Search Results (372)

Lightweight polymer composites with effective electromagnetic interference (EMI) shielding are of increasing interest for advanced electronic and aerospace applications; however, conventional glass fiber-reinforced polymers (GFRPs) exhibit inherently low electrical conductivity, limiting their shielding performance. In this study, a hierarchical hybrid conductive architecture was developed by integrating graphene-coated multiaxial glass fiber fabrics with carbon black (CB)-reinforced epoxy matrices to enhance EMI shielding behavior in the X-band (8–12 GHz). Graphene coatings were deposited onto glass fibers via a surfactant-assisted ultrasonic dispersion method, while carbon black (0–1 wt.%) was incorporated into the epoxy matrix using ultrasonication-assisted mixing. Multilayer composites were fabricated using a vacuum bagging process. X-ray diffraction analysis revealed that the composites retained a predominantly amorphous epoxy/glass fiber matrix while exhibiting broad carbon-related diffraction features associated with disordered graphitic domains. Electrical conductivity measurements indicated that all composites remained in the insulating regime (~10⁻⁹ S/m), suggesting that a fully interconnected conductive network was not established within the investigated filler range. Despite the absence of a continuous conductive network, measurable EMI shielding performance was achieved. The composite containing 0.25 wt.% CB exhibited the highest shielding effectiveness, reaching approximately 12 dB at ~11.2 GHz. Analysis of the shielding contributions showed that absorption contributions (SEA) were consistently higher than reflection contributions (SER) across the studied frequency range. Morphological observations revealed that well-dispersed CB at low loading facilitated the formation of localized conductive domains that may contribute to tunneling-assisted polarization and interfacial charge accumulation. At higher CB contents, particle agglomeration reduced dispersion quality and limited effective pathway formation, while dynamic mechanical analysis indicated enhanced stiffness at low CB loading. FTIR results confirmed the absence of new chemical bonding, indicating that CB acts as a physically dispersed conductive filler. Overall, the results show that effective EMI shielding can be achieved in electrically insulating composites through the combined effect of hierarchical structural design and localized conductive features. This approach provides a practical pathway for developing lightweight EMI shielding materials with controlled filler loading and preserved structural integrity for aerospace and electronic applications. Full article

Hyperspectral imaging (HSI) provides rich spatial and spectral information for remote sensing, mineral exploration, and biomedical analysis, but its limited spatial resolution and sensor imperfections lead to blurred, noisy, and mixed-pixel observations. Addressing these degradations jointly—rather than sequentially—has been shown to improve physical interpretability, yet existing joint deblurring–unmixing methods rely primarily on hand-crafted regularizers that do not fully exploit spatial–spectral structure. Meanwhile, recent plug-and-play (PnP) approaches applied to HSI leverage deep priors but focus solely on either deconvolution or unmixing in isolation. To bridge this gap, we formulate the joint inverse problem of hyperspectral deblurring and spectral unmixing and propose, to our knowledge, the first plug-and-play framework tailored for this coupled task using the Alternating Direction Method of Multipliers (ADMM) and a pretrained deep denoiser (DnCNN) as an implicit PnP prior. Our method uses the natural splitting properties of ADMM to separate a physics-driven subproblem that enforces fidelity to the hyperspectral forward model, which includes linear mixing and blur under a linear, space-invariant convolution approximation, from the data-driven prior step. This synergy of model-based fidelity and learned spatial prior enables more accurate abundance estimates than those obtained with approaches relying solely on analytical regularizers. Experimental results on real hyperspectral datasets demonstrate that the proposed Plug-and-Play Joint Deconvolution and Unmixing (PnP-JDU) method outperforms conventional unmixing baselines, stand-alone PnP unmixing methods, and the Deblurring and Sparse Unmixing via the Alternating Direction Method with Total Variation (DSUnADM-TV) baseline in reconstruction and abundance accuracy metrics. Across the tested datasets and imaging conditions, PnP-JDU achieves lower RMSE, higher PSNR, lower reconstruction and abundance errors, and lower SAD values, while preserving fine spatial details and producing physically meaningful abundance maps. Full article

Background/Objectives: The aim of current study was the significant improvement of both the flowability and the compressibility of mesoporous silica microparticles (MSMs), to enable the formulation a potential drug delivery system. MSMs are of emerging interest in the pharmaceutical industry, due to their numerous advantages and versatile applicability, such as improvement in aqueous solubility and epithelial permeability, thus enhancing the oral bioavailability of drugs. However, the formulation of these types of materials has been a major challenge. This problem originates from poor powder flow characteristics due to particle properties. Methods: A binder-free high-shear wet granulation (HSWG) process was performed to improve the flowability and compressibility of the model material, meanwhile preserving its porosity. The prepared granules were characterized by particle size, size distribution, yield percentage, particle morphology, porosity, powder flowability, crushing strength, and stability. Micro-CT measurements were performed to examine the structure of the granules and to see the internal segmentation resulted by the two-step granulation process. The granules were compressed into tablets to evaluate the compressibility behavior based on the models of Kawakita and Walker. The physical parameters of the compressed tablets, such as breaking hardness, tensile strength, and thickness, were tested. Results: The prepared granules were evaluated successfully according to the mentioned properties and found to be satisfactory compared to the raw materials. The binder-free method appeared to be effective, thus the use of binders may be avoided if the process is designed well and critical process parameters (CPPs) selected carefully. The granules showed good stability over a one-year testing period. The micro-CT test also verified the success of the initial concept of preparing core–shell structured granules, and enabled the determination of macropores. Nevertheless, the results were completed with BET measurements to determine specific surface area of the granules. Conclusions: The effect of the critical process parameters of the granulation process on all the mentioned attributes was investigated and since major differences were observed between the batches, the effect of the selected CPPs were also verified. Full article

Low-frequency sound insulation remains a major challenge for conventional passive materials, as improved attenuation is usually achieved at the expense of increased thickness and mass. In this work, a smooth fixed third-order ring-curve fractal-maze acoustic metamaterial is proposed for compact low-frequency sound insulation, and a physics-guided long short-term memory–physics prediction surrogate network (LSTM–PPS-Net) tandem framework is developed for its full-spectrum inverse design. Different from conventional Hilbert-type, right-angled, or sharply folded labyrinthine structures, the proposed topology uses recursively arranged curved channels to extend the effective acoustic propagation path and enhance phase accumulation within a limited space. Based on this mechanism, four physically meaningful parameters, namely slit width d, characteristic radius R₃, wall thickness t_w, and inter-column spacing l_E, are selected to construct a low-dimensional design space. A COMSOL–MATLAB automated finite-element method (FEM) workflow is established to generate 1000 valid transmission-loss (TL) spectra over 100–1700 Hz with a 5 Hz interval. For forward prediction, PPS-Net is developed by integrating geometry encoding, frequency-conditioned spectral decoding, and peak-weighted learning. The proposed PPS-Net achieves the best prediction accuracy among the tested models, with a mean absolute error (MAE) of 0.75 dB, a root mean square error (RMSE) of 1.88 dB, and a coefficient of determination (R²) of 0.96, outperforming multi-layer perceptron (MLP), convolutional neural network (CNN) and Transformer models under the same dataset and training protocol. For inverse design, the LSTM encoder extracts frequency-ordered spectral features from the target TL curve, while the frozen PPS-Net decoder provides differentiable acoustic-response feedback, thereby addressing the non-unique mapping from acoustic response to structural parameters. Furthermore, a compactness-oriented optimization strategy is introduced to balance spectral consistency, peak alignment, bandwidth preservation, and occupied-area reduction. In two representative cases, the optimized designs reduce the occupied area by approximately 21% in both representative cases, while maintaining the target attenuation characteristics after FEM verification. These results demonstrate that the proposed framework provides an efficient and physically interpretable route for the full-spectrum inverse design and compact optimization of low-frequency acoustic metamaterials. Full article

Background: Regular physical activity induces physiological cardiac remodeling (“athlete’s heart”), which may overlap with pathological hypertrophy. Regional echocardiographic and electrocardiographic data among young African adults are limited. This study evaluated how graded physical activity relates to cardiac structure and function among university students in Ghana. Methods: In this comparative cross-sectional study, 174 apparently healthy students aged 18–30 years were categorized into four physical activity groups in the preceding six months: level 1, no regular exercise (n = 29, 16.7%); level 2, <30 min/day of exercise (n = 41, 23.6%); level 3, 30 to 60 min/day of moderate exercise (n = 29, 16.7%); and level 4, >1 h/day of vigorous exercise (n = 75, 43.1%). Anthropometry, blood pressure, 12-lead electrocardiography, and comprehensive transthoracic echocardiography were obtained. Cardiac indices were compared across activity levels using the Kruskal–Wallis or Welch’s ANOVA test, with post hoc comparisons and regression analyses performed where appropriate. Results: Participants were predominantly male (56.3%), with a mean age of 22.3 ± 3.50 years, BMI of 23.0 ± 4.39 kg/m², systolic blood pressure of 118 ± 13.0 mmHg, diastolic blood pressure of 71.3 ± 9.11 mmHg, and heart rate of 66.9 ± 10.9 bpm. Compared with sedentary participants, those in level 4 had a higher IVSd (9.87 ± 1.61 vs. 8.17 ± 1.47 mm, p < 0.001), LVIDd (43.6 ± 6.96 vs. 40.2 ± 3.58 mm, p = 0.002), LVPWd (10.1 ± 1.95 vs. 8.91 ± 1.60 mm, p = 0.003), and LVM (54.6 ± 7.45 vs. 47.1 ± 6.57 g, p < 0.001). EDV and ESV also increased with activity (90.1 ± 24.8 vs. 69.7 ± 17.8 mL, p < 0.001; 32.4 ± 12.8 vs. 25.6 ± 6.52 mL, p = 0.023). Systolic function was preserved across groups, with an EF of 59.3 ± 4.86% in level 4 vs. 58.3 ± 5.34% in level 1 (p = 0.707). Level 4 participants had a higher SV (57.6 ± 16.7 vs. 46.3 ± 10.4 mL, p = 0.003), CO (3.83 ± 1.17 vs. 3.05 ± 0.70 L/min, p = 0.022), and CI (2.19 ± 0.66 vs. 1.77 ± 0.37 L/min/m², p = 0.015). Bradycardia was most frequent in level 4 (35.8% vs. 18.2% in level 1, p = 0.041), and PR interval was longer in participants exercising ≥30 min/day than in those exercising <30 min/day (166 ± 23.2 vs. 162 ± 21.8 ms, p = 0.031). Conclusions: In young African university students, greater physical activity was associated with mild physiological remodeling, including a higher left ventricular wall thickness, cavity size, and mass, while systolic and diastolic indices remained preserved. The mean values in the most active group were 9.87 mm IVSd and 10.1 mm LVPWd with preserved EF, supporting activity-related adaptation rather than overt pathological hypertrophy and highlighting the need for population-specific cardiovascular interpretation. Full article

Almonds (Prunus dulcis; family Rosaceae) contain 18–25% protein (dry weight). They are an important plant-based protein source in dairy alternatives and other functional foods. The hard and dense nature of almond kernels and the localization of proteins with lipid bodies in the cotyledons of almond seeds make it challenging to recover protein from the seed efficiently and preserve its function. Therefore, this review evaluates the influence of pretreatments, including blanching, grinding, and defatting, on almond protein recovery and functionality, and compares conventional and emerging technologies for almond protein. Traditional protein extraction techniques such as alkaline extraction–isoelectric precipitation (AE–IEP), aqueous extraction, and salt extraction provide moderate-to-high protein yields, but harsh processing conditions denature the proteins, decrease solubility, and cause functional properties to be lost. On the other hand, emerging protein extraction technologies (including enzyme-assisted aqueous extraction (EAE) ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), high-pressure processing (HPP), and pulsed electric field (PEF) treatment) improve protein recovery, resulting in protein extract with superior functional properties and reduced allergenicity. However, their application in industry remain challenging. This review reveals that pretreatment approaches and conditions/parameters significantly influence protein extraction efficiency and the functional and structural properties of almonds, and that no single method is universally optimal. This review concludes that controlled enzymatic hydrolysis combined with physical pretreatment may be the best approach for producing high-value-added almond protein ingredients with specific techno-functional properties for use in plant-based beverages, hypoallergenic products, or nutraceuticals. More research is needed to develop an efficient, applicable, sustainable and eco-friendly almond protein extraction process, optimizing processing conditions to achieve high protein recovery while retaining desirable functional properties, and reduce operating costs. Full article

The counterfeiting of official documents and banknotes represents a critical threat to global security and requires robust and low-cost protection techniques. This work presents an innovative information security system that uses photoluminescent inks for chromatic multiplexing of QR codes. Unlike conventional cryptographic methods, the proposed approach employs physical-layer information hiding through the superposition of two QR codes encoded in magenta and cyan colors on a white background. The controlled interaction between these codes generates an additional logical state that enables a third representation of information through pixel-level operations. The resulting chromatic QR code remains visually imperceptible under ambient illumination and can be reliably recovered through chromatic demultiplexing and thresholding process. Additionally, its visibility can be enhanced under ultraviolet (UV) excitation due to photoluminescent behavior and spectral response variations. The experimental results demonstrate that both encoded data layers can be extracted independently with high fidelity using standard CMOS sensors, while preserving structural integrity and decodability. The proposed scheme increases information density within a single optical tag while improving resistance against unauthorized replication and visual forgery. Full article

Bauhinia forficata leaves were subjected to ultrasonic pretreatment and subsequently dried using a refractance window (RW) and tray drying (TD). The physical, chemical, and biological properties of the dried leaves were evaluated under both drying methods, with and without ultrasound. RW combined with ultrasound (RW-US) resulted in the shortest drying time (90 min) and the lowest values of water activity (0.21), color difference (ΔE = 0.61), and maximum shear force (14.72 N), indicating improved drying efficiency and texture preservation. In addition, the RW-US samples exhibited the highest water solubility capacity (13.75%), water absorption capacity (5.56 g water/g dry matter), and swelling power (9.95%). With respect to structural changes, thickness showed the greatest percentage reduction during drying. The RW-US treatment also preserved bioactive compounds more effectively, yielding the highest total polyphenol content (61.96 mg GAE/g extract), flavonoid content (308.44 mg QE/g extract), antioxidant activity (60.50% by DPPH• and 70.15% by ABTS•+), and chlorophyll content (2.65 mg/g), the values of which were closest to those of fresh leaves. None of the extracts showed cytotoxic effects, with respect to hypoglycemic activity, the best treatments were RW, RW-US, and TD, which resulted in glucose reductions of 51.64%, 41.95% and 39.70%, respectively. Overall, RW-US drying preserved most of the physical, chemical, and biological properties, resulting in the production of a potential functional ingredient for foods. Full article

Autonomous driving systems face critical perception failures in dense fog, where conventional RGB cameras suffer from severe degradation due to atmospheric scattering and reduced visibility. This paper presents an adaptive multi-modal fusion framework that synergistically integrates gated imaging with 3D LiDAR point clouds to achieve robust obstacle detection under visibility conditions as low as 50 m. Unlike standard cameras that passively capture scattered ambient light, gated cameras employ time-synchronized active illumination to physically filter backscattered photons, preserving structural features even in low-visibility scenarios. We propose a novel Adaptive Feature-Weighting Network (AFW-Net) that dynamically adjusts sensor modality contributions based on real-time environmental degradation assessment. The framework incorporates three key innovations: (1) a cross-modal feature extraction module that exploits the complementary physical properties of gated imaging and LiDAR, (2) an attention-based adaptive fusion mechanism that quantifies per-modality reliability through uncertainty estimation, and (3) a degradation-aware training strategy using weather-specific augmentation. Extensive experiments on the Princeton Automated Driving Dataset demonstrate that our approach maintains detection average precision (AP) above 82% under dense fog conditions (50 m visibility), representing a 23.7% improvement over state-of-the-art RGB-LiDAR fusion methods that exhibit substantial performance degradation to 58.4% AP. Ablation studies validate the necessity of each component, and cross-dataset evaluation confirms the generalization capability of the proposed framework. The adaptive weighting mechanism proves particularly effective, dynamically rebalancing modality contributions across the gated imaging and LiDAR branches while maintaining LiDAR geometric constraints. This work establishes a robust perception paradigm for safety-critical autonomous systems operating in low-visibility environmental conditions. Full article

In sparse representation learning-based linear k-nearest neighbors methods, the linear representation assumption frequently fails when applied to nonlinear distributed data, leading to degraded generalization and a loss of physical interpretability. To address this, we propose the Kernelized Manifold-Optimized Linear Nearest Neighbor (KMOLNN) method. Methodologically, KMOLNN projects the data into a high-dimensional kernel space to capture the nonlinear relationships, while introducing an adaptive manifold-preserving regularization term—via an adaptive Laplacian matrix—to dynamically preserve the local geometric structures. Theoretically, this study provides a mathematical proof of the nearest neighbor group effect for the kernel framework and reveals that its weight optimization behavior implicitly implements the Bayesian decision rule. Furthermore, we derive a rigorous generalization error bound using Rademacher complexity to validate its theoretical robustness. Empirically, we evaluate KMOLNN on 15 small-to-medium-scale benchmark datasets against eight comparative methods, including recent variants. The results demonstrate significant numeric superiority, with KMOLNN achieving an average accuracy of 90.76% and a Macro F1-score of 88.62% across the evaluated datasets. Finally, we present a comprehensive runtime analysis, explicitly acknowledging that these gains in generalization capability and theoretical interpretability present a practical trade-off, requiring increased computational runtime due to the iterative alternating optimization process. Full article

The sustainable recovery of high-value metals from wastewater has garnered significant attention in light of the circular economy and environmental preservation. Because of its appealing characteristics, membrane separation technology is essential for the sustainable and effective recovery of valuable metals from wastewater, in contrast to conventional methods, which are chemical- or energy-intensive. In this study, a rational design approach was utilized to synthesize a metal–organic framework (MOF) using a deep eutectic solvent (DES) as a mediating medium to control the reaction of framework formation and particle properties. While DESs have been widely used for the physical modification of materials, their role as a chemically modifying medium during MOF synthesis for structural tailoring remains less explored. This synthesized MOF (DM-Zn-PDC@MOF) was further introduced as filler in polysulfone (PSf)-based mixed matrix membranes (MMMs). The performance of DM-Zn-PDC@MOF within the polymer matrix was examined. Several characterization techniques were used to thoroughly analyze the morphological, chemical, and physical characteristics of the MMMs and DM-Zn-PDC@MOF. The addition of the filler material significantly enhanced the membrane characteristics, including pure water flux, hydrophilicity, porosity, surface roughness, pore size, and heavy metal resource recovery in comparison with the pristine membrane. Stable incorporation of the filler within the membrane matrix was indicated by much less filler leaching (<5%) at all concentrations. With DM-Zn-PDC@MOF loading, the pure water flux increasedmore than nine times from 102.8 L/m²h (M-0) to 971.5 L/m²h (M-4). The functionalized membranes showed better flux retention in high-value heavy metal resource recovery using simulated wastewater: 871.8 L/m²h when filtering a Pb(II) ion solution (compared to M-0 with flux 120.6 L/m²h) and 526.8 L/m²h when filtering a Cr(III) ion solution (compared to M-0 with flux 97.1 L/m²h). These values represented approximately 7-fold and 5-fold improvements, respectively. Overall, Pb⁺² > Cr⁺³, but the rejection of Cr(III) ions was also improved, when compared with M-0. The high flux of the membrane makes it easier to process large volumes and concentrate metals in the retentate, turning diluted contaminated streams into a concentrated feedstock for subsequent recovery procedures. Full article

As critical air-dropped acoustic sensors for underwater target detection, sonobuoys are frequently compromised by severe hydrodynamic self-noise induced by sea-surface wave excitation, which masks target signals and degrades detection performance. While structural optimizations have traditionally been employed, effective signal-processing-based noise suppression remains challenging because the noise is non-stationary and physically coupled with buoy motion. To address the limited physical interpretability of conventional decomposition methods, this study proposes a physically guided self-noise suppression framework: VMD Constrained by DCCA Correlation (VMD-DCCA). The main contribution is the incorporation of the Detrended Cross-Correlation Analysis (DCCA) coefficient between the sonobuoy’s vertical velocity and the acoustic data as a correlation-dependent constraint within the Variational Mode Decomposition (VMD) optimization process. This motion prior allows more targeted isolation of motion-induced components than standard data-driven decomposition. Simulation and controlled water-tank results show that VMD-DCCA outperforms EEMD and standard VMD, achieving an SNR improvement of approximately 15 dB at an input SNR of −9 dB. The reconstructed signal also preserves visible narrowband spectral lines in the time-frequency representation. These results demonstrate the potential of the proposed method for controlled or post-processing sonobuoy self-noise reduction, while validation under irregular open-ocean conditions remains necessary. Full article

In this paper, we propose a novel Depth from Focus (DFF) framework that formulates depth estimation as an energy minimization problem and unrolls the corresponding iterative optimization into a trainable neural architecture. Given a focal stack, a deep feature extractor constructs a learned focus volume that encodes defocus and structural cues. Based on this representation, multiple candidate depth maps are generated using a plane-based probabilistic formulation, while an attention mechanism adaptively assigns pixel-wise confidence weights to each candidate. The depth estimation is performed through an iterative refinement process, where each stage corresponds to a learned proximal update implemented via lightweight conditional networks. These updates incorporate focus consistency, adaptive step sizes, and learned regularization priors, enabling effective integration of physical imaging constraints with data-driven modeling. A final refinement module further enhances prediction accuracy by fusing the refined depth, focus volume features, and candidate hypotheses to estimate residual corrections. The entire framework is trained end-to-end, ensuring coherent optimization across all components. Experimental results demonstrate that the proposed method achieves improved robustness and accuracy, particularly in low-texture and noisy regions, while preserving interpretability through its unfolding-based design. Full article

Thin-walled composite beams with unsymmetric laminates are attracting increasing attention in lightweight aerospace and mechanical structures because they enable enhanced stiffness tailoring and weight reduction beyond the limitations of conventional symmetric stacking sequences. However, despite their practical relevance, unsymmetric thin-walled laminates have received comparatively limited attention in the available buckling literature due to the additional complexity introduced by membrane–bending coupling effects. This study presents an efficient and physically transparent formulation for the buckling analysis of thin-walled composite beams with both symmetric and unsymmetric layups by combining Generalized Beam Theory (GBT) with the Ritz method. The proposed GBT-Ritz framework captures global, local, distortional, torsional, and shear-related deformation modes while consistently incorporating laminate coupling effects associated with unsymmetric configurations. The formulation is applicable to open, closed, branched, and unbranched cross-sections commonly encountered in aerospace structures. Validation against ABAQUS V2017 shell finite element models demonstrates excellent agreement (with discrepancies generally below 6%) in predicting critical buckling loads and mode shapes for various geometries and boundary conditions. The results show that unsymmetric laminates can significantly influence buckling behavior, particularly in open sections and intermediate beam lengths where coupling effects become dominant. Compared with conventional finite element approaches, the proposed method achieves substantially lower computational cost (providing speed-up factors of 1.5 to 2.5) while preserving clear physical insight into interacting instability mechanisms. Overall, the developed framework provides an efficient and practically relevant tool for the analysis and design of advanced thin-walled composite structures with tailored unsymmetric laminates. Full article

In collaborative robot lead-through teaching, fixed admittance parameters impose an inherent trade-off between operational ease and motion stability. This paper proposes a SAC-optimized fuzzy variable admittance control method (SAC-FAC). A fuzzy variable admittance controller (FAC) quantifies the operator’s motion and turning intents using interaction-force and end-effector motion information, and modulates the damping coefficient online via interpretable fuzzy rules. Soft Actor-Critic (SAC) searches offline for a well-balanced membership-function configuration on an episode basis in simulation, and the optimized configuration is then fixed for deployment. A saturation mechanism in the reward function suppresses degeneration of the damping configuration toward its physical lower bound. To counter parameter degradation under high-disturbance training, potential-based reward shaping and performance-gated curriculum learning are jointly introduced to promote stable convergence. Ablation studies and comparisons with four alternative optimizers verify the training design and support the suitability of SAC in this framework. Experiments on a UR10 collaborative robot platform with three trajectory types show that, relative to the hand-tuned FAC, SAC-FAC reduces the mean trajectory tracking error, work per unit path, and root-mean-square interaction force by 19.5%, 11.6%, and 6.8%, respectively, with more evident advantages on the compound and 3D ramp trajectories while preserving the interpretability of the fuzzy rule structure. Full article