Search Results (93)

The global transition from petrochemical to sustainable bio-based plastics has been strongly supported by electrospinning (ES), a versatile nanotechnology enabling the fabrication of ultrathin fibers with multifunctional properties. The solution ES process alongside the uniform fibers, a characteristic “beads-on-string” morphology, consisting of alternating cylindrical and spindle-like segments, is frequently observed. Once considered undesirable, these structures are now recognized as functional fibrous architectures with enhanced properties. This work explores the valorization of beaded fibers through combined experimental characterization and modeling, aiming to evaluate the impact of beading on drug diffusion and delivery performance. Poly(3-hydroxybutyrate) (PHB) was selected as the model biopolyester and dipyridamole (DPD) as the model drug. Ultrathin fibers were fabricated using the laboratory electrospinning device, EFV-1 (ICP, Moscow, Russia). The distance between the capillary nozzle and the anodic collector was set to 180 mm, with the capillary tip radius equal to 0.35 mm, and applied voltage between the electrodes was kept constant at 18 kV. Drug release profiles were obtained by simulating DPD diffusion in ellipsoidal (beads) and cylindrical fiber domains. Ultrathin fibers were fabricated by solution electrospinning under environmental conditions (at ambient temperature, 50% relative humidity). Morphology was analyzed via SEM, thermal properties via DSC, and structure via FTIR spectroscopy at different temperatures, including the melting point (~170 °C). Drug release kinetics were monitored using a UV-Vis spectroscopy. The impact of DPD diffusion within the ellipsoidal and cylindrical constituents of polymer filaments was considered to modulate release profiles for the development of innovative pharmaceutical platforms. Diffusion controlled drug release was computationally modeled using a specially designed simulation program, in good agreement with experimental data. The results demonstrate that morphological parameters significantly affect diffusion and release kinetics. The controlled exploitation of bead-on-string architectures may enable the design of electrospun materials with tunable absorption of pollutant filtration, mechanical performance, and flexibility in drug release profiles, for sustainable biopolymers like PHB. Full article

Digital predistortion (DPD) is essential for mitigating nonlinear distortion in radio-frequency (RF) power amplifiers (PAs), particularly in modern multimode transmitters. Among the existing approaches, the neural-network-based DPD reference model adopted in this work is attractive due to its high modeling accuracy and effective predistortion capability. However, its practical implementation is hindered by the computational complexity of the preprocessing stage, which relies on magnitude extraction, phase normalization, and trigonometric operations. Motivated by this limitation, this work proposes a simplified hardware-efficient formulation, derived from an existing real-valued three-layer perceptron (TLP)-based DPD model, for multimode PA linearization. The proposed approach preserves the main characteristics of the reference model while replacing conventional magnitude and phase normalization with a simplified feature representation derived from complex-valued signal products, eliminating square-root, reciprocal, and trigonometric operations. Two configurations are investigated: a single-network formulation and an iterative cascaded structure composed of compact networks trained sequentially. Simulation results demonstrate accuracy comparable to the reference model while reducing computational complexity by up to 34% in multiplications, 25% in additions, and 73.9% in LUT usage, making the proposed approach suitable for FPGA and ASIC implementations. Full article

A digital predistortion (DPD) scheme for concurrent multi-beam transmission in fully digital multiple-input, multiple-output (MIMO) systems, using Fast Fourier Transform (FFT) beamforming and so-called virtual-array processing, is proposed. In a MIMO array with nonlinear power amplifiers (PAs), transmitting multiple beams concurrently yields intermodulation products that end up in both user and non-user directions. In the setting with few users in a large array, the array dimension will typically be much larger than the number of generated intermodulation products. At the same time, linearization per PA is excessively costly for large arrays. This work shows that it is instead possible to linearize the system by producing predistorted user beams, and non-user intermodulation products, through DPD processing in a virtual array of a much smaller dimension than the physical array. Theoretical derivations and simulation examples show how this approach can lead to manyfold reductions in DPD complexity. Full article

Rational design of high-performance viscosifying polymers is critical for enhancing supercritical CO₂ flooding efficiency in enhanced oil recovery (EOR). Traditional experimental and simulation approaches are limited in exploring the vast design space of polymer architecture, flexibility, and intermolecular interactions. This work presents an integrated machine learning (ML) and mesoscopic simulation framework using Dissipative Particle Dynamics (DPD) to accelerate the development of tailored polymeric thickeners. We systematically investigate synergistic effects of linear and branched polymer blends on solvent viscosity under Poiseuille flow, representative of flow in micro-fractures and pore throats. Key molecular descriptors are varied to generate a comprehensive rheological database. This data trains a deep neural network (DNN) surrogate model linking molecular parameters to macroscopic viscosity. The DNN is coupled with gradient ascent optimization for inverse design, enabling rapid virtual screening of thousands of formulations. A focused case study demonstrates that the star-like architectures with associative cores and semi-flexible backbones outperform linear analogs for supercritical CO₂ viscosity enhancement. The optimal candidate—a four-arm star polymer with linear side chains—was validated by DPD simulation. This multiscale “simulation-to-surrogate” methodology bridges molecular design with continuum-scale flow behavior, offering a transformative tool for formulating cost-effective, efficient, and sustainable next-generation EOR chemicals. Full article

Homopolymer addition is a widely used strategy to dilate the microdomain spacing of block copolymers, yet the attainable dilation is often limited by macrophase separation in conventional blends at elevated homopolymer loading. In this work, we investigate an architectural route to suppress macrophase separation while retaining homopolymer-driven dilation: a covalently hybridized bottlebrush copolymer (CH-BBC), a copolymacromer-like bottlebrush architecture in which symmetric AB diblock side chains and A-type homopolymer side chains are covalently grafted to a common backbone. Using dissipative particle dynamics (DPD) simulations, we directly compare the phase behavior of CH-BBC melts with that of composition-matched blends of symmetric AB diblocks and A-type homopolymers. Across the explored window, CH-BBC exhibits microphase morphologies and disorder without an observable two-phase region, whereas the corresponding blends show extensive two-phase coexistence at elevated homopolymer loading. Lamellar analysis and one-dimensional density decompositions further reveal that CH-BBC enables substantially larger microphase dilation and stronger selective swelling of the A-rich domain because tethered A-type homopolymer segments preferentially occupy and dilate the A-rich domain interior while diblock A segments remain localized near interfaces. Full article

The dynamic self-assembly and phase separation of donor–acceptor blends are processes that dictate the nanoscale morphology in organic solar cells. Here, we employ a fluidics-inspired framework, integrating dissipative particle dynamics simulations with percolation theory, to investigate the morphogenesis of two non-fullerene systems: P3HT-PPerAcr and P3HT-PFTBT. We analyze monomeric and homopolymer blends, and copolymer macrostructures, focusing on how key parameters such as temperature and polymer chain flexibility govern the dynamic evolution towards percolating networks. Our simulations captured the fundamental fluidic behavior and universal scaling near the critical percolation threshold (χ_c). The critical exponent β revealed distinct universality classes dictated by system compatibility and flexibility: monomeric and flexible homopolymer blends below the critical temperature (T_c) exhibit mean field behavior (β ≈ 1). In contrast, monomeric systems above χ_c and flexible copolymers below χ_c display 3D percolation behavior (β ≈ 0.45). In the case of flexible copolymeric macromolecules, above percolation threshold a quasi-bidimensional behavior emerge with (β ≈ 0.1). Notably, semi-rigid and rigid homopolymeric and copolymeric linear architectures induce a dimensional crossover, yielding quasi-2D (β ≈ 0.14) and quasi-1D (β ≈ 0.0) morphologies. These findings establish a direct link between tunable fluidic interactions, chain dynamics, and the emergence of optimal bicontinuous percolation networks. Full article

The density power divergence (DPD) is a well-studied member of the Bregman divergence family and forms the basis of widely used minimum divergence estimators that balance efficiency and robustness. In this paper, we introduce and study a new sub-class of Bregman divergences, termed the exponentially weighted divergence (EWD), designed to generate competitive and practically interpretable inference procedures. The EWD is constructed so that its associated weight function remains bounded within the interval $[0, 1]$, which facilitates a transparent interpretation of robustness through controlled downweighting of low-density observations and avoids excessive influence from high-density points. We develop minimum EWD estimators (MEWDEs) within a general framework accommodating independent but non-homogeneous data, thereby extending classical minimum divergence theory beyond the i.i.d. setting. Under standard regularity conditions, we establish Fisher consistency and asymptotic normality, and we analyze robustness properties through influence function calculations. The EWD framework is further extended to parametric hypothesis testing, for which we derive the asymptotic null distribution of a Bregman divergence-based test statistic. Extensive simulation studies and real-data applications demonstrate that the proposed estimators perform comparably to, and often more robustly than, existing DPD-based procedures, particularly under moderate to heavy contamination, while retaining high efficiency under clean data. Overall, the EWD provides a tractable and interpretable alternative within the Bregman divergence class for robust parametric estimation and testing. Full article

Thermosensitive nanogels undergo a volume phase transition in response to temperature changes, making them promising candidates for applications, such as water pollutant remediation and drug delivery. In this study, we investigated the thermosensitive volume phase transition of a neutral poly(N-isopropylacrylamide) (PNIPAM) nanogel using coarse-grained dissipative particle dynamics (DPD) simulations conducted using ESPResSo software with varying bead volumes. Langevin dynamics simulations were employed to compare the results. In DPD simulations, water is explicitly treated, whereas in Langevin dynamics, it is treated implicitly, and hydrophobic interactions are represented by an attractive potential between monomer beads. Our results, including the radius of gyration and various radial distribution functions, revealed a clear volume phase transition as the temperature varied, transitioning from an expanded state to a collapsed state. Notably, the volume phase transition observed in Langevin simulations is attributed to the attractive potential between the PNIPAM monomers, whereas in the DPD simulations, it arises from implicit hydrophobic interactions, obviating the need for an additional attractive potential between the monomer beads. This implicit hydrophobic effect originates from the temperature dependence of the Flory–Huggins interaction parameter. Full article

To investigate the accumulation and transport behavior of non-spherical particles during rotary drilling in extended-reach horizontal wells, a CFD–DEM numerical simulation study was carried out based on actual field drilling parameters. The effects of flow rate, drillpipe rotation speed, drilling fluid viscosity, and particle shape on cutting transport were systematically analyzed in terms of spatial distribution of particle concentration, microscopic movement velocity of particles, and annular pressure drop. A dimensionless pressure-drop–flow-pattern chart was then constructed to characterize the coupled flow–particle transport behavior. The results indicate that flow rate, rotation speed, viscosity, and cutting shape all markedly affect the transition from a stationary cutting bed to suspended transport. Increasing the flow rate, rotation speed, and viscosity promotes hole cleaning. However, once these parameters exceed a certain threshold, further improvements in cutting removal are accompanied by a sharp increase in annular pressure drop. The final Π–DPD dimensionless chart was developed, which can be used for rotary drilling parameter optimization in extended-reach wells, and Π ≈ (2.5–3.1) × 10⁴ is recommended as the preferred range. Full article

The demand for higher data rates and energy efficiency in wireless communication systems drives power amplifiers (PAs) into nonlinear operation, causing signal distortions that hinder performance. Digital Pre-Distortion (DPD) addresses these distortions, but existing systems face challenges with complexity, adaptability, and resource limitations. This paper introduces DRL-DPD, a Deep Reinforcement Learning-based solution for DPD that aims to reduce computational burden, improve adaptation to dynamic environments, and minimize resource consumption. To ensure safety and regulatory compliance, we integrate an ad-hoc Safe Reinforcement Learning algorithm, CRE-DDPG (Cautious-Recoverable-Exploration Deep Deterministic Policy Gradient), which prevents ACLR measurements from falling below safety thresholds. Simulations and hardware experiments demonstrate the potential of DRL-DPD with CRE-DDPG to surpass current DPD limitations in both local and remote configurations, paving the way for more efficient communication systems, especially in the context of 5G and beyond. Full article

The increasing global population of the elderly and rising life expectancy have made osteoporosis a more severe public health issue, necessitating the development of safer and more effective therapeutic strategies. This study investigated the osteoprotective effects of low, medium, and high doses of oyster peptide (OP) in dexamethasone (DEX)-induced osteoporotic rats. Pathological analysis showed that OP treatment effectively mitigated bone loss and repaired bone microarchitecture deterioration caused by DEX administration. In the OP groups, levels of the osteogenic markers osteocalcin (OCN) and osteoprotegerin (OPG) were significantly higher than in the DEX group. Moreover, levels of the osteoclastic markers RANKL, Cathepsin K (Cath-K), MMP-9, C-terminal telopeptide of type I collagen (CTX-1), and Deoxypyridine (DPD) were significantly lower. Bone proteomic analysis of the DEX and OP groups revealed that differentially expressed proteins were significantly enriched in pathways related to extracellular matrix and structural reorganization, ECM–receptor interaction, and PI3K-Akt signaling. Furthermore, virtual screening simulations indicated that peptides with lengths ranging from 11 to 20 amino acid residues were involved in modulating the activity of key receptors in these pathways, including Integrins α5β1, Integrins αvβ3, and EGFR. Collectively, these results demonstrate the significant potential of OP as a novel therapeutic agent for osteoporosis. Full article

The increasing need for high-data-rate wireless applications in 5G and IoT networks requires sophisticated power amplifier (PA) designs in the sub-6 GHz spectrum. This work introduces a high-efficiency Gallium Nitride (GaN)-based power amplifier optimized for the 2.3–2.5 GHz frequency band, using digital pre-distortion (DPD) to improve spectral fidelity and reduce distortion. The design employs load modulation and dynamic biasing to optimize power-added efficiency (PAE) and linearity. Simulation findings indicate a gain of 13 dB, a 3 dB compression point at 29.7 dBm input power, and 40 dBm output power, with a power-added efficiency of 60% and a drain efficiency of 65%. The power amplifier achieves a return loss of more than 15 dB throughout the frequency spectrum, ensuring robust impedance matching and consistent performance. Electromagnetic co-simulations confirm its stability under high-frequency settings, rendering it appropriate for next-generation high-efficiency wireless communication systems. Full article

Membrane tubes, a class of soft biological confinement for ubiquitous transport intermediates, are essential for cell trafficking and intercellular communication. However, the confinement interaction and directional migration of diffusive nanoparticles (NPs) are widely dismissed as improbable due to the surrounding environment compressive force. Here, combined with the mechanics analysis of nanoparticles (such as extracellular vesicles, EVs) to study their interaction in confinement, we perform dissipative particle dynamics (DPD) simulations to construct a model that is as large as possible to clarify the submissive behavior of NPs. Both molecular simulations and mechanical analysis revealed that the interactions between NPs are controlled by confinement deformation and the centroid distance of the NPs. When the centroid distance exceeds a threshold value, the degree of crowding variation becomes invalid for NPs motion. The above conclusions are further supported by the observed dynamics of multiple NPs under confinement. These findings provide new insights into the physical mechanism, revealing that the confinement squeeze generated by asymmetric deformation serves as the key factor governing the directional movement of the NPs. Therefore, the constraints acting on NPs differ between rigid confinement and soft confinement environments, with NPs maintaining relative stillness in rigid confinement. Full article

With the continuous expansion of communication bandwidth, accurately modeling the non-linear characteristics of power amplifiers has become increasingly challenging, directly affecting the performance of digital pre-distortion (DPD) technology. The high peak-to-average power ratio and complex modulation schemes of wideband signals further exacerbate the difficulty of DPD implementation, necessitating more efficient algorithms. To address these challenges, this paper proposes a wideband DPD algorithm based on edge signal correction. By acquiring signals near the center frequency and comparing them with equally band-limited feedback signals, the algorithm effectively reduces the required processing bandwidth. The incorporation of cross-terms for model calibration enhances the model fitting accuracy, leading to significant improvement in pre-distortion performance. Simulation results demonstrate that compared with traditional DPD algorithms, the proposed method reduces the error vector magnitude (EVM) from 1.112% to 0.512%. Experimental validation shows an average improvement of 11.75 dBm in adjacent channel power at a 2 MHz frequency offset compared to conventional memory polynomial DPD. These improvements provide a novel solution for power amplifier linearization in wideband communication systems. Full article

This article presents a Synthetic-Aperture Distributed Phased Array (SADPA) framework to address emitter localization challenges in dynamic environments. Building on Distributed Synthetic-Aperture Radar (DSAR) principles, SADPA integrates distributed phased arrays with motion-induced phase compensation, enabling coherent aperture synthesis beyond physical array limits. By analytically modeling and compensating nonlinear phase variations caused by platform motion, we resolve critical barriers to signal integration while extending synthetic apertures. An improved MUSIC algorithm jointly estimates emitter positions and phase distortions, overcoming parameter coupling inherent in moving systems. To quantify fundamental performance limits, the Cramer–Rao bound (CRB) is derived as a theoretical benchmark. Numerical simulations demonstrate the SADPA framework’s superior performance in multi-source resolution and positioning accuracy; it achieves 0.012 m resolution at 10 GHz for emitters spaced 0.01 m apart. The system maintains consistent coherent gain exceeding 30 dB across both the 1.5 GHz communication and 10 GHz radar bands. Monte Carlo simulations further reveal that the MUSIC-DPD algorithm within the SADPA framework attains minimum positioning error (RMSE), with experimental results closely approaching the theoretical CRB. Full article