Search Results (9,540)

In this research, PLA biocomposites reinforced with 20 wt% flax fibers modified with 1, 5, 10, and 20% concentrations of trans-cinnamic acid (TC) were prepared. The materials were systematically characterized to evaluate their structural, thermal, viscoelastic, surface, and functional properties. Thermal stability and phase transitions were analyzed using thermogravimetric analysis (TG) and differential scanning calorimetry (DSC), while viscoelastic behavior and molecular relaxation processes were investigated by dynamic mechanical analysis (DMA). To elucidate failure mechanisms and interfacial quality, fracture surface morphology after tensile testing was observed using scanning electron microscopy (SEM). Surface wettability was determined through water contact angle measurements, and antibacterial activity against Escherichia coli and Staphylococcus aureus was evaluated to assess the functional potential of the developed biocomposites. The results demonstrated that moderate fiber modification improved interfacial adhesion and enhanced thermo-mechanical performance. The highest contact angles were observed for 5% and 10% TC concentrations, indicating increased surface hydrophobicity, while strong antibacterial activity (R ≥ 6) was achieved for 10% and 20% TC. The research confirms that trans-cinnamic acid concentration governs multiple structure–property relationships, enabling controlled tuning of mechanical reinforcement and antibacterial functionality. Full article

Accurate wind speed forecasting is critical for reliable wind-power integration, yet it remains challenging due to the strongly non-stationary and inherently multi-scale nature of atmospheric processes. While deep learning models—such as LSTM, GRU, and Transformer architectures—achieve competitive short- and medium-term performance, they frequently suffer from spectral bias, hyperparameter sensitivity, and reduced generalization under heterogeneous operating regimes. To address these limitations, we propose a multi-scale spectral–recurrent framework, termed RFF-RNN, which integrates multi-band Random Fourier Feature (RFF) encodings with parameterizable recurrent backbones. A key innovation of our approach is the deliberate relaxation of strict shift-invariance constraints; by jointly optimizing spectral frequencies, phase biases, and bandwidth scales alongside the neural weights, the framework dynamically shapes a fully data-driven spectral embedding. To ensure robust adaptation, we employ a two-stage optimization strategy combining gradient-based inner-loop learning with outer-loop Bayesian hyperparameter tuning. Our extensive evaluations on a controlled synthetic benchmark and six geographically diverse real-world wind datasets (spanning the USA, China, and the Netherlands) demonstrate the superiority of the proposed framework. Statistical validation via the Friedman test confirms that RFF-enhanced models—particularly RFF-GRU and RFF-LSTM—systematically outperform standard recurrent networks and state-of-the-art Transformer architectures (Autoformer and FEDformer). The proposed approach yields significantly lower error metrics (MAE and RMSE) and higher explained variance ($R^2$), while exhibiting remarkable resilience against error accumulation at extended forecasting horizons. Full article

Background/Objectives: Magnetic nanoparticles have emerged as powerful tools for biomedical imaging, targeted drug delivery, and hyperthermia therapy. Magnetic particle imaging (MPI) is among the most promising technologies built around its properties: a radiation-free, quantitative tomographic modality that detects superparamagnetic iron oxide nanoparticles (SPIONs) directly against a biologically silent background. This review synthesizes MPI’s physical principles, nanoparticle design strategies, and preclinical applications within the broader landscape of magnetic material engineering for biomedical use. Methods: A systematic review was conducted covering MPI signal generation and image reconstruction, nanoparticle core synthesis and surface coating approaches, and preclinical applications, spanning cell tracking, oncological imaging, vascular perfusion, neuroimaging, and MPI-guided theranostics. Studies were selected to provide quantitative benchmarks and direct comparisons with competing modalities where available. Results: MPI delivers signal-to-background ratios above 1000:1, iron-mass linearity at R² ≥ 0.99, regardless of tissue depth, and acquisition rates up to 46 volumes per second. Tracer architecture—encompassing single-core particles, multicore nanoflowers, and stimuli-responsive cluster designs—is the primary determinant of sensitivity, environmental robustness, and theranostic capability. Preclinical results include detection of cell populations in the low thousands, earlier ischaemia identification than diffusion-weighted MRI, real-time drug release quantification, and spatially confined tumour hyperthermia. Three translational bottlenecks are identified: the absence of a clinically approved tracer with optimal relaxation dynamics, hardware performance losses when scaling to human-bore systems, and overestimation of passive tumour accumulation in murine models. Conclusions: MPI illustrates how progress in magnetic material design directly expands clinical imaging and theranostic possibilities. Successful translation will require indication-driven, interdisciplinary development that integrates materials science, scanner engineering, and regulatory strategy in parallel. Full article

Entropic soft-min operators are widely used to obtain smooth approximations of minimum and argmin mechanisms in optimization, machine learning, and reinforcement learning. The quality of this approximation is controlled by an inverse temperature parameter that governs the trade-off between smoothness and fidelity, yet its selection is usually based on global heuristics or worst-case bounds that do not account for the geometry of the candidate cost vector. This study investigates the calibration of the inverse temperature parameter from a geometry-aware perspective, with explicit guarantees on the approximation error between the entropic soft-min and the exact minimum value. After establishing the structural properties of the relaxation error, including monotonicity with respect to the inverse temperature and its dependence on the geometry of the near-optimal set, we introduce a conformal calibration rule that selects the smallest inverse temperature, ensuring that a prescribed upper quantile of the approximation error remains below a target tolerance with distribution-free finite-sample validity. The resulting selector adapts to the geometry distribution represented in the calibration population and provides a principled alternative to mean-based and worst-case tuning rules. Numerical experiments, including geometry-controlled benchmarks and a contextual bandit setting illustrating the impact of geometry-aware calibration on decision-making under estimated action values, show that the proposed method accurately tracks oracle calibration temperatures, preserves the desired operator-level coverage, and makes explicit how geometric heterogeneity governs the effective sharpness required by the soft-min approximation. Additional shifted evaluations illustrate the role of exchangeability in the validity guarantee and the consequences of transferring temperatures across populations with different near-optimal geometries. Full article

To address the issues of information leakage risks faced by the base station (BS) when communicating with multiple users in an integrated sensing and communication (ISAC) system, as well as the blockage of the direct link between the BS and the target to be detected, which limits sensing functionality, this paper introduces the active intelligent reflecting surface (IRS) into the ISAC system. By creating a virtual line-of-sight (LoS) path, signal blockage is effectively mitigated, while the active IRS enhances the incident signal strength and adjusts the reflection phase shifts, thereby improving the reliability and security of communication. This paper proposes a joint optimization scheme for the active IRS-assisted ISAC system, which jointly designs the BS beamforming and the IRS reflection coefficient matrix. A non-convex optimization problem is formulated with the objective of maximizing the radar output signal-to-noise ratio (SNR) subject to communication performance constraints. To solve this problem, this paper employs an iterative algorithm based on alternating optimization (AO), fractional programming (FP), and semidefinite relaxation (SDR). Simulation results demonstrate that the proposed scheme significantly outperforms the benchmark schemes without IRS assistance and with passive IRS assistance in terms of enhancing the sensing performance of the ISAC system. Full article

The energy-intensive nature of primary aluminum production necessitates advanced computational tools for process optimization. This study presents a coupled electro-thermal model of an aluminum reduction cell, developed within the framework of smart manufacturing. Using the finite difference method (FDM) implemented in MATLAB R2025b, the model resolves the three-dimensional configuration of a cell with eight prebaked anodes across four distinct physical domains (electrolyte, anodes, cathode, and gas phase). The computational grid comprises approximately 45,000 nodes with refined vertical resolution (Δz = 0.025 m) in the interelectrode gap. The electrostatic solution converges within 150–200 iterations using successive over-relaxation (SOR, ω = 1.5), with a total runtime under 15 min for 30,000 s of simulated physical time on a standard desktop workstation. Simulation results reveal characteristic temperature profiles with maxima reaching 1150 °C and a thermal uniformity index of approximately 130 °C across the central cross-section. The predicted specific energy consumption of 14.0 MWh/t Al aligns with industrial benchmarks. This computationally accessible virtual testbed enables rapid assessment of design modifications and process parameters, supporting the goals of energy efficiency and enhanced operational stability in primary aluminum production. Full article

This study presents a predictive manufacturing framework for customizing the biomechanical response of a 3D printed ergonomic armrest based on relaxed Voronoi metamaterials. A double curved armrest geometry was combined with parametric lattice generation, stereolithography printing in BioMed Elastic 50A resin, uniaxial compression testing of cylindrical lattice specimens, and homogenized finite element simulations using a CT derived forearm model under 15, 30, and 45 N loading. The results showed that both cell size and ligament thickness strongly affected compressive behavior, with smaller cells and thicker ligaments producing higher stiffness and earlier densification. Among the uniform configurations selected for simulation, the E-9-1.5 lattice provided the most balanced response, maintaining contact pressure below about 70 kPa up to 45 N, whereas the stiffer E-7-1.5 configuration exceeded 160 kPa and the E-7-1 configuration surpassed 100 kPa at higher load. Based on these findings, a functionally graded Voronoi concept was developed to combine a more compliant central zone with a stiffer peripheral support region while preserving conformity to the complex armrest boundary. Overall, the results show that relaxed Voronoi lattices offer a computationally efficient route toward anatomically conforming and mechanically tunable cushioning interfaces. Full article

Silver-coated copper (Cu@Ag) core–shell nanoparticles are promising interconnect materials for electronic packaging due to their high conductivity, oxidation resistance, and reduced use of precious metals. However, the key factors governing their sintering behavior and mechanical performance are not fully understood. In this study, molecular dynamics simulations were performed to examine the effects of sintering pressure (300–700 MPa), temperature (500–700 K), particle size, and silver shell thickness on atomic diffusion, microstructural evolution, and mechanical properties. Results show that higher pressure improves particle contact, accelerates densification, and strengthens interfacial bonding, with optimal performance achieved at 600–700 MPa. Elevated temperatures enhance atomic mobility, promoting neck growth and pore elimination, with the most active diffusion observed between 650 K and 700 K. Particle size and shell thickness also affect sintering: the Ag6Cu3 configuration exhibits the highest atomic mobility and a balanced combination of strength and ductility. Moderately thick silver shells facilitate surface diffusion and interfacial interdiffusion, while mechanisms such as the Kirkendall effect and local plastic relaxation reduce defect density, yielding stable sintered structures. These findings provide atomic-scale insights into the sintering mechanisms of Cu@Ag nanoparticle solder pastes and offer guidance for optimizing processing parameters in high-performance electronic packaging applications. Full article

Inverse synthetic aperture radar (ISAR) image classification plays a crucial role in remote sensing, traffic monitoring, and maritime surveillance. However, existing methods often suffer from limited labeled data, degraded image quality, and the insufficient adaptability of conventional loss functions. To address these issues, this paper proposes AdaProtoNet, a few-shot ISAR image classification framework based on a ResNet10 backbone and a combined adaptive and cross-entropy loss function. The model adopts a Prototypical Network architecture that balances feature extraction and class discrimination. A customized multicategory ISAR dataset is constructed through 3D target modeling and simulated radar imaging to support few-shot learning. Within the meta-learning paradigm, AdaProtoNet generates class prototypes by averaging support features and performs classification via Euclidean distance measurement. Experimental results demonstrate that AdaProtoNet achieves higher overall accuracy (OA) and stronger generalization than conventional ISAR classification methods. These findings highlight the effectiveness of adaptive-margin optimization in few-shot learning and provide guidance for the development of next-generation remote sensing recognition systems. Full article

Mechanical and thermal relaxation times are of utmost importance in determining the thermal quality of micro- and nano-resonators. The interplay between mechanical and thermal activity governs energy dissipation in these resonators. In a recent paper, an analytical thermal model was developed to incorporate mechanical and thermal relaxation times, thereby increasing the quality factor under mechanical damage, while accounting for static prestress in a micro-viscothermoelastic resonator. The effects of the relaxation time parameters and static prestress on the thermal quality factor have been addressed. This model assumes that static prestress can serve as a tuning knob for significant improvements in thermal efficiency variables. The mechanical and thermal relaxation times, isothermal frequency, and mechanical damage parameter have substantial effects on the resonator’s thermal quality factor. Full article

The synergistic utilization of waste plastics and tires in asphalt modification is a highly promising sustainable strategy. However, the differential impacts of distinct plastic molecular architectures on the performance and network evolution of rubber-modified asphalt remain fundamentally unclear. This study systematically investigated the physical, rheological, and microstructural properties of composite asphalts modified with desulfurized rubber powder (DRP) and four representative plastics: polyethylene (PE), styrene–isoprene–styrene (SIS), styrene–ethylene–butylene–styrene (SEBS), and styrene–butadiene–styrene (SBS). Furthermore, the pavement performance of the asphalt mixtures prepared via dry and wet methods was comparatively evaluated. Microstructural and spectroscopic analyses revealed that the composite modification was primarily governed by physical blending and swelling. The non-polar, semi-crystalline PE resulted in severe phase separation and extreme low-temperature brittleness. Conversely, the saturated hydrogenated mid-blocks of SEBS endowed the asphalt with the highest high-temperature rutting resistance but severely compromised its low-temperature stress relaxation. Remarkably, SBS interacted synergistically with DRP to form a highly homogeneous and densely interwoven three-dimensional network, thereby achieving an optimal viscoelastic balance, outstanding storage stability, and superior low-temperature ductility. Pavement performance tests further demonstrated that the wet method significantly outperformed the dry method for block copolymers by facilitating sufficient pre-swelling. Overall, the SBS-DRP composite-modified asphalt prepared via the wet method exhibited the most exceptional and balanced comprehensive pavement performance, providing a robust theoretical foundation for the sustainable and high-value recycling of multi-source solid wastes in paving engineering. Full article

The precipitation of nanoscale HfO₂ plays a critical role in the high-temperature creep properties of Fe-Cr-Al electrical heating alloys. However, the atomic-scale initial nucleation and growth mechanisms remain unclear, hindering the precise design of precipitates based on Hf microalloying. In this study, classical molecular dynamics simulations implemented in LAMMPS were employed to investigate the formation and evolution of Hf-O clusters at 1773 K, 1873 K, and 2000 K. The Fe-Cr-Al-Hf-O system was described by hybrid potential functions, whose reliability was verified by lattice-parameter calculations in good agreement with literature values. The simulation results demonstrate that Hf atoms and O atoms attract each other, forming stable Hf-O clusters. At higher temperatures, the diffusion capabilities of Hf and O atoms are enhanced, the number of Hf-O bonds grows, and the size of the largest cluster expands, indicating that elevated temperatures promote cluster growth. The calculated diffusion activation energy of Hf and O atoms indicates that increasing temperature promotes O atom diffusion more significantly. Analysis of the cluster radius of pair gyration and average atomic energy reveals that Hf-O clusters formed at 1873 K exhibit more compact and stable structural characteristics. Radial distribution function analysis further revealed that the atomic arrangement of neighboring atoms in Hf-O clusters closely resembles the relaxed HfO₂ crystal structure at the same temperature, indicating that Hf-O clusters serve as critical nucleation cores promoting the precipitation of HfO₂ crystals. This study elucidates the dynamic formation mechanism and structural evolution of Hf-O clusters in Fe-Cr-Al alloys at the atomic scale, providing valuable guidance for the optimized design of precise control over HfO₂ nanoprecipitates. Full article

We utilized molecular dynamics (MD) simulations to explore the interaction of the RGD peptide with the αvβ3 integrin receptor, a key process for targeted drug delivery to tumors. The goal of these simulations was to model the transport of boron atoms by the RGD peptide and to characterize the binding event between this vector and its receptor. The study focused on the interaction processes and spatial arrangements of the solvated RGD–integrin system. Simulations were run for 100 ns to achieve relaxed-state configurations. Our model featured two RGD peptides: one pre-localized within the integrin’s binding site and another initially positioned externally. The external peptide was observed to diffuse freely and subsequently bind to the αvβ3 integrin. This spontaneous binding event provides valuable insights into the pharmacological specificity and mechanisms of the RGD–integrin interaction, informing the design of effective drug delivery systems. Full article

Topological insulators (TIs) feature unique Dirac fermion-hosting surface states with exceptional electronic properties, rendering them promising candidates for optoelectronic and spintronic applications. Herein, we investigate the relaxation dynamics of photoexcited carriers in Bi₂Se₃ films via optical pump–terahertz (THz) probe spectroscopy (OPTP) at room temperature. Under 800 nm pump pulse excitation, the time-dependent real part of the pump excitation conductivity Δσ exhibits a positive-to-negative sign reversal as carriers relax toward equilibrium, which is further validated by frequency-dependent conductivity spectra at varied pump-probe delays. The initial positive Δσ originates dominantly from bulk carrier contributions, while the negative component at prolonged delays is ascribed to Dirac surface states, driven by enhanced scattering of photoexcited carriers. Using the Drude–Smith model to fit the differential conductivity spectra, we quantitatively extracted time-dependent transport parameters of bulk and surface states. These results unravel the comprehensive carrier relaxation mechanism in Bi₂Se₃, clarify the distinct roles of surface and bulk contributions, and lay the groundwork for designing TI-based THz devices. Full article

In this paper, we investigate the multi-scale dynamics of a singularly perturbed Rosenzweig–MacArthur model with a generalist predator and identify dynamical phenomena, including equilibrium bifurcations, supercritical or subcritical singular Hopf bifurcations, canard explosion bifurcations and homoclinic bifurcations. Specifically, the system exhibits a globally stable node, a headless canard cycle evolving into a homoclinic cycle, a headed canard cycle encompassing either a headless canard cycle or a homoclinic cycle, and so on. Notably, near the boundary equilibrium, these cycles exhibit a diminutive beard-shaped structure whenever it aligns with the transcritical non-normally hyperbolic point. The numerical simulations confirm the occurrence of a canard explosion, relaxation oscillation, and an inverse canard explosion phenomena not previously reported in singularly perturbed systems with both a transcritical point and a canard point. In brief, our results demonstrate that the generalist predation can cause richer bifurcations and dynamics. Full article