Search Results (2,132)

Thermoplastic composites are growing in popularity in the aerospace and automotive industries; they enable weldable and recyclable structures. Resistance welded hybrid thermoplastic and metal joints are attractive for rapid assembly, but the thermal mismatch between metals and polymers introduces residual stresses, which can drive edge debonding and compromise durability. This study presents fabricated single-lap PC/CF–Al7075 coupons with measured mid-span bow resulting from welding, evaluated bond quality by step-heating thermography, and an evaluated framework for residual stress prediction using Ansys complemented by a bimetal analytical check. Three thermal cycles were examined with different temperature gradients (200, 220, 240 °C): the measured bow was 16.5 mm and remained constant, whereas analytical calculation increased with ΔT similarly to the FEM prediction. The current FEM under predicted the bow (Mean Absolute Percentage Error is 21%), showing stress contours that decay with distance from the bond and revealing pronounced peaks in both σ_xx and σ_zz components at weld edges, consistent with shear-lag theory. FEM returned edge-peaked peel rising from 43 to −64 MPa and σ_xx was up to 12% more compressive than analytical calculation; an effective CF/PC CTE of 1.5 × 10⁻⁶ K⁻¹ reconciled curvature with test better than catalogue values. The temperature insensitive bow is attributed to polycarbonate flow/viscoelastic relaxation above Tg and hot relaxation in aluminum, with effects not represented in the elastic models. Edge peel and shear govern initiation risk. Full article

Existing contrastive deep graph clustering methods typically employ fixed-threshold strategies when constructing positive and negative sample pairs, and fail to integrate both graph structure information and clustering structure information effectively. However, this fixed-threshold and binary partitioning approach is overly rigid, limiting the model’s utilization of potentially learnable samples. To address this problem, this paper proposes a contrastive deep graph clustering model with a progressive relaxation weighting strategy (DCPRES). By introducing the progressive relaxation weighting strategy (PRES), DCPRES dynamically allocates sample weights, constructing a progressive training strategy from easy to difficult samples. This effectively mitigates the impact of pseudo-label noise and enhances the quality of positive and negative sample pair construction. Building upon this, DCPRES designs two contrastive learning losses: an instance-level loss and a cluster-level loss. These respectively focus on local node information and global cluster distribution characteristics, promoting more robust representation learning and clustering performance. Extensive experiments demonstrated that DCPRES significantly outperforms existing methods on multiple public graph datasets, exhibiting a superior robustness and stability. For instance, on the CORA dataset, our model achieved a significant improvement over the static approach of CCGC, with the NMI increasing by $4.73\%$, the ACC by $4.77\%$, the ARI value by $7.03\%$, and the F1-score by $5.89\%$. It provides an efficient and stable solution for unsupervised graph clustering tasks. Full article

In this article, an ecological composite based on a neat polylactide with 50 and 75% degrees of coffee particles and eggshells as an infill and organic filler, was developed. It has been shown that the content of fillers used reduced the mechanical properties, increasing the possibility of environmental degradation and accelerating the biodegradation process. During the additive production of polylactide with 10% of coffee grounds as a filler, it was possible to reduce the additive manufacturing temperature, which reduced the process time, energy costs, carbon dioxide emissions and the amount of polymer that may affect the environment. The structure of polylactide enriched with hen eggshells is characterized by roan and irregular shapes, which can cause a high tendency to form a concentration of cracks in these areas. Based on the results obtained from the stress relaxation test, the Zener model was used to describe a creep model of the produced ecological composites. The polymer composition of coffee grounds and eggshells shows a tendency to creep faster than pure polylactide and with different degrees of infill. Voids reduce the strength of composite materials, which increases the creep potential of samples with incomplete degrees of infill. Full article

A continuum model for describing pseudo-turbulent flows of a dispersed phase is developed using a statistical approach based on the kinetic equation for the probability density of particle velocity and temperature. The introduction of the probability density function enables a statistical description of the particle ensemble through equations for the first and second moments, replacing the dynamic description of individual particles derived from Langevin-type equations of motion and heat transfer. The lack of detailed dynamic information on individual particle behavior is compensated by a richer statistical characterization of the motion and heat transfer within the particle continuum. A numerical simulation of the unsteady flow of a gas–particle suspension generated by the interaction of a shock wave with a particle cloud is performed using an interpenetrating continua model and equations for the first and second moments of both gas and particles. Numerical methods for solving the two-phase gas dynamics equations—formulated using a two-velocity and two-temperature model—are discussed. Each phase is governed by conservation equations for mass, momentum, and energy, written in a conservative hyperbolic form. These equations are solved using a high-order Godunov-type numerical method, with time discretization performed by a third-order Runge–Kutta scheme. The study analyzes the influence of two-dimensional effects on the formation of shock-wave flow structures and explores the spatial and temporal evolution of particle concentration and other flow parameters. The results enable an estimation of shock wave attenuation by a granular backfill. The extended pressure relaxation region is observed behind the cloud of particles. Full article

Lightweight magnesium alloys are gaining increasing attention as potential structural materials for automotive and aerospace applications due to their high specific strength and excellent recyclability. However, their formability and mechanical performance are often limited by strong basal texture and limited recrystallization during thermomechanical processing. In this context, the present study systematically investigates the effect of post-annealing treatment on the microstructural evolution, recrystallization behavior, and mechanical response of a hot-rolled Mg-3Al-1Zn-1Ca alloy. Detailed microstructural characterization revealed that Al₂Ca precipitates were uniformly distributed along grain boundaries in the as-received (AR) condition, where they contributed to significant pinning of boundary migration. Post-annealing treatment (350 °C, furnace cooling) resulted in non-uniform grain coarsening, driven by the interplay of precipitate pinning and differential stored strain energy, while also facilitating particle-stimulated nucleation (PSN) and recrystallization. Electron backscatter diffraction (EBSD) analysis confirmed a substantial increase in the fraction of high-angle grain boundaries and recrystallized grains in the heat-treated (HT) state, with kernel average misorientation (KAM) and grain orientation spread (GOS) analyses indicating pronounced recovery of lattice distortions. Mechanical testing demonstrated a significant decrease in yield strength (263 MPa to 187.4 MPa) and hardness (65.7 to 54.1 HV) due to dislocation annihilation and stress relaxation, while ultimate tensile strength remained nearly unchanged (~338 MPa) and ductility improved markedly (12.6% to 16.4%). These findings highlight the dual role of Al₂Ca precipitates in promoting recrystallization through PSN while simultaneously restricting excessive grain growth through Zener pinning. Full article

Electrospinning is a versatile technique used to manufacture nanofibers by applying an electric field to a polymer solution. This method has gained significant interest in the biomedical, pharmaceutical, and materials engineering fields due to its ability to produce porous structures with a high specific surface area, making it ideal for applications such as wound dressings, controlled drug delivery systems, and tissue engineering. The materials used in electrospinning play a crucial role in determining the final properties of the obtained nonwoven nanofibers. Among the most studied substances are chitosan, collagen, and fish-derived gelatin, which are biopolymers with high biocompatibility. These materials are especially used in the medical and pharmaceutical fields due to their bioactive properties. In combination with synthetic polymers such as polyethylene glycol (PEG) and polyvinyl alcohol (PVA), these biopolymers can form electrospun fibers with improved mechanical characteristics and enhanced structural stability. The characterization of these materials was performed using modern characterization techniques, such as one-dimensional (1D) proton NMR spectroscopy (¹H), for which the spin–spin relaxation time distributions T₂ were characterized. Additionally, two-dimensional (2D) measurements were conducted, for which EXSY T₂-T₂ and COSY T₁-T₂ exchange maps were obtained. The characterization was complemented with FT-IR spectra measurements, and the nanofiber morphology was observed using SEM. As a novelty, machine learning methods, including artificial neural networks (ANNs), were applied to characterize the local structural order of the produced nanofibers. In this study, it was shown that the nanofiber nonwoven materials made from PVA are characterized by a degree of order in the range of 0.27 to 0.61, which are more ordered than the nanofibers made from chitosan and fish gelatin, characterized by an order degree ranging from 0.051 to 0.312, where 0 represents the completely unordered network and 1 a fully ordered fabric. Full article

Protein–protein docking is a cornerstone of computational structural biology, yet its reliability for large, multimeric assemblies remains uncertain. Standard workflows typically include geometry optimization or molecular dynamics equilibration to relieve local strains and improve input quality, but the extent to which these preparatory steps alter docking outcomes has not been systematically evaluated. Here, we address this question using the mitochondrial chaperonin Hsp60, a dynamic double-ring complex essential for protein folding, and MIX, a kinetoplastid-specific protein with unresolved function, as a stress test system. By comparing docking predictions across minimized, equilibrated, and ensemble-refined structures of Hsp60 in three conformational states (apo, ATP-bound, and ATP–Hsp10), we show that structural relaxation profoundly reshapes the docking landscape. Minimization alone often yielded favorable scores but localized binding, while longer MD trajectories exposed alternative sites, including central cavity, equatorial ATP pocket, and apical domain, each consistent with distinct regulatory hypotheses. These findings reveal that docking outcomes are highly sensitive to receptor preparation, especially in complexes undergoing large conformational transitions. More broadly, our study highlights an underappreciated vulnerability of docking pipelines and calls for ensemble-based and dynamics-aware approaches when predicting interactions in large biomolecular machines. Full article

Background and Clinical Significance: Tandem pathology at the dominant-hemisphere middle cerebral artery (MCA)—combining a wide-neck bifurcation aneurysm that shares the neck with both M2 origins and a short dorsal M1 fusiform dilation embedded in the lenticulostriate belt—compresses the therapeutic margin and complicates device-first pathways. We aimed to describe an anatomy-led, microscope-only sequence designed to secure an immediate branch-definitive result at the fork and to remodel dorsal M1 without perforator compromise, and to place these decisions within a pragmatic perioperative framework. Case Presentation: A 37-year-old right-handed man with reproducible, load-sensitive cortical association and capsulostriate signs underwent high-fidelity digital subtraction angiography (DSA) with 3D rotational reconstructions. Through a left pterional approach, vein-respecting Sylvian dissection achieved gravity relaxation. Reconstruction proceeded in sequence: a fenestrated straight clip across the bifurcation neck with the superior M2 encircled to preserve both M2 ostia, followed by a short longitudinal clip parallel to M1 to reshape the fusiform segment while keeping each lenticulostriate mouth visible and free. Temporary occlusion windows were brief (bifurcation 2 min 30 s; M1 < 2 min). No neuronavigation, intraoperative fluorescence, micro-Doppler, or intraoperative angiography was used. No perioperative antiplatelets or systemic anticoagulation were administered and venous thromboembolism prophylaxis followed institutional practice. The bifurcation dome collapsed immediately with round, mobile M2 orifices, and dorsal M1 regained near-cylindrical geometry with patent perforator ostia under direct inspection. Emergence was neurologically intact, headaches abated, and preoperative micro-asymmetries resolved without new deficits. The early course was uncomplicated. Non-contrast CT at three months showed structurally preserved dominant-hemisphere parenchyma without infarction or hemorrhage. Lumen confirmation was scheduled at 12 months. Conclusions: In dominant-hemisphere tandem MCA disease, staged, perforator-sparing clip reconstruction can restore physiologic branch and perforator behavior while avoiding prolonged antiplatelet exposure and device-related branch uncertainty. A future-facing pathway pairs subtle clinical latency metrics with high-fidelity angiography, reports outcomes in branch- and perforator-centric terms, and, where available, incorporates patient-specific hemodynamic simulation and noninvasive lumen surveillance to guide timing, technique, and follow-up. Full article

Dielectric characterization offers valuable insights into fruit structure, ripening, and storage stability. However, systematic studies on apples are still limited. This work elucidates the electrical and physicochemical properties of a specific variety of apples, Malus domestica, using Electrochemical Impedance Spectroscopy (EIS), a non-destructive, fast and cost-effective technique, suitable for real-time quality assessments. The apple samples were analyzed over the frequency range of 20 Hz–120 MHz at 25 °C, and impedance data were modeled using equivalent circuits and dielectric relaxation models. Physicochemical analyses confirmed a high moisture content (84%, wwb), pH 4.81, TSS 14.58 °Brix, and acidity 0.64%, which is typical of fresh Red Delicious apples. Impedance spectra revealed semicircular and Warburg elements in Nyquist plots, indicating resistive, capacitive, and diffusive processes. Equivalent circuit fitting with the proposed R-C-Warburg impedance model outperformed (R² = 0.9946 and RMSE = 6.610) the classical Cole and Double-Shell models. The complex permittivity (ε) represented a frequency-dependent ionic diffusion, space-charge polarization, and dipolar relaxation decay, while electrical modulus analysis highlighted polarization and charge carrier dynamics. The translational hopping of charge carriers was confirmed through AC conductivity following Jonscher’s power law with an exponent of ƞ = 0.627. These findings establish a comprehensive dielectric profile and advanced circuit fitting for biological tissues, highlighting a promising non-invasive approach using EIS for real-time monitoring of fruit quality, with direct applications in post-harvest storage, supply chain management, and non-destructive quality assurance in the food industry. Full article

This work is motivated by the need to enhance efficiency and radiation resistance and reduce weight in high-performance photovoltaic devices, with applications spanning both terrestrial and space environments. Metamorphic buffers are key enablers for reducing defect formation in lattice-mismatched structures, which are among the most widespread technologies for high-efficiency photovoltaic energy conversion. Although many systems have been created, absolute certainty about the effective relaxation mechanism remains unattained. In this work, MOVPE-grown step-graded buffers with variable In content were obtained on Ge substrates and investigated to identify the critical thresholds that govern strain relaxation and defect formation. The results show that the buffers are fully strained when the In top-layer content is <6.0%, while a degree of relaxation in the entire structure appears when the In top-layer content is >6.0%. In addition, the relaxation phenomenon is paralleled by the formation of a tilt angle between the layers and the substrate. We also found evidence that the appearance of relaxation is not limited to the upper layer but is presented by the structure as a whole. The effects of Te doping inside the InGaAs layers were also investigated: Te does not influence the structure of the crystal, but it introduces a Burstein–Moss blue shift in the photoluminescence energy of about 20 meV. Eventually, to reduce defect formation with the goal of achieving high-efficiency photovoltaic devices, a thick layer with a lower In content was grown onto the overshoot material (In_0.12Ga_0.88As). The results obtained confirm the high quality of the buffers and unveil the critical points, which are responsible for the most important changes in the buffer architecture and should be considered in future material engineering. The results provide valuable insights for the design of high-performance, sustainable photovoltaic devices and contribute to the advancement of III–V semiconductor integration on Ge substrates. Full article

Thermoset polyurethanes invite industrial interest for their versatility and chemical and mechanical resistance due to their permanently crosslinked networks; yet this structural feature severely limits their recyclability. Recent advances in Covalent Adaptable Networks (CANs), enabled by Dynamic Covalent Chemistry (DCC), have demonstrated promising pathways toward reprocessability through bond-exchange mechanisms. However, no clear link has yet been identified between material properties and the retention of performance after reprocessing. This work investigates the role of crosslink density as a key factor in determining the reprocessability of polyurethane networks. Two model systems with comparable compositions but distinct crosslink densities were synthesised, reprocessed, and compared. Relaxation analysis based on the Maxwellian approach proved insufficient to predict reprocessing outcomes. Only the highly crosslinked network yielded homogeneous reprocessable films with significant retention of mechanical performance, whereas the less crosslinked network resulted in incoherent materials with markedly reduced properties. The application of Kohlrausch–Williams–Watts (KWW) fitting revealed that dynamic covalent exchange dominates relaxation in the highly crosslinked system, while in the looser network, relaxation is governed by soft segment mobility, hindering effective network reformation. These findings underscore the pivotal role of crosslink density in determining the recyclability of thermoset polyurethanes and provide new insights for the rational design of reprocessable materials. Full article

We develop a convergence framework for Grünwald–Letnikov (GL) fractional and classical integer difference operators acting on sequences in fuzzy-paranormed (fp) spaces, motivated by data that are imprecise and contain sporadic outliers. Fuzzy paranorms provide a resolution-dependent notion of proximity, while statistical and lacunary statistical convergence downweight sparse deviations by natural density; together, they yield robust criteria for difference-filtered signals. Within this setting, we establish uniqueness of fp–${\mathsf{\Delta }}^m$ statistical limits; an equivalence between fp-statistical convergence of ${\mathsf{\Delta }}^m$ (and its GL extension ${\mathsf{\Delta }}^{\alpha }$) and fp-strong p-Cesàro summability; an equivalence between lacunary fp-${\mathsf{\Delta }}^m$ statistical convergence and blockwise strong p-Cesàro summability; and a density-based decomposition into a classically convergent part plus an fp-null remainder. We also show that GL binomial weights act as an ${\ell }^1$ convolution, ensuring continuity of ${\mathsf{\Delta }}^{\alpha }$ in the fp topology, and that nabla/delta forms are transferred by the discrete Q–operator. The usefulness of the criteria is illustrated on simple engineering-style examples (e.g., relaxation with memory, damped oscillations with bursts), where the fp-Cesàro decay of difference residuals serves as a practical diagnostic for Cesàro compliance. Beyond illustrative mathematics, we report engineering-style diagnostics where the fuzzy Cesàro residual index correlates with measurable quantities (e.g., vibration amplitude and energy surrogates) under impulsive disturbances and missing data. We also calibrate a global decision threshold ${\tau }_{\mathrm{glob}}$ via sensitivity analysis across $(\alpha ,p,m)$, where $m\in \mathbb{N}$ is the integer difference order, $\alpha >0$ is the fractional order, and $p\ge 1$ is the Cesàro exponent, and provide quantitative baselines (median/M-estimators, ${\ell }_1$ trend filtering, Gaussian Kalman filtering, and an $\alpha$-stable filtering structure) to show complementary gains under bursty regimes. The results are stated for integer m and lifted to fractional orders $\alpha >0$ through the same binomial structure and duality. Full article

This study reports the synthesis, structural characterization, and electromagnetic shielding performance of tantalum (Ta)-doped nickel ferrite (NiFe₂O₄) composites reinforced with chopped strands. Ta-doped NiFe₂O₄ powders were prepared via the conventional mixed-oxide route and sintered at 1200 °C for 4 h, resulting in a well-crystallized single-phase spinel structure. Comprehensive structural and chemical analyses were carried out using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS), confirming the successful incorporation of Ta into the NiFe₂O₄ lattice and the uniform microstructural distribution. The ferrite powders were subsequently embedded with chopped strands and epoxy resin through hot pressing to fabricate composites with varying filler contents. The electromagnetic interference (EMI) shielding effectiveness (SE) of the composites was systematically evaluated in the 7–18 GHz frequency range using a network analyzer (NA). The optimized composite, with a thickness of 1.2 mm, demonstrated a maximum SE of 34.74 dB at 17.4 GHz, primarily attributed to interfacial polarization, dipolar relaxation, and multiple scattering effects induced by the chopped strands. The results indicate that the shielding performance of the composites can be precisely tuned by modifying the filler concentration and microstructural characteristics, enabling selective frequency-band applications. Overall, this work highlights the potential of Ta-doped NiFe₂O₄/chopped strand composites as lightweight, cost-effective, and high-performance candidates for advanced microwave absorption and electromagnetic shielding applications in defense, and next-generation communication technologies. Full article

Under extreme operating conditions, the internal lubricating flow field of high-speed gear transmission systems exhibits a transient oil–gas multiphase flow, predominantly governed by cavitation-induced phase transitions and turbulent shear. This phenomenon involves complex mechanisms of nonlinear multi-physical coupling and energy dissipation. Traditional lubrication theories and single-phase flow simplified models show significant limitations in capturing microsecond-scale flow features, dynamic interface evolution, and turbulence modulation mechanisms. To address these challenges, this study developed a cross-scale coupled numerical framework based on the Lattice Boltzmann method and large eddy simulation (LBM-LES). By incorporating an adaptive time relaxation algorithm, the framework effectively enhances the computational accuracy and stability for high-speed rotational flow fields, enabling the precise characterization of lubricant splashing, distribution, and its interaction with air. The research systematically reveals the spatiotemporal evolution characteristics of the internal flow field within the gearbox and focuses on analyzing the nonlinear regulatory effect of baffle geometric parameters on the system’s energy transport and dissipation characteristics. Numerical results indicate that the baffle structure significantly influences the spatial distribution of the vorticity field and turbulence intensity by reconstructing the shear layer topology. Low-profile baffles optimize the energy transfer pathway, effectively reducing the flow enthalpy, whereas excessively tall baffles induce strong secondary recirculation flows, exacerbating vortex-induced energy losses. Simultaneously, appropriately increasing the spacing between double baffles helps enhance global lubricant transport efficiency and suppresses unsteady dissipation caused by localized momentum accumulation. Furthermore, the geometrically optimized double-baffle configuration can achieve synergistic improvements in lubrication performance, oil film stability, and system energy efficiency by guiding the main shear flow and mitigating localized high-momentum impacts. This study provides crucial theoretical foundations and design guidelines for developing the next generation of theory-driven, energy-efficient lubrication design strategies for gear transmissions. Full article

Chitosan films obtained by solution casting were investigated by broadband dielectric spectroscopy (BDS) to explore both their glass transition and the effects of thermal annealing on molecular dynamics, deriving from residual water content as well as from cold crystallization. Glass transition at low temperatures could be evidenced in as-produced as well as thermally annealed films, where non-Arrhenian dielectric relaxation processes, consistent with a structural (α) relaxation, could be detected. The process detected at low temperatures could reflect the dynamics of residual water slaved by the polymer matrix. Secondary (β) relaxations, along with a slow process ascribed to interfacial polarization at the amorphous/crystalline interfaces, were concurrently detected. In most cases, a further Arrhenian process at intermediate temperatures (α_c) was present, also indicative of crystallization. Notably, the α processes, due to the primary relaxation of the polymer matrix plasticized by water, could be discriminated from other processes, present in the same frequency range, thanks to improvements in the dielectric fitting strategy. All relaxation processes showed the expected dependence on T_a. The more accurate exploration of the glass transition for chitosan helps to better rationalize its crystallization behavior, in view of an optimized application of this biopolymer. Full article