storage in the CCTO/PDMS composite material reached 900 nF and 450 nJ, respectively, with increased filler content. Additionally, increased pressure on the capacitive model with varied filler content showed promising effects on mechanical properties. The interaction between BT filler and the polymer matrix significantly altered the electrical properties of the model, primarily depending on the composite’s permittivity. This study provides comprehensive insights into the effects of varied filler concentrations on estimating mechanical and electrical properties, aiding in the development of real-world pressure-based capacitive models. Full article

This research investigates the application of supercritical carbon dioxide (CO₂) within carbon capture, utilization, and storage (CCUS) technologies to enhance oil-well production efficiency and facilitate carbon storage, thereby promoting a low-carbon circular economy. We simulate the flow of supercritical CO₂ mixed with associated gas (flow rates 3–13 × 10⁴ Nm³/d) in a miniature venturi tube under high temperature and high-pressure conditions (30–50 MPa, 120–150 °C). Accurate fluid property calculations, essential for simulation fidelity, were performed using the R. Span and W. Wagner and GERG-2008 equations. A dual-parameter prediction model was developed based on the simulation data. However, actual measurements only provide fluid types and measurement data, such as pressure, temperature, and venturi differential pressure, to determine the liquid mass fraction (LMF) and total mass flow rate (m), presenting challenges due to complex nonlinear relationships. Traditional formula-fitting methods proved inadequate for these conditions. Consequently, we employed a Levenberg–Marquardt (LM) based neural network algorithm to address this issue. The LM optimizer excels in handling complex nonlinear problems with faster convergence, making it suitable for our small dataset. Through this approach, we formulated dual-parameter model equations to elucidate fluid flow factors, analyzing the impact of multiple parameters on the LMF and the discharge coefficient (C). The resulting model predicted dual parameters with a relative error for LMF of ±1% (Pc = 95.5%) and for m of ±1% (Pc = 95.5%), demonstrating high accuracy. This study highlights the potential of neural networks to predict the behavior of complex fluids with high supercritical CO₂ content, offering a novel solution where traditional methods fail. Full article

The water-impacting behavior of a wedge is often studied in the slamming phenomenon of ships and aircraft. Many scholars have proposed theoretical models for studying the water-impacting problem of a wedge, but these models still have some shortcomings. This study combines Von Karman’s method, the Generalized Wagner Model (GWM), and Modified Logvinovich Model (MLM) to establish a converged theoretical Von Karman-GWM-MLM (VGM) model. The VGM model utilizes added mass to replace the fluid influence, which is derived from the velocity potential and boundary conditions. Considering the influence of impulse, the velocity is determined by the momentum theorem. Subsequently, the pressure, resultant force, and acceleration of the wedge can be calculated. By comparing with the published test data of other scholars, it is found that the velocity, acceleration, pressure, and force of the wedge obtained by the VGM model reached a consensus with experiments. The validity and accuracy of the VGM model are also verified. The efficiency and accuracy of problem-solving are both balanced when using the VGM model. The establishment of the VGM model is significant for solving water-impacting problems related to wedges. Full article