Search Results (4,664)

Polydicyclopentadiene (PDCPD), an emerging environmentally friendly material, has been widely applied in lightweight structural shells; however, its extension to high-value electronic applications remains challenging. In this work, we developed a novel vinyl-SiO₂@NaNbO₃ (VSN) core–shell structure with a high surface vinyl concentration (1.26 mmol/g) and excellent thermal stability, making it highly suitable for co-polymerization with polymers. Through ring-opening metathesis polymerization, the influence of VSN on the mechanical, thermal, and dielectric properties of PDCPD composites was systematically investigated. The vinyl groups on the VSN surface provide strong interfacial compatibility with the PDCPD matrix. With only 1.0 wt% loading, the composites show significant performance improvements: the heat deflection temperature and glass transition temperature increased to 139.3 °C and 150.43 °C, respectively, while the dielectric constant at 1 kHz rises to 4.13 with an ultralow dielectric loss of 0.035%. Meanwhile, the composites maintain high mechanical strength and solvent resistance. This study not only establishes a facile strategy for fabricating highly compatible inorganic additives but also offers new opportunities for expanding PDCPD into advanced dielectric and electronic applications. Full article

This work supports the circular economy and sustainable material by facilitating the creation of low-carbon materials with enhanced elimination of nutrients from wastewater, thereby assisting in preventing eutrophication. Porous geopolymers, owing to their distinctive pore structure and numerous superior properties, including noise reduction and thermal insulation, have a wide range of potential applications in the building sector, chemical industry, and water treatment. Developing low-carbon-footprint porous geopolymer materials is an important step toward creating multipurpose lightweight materials that can serve as structural materials and, at the same time, as adsorbents. In this study, it was revealed that the porous material created during the hydrothermal synthesis of (lime–Portland cement-based aerated composition), by replacement of sand with wood biomass bottom ash (WBA), can be used as porous aggregates (PA) for adsorbent development. PA was produced with an apparent porosity of 65%, a density of 610 kg/m³, and a compressive strength of 2.0 MPa. The effectiveness of employing an air-entraining additive (AEA) and creating PA in geopolymers was tested. A different-molarity activator was used, and wood biomass fly ash (WFA) and metakaolin (MK) waste were used as precursors for the synthesis of porous geopolymers. Using an air-entraining admixture in geopolymers allows for the production of lightweight geopolymers with densities up to 1400 kg/m³, compressive strengths up to 8.0 Mpa, and apparent porosities up to 38.4%. Such properties, together with their low cost, offer good prospects for geopolymers in the construction industry. By utilizing PA in the geopolymer composition, a lightweight geopolymer (GPA) with a density of 985 kg/m³ and a compressive strength of 3.9 Mpa, with 42.0% apparent porosity, was obtained. The materials effectively removed phosphorus from biologically treated wastewater: PA had an efficiency of up to 82.5%, the geopolymer with AEA had an efficiency of up to 88.4%, and GPA had an efficiency of up to 97%. The created GPA enhances the adsorbent’s sorption capacity, resulting in extremely high phosphorus uptake efficiency. Full article

This study focuses on the machinability optimization of bio-waste corn husk fiber–reinforced epoxy composites during drilling, with the objective of minimizing delamination and improving hole quality required for mechanical fastening applications. While natural fiber composites have been widely investigated, systematic statistical optimization of drilling parameters for corn husk fiber composites remains limited. The novelty of this work lies in identifying the dominant drilling parameter and establishing a clear damage-control strategy using a Taguchi L16 design coupled with ANOVA. Drilling experiments were conducted by varying spindle speed (1000, 1500, 2000, and 2500 rpm), drill diameter (6, 8, 10, and 12 mm), feed rate (00.05, 0.10, 0.15, and 0.20 mm/rev), and point angle (90°, 100°, 110°, and 120°). The results show that the drill diameter is the governing factor affecting delamination, contributing 73.52% of the total variation, followed by spindle speed (22.68%), whereas feed rate (3.14%) and point angle (0.38%) have minimal influence. The optimal condition (2500 rpm, 6 mm drill diameter, and 0.05 mm/rev feed rate) produced the lowest delamination and improved surface integrity. Microscopic observations confirmed reduced fiber pull-out and matrix cracking under these conditions. The main advantage of the proposed approach is the clear identification of parameter priority, enabling the industry to control drilling damage by primarily selecting appropriate drill diameter and spindle speed. The findings provide practical machining guidelines for the use of corn husk fiber composites in lightweight panels, automotive interior parts, and secondary structural components where reliable bolted joints are required. Full article

The integration of wearable inertial measurement units (IMU) in animal welfare Internet of Things (IoT) systems has become crucial for monitoring animal behaviors and enhancing welfare management. However, the vulnerability of IoT devices to network and hardware attacks poses significant risks, potentially compromising data integrity and misleading caregivers, negatively impacting animal welfare. Additionally, current animal monitoring solutions often rely on intrusive tagging methods, such as Radio Frequency Identification (RFID) or ear tagging, which may cause unnecessary stress and discomfort to animals. In this study, we propose a lightweight integrity and provenance-oriented security stack that complements standard transport security, specifically tailored to IMU-based animal motion IoT systems. Our system utilizes a 1D-convolutional neural network (CNN) model, achieving 88% accuracy for precise motion recognition, alongside a lightweight behavioral fingerprinting CNN model attaining 83% accuracy, serving as an auxiliary consistency signal to support collar–animal association and reduce mis-attribution risks. We introduce a dynamically generated pre-shared key (PSK) mechanism based on SHA-256 hashes derived from motion features and timestamps, further securing communication channels via application-layer Hash-based Message Authentication Code (HMAC) combined with Message Queuing Telemetry Transport (MQTT)/Transport Layer Security (TLS) protocols. In our design, MQTT/TLS provides primary device authentication and channel protection, while behavioral fingerprinting and per-window dynamic–HMAC provide auxiliary provenance cues and tamper-evident integrity at the application layer. Experimental validation is conducted primarily via offline, dataset-driven experiments on a public canine IMU dataset; system-level overhead and sensor-to-edge latency are measured on a Raspberry Pi-based testbed by replaying windows through the MQTT/TLS pipeline. Overall, this work integrates motion recognition, behavioral fingerprinting, and dynamic key management into a cohesive, lightweight telemetry integrity/provenance stack and provides a foundation for future extensions to multi-species adaptive scenarios and federated learning applications. Full article

With the rapid development of aerospace technology towards hypersonic vehicles, the synergistic demand for lightweighting and high-efficiency thermal insulation performance of ablation-resistant thermal insulation materials is becoming increasingly urgent. In this study, nanoporous phenolic resin was used as the matrix to prepare quartz fiber-reinforced phenolic aerogel composites (QF/PF), mullite fiber-reinforced phenolic aerogel composites (MF/PF), and carbon fiber-reinforced phenolic aerogel composites (CF/PF), and the influence mechanisms of different reinforcing fibers on the properties of the composites were systematically investigated. QF/PF exhibits optimal thermal insulation performance with a thermal conductivity of 0.1 W/(m·K) at 20–200 °C, followed by MF/PF with a thermal conductivity of 0.11 W/(m·K). Relatively weak thermal insulation performance is demonstrated in CF/PF, whose thermal conductivity reaches 0.14 W/(m·K). However, in terms of mechanical properties, CF/PF is outstanding, with a tensile strength of 54.62 MPa and a bending strength of 29.69 MPa. In addition, the most excellent ablation resistance is displayed in CF/PF, with a linear ablation rate of 0.13 mm/s and a mass ablation rate of 0.0435 g/s, which are significantly lower than QF/PF and MF/PF. This study provides an important basis for the selection of reinforcing fibers in different application scenarios. QF/PF or MF/PF is preferred for high thermal insulation requirements. CF/PF is favored for high load-bearing requirements or extreme ablative environments. Full article

Real-time semantic segmentation has been widely adopted in resource-constrained applications such as mobile devices, autonomous driving, and drones due to its high efficiency. However, existing lightweight networks often compromise segmentation accuracy to reduce parameter count and improve inference speed. To achieve an optimal balance among accuracy, latency, and model size, we propose the Dilated–Separable Convolution and Attention Fusion Network (DSAFNet), a lightweight real-time semantic segmentation network based on an asymmetric encoder–decoder framework. DSAFNet integrates three core components: (i) the Double-Layer Multi-Branch Depthwise Convolution (DL-MBDC) module that fuses channel splitting and multi-branch depthwise convolutions to efficiently extract multi-scale features with minimal parameters; (ii) the Multi-scale Dilated Fusion Attention (MDFA) module that utilizes factorized dilated convolutions and channel-spatial collaborative attention to expand the receptive field and reinforce key contextual features; (iii) the Multi-scale Attention Lightweight Decoder (MALD) that integrates multi-scale feature maps to generate attention-guided segmentation results. Experiments conducted on an RTX 3090 platform demonstrate that DSAFNet, with only 1.00 M parameters, achieves 74.78% mIoU and a frame rate of 74.74 FPS on the Cityscapes dataset, while 70.5% mIoU and a frame rate of 89.5 FPS on the CamVid dataset. Full article

Real-time object detection on resource-constrained edge devices remains a critical challenge in precision agriculture and autonomous systems, particularly when integrating advanced multi-modal sensors (RGB-D, thermal, hyperspectral). This paper introduces FEGW-YOLO, a lightweight detection framework explicitly designed to bridge the efficiency-accuracy gap for fine-grained visual perception on edge hardware while maintaining compatibility with multiple sensor modalities. The core innovation is a Feature Complexity Descriptor (FCD) metric that enables adaptive, layer-wise compression based on the information-bearing capacity of network features. This compression-guided approach is coupled with (1) Feature Engineering-driven Ghost Convolution (FEG-Conv) for parameter reduction, (2) Efficient Multi-Scale Attention (EMA) for compensating compression-induced information loss, and (3) Wise-IoU loss for improved localization in dense, occluded scenes. The framework follows a principled “Compress, Compensate, and Refine” philosophy that treats compression and compensation as co-designed objectives rather than isolated knobs. Extensive experiments on a custom strawberry dataset (11,752 annotated instances) and cross-crop validation on apples, tomatoes, and grapes demonstrate that FEGW-YOLO achieves 95.1% mAP@0.5 while reducing model parameters by 54.7% and computational cost (GFLOPs) by 53.5% compared to a strong YOLO-Agri baseline. Real-time inference on NVIDIA Jetson Xavier achieves 38 FPS at 12.3 W, enabling 40+ hours of continuous operation on typical agricultural robotic platforms. Multi-modal fusion experiments with RGB-D sensors demonstrate that the lightweight architecture leaves sufficient computational headroom for parallel processing of depth and visual data, a capability essential for practical advanced sensing systems. Field deployment in commercial strawberry greenhouses validates an 87.3% harvesting success rate with a 2.1% fruit damage rate, demonstrating feasibility for autonomous systems. The proposed framework advances the state-of-the-art in efficient agricultural sensing by introducing a principled metric-guided compression strategy, comprehensive multi-modal sensor integration, and empirical validation across diverse crop types and real-world deployment scenarios. This work bridges the gap between laboratory research and practical edge deployment of advanced sensing systems, with direct relevance to autonomous harvesting, precision monitoring, and other resource-constrained agricultural applications. Full article

Calcium carbide slag and silica fume was used as a cement replacement material, combined with excavated soil and EPS (expanded polystyrene) particles, to develop a new green and low-carbon lightweight fluid solidified soil (LFSS). Focusing on the performance regulation of LFSS, this study adopted the paste volume ratio (PV, defined as the volume ratio of paste to total mixture) and the water–binder ratio (w/b) to systematically construct a mix ratio design system and proposed EPS particle interface modification and shell formation technology to improve the weak interface bonding between EPS and the matrix. Firstly, based on the paste volume method, the effects of PV and w/b on the flowability and strength of LFSS were analyzed, and a linear correlation model between the water–solid volume ratio and flowability, as well as a quadratic function prediction model for 28-day strength, was established. Secondly, the “core–shell structure” of EPS particles was constructed by combining EVA (ethylene-vinyl acetate) modification with the coating of calcium carbide slag–silica fume paste. Considering the influence of the coating method, w/b, and material mass ratio on interface bonding comprehensively, the optimal process parameters were determined to achieve the interface reinforcement of EPS particle. The results showed that the water–solid volume ratio was significantly linearly correlated with the flowability of LFSS. PV and w/b respectively controlled the framework formation and pore structure evolution of LFSS, with optimal overall performance at PV = 0.55 and w/b = 2.5. The modification shell formation significantly reduced the shell loss rate of EPS particles and increased the 28-day compressive strength of LFSS by 21.7%. SEM (scanning electron microscope) and EDS (energy-dispersive spectroscopy) analysis further revealed that the shell-formation technique promoted the densification of the interface transition zone, enhanced the deposition of hydration products, and strengthened the synergistic effect of Na and Ca elements, thereby significantly improving interface bonding and overall structural stability. This study established a “mix ratio optimization-modification and shell formation” dual-regulation mechanism, providing an effective technical approach and theoretical basis for the engineering application of calcium carbide slag–silica fume-based LFSS. Full article

Digitally configurable DC/DC converters provide built-in telemetry signals that offer new opportunities for operational data-driven monitoring in embedded energy systems. However, exploiting these signals for intelligent condition monitoring remains challenging due to limited computational resources and the need to preserve the safety and determinism of power supply control. This work investigates the combination of digitally configurable DC/DC converters and embedded artificial intelligence for resource-efficient load and condition monitoring based exclusively on converter-side power telemetry. A lightweight, feature-based current analysis pipeline is proposed, incorporating domain-informed temporal and electric features. Three representative machine learning model classes, Random Forest, Support Vector Machine, and a Neural Network, are evaluated. The approach is implemented on an ESP32-class microcontroller operating as a dedicated monitoring unit, fully separated from the safety-critical power supply control. Experimental validation on a laboratory demonstrator shows that classification accuracies of up to 99% can be achieved for four system states using only five features at a 100 Hz telemetry sampling rate, while remaining within typical embedded memory constraints. The results demonstrate that converter-internal telemetry enables effective and scalable condition monitoring without additional sensors, supporting the combination of embedded intelligence and digitally configurable power supplies for industrial applications. Full article

To recognize radar compound jamming under complex electromagnetic environments, this paper proposes a lightweight multi-feature fusion network for compound jamming recognition. Three complementary time–frequency representations are employed to extract various features of compound jamming, which are processed by a multi-branch architecture for parallel, multi-scale feature learning. Attention mechanisms are incorporated to enhance the discriminative characteristics of jamming, and a weighted fusion strategy is adopted to integrate multi-channel features effectively. Furthermore, an improved lightweight module, GSENet, is introduced to construct the recognition network with low complexity. Experiments on simulated radar jamming datasets demonstrate that the proposed network achieves over 87% recognition accuracy for seven compound jamming types under low jamming-to-noise ratio (JNR) conditions while maintaining a parameter count below 0.14 M. These results indicate that the proposed network provides an effective trade-off between recognition performance and model complexity, making it suitable for electronic counter-countermeasure (ECCM) applications. Full article

In modern smart dairy farms, precise feed management and accurate monitoring of dairy cows’ feeding behavior are crucial for improving production efficiency and reducing feeding costs. However, in practical applications, complex environmental factors such as varying illumination, frequent occlusion, and dense multi-targets pose significant challenges to real-time visual perception. To address these issues, this paper proposes a lightweight multi-target detection model, BFDet-YOLO, for the joint detection of dairy cows’ feeding behavior and feed density levels in pasture environments. Based on the YOLOv10 framework, the model incorporates four targeted improvements: (1) a bidirectional feature fusion network (BiFPN) to address the insufficient multi-scale feature interaction between dairy cows (large targets) and feed particles (small targets); (2) a lightweight downsampling module (Adown) to preserve fine-grained features of feed particles and reduce the risk of small target miss detection; (3) an attention-enhanced detection head (SEAM) to mitigate occlusion interference caused by cow stacking and feed accumulation; (4) an improved bounding box regression loss function (DIoU) to optimize the localization accuracy of non-overlapping small targets. Additionally, this paper constructs a pasture-specific dataset integrating dairy cows’ feeding behavior and feed distribution information, which is annotated and expanded by combining public datasets with on-site monitoring data. Experimental results demonstrate that BFDet-YOLO outperforms the original YOLOv10 and other mainstream target recognition models in terms of detection accuracy and robustness while maintaining a significantly streamlined model scale. On the constructed dataset, the model achieves 95.7% mAP@0.5 and 70.7% mAP@0.5:0.95 with only 1.85 M parameters. These results validate the effectiveness and deployability of the proposed method, providing a reliable visual perception solution for intelligent feeding systems and smart pasture management. Full article

Hypersonic vehicles impose stringent requirements on lightweight structures to maintain mechanical integrity under extreme thermal environments. Bismaleimide (BMI)-based carbon fiber-reinforced polymer (CFRP) composites, featuring a high glass transition temperature and excellent thermal stability, are regarded as promising candidates for such applications. However, the high curing temperature and narrow processing window of BMI resins make it challenging to manufacture stiffened-shell structures with low defect levels and high fiber volume fractions. In this study, an integrated manufacturing route—hot-melt prepregging–filament winding–matched-metal mold forming—is proposed, and the key processing parameters are optimized via single-factor experiments and the Box–Behnken response surface methodology. The tensile strength of the laminate is selected as the response variable to evaluate the effects of the compression displacement (A), thermal consolidation/bonding temperature (B), heating rate (C), and cooling rate (D). The results reveal a unimodal dependence of the tensile strength on each parameter, with the significance ranking B > D > A > C; moreover, the A–B and A–D interactions are significant (p < 0.01). The established quadratic regression model exhibits good agreement with experimental data (R² = 0.974; R²_adj = 0.949). The predicted optimum conditions are A = 0.07 mm, B = 114.93 °C, C = 1.35 °C·min⁻¹, and D = 4.58 °C·min⁻¹, corresponding to a predicted tensile strength of approximately 2287 MPa. Validation experiments yielded 2291 MPa, in excellent agreement with the prediction. Microstructural observations indicate tight interlaminar bonding and a pronounced reduction in voids under the optimized conditions. Applying the optimized process to fabricate stiffened-shell demonstrators achieves a fiber volume fraction of >60% and a void content of <1%. This work provides a quantitatively defined processing window and parameter optimization basis for the high-quality manufacturing of BMI-CFRP stiffened-shell structures, with significant engineering relevance. Full article

Aluminum–lithium (Al-Li) alloys are extensively employed in aerospace and space structures because of their low density, high specific stiffness, and excellent fatigue resistance. However, welding of these alloys remains challenging, since the joints typically exhibit unique microstructural features, including equiaxed grain zones (EQZ) along the fusion boundary and coarse columnar grains in the fusion zone, which degrade mechanical performance and increase susceptibility to cracking. This review provides an overview of the generational evolution of Al-Li alloys and their associated weldability, highlights the advantages and limitations of major welding processes, such as laser, arc, and hybrid techniques, and systematically examines the formation mechanisms of EQZ, columnar grains, and equiaxed grain bands. Various strategies for microstructural control are compared, including filler design, pulsed current, and external-field-assisted welding. Special attention is given to grain refinement achieved through heterogeneous nucleation, dendrite fragmentation, and columnar-to-equiaxed transition. Finally, prospects for advanced microstructural control strategies are discussed, with the goal of achieving high-quality welds for next-generation lightweight structural applications. Full article

Wampee (Clausena lansium) harvesting currently relies heavily on manual labor, but automation is significantly hindered by clustered fruit growth patterns, small fruit sizes, and complex orchard backgrounds, which make accurate detection highly challenging. This study proposes Wampee-YOLO, a lightweight and high-precision model based on the YOLO11n architecture, specifically designed for real-time wampee detection in natural orchard environments. The proposed model integrates several architectural enhancements: the RFEMAConv module for expanded receptive fields, an AIFI module for improved small target interaction, and a C2PSA-MSCADYT structure to boost multi-scale adaptability. Additionally, a Triplet Attention mechanism strengthens multi-dimensional feature representation, while an AFPN-Pro2345 neck structure optimizes cross-scale feature fusion. Experimental results demonstrate that Wampee-YOLO achieves an mAP₅₀ of 90.3%, a precision of 92.1%, and F1 score of 87%. This represents a significant 3.4% mAP₅₀ improvement over the YOLO11n baseline, with a slight increase to 3.28 M parameters. Ablation studies further confirm that the AFPN-Pro2345 module provides the most substantial performance gain, increasing mAP₅₀ by 2.4%. The model effectively balances computational efficiency with detection accuracy. These findings indicate that Wampee-YOLO offers a robust and efficient visual detection solution suitable for deployment on resource-constrained edge devices in smart orchard applications. Full article

Polypropylene (PP) nonwoven is widely used in biomedical textiles because of its lightweight and mechanical durability; however, its inherent hydrophobicity and chemical inertness limit further surface functionalization. In this study, a plasma-assisted UV grafting strategy was developed to fabricate thermo-responsive and antibacterial hydrogel coatings on PP nonwoven. Atmospheric-pressure plasma jet (APPJ) treatment was first employed to activate the PP nonwoven surface, followed by UV-induced graft polymerization of chitosan and N-isopropylacrylamide (NIPAAm), forming a chitosan-co-PNIPAAm hydrogel immobilized on the nonwoven substrate. Surface characterization using water contact angle measurement, Fourier transform infrared spectroscopy, and scanning electron microscopy confirmed effective plasma activation and successful hydrogel grafting. APPJ treatment significantly enhanced surface wettability, whereas subsequent UV grafting formed a continuous hydrogel on the PP nonwoven surface. The modified nonwoven exhibited distinct thermo-responsive swelling behavior in aqueous and simulated physiological environments, associated with the temperature-sensitive characteristics of the PNIPAAm component. In addition, the incorporation of chitosan imparted pronounced antibacterial activity against Escherichia coli, with inhibition zone diameters ranging from 14 to 16.5 mm, indicating high antibacterial sensitivity. Preliminary cytocompatibility evaluation further demonstrated favorable cell viability on the modified surfaces. This study demonstrates a scalable and low-temperature surface engineering approach for integrating stimuli-responsive and antibacterial hydrogel functionality into nonwoven polymer substrates, offering potential for advanced biomedical textile applications. Full article