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The utilization of high-strength and tough petroleum casing steel has become the focus of various research groups worldwide, particularly in the past decade. However, defining its forming behavior presents several challenges, stemming from the complexity of enhancing strength and toughness on the steel's characteristics. To address this issue, this study employed a low-carbon microalloyed steel and introduced V and Nb elements sequentially, resulting in three groups of steels under investigation. Subsequently, mechanical properties and microstructure characteristics were examined. Interestingly, the addition of V and Nb elements resulted in both tensile and yield strengths above 1200 MPa and 1173 MPa, respectively, satisfying the criteria for the V170 oil casing. Furthermore, after adding V, V(C,N) precipitates uniformly in the steel. Still, the Nb element is not completely precipitated as there is a significant amount of Nb in solid solution. The effective grain size of the investigated steel increases from 0.92 μm to 0.98 μm after adding V and Nb elements, accompanied by a reduction in the fraction of HAGB from 47.4 % to 42.4 %. Therefore, V element plays a vital and positive role in improving the mechanical properties of the experimental steel, while Nb element has only a minor effect.

This study aimed to solve the quantitative problem of the magnetic flux leakage (MFL) internal detection signals of long-distance oil and gas pipelines. Based on the magnetic charge theory, the numerical–analytical model for MFL internal detection of pipeline complex defects was constructed by introducing stress factor in this work. This model was used to investigate the changes of magnetic signals caused by different division sizes of the defective end-face. The distribution pattern of MFL signal under different geometric sizes and stresses of defective end-face was analyzed, and its influence on the characteristics of magnetic signals was assessed. The effectiveness of the model was verified through experiments. The results indicated that the overall accuracy of magnetic signal analysis by determining the optimal division size on the end face defects was improved by 35% compared with the conventional magnetic charge model, in which the calculation accuracy for defects with depth less than 25% of wall thickness was 41%. The axial signal component had extreme values. The radial component showed sinusoidal fluctuations, accompanied by peaks and troughs. The magnetic signal component increased non-linearly with the increase of the depth and width of the defect, and the incremental change increased first and then decreased. The magnetic signal components exponentially decreased with the increase of defect stress under magnetic saturation, and the decay rate gradually decreased with the increase of stress.

Diabetic wound complications are financially costly and difficult to heal in worldwide. Whereas the therapies of diabetic wound, such as wound dressing, endocrine therapy or flap-transplantations, were not satisfied. Based on our previous study of exosome secreted by adipose-derived stem cell (ADSC-exo), we loaded ADSC-exo into the matrix metalloproteinase degradable polyethylene glycol (MMP-PEG) smart hydrogel. Physical and chemical properties of ADSC-exo@MMP-PEG smart hydrogel were tested by scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), weight loss examination, etc. As the hydrogel degraded in response to MMP, ADSC-exo was released and subsequently enhanced cell function via Akt signaling. Moreover, treatment with ADSC-exo@MMP-PEG smart hydrogel significantly relieved the H2O2-induced oxidative stress, which was widely recognized as a major cause of diabetic wound nonhealing. Similar results were achieved in mice diabetic wound models, in which the ADSC-exo@MMP-PEG treatment group displayed a significantly accelerated wound healing. To summarize, the present smart hydrogel with enzyme-response and exosome-release was proved to be benefit for diabetic wounds healing, which provides a reliable theoretical basis for application of ADSC-exo in treatment of diabetic wounds.

Summary: The entorhinal-hippocampal circuit can encode features of elapsed time, but nearly all previous research focused on neural encoding of “implicit time.” Recent research has revealed encoding of “explicit time” in the medial entorhinal cortex (MEC) as mice are actively engaged in an interval timing task. However, it is unclear whether the MEC is required for temporal perception and/or learning during such explicit timing tasks. We therefore optogenetically inactivated the MEC as mice learned an interval timing “door stop” task that engaged mice in immobile interval timing behavior and locomotion-dependent navigation behavior. We find that the MEC is critically involved in learning of interval timing but not necessary for estimating temporal duration after learning. Together with our previous research, these results suggest that activity of a subcircuit in the MEC that encodes elapsed time during immobility is necessary for learning interval timing behaviors.