Search Results (3,063)

As the lightest metallic structural material, magnesium alloys face a fundamental trade-off between strength and ductility, limiting their broader application. This study investigates a processing approach to overcome this limitation by systematically comparing the effects of direct extrusion and a multi-directional forging (MDF) combined extrusion process on a Mg-8Gd-4Y-1Nd-0.5Zr alloy. The results demonstrate that MDF pretreatment effectively refines grains and enhances dynamic precipitation. It also significantly weakens the texture, reducing the intensity from 11.14 to 3.98 and tilting the {0001} basal planes by approximately 30° from the extrusion direction. This texture weakening is attributed to the combined effects of particle-stimulated nucleation (PSN) and the orientation diversity introduced by pre-forging, which promote orientation randomization during recrystallization. The alloy processed by the combined route exhibits an excellent strength–ductility synergy in the as-extruded state, with ultimate tensile strength, tensile yield strength, and elongation reaching 315 MPa, 228 MPa, and 13.1%, respectively. After peak aging, the strength further increases to 429 MPa and 323 MPa while maintaining a ductility of 7.3%. Schmid factor analysis confirms that the combined process facilitates the activation of non-basal slip and improves strain compatibility through multi-slip activity, providing an effective pathway for developing high-performance wrought magnesium alloys. Full article

The operational reliability of aero-engines is critically dependent on the health of rolling element bearings, while incipient fault diagnosis remains particularly challenging under small-sample conditions. Although multiscale entropy methods are widely used for complexity analysis, conventional coarse-graining strategies suffer from severe information loss and unstable estimation when data are extremely limited. To address this, the primary objective of this study is to develop a robust diagnostic framework that ensures feature consistency and classification stability even with minimal training samples. Specifically, this paper proposes an integrated approach combining Refined Time-shifted Multiscale Rating Entropy (RTSMRaE) with an Animated Oat Optimization (AOO)-optimized Extreme Learning Machine (ELM). By introducing a refined time-shift operator and a dual-weight fusion mechanism, RTSMRaE effectively preserves transient impulsive features across multiple scales while suppressing stochastic fluctuations. Meanwhile, the AOO algorithm is employed to optimize the input weights and hidden biases of the ELM, alleviating performance instability caused by random initialization and improving generalization capability. Experimental validation on both laboratory-scale and real-world aviation bearing datasets demonstrates that the proposed RTSMRaE-AOO-ELM framework achieves a diagnostic accuracy of 99.47% with a standard deviation of ±0.48% using only five training samples per class. These results indicate that the proposed method offers superior diagnostic robustness and computational efficiency, providing a promising solution for intelligent condition monitoring in data-scarce industrial environments. Full article

This study evaluates whether a multimodal retrieval-augmented generation (RAG) pipeline originally developed for early modern woodcuts can be effectively transferred to the domain of medieval manuscript illumination. Using a dataset of Wenzelsbibel miniatures annotated with Iconclass, the pipeline combined page-level image input, LLM description generation, vector retrieval, and hierarchical reasoning. Although overall scores were lower than in the earlier woodcut study, the best-performing configuration still substantially surpassed both image-similarity and keyword-based search, confirming the advantages of structured multimodal retrieval for medieval material. Truncation analysis further revealed that many errors occurred only at the deepest Iconclass levels: removing levels raised precision to 0.64 and 0.73, with average remaining depths of 5.49 and 4.49 levels, respectively. These results indicate that the model’s broader hierarchical placement is often correct even when fine-grained specificity breaks down. Taken together, the findings demonstrate that a woodcut-oriented RAG pipeline can be meaningfully adapted to manuscript illumination and that its strengths lie in contextual reasoning and structured classification. Future improvements should incorporate available textual metadata, explore graph-based retrieval, and refine Iconclass-driven pathways. 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

Accurate and robust visual localization under changing environments remains a fundamental challenge in autonomous driving and mobile robotics. Traditional handcrafted features often degrade under long-term illumination and viewpoint variations, while recent CNN-based methods, although more robust, typically rely on coarse semantic cues and remain vulnerable to dynamic objects. In this paper, we propose a fine-grained semantics-guided feature extraction framework that adaptively selects stable keypoints while suppressing dynamic disturbances. A fine-grained semantic refinement module subdivides coarse semantic categories into stability-homogeneous sub-classes, and a dual-attention mechanism enhances local repeatability and semantic consistency. By integrating physical priors with self-supervised clustering, the proposed framework learns discriminative and reliable feature representations. Extensive experiments on the Aachen and RobotCar-Seasons benchmarks demonstrate that the proposed approach achieves state-of-the-art accuracy and robustness while maintaining real-time efficiency, effectively bridging coarse semantic guidance with fine-grained stability estimation. Quantitatively, our method achieves strong localization performance on Aachen (up to 88.1% at night under the $(0.2^{\circ },0.25,\mathrm{m})$ threshold) and on RobotCar-Seasons (up to 57.2%/28.4% under the same threshold for day/night), demonstrating improved robustness to seasonal and illumination changes. Full article

Zirconia-based ceramics find wide application in engineering due to their very high hardness, resistance to elevated temperatures, and high fracture toughness. Among stabilizers of the advantageous tetragonal zirconia phase, yttria allows for better grain size refinement than ceria does; thus, Y₂O₃ is the most widely used. In the present study, comparative analysis was performed for yttria-stabilized zirconia (YSZ) and ceria-stabilized zirconia (CSZ) in terms of sinterability, densification, and mechanical properties, including hardness and resistance to plastic deformation. The results proved that CSZ sintered in similar conditions as YSZ exhibits similar properties, including an elastic modulus of 200–220 GPa and H/E of 0.070–0.076. In particular, the hardness of the ZrO₂–5 wt% CeO₂ ceramic appeared to be 14.6 ± 0.5 GPa, close to that of ZrO₂–3 wt% Y₂O₃, which was 14.20 ± 0.74 GPa. However, SiC addition to ZrO₂–5 wt% CeO₂ composites increased hardness substantially up to 16.8 ± 0.8 GPa. Moreover, the fracture toughness of CSZ was 2.5 times higher than that of YSZ sintered under identical conditions. Thus, CeO₂ can be a good, cheaper alternative to the traditionally used Y₂O₃ stabilizer for submicron-grained tetragonal zirconia ceramics. Full article

In response to the high stiffness and hardness levels of alumina ceramic materials, the traditional SHPB (split Hopkinson pressure bar) experimental setup has been modified. This study analyzes the propagation patterns of stress waves in the SHPB system after adding cushion blocks. Experiments demonstrated that the modified SHPB apparatus can effectively perform dynamic mechanical property tests on alumina ceramics. The JH-2 constitutive damage model parameters for alumina ceramics were determined based on theoretical analysis and static/dynamic experimental data. An LS-DYNA numerical model for the impact compression simulation of alumina ceramics was established to investigate the effects of stress waves with three wavelengths (300 mm, 400 mm, and 600 mm) at the same impact velocity, along with the dynamic fragmentation process. The results indicate that alumina ceramics exhibit strain rate hardening effects in compressive strength, failure strain, and elastic modulus under high strain rates; compressive strength and failure strain show positive correlations with stress wave wavelength under high strain rates; and microcracks initially nucleate preferentially along grain boundaries on the end surfaces, forming annular damage zones symmetrically about the central axis. This study presents a modified SHPB setup that improves test capability for high-hardness ceramics, rather than overturning classical methodologies. The absence of a direct comparison with unmodified setups stems from the known limitations of conventional systems in handling small-diameter alumina specimens without bar damage—a challenge addressed proactively in this work through impedance-matched cushion blocks and refined data processing. Full article

Context: Code recommendation plays an important role in improving programming efficiency and software quality. Existing approaches mainly focus on method- or API-level recommendations, which limits their effectiveness to local code contexts. From a multi-stage recommendation perspective, class-level code recommendation aims to efficiently narrow a large candidate code space while preserving essential structural information. Objective: This paper proposes RioCC, a class-level code recommendation framework that leverages deep forest-based code clone detection to progressively reduce the candidate space and improve recommendation efficiency in large-scale code spaces. Method: RioCC models the recommendation process as a coarse-to-fine candidate reduction procedure. In the coarse-grained stage, a quick search-based filtering module performs rapid candidate screening and initial similarity estimation, effectively pruning irrelevant candidates and narrowing the search space. In the fine-grained stage, a deep forest-based analysis with cascade learning and multi-grained scanning captures context- and structure-aware representations of class-level code fragments, enabling accurate similarity assessment and recommendation. This two-stage design explicitly separates coarse candidate filtering from detailed semantic matching to balance efficiency and accuracy. Results: Experiments on a large-scale dataset containing 192,000 clone pairs from BigCloneBench and a collected code pool show that RioCC consistently outperforms state-of-the-art methods, including CCLearner, Oreo, and RSharer, across four types of code clones, while significantly accelerating the recommendation process with comparable detection accuracy. Conclusions: By explicitly formulating class-level code recommendation as a staged retrieval and refinement problem, RioCC provides an efficient and scalable solution for large-scale code recommendation and demonstrates the practical value of integrating lightweight filtering with deep forest-based learning. 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

Medical image classification is a fundamental task in medical image analysis and underpins a wide range of clinical applications, including dermatological screening, retinal disease assessment, and malignant tissue detection. In recent years, diffusion models have demonstrated promising potential for medical image classification owing to their strong representation learning capability. However, existing diffusion-based classification methods often rely on oversimplified prior modeling strategies, which fail to adequately capture the intrinsic multi-scale semantic information and contextual dependencies inherent in medical images. As a result, the discriminative power and stability of feature representations are constrained in complex scenarios. In addition, fixed noise injection strategies neglect variations in sample-level prediction confidence, leading to uniform perturbations being imposed on samples with different levels of semantic reliability during the diffusion process, which in turn limits the model’s discriminative performance and generalization ability. To address these challenges, this paper proposes a Confidence-Guided Adaptive Diffusion Network (CGAD-Net) for medical image classification. Specifically, a hybrid prior modeling framework is introduced, consisting of a Hierarchical Pyramid Context Modeling (HPCM) module and an Intra-Scale Dilated Convolution Refinement (IDCR) module. These two components jointly enable the diffusion-based feature modeling process to effectively capture fine-grained structural details and global contextual semantic information. Furthermore, a Confidence-Guided Adaptive Noise Injection (CG-ANI) strategy is designed to dynamically regulate noise intensity during the diffusion process according to sample-level prediction confidence. Without altering the underlying discriminative objective, CG-ANI stabilizes model training and enhances robust representation learning for semantically ambiguous samples.Experimental results on multiple public medical image classification benchmarks, including HAM10000, APTOS2019, and Chaoyang, demonstrate that CGAD-Net achieves competitive performance in terms of classification accuracy, robustness, and training stability. These results validate the effectiveness and application potential of confidence-guided diffusion modeling for two-dimensional medical image classification tasks, and provide valuable insights for further research on diffusion models in the field of medical image analysis. Full article

With surging demand for oil and gas resources, staged fracturing is becoming extremely important, and fracturing material is the key factor in exploration. Recently developed Mg-Al alloys cannot simultaneously achieve high strength and rapid degradation, limiting their widespread application in the exploration. To address this issue, this study utilized the rapid densification technology of spark plasma sintering (SPS) to sinter Mg, Al, and Fe powders at a ratio of Mg-5Al-Fe (0, 2, 4, 6 wt.%) under a temperature of 510 °C and a pressure of 35 MPa for 800 s. And this study conducted investigations on the microstructure, mechanical strength and degradation rate of the alloy through scanning electron microscope, hardness and compression tests, as well as immersion experiments. The results indicated that SPS enabled rapid powders densification and grain refinement, and the addition of Fe particles formed a second-phase strengthening which could block dislocation, thereby increasing mechanical strength. The ultimate compressive strength (UCS) was increased from 189.37 ± 6.12 MPa for Mg-5Al to 421.21 ± 12.31 MPa for Mg-5Al-6Fe. Furthermore, the addition of Fe accelerated the degradation rate, with the Mg-5Al-6Fe alloys reaching 45.26 ± 2.6 mm/year. Meanwhile, the alloys also had a low density of 1.38 ± 0.027–1.53 ± 0.030 g/cm³, which could effectively reduce the pumping energy consumption of fracturing fluids. These characteristics met the core requirements of degradable fracturing balls, showing the great potential of Mg-5Al-Fe alloys for staged fracturing. Full article

The simultaneous enhancement of hardness and toughness in WC-Co cemented carbides remains a critical and persistent challenge for advanced cutting-tool applications, where conventional materials often suffer from inherent property trade-offs. In this study, a novel composite ceramic material—WC-(TiZrHfNbTa) (C, N) high-entropy carbonitride (HECN)-Co composite—was successfully fabricated via dry ball milling and spark plasma sintering (SPS) at 1300 °C following 90 h of ball milling. By incorporating varying amounts of HECN (0–15%, mass fraction, same below), the microstructure and mechanical properties of the composites were systematically tailored. The results demonstrate that the addition of HECN effectively refines the WC grains and increases the material density, leading to a pronounced improvement in hardness. Notably, the composite with 10% HECN (WC-10%HECN-9Co) exhibits an optimal balance of hardness and fracture toughness, achieving a Vickers hardness of 2375 ± 25 HV30 and a fracture toughness of 12.9 ± 1.1 MPa·m^1/2. In contrast, excessive HECN addition (15 wt.%) induces excessive grain refinement, which significantly impairs toughness. Our study demonstrates that the introduction of (TiZrHfNbTa) (C, N) HECN as a reinforcing phase offers a viable and effective strategy for designing cemented carbides with an exceptional hardness–toughness synergy, showing great promise for demanding cutting applications such as high-speed machining and the processing of hard-to-cut materials. Full article

The addition of Cu or La plays an important role in microstructure and property manipulation of 4xxx series Al-Si alloys. However, the effects of Cu-La hybrid modification on the microstructure and properties of Al-5Si alloys and welding performance remain unclear. In this paper, the influence of Cu-La addition on the strength and elongation of one commercial Al-5Si alloy and the welding joint characterization are investigated. The results show that the addition of Cu-La can refine α-(Al) and Fe-rich phase and improve the fluidity. Meanwhile, the elongation can be improved by Cu-La microalloying, which is beneficial for the manufacturing filler wire. The uniform distribution of Cu in the alloy but not segregation at grain boundaries due to La addition is the key factor to adjust the mechanical properties. Moreover, the filler materials were used to conduct metal inert gas welding on 6061 alloy. It reveals that, with Cu-La addition, the weld pool width increases and porosity defect decreases significantly. This is ascribed to Cu-La co-addition enhancing wettability and fluidity, which improves the welding performance. Our results offer an effective strategy for manufacturing and optimizing welding performance of welding wires. Full article

Austenitic Fe-Mn-Al-C lightweight steels have attracted considerable interest for automotive applications due to their exceptional specific strength, where κ-carbides precipitation critically influences mechanical properties. This review systematically examines the crystal structure, classification, and precipitation kinetics of κ-carbides, emphasizing their spatial distribution-dependent effects: coarse κ-carbides at austenite grain boundaries induce embrittlement and degrade toughness, while nanoscale κ’-carbides within grains enhance strength and ductility through dislocation interactions (e.g., Orowan bypassing and shearing), activating deformation mechanisms such as Dynamic Slip Band Refinement (DSBR), Shear Band-Induced Plasticity (SIP), and Microband-Induced Plasticity (MBIP). Thermodynamic calculations guide alloy design to ensure a single-phase austenite structure at the typical hot-rolling finishing temperature (~900 °C), avoiding harmful phases while promoting beneficial precipitates. Mn suppresses κ-carbide formation, whereas Al and C act as promoters, with intragranular κ’-carbides favoring higher Al/C concentrations (e.g., >6.2% Al and >1.0% C). Heat treatment parameters critically influence κ-carbide distribution, where rapid cooling (e.g., water quenching) suppresses κ-carbides, and subsequent aging (500–700 °C) enables homogeneous precipitation of κ’-carbides. Pre-deformation prior to annealing further accelerates κ-carbide nucleation by introducing crystal defects. Optimal performance requires integrated composition-processing-microstructure optimization to achieve a nnanoscaleκ’-carbide-strengthened austenite matrix through controlled composition and thermo-mechanical processing to achieve an optimal strength-ductility balance. Full article

The rapid growth of sensor data in IoT and Digital Twins necessitates high-performance spatial indexing. Traditional indexes like Rtrees suffer from high storage overhead, while state-of-the-art learned indexes like GLIN encounter a “Refinement Bottleneck” due to coarse-grained Minimum Bounding Rectangle (MBR) filtering. Furthermore, existing solutions often trade update throughput for query accuracy, failing in dynamic IoT workloads with concurrent reads and writes. We propose DyGLIN (Dynamic Generate Learning-Based Index), a dynamic, read-optimized learned spatial index tailored for high-frequency sensor streams. DyGLIN introduces a decoupled leaf architecture separating query processing from data maintenance. To accelerate queries, we implement a hierarchical filtering pipeline using hierarchical MBRs (HMBR) and Cuckoo Filters to aggressively prune false positives. For maintenance, a Delta Buffer mechanism amortizes update costs, while logical deletion ensures high throughput. Experiments on real-world datasets show that DyGLIN reduces query latency by 26.4% [95% CI: 20.1%, 38.6%] compared to GLIN. It achieves 30.0% [95% CI: 21.4%, 35.9%] higher insertion throughput and superior deletion performance, with only an 18.5% [95% CI: 16.8%, 19.8%] increase in memory overhead. Full article