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Al-Si coated ultra-high strength steel（UHSS）has been commonly applied in hot stamping process.The influence of austenitizing temperature on microstructure of Al-Si coating of UHSS during hot stamping process and its tribological behavior against H13 steel under elevated temperature were simulatively investigated.The austenitizing temperature of Al-Si coated UHSS and its microstructual evolution were confirmed and analyzed by differential scanning calorimetry and scanning electron microscopy.A novel approach to tribological testing by replicating hot stamping process temperature history was presented.Results show that the hard and stable phases Fe_2Al_5＋FeAl_2 formed on Al-Si coating surface after exposure to 930°C for 5 min,which was found to be correlated to the tribological behavior of coating.The friction coefficient of coated steel was more stable and higher than that of uncoated one.The main wear mechanism of Al-Si coated UHSS was adhesion wear,while abrasive wear was dominant for the uncoated UHSS.

In heavy forging, a manipulator is indispensable to assist and help the precision of the forming process. This paper presents a multi-system simulation methodology combining the forging finite element method (FEM) simulation and the kinematics analysis to evaluate the mutual reaction loads between the forging process and the assisting manipulator. The forging is realized by the thermal–mechanical FEM simulation and the kinematics movements are analyzed based on the statics and dynamics modeling of the manipulator. The reaction load generating from the forging process to the manipulator clamps is treated as an input parameter for the kinematics analysis system, which will then calculate the movement of the manipulator. And this movement is regarded as the passive compliant movement constraint and applied on the forging process through the manipulator clamps. Using this coupled system, the study compares the reaction loads with and without the active vertical compliant movement and/or the passive horizontal compliant movement and reveals the effect of these compliant movements on the reaction loads.

Sedimentary facies modeling is a critical approach for understanding geological phenomena, yet the strong heterogeneity of reservoir systems poses a serious challenge for their refined characterization. In this study, we innovatively propose an interpretable attention-guided generative adversarial network framework with dual-domain learning, which achieves precise sedimentary facies modeling under the constraints of well facies and soft probability data. Specifically, we first effectively extract and preserve prior information of sedimentary facies models from both spatial and frequency domain perspectives. Then, during simulation, to enhance the capability of the network model for finely characterizing complex heterogeneous models, cross-spatial attention mechanisms are designed to effectively capture short-range and long-range dependencies between multi-scale pattern features. Additionally, through systematic feature map visualization analysis, we elucidate the processes of conditional fitting and complex sedimentary facies model reconstruction, intuitively demonstrating the functional mechanisms of each module. Finally, systematic experiments are conducted on multiple datasets to validate the effectiveness of the proposed method. The results demonstrate that the generated sedimentary facies models exhibit high consistency with training datasets in terms of visual realism and statistical indicators. Quantitative comparisons reveal remarkable performance of the method, achieving low Wasserstein distance (0.09), Kernel Inception Distance (0.0017) and Kernel Maximum Mean Discrepancy (0.21). These findings further confirm the high realism of the generated realizations regarding pattern features. This study offers a reliable and practical method for geological reservoir modeling, thereby advancing quantitative, precise geological research with broad application prospects.

The laminar sedimentary structures of saline lacustrine mixed rocks affect both organic matter enrichment and reservoir storage performance. However, due to the small-scale nature of laminae, large-scale identification using well-logging data during reservoir exploration and development remains challenging. It is necessary to introduce a research method to identify and characterize the development of different types of laminae. Based on analyses of typical cores, XRD data, and well-logging curves from the upper member of the Xiaganchaigou Formation in the Yingxi area, five main types of laminae and six lamina combinations were classified. A Transformer-based intelligent recognition method was then applied to identify these lamina combinations from well-log data, with the Random Forest algorithm used as a comparative benchmark. Verification results show that the Transformer model achieves a higher total accuracy of 84% in lamina combination recognition. This study proposes a new approach for the conventional well-log characterization of laminae, in which the classification is established from the perspective of laminae genesis. It reflects the development patterns of lamina combinations driven by paleoenvironmental changes, and selects an appropriate intelligent recognition method to address the challenges in well-log characterization of such reservoirs. In terms of engineering applications, this study can accurately indicate the positions of high-quality reservoirs within sedimentary cycles during field development. It provides a sedimentary facies-controlled basis for the three-dimensional characterization of reservoir quality, thereby offering a valuable reference for the exploration and development of reservoirs formed under similar sedimentary conditions.

This study applies cluster analysis to identifying two frequently blended fibers, lyocell and cotton, based on the six characteristic parameters extracted from the automatic fiber identification system presented in the previous publications. Two independent parameters are first derived from the six characteristic parameters by using factor analysis. Second, a probability density distribution map of the two indirect parameters is established through sample observations. Finally, the clusters of lyocell and cotton fibers in the probability density distribution map are segmented according to contour lines and distance. The experiment showed that the accuracy of lyocell and cotton fiber identification with the cluster analysis is above 95%.

For automatic pilling evaluation of textiles, the depth information is one of the most critical and effective features in extracting pills from fabric image. Laser-scanning techniques are often used for acquiring 3D depth images. However, due to the high-cost and low-efficiency of Laser-scanning system, researchers have found it unsuitable for fabric analysis. This paper illustrates a new approach for acquiring the depth image used to extract pills by introducing the method of Depth From Focus (DFF). This approach firstly captures a sequence of images of the same view at different focal positions under the automatic optical microscope. Then the best-focused position (z) of each pixel(x, y) was determined by choosing the layer of image declaring the max sharpness and formed the depth image. This paper proposed a new sharpness-evaluation criterion which was based on the variance of gradients. Afterwards, a few basic points indicating the background area was selected from the depth image, and then the depth coordinates (x, y, z) at these basic points were used to calculate a predicted background plane. Via the background plane, pills above the background were extracted. A fabric sample with a single fiber upon it was presented to illustrate the process and result of the approach.

A spherical-like Ni0.6Co0.2Mn0.2（OH）2 precursor was tuned homogeneously to synthesize LiNi0.6Co0.2Mn0.2O2 as a cathode material for lithium-ion batteries.The effects of calcination temperature on the crystal structure,morphology,and the electrochemical performance of the as-prepared LiNi0.6Co0.2Mn0.2O2 were investigated in detail.The as-prepared material was characterized by X-ray diffraction,scanning electron microscopy,laser particle size analysis,charge–discharge tests,and cyclic voltammetry measurements.The results show that the spherical-like LiNi0.6Co0.2Mn0.2O2 material obtained by calcination at 900°C displayed the most significant layered structure among samples calcined at various temperatures,with a particle size of approximately 10 μm.It delivered an initial discharge capacity of 189.2 m Ah×g-1 at 0.2C with a capacity retention of 94.0% after 100 cycles between 2.7 and 4.3 V.The as-prepared cathode material also exhibited good rate performance,with a discharge capacity of 119.6 m Ah×g-1 at 5C.Furthermore,within the cut-off voltage ranges from 2.7 to 4.3,4.4,and 4.5 V,the initial discharge capacities of the calcined samples were 170.7,180.9,and 192.8 m Ah×g-1,respectively,at a rate of 1C.The corresponding retentions were 86.8%,80.3%,and 74.4% after 200 cycles,respectively.

Aim： To investigate whether nerve growth factor （NGF） induced angiogenesis of bone marrow mesenchymal stem cells （MSCs） and the underlying mechanisms. Methods： Bone marrow MSCs were isolated from femors or tibias of Sprague-Dawley rat, and cultured. The cells were purified after 3 to 5 passages, seeded on Matrigel-coated 24-well plates and treated with NGF. Tube formation was observed 24 h later. Tropomyosin- related kinase A （TrkA） and p75NTR gene expression was examined using PCR analysis and flow cytometry. Growth curves were deter- mined via cell counting. Expression of VEGF and pAkt/Akt were analyzed with Western blot. Results： NGF （25, 50, 100 and 200 pg/L） promoted tube formation of MSCs. The tubular length reached the maximum of a 2.24-fold increase, when the cells were treated with NGF （50 pg/L）. NGF （50 pg/L） significantly enhanced Akt phosphoryiation. Pretreatment with the specific PI3K inhibitor LY294002 （10 pmol/L） blocked NGF-stimulated Akt phosphorylation, tube formation and angiogenesis. NGF （25-200 pg/L） did not affect the expression of TrkA and vascular endothelial growth factor （VEGF）, but significantly suppressed the expression of p75NTR. NGF （50 pg/L） markedly increased the proliferation of MSCs. Conclusion： NGF promoted proliferation of MSCs and activated the PI3K/Akt signaling pathway, which may be responsible for NGF induction of MSC angiogenesis.

Electrospun nanofibers are regarded as a promising candidate for filtration because of their prominent size effect. The precise control of nanofiber diameter is the key to the material’s outstanding filtration capability, but it remains a challenge. Herein, an electrohydrodynamic scaling model is established for the accurate prediction of fiber diameter based on the velocity of the whipping jet. In this model, the velocity reflects jet stretching and is tuned by adding salt. The theoretical predictions agree well with the experimental results. With the proposed diameter model, the filtration property of the membrane is significantly optimized by governing the fiber diameter. When the nanofiber is slenderized to 183 nm, an ultralight (0.521 g m−2) membrane exhibits high filtration efficiency (99.93%) and low pressure drop (105.2 Pa) against ultrafine aerosol particles (≤0.26 µm) under an airflow face velocity of 5.33 cm s−1. These findings demonstrate that the established surface charge-based diameter model provides an excellent platform to timely and accurately control the nanofiber diameter via the velocity of whipping jets for high-efficiency air filtration.