Search Results (2,163)

Transitioning cultivated land from a narrowly defined production resource into a coordinated multifunctional asset is a cornerstone of agricultural modernization. Despite this imperative, current land utilization in China remains largely constrained by a singular production focus, resulting in suboptimal multifunctionality. Although digitally driven agricultural new quality productive forces (ANQPFs) are posited as a critical catalyst for functional restructuring, empirical evidence quantifying their relationship with cultivated land multifunctionality (CLM) remains limited. To examine the association between ANQPF and CLM, this study employs panel data from 115 prefecture-level cities across the Yangtze River Basin, China, spanning the period 2013–2023. The empirical results indicate that ANQPF is significantly and positively associated with CLM. These associations are robust to a battery of robustness checks, and endogeneity tests provide additional evidence supportive of a positive association. Transmission pathway analysis suggests that the positive association operates through three pathways: increasing the main business revenue of agricultural product processing enterprises above a designated size, expanding the number of agricultural technology patents, and improving the level of agricultural socialized services. Subgroup analyses, supplemented by Chow tests of coefficient equality, reveal that these associations tend to be larger in non-major grain-producing regions, areas with lower per capita GDP, and the upper and middle reaches of the Yangtze River Basin. Threshold effect analysis further demonstrates that once ANQPF exceeds a certain level, the positive association exhibits diminishing marginal returns; a similar but weaker pattern is observed for leading enterprises. These findings provide policy implications for developing ANQPF in accordance with local conditions and for synergistically optimizing CLM patterns. Full article

To investigate the effect of microalloying on hydrogen embrittlement resistance of hot-stamped steels with strength levels of 1.8 GPa and above, six composition schemes were designed based on conventional 34MnB5 steel, including three routes, namely Nb, V, and Nb–V. U-bend constant-strain bending tests and slow strain rate tensile (SSRT) tests were conducted on quenched specimens for each scheme. Results indicated that the Nb-containing compositions exhibited superior hydrogen embrittlement resistance. The mechanism by which microalloying refines the martensitic microstructure of 34MnB5 in the quenched state and enhances its resistance to hydrogen embrittlement was studied in detail. It was found that Nb exhibits stronger effects than V in refining and homogenizing the martensite structure. The fundamental reasons for Nb’s enhanced ability to pin austenite grain boundaries at high temperatures—leading to better microstructural refinement and homogenization—are its higher temperature range for second-phase precipitation, greater driving force for grain boundary diffusion, lower austenite grain boundary diffusion coefficient, and weaker tendency for high-temperature coarsening of precipitates. The microstructural refinement and homogenization induced by Nb addition are more pronounced than those achieved by combined additions of Nb and V. Furthermore, within the concentration range of 0–0.1%, the amount of Nb is positively correlated with the degree of microstructural refinement and homogenization. By reducing martensite lath size through microalloying, multiple microstructural modifications occur: decreased density of geometrically necessary dislocations (GNDs) in the matrix, significant increase in interface density—especially a higher proportion of high-angle grain boundaries—reduced number of Σ3 special harmful grain boundaries, weakened matrix texture intensity, fewer twin martensites, and smaller twin martensite sizes. These factors collectively contribute significantly to the improved hydrogen embrittlement resistance of Nb-containing steels. Full article

Landslide-induced surges and subsequent dam breaching constitute severe cascading hazards in mountainous gorges. Conventional steady-flow sediment theories fail to describe these extreme, unsteady processes, and existing research focuses on wave propagation rather than surge-driven erosion mechanisms. Using the Baige landslide dam as a prototype, this study combines 1:100 physical model tests with CFD-DEM simulations to investigate how landslide fall height, water depth, and sediment gradation govern surge propagation, dam scour, and sediment transport. The results show the surge amplitude reaches 38.21 cm under high-fall, deep-water conditions and decays nonlinearly. Fine-grained beds exhibit suspended-load transport (max concentration 15.2%), whereas coarse-grained beds develop scour pits via bedload transport, with deposition volume increasing ~230%. Sediment transport follows a three-stage spatial pattern: intense erosion near the dam (max depth 2.9 cm), grain-size-sorted deposition in the middle reach (max height 4.6 cm), and fine-sediment accumulation downstream. The numerical results agree well with experiments. The constructed “water depth–gradation–energy” risk assessment matrix supports refined prediction and mitigation of landslide dam-break cascading hazards. Full article

CrAlN PVD coatings are frequently used to protect cutting tools because of their superior hardness and wear resistance. However, the influence of carbon addition to such coatings remains largely unexplored. Therefore, Cr-Al-C-N coatings were deposited by HiPIMS using a commercial PVD unit equipped with a segmented sputter target. The target consisted of an upper half of Cr and a lower half of Al. Carbon was added under reactive sputtering conditions with the aim of reducing internal stress and introducing a friction-reducing component. A range of different Al-Cr-C compositions could be efficiently explored by varying the acetylene reactive gas flow. Depending on the positioning of the samples, Cr/Al ratios could be varied between about 4/1 and 1/2 while three different levels of carbon concentration (0, 11, and 25 atomic % of total coating composition) were investigated. It was found that an intermediate carbon concentration effectively increased the hardness of Cr-rich coatings, achieving maximum plastic hardness values over 40 GPa. With increasing Al content, hardness drops to below 30 GPa. The cubic CrN phase with mostly 200-oriented grains was detected for most variants. With increasing Al and C contents, a rapid decrease in crystallite size is observed, accompanied by a reduced intensity of the (200) XRD reflection. A turning test on stainless steel showed decreasing flank wear with higher Al contents. However, no improvement associated with carbon addition could be confirmed within the investigated concentration range. Full article

Rain gardens are widely used for stormwater treatment; however, the performance of alternative sorbent materials under varying rainfall conditions remains insufficiently understood. In this study, the removal of nutrients and heavy metals was evaluated in laboratory-scale rain gardens amended with halloysite of two grain size fractions (1–2 mm and 2–4 mm) under rainfall events lasting 30 and 120 min. Three column systems were tested: a reference column containing dolomite, sand, and gravel (C1), and two halloysite-amended columns (C2 and C3). Synthetic stormwater containing N–NH₄⁺, N–NO₃^–, P–PO₄^3–, Cu, Zn, and Pb was applied. Halloysite improved nutrient removal in all rainfall scenarios compared with the reference column. Dissolved inorganic nitrogen (DIN) removal reached 84% in column C3, whereas soluble reactive phosphorus (SRP) removal reached 85% in column C2. Complete removal of Cu, Zn, and Pb was observed in all columns, highlighting the dominant role of dolomite in heavy metal retention. These findings demonstrate the potential of halloysite as a nutrient-removing amendment in rain garden. Full article

Composition criteria for refractory high-entropy alloys (RHEAs) reliably predict whether a candidate composition forms a single-phase body-centred-cubic (BCC) solid solution, but not which BCC-confirmed composition will be strongest. Here we revisit seven previously reported RHEA compositions on freshly arc-melted material of our own, confirm each as single-phase BCC using full-spectrum X-ray diffraction re-indexing, and screen ten descriptors obtainable before mechanical testing against their room-temperature compressive yield strength: five compositional (mean atomic radius r⁻, mixing enthalpy ΔH_mix, atomic-size mismatch δ, VEC, and melting point T_m), two lattice-scale (Nelson–Riley parameter a₀ and Williamson–Hall apparent microstrain ε) and three microstructural (KAM, ELM₁₅, and grain ECD). Only ΔH_mix ranks the strengths, and its direction inverts the usual expectation: the less negative the mixing enthalpy, the stronger the alloy. Refractoriness carries no ranking information, and the most refractory member, NbMoTaW, is second weakest of six. At n = 6 only a perfect ranking reaches a Benjamini–Hochberg q below 0.05 across ten descriptors, so the q of 0.167 obtained here measures cohort resolution: a ranking of this magnitude clears the corrected threshold from eight alloys upwards. Mean atomic radius separately predicts a₀ across all seven alloys. Full article

Pig detection in intelligent livestock farming is challenging due to the difficulty of jointly capturing global context and multi-scale features in complex environments. To address this, we propose MSCA-RTDETR (Multi-scale Content-Aware Real-Time Detection Transformer), which enhances the Real-Time Detection Transformer (RTDETR) architecture with two complementary components. First, we introduce a Content-Aware Token Selection (CATS) backbone that uses content-aware weighting and Top-K sparse attention to efficiently model long-range dependencies and global context. Second, we design a Multi-scale Interaction Residual Block (MIRB) that employs parallel convolutional kernels ($3\times 3$ and $5\times 5$) to capture fine-grained local details and broad contour information, handling scale variations due to different viewing distances and pig sizes. On a custom dataset of 8070 images (6955 training, 1115 testing) collected from a commercial pig farm, MSCA-RTDETR outperforms existing detectors, improving AP, $A{P}_{50}$, and $A{P}_{75}$ by 0.6%, 0.3%, and 0.9% respectively over the strong RTDETRv1 baseline. The model demonstrates effective detection on our self-built dataset, offering a practically viable and accurate solution for intelligent livestock farming. Full article

To further improve the impact toughness of aged 6082 aluminum alloy, electromagnetic shocking treatment (EST) was applied to IHC (solution treatment + unidirectional compression + peak aging) samples. The mechanical properties and impact toughness of the IHC and EST samples were evaluated through room-temperature tensile tests and Charpy impact tests, respectively. The results indicate that, compared to the IHC samples, the EST samples exhibit higher tensile strength (an increase of approximately 9.4%), greater elongation, and significantly higher impact energy (an increase of approximately 26.5%). Microstructural characterization reveals that, compared to the IHC samples, the EST samples possess a lower dislocation density, a larger grain size, and shorter precipitates. Striped grain boundaries were observed in both IHC and EST samples, but they were considerably more pronounced in the EST samples. This indicates that more distinct interface wetting occurred in the EST samples, which promoted grain growth to some extent, a reduction in dislocation density, precipitate dissolution, and the occurrence of interface bridging. This paper primarily investigates the microstructural evolution within the alloy under EST and discusses how these microstructural changes influence the alloy’s performance, thereby providing a novel approach to enhancing the impact toughness of aluminum alloys. Full article

The Ti-6Al-4V alloy is widely used in aerospace and deep-sea applications due to its exceptional strength and corrosion resistance. However, its application is often constrained by high deformation resistance and a narrow hot-working temperature window, primarily attributed to its heat and mass transfer characteristics. To address these limitations, a novel Ti-6Al-1.3V-0.9Fe alloy was designed with an equivalent molybdenum content. In this study, Gleeble thermal simulation tests were conducted to investigate the impact of Fe on the hot deformation behavior under various conditions and to identify the optimal processing window for this alloy. The effects of deformation temperature and strain rate on the flow stress curves and peak stress were systematically analyzed, along with the role of Fe in microstructural evolution during hot deformation. The results demonstrate that the addition of Fe significantly refines the grain size of the Ti-6Al-1.3V-0.9Fe alloy. As expected, the flow stress decreases with increasing deformation temperature and increases at higher strain rates. Under high-temperature and low-strain-rate conditions, the alloy exhibits steady-state flow behavior, indicating improved hot workability. Based on the constitutive modeling, the apparent activation energy (Q) for hot deformation was calculated to be 503.81 kJ/mol. Finally, the optimal hot-working parameters for the Ti-6Al-1.3V-0.9Fe alloy were identified as a temperature range of 760 °C to 860 °C and a strain rate between 0.01 and 0.16 s⁻¹. Full article

A solid solution treatment (SST) followed by single-stage aging (0–30 h, 140 °C) was performed on 7075 aluminum alloy specimens with longitudinal axes oriented at 0°, 45° and 90° to the rolling direction. The mechanical properties and microstructure were analyzed by tensile testing, optical microscope (OM), electron backscatter diffraction (EBSD), scanning electron microscope (SEM) and transmission electron microscope (TEM). The results show that the average tensile strengths of the as-received 7075 aluminum alloy in the three directions were 304 MPa (0°), 295 MPa (45°) and 297 MPa (90°), respectively, with an anisotropy index (AI) of 0.97, indicating that the as-received samples exhibited negligible anisotropic mechanical properties. After SST, elongated grains with coarse size were formed, which is primarily attributed to the inheritance of the deformed fiber texture introduced by hot rolling. EBSD analysis of the 30 h aged specimens revealed that, within the same analyzed area, the total grain-boundary length in the 45° direction (16.4 cm) was much larger than that in the 0° (10.4 cm) and 90° (13.5 cm) directions. As the grain morphology showed no significant change between the SST and aged conditions, this grain-boundary distribution was representative of the microstructural state established during SST and persisted throughout the artificial aging process, contributing to the anisotropic mechanical properties. During artificial aging, prolonged aging time significantly facilitated the precipitation, with the 30 h aged sample exhibiting a significantly higher density of precipitates compared to the 6 h aged sample, leading to enhanced mechanical properties. The tensile strengths of the 30 h aged samples increased to 165 MPa, 236 MPa and 196 MPa in the three directions, respectively. Meanwhile, due to the fixed crystallographic orientation relationship between the precipitates and the Al matrix, the precipitates tended to form on specific planes, which enhanced the anisotropic mechanical properties. Consequently, the AI value increased from 0.97 (as-received) to 1.43 (30 h aged) with prolonged aging time. Full article

At present, the detection methods for the microstructure and mechanical properties of aluminum alloys are mainly based on SEM, EBSD, TEM, tensile tests, and microhardness tests, which are time-consuming and destructive. In this paper, laser ultrasonic non-destructive detection is employed to obtain ultrasonic signals from Al-Cu-Mg aluminum alloy subjected to various heat treatment processes. The results reveal empirical correlations between the characteristic values of the ultrasonic signals and the material’s state. Specifically, the characteristic values exhibit an inverse correlation with the precipitated phase content. When both the precipitated phase content and the average grain size vary significantly, distinct deviations in the characteristic values are observed, which can serve as indicators of microstructural changes. The extracted ultrasonic eigenvalues also show promising, empirically derived correlations with mechanical properties, with frequency-domain attenuation coefficients demonstrating relatively higher sensitivity based on fitting analyses within the current dataset. These observed variations are tentatively discussed as plausible consequences of grain boundary scattering and changes in matrix solid solution strengthening associated with precipitate dissolution. Overall, the findings suggest the potential of laser ultrasonics as a rapid non-destructive evaluation tool, providing a preliminary scientific basis for further development of methods to assess the microstructure and mechanical properties of Al-Cu-Mg alloys within the tested parameter space. Full article

Robot-assisted surgery has significantly expanded the possibilities of minimally invasive procedures by providing enhanced dexterity and visualization. However, the lack of direct haptic feedback still limits the surgeon’s ability to accurately assess instrument–tissue interactions, motivating the need for automatic intraoperative assistance systems. During vesicourethral anastomosis (VUA) in robot-assisted radical prostatectomy (RARP), accurate engagement of the bladder and urethral mucosa is essential to ensure proper tissue approximation and watertight closure. Nevertheless, automatic identification of fine-grained needle–tissue interactions during this phase remains largely unexplored. In this work, we propose a proof-of-concept framework for vision-based needle–tissue interaction analysis in RARP endoscopic videos, combining semantic segmentation, geometric proximity analysis, and motion coherence estimation to identify biomechanically plausible interaction events. Two independent transformer-based models were fine-tuned for semantic segmentation of the mucosal tissue and the surgical needle using a patient-level split of six real-world RARP procedures, comprising four procedures for training, one for validation, and one for independent testing. The models achieved Dice scores of 0.837 and 0.774, respectively. The segmentation outputs were subsequently used to drive a motion-aware interaction analysis pipeline, combining geometric proximity estimation between the needle endpoint and the mucosal tissue with optical-flow motion coherence analysis. The proposed interaction framework was evaluated on an independent test set, achieving a specificity of 0.933 and a recall of 0.667. An ablation study further demonstrated the complementary contribution of geometric proximity and motion coherence cues for needle–tissue interaction detection. Although limited by the retrospective nature and size of the dataset, this study introduces a low-latency, end-to-end framework for interaction-aware surgical scene understanding during RARP. The proposed approach represents an initial step toward the development of context-aware intraoperative guidance systems for robotic urologic surgery. Full article

Gleeble-3800 thermal simulation testing machine was adopted to investigate the evolution laws of microstructure and properties in the coarse-grained heat-affected zone (CGHAZ) of pipeline steels with different Cr mass fractions (0.2, 0.5, 0.8 wt.%) under welding heat inputs ranging from 8 kJ/cm to 20 kJ/cm. Combined with optical microscopy, scanning electron microscopy and electron backscatter diffraction, the coupled influencing mechanism of heat input and Cr content on the properties of CGHAZ was systematically analyzed. The results show that as the Cr content increases, the range of valid welding heat input for maintaining satisfactory CGHAZ impact toughness gradually narrows with increasing Cr mass fraction of the steel. Specifically, the 0.2Cr experimental steel maintains high toughness under a thermal input ranging from 8 to 20 kJ/cm, the 0.5Cr experimental steel exhibits relatively high toughness in the range 8 to 13 kJ/cm, while the 0.8Cr experimental steel shows high toughness only at 15 kJ/cm. The coupled effect of weld heat input and Cr content on CGHAZ properties originates from a combination of microstructural composition types, phase fractions, substructures, and grain sizes. Increasing the heat input and Cr content leads to a reduction in the bainite ferrite with superior toughness and an increase in the large-sized granular bainite with inferior toughness. Meanwhile, the effective grain size of the overall microstructure first decreases and then rises. Grain coarsening and an increased fraction of Martensitic/Austenitic (M/A) constituent are the key factors responsible for the deterioration of CGHAZ toughness in deep-sea oil and gas transportation pipeline steel. Full article

During hot deformation of GH4706 alloy forgings, to achieve effective control over the uniformity of its microstructure, it is first necessary to establish a quantitative relationship between the microstructural characteristics of an entire hot-formed turbine disk forging and process parameters using reliable methods. Based on hot compression test results of GH4706 alloy at temperatures ranging from 950 °C to 1150 °C and strain rates from 0.001 s⁻¹ to 1 s⁻¹, this study developed a microstructure evolution model. Multi-scale high-precision numerical simulations were performed to predict the parameter field distribution and microstructure distribution of the turbine disk. The results reveal that the inhomogeneity of strain distribution is the primary cause of mixed grain formation. Statistical comparisons between simulation predictions at six validation points and industrial experimental data revealed the following relative deviations: 5.21% for average grain size (AVG), 9.65% for the standard deviation of grain size distribution (SD), and 5.31% for DRX fraction. Additionally, the standard deviations of prediction error for these three parameters are 3.26% for AVG, 4.06% for SD, and 3.12% for DRX fraction. These results demonstrate that the multi-scale dynamic recrystallization model developed in this study exhibits satisfactory prediction accuracy and stability. The modeling approach presented in this paper is of great significance for precisely controlling the uniformity of microstructural distribution during the hot deformation of GH4706 alloy. Full article

On-device plant disease perception must reconcile two competing demands: enough spatial detail to localise diseased tissue within a field image and a memory and compute budget small enough for microcontroller-class hardware. Single-network solutions that jointly learn a pixel-wise mask and a fine-grained disease label tend to oversubscribe both the Flash and activation SRAM of such devices. This paper presents Chroma-Sense 2.0, a two-stage lightweight pipeline that decouples the two subproblems and sizes each stage for its own budget. The two stages run sequentially on the same frame: Stage 1 is a per-channel convolutional classifier, derived from Chroma-Sense, that names the disease, and Stage 2 is a compact ESPNet segmenter that produces a binary diseased-versus-healthy mask localising it. Because the stages run one after the other rather than concurrently, the peak working memory of the pipeline is the maximum of the two stages rather than their sum. We evaluate the pipeline on the in-the-wild PlantSeg dataset using a curated 10-species, 34-class subset and a leakage-controlled protocol in which all training crops are derived from PlantSeg’s official training images and all reported metrics are measured on a held-out test set of 5002 crops built from the official test images. The segmentation stage attains a mean foreground recall of 0.97 (mean foreground IoU of 0.49; 0.53 pooled over pixels), a deliberately recall-oriented operating point. Against Fast-SCNN, a small U-Net, LR-ASPP, and DeepLabV3+, ESPNet is the smallest-footprint model (140k parameters, 193 KB Int8 Flash) while retaining the highest foreground recall; the per-channel classifier reaches accuracies comparable to much larger ImageNet-pretrained backbones (MobileNetV3 and EfficientNet) using 10–13× fewer parameters. End to end, the coupled pipeline classifies the disease correctly on 87.8% of the test crops. An on-device profile on the OpenMV H7 and H7 Plus shows that the binding constraint at 256 × 256 is the segmenter’s ≈4 MB contiguous activation arena, rather than parameter Flash: Even on the 32 MB-SDRAM H7 Plus, the usable interpreter heap is only about 4 MB, and the arena cannot be allocated as a single contiguous block from it, so on the tested firmware, the classifier runs on microcontrollers while the segmenter does not; the full pipeline instead fits the gigabyte-scale single-board-computer tier (for example, Raspberry Pi or NVIDIA Jetson Nano), and enabling the segmenter to run on microcontrollers remains the open gap. Full article