Search Results (9,159)

The increasing demand for sustainable polymer composites has driven the development of hybrid laminates that combine natural, recycled, and synthetic reinforcements while maintaining adequate mechanical performance. However, the combined influence of stacking sequence and mineral filler addition on the mechanical behavior of such sustainable hybrid systems remains insufficiently understood. In this study, sustainable hybrid laminated composites based on epoxy reinforced with glass fiber (G), bamboo fiber (B), and flex banner (F) were fabricated with varying stacking sequences and calcium carbonate (CaCO₃) filler contents (0 and 1 wt.%). A total of nine laminate configurations were produced and evaluated through flexural and impact testing. The results demonstrate that mechanical performance is strongly governed by laminate architecture and filler addition. The bamboo-dominant G/B/B/B/G laminate containing 1 wt.% CaCO₃ exhibited the highest flexural strength (191 MPa) and impact resistance (0.766 J/mm²), indicating a synergistic effect between reinforcement arrangement and CaCO₃-induced matrix strengthening. In contrast, the lowest performance was observed for the G/F/B/F/G configuration without filler. Overall, all hybrid composites outperformed neat epoxy, highlighting the potential of bamboo–flex banner hybrid laminates with CaCO₃ filler for sustainable composite applications requiring balanced mechanical properties. This work aligns with SDG 12 by promoting resource-efficient circular-economy practices through the utilization of flex banner material and natural fibers as reinforcements in epoxy-based hybrid composites. Full article

Given automotive PEMFCs’ susceptibility to thermal runaway and uneven temperature distribution under high-power-density operation, this study proposes a novel embedded pulsating heat pipe cooling system. The core innovations of this work are threefold, fundamentally distinguishing it from prior PHP cooling approaches: (1) an embedded PHP cooling plate design that integrates the heat pipe within a unified copper plate, eliminating the need for external attachment or complex bipolar plate channels and enhancing structural compactness; (2) a system-level modeling methodology that derives an effective thermal conductivity (k_eff ≈ 65,000 W·m⁻¹·K⁻¹) from a thermal resistance network for seamless integration into a full-stack CFD model, significantly simplifying the simulation of the passive PHP component; and (3) a parametric system-level optimization of the secondary active cooling loop. Numerical results demonstrate that the system achieves an exceptional maximum temperature difference (ΔT_max) of less than 1.7 K within the PEMFC stack at an optimal coolant flow rate of 0.11 m/s, far surpassing the performance of conventional liquid cooling baselines. This three-layer framework (PHP heat transfer, cooling plate conduction, liquid coolant convection) offers robust theoretical and design support for high-efficiency, passive-dominant thermal control of automotive fuel cells. Full article

Genomic selection (GS) estimates the GEBV from genome-wide markers to reduce generation intervals and optimize germplasm selection, which is particularly advantageous for high-cost or late-expressed traits. While models like GBLUP are popular, they assume a polygenic architecture. In contrast, the Bayesian alphabet and machine learning (ML) can accommodate other types of genetic architectures. Given that no single model is universally optimal, stacking ensembles, which train a meta-model using predictions from diverse base learners, emerge as a compelling solution. However, the application of stacking in GS often overlooks non-additive effects. This study evaluated different stacking configurations for genomic prediction across 10 simulated traits, covering additive, dominance, and epistatic genetic architectures. A 5-fold cross-validation scheme was used to assess predictive ability and other evaluation metrics. The stacking approach demonstrated superior predictive ability in all scenarios. Gains were especially pronounced in complex architectures (100 QTLs, h² = 0.3), reaching an 83% increment over the best individual model (BayesA with dominance), and also in oligogenic scenarios with epistasis (10 QTLs, h² = 0.6), with a 27.59% gain. The success of stacking was attributed to two key strategies: base learner selection and the use of robust meta-learners (such as principal component or penalized regression) that effectively handled multicollinearity. Full article

Tylorrhynchus heterochaetus is an aquatic food with both edible and medicinal value in China. With a protein-rich body wall, it has strong potential for producing bioactive peptides. To explore its potential as a source of immunomodulatory peptides, in this study, flavor enzymes were selected as the optimal hydrolases, and the hydrolyzed products were subjected to ultrafiltration fractionation. The <3000 Da portion exhibited the most effective immune-stimulating activity in RAW 264.7 macrophages, enhancing phagocytosis and promoting the secretion of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6) and nitric oxide (NO) in a concentration dependent manner. Peptide omics analysis, combined with the activity and safety screened by bioinformatics, identified 43 candidate peptides. Molecular docking predicts that three novel peptides, LPWDPL, DDFVFLR and LPVGPLFN, exhibit strong binding affinity with toll-like receptor 4/myeloid differentiation factor-2 (TLR4/MD-2) receptors through hydrogen bonding and hydrophobic/π stacking interactions. Synthetic verification confirmed that these peptides were not only non-toxic to cells at concentrations ranging from 62.5 to 1000 µg/mL, but also effective in activating macrophages and stimulating the release of immune mediators. This study successfully identified the specific immunomodulatory peptides of the Tylorrhynchus heterochaetus, supporting its high-value utilization as a natural source of raw materials for immunomodulatory functional foods. Full article

Under sparse aperture (SA) conditions, inverse synthetic aperture radar (ISAR) imaging becomes a severely ill-posed inverse problem due to undersampled and noisy measurements, leading to pronounced degradation in azimuth resolution and image quality. Although deep learning approaches have demonstrated promising performance for SA-ISAR imaging, their practical deployment is often hindered by black-box behavior, fixed network depth, high computational cost, and limited robustness under extreme operating conditions. To address these challenges, this paper proposes an ADMM Denoising Deep Equilibrium Framework (ADnDEQ) for SA-ISAR imaging. The proposed method reformulates an ADMM-based unfolding process as an implicit deep equilibrium (DEQ) model, where ADMM provides an interpretable optimization structure and a lightweight DnCNN is embedded as a learned proximal operator to enhance robustness against noise and sparse sampling. By representing the reconstruction process as the equilibrium solution of a single-layer network with shared parameters, ADnDEQ decouples forward and backward propagation, achieves constant memory complexity, and enables flexible control of inference iterations. Experimental results demonstrate that the proposed ADnDEQ framework achieves superior reconstruction quality and robustness compared with conventional layer-stacked networks, particularly under low sampling ratios and low-SNR conditions, while maintaining significantly reduced computational cost. Full article

This study utilizes COMSOL Multiphysics (version 6.0) to design a planar ultra-broadband optical absorber with a multilayer configuration. The proposed structure consists of seven stacked layers arranged from bottom to top: W (h1, acting as a reflective substrate and transmission blocker), WSe₂ (h2), SiO₂ (h3), Ni (h4), SiO₂ (h5), Mo (h6), and SiO₂ (h7). One key finding of this study is that, when all other layer thicknesses are fixed, variations in the Mo layer thickness systematically induce a redshift in both the short- and long-wavelength cutoff edges. Notably, the long-wavelength cutoff exhibits a larger shift than the short-wavelength edge, resulting in an increased absorption bandwidth where absorptivity remains above 0.900. The second contribution is the demonstration that this planar structure can be readily engineered to achieve ultra-broadband absorption, spanning from the near-ultraviolet and visible region (360 nm) to the mid-infrared (6300 nm). An important characteristic of the proposed design is that the thickness of the h7 SiO₂ layer influences the cutoff wavelength at the short-wavelength edge, while the thickness of the h6 Mo layer governs the cutoff position at the long-wavelength edge. This dual modulation capability allows the proposed optical absorber to flexibly tune both the spectral range and the bandwidth in which absorptivity exceeds 0.900, thereby enabling the realization of a wavelength- and bandwidth-tunable optical absorber. Full article

A low-power IPv6 mesh standard, Thread, is gaining traction in smart-home, building-automation, and industrial IoT deployments. It extends mesh connectivity with the help of Router-Eligible End Devices (REEDs), which can be promoted to, or demoted from, the router status. Promotion and demotion hinge on two tunable parameters, the router upgrade and the router downgrade thresholds. Yet the OpenThread reference stack ships with fixed values (16/23) for these thresholds. This paper presents a systematic study of how these thresholds shape router-election dynamics across diverse traffic loads and network topologies. Leveraging an extended OpenThread Network Simulator, a sweep through both router upgrade and router downgrade thresholds with different gaps was performed. Results reveal that the default settings may over-provision routing capacity and may result in increased frame retransmissions, wasting airtime and reducing energy efficiency. Full article

Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques have received rapid advance in recent years, and the Multi-temporal InSAR (MT-InSAR) has been widely applied in various earth observations. Distributed scatterer (DS) InSAR is one of the most advanced MT-InSAR methods, and has overcome the limitation of the lack of enough measurement points in the low coherent regions for traditional methods. While the Joint-Scatterer InSAR (JS-InSAR) is the extension of DS InSAR method, which exploited the overall information of Joint Scatterers to carry out DS identification and phase optimization. And it can avoid the inaccuracy caused by the offset errors between scatterers in complex terrain areas. However, the intensive computation and low efficiency have severely restricted the application of JS-InSAR, especially when dealing with massive and long historical SAR images. As the sequential estimator has proven to successfully improve the efficiency of MT-InAR and obtain near-time deformation time series, in this work, we proposed the sequential-based JS-InSAR (S-JSInSAR) method with flexible batches. This method has adaptively divided large single look complex (SLC) stack into different batches with flexible number and certain overlaps. Then, the JS-InSAR processing is performed on each batch, respectively, and these estimated results are integrated into the final deformation time series based on the connection mode. Thus, S-JSInSAR can efficiently process large InSAR dataset, and mitigate the decorrelation effect caused by long temporal baselines. To demonstrate the effectiveness of the S-JSInSAR, a multi-year of 145 Sentinel-1 ascending SAR images in Tangshan, China, were collected to estimate the long deformation time series. And the results compared with other methods have shown the processing time has substantially decreased without the loss of deformation accuracy, and obtain deformation spatial distribution with more details in local regions, which have well validated the efficiency and reliability of the proposed method. Full article

This study analyzes the mechanical behavior of a quasi-isotropic biaxial glass fiber–vinyl ester composite in a multiaxial stress condition and the effect of the orientation of the fibers. A ply structure was created through the process of vacuum infusion using six layers of biaxial fabric that were oriented to 15°. Tensile samples were isolated at 0, 15, 30, 45 and 90 degrees relative to the warp direction. It was found that strength and stiffness strongly depend on orientation, with maximum tensile strengths of 157.2 MPa at 90° and 125 MPa at 0°, and minimum tensile strengths 59.6 MPa at 15°, showing fiber and shear failures, respectively. MAT_124 underwent finite element analysis in LS-DYNA, and the results were excellent, with a difference of less than 1.5%. Three-point bending and Charpy impact tests indicated that flexural properties were lower at 15° and 90°, whereas off-axis orientations were generally better at impact energy absorption, although at 45°, binding sites were few and far between. The results have important implications for the design of laminates subjected to complicated loads. Full article

Human Activity Recognition (HAR) has become crucial across various domains, including healthcare, smart homes, and security systems, owing to the proliferation of Internet of Things (IoT) devices. Several Machine Learning (ML) techniques, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), have been proposed for HAR. However, they are still deficient in addressing the challenges of noisy features and insufficient data. This paper introduces a novel approach to tackle these two challenges, employing a Deep Learning (DL) Ensemble-Based Stacking Neural Network (SNN) combined with Generative Adversarial Networks (GANs) for HAR based on ambient sensors. Our proposed deep learning ensemble-based approach outperforms traditional ML techniques and enables robust and reliable recognition of activities in real-world scenarios. Comprehensive experiments conducted on six benchmark datasets from the CASAS smart home project demonstrate that the proposed stacking framework achieves superior accuracy on five out of six datasets when compared to literature-reported state-of-the-art baselines, with improvements ranging from 3.36 to 39.21 percentage points and an average gain of 13.28 percentage points. Although the baseline marginally outperforms the proposed models on one dataset (Aruba) in terms of accuracy, this exception does not alter the overall trend of consistent performance gains across diverse environments. Statistical significance of these improvements is further confirmed using the Wilcoxon signed-rank test. Moreover, the ASGAN-augmented models consistently improve macro-F1 performance over the corresponding baselines on five out of six datasets, while achieving comparable performance on the Milan dataset. The proposed GAN-based method further improves the activity recognition accuracy by a maximum of 4.77 percentage points, and an average of 1.28 percentage points compared to baseline models. By combining ensemble-based DL with GAN-generated synthetic data, a more robust and effective solution for ambient HAR addressing both accuracy and data imbalance challenges in real-world smart home settings is achieved. Full article

Perpendicular magnetic tunnel junctions (p-MTJs) rely on synthetic antiferromagnets (SAFs) as reference layers to achieve strong perpendicular magnetic anisotropy (PMA) together with stable interlayer exchange coupling. In this study, we present a comparative materials study of buffer and spacer layer engineering in Co/Pt-based perpendicular synthetic antiferromagnets (p-SAFs). The influence of buffer layer selection, number of multilayer repeats, and annealing at 330 °C for 30 min on PMA and interlayer exchange coupling is systematically examined. Co/Pt multilayers with four and six repeats were grown on Ta/Ru and Ta/CuN buffer layers separately, followed by the fabrication of SAF structures incorporating Ru spacers with thickness between 0.60 and 0.80 nm. Magnetic measurements show that Ta/Ru-buffered structures exhibit squarer hysteresis loops, higher remanence, and greater tolerance to annealing at 330 °C for 30 min compared to Ta/CuN-buffered counterparts. The SAF structures display clear two-step magnetization reversal and robust antiferromagnetic coupling across the investigated Ru thickness range, with large exchange fields and bias fields in the deposited state. Although annealing reduces the absolute coupling strength, a Ru spacer thickness of 0.60 nm retains the strongest antiferromagnetic response within the studied thermal budget. These results underscore the importance of comparative buffer and spacer layer engineering and provide materials insights into the design of Co/Pt-based p-SAF reference stacks that may inform future p-MTJ structures. Full article

Thorium carbide (ThC${}_{2\pm x}$) nano-structured thin disc-like pellets were produced from thoria nanoparticles (ThO${}_2$-NP) and multi-walled carbon nanotubes (MWCNT). These composites are to be studied as a target material candidate for radioactive ion beam (RIB) production via nuclear reactions upon impact with high-energy proton beams on a stack of solid pellets. The ThO${}_2$-NP precursor was produced via precipitation of thorium oxalate from a thorium nitrate solution with oxalic acid and subsequent hydrothermal oxidation of the oxalate, creating the thoria nanoparticles. The ThO${}_2$-NP were then mixed with MWCNT in isopropyl alcohol and sonicated by two different methods to create a nanoparticle dispersion. This dispersion was then heated under medium vacuum to evaporate the solvent; the resulting powder was pressed into pellets and taken to an inert-atmosphere oven, where it was heated to 1650 ${}^{\circ }$C and carbothermally reduced to ThC${}_{2\pm x}$. The resulting pellets were characterized via XRD, SEM-EDS, and Raman spectroscopy. The resulting thorium pellets exhibited, at most, trace levels of the oxide precursor. Furthermore, the nanotube structures were still present in the final product and are expected to contribute positively towards faster radioisotope release times by lowering isotope diffusion times, which is required for the efficient extraction of the shortest-lived (<1 s half-life) radioisotopes. Full article

Simultaneous optimization of vehicle routing and cargo loading is essential for reducing operational costs and improving the environmental performance of logistics systems. To overcome the limitations of traditional sequential approaches to the one-to-many pickup and delivery vehicle routing problem with three-dimensional loading constraints (3L-PDVRP), this paper proposes a deeply coupled hybrid genetic algorithm (HGA). The algorithm adopts a grouping-based genetic encoding strategy to accommodate variable fleet sizes and incorporates a tree-search-based loading module. A dynamic three-dimensional loading feasibility verification mechanism is embedded directly into the evolutionary search so that routing decisions are continuously guided by fragility, stacking stability, support constraints, and other loading constraints. In addition, an adaptive hybrid insertion strategy is employed to balance global exploration and local exploitation during route construction and repair. Extensive computational experiments on extended benchmark instances derived from standard datasets show that the proposed method consistently outperforms a large neighborhood search (LNS)-based baseline from the literature, reducing the average total travel distance by 10.60% and increasing the average vehicle loading rate by 2.76%. These results indicate that the proposed HGA provides an effective approach to the synergistic optimization of routing and loading in one-to-many distribution settings, offering practical value for lowering transportation costs and supporting more sustainable logistics operations. This methodology provides decision support for logistics enterprises, reducing travel distances while ensuring three-dimensional loading feasibility, thereby enabling greener and safer transportation operations. Full article

Numerical simulations of the forming limit diagram (FLD) for SUS430/Al1050/TA1 laminated metal composites (LMCs) are conducted through the crystal plasticity finite element (CPFE) model integrated with the Marciniak–Kuczyński (M–K) theory. Representative volume elements (RVEs) that reconstruct the measured crystallographic texture, as characterized by electron backscatter diffraction (EBSD), are developed. The optimal grain number and mesh density for the RVE are calibrated through convergence analysis by curve-fitting simulated stress–strain responses to the uniaxial tensile data. The established multi-scale model successfully predicts the FLDs of the SUS430/Al1050/TA1 laminated sheet under two stacking sequences, namely, the SUS layer or the TA1 layer in contact with the die. The Nakazima test results validate the effectiveness of the proposed model as an efficient and accurate predictive tool. This study extends the CPFE–MK framework to multi-layer LMCs, overcoming the limitations of conventional single-layer models, which incorporate FCC, BCC, and HCP crystalline structures. Furthermore, the deformation-induced texture evolution under different loading paths is analyzed, establishing the relationship between micro-scale deformation mechanisms and the macro-scale forming behavior. Full article

Target-oriented grasping has become increasingly important in household and industrial environments, and deploying such systems on mobile robots is particularly challenging due to limited computational resources. To address these limitations, we present an efficient framework for real-time target-oriented grasping on resource-constrained platforms, supporting both click-based grasping for unknown objects and category-based grasping for known objects. To reduce model complexity while maintaining detection accuracy, YOLOv8 is compressed using a structured pruning method. For grasp pose generation, a pretrained GR-ConvNetv2 predicts candidate grasps, which are restricted to the target object using masks generated by MobileSAMv2. A geometry-based correction module then adjusts the position, angle, and width of the initial grasp poses to improve grasp accuracy. Finally, extensive experiments were carried out on the Cornell and Jacquard datasets, as well as in real-world single-object, cluttered, and stacked scenarios. The proposed framework achieves grasp success rates of 98.8% on the Cornell dataset and 95.8% on the Jacquard dataset, with over 90% success in real-world single-object and cluttered settings, while maintaining real-time performance of 67 ms and 75 ms per frame in the click-based and category-specified modes, respectively. These experiments demonstrate that the proposed framework achieves high grasping accuracy and robust performance, with a efficient design that enables deployment on mobile and resource-constrained robots. Full article